Assessment of the Environmental Performance and Sustainability of

Biodiesel in Canada

Jim Rollefson Gloria Fu

Albert Chan

November 2004

Prepared for: Ontario Ministry of Agriculture and Food

Agriculture and Agri-Food Canada Environment Canada

Industry Canada Natural Resources Canada

PREAMBLE

Over the last five years, the environmental and socio-economic impacts of biodiesel have been studied by a number of groups. Notable among them are the National Renewable Energy Laboratory (NREL) of the USA, the Levelton Group in updating the Genius model of greenhouse gas emissions for Natural Resources Canada (NRCan), the Ontario Ministry of Agriculture and Food, the Australian Greenhouse Office, and K. Scharmer of Germany. These studies have been substantial in scope and constitute good building blocks for further investigation in the same field. In 2002, an NRC team of researchers initiated a study to assess the environmental and sustainable performance of biodiesel in Canada. Partially funded by the Ontario Ministry of Agriculture and Food, together with federal departments including Agriculture and Agri-Food Canada, Environment Canada and Industry Canada, the NRC study aims to build on the current knowledge base by validating existing data, incorporating more recent Canadian data where available, and including a specific innovative biodiesel production process that is being developed in Canada. This will allow an objective assessment of biodiesel relative to conventional diesel to support technological innovation and policy analysis in the Canadian context. In order to maximize the opportunity to obtain input from all the groups that have a stake in the outcome of the study, a broad-based Advisory Board was formed at the outset of the project, not only to help steer the research but also to ensure the best available data were properly used and interpreted in the study. The following is a list of the stakeholder groups represented on the Advisory Board for the project: • Ontario Ministry of Agriculture and Food • Agriculture and Agri-Food Canada • Environment Canada • Industry Canada • Natural Resources Canada • Ontario Soybean Growers Association • Canadian Renewable Fuels Association • Rothsay • Biox Corp. The NRC study encompasses an assessment of the environmental performance of biodiesel, together with an analysis of the longer term potential of biodiesel in the context of achieving sustainability objectives. Whereas the first part of the project is focused on a Life Cycle Analysis (LCA) of biodiesel, the second part of the project attempts to address a number of systemic issues that could impact on the sustainable use of biodiesel in Canada. The Office of Energy Efficiency of NRCan has made available its substantial Canadian data set and analysis associated with conventional diesel production and with other pertinent processes like oil crushing through the Genius spreadsheet model. Rothsay has provided the data for waste cooking oil and for animal rendering material, while Biox has supplied data for its biodiesel production process. The NRC project team comprises Jim Rollefson, Gloria Fu and Albert Chan. Steve Smyth, a Masters student from Carleton University, performed the analysis on the air quality modelling aspect of the project.

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PART I Life Cycle Analysis of Biodiesel

Jim Rollefson and Gloria Fu1 Foreword The National Research Council of Canada (NRCC) undertook this study on behalf of the Ontario Ministry of Agriculture and Food (OMAF) and other funding partners with the intention of providing a relatively straightforward Life Cycle Inventory of biodiesel, with additional research to put the biodiesel option in the context of overall sustainability. In the end, far more scientific research and analyses were required than had been anticipated. These analyses have had to tackle some fundamental issues like sustainable agricultural practices and the nitrous oxide emissions associated with agricultural land. In this context, the analyses constitute scientific investigation that calls for peer review and feedback. To facilitate both the rapid comprehension of the findings and to acknowledge the significant progress of other groups working in the field, the report has included key figures and tables from other sources, most notably, the National Renewable Energy Laboratory, the Environmental Protection Agency of the USA and the BioDiesel project in Montreal. Further consultation with these groups is called for to ensure a common understanding of the issues presented. Both the scope and significance of some of the findings in this final report submitted to OMAF are therefore recognized by the NRCC to warrant further research and especially further communication to take full advantage of the work undertaken to date. OMAF and the Canadian ministries will be kept abreast of the refinement of the scientific developments as progress is made pursuant to the release of this final report submitted to the Ministry. Executive Summary Introduction Throughout Europe and North America biodiesel is being promoted as a bio-based fuel that can displace some of the transport diesel derived from fossil fuels. The production and use of biodiesel would reduce greenhouse gas emissions and, to a lesser degree, certain pollutants like particulate emissions in the urban environment. Biodiesel would show the way towards a more sustainable future with less reliance on the fossil fuels. It has been recognized generally that the biodiesel can displace a small relative portion of the total diesel supply, e.g. on the order of 1 to 5%, but the scale of production could still be important enough to offer significant new market opportunities for products like animal tallow, canola and soybean oils and yellow grease derived from used cooking oil.

1 Jim Rollefson is the analyst and author of the text. Gloria Fu performed all LCI operations with the software GaBi.

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However, it is prudent to assess thoroughly the purported advantages and determine the total impact of biodiesel through life cycle analysis (LCA). This study undertakes a series of analyses within the overall LCA framework to provide a better perspective of the most important issues related to biodiesel use and production in Canada. This study strives to complement and advance the results of similar studies undertaken in the USA and Europe within the last ten years. It delves in more depth into areas where significant advances are being made or where the issues remain contentious. In a few cases, notably the predictions for the impact of biodiesel on NOx emissions from new diesel motors, the available research results are too unclear and contradictory to give definitive answers, but hopefully the specific research required to address the questions to be addressed will be laid out more clearly. The most relevant report available at the initiation of this LCA study was a 1998 publication of the National Renewable Energy Laboratory. It still remains a highly pertinent study and reference and updates to data within it will be often made in the course of this report. EPA has recently produced a valuable overview of biodiesel exhaust emission results.2 In December of 2003 Delucchi and Lipman3 put out another study of relevance to the current topic. They have raised important issues relating to the overall impact of biodiesel on the reduction of greenhouse gas emissions. They have indicated that anthropogenic nitrous oxides from agricultural fields can play a very significant role in GHG emission estimates from biodiesel. This same conclusion had been reached early on in this study and some more detailed analyses based on recent Canadian findings are given. In the Canadian context, the most important similar study is one recently completed by Levelton4 for Natural Resources Canada in order to update the Genius model, which is used to estimate various greenhouse gas emission scenarios in Canada. The NRC has strived to develop a certain transparency to the integration of biodiesel issues into this Genius model so that its results can be further refined and more readily interpreted.

2 A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, EPA Draft Technical Report, October 2002. http://www.epa.gov/otaq/models/analysis/biodsl/p02001.pdf 3Delucchi, Mark A. and Lipman, Timothy. A Lifecycle Emission Model (LEM): Lifecycle Emissions From Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, heating and Cooking Fuels and Materials, UCD-ITS-RR-03-17D, available at http://www.its.ucdavis.edu/faculty/delucchi.htm. 4 Levelton Engineering Ltd. and S&T Squared Consultants Inc. 2002. Assessment of Biodiesel and Ethanol Diesel Blends, Greenhouse Gas Emissions, Exhaust Emissions, and Policy Issues. Prepared for Natural Resources Canada. www.ghgenius.ca (under Reports, see Biodiesel and Ethanol Diesel Blends. September 2002.)

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The cradle-to-grave perspective inherent in a biodiesel LCA with three different feedstocks entails a very wide scope of analyses. Advantage has been taken of the LCA project to delve into a number of sustainability issues of not only biodiesel but of the evolving agricultural practices in Canada. The biodiesel life cycle can be divided into four stages when the initial feedstock is either canola or soybean:

• Agricultural production, • Transformation into oil and meal, i.e. oilseed crushing operations, • Oil conversion to biodiesel (transesterification), and • Biodiesel use in urban bus operations.

For the biodiesel production from rendering and waste oil feedstocks, the last two stages above are preceded by only the transport of what is considered waste material. Specific New/Revised Analyses and Updated Data within this Report

• LCA results for the production and use of biodiesel from soybean, canola and used cooking oil are determined based on data specific to Canada.

• A LCA for vegetable-oil biodiesel is determined in such a way that the main biodiesel byproduct, protein meal, is accounted for by a LCA system expansion where the meal’s net incremental output for unit biodiesel output is kept to zero, thus avoiding the need for an allocation formula.

• The analysis extends to determining some of the greenhouse gas implications of the latest proposals made by the Canadian Food Inspection Agency to control the spread of bovine spongiform encephalopathy (BSE). More explicitly, consideration is given to the roles that could be played by biodiesel production and the use of potentially BSE-contaminated meat and bone meal for energy in the cement industry.

• The GHG impact of the nitrogen fixation of soybean is given a new perspective with the Canadian use of rhyzobial inoculants and no nitrogen fertilizer.

• Life cycle and environmental implications of new herbicide-resistant seeds are considered along with refinements to cultivation techniques, including conservation and no till regimes.

• A probable range of life cycle inventory input values for various forms of fertilizer manufactured in Canada is given.

• New analysis is carried out of the estimation methodologies for nitrous oxide emissions from nitrous fixing crops and new approaches are suggested to put these methodologies on a firmer scientific basis.

• Canadian oil crushing emissions of hexane are considered in the light of data from the National Pollutant Release Inventory and of new analyses.

• Two different processes for the conversion of the oils to biodiesel are analyzed. Attention is drawn to the impact of producing high-grade glycerine as opposed to soapstock.

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• The emission data for biodiesel are analyzed in light of the most recent data from Canada and the USA in such a way that the impact of biodiesel per se is more readily recognized. There are as well recommendations for new research studies to address some of the outstanding issues, in particular, the differences in biodiesel emissions due to different feedstocks.

• The life cycle analyses with extensive used of system expansion provide new insights into the use of system expansion and the very substantial impact that it can have on results.

Baseline Diesel Characteristics and their Impact on GHG Emission Estimates The Biobus5 results on biobiesel blend emissions and performance have been used in this study because of the extensive testing undertaken to ensure their accuracy. However, one characteristic of diesel base stock used in the Biobus research does not match up well with its counterpart for “average” Canadian diesel with a significant heavy oil component. The diesel used in Biobus was from off-shore light crude. With its low aromatic content it had a higher heating value (HHV) of 43.5MJ/kg. The HHV for the average diesel in Canada in the Genius model is 45.8MJ/kg, i.e. a 5% difference. To be able to use the Biobus results in a consistent manner the HHV of 43.5MJ/kg has been extensively used in this study. However, it must be noted that this tends to overestimate the greenhouse gas emissions from both the upstream and exhaust emissions by the same 5% when values are given in terms of specific emissions per unit energy output instead of per unit mass. For instance Biobus diesel exhaust emissions for CO2 were determined to be 221g/MJout (see Table 7.8.1) whereas diesel emissions based on the carbon fraction of average Canadian diesel, 0.858 and the HHV of 45.8MJ/kg would lead to emissions of 213g/MJout.6 In the tables the results are all expressed for an HHV of 43.5MJ/kg, but the way in which it tends to overestimate GHG emissions by about 5% from some diesel sources should be recognized. This also impacts on the calculation of the GHG benefits of biodiesel as will be seen immediately below. Principal Conclusions Some of the principal conclusions are as follows.

5 Biobus Project Committee Members. 2003. Biobus - Biodiesel Demonstration and Assessment with the Société de transport de Montréal. Final Report. http://www.stcum.qc.ca/English/info/a-biobus-final.pdf 6 This number is the product of the carbon fraction and the ratio of the atomic weights of carbon dioxide to carbon divided by the product of the HHV and the conversion efficiency of chemical to mechanical energy in the diesel (effH): [(0.858)(44)/12]/[(45.8)(0.322)] = 0.213kg.

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Biodiesel Use Significantly Reduces Greenhouse Gas (GHG) Emissions Biodiesel is an attractive option for GHG emission reduction from the viewpoint of GHG impact per unit energy output7 or per unit mass. With mass allocation used to account for byproducts formed, biodiesel derived from yellow grease (used cooking oil) leads to net GHG emissions per unit energy output from a heavy duty vehicle of about 26g/MJout compared to 293-308g(CO2)/MJout

8 for diesel in Canada. This translates into a net reduction of some 3.5kg(CO2eq) for each kilogram of biodiesel used. This latter number is based on the factor of 12.85 MJ/kg for converting biodiesel mass to energy output. It is the product of the higher heating value (HHV) of biodiesel taken as 39.9MJ/kg and the efficiency (effH) of 0.322 for conversion of the chemical energy of the biodiesel to mechanical energy delivered to the crank.9 So for each megajoule of output energy delivered by biodiesel rather than diesel the GHG reduction is: [(300- 26)g(CO2)/MJ][12.85MJ/kg] = 3500g(CO2)/kg. Under system expansion where more appropriate credit is given to the high-purity glycerine byproduct of the biodiesel production process the net GHG emissions for biodiesel derived from used cooking oil actually become negative, -21g/MJout (see next section for explanation). Then the net reduction of GHG emissions becomes 4.2kg(CO2eq) for each kilogram of biodiesel used. The biodiesel use is particularly promising in the Canadian context as it displaces substantially higher upstream diesel emissions than in the USA. Based on Genius data Canadian upstream emissions of carbon dioxide alone ranged from 677g(CO2)/kg for onshore light diesel to 1115g/kg for heavy oil. In the NREL study the upstream values for the USA ranged from 443g/kg to 535g/kg. Canadian upstream GHG emissions ranges from 872 to 1388g(CO2eq)/kg when methane and nitrous oxide are taken into account. Biodiesel emissions from other sources using mass allocation range from 29, to 52 to 82g(CO2)/MJ respectively for biodiesel from tallow, soybean and canola. The following table captures LCA results using mass allocation and system expansion.

7 The functional unit of this LCA study is the energy delivered to the crank of the diesel engine and the typical unit in most American studies is the brake horsepower-hour (bhp-h). 1bhp-h = 2.685MJ. For a city bus route the conversion to distance traveled would be 7.54MJ/km whereas for more highway travel the energy requirement would be reduced to about 5.82km/MJ. 8 The value of 308 is based on the Biodiesel diesel characteristics for an off-shore light source, whereas the value of 293 is 5% lower and based on the HHV of an “average” Canadian diesel with a heavy oil component. The net GHG reduction per kilogram of biodiesel would be in the range from 3430-3620g(CO2)/MJout. This is why the net reduction has been rounded to 3500g. 9 The mechanical energy delivered to the wheels would be some 15-20% less due to transmission losses.

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Table ES1 Biodiesel GHG emissions for different feedstocks Biodiesel (g(CO2-eq.)/MJ)

Yellow Grease biodiesel1,2

Tallow biodiesel

Canola biodiesel

Soy biodiesel7

Without allocation 27.73 42.19 176.83 189.12 With mass allocation (for glycerin and protein meal) 25.86 28.63 81.8 51.9 With system expansion (protein displacement) -213

-406.94

-610.8 114.24 -179.4 NA5

With system expansion (Meat and Bone Meal as energy source)6 -355

1. All biodiesel results are based on the Biox process for biodiesel production. 2. Biodiesel exhaust emissions here are considered to be of biologic origin and are not

counted. This is consistent within the overall LCA if the byproduct glycerin displaces a carbon of non-biological origin, as it would do for synthetic glycerin. However, if the byproduct glycerin is displacing glycerin of biological origin then the accounting of the non-biological carbon atom in each methyl ester chain would be appropriate. This would mean 11.4g should be added to all biodiesel emissions due to the biodiesel exhaust emissions. This latter number is derived from the good approximation that the methyl ester biodiesel is mainly an 18-carbon fatty acid of biological origin with the extra carbon of the carboxylic group coming from the methanol used in the biodiesel production reaction. Therefore 1/19 of the biodiesel exhaust emissions are not of biological origin.

3. The system expansion yields a negative result here because of the very high purity (99.7%) of the glycerin from the Biox process. The energy required for the biodiesel and glycerin production is less than that necessary for synthetic glycerin production. If a process obtained a much lower purity such as in the NREL study then the byproduct would be more appropriately called soapstock as opposed to glycerin and the byproduct would not lead to the CO2 credit as given above. For instance, if the glycerine was of such poor quality that its most practical use would be as an energy source to displace some of the natural gas required for rendering, the system expansion result would be 2.5g(CO2eq/MJ).

4. The upper value is based on a calculation based on the IPCC adjusted methodology for nitrous oxide emission for both oilseeds while the lower value is based on the same estimate for soybean emissions combined with the experimental measurements of canola emissions by Lemke (see Table 3.8.1). The system expansion result listed under canola is actually for an increase in canola production combined with a reduction in soybean such that no net protein is produced. The system expansion also takes account of the glycerine offset. These figures are derived from tallow where the byproduct of meat and bone meal (MBM) can be used as protein feed for animals.

5. The specific system expansion of this study yields a net increase in oil production but no net increase in protein by increased agricultural production of canola and decreased soybean production. In this context no value can be given for a soybean-based biodiesel with the system expansion.

6. This represents the case where MBM is not useable for animal feed because of BSE risk (see 8.1.1) and the MBM is used for energy production in the cement industry.

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The overall GHG impact of biodiesel is seriously limited by the supply of economically-priced feedstock. Because of this and the cold-flow properties biodiesel blends for general use in Canada will most likely be blended to 1-5%. The impact of biodiesel on GHG emissions as a percentage of total Canadian GHG emissions will therefore be small in a global context. The recent announcement of a 60ML/a biodiesel production plant to be built by Biox brings this into perspective. If this biodiesel were to be derived mainly from used cooking oil the GHG emission reduction within the framework of the LCA would be some 185Gg(CO2eq)/a10 based on simple mass allocation or 220Gg(CO2eq)/a with system expansion accounting for the glycerine production. (Note that 1Gg = 1 kilotonne.) This is an excellent result even if it only constitutes some 0.5% of the Canadian GHG emissions associated with diesel on-road transport, tallied for 2001 to be 39,839Gg(CO2eq)/a.11

It should be noted that the reductions given above cannot be assumed to give net GHG balance for biodiesel under all circumstances. The analyses here account for all the processes that convert the used cooking oil into yellow grease and then to biodiesel. The system boundary does not encompass the alternative option of used cooking oil ending up as a high-energy animal food additive. If the biodiesel production were to mean that alternate animal energy supplements like palm oil would have to be brought in to meet livestock demand then the net GHG balance would be affected in a new expanded system.12 Currently, market prices reflect the good value of yellow grease as an animal feed supplement. Because of the strong demand for yellow grease in Chinese and North American markets for this application it would be difficult on an economic basis to divert much yellow grease supply towards biodiesel in Western Canada.

System Expansion versus Mass Allocation The LCA results are very much dependent on the framework, be it system expansion or mass allocation, within which the analyses are undertaken. This is clear from the table given above. The system expansion gives a more accurate analysis of the real impact of different options. However the interpretation of results from the expanded system requires special attention to detail. The good news is that whatever the framework there remains a major GHG emission reduction due to the use of biodiesel rather than diesel. The system expansion to account for the glycerine produced as a byproduct of the biodiesel production is relatively simple. The glycerine production from biodiesel was deemed to be offset by an equal reduction in production from the source most likely to be affected by the former. There would be no net change in 10 This is calculated as (60ML/a)(0.88kg/L)(3.5kg(CO2eq/kg) = 185Gg(CO2eq)/a 11Environment Canada. Canada’s Greenhouse Gas Inventory, 1990-2001 http://www.ec.gc.ca/pdb/ghg/canada_2001_e.cfm 12 This topic is brought up in a number of studies where consideration is given to what is and is not “surplus” feedstock for biodiesel. The Canadian situation was considered in a 2004 study, Biodiesel and Other Chemicals from Vegetable Oils and Animal Fats, undertaken by PRA Inc. for Agriculture and Agri-Food Canada.

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glycerine available and the differences in environmental burdens associated with each process were tallied to provide the overall environmental impact of the expanded system, i.e. the difference was taken as the difference between the environmental burden of producing one unit of glycerine from the biodiesel process minus one the same burden for one of synthetic glycerine. This has become one of the more contentious points in this study. Another option was to consider that biodiesel glycerine production would be offset by reduction of production in glycerine of organic origin instead of the synthetic glycerine. The decision to base the expansion on synthetic glycerine was both pragmatic and based on the evidence available. World production of synthetic glycerine has dropped substantially as biodiesel production has been promoted in Europe. This is logical because of its premium cost and its fossil fuel origin. Furthermore, the biologically-based glycerine is largely a byproduct of other processes and its supply is less easily influenced by supply and demand. On the pragmatic side, because the glycerine from organic material is produced mainly as a byproduct of other processes its use in the system expansion would have become more complex as the expansion would then have to account for the other products produced along with the biological-based glycerine – as is done in this study in examining the impact of protein meal production with canola and soybean. There is one key caveat in the system expansion and that is that the byproducts must be strictly comparable. In most biodiesel productions processes the glycerine is relatively impure and is better qualified as soapstock. Considerable energy, and therefore GHG emissions, would be necessary to upgrade the base feedstock to synthetic glycerine. However, the Biox process glycerine is comparable based on its purity of 99.7%. The results with the system expansion for this glycerine production are quite remarkable. Synthetic glycerine production is highly energy intensive because of the embodied energy in its feedstocks. This energy needed for synthetic glycerine is actually more than goes into the biodiesel production. The end result for biodiesel production from used cooking oil is to have the net GHG emissions go negative to –21g(CO2eq)/MJout instead of the +26g(CO2eq)/MJout with just mass allocation (see Table 8.0.2). If biodiesel production rises substantially and processes are used that produce this very high purity glycerine then the synthetic production will likely be curtailed and the comparison will then have to be made with other forms of organically-based glycerines. The second major byproduct that must be accounted for in a system expansion is the protein meal that is produced at the same time as tallow or of either of the vegetable oils. For the oilseeds the system expansion is basically the kind of

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response that would come from a market demand for more biodiesel but no new protein meal. Because of the difference in the ratios of oil to protein meal this would be accomplished by the additional production of 3.36 units of canola and 2.25 units less of soybean. The European response of enhancing rapeseed production relative to other oilseeds to meet the high demand for biodiesel reflects this kind of situation. The consequences of this system expansion to account for the byproduct of protein meal are even more remarkable than those for glycerin. This system expansion amounts to reducing soybean production while augmenting that of canola. This makes GHG emission estimates very sensitive to any differences in emission estimates of these two agricultural crops. This sensitivity is most obvious when the impact is seen of a relatively high estimate for canola based on the IPCC methodology compared to a much lower estimate based on experimental data from Western Canada. The soybean estimate is kept constant based on the adjusted IPCC methodology. The swing is from +114g(CO2eq)/MJout based on the former down to –179g(CO2eq)/MJout based on the experimental data. With this level of sensitivity and the large discrepancies between different estimates and measurements of nitrous oxide emissions there must clearly be great caution in using these numbers. It should also be pointed out that if lower estimate for soybean emissions were used in the system expansion (which is probably closer to reality in most instances) then the emissions estimates for the system expansion would become less negative. If the system expansion results given above are remarkable, the very high negative values for tallow in Table ES1 could almost be considered startling. However, the results become more readily understood if they are put in the context of the LCA boundary and what the system expansion entailed. In this study the collected residues from the slaughterhouse were considered as the feedstocks to the rendering industry. The LCA included all the inputs from the transport to the rendering industry onwards but did not assign a portion of the GHG emissions due to the raising of the cattle, e.g. from methane produced by the ruminants. In essence, the slaughterhouse residue is considered as a waste product of negligible value until it is processed by the rendering industry. The system expansion was set up such that for every unit production of a byproduct in the process, a logical alternative source of the byproduct was decreased by one unit and accounted for in the LCA. Ten kilograms of slaughterhouse residue are processed to yield one kilogram of tallow and 2.6 kilograms of meat and bone meal (MBM) is offset by a combined processing of 2.22 kg of canola oilseed minus 5.38 kg of soybean (see equation 1.6.2) to yield not net protein. When the MBM cannot be used for protein because of possible BSE contamination, it becomes an energy source for the cement industry and therefore the requirement for coal is reduced. For each 2.6 kilograms of MBM

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used in this way there is a reduction of 1.9 kilograms of coal consumed. This leads to the very favourable GHG balance for this system expansion. In recent times the industry responsible for the recuperation and processing of deadstock has been financially stressed and the financial incentives to farmers to exploit these services have all but disappeared. Nevertheless, this report clearly shows that the rendering of such stock and of other slaughter residue is very advantageous from an environmental standpoint. Clearly, all measures necessary to ensure proper rendering of all these residues should be taken. Nitrous Oxide Emissions These emissions have a global warming potential 310 times higher than CO2. Even low emissions can lead to a significant GHG impact. Nitrous oxide emissions are also notoriously sporadic and measurement and predictions difficult to make. The National Renewable Energy Laboratory in the USA concluded in 1999 that: “the lack of consistent data and high degree of variability in soil emission measurement prevents us from deriving a meaningful expected soil emission estimate for soybeans. … For this model the field emissions for N2O and NOx will not be reported to prevent any misinterpretation of the overall results.” The different estimates of nitrous oxide emissions continue to range over a wide range in 2004. Within its revised guidelines for national greenhouse gas inventories of 1996, the Intergovernmental Panel on Climate Change (IPCC) has established a framework for analysis of nitrous oxide emissions and has made estimates of various emission factors. However, the extent to which these guidelines may or may not be accurate is reflected in the difference in the estimates of Agriculture Canada for nitrous oxide emissions from N-fixing crops using the adjusted approach and the original IPCC guidelines. Desjardins et al13 estimate 2.1 Gg and 12.8 Gg of N20 in Canada from the adjusted and original methodologies respectively. All of this means that under certain scenarios the nitrous oxide emissions from fields can lead to significant reductions in the GHG benefits of vegetable-oil biodiesel. However, under the most plausible hypotheses supported by Canadian data, the net benefits of biodiesel in GHG emission reduction are still large. Even with the more plausible estimates for N2O emissions there is a significant impact related to them. They largely explain, for instance, the higher net GHG emissions from canola because of the nitrous oxide emissions associated with the intensive nitrogen fertilizer used for growing this oilseed. Recent experimental data from Canada tends to confirm that the lower estimates of the Canadian “adjusted” emission factors are closer to the real values than the formal IPCC guidelines of 1996. This provides some confidence in the lower 13 Desjardins, R.L, Janzen, H.H., Lemke, R. 2002. Regional and National Estimates of the Annual Nitrous Oxide Emissions from Agroecosystems in Canada using the Revised IPCC Methodology.

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GHG emission estimates for nitrous oxide emissions given in this report. This doesn’t mean that the estimation methodologies for nitrous oxide emissions from agricultural sources are on a sound footing. In Appendix A4 of this report is a draft article, Towards a Better Estimation Methodology for Nitrous Oxide Emissions from Nitrogen-Fixing Crops. This article indicates that significant modifications to the current methodologies must be envisaged. It shows that the scientific basis for the equations to estimate nitrous oxide emissions as set out in the 1996 IPCC Revised Guidelines is simply not sound, and it goes on to suggest some approaches that could lead to more useful estimates. Agricultural Inputs and Practices Soybean and canola agricultural practices have been significantly modified since the mid nineties. This has meant that many of the data sets used by NREL in its LCA of biodiesel from soybean are in need of being updated. The revised practices include the wide acceptance of herbicide-resistant seed and the increased adoption of zero and reduced tillage. In this same time frame there have also been numerous studies done to help optimize the agricultural practices by Agriculture and Agri-Food Canada and universities such as Guelph. Such studies have concluded that it is economically unjustifiable in the vast majority of cases to add nitrogen fertilizer to nitrogen-fixing soybean. This has a significant impact on GHG emission estimates for soybean production. The use of inoculants and the elimination of any nitrogen fertilizer on the soybean crop reduce greenhouse gas (GHG) emissions (based on the IPCC methodology as adjusted by Agriculture Canada) from 29.5g(CO2eq)/kg soybean to 25.9g(CO2eq)/kg of soybean yield. If the farm practices are adjusted to take advantage of the nitrogen from the soybean biomass residue for next year’s crop the net GHG emissions fall by a further 25% to 18.5g(CO2eq)/kg soybean because of the reduced nitrous oxide emission reduction and the credit for N-fertilizer reduction (see Table 3.9.2). As the emissions due to agriculture constitute about 39% of the total emissions of soybean biodiesel the overall impact of taking advantage of the fertilizer credit is about a 10% reduction in GHG emissions (see Figure 8.0.1 and Table 8.0.2). In summary, the integration of new agricultural practices which take much better account of the nitrogen fixation of soybean and its impact on subsequent crops in a crop rotation end up having some potential for reducing Canadian GHG emissions. Furthermore, the measures are extremely cost effective, actually reducing costs to the farmer. During this study it was found that there was a significant variation in the estimated input requirements for various fertilizers. This led to widely divergent GHG impacts associated with their production. Some fundamental analyses of the fertilizer production process have therefore been undertaken and a probable range of values for Canadian fertilizer production have been calculated and presented in Table 3.5.3.

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Conservation tilling have significant positive environmental impacts at the oilseed production phase in terms of reduced erosion etc. but has a relatively small impact within the scope of the overall biodiesel production on such issues as GHG emissions. Oilseed Crushing Operations The study confirms the NREL report that hexane emissions from crushing operations can be an important source of volatile organic compounds from soybean and canola oil production within the framework of the biodiesel LCA. However, new regulations will bring down these emissions substantially in the USA. In Canada data from the National Pollutant Release Inventory suggests the releases of hexane from Canadian crushing operations are at levels where the hexane emissions are on average below the new USA regulations. Biodiesel Production Production of biodiesel and its co-product, glycerin, is energy intensive but the net energy output from biodiesel remains substantial. If credit is given for the displacement of synthetic glycerin production by the co-product, the net energy consumption to be attributed to biodiesel actually becomes negative. In this study calculations of energy and material requirements were based on the technologies of Biox and Lurgi. In some other reports a comparison of the two technologies indicated a very substantial difference in the energy requirements for the two processes. However, we noted that the LCA system boundary within the supposedly comparable systems were not the same. When the boundaries are made more equivalent by proceeding to the same degree of refinement of the byproducts, then the differences are much smaller. The Biox process remains more energy intensive but it has the advantage of being a highly versatile system that can accept a high level of free fatty acids in its feedstock. It also produces an anhydrous glycerin of 99.7% purity that leads to highly significant GHG emission reductions because it can justifiably be considered to displace synthetic glycerin production. Biodiesel Combustion and Efficiency in Transport Vehicles The energy conversion efficiency from the chemical energy of the biodiesel or diesel (as reflected by either the higher or lower heating values) to the energy delivered to the heavy-duty vehicle is essentially the same for biodiesel and diesel. This is borne out by many studies and it is probably accurate to within one percent. However, if the baseline diesel fuel is particularly deficient in either lubricity or in cetane number the biodiesel would enhance engine performance and this may be reflected in a slightly increased efficiency for the biodiesel-diesel blend. There remains a high degree of scatter in exhaust emission measurements from different biodiesel trials. However, some reasonable approximation can be made

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for most emissions based on the good agreement between data from the EPA comprehensive study and from the recent Biobus project in Canada. Biodiesel is particularly advantageous in reducing emissions of particulate matter and carbon monoxide. The different biodiesel feedstocks play an important role in determining certain exhaust emissions, including polycyclic aromatic hydrocarbons and NOx. Absolute exhaust emission reductions are dependent on the base diesel used in the blend as well as the engine technology applied, e.g. mechanical versus electronic injection. A variation in injection timing due to biodiesel has played an important role in enhanced NOx emissions in many studies. Under certain conditions it appears that the change in NOx exhaust due to biodiesel can be kept small, and even slightly negative. New research is needed to determine under what conditions, and with what pretreatment or additives this can be achieved. This research will be especially important with new diesel technologies like exhaust gas recirculation that are coming to the fore. It should be recognized that whether the exhaust NOx emissions are slightly higher or lower there is actually a substantial reduction in overall NOx emissions with biodiesel over the entire life cycle. In this study, the estimates for the impact of biodiesel as a percent of its energy output or mass input were largely based on data and analysis of 20% by volume blends of biodiesel, while it was recognized that typical blends in Canada will be less. The intrinsic errors in estimating the specific impact of biodiesel in experiments with very low-ratio blends are too high to yield meaningful results. It is felt that the estimates of biodiesel impact at 20% can be used to establish a good conservative estimate of lower-ratio blends when the relative proportions of biodiesel are taken into account. Indeed, it is anticipated that the results should be better at the lower ratios. Biodiesel advantages dominate at the lower ratios while the disadvantages, e.g. in timing adjustments, appear at the higher ratios. It should be noted that all the life cycle analyses performed have not taken into account capital infrastructure requirements and the life cycle inventory associated with the buildings and equipment. However, as most of the processes are highly energy intensive this does not introduce serious errors into the analyses.

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Table of Contents Foreword ............................................................................................................................. i

Executive Summary ............................................................................................................ i

Introduction.................................................................................................................... i

Specific New/Revised Analyses and Updated Data within this Report................... iii

Baseline Diesel Characteristics and their Impact on GHG Emission Estimates ... iv

Principal Conclusions .................................................................................................. iv Biodiesel Use Significantly Reduces Greenhouse Gas (GHG) Emissions................. v System Expansion versus Mass Allocation .............................................................. vii Nitrous Oxide Emissions ............................................................................................ x Agricultural Inputs and Practices............................................................................... xi Oilseed Crushing Operations .................................................................................... xii Biodiesel Production................................................................................................. xii Biodiesel Combustion and Efficiency in Transport Vehicles................................... xii

Table of Contents ............................................................................................................ xiv

List of Figures ................................................................................................................ xvii

List of Tables ................................................................................................................... xix

1. Life Cycle Analysis Considerations for Biodiesel......................................................... 1

1.1 Purpose of the LCA ................................................................................................ 1

1.2 The Functional Unit and Scope-related Issues..................................................... 1

1.3 System Expansion versus Allocation..................................................................... 2

1.4 Detailed Analysis of System Boundary Expansion to Account for Co-Products......................................................................................................................................... 3

1.4 Detailed Analysis of System Boundary Expansion to Account for Co-Products......................................................................................................................................... 4

1.5 System Expansion Approach for the Technologically Whole System (TWS) for Biodiesel ......................................................................................................................... 8

1.6 Values of the Constants to be used in the System Expansion of this Study..... 11

2 Diesel Oil Life Cycle Analysis for Canada................................................................... 13

2 Diesel Oil Life Cycle Analysis for Canada................................................................... 13

2.1 Diesel LCI Estimates from the Genius Model.................................................... 13

2.2 Exhaust Emission Discussion ................................................................................... 17

2.3 Comparison with other data ...................................................................................... 18

3. Upstream Analysis of Soybean and Canola - Agricultural Production..................... 20

3.1Highlights of Agricultural Analysis Updating Findings of NREL and Levelton....................................................................................................................................... 20

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3.1.1Yield.................................................................................................................. 20 3.1.2 Fertilizer and Inoculants .................................................................................. 21 3.1.3Herbicide-resistant Seed and Herbicide Use..................................................... 21 3.1.4 Cultivation Practices and Farm Energy Use .................................................... 22

3.2 Detailed Agricultural Analyses............................................................................ 22 3.2.1 Soybean Yield In Canada................................................................................. 22 3.2.2 Soybean Fertilizer and Inoculant Use .............................................................. 24

3.3 Upstream Canola Analyses .................................................................................. 27 3.3.1 Canadian Canola Yield .................................................................................... 27 3.3.2 Canola Fertilization.......................................................................................... 28

3.4 Field Energy Requirements ................................................................................. 29 3.4.1 Background...................................................................................................... 29 3.4.1 Estimates for Field Energy Requirements ....................................................... 31

3.5 Upstream Fertilizer Life Cycle Inventory .......................................................... 32

3.6 Herbicide-resistant Soybean and Herbicide Use................................................ 40

3.7 Canola Herbicides and Herbicide-Resistant Varieties ...................................... 44

3.8 Nitrous Oxide Emissions ...................................................................................... 45

3.9 Life Cycle Analysis of the Agricultural Production of Canola and Soybean .. 49

4. Oilseed Crushing Operations ...................................................................................... 55

4.1 Canadian Canola Crushing Characteristics....................................................... 57

4.2 Soybean Crushing Operations ............................................................................. 58

4.3 Life Cycle Inventory of Canola and Soybean Crushing Operations................ 60

5. Analyses of Other Biodiesel Feedstocks...................................................................... 62

6. Biodiesel Production.................................................................................................... 68

6.1 BIOX Process Summary....................................................................................... 68

6.2 The Lurgi Process ................................................................................................. 71

6.3 LCI Results for Biodiesel Production ................................................................. 72

7. Downstream Emissions of Diesel and Biodiesel......................................................... 78

7.1 The Challenge of Data Accuracy ......................................................................... 79

7.2 The LCA Framework for Exhaust Emission Analysis ...................................... 79

7.3 Integration of EPA relative emission data for biodiesel blends into framework....................................................................................................................................... 82

7.4 Preliminary Analysis of Parameters Likely to Affect Exhaust Emissions....... 82

7.5 Chemical Differences between Biodiesel and Diesel no.2 and their Impact on Emissions ..................................................................................................................... 83

7.5.1 Biodiesel as a Methyl Ester.............................................................................. 83

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7.5.2 Biodiesel’s Straight Hydrocarbon Chains, their Unsaturated Bonds and their Relationship to Combustion Characteristics............................................................. 85

7.5 The EPA Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions as Interpreted in the light of this Report’s Preliminary Analysis .......................... 94

7.6 Interpretation of Biobus Emissions Data from Different Sources of Biodiesel..................................................................................................................................... 100

7.6.1 Biobus NOX Emissions ................................................................................. 101 7.6.2 Biobus Polycyclic aromatic Hydrocarbon (PAH) Emissions ........................ 101 7.6.3 Biobus Other Exhaust Emissions................................................................... 102

7.7 Specific Life Cycle Analysis Parameters to be Used in this Study ................. 102 7.7.1 Biodiesel’s Carbon Dioxide Emissions of Biological Origin ........................ 103

7.8 Biodiesel Exhaust Emissions .............................................................................. 104

8. Life Cycle Analysis of Entire Biodiesel Cycle........................................................... 108

8.1 Biodiesel System Expansion Results.................................................................. 112 8.1.1 Biodiesel System Expansion from Feedstock where Bovine Spongiform Encephalopathy (BSE) is a Significant Risk Factor ............................................... 113

8.2 The Role of Biodiesel in the Deadstock and Rendering Industry................... 115

8.3 Summary Conclusion of the Life Cycle Analysis ............................................. 116

9. References .................................................................................................................. 117

Appendices...................................................................................................................... 125

Appendix A1 Comparison of Upstream Emissions for Canadian and USA Diesel..................................................................................................................................... 125

Appendix A2 Opportunity for Refining Agricultural Fieldwork Data for Life Cycle Analyses of Soybean and Canola Production in Canada............................ 130

A2.2 The F4E2 Model ............................................................................................ 131 A2.3 Needs for New Surveys and Analyses ........................................................... 132 A2.4 Recommendations.......................................................................................... 133

References for A2........................................................................................................... 134

Appendix A3 Data from other sources for vegetable oil extraction and refining136

Appendix A4 Towards a Better Estimation Methodology for Nitrous Oxide Emissions from Nitrogen-Fixing Crops.......................................................................................... 138

The IPCC Methodology for Nitrogen-Fixing Crops and its Inconsistencies .............. 138

The Equation for Nitrogen in Crop Residues Returned to the Soil ............................. 139

The Equation for Estimating the Nitrogen Fixed from the Air ................................... 142

Nitrogen fixation and its Potential to Enhance Nitrous Oxide Emissions.................. 144

Analysis of Nitrous Oxide Emissions from Soybean Crops ......................................... 147

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List of Figures Figure 1.2.1 System Boundaries and Data Sources for Biodiesel LCA.................3 Figure 3.8.1 Schematics of IPCC Methodology for the Estimation of N2O Emissions ...........................................................................................................46 Figure 5.1a Yellow Grease Production from Waste Cooking Oil.........................62 Figure 5.1b Mass and Energy Balances for Yellow Grease Production from Used Cooking Oil .........................................................................................................63 Figure 5.2a Tallow Production from Slaughter House Residues.........................63 Figure 5.2a Tallow Production from Slaughter House Residues.........................64 Figure 5.2b Mass and Energy Balances for Tallow Production from Slaughter House Residues..................................................................................................65 Figure 6.2.1 Simplified Lurgi Process Diagram...................................................71 Figure 7.5.1 Injection Pressure for Biodiesel, Diesel and 20% Biodiesel Blends, according to Tat and Van Gerpen.......................................................................84 Figure 7.5.2.1 Correlation of Aromatic Content and Density in Diesel Fuels according to the EPA ..........................................................................................86 Figure 7.5.2.2 Correlation of Cetane Number and Aromatic Content of Diesel Fuels according to the EPA ................................................................................86 Figure 7.5.2.3 NOx Reduction Due to Cetane Enhancers Effective at 1% Concentration or Less according to the EPA ......................................................87 Figure 7.5.2.4 Heat Release as a function of Crank Angle for Biodiesel and Diesel Fuels according to Tat .............................................................................88 Figure 7.5.2.5 Heat Release versus Crank Angle for Diesel and Soybean Biodiesel according to Van Gerpen.....................................................................90 Figure 7.4.2.6 Brake Specific NOx Emissions from Biodiesel and Diesel Fuels according to Tat ..................................................................................................91 Figure 7.4.2.7 Equivalence Ratio and Temperature Influence on NO and Soot Formation according to Akihama et al.................................................................93 Figure 7.5.1 Average exhaust emission impacts of biodiesel for heavy-duty highway engines, EPA results ............................................................................94 Figure 7.5.2 Exhaust emission impacts of 20%vol biodiesel for soybean-based biodiesel added to an average base fuel, EPA results........................................95 Figure 7.5.3 Base fuel effects on particulate matter exhaust emissions, EPA results .................................................................................................................96 Figure 7.5.4 Base fuel effects on hydrocarbon exhaust emissions, EPA results 96 Figure 7.5.5 Base fuel effects for NOx exhaust emissions, EPA results .............97 Figure 7.5.6 Biodiesel source effects for NOx exhaust emissions, EPA results..98 Figure 7.5.7 Biodiesel source effects for particulate matter exhaust emissions, EPA results .........................................................................................................98 Figure 7.5.8 Biodiesel source effects for carbon monoxide exhaust emissions, EPA results .........................................................................................................99 Figure 7.5.9 Biodiesel impacts on CO2 exhaust emissions, EPA results..........100 Figure 7.6.1 Biobus NOx Emissions .................................................................101 Figure 7.6.2 Biobus Polycyclic aromatic Hydrocarbon (PAH) Emissions ..........102 Figure 7.6.3 Biobus Exhaust Emissions with Electronic Fuel Injection .............102 Figure 8.0.1 GHG Emissions for Biodiesel, Base Cases with Mass Allocation .109

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Figure A2.1 (Figure 2 of Grisso et al)................................................................130

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List of Tables Table ES1 Biodiesel GHG emissions for different feedstocks ............................. vi Table 2.1.1 Total Upstream Emissions for 50ppm Diesel in Canada Anticipated for 2005 from the Genius Model..........................................................................14 Table 2.1.2 Leaks and Flares (undifferentiated data)..........................................14 Table 2.1.3 Oil Production ..................................................................................15 Table 2.1.4 Oil Transport to Refinery ..................................................................15 Table 2.1.5 Oil Refining ......................................................................................15 Table 2.1.6 Fuel Storage and Distribution...........................................................16 Table 2.1.7 Fuel Dispensing ...............................................................................16 Table 2.1.8 Upstream Emission Summary for diesel with 500ppm maximum sulphur ................................................................................................................16 Table 2.3 NREL Upstream Overview of Diesel Emissions..................................18 Table 3.2.1 Canadian Soybean Agricultural Production 1998 – 2002.................22 Table 3.2.2 Soybean Yield as a Function of Soil and Crop Heat Units ...............23 Table 3.3 Canadian Canola Yield .......................................................................28 Table 3.4.1 Changes in Tillage System Prevalence from 1991 through 2001 ....30 Table 3.4.2 Total Energy Outputs and Inputs for Conventional and Zero Tillage according to Lindwall et al...................................................................................31 Table 3.5.1 Comparison of SimaPro and DEAM Data (from NREL study)..........33 Table 3.5.2 Consumption of Energy and Resources for the Production of N-fertilizers .............................................................................................................35 Table 3.5.3 Natural Gas Requirements for N-Fertilizer Production1....................37 Table 3.6.1 NREL Results for LCI of Soybean Agriculture (for 1 kg of soybean) 43 Significant LCI Emissions related to Agrochemical Production Inputs ................43 Table 3.8.1 Experimental Data for N20 Emissions (by R. Lemke) ......................49 Table 3.9.1a LCI Results for 1kg Canola/soybean produced – base case..........50 Table 3.9.1 Sensitivity of GHG Emissions to N2O Estimates for 1kg canola ......52 Table 3.9.2 Sensitivity of GHG Emssions to N2O Estimates for 1kg Soybean ...53 Table 3.9.3 Sensitivity of GHG Emissions to Conservation and Conventional Till for 1 kg canola produced ....................................................................................54 Table 3.9.4 Sensitivity of GHG Emissions to different estimates of energy requirements for N-fertilizer production (for 1kg canola produced) .....................54 Table 4.0.1 N-Hexane emissions from Canada’s National Pollutant Release Inventory .............................................................................................................56 Table 4.0.2 Hexane Emissions as related to Grain Processed and Oil Produced in 1999 ................................................................................................................56 Table 4.1.1..........................................................................................................57 Table 4.1.2 Process inputs and outputs for oil extraction of canola ....................58 Table 4.2a Overall inputs and outputs for soybean crushing (from NREL study except hexane emissions) ..................................................................................59 Table 4.2b Mass Allocation for the generic soybean crushing facility (NREL study) ..................................................................................................................59 Table 4.1.3 LCI Data for Crushing of 1kg of Canola and of Soybean with mass allocation1 ...........................................................................................................61 Table 5.1 LCI of Yellow Grease Production from Used Cooking Oil ...................65

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Table 5.1 LCI of Yellow Grease Production from Used Cooking Oil ...................66 Table 5.2 LCI of Tallow Production from Slaughterhouse Residues...................67 Table 6.1 Mass and Energy Balances for the BIOX Process..............................70 Table 6.2.1 First Order Energy and Mass Requirements for the Lurgi Process..71 Table 6.3.1a LCI for BIOX Biodiesel Conversion Process with no mass allocation............................................................................................................................72 Table 6.3.1b LCI for BIOX Biodiesel Conversion Process with no mass allocation............................................................................................................................73 Table 6.3.2a LCI for Lurgi1 Biodiesel Conversion Process with no mass allocation............................................................................................................................74 Table 6.3.2b LCI for Lurgi Biodiesel Conversion Process with no mass allocation............................................................................................................................75 Table 6.3.3 Synthetic Glycerine Production according to Ahmed et al................76 Table 6.3.4 Synthetic Glycerine Energy Content according to Scharmer and Gosse .................................................................................................................77 Table 6.3.5 GHG Emission Estimates for the Biox Biodiesel Production ............77 Table 6.3.6 GHG Emission Estimates for the Lurgi Biodiesel Process1..............77 Table 7.4.1 Approximate composition of canola, soy oils and yellow grease......83 Table 7.5.2.1 Cetane Number of Fatty Acid Methyl Esters .................................87 Table 7.8.1 Tail-pipe emissions attributable1 to biodiesel for electronic injection engine (g/MJ)2...................................................................................................105 Table 7.8.2 Tail-pipe emissions attributable to biodiesel for mechanical injection engine (g/MJ)....................................................................................................106 Table 7.8.3 Specific Exhaust Emissions of 20%vol Biodiesel-Diesel Blends a. for electronic injection engine (g/MJ)......................................................................107 Table 8.0.1 LCI Results for Diesel and Biodiesel with mass allocation .............108 Table 8.0.2 GHG Emissions for the Life Cycle of Canadian Diesel and Biodiesel..........................................................................................................................111 Table A2.3 Comparison of SVFE for Gear Up and Full Throttle .......................132 Table A2.4a Diesel Fuel Consumption (FC) for Field Activities in g/ha and g/(kg(yield)/ha)1.................................................................................................133 Table A2.4b Fuel use (liter/hectare)..................................................................134 Table 1 Comparison of Measured and Estimated (IPCC Methodology) Nitrous Oxide Emissions for Eastern Canada ...............................................................152 Table 2 Illustration of Methodology to Estimate Nitrous Oxide Emissions Due to Thaw-Freeze Cycles .........................................................................................153 Table 3 Soybean Yield as a Function of Soil and Crop Heat Units ...................155

1

1. Life Cycle Analysis Considerations for Biodiesel 1.1 Purpose of the LCA The purpose of this study is to quantify and assess the potential environmental impacts of biodiesel over its entire life cycle, and to compare the environmental performance of biodiesel with that of petrodiesel. 1.2 The Functional Unit and Scope-related Issues As the counterpart to biodiesel, 50 ppm sulphur-content petroleum diesel is considered in the study. 500 ppm sulphur is the current upper limit in Canada until June 2006, after which, the limit drops to 15 ppm. This new sulphur limit will have major impact on the tailpipe emissions sulphur dioxide and and it will enable significant post-combustion reduction technologies for PM and NOx. While good data on 15 ppm-sulphur diesel production is not yet available, good data for 50 ppm-sulphur diesel is available from the Genius model of NRCan. The LCA is based on the production and use of petroleum diesel and biodiesel in central Canada, including Ontario. The study will model commercial technology as it exists today. Biodiesel is typically blended with diesel. Because of the limited availability of economic biodiesel feedstock and the relatively high cloud point of biodiesel Canadian biodiesel is unlikely to exceed 20% by volume use in the foreseeable future. In fact, 1-5% formulations are most likely. This study will therefore consider biodiesel use in blends, but as in the Australian study, highlighting the impact of the biodiesel per se through the choice of the functional unit separating out the role of the biodiesel within the blend. This LCA endeavours to provide a measure of the environmental consequences of biodiesel fuel delivering one megajoule of output energy through the heavy-duty engine of an urban bus as measured by engine transient tests14 and as verified in actual performance in Montreal. The megajoule of output energy becomes therefore the functional unit of this study. A related unit often used in North America is the brake horsepower.hour where 1bhp.h = 2.685MJ. This functional unit of output energy can be directly related to the requirements to travel a specified distance, i.e. one kilometer, under specified driving conditions. These conversions are provided in the report as well to provide a wider perspective to the information. The principal system boundary and data sources for the LCA study are shown in Figure 1.2.1. There are two very significant co-products in most biodiesel processes. With canola, soybean and rendering feedstock there is protein meal. The one current exception to a feedstock not having a protein byproduct is waste cooking oil. In the transesterification process, there is a glycerine co-product. It

14 More specifically the test used in the Biobus project and therefore applied in this study is the U.S. EPA Heavy Duty Engine Transient Test, Code of U.S. Federal Regulations (CFR) 40, Part 86.

2

is valuable in its own right and its economic and environmental value is far from negligible. Figure 1.2.1 also shows diversion from landfill of deadstock and waste oil. One estimate has 10% of waste cooking oil and 20% of deadstock going to landfill. This LCA study shows that such practices thwart a major opportunity to reduce GHG emissions by more appropriate use of rendering byproducts. 1.3 System Expansion versus Allocation One of the most controversial aspects of previous analyses of biodiesel use has been the allocation approach followed to account for the co-products. A mass allocation is simple to apply, but it fails to take account of the economic and environmental value of the co-products. An economic allocation, as was proposed in a recent Australian study, has its own problems, not the least of which is the high volatility of prices associated with the co-products. In accordance with ISO 14041, the NRC is proceeding with a system expansion approach as opposed to any allocation formula. In a so-called “technologically whole system” (TWS) approach, all of the environmental impacts are assigned initially to the main product, in this case biodiesel, and then this impact is adjusted to take account of the avoided environmental burden due to the production of the associated co-product. So in the case of the canola co-product which is its protein meal, account is taken of its impact in displacing the need for more protein production from soybean. To make the impact of these co-products on the LCA as transparent as possible, the NRC study will tend to present results with no account taken for the co-products, and then progressively showing the impact of each co-product.

3

Figure 1.2.1 System Boundaries and Data Sources for Biodiesel LCA

Transesterification of animal fats and vegetable oils to

biodiesel (BIOX process)

Collecting used cooking oil

Cooking oil production and

Yellow grease, Tallow production

(Rothsay)

Collecting residues from

slaughterhouse, packinghouse,

Livestock production Meat production

Fuel storage and distribution (NRCan Genius)

Combustion of fuel in vehicle (Biobus Study, EPA Compilation Study of 2002)

Fuel dispensing (NRCan Genius)

Crude oil extraction & petrodiesel production (NRCan Genius)

Methanol production H2SO4 production

NaOH production N2 production

Energy production

Protein MealZero Net

Protein Meal

Displaced Protein Meal Production

(soy, canola production)

( Minus

Glycerine Zero Net Glycerine

Displaced Synthetic Glycerine

( Minus

Combined agricultural production of Canola and Soybean (zero net production of protein meal)

4

1.4 Detailed Analysis of System Boundary Expansion to Account for Co-Products It was noted in the National Renewable Energy Laboratory study15, that the most contentious issue was that of allocation. NREL employed mass allocation to account for the co-product of protein meal while a recent Australian study employed both an economic allocation methodology and a system expansion approach.16 The latter is the preferred option according to ISO14041. The primary results for oilseed-based biodiesel in this report are therefore based on this expansion technique. Unfortunately, as will be seen later, the distinction between emissions from canola and soybean-based biodiesel will be difficult to make using the system boundary expansion. Therefore some analyses will employ a mass allocation to show differences in soybean and canola production. Within ISO14041 for LCA it is stated: “Step 1: Wherever possible, allocation should be avoided by: 1) dividing the unit process to be allocated into two or more sub-processes and collecting the input and output data related to these sub-processes; 2) expanding the product system to include the additional functions related to the co-products…” In cases of interest to biodiesel where protein and oil are co-products, the separation into two process lines is simply not feasible because some of the key processes, e.g. cultivation and crushing, or thermal rendering, are one and the same. That means that the first option under ISO 14041 is not open. The crushing and rendering processes both give valuable oil or tallow and a high-protein meal, so the issue of co-product generation cannot simply be ignored. So the next option under ISO 14041, which is an appropriate expansion of the system boundary, should be considered. This is made practical because of the relative complementarity of soybean and canola production. If there is a very heavy demand for protein meal, the emphasis will be on soy production; if the market demand is much greater for oil, the tendency will be toward higher canola production. As will become apparent later in the analysis, this allows a reasonable system closure even though additional functions are associated with other co-product processes. Tillman et al17 distinguish three types of boundary for LCA:

A. Process tree, 15 Sheehan, John, Camobreco, Vince, Duffield, James, Graboski, Michael, Shapouri, Housein. 1998. Life Cycle Inventory of Biodiesel and Petroleum Diesel for Use in an Urban bus, prepared by the National Renewable Energy Laboratory (NREL) for the U.S. Department of Agriculture and U.S. Department of Energy, May 1998. 16 Beer, Tom, Grant, Tim, Morgan, Geoff. Lapszewicz, Jack, Anyon, Peter, Edwards, Jim, Nelson, Peter Watson, Harry & Williams, David. 2004. Comparison of Transport Fuels Final Report (EV45A/2/F3C) to the Australian Greenhouse Office on the Stage 2 study of Life-cycle Emissions Analysis of Alternative Fuels for Heavy Vehicles. http://www.greenhouse.gov.au/transport/comparison/ 17 Tillman, A-M., Baumann, H., Eriksson, E. and Rydberg T. 1991. Life cycle analysis of packaging materials. Calculation of environmental load. Göteborg: Chalmers Industriteknik.

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B. Technological whole system, C. Socio-economic whole system.

A. The process tree approach limits the study to the immediate product and processes and, if co-products are involved, means that some form of allocation formula must be used for distributing the environmental burden among the co-products. B. The technological whole system approach considers the functions fulfilled by the system. An effort is made to isolate the key function, in our case, biodiesel production and its use in transport, and the boundary is extended in such a way as to keep functionality provided by other products or outputs to a constant. This can be accomplished in the case of biodiesel by examining combined processes that together yield a net increase in biodiesel without a net increase in the major co-product of protein meal. It is important to determine what would happen on the margin to ensure the co-product functionality is constant. So, if the biodiesel process produces protein as a co-product, the question becomes what other process output would be logically decreased to maintain the overall functional output for protein. The LCA boundary is increased to consider this second process and to take account of its decreased output in the overall LCA. C. The socio-economic whole system is, as the name suggests, even more inclusive. This will usually be open to a much wider interpretation of what should or should not be in the LCA. It is important to remember that an important consequence of a fuller economic study is, as stated by Tillman et al: “The comparison between alternatives is usually done using the functional unit as the basis for comparison. In an SWS, the number of functional units, i.e. the production volume, may vary between alternatives compared. In such cases, the functional unit, being the basis for calculation, cannot be used as the basis for comparison.”

To retain the usefulness of the functional unit, the NRC study will work in the initial phase mainly within the confines of the technological whole system but will also adjust this system to account for important economic factors. Within this framework, Weidema18 has worked to establish rigorous conditions for identifying and including the effects of alternate processes on the margin to retain overall co-product functionality as a constant. 18 Weidema B P. 1999. Some important aspects of market-based system delimitation in LCA - with a special view to avoiding allocation. Positioning paper for joint workshop of the Dutch and Danish LCA methodology projects. http://www.lca-net.com/publ/delimitation.asp Weidema, B.P. 1999. System Expansions to Handle Co-products of Renewable Materials. 7th LCA Case Studies Symposium SETAC-Europe. Weidema, B.P. 2001. Addendum to the article “Avoiding co-product allocation in life-cycle assessment”. Journal of Industrial Ecology 4(3):11-33.

6

“However, these perceived problems can be solved by adapting a stringent procedure for identifying the affected processes, earlier presented in Weidema et al., leading to the conclusion that allocation can (and shall) always be avoided in prospective life cycle assessments. The following figure shows how the co-producing process has one determining product (product A), i.e. the product that determines the production volume of that process. This is not necessarily the product used in the specific life cycle study. In the figure, also just one co-product is shown, but in practice there may be any number of co-products, while at any given moment there can be only one determining product. That a product is determining the production volume of a process, is the same as saying that this process will be affected by a change in demand for this product. How to identify the processes affected by a change in demand (which is also the processes to be included in a prospective life cycle study) has been shown in Weidema et al.. To say that there can be only one determining product at any given moment, is not the same as saying that the other co-products are not of importance. That the co-products can obtain a certain price on the market may well be a precondition for the process to expand its production volume. But when this precondition is fulfilled, it is still only a change in demand for the determining product that will be able to affect the production volume of the process. For example, out of the total income of growing sunflowers, 63% comes from selling the oil and 37% from selling the protein-containing pressing cake as animal fodder. Thus, it is unlikely that more sunflowers would be grown if it were not possible to sell additional sunflower pressing cakes. Yet, it is not the demand for fodder cakes that determines the production of sunflowers, since an increased demand for protein can be met at a lower cost by producing soybeans. Thus, the determining product for sunflowers is the sunflower oil, which is in demand for its particular composition of fatty acids.

7

Performing a system expansion in relation to co-products is exactly to identify how the production volume of the processes in the above figure, will be affected by a change in demand for the product that is used by the life cycle study in question (both when this is the determining product for the co-producing process (A) and when it is the product in which the co-product is utilized (B)).” Weidema expresses the rules most recently as:

1. “The co-producing process (and its exchanges) shall be ascribed fully (100%) to the determining co-product for this process (product A),

2. Under the conditions that the dependent co-products are fully utilized in other processes, product A shall be credited for the processes that are displaced by the dependent co-products. The intermediate treatment shall be ascribed to product A. If there are differences between a dependent co-product and the product it displaces, and if these differences cause any changes in the further life cycles in which the co-product is used, these changes shall likewise be ascribed to product A.

3. When a dependent co-product is not utilized fully (i.e., when part of it must be regarded as a waste), the intermediate treatment shall be ascribed to product B, while product B is credited for the avoided waste treatment of the co-product.”

It is a great oversimplification to conclude, as Kim and Dale19 have done with this kind of system expansion, that: “The underlying assumption in the system expansion approach is that product systems with an equivalent function have the same environmental burdens.” It is only to the extent that a second process has been identified which is the logical alternative option to retain the overall co-

19 Kim, S. and Dale, B. 2002. Allocation Procedure in Ethanol Production System from Corn Grain. International Journal Life Cycle Assessments. 7(4):237-243.

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product function at a constant can the link be made between the environmental burdens of the first and second processes. The system boundary for biodiesel production relevant to the rendering industry is given in Figure 1.2.1. The details of the system function expansion to account for the protein co-product and the glycerine by-product are explained below. 1.5 System Expansion Approach for the Technologically Whole System (TWS) for Biodiesel The most attractive economic options for the production of biodiesel in Canada come from waste cooking oil recovery, animal rendering operations and seriously off-specification canola beans. However, the initial analysis is carried out considering soybean and canola oil production so that some continuity can be established with the previous analyses of Weidema, and Kim and Dale. The analysis will then be extended to the more economic feedstocks. The terms to be used are as follows:

E = the environmental burdens per kilogram associated with the product which is indicated by its subscript.20 a = kilogram amount of co-product output per kilogram of feedstock. c = canola s = soy bean o = oil t = total (sum of cultivation and crushing operations) p = protein b is typically introduced into the equations as the displacement ratio, which reflects the differential value of two co-products in the market. However, in this analysis the co-products are defined in such a way that this displacement ratio is always 1.

What can be reasonably well known in the production and processing of soybean and canola is the total environmental burdens expressed as Est and Ect. The question becomes: Can a logical system expansion for TWS be undertaken to determine Eco, Eso, Ecp and Esp as a function of the two known quantities? There are initially two equations but with four unknowns. Ect = acoEco + acpEcp (1.5.1) Est = asoEso + aspEsp (1.5.2)

20 Where this environmental analysis follows through more than one stage, e.g. through agricultural production and through crushing, the consistency of the equations is maintained by having Et refer to the burden per unit output of the first stage. This becomes the environmental burden per unit feedstock input to the second. Therefore, Ect continues to refer to the environmental burden per kilogram of canola grain, first grown, then processed.

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The following factors are important to retain in allowing the remaining relationships to be established. Both soy oil and canola oil can be considered as all purpose vegetable oils on the market and have basically the same functional value, i.e., the displacement ratio for them is one. The primary values of both soy meal and canola meal are determined by their protein contents. Other factors such as metabolizable energy and fat are slightly different, and canola meal has more restrictions in its use with certain animals, for instance, in poultry. Nevertheless, their overall value when measured in terms of protein value is equivalent for many animal applications. This conclusion has been reached in a number of studies. Nelson and Landblom21, for instance, compared the economic value of canola and soybean meal as supplemental protein for growing steers. Field trials and economic analysis led to the conclusion that the value of a canola meal of 38% protein relative to a soy meal of 44% protein was 84%. The ratio of 38/44 is 86% so it can be seen that to a very good approximation the overall value of soy and canola protein is equivalent. It is because of the equivalence of the protein content as opposed to the total respective quantities of meal that the equations are developed as a function of protein. In this way the displacement ratio can again be put to 1. The final key factor to retain is that soy and canola are naturally complementary products because of their very different ratios of oil to protein output. For canola this ratio is slightly less than 2, whereas for soy the ratio is less than 0.5. In the language of Weidema, the determining product for canola is reasonably taken to be the oil, while for soybean the determining product is protein. These factors can be used to determine the environmental burdens of the co-products as follows. Equation (1) is rewritten as Eco = (Ect – acpEcp)/aco (1.5.1a) To keep the overall animal feed functionality constant, any increase in canola protein would be logically met by a decrease in the determining soy protein output. Therefore we substitute Esp for Ecp where from equation (2), Esp = (Est – asoEso)/asp (1.5.2a)

21Nelson, J.L. and Landblom, D.G. Canola Meal vs Soybean Meal and Two Levels of Protein and Backgrounding Steer Calves, http://www.ag.ndsu.dodak.edu/dickinso/research/1990/rpt2.htm

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Eco = {Ect – acp(Est - asoEso)/asp}/aco (1.5.3) As the determining oil product is canola oil, then Eco can be substituted for Eso in (3) to give: Eco = {Ect – (acp/asp)Est}/{aco - acpaso/asp} (1.5.4a) The physical interpretation of this equation is that a production of 1/{aco - acpaso/asp} units of canola minus the production of (acp/asp)/{aco - acpaso/asp} units of soy would yield one kilogram of oil and no meal. This environmental impact calculation is now explicitly dependent not only on the main process stream for canola, it is dependent on the environmental impact of the co-product displaced. This interpretation is vital in understanding how this system expansion approach is fundamentally different from mass or other attribution formulae. A set of four equations with four variables has now been set up which allows all variables to be expressed as functions of the known values of Ect and Est. Based on the same arguments then, Esp can be expressed as: Esp = {Est – (aso/aco)Ect}/{asp – asoacp/aco} (1.5.4b) These equations allow the determination of environmental impacts up to the point where the biodiesel feedstock of crude (non-waste) vegetable oil and the co-product meal is produced. The analysis can now readily be extended to the rendering process where the co-products will be the oil/tallow feedstock plus an animal meal. Again, while the animal meal has even more restrictions in its use, where it can be used, its value is to a good approximation proportionate to its protein content. This allows the following equations to be developed: Ert = aroEro + arpErp (1.5.5) On the margin Erp = Esp, Ert = aroEro + arp{Est – (aso/aco)Ect}/{asp – asoacp/aco} or Ero = [Ert - arp{Est – (aso/aco)Ect}/{asp – asoacp/aco}]/aro (1.5.6) where Ert is the environmental burden of the total rendering process Ero is the environmental burden of just the oil/tallow production and Erp is the environmental burden of the animal meal protein content

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The physical interpretation of this formula is that the environmental burden associated with the production of one kilogram of biodiesel feedstock is the burden associated with the total rendering process minus the burden associated with the combination of soy and canola production that would just offset the animal protein production from the rendering process. The next step in the process of biodiesel production is simpler to analyze from an environmental standpoint. There is one product, biodiesel, and one valuable by-product in the form of glycerin. In the case of converting rendering oil and tallow to biodiesel and the glycerin, one can write: Ebt = aboEbo + abgEbg (1.5.7) In the case of the Biox biodiesel production, the glycerin byproduct, although still to be considered crude, is far superior to most normal feedstocks in that it is anhydrous and 99.7% pure, close to synthetic glycerin quality. Under these conditions, equation (1.5.7) becomes: Ebo = {Ebt – abg(Esg - Epurg)}/abo (1.5.8) where Ebo is the upstream environmental burden of the biodiesel production per unit biodiesel output Ebt is the total environmental burden of producing both the biodiesel and glycerin Esg is the environmental burden of producing a kilogram of synthetic glycerin Epurg is the environmental burden of any purification steps needed to bring the glycerin byproduct to the equivalent purity of synthetic glycerin. In the Biox process with the very high purity of the glycerin the Epurg is assumed to be negligible. In the case of biodiesel production from waste cooking oil, the same equation (1.5.8) would apply but the calculation of Ebo would be simpler than (1.5.7) in that no protein is produced. These equations can lead to the graphic representation of the system expansion presented in Figure 1.2.1. 1.6 Values of the Constants to be used in the System Expansion of this Study One kilogram of canola seed is estimated to yield on average 0.411kg of canola oil and 0.589kg of canola meal with protein content of 38%. Therefore,

aco = 0.411, acp = (0.589)(0.38) = 0.2238

One kilogram of soybean seed is estimated to yield on average 0.170kg of soy oil and 0.760kg of soy meal with a protein content of 44%. Therefore,

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aso = 0.170 asp = (0.760)(0.44) = 0.3344

One kilogram of slaughterhouse residues (63.7% H2O) is estimated to produce 0.10kg of tallow and 0.26 kg of animal meal with protein content of 50%. Therefore,

aro = 0.10 arp = (0.26)(0.50) = 0.13

If the first four constants are substituted into the equation 1.5.4a, it becomes:

Eco = 3.36 Ect – 2.25 Est. (1.6.1)

The interpretation of this formula is that an increase of production of 3.36kg of canola accompanied by a reduction of 2.25kg of soybean sent for crushing yields one additional kilogram of oil and no net production of protein meal. Therefore the system expansion applies not specifically to canola, and especially not to soybean, but rather to an oilseed response to increased demand for oil but not for protein meal. In the case of slaughterhouse residues the system expansion yields:

Ero = 10Ert-5.38Est+2.22Ect (1.6.2)

It takes 10kg kilograms of residues to produce 1kg of tallow and to offset the 1.3kg of protein produced with this oil it would take the processing of 5.38kg less of soybean and 2.22 more of canola. This is derived from the combination of soybean and canola production that produces one net kilogram of protein meal without any additional oil as reflected by the equation:

Esp = 4.14Est - 1.71Ect (1.6.3)

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2 Diesel Oil Life Cycle Analysis for Canada Diesel oil production in Canada is significantly different from most other countries, mainly due to the ever-increasing portion of its diesel derived from heavy and tar sand synthetic oil. The energy requirement for this synthetic oil is substantial. The tar or bitumen must be separated from the sand/clay by a process that typically includes steam, hot water and caustic soda. It is diluted with naphtha and then centrifuged to produce the liquid bitumen. This bitumen is then upgraded in a coking process and through hydrogenation to yield the synthetic oil. There is currently a significant transition taking place in the Canadian oil sector as it goes from producing a diesel with a regulated maximum of 500 ppm of sulphur for all on-road applications to one that will have a maximum of 15 ppm by 2006. This will dramatically lower the sulphur emissions from diesel, but even more importantly, enable effective post-combustion emission treatment processes. This does however increase refinery energy requirements. 2.1 Diesel LCI Estimates from the Genius Model To bring this all into perspective, the life cycle inventories of diesel derived from three different sources of crude oil are given along with some analysis to indicate the differences that emerge from the transition from 500-ppm to 50-ppm sulphur content. The figure of 50 ppm rather than 15 ppm is used because the source of data in this analysis is the Genius model22 of NRCan that currently has a lowest sulphur content of 50ppm. This captures the most significant portion of the additional energy inputs to produce the ultra-low sulphur diesel. There will be some additional energy costs in going from 50 to15 ppm but industry analysts indicate that these incremental energy inputs are low.23 There is one significant simplification that has been applied to the analysis and it comes from the current version of Genius. The crude oil refining actually produces a series of products from gasoline to diesel to heavy fuel oil, to asphalt and petroleum coke. In this analysis there is no specific breakdown for the costs associated with the refining of diesel. The LCI figures allocate to diesel the same portion of the total production and refining inputs and emissions as the mass fraction of diesel produced from the crude. The diesel oil characteristics used in Genius are as follows:

Density 0.843kg/L with a carbon fraction 0.858,

22 More specifically, the version 2.3a (2004) of Genius has been used in this study. 23 To go from 50 to 15 ppm requires mainly new capacity in the hydrotreating reactors and some additional hydrogen. One rough estimate would have the incremental energy requirement for sulphur removal proportional to the sulphur reduction. So to go from 50 to 15 ppm would take (35/450) of the energy to go from 500 to 50 ppm. If this is directly reflected in CO2 emissions, the difference in refinery emissions for the further sulphur reduction to 15 ppm would be for Central Canada, {382.9 (CO2 refinery emissions 50ppm) – 263.9 (CO2 refinery emissions 500ppm)][35/450] = 9.3g(CO2)/kg. This would increase total upstream emissions from 959 to 969g/kg or by about 1%.

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Energy Content (HHV) 45.8MJ/kg24.

Table 2.1.1 Total Upstream Emissions for 50ppm Diesel in Canada Anticipated for 2005 from the Genius Model

onshore offshore heavy Central2

Upstream Emissions1 (g/kg diesel) Year 2005 0.005% S 0.005% S 0.005% S 0.005% S

CO2 (not including other pollutants) 676.91 760.46 1115.3 958.99 CH4 8.6665 9.3218 12.105 10.879 N2O 0.0411 0.0444 0.0586 0.0523

CFCs+HFCs 4E-05 5E-05 5E-05 5E-05 CO 3.5027 4.5055 8.7645 6.8885 NOx 4.2337 5.3809 10.253 8.1071

VOC-Ozone weighted 0.9684 1.0284 1.2832 1.171 SOx 1.46 1.5515 1.9401 1.7689 PM 0.2618 0.2929 0.4249 0.3668

GHG emissions g(CO2eq)/kg 871.65 969.98 1387.67 1203.66 1. Upstream includes oil production, leaks and flares, oil transportation to refinery,

refining, fuel storage and distribution and dispensing. 2. The central Canada mix fro 2005 from Genius model, version 2.3a, is comprised

of 27% light crude onshore (or conventional), 10% light offshore and 63% heavy oil. Heavy includes synthetic oil from the tar sands and heavier grades from Western Canada. The heavy oils are not differentiated for their overall energy inputs. Actual heavy oil input to diesel in 2004 for Ontario and Quebec is only about 30% so the estimate for of 63% for 2005 is high. The mix used in this report should therefore be considered as a mix appropriate to an average over central and western Canada.

Tables 2.1.2 through 2.1.7, Emission Breakdown by Stage (g/kg diesel) for 50ppm sulphur

Table 2.1.2 Leaks and Flares (undifferentiated data) CH4 and CO2 leaks and flares

All Sources of Diesel

CO2 - not including other pollutants 37.245 CH4 6.0196 N2O 0 CO 0 NOx 0

VOC, Ozone-Weighted based on Sheet F factors 0.3171 SOx 0.2629

CFCs+HFCs 0 PM 0

24 This energy content appears to reflect the energy content of Western Canadian oil with a high aromatic content. Higher aromatic content leads to higher energy content. In the Biobus study the diesel no. 2 came from off-shore oil and it has a HHV of 43.5MJ/kg.

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Table 2.1.3 Oil Production Oil Production (g/kg diesel):

Oil production Onshore Offshore Heavy Central Canada

0.005% S 0.005% S 0.005% S 0.005% S CO2 - not including other pollutants 208.57 285.57 612.58 468.54

CH4 1.6306 2.2346 4.7996 3.6698 N2O 0.0083 0.0113 0.0244 0.0186 CO 2.4973 3.4215 7.3466 5.6176 NOx 2.8577 3.915 8.4054 6.4274

VOC, Ozone-Weighted based on Sheet F factors

0.1496 0.2049 0.4397 0.3363

SOx 0.2282 0.3125 0.6707 0.5129 CFCs+HFCs 9E-07 1E-06 2E-06 2E-06

PM 0.0779 0.1066 0.2283 0.1747

Table 2.1.4 Oil Transport to Refinery Oil Transport to Refinery:

Oil transport to refinery

Onshore Offshore Heavy Central Canada

0.005% S 0.005% S 0.005% S 0.005% S CO2 - not including other pollutants 7.8214 7.8441 7.9406 7.8981

CH4 0.0493 0.0494 0.0502 0.0499 N2O 0.0004 0.0004 0.0004 0.0004 CO 0.0127 0.013 0.0141 0.0136 NOx 0.0249 0.0252 0.0265 0.0259

VOC, Ozone-Weighted based on Sheet F factors

0.1014 0.1014 0.1014 0.1014

SOx 0.0237 0.0237 0.0238 0.0237 CFCs+HFCs 1E-06 1E-06 1E-06 1E-06

PM 0.0055 0.0055 0.0055 0.0055

Table 2.1.5 Oil Refining Refining (g/kg diesel):

Refinery

Onshore Offshore Heavy Central Canada

0.005% S 0.005% S 0.005% S 0.005% S CO2 - not including other pollutants 363.44 369.21 393.73 382.93

CH4 0.8435 0.8888 1.0812 0.9964 N2O 0.0297 0.03 0.0309 0.0305 CO 0.5975 0.6668 0.9611 0.8315 NOx 0.8083 0.8875 1.2242 1.0759

VOC, Ozone-Weighted based on Sheet F factors

0.3483 0.3524 0.37 0.3623

SOx 0.8657 0.872 0.8989 0.8871 CFCs+HFCs 6E-07 6E-07 7E-07 7E-07

PM 0.1043 0.1064 0.1156 0.1115

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Table 2.1.6 Fuel Storage and Distribution Fuel Storage and Distribution (g/kg diesel):

Fuel storage and distribution

Onshore Offshore Heavy Central Canada

0.005% S 0.005% S 0.005% S 0.005% S CO2 - not including other pollutants 54.773 55.525 58.72 57.313

CH4 0.1137 0.1196 0.1447 0.1336 N2O 0.0023 0.0024 0.0025 0.0024 CO 0.3935 0.4025 0.4409 0.424 NOx 0.5346 0.5449 0.5888 0.5695

VOC, Ozone-Weighted based on Sheet F factors

0.0498 0.0503 0.0526 0.0516

SOx 0.0598 0.0607 0.0642 0.0626 CFCs+HFCs 4E-05 4E-05 4E-05 4E-05

PM 0.0728 0.0731 0.0742 0.0737

Table 2.1.7 Fuel Dispensing Fuel Dispensing (g/kg diesel):

Fuel dispensing Onshore Offshore Heavy Central Canada

0.005% S 0.005% S 0.005% S 0.005% S CO2 - not including other pollutants 5.061 5.0629 5.0713 5.0676

CH4 0.0097 0.0098 0.0098 0.0098 N2O 0.0003 0.0003 0.0003 0.0003 CO 0.0017 0.0018 0.0019 0.0018 NOx 0.0083 0.0083 0.0084 0.0083

VOC, Ozone-Weighted based on Sheet F factors

0.0023 0.0023 0.0023 0.0023

SOx 0.0197 0.0197 0.0198 0.0197 CFCs+HFCs 7E-09 7E-09 7E-09 7E-09

PM 0.0013 0.0013 0.0013 0.0013 The following table allows some comparison of requirements of 50ppm versus 500 ppm upstream data summaries.

Table 2.1.8 Upstream Emission Summary for diesel with 500ppm maximum sulphur

Upstream Internal Combustion Engines Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 (not including other pollutants) 561.73 643.06 988.35 836.29

CH4 8.42 9.06 11.78 10.59 N2O 0.038 0.041 0.055 0.049

CFCs+HFCs 4.46E-05 4.49E-05 4.58E-05 4.54E-05 CO 3.40 4.38 8.55 6.71 NOx 3.99 5.11 9.88 7.78

VOC-Ozone weighted 0.95 1.01 1.26 1.15 SOx 1.29 1.38 1.76 1.59 PM 0.235 0.265 0.394 0.337

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The following table is added to give some perspective on the relative importance of the upstream versus the direct exhaust emissions for the diesel. Table 2.1.9 Urban Autobus Emissions not including upstream emissions according to Genius Model (g/kg diesel) (undifferentiated data for different oil sources for 50-ppm maximum sulphur content)

onshore offshore heavy Central

Vehicle operation Results for Buses (g/kg diesel)

Year 2005 0.005% S 0.005% S 0.005% S 0.005% S CO2 (not including other pollutants) 3757.7 3757.7 3757.7 3757.7

CH4 0.2451 0.2451 0.2451 0.2451 N2O 0.1655 0.1655 0.1655 0.1655

CFCs+HFCs 0.0041 0.0041 0.0041 0.0041 CO 41.908 41.908 41.908 41.908 NOx 51.905 51.905 51.905 51.905

VOC-Ozone weighted 3.9476 3.9476 3.9476 3.9476 SOx1 0.3901 0.3901 0.3901 0.3901 PM 1.6683 1.6683 1.6683 1.6683

GHG emissions (g(CO2eq)/kg 3814.15 3814.15 3814.15 3814.15 1. SOx emissions for 15 ppm diesel would be 15/50 = 0.3 of these 50 ppm values. SOx emissions for 500 ppm maximum sulphur do not scale directly as there is enough sweet crude oil available that the average sulphur content is often well below 500 ppm. Genius estimates SOx emissions for 500 ppm max diesel to be 1.48g/kg (diesel). 2.2 Exhaust Emission Discussion While the exhaust emissions of carbon dioxide from the combustion of the diesel are predominant over the upstream emissions, the upstream values remain important. In the GENIUS model the upstream emissions range from 18% of direct combustion emissions for light offshore crude to 29.7% for diesel derived from heavy oil. The upstream CO2 emissions for the composite diesel oil of central Canada is 25.5% of the direct combustion emissions. The fugitive methane emissions are almost entirely from the upstream. The highest percentage of methane emissions from direct combustion relative to upstream losses is for a light off-shore oil at 2.8%. For heavy oil the percentage drops to 2%. The major portion of the upstream methane losses come from the leaks and flaring, although in the case of heavy oil the oil production is a major contributor, 4.8 g/kg relative to 6.02 g/kg for the leaks and flares. The methane emissions as a portion of the total upstream greenhouse gas emissions are 18 to 21% (taking into account its multiplying factor of 21 relative to CO2). However, the importance of the methane emissions relative to the GHG emission total for both upstream and combustion drops to about 5%. Nitrous oxide from diesel, even with an equivalent GHG potential of 310 times that of carbon dioxide on a per unit mass basis, remains a negligible factor in the diesel life cycle at about 0.3% of the total of upstream and combustion GHG emissions.

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The major difference for 50 vs 500ppm max sulphur content is in the additional energy requirement to remove the sulphur. This is reflected in the increased carbon dioxide emissions of about 117g/kg for the light oil sources and 127g/kg for the heavy oil. Upstream SOx emissions are also anticipated to increase through the efforts to reduce the sulphur content. This upstream increase is about 0.17g/kg but this is more than offset by the difference during combustion where the decrease is about 1.1g/kg from the higher to the lower sulphur content. 2.3 Comparison with other data The NREL report gives a detailed breakdown of processes, inputs and emissions that allows the US values for the five steps to be compared to the estimates given for Canada. The overview is given in the following table for diesel fuel of 500ppm so it should be compared to Table 2.1.8 above.

Table 2.3 NREL Upstream Overview of Diesel Emissions NREL Upstream Overview

Results for Buses – petrol diesel (500ppm) g/kg diesel Year 2005 Domestic Foreign

CO2 (not including other pollutants) 443.1968 534.4871CH4 0.859716 1.413972N2O 0.052901 0.02244 CO 0.368782 0.430242NOx 1.214151 1.16867

Hydrocarbons (except methane) 0.16157 0.186001SOx 4.142979 4.426159

CFCs+HFCs PM (unspecified) 0.727155 0.750804

The more detailed breakdown of both NREL data for 500-ppm diesel and for Canadian 500-ppm diesel is given in Appendix A1. It can be seen that the oil extraction and processing before refining is far more energy intensive than in the USA. The end result is that for central Canada the upstream carbon dioxide emissions are 836g(CO2)/kg(diesel) while the equivalent value for the USA is from 443 to 534g/kg depending on whether the oils source is domestic or foreign. If one totals the global warming potential for the three greenhouse gases in upstream and final combustion for 50ppm diesel, the importance of the upstream numbers in determining the greenhouse impact of displacing Canadian diesel becomes most striking. The total CO2 equivalent emissions for oil production averaged for Central Canada is 1204g(CO2eq)/kg and the combustion figures of Genius are 3814g/(CO2eq)/kg. The upstream value is some 32% of the downstream value. The total GHG emissions based on the Genius model become 5018g(CO2eq)/kg with 24% from upstream emissions. In summary, because of the very substantial upstream energy requirements for Canadian diesel there is also a significantly greater GHG benefit in displacing its use.

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To give some representative value of the diesel emissions that could be displaced by biodiesel use, this study considers the central Canada mix for the base diesel, 63% from heavy oil and 37% from light oil sources.

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3. Upstream Analysis of Soybean and Canola - Agricultural Production In this chapter the agricultural practices as they relate to soybean and canola are examined to determine their impacts on agricultural sustainability and, more specifically on the impacts of biodiesel derived from these products. An emphasis is put on optimizing yield in a sustainable manner. The following themes all emerge from this main issue:

• Yield as a function of climate, soil, and farm practices, • Fertilizer Production and Use, and Impact of Inoculants on Fertilizer Use, • GM-modified, herbicide-resistant seeds and impact on herbicide use, • Cultivation Practices and Farm Energy Use, • Nitrous oxide emissions related to the fertilizer use and farm practices.

The 1998 NREL report of the life cycle inventory of biodiesel remains one of the clearest documents in explaining the various factors influencing environmental impacts of soybean production for biodiesel. However, certain data sets used by NREL, e.g. on herbicide use, fertilizer production inputs, tilling practices, are outdated. This report introduces more recent data and provides new interpretation to allow a comparison of the results with NREL’s as well as Levelton’s in a more recent Canadian study. As for nitrous oxide emissions NREL concluded: “the lack of consistent data and high degree of variability in soil emission measurement prevents us from deriving a meaningful expected soil emission estimate for soybeans. … For this model the field emissions for N2O and NOx will not be reported to prevent any misinterpretation of the overall results.” This report will delve into nitrous oxide emissions. At the very least, it is hoped that the analyses will identify more clearly the issues to be addressed. 3.1Highlights of Agricultural Analysis Updating Findings of NREL and Levelton 3.1.1Yield This report provides Canadian data on soybean production and relates it to soil and climate conditions. Canadian soybean production in recent years has been highly variable and therefore it is challenging to assign a typical or average yield. Nevertheless, the analysis has led to a current estimate for Canada of 2.6 tonnes/ha, intermediate to the value of 2.4 tonnes/ha of NREL and 2.7 tonnes/ha as given by Levelton. The yield of soybean is highly dependent on climate and soil conditions. Pilot plot yields indicate that there is substantial potential for yield improvement, as long as there is no significant shift in planting in marginal land (cool climate, clay soils) as opposed to optimal warm climate, loam soils. The sustainability issues surrounding these variables are considered in this chapter.

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3.1.2 Fertilizer and Inoculants Both the NREL and Levelton report estimate low level use of nitrogen fertilizer (9-11kgN/ha) on soybean crops. However, the economic or environmental case can no longer be made for the use of manufactured nitrogen fertilizer in soybean production, and all extension offices of provinces and states are advising against such nitrogen fertilizer use. Manure fertilization may remain a sustainable option where the prime value sought is in the phosphate and potassium addition. The major progress that has been made to eliminate the residual need for nitrogen fertilizer is the development and use of inoculants of Bradyrhizobium japonicum that spur nodule development and nitrogen fixation in soybean. This inoculant is most important in fields where soybean has not been planted for many years. Before the development of these inoculants the farmers would need to use nitrogen fertilizer until the nodular microbial population developed in the soil. This chapter shows how this new pattern of lower nitrogen use has positive environmental impacts. It has been very difficult to analyze with any accuracy the upstream life cycle inventory (LCI) of the fertilizers. Different data sets from suppliers like SimaPro yield very different values, and these divergent values include the GHG impact. Nitrogen fertilizers have by far the biggest impact on GHG emissions, due to the high energy input necessary for ammonia production. It is recognized that there have been dramatic improvements in energy efficiency over the last few decades, but quantifying these gains remains problematic. This study cannot give definitive numbers but it can shed light on the fertilizer industry and provide some updated range of impacts from fertilizer production in Canada and also indicate some legitimate targets for improved efficiency. The analyses also should help emphasize the importance of the benchmarking exercise of nitrogen fertilizer production that is anticipated in the coming year by the Office on Energy Efficiency of NRCan in collaboration with fertilizer producers. 3.1.3Herbicide-resistant Seed and Herbicide Use There has been a major shift in the type of herbicides over the last ten years due to the introduction of glyphosate-resistant soybean. Glyphosate use has gone up remarkably and is many times larger than the 4% of herbicides listed in Table 58 of the NREL report. The advantages and disadvantages of the seed and the herbicide use are among the most hotly debated subjects in agriculture today. Our analysis indicates that the environmental impacts can be positive but that attention must be paid to adopting the proper farm management processes to achieve this. There are some somewhat ill-appreciated advantages to the latest developments in glyphosate use. This product facilitates the new conservation and no till practices. Glyphosate has one of the lowest vapor pressures of the surface-applied herbicides and, because of this, the inadvertent volatilization of the

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herbicide and potential damage to other crops and animals (including humans) is considerably reduced. 3.1.4 Cultivation Practices and Farm Energy Use There has been a significant shift in the last decade to low-till and no-till cultivation practices for soybean. This can in part be linked to the wider use of the herbicide-resistant varieties. One function of cultivation is to reduce weed competition with crops and the new possibility of using efficient post-emergent herbicides makes the reduced cultivation much more practical. In soybean production the reduced tillage is particularly interesting from an environmental standpoint as the soybean plants provide relatively low protection to erosion with more aggressive cultivation techniques. Since the NREL and Levelton studies there has been some in-depth analyses of the energy requirements on farms in Canada by Dyer and Desjardins. These results are consistent with the previous estimates made, so they confer a further degree of confidence in this and previous analyses. They also enable some simple sensitivity analyses to show the impacts of different cultivation practices on energy use. 3.2 Detailed Agricultural Analyses 3.2.1 Soybean Yield In Canada In Table 3.2.1 is presented the soybean production, harvested area and yield.25

Table 3.2.1 Canadian Soybean Agricultural Production 1998 – 2002 Canadian Soybean Yield Year 1998/99 1999/20002000/20012001/20022002/2003 5-year av 4-yr av* Acres harvested 2420600 2479880 2620670 2584500 2529280 2526986 2512608Hectares harvested 980000 1004000 1061000 1046356 1024000 1023071 1017250Production (tonnes) 2737000 2781000 2703000 1635000 2335100 2438220 2639025Yield (tonnes/ha) 2.79 2.77 2.55 1.56 2.28 2.38 2.59Notes: 40bu/acre = 2.69tonnes/ha, *4 year average excludes 2001/2002 The most remarkable feature of this table is the evidence of the exceptionally poor harvest of 2001. This was caused by a combined severe drought and aphid infestation. The question becomes to what extent do these five years of data form a representative sample and constitute a basis for future projections. According to OMAF officials, the 2001 drought is highly atypical and with a probability lower than one in twenty. So this year should not be factored in as a one in five occurrence as would be done with a five-year averaging process. As the years 2000 and 2002 were not good years either, there appears to be a good balance of climatic variation over the fours years 1998 through 2002 excluding

25 The table is derived from data of Statistics Canada, Cereal and Oilseeds Review and the Ontario Soybean Growers Association, as published on the Web at http://www.soybean.on.ca/stats.htm.

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2000. This is considered as the representative set at this time and therefore the average yield for Canada is estimated to be 2.6 tonnes/ha. Levelton has used 2.7 tonnes/hectare for a Canadian average and this appears to be based on the 40bu/acre (2.69 tonnes/ha) target that canola producers have set over recent years. NREL data sets arrived at a 36 bu/acre or 2.42 tonnes/ha average yield over the key soybean producing states of the USA. The NREL data also shows strong variations among different states and it is worthwhile to consider how the Canadian and USA data can be related. Ohio is immediately across Lake Erie from the southern Ontario area of significant soybean cultivation. It had a three-year average ending in 1992 or 38bu/ac and 41bu/ac ending in 1994. When reference is made to the results on climate below these results from Ohio appear to present a consistent overview of soybean yield. Soybean yield is dependent on heating days, soil conditions as well as available water levels. The results from various variety trials over 2001-2003 have been grouped together here to show the variations.26

Table 3.2.2 Soybean Yield as a Function of Soil and Crop Heat Units

Crop Heat Units 2400 ±100

2650±150

2800±100

3100±200 clay

3100±200 loam

3400±100 clay

3400±100 loam

Soybean Yield ((t/ha) 2.93 2.83 3.23 2.35 3.40 2.53 3.50 The dependence on soil type is important, with a 45% increase in yield in going from clay to loam in the areas of 3100 crop heat units. One of the reasons likely for the poor performance of soybean on clay soils with poor drainage is the adverse impact that such conditions have on the aerobic nodular bacteria responsible for nitrogen fixation (see more complete discussion below). Very remarkable is the sharp difference between these results and the average yields for the whole crop area as given in Table 3.2.1. The differences could be due to four factors.

• First and foremost, the trials are to determine the progress in the breeding and choice of new soybean seed varieties. Varieties have improved yield dramatically over the last half century and it is clear that progress continues to be made.

• The management of all the crop plots follows best practice principles in planting, fertilization and herbicide use so the various plot results can be considered comparable. If a significant number of farmers are not following the same best practices in normal farming operations, yield loss can be expected.

26 Ontario Oil & Protein Seed Crop Committee (OOPSCC). Ontario Soybean Variety Trials for 2001-2003. http://www.oopscc.org/2004rpt.pdf.

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• The plots are chosen to be relatively homogeneous in structure so this naturally tends to favour the choice of more productive land. Horst Bohner, soybean specialist with OMAF, has suggested this can represent a good 10% difference in anticipated results from plots as compared to full field operations.

• Finally, it may be that a significant portion of the soybean crop is now being planted in more marginal land, i.e. clay soils with low heat units (less than 2700).

If the first two factors are predominant, then there is clearly the opportunity for considerable improvement in future yields of soybean. If the marginal land use is a major factor then it indicates that some attention should be paid to any policies that encourage higher production of soybeans. Are the overall environmental impacts associated with farming marginal land significantly worse than for more productive land? This issue will be considered further in this chapter when the core information base to do this assessment has been presented. 3.2.2 Soybean Fertilizer and Inoculant Use As a legume the soybean plant has the important advantage of nitrogen fixation. This is established through the symbiotic relationship between the plant and the bacteria bradyrhizobium japonicum. (Bradyrhizobia have been separated from other nodule bacteria of the group rhizobia because of their slow growth. Japonicum is the particular species associated with soybean.) The plant forms nodules to support these bacteria and they in turn furnish all the nitrogen necessary for the plant. There are three factors that limit this action.

• If the field has not been previously planted to a soybean crop the initial very small population of nodular bacteria present in the soil cannot build up fast enough to support normal growth of the soybean.

• If there are high levels of nitrogen already available in the soil, e.g. through fertilization, then the nodule formation is suppressed until nitrogen levels diminish,

• Bradyrhizobia are aerobic bacteria and therefore require reasonable oxygen levels in the soil. The bacteria and hence the soybeans do not grow well in poorly drained land with high clay content.

Remedial action in the first two cases is now simple. Inoculants of the nodular bacteria have now been developed that are highly effective. Trials have shown excellent yield response of 20-30% improvement.27 Nitrogen fertilization of soybeans should be avoided to encourage the nodule development and nitrogen fixation by the plant itself. Recent studies indicate no real gains can be achieved through the nitrogen fertilizer use except under extremely low levels of available nitrogen, for instance, caused by soil imbalance where the C:N ratio becomes very high due to integration of wheat straw into an 27 Bohner, Horst. 2003. Head-to-Head Comparison of Soybean Inoculants in 2002. http://www.gov.on.ca/OMAFRA/english/crops/field/news/croptalk/2003/ct_0103a8.htm

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already nitrogen-depleted soil.28. As the fertilizer represents a cost to the farmer, the already common practice of no nitrogen fertilization of soybean is anticipated to become virtually universal, except when some farmers have P and K available mainly as manure and the nitrogen comes with this manure. Not only should nitrogen fertilization of soybean be generally avoided, it is good farming practice to reduce the nitrogen fertilization of the following crop because of the readily available nitrogen left in the biomass residue and in the rhizodeposition of the soybean. For a soybean crop yielding 3476kg/ha (14% humidity) Goss et al29 estimated a net nitrogen credit 25±8kg(N)/ha for the following crop due to the soybean. This credit was determined by comparing the corn yield planted after the soy crop with corn yields from equivalent areas but with varying additions of nitrogen fertilizer. The corn yield after soybean with no fertilizer was equivalent to the corn yield with a fertilizer input of 25kg(N)/ha. The credit applied only to a nodulating soybean variety so this can clearly be associated with the fixation of nitrogen by the legume. As the impact of the soybean should be proportional to yield, it appears that a N fertilizer credit of some 25/3.476 = 7kg(N)/tonne yield may be attributed to the normal nodulating soybean. This would be some 18kg(N)/ha for an average yield of 2.6tonne/ha.30 This credit is in spite of the fact that there may a net loss of nitrogen from the soil as a whole after soybean. That is to say that there may be more nitrogen taken away in the protein of the soybean harvest than is put in by the nitrogen fixation process. The net credit of about 7kg(N)/tonne(soybean yield) has been experimentally determined by others31 as well. Its reality is becoming more widely accepted and integrated in crop guidelines for fertilizer input. This is in spite of

28 Whitney, David A. Kansas State University, Soybean Production Handbook. http://www.oznet.ksu.edu/library/crpsl2/c449.pdf 29 Goss, M.J., A, de Varennes, Smith, P.S. and Ferguson, J.A. N2 fixation by soybeans grown with different levels of mineral nitrogen, and the fertilizer replacement value for a following crop, Can. J. Soil Sci. :139-145. 30 The importance of the C:N ratio in establishing the nitrogen credit emerges from an analysis in the April 21, 2000 bulletin, Thinking about Nitrogen Recommendations, by Emerson Nafziger and Robert Hoeft of Illinois University, http://www.gocorn.net/mag_Fertilizer6.htm. “It is a technical point, but the credit given to corn following soybean is more likely a lower penalty than when corn follows corn. The penalty comes from the fact that as crop residue breaks down in the soil, nitrogen is absorbed from the soil, making less available for the crop. Because soybean leaves much less crop residue, and the residue has higher nitrogen content than that of corn, its breakdown ties up less nitrogen. In fact, when we grow corn following corn after removing cornstalks from the field, the response to nitrogen is similar to that for corn following soybean. And removing soybean residue before the following corn crop does not affect the nitrogen response of the corn crop.” 31 Staggenborg et al have estimated an apparent supply of nitrogen to a wheat crop following a soybean crop of 21kg(N)/ha. Staggenborg, S.A., Whitney, D.A., Fjell, D.L. and Shroyer, J.P. 2003. Seeding and nitrogen rates required to optimize wheat yields following grain sorghum and soybean. Agron. J. 95:253-259.

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the calculations that appear to show a net loss of some 6kg(N)/tonne yield32 based on estimates of nitrogen fixed and nitrogen removed with the soybean crop. The apparent net loss of soil nitrogen in many experiments may be larger than it is in reality. This relates back to the difficulty in getting accurate measurements and estimates of the nitrogen balance for such crops because of the substantial senescent leaf biomass of soybean and of the rhizodeposition term that is rarely taken into account (see Appendix A4 on these issues). For this LCA the baseline scenario has a net nitrogen fertilizer input of zero for soybean. An optimistic scenario for sensitivity analysis includes a credit for the reduced fertilizer use in the subsequent year of 7kg(N)/tonne yield (leading to reduced nitrous oxide emissions) while the pessimistic scenario for comparison is that of the current Genius model with 8.9kg(N)/ha of fertilizer added to the soybean crop. It is advisable to undertake soil sampling and determine what levels of phosphate and potash should be added for good soybean yield.33 These specific fertilizer requirements at seeding time are a reflection of the crop rotation history of the soil combined with the soybean requirements. The long-term impact of the soybean on fertilizer use is better estimated from the effective removal of P and K through the grain harvest. The Canadian Fertilizer Institute has summarized nutrient uptake and removal estimates for different crops in Canada.34 A conversion of the values given by this reference leads to average values of 14 and 23kg/tonne respectively for P2O5 and K2O. The same two estimates for fertilizer removal per extracted tonne of grain can be obtained from analysis of the National Plant Food Institute as interpreted by the states of Ohio and Kansas. Some soils have a natural supply of phosphate that would reduce fertilizer use. However, in a sustainable system it is estimated here that the P and K must be made up eventually through fertilizer use at these rates. With a harvest of 2.6 tonnes/ha, this represents fertilizer rates of 36 and 60 kg/ha

32 There is typically a measured loss of nitrogen because of the high protein content of the oilseed that is harvested. The factors used for converting soybean nitrogen to protein content are 5.71 and 6.25. The factor of 5.71 is the more accurate number for converting nitrogen content to protein where the protein is strictly limited to those amino acids that the body can exploit directly as proteins. The factor of 6.25 is applicable to oil seeds in general but it overestimates true protein availability of soybean. However, the soybean industry widely uses the 6.25 conversion factor, see, for example, http://www.grainscanada.gc.ca/Quality/Soybean/2001/soybean01hs05-e.htm. The factor of 6.25 yields what more accurately is referred to as “crude protein content”, see http://www.fao.org/documents/show_cdr.asp?url_file=/DOCREP/006/Y5022E/y5022e03.htm Average crude protein content of Canadian soybean seed is 41.9%, so a yield of 3476kg/ha (14% humidity) means a nitrogen removal of (3476kg/ha)(.419)/{(1.14)(6.25))} = 204kg(N)/ha. As the average fixed nitrogen was estimated at 183kg(N)/ha, the net loss is about 21kg(N)/ha or 6kg(N)/tonne yield. 33 OMAF. Agronomy Guide for Field Crops (Chapter 4). http://www.gov.on.ca/OMAFRA/english/crops/pub811/4fert.htm - phosphate 34 Canadian Fertilizer Institute. Nutrient Uptake and Removal by Field Crops. Eastern Canada. 2001. http://www.cfi.ca/uploaddocuments/d160%2BNU%5FE%5F01%2Epdf

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respectively for P2O5 and K2O. The NREL report cites a Chapman and Carter article in 1976 that determines requirements based on soybean harvest as 15 and 20 kg for P and K per tonne of yield. Actual fertilizer use in the USA per hectare based on a report of Ali and McBride as cited by NREL would lead to an estimate of 14kg and 24kg for P and K per tonne yield when the average yield of the NREL study is used. The analysis here appears therefore to be consistent with the NREL study. Levelton, in contrast, has estimated higher phosphorous than potassium requirements, 12 kg and 7 kg per tonne yield for the components of P and K. The most likely explanation for the difference between the results is that the soybean impact on potassium fertilizer use would most often be seen in the year following the soybean use. This is why the estimate for fertilizer requirements is based here on the potassium depletion of the soybean harvest. There has been an in-depth analysis undertaken for the agricultural practices associated with soybean production. This appeared warranted considering a number of different viewpoints presented in the literature concerning the value and impact of this leguminous crop for its nitrogen-fixing capacity. 3.3 Upstream Canola Analyses For canola, there appears to be a fair convergence of data from different sources. The one area where progress appears to be changing the estimates is in the impact of conservation tilling on energy requirements for field operation. Even the “conventional” tilling has changed substantially in Western Canada with the total replacement of the moldboard plow by the chisel plow. Further efficiencies are realized in conservation tillage. So the values cited in the literature of about 70L/ha are being replaced in this study by values between 37-54L/ha. 3.3.1 Canadian Canola Yield Canola yield is mainly influenced by the level of rainfall in the west. Below are yield values over the past 18 years along with the average. Canola yield appears to be little affected by the specific tilling practice so average values will be used throughout this study.

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Table 3.3 Canadian Canola Yield35

Year Yield (kg/ac)

Yield1 (kg/ha)

1986 572 1413 1987 576 1423 1988 459 1134 1989 445 1100 1990 523 1292 1991 544 1344 1992 526 1300 1993 526 1300 1994 526 1300 1995 485 1198 1996 607 1500 1997 526 1300 1998 566 1399 1999 647 1599 2000 607 1500 2001 526 1300 2002 526 1300 2003 567 1401

Average 1339 1. Conversion Factors: Note that, on average, a bushel contains 24kg of canola, so that to convert from bu/ac to kg/ha the multiplication factor is (1bu/ac)(24kg/bu)(2.47ac/ha) = 59.3. The average canola production is therefore 1339/59.3 = 22.6bu/ac. For crop planning purposes Saskatchewan farmers fertilizer the crops in the expectation of a 26bu/ac (1542kg/ha) crop. 3.3.2 Canola Fertilization The outreach officials of the Saskatchewan department of Agriculture, Food and Rural Revitalization work closely with Agriculture Canada scientists to determine recommended operations for growing canola. This study will therefore take advantage of the data on recommended fertilizer use and the latest estimates of field energy requirements that are available.36 The N-fertilizer application rate is suggested as 67kg/ha (60lb/ac) for planting in stubble and half of this rate for a fallow field. The fertilizer rate for stubble crops will be used in this study because it appears to reflect a value that must be input to sustain the soil characteristics in the long run. 35 Statistics Canada - Field Crop Reporting Series –as provided by the Canola Council http://www.canola-council.org/markets/acreageproductionandyield.html 36 Saskatchewan Agriculture, Food and Rural Revitalization. Crop Planning Guide 2004. http://www.agr.gov.sk.ca/docs/econ_farm_man/production/cereals/cpgblack04.pdf.

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The recommended P-fertilizer application has been lowered slightly in Saskatchewan and it is now at 22.5kg/ha (20lb/ac). The equivalent P2O5 lost in the seed harvest is 18.4kg/tonne yield so for the average yield of 1.34 tonnes/ha the phosphate fertilizer requirement from a long-term sustainability view would be 24.6kg/ha which is reasonably close to the recommendation. Sulphur and potash requirements are very dependent on soil conditions. Many fields have an adequate supply of K2O from the slow conversion of mineral within the soil. However, other soils are deficient and the potassium fertilizer is important to optiimize yield. The losses of K20 and sulphur through the grain removal are respectively 9.2kg/tonne yield and 6.6kg/tonne yield.37 The potassium fertilizer addition has been integregated into this LCA from the perspective of long-term sustainability of the soil. The sulphur input to the soil is most likely to be in the form of ammonium sulphate. From Table 3.5.3 it can be seen that the typical nitrogen-sulphur fertilization in the form of ammonium sulphate would lead to only a minimal difference (either positive or negative depending on whether the comparison is with ammonia or urea) on the energy requirement per unit nitrogen input. For this reason the sulphur addition is deemed to have negligible overall impact on the LCA. 3.4 Field Energy Requirements 3.4.1 Background Over the past twenty to thirty years there have been a number of factors decreasing the Canadian field energy requirements and a few increasing them. Among the former are:

• A major shift from to in the use of the chisel plow instead of the mouldboard plow for primary tillage,

• A major shift towards conservation (mini-till) and no-till practices, • Improvement of some 4.6% in the efficiency of farm diesel equipment over

the past twenty years.38 Among the factors increasing energy requirements are:

• Reduced number of active farmers means emphasis must be given to speed of operations versus energy efficiency of them.

• Transport of grain from field to farm granary, and from farm granary to rail line are both longer because of bigger farms and closure of rail lines.

37 Canola Council of Canada. http://www.canola-council.org/, 38 Grisso, R.D., Kocher, M.F. and Vaughan, D.H. 2004. Predicting tractor fuel consumption. Applied Engineering in Agriculture (in press), as now appearing on R. Grisso’s Web site. This paper is pertinent to the Canadian agricultural analysis as it offers a good validation and update of some standards of the American Society of Agricultural Engineers that have been used by Agriculture Canada in energy requirements of agricultural machinery. See a brief discussion of these issues in Appendix A2.

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The importance of the first improvement was well recognized by Dyer and Desjardin (2003b). When they compared results from the Farm Fieldwork and Fossil Fuel Energy and Emissions (F4E2) model with survey results based on Farm Energy Use Surveys (FEUS) for 1996 they realized that they needed to convert modeled results for primary tillage from mouldboard use to chisel plow use in Western Canada to get a good fit. In effect, the use of the mouldboard plow in Western Canada has virtually disappeared because:

• It has been found unnecessary as a soil preparation technique for the soil and climatic conditions.

• It can lead to serious soil erosion. • It is a more energy intensive process than the chisel plow and is therefore

more costly. The implications of this change in the energy intensity of agricultural operations are important. The modeled diesel energy consumption in Canada according to F4E2 dropped from 82.5PJ to 71.6PJ. (The FEUS survey results were 72PJ for diesel use in 1996.) The transformation from conventional to conservation or no till is also remarkable. The following numbers are extracted from Table 3 of Dyer and Desjardins (2004).

Table 3.4.1 Changes in Tillage System Prevalence from 1991 through 2001 Prairies Quebec/Ontario Canada 1991 Conventional 67% 80% 69% Conservation 26% 16% 24% No till 7% 4% 7% 1996 Conventional 52% 64% 53% Conservation 32% 21% 31% No till 16% 15% 16% 2001 Conventional 38% 59% 40% Conservation 31% 21% 30% No till 31% 20% 30% Saskatchewan estimates about a 30% reduction in field energy requirements in going from conventional till (on stubble) to conservation till. It should be clear that the recent improvements in farm energy efficiency outweigh the potential setbacks such as longer transport runs. It is important to note that these fuel consumption figures considered on their own do not necessarily yield the most efficient energy options. The changes that

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may be required in other variables may outweigh the direct diesel energy reduction. The following is a table directly extracted from Lindwall et al. 39

Table 3.4.2 Total Energy Outputs and Inputs for Conventional and Zero Tillage according to Lindwall et al

Of the three crops only canola ends up requiring slightly poorer ratio of output/input energy with a no till regime. This is associated with higher N-fertilizer requirements for canola under no till. A conservation or mini-till regime would allow better N access by the canola and this would tend to make it superior (in the sense of output/input ratio) to the conventional till. Analyses presented later will show that in terms of the total LCA of biodiesel the fuel consumption for field work of canola and soybean will not be a major factor. Biodiesel production requirements, embodied energy in fertilizer, possible nitrous oxide emissions from fields, etc. end up more important. Therefore, some simple analyses employing the latest Saskatchewan estimates are used. 3.4.1 Estimates for Field Energy Requirements The 2004 Crop Planning Guide of Saskatchewan Agriculture, Food and Rural Revitalization provides the following estimates for overall diesel fuel consumption for canola field operations for three different cases:

• Conventional till and seeding in stubble1 54L/ha or 46.5kg/ha2 • Till into fallow: 44.5L/ha or 37.8kg/ha • Conservation till and direct seeding: 37L/ha or 31.4kg/ha 1. This takes account of the chisel plow use. 2. This is based on a specific gravity of 0.85 the diesel.

To ensure that the direct field energy requirements are fully integrated into the LCA the conventional till figure40of 54L/ha is to be used as the base case and the conservation till total is to be used for sensitivity analysis.

39 Lindwall, Wayne, McConkey, Brian, Campbell, Con and Lafond, Guy. 1998. Twenty Years of Tillage, “What have we learned?” http://www.reducedtillage.ca/20yearsct.pdf.

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3.5 Upstream Fertilizer Life Cycle Inventory In a Life Cycle Analysis it is critical to be able to trace back the consequences of the manufacture of goods like fertilizer. It is well understood that nitrogen fertilizer manufacture is very energy intensive due to the initial step of ammonia production. This serves as a feedstock for the other forms of nitrogen fertilizer such as ammonium nitrate and urea. However, it is very difficult to discern what is the most reliable public data on this and consequent production steps. For instance, a few data sets from SimaPro relating to ammonia production have been examined and they are highly inconsistent. This calls into question the ability to arrive at reliable life cycle inventory numbers for all the nitrogen fertilizers. The NREL has used the database, DEAM (Data for Environmental Analysis and Management) as produced by Ecobilan of PricewaterhouseCoopers. They have taken ammonium nitrate, triple superphosphate and K2O as representative compounds for the NPK components of fertilizer. The core data used on fertilizer inputs can be derived from Table 62 of the NREL report. SimaPro has data sets for nitrogen and phosphate fertilizers but they differ to such an extent from the DEAM data (see Table 3.5.1) that we have found it necessary to return to basic principles and some understanding of the processes involved to arrive at some reasonable estimates of the inventory for nitrogen fertilizer.

40 In a more recent study by Dyer and Desjardins (2003b), the actual tillage practices of 1996 were integrated into the F4E2 model estimates of energy use and this produced an estimate some 5% below the FEUS survey results. It is possible that the higher transport requirements with diesel-powered vehicles may account for this difference. As the use of conventional tilling estimate gave an excellent overall fit with survey results, this is to be used as the conservative base case.

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Table 3.5.1 Comparison of SimaPro and DEAM Data (from NREL study) Raw materials per kilogram nitrogen of fertilizer

NREL, N SimaPro, N fertilizer

NREL, P SimaPro, P

coal (in ground) kg 0.14 0.008 0.15 0.0394 oil (in ground) kg 0.005 0.002 0.04 Natural gas (in ground) kg 1.25 0.538 0.12 0.105 Phosphate rock (in ground) kg 0 4.16 Limestone (CaCO3, in ground) kg 0.03 1.31E-08 0.03 Water used (total) liter 1.5 0.43 Air emissions CO2 (fossil) g 3903.9 576 832.9 347 CH4 (Methane) g 7.04 0.34 2.18 0.404 Nitrous oxide (N2O) g 0.018 3.09 0.028 0.001 CO g 12.54 0.059 0.41 0.038 Hydrocarbons (except

Methane) g 0.007 0.009 1.74

Hydrocarbon (unspecified) g 0.01 0.06 Particulates (PM10) g 2.43 0.263 0.42 0.1 Particulates (unspecified) g 2.0 0.31 2.21 0.24 Sulfur oxides (SOx as SO2) g 30.5 0.156 3.27 15 Nitrogen oxides (NOx as NO2) g 15.0 3.86E-05 2.71 6.49 HCl g 0 0.005 0.081 0.017 Ammonia (NH3) g 102.07 0.4 4.0E-05 3.57E-04 Water emissions BOD5 g 1.86 5.99E-06 0.02 1.74E-05 COD g 5.6 1.23E-04 0.17 3.8E-04 Solid waste (non-hazardous) kg 0.05 0.056 Total primary energy MJ 71.1 0.7 14.4 Fossil energy MJ 70.9 0.733 14.2 Worrell and De Beer published a review of the energy savings potential in the nitrogen fertilizer industry in 1994. The paper has the merit of giving enough information to allow some further analysis to be made to determine which numbers may be appropriate, at least for the purposes of GHG emission calculations. The first point that should be made is that the industry in the Netherlands had lowered the specific energy requirements for the base feedstock of ammonia from about 55GJ/tonne (GJ expressed here as LHV or lower heating value) to 33.5 over the period 1977-1988. Best available technology had demonstrated at the same time plants working at 28GJ/tonne. Clearly the industry is transforming itself in a major way and the degree to which old plant performance is integrated into a data set would clearly have profound effect on the data. This may explain part of the major difference between different data sets.

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The article divides the fertilizer process into the various key production processes for ammonia, nitric acid, urea and ammonium nitrate. The data on the “current” technology of the article is for 1992 but the results can be applied well beyond this date as the analysis extends to the promising technological options and their cost effectiveness. One set of options all have less than a three-year payback, and in this analysis, this is taken as a minimum baseline to which the North American industry should be aspiring. It is recognized that because of the much lower energy prices in the North American market there has been less pressure to improve the energy intensity of fertilizer production and this is still reflected by higher energy consumption in Canada. The analysis here begins with a slightly revised presentation of the Table 5 from the Worrell and De Beer paper. The data has been transformed to give the higher heating value (HHV) instead of the LHV in line with other calculations in Canada of energy intensity. For natural gas in Canada this meant multiplying lower heating values by 1.12.

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Table 3.5.2 Consumption of Energy and Resources for the Production of N-fertilizers adapted from the original material of Worrell and Blok Resource Ammonia Urea Nitric Acid

Ammonium Nitrate

Ammonia (g/kg) 568 280 208 CO2 (g/kg) 735 Nitric Acid (g/kg) 765 Supplementary Natural gas (HHV) for steam (MJ/kg)

35.4 2.84 High eff -2.71 Low eff. 0

High eff. -0.67 Low eff. 1.1

Total natural gas (MJ/kg) required2

35.4 High eff. 22.9 Low eff.3 28.2

High eff. 7.2 Low eff. 9.9

High eff. 12.2 Low Eff. 16.04

Total natural gas (MJ/kg) required under low efficiency scenario integrating in impact of 1995 estimate of Cdn ammonia production energy intensity

47.4 35.0 13.3 21.1

Supplementary Electricity (MJ/kg)5

0.36 0.08 0.11 0.004

1. Minus indicates steam export potential with an integrated, dual-pressure system. 2. This includes embodied energy from feedstock of ammonia or nitric acid. 3. In the low efficiency scenario some 60% of the carbon dioxide is assumed to be provided

by capture of CO2 from ammonia and 40% produced separately from natural gas combustion to add (0.4)(49.6MJ/kgNG)*(0.735kgCO2)/2.75kgCO2/kg(NG)= 5.3MJ/kg The Blue Johnson Consulting Group estimate of supplementary natural gas requirement for urea (see reference below) was 6.8MJ/kg of urea. The supplementary demand for natural gas in this low efficiency scenario may therefore be somewhat pessimistic at 5.3+2.8 = 8.1 MJ/kg.

4. The low efficiency ammonium nitrate case is based on no capture of the potential gas export from the nitric acid production plus the additional energy of older plants needed for the ammonium nitrate production itself.

5. To calculate total electrical energy needed for fertilizers other than ammonia account would need to be taken of electrical energy embedded in the feedstocks such as ammonia.

6. Electricity for ammonia production is not explicitly given in the articles of Worrell. An estimate of 0.3 MJ/kg of ammonia has been used. This is somewhat less than 1% of energy requirement and is the right order of magnitude.

From the table it can readily be seen where the big progress can be made in fertilizer efficiency. The initial ammonia production through the steam reformation to yield hydrogen and the subsequent reaction of hydrogen with nitrogen is highly

36

energy intensive. However, the processes for nitric acid and ammonium nitrate manufacturing are both exothermic. Careful integration of some of the steam heat processes can lead to the excess heat from the nitric acid and ammonium nitrate production being fed back to other processes. In older technology, or in areas where the natural gas feedstock price is low, there has been a tendency to keep the processes separated and even the exothermic processes end up requiring significant energy. For instance, in ammonium nitrate production, the non-integrated plants tend to consume even more steam energy than the integrated ones are actually able to deliver as excess energy. This difference is shown in the table by providing two scenarios for ammonium nitrate production In urea production, very low energy costs would tend to have the carbon dioxide produced directly from burning fossil fuels. However, this isn’t necessary as the carbon dioxide can be stripped as a byproduct from the ammonia production for making urea. While urea formation is actually a two-step process, for our purposes it can be represented by the simple equation,

2NH3 + CO2 ---> (NH2)2CO +H2O. (3.5.1)

The stoichiometry then indicates that 60kg of urea requires 34kg of ammonia and 44kg of carbon dioxide. The implications of various technology options given above can now be analyzed in the light of where the Canadian fertilizer has been and where other current estimates indicate where the industry has achieved at this time. A recent survey conducted by Blue Johnson Consulting Group and cited by Canada’s National Climate Change Process41 provided an estimate for energy use in North American ammonia production. The figure was adjusted to Canadian industry by assuming about a 10% higher efficiency. For anhydrous ammonia this leads to an estimated requirement of 44MJ/kg(N) for natural gas and 0.4MJ/kg(N) for electricity in Canada. Coxworth made some estimates of where the fertilizer industry was in 1995. The estimation techniques of Coxworth were adapted from earlier work by Mudahar et al, 1982. Their techniques provide a means of assessing not only ammonia but the other fertilizers as well. In the following table the energy intensity of urea and ammonium nitrate relative to ammonia are compared using the approaches of Coxworth and Worrell and De Beer. The practice of providing energy values normalized to nutrient content has been used as it helps put the results in the light of the agricultural impact of the fertilizer. For the year 1995 Coxworth estimated that energy requirements for ammonia production in Canada were 57.6MJ/kg(N) and averaged fertilizer energy intensity of 66.1MJ/kg. By 1998 the Canadian Agricultural Energy End-use Data Analysis Center had adjusted the fertilizer estimate downward for their current year by

41 Canada’s National Climate Change Process (NCCP). Greenhouse Gas Emissions and the Canadian Fertilizer Industry. 2002. http://www.nccp.ca/NCCP/national_stakeholders/pdf/1_d_fertilizer_industry_overview_e.pdf

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34.5%42. This was to account for technological improvements claimed by the industry. If the 34.5% reduction were applied to ammonia itself this would lead to a revised energy intensity of 37.7MJ/kg(N). This intensity appears too low based both on the survey results and the major changes this would imply in the industry. The 44.4MJ/kg(N) is actually equivalent to 23% improvement in ammonia itself. However, as will become apparent, there appears to be enough “low-hanging fruit” in other parts of the fertilizer industry that could lead to, for instance, a 30% improvement in the energy intensity of the urea production over the last few years, see Optimistic Estimate below.

Table 3.5.3 Natural Gas Requirements for N-Fertilizer Production1

Worrell MJ/kg(N)

1995 Scenario (low eff) MJ/kg(N)

Coxworth2 Estimate for 1995 MJ/kg(N)

Optimistic Estimate3 2003 Canada MJ/kg(N)

Optimistic Estimate 2003 Canada kg(CO2)/kg(N)

Slow Improvement Estimate4 Canada 2003 MJ/kg(N)

Slow Improvement Estimate Canada 2003 kg(CO2)/kg(N)

Ammonia 43.0 57.6 57.6 44.0 2.44 47.8 2.65 Urea 49.1 75.0 76.1 53.0 2.94 61.9 3.43 Ammonium nitrate

34.9 60.3 66.6 46.9 2.60 50.6 2.81

UAN 65.1 Ammonium sulfate

60.4

Mono and diammonium phosphate

59.5

1. All the estimates are based on natural gas delivered to the site and used uniquely for the manufacturing. Mixing and transport are considered separately. The full LCA should account for upstream natural gas production and transport for gross energy inputs and complete CO2 emissions.

2. The Coxworth estimates are taken from Appendix A of http://www.usask.ca/agriculture/caedac/PDF/mcrae.PDF.

3. The optimistic scenario assumes ammonia production is 10% more efficient in Canada than the USA survey estimate, that f=0.9, and Snit +SAN are respectively 0 and 1.1, i.e. there is still no significant energy recovery from nitric acid and ammonium nitrate exothermic processes through integrated, dual-pressure steam systems.

4. The slow improvement estimate is based on ammonium production with the same efficiency as in USA and with f=0.7.

All the scenarios given above, except for the Coxworth estimate for 1995, have been made consistent with the Table 3.5.1 by using it as a flow chart to derive the impact of the ammonia production and its derivatives on subsequent process steps in the manufacture of urea and ammonium nitrate. Urea and ammonium nitrate energy intensity are therefore derived from the following equations respectively: 42 As cited in the previous NCCP reference.

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EI(U) = 0.568EIA + (1-f)(0.735)ECO2 +2.54 EI(AN) = 0.208 EIA + 0.765(0.280 EIA + Snit) +SAN

EI(UN) = 60/28EI(U) EI(ANN) = (80/28)EI(AN) Where EI(U) = Energy intensity per unit mass of urea in MJ/kg, EI(UN) = Energy intensity per unit mass of N nutrient content in urea, MJ/kg(N) EI(AN) = Energy intensity of ammonium nitrate EI(ANN) = Energy intensity per unit mass of N nutrient content in AN, MJ/kg(N) EIA = Energy intensity of ammonia production, MJ/kg f = fraction of CO2 derived from stripping of CO2 from ammonia process ECO2 = Energy requirement to produce 1kg of CO2 Snit = Supplementary steam requirement for 1kg of nitric acid SAN = Supplementary steam requirement for 1kg of ammonium nitrate.

The 1995 scenario using the flow logic above was developed to gain some better perspective of the implications of the Coxworth estimates. The ammonia estimate was simply made to agree with that of Coxworth. For urea it was assumed that 50% of the CO2 needed for the reaction was still coming from separate fossil fuel burning. The close agreement between the Coxworth estimate of 76.1 versus 75.0MJ/kg for the scenario indicates that the industry was still probably obtaining some 40% of its CO2 from such fossil burning in 1995. However, this can be lowered readily by proper stripping technology in the ammonia production and it is assumed in the optimistic estimate that 90% of the CO2 is now coming as an ammonia byproduct. The optimistic scenario still includes no steam recovery from the nitric acid and ammonium nitrate processes as no new plants have been built in Canada since 1992. The dual pressure, integrated system that makes steam recovery possible is much easier to implement in new plants. If Coxworth’s 1995 estimate for ammonium nitrate is accurate, its comparison with the scenario for low efficiency above indicates the extent to which the Canadian industry could improve even without implementation of the most advanced technologies for N-fertilizer integration. Levelton assumes that the energy intensity of nitrogen fertilizer production is the same in Canada as it is in the US, and the energy intensity of nitrogen fertilizer production improves by 0.3% per year from the 1994 base year. The energy data in 1994 is derived from US Census data - energy-in/product-out ratio. This leads to an estimate of 59.8MJ/kg(N) for the fertilizer manufacturing industry as an average for all types of fertilizer. Levelton also shows additional requirements of 0.52 and 0.74MJ/kg(N) for the fertilizer mixing and transport respectively. The mixing and transport have not been accounted for in the previous figures put forward for nitrogen fertilizer and these estimates of Levelton will be integrated into the LCA. The Levelton estimate is about 10% less than the 66MJ/kg estimate made by Coxworth for 1995. The latter’s estimate based on the output of all the different N

39

fertilizers, their estimated energy intensities and their share of the total market. The major difference between this report’s optimistic estimate and that of Levelton is the latter’s estimate of a slow rate of improvement of 0.3% per year for the industry. Given all the approximations made in each of these analyses, it is concluded that the estimates can only be considered accurate to within 10-15%. One particularity of the Canadian fertilizer industry is that one major fertilizer producer, Agrium, has access to hydrogen as a byproduct of the conversion of tar sands to petroleum in Joffre, Alberta. This plant has a capacity of 450kilotonnes/a, enough to make a significant difference in energy intensity for fertilizer produced in the West as opposed to Eastern Canada. One of the primary goals here is to arrive at a reasonable estimate of greenhouse gas emissions based on these numbers. This, in turn, raises the issue of how to account for the CO2 used in urea production. Credit can be given to the urea production for the consumption of the gas, but, if this is done, the same amount must be considered as an additional CO2 emission from the field. There are many soil bacteria with the urease enzyme that rapidly convert urea into ammonium hydroxide and carbon dioxide,

(NH2)2CO + 2H2O ----> 2NH4OH +CO2. (3.5.2)

In any LCA of fertilizer it is important to identify how this CO2 is taken into account. The simple rule is that if it is credited at one point, it must be considered an emission at a later point. To simplify the treatment of urea and make it more directly comparable to other nitrogen fertilizers the CO2 in urea will be credited and debited at the same point in the initial production phase. If additional CO2 appears to have input from other than ammonia byproduct recovery, this will be considered as an additional GHG emission in the total process accounted for the higher energy input. Up until this point the GHG emissions from fertilizer production have been uniquely related to the natural gas input. There is some additional electrical input. In the Netherlands reference, about 0.7GJ of the 36GJ primary energy consumption per kg N of ammonium nitrate is traced to electricity. The actual electricity consumed, taking into account their 39% efficiency factor for electrical energy production is 0.26GJ/kg(N) of the fertilizer. This is less than one percent of the total energy. The other survey result of 0.4MJ/kg (N) from Blue Johnson Consulting tends to confirm that somewhat less than 1% of the energy for the industry comes from electrical consumption. Given the margin of error in the overall estimates this need not be further explored. A value of 0.4MJ/kg(N) is taken as the electrical input. If account is to be taken of the impact of the form of fertilizer used in soybean and canola then some reference to practices in Western and Eastern Canada

40

where the two crops tend to be concentrated can be made. Overall, urea use, normalized by nitrogen content, dominates in Canada, followed closely by anhydrous ammonia. There are, however, significant differences in use for different parts of Canada.43 Western Canada consumes almost equal quantities of urea and anhydrous ammonia fertilizer, again normalized by N context, while Eastern Canada has very low consumption of the ammonia. As a basic simplification urea will be assumed as the fertilizer of choice for the two crops. 3.6 Herbicide-resistant Soybean and Herbicide Use One of the most remarkable changes in North American agricultural practice over the last fifteen years has been the widespread adoption of genetically-modified crops with herbicide resistance. Fully 75% of soybean crops in the USA were herbicide resistant in 2002.44. One reason for this popularity has been a very significant move over the past few years to no till or conservation till. The herbicide-resistant soybean facilitates these options, and this, in turn, reduces negative environmental impact related to wind and water erosion of soil. A typical spreadsheet analysis45 shows an economic advantage for the herbicide-resistant varieties based on much lower herbicide cost. However, aggregate numbers on herbicide use are not showing this level of herbicide reduction. There are likely a few reasons for this apparent contradiction. The first reason is the correlation of the rapid increase in use of herbicide-resistant (HR) seed with the use of no or conservation till. The latter could well result in higher herbicide use counterbalancing the reduction possible through the resistant seed use. Secondly, it may be that farmers are simply using too much herbicide with this type of seed. The major herbicide resistance is aimed at permitting the use of glyphosate (Roundup is one brand name). Glyphosate is a post-emergent, broad-spectrum herbicide effective in foliar application. Many farmers complement its use with a pre-emergent herbicide. The combination of such herbicides leads to a very “clean” field that the farmer appreciates. However, the current levels of herbicide use may not be justified. The recent study by Heatherly et al46 indicates no advantage to the pre-emergent herbicide use from an economic standpoint. This study also corroborates earlier research indicating that nitrogen fertilizer on soybean has normally no impact on yield and represents an economic loss to the farmer.

43 Agriculture and Agri-Food Canada. Canadian Fertilizer Consumption, Shipments and Trade. Tables 1.4 and 1.8. http://www.agr.gc.ca/policy/cdnfert/text99-00.pdf. 44 Economic Research Service, US Department of Agriculture. Genetically Engineered Crops U.S. Adoption & Impacts in Agricultural Outlook, September 2002. http://www.ers.usda.gov/publications/agoutlook/sep2002/ao294h.pdf 45 Ontario Ministry of Agriculture and Food. 2004. Soybeans, Crop Budgets. http://www.gov.on.ca/OMAFRA/english/crops/field/soybeans.html - budget 46 Heatherly, Larry G., Spurlock, Stan R. and Reddy, Krishna N. 2003. Influence of Early-Season Nitrogen and Weed Management on Irrigated and Nonirrigated Glyphosate-Resistant and Susceptible Soybean. Agronomy Journal 95:446-453. http://agron.scijournals.org/cgi/content/abstract/95/2/446

41

The same study does not support the conclusion that there is an intrinsic yield reduction due to the use of the HR seed. There have been a number of reasons suggested for lower yield, including:

1. Cultivar lag in HR seeds, meaning that the advances in the genetics of soybean is rapid enough that the extra few years that it takes to integrate the HR gene into the best cultivars means an HR yield deficit when the most recent cultivars from non-HR and HR seeds are compared.

2. Cultivar drag, meaning the expression of the HR gene takes up some of the energy of the plant and lowers yield.

3. Glyphosate, integrated into the soil, is relatively toxic to Bradyrhizobium japonicum and its use would retard nodule formation and lower yield.

4. Extensive, repeated use of glyphosate will lead to the evolutionary response of some weeds to become glyphosate resistant and this will occasion higher herbicide doses to control weeds.

There is certainly some basis to the first point as can been seen from a comparison of new HR and non-HR cultivars.47 However, the industry is increasingly aware of this issue and is striving to get the best cultivars converted in a timely manner. As for the second point raised above, there is simply little evidence to support it. The third point is a critical one. If glyphosate use can be expected to reduce significantly nitrogen fixation and reduce yield, one of the key environmental advantages of soybean has been diminished. Glyphosate is somewhat toxic to Bradyrhizobium, the question becomes whether the herbicide’s impact can be kept low enough to retain sufficient nitrogen fixation. The answer appears to be yes. A recent study by Hoagland et al48 comes to this conclusion, “Subtle reductions of nodulation in RR soybeans using label rates of glyphosate can occur, but these effects may be of minimal consequence due to the potential of soybeans to compensate for short durations of stress.” An earlier study by King et al49 had indicated some potential impact under stressed (drought) conditions. However, the report of Heatherly et al cited above actually shows higher yields under non-irrigation conditions for the HR cultivar. The conclusion reached here is that farmers must be aware of the potential impact and avoid glyphosate use when a large portion of the herbicide will not be intercepted by the foliar surface and when rainfall within a few hours of

47 Bohner, Horst. 2003. What About Yield Drag on Roundup Ready Soybean? http://www.gov.on.ca/OMAFRA/english/crops/field/news/croptalk/2003/ct_0303a9.htm 48 Hoagland, R.E., Reddy, K.N., Zablotowicz, R.M. 1999. Effects of glyphosate on Bradyrhizobium japonicum interactions in Roundup-Ready soybeans. Weed Science Society of America Abstracts. Vol. 39. http://www.biotech-info.net/bradyrhizobium.html 49 King, C.Andy, Purcell, Larry C., Voires, Earl D. 2001. Plant Growth and Nitrogenase Activity of Glyphosate-Tolerant Soybean in Response to Foliar Glyphosate Applications. Agronomy Journal 93: 179-186. http://agron.scijournals.org/cgi/content/abstract/93/1/179

42

application is anticipated. This would tend to integrate the glyphosate into the soil where it does no good in the first place as an herbicide but it does retard early nitrogen fixation. The fourth point raised of weeds developing glyphosate resistance under heavy evolutionary pressure is valid. This is a legitimate concern and again exemplary farm practice must be reinforced to avoid its negative impact. It is critical to limit the evolutionary pressure on the soil by proper crop rotation techniques. HR soybean and HR corn seeding should not follow one another so other techniques to control weeds become the rule for alternating years at the least. In conclusion, some of the potential negative impacts of HR seed use are real, but they can be well controlled by proper farm management and techniques. While there has been considerable emphasis on the Web concerning the negative impacts of HR soybean, some of the positive impacts have been ignored. One benefit is the facilitation of conservation tillage techniques. A second is the reduced volatilization of herbicide due to more intensive glyphosate use. The NREL study in Table 58 lists the average use of different herbicides for soybean over the years 1990-1992. Tables 59, 60 and 61 of the same report provide emission factors for volatilization based on the Environmental Protection Agency’s AP-42 tables to estimate VOC due to both the herbicide and the inert material associated with it. Glyphosate has a vapour pressure of 3.8E–08 mm Hg and this is considerably lower than most other herbicides. For surface-applied agrochemicals of vapour pressure from 1E-04 to 1E-06 the EPA AP-42 gives an emission factor or 35%. The average vapour pressure for the herbicides used in 1990-1992 fell in this range so this emission factor was used to estimate herbicide VOC emissions from soybean crops of 0.38kg/ha. NREL also anticipates VOC content from the inert ingredients of the herbicides to be 0.99kg/ha, based on an average 62% inert content of which 32% is in the form of VOCs. NREL estimates that all of this volatilizes. EPA concludes that with the small addition from soil-incorporated pesticides the total VOC emissions are 1.39kg/ha or 31% of all chemicals applied. The vapour pressure of glyphosate is actually below the range covered by the EPA tables and the emission factor of 35% is definitely inappropriate. If such an emission factor were applicable the very broad-spectrum glyphosate would have a devastating impact on surrounding land. The fact that this doesn’t take place is one indication of a much lower volatilization. Keith Reid, soil specialist with OMAF, estimates overall emissions from the herbicides as being 1-2%. This appears to be somewhat low for some of the more volatile pre-emergent herbicides but this range should be close to representative for glyphosate.

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The inert VOC ingredient that is typically used with glyphosate is ethoxylated tallowamine. In one formulation of Roundup the active ingredient is 41%. There is 44% water 15% of the VOC. This particular VOC is introduced as a surfactant to encourage better foliar uptake on the plant. Other formulations of glyphosate have lesser amounts of the surfactant and some have none. The surfactant is unlikely to completely volatilize. Herbicide product labels indicate that it is converted by microbial action to other forms within one week. As a hydrocarbon with an amine chain this appears to be a reasonable assertion. The above information must be translated into revised estimates of VOC emissions from soybean crops. Glyphosate constituted only 4% of herbicide use in the USA in 1992 (from which NREL based its estimates) but by 1996 this has already risen to 14.6% (3949 tonnes out of 27069 tonnes) of herbicide used on soybean.50 By 2002 the glyphosate share of the market for soybean is probably in the range of 25-50%. The tools currently being used by the National Research Council have very little life cycle inventory information on the various herbicides. Therefore a simplified approach is to be taken. It is noted from the NREL study that the only emissions where the agrochemical production (category for the herbicides and pesticides) are of any real significance relative to the total agricultural emissions are in the following table.

Table 3.6.1 NREL Results for LCI of Soybean Agriculture (for 1 kg of soybean)

Significant LCI Emissions related to Agrochemical Production Inputs from NREL Study, Life Cycle Inventory of Biodiesel and Petroleum Diesel for Use in an Urban Bus Air Emissions Soybean

Agriculture Total (g/kg)Agrochemical Component

(g/kg) Agrochemical Component

(% of agriculture total)

CO2 182.9 11.6 6 CH4 0.18 0.04 22 N2O1 0.0084 0.0014 17

NMHC2 0.344 0.114 33 1. The NREL study did not include any N2O field emissions which would be by far the most

prominent source of nitrous oxide. 2. Non-methane hydrocarbon emissions.

According to our analysis the non-methane hydrocarbon emissions constitute the main change due to new agricultural practices and the lower volatility of 50 Fernandez-Conejo, Jorge and Jans, Sharon. 1999. Pest Management in U.S. Agriculture. Agricultural Handbook No. 717, Appendix III, Tables on Pesticide Use by Crop and Active Ingredient. http://www.ers.usda.gov/publications/ah717/ah717i.pdf.

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herbicides like glyphosate. To establish the impact of glyphosate on VOC emissions a rough approximation of 25% share (probably much higher now) for its market share for soybean is assumed and its VOC emissions from the active ingredient is estimated at 2% with no air emissions for its inert component. For the remaining other herbicides used, the soybean data will be based on statistics of the NREL study. It is conservatively assumed that herbicide use overall has remained constant at 1.46kg(active ingredient)/hectare of which 0.365 kg is now glyphosate and 1.095kg comes from other active materials from the NREL list. For these latter active materials it is assumed that they constitute 32% of the total combination of inert and active ingredients for the herbicides and pesticides. The NREL figure of an average VOC content of 32% for the inert ingredients is used to give VOC emissions of (1.095)(68/32)(0.32) = 0.74kg/ha. 26% of the active materials (other than glyphosate) is assumed to be incorporated in the soil and the air emissions are 5.2% of this number, i.e. (0.26)(1.095)(.052) = 0.014kg/ha. The remaining 0.81kg of active ingredients is surface applied with an emission rate of 35% to yield an additional 0.28kg/ha. All emissions from active ingredients are considered as chlorinated hydrocarbons. 3.7 Canola Herbicides and Herbicide-Resistant Varieties The penetration of the herbicide-resistant varieties for canola is even more pronounced than for soybean in Canada. Estimates of 80 to 85% penetration of the market for these resistant varieties are advanced. Besides glyphosate-resistant varieties there are also in wide use glufosinate-resistant, e.g. Liberty Link, and Odyssey and Pursuit-resistant canola varieties, e.g. Clearfield. The situation has evolved so rapidly that there do not appear to be good statistics upon which to base a good analysis of the current herbicide use with canola. The environmental concerns appear to be much more focused on the potential impacts on water than of air for such compounds as glufosinate.51 Glufosinate is actually used as an ammonium glufosinate salt with a melting point of 215C so its air emissions will be extremely low. It has an estimated vapour pressure of just 9.1X10-12 mm Hg at 25C. The other types of pesticides that are commonly used with canola are seed treatments of combined fungicide and insecticide. As the seeds are incorporated in the soil there air emissions are expected to be low. In summary, overall air emissions from herbicides for canola are anticipated to be much lower than predicted in the 1998 NREL study that examined soybean agriculture to produce biodiesel. As a very rough approximation the air emissions

51 See, for example, Faber, Marvin J., Thompson, Dean G., Stephenson, Gerald R. and Kreutzweiser, David P. Impact of Glufosinate-ammonium and Bialaphos on the Zooplankton Community of a Small Eutrophic Northern Lake. Environmental Toxicology and Chemistry: Vol. 17, No. 7, pp. 1291-1299.

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from the herbicides for canola are set in this LCA at 2% of a total active ingredient use per unit hectare of the same magnitude as for soybean. The similar overall level of herbicide use is based on Ontario and Saskatchewan costing data that shows similar total costs per hectare for herbicide treatment of the two crops. It should be reiterated, however, that the air emissions are probably not the critical environmental issue. The breakdown products of glufosinate are known to be water soluble and can be toxic to zooplankton under conditions that exist in Western Canada. Therefore, the focus should be on avoiding any significant run-off of the herbicides to small lakes and sloughs. 3.8 Nitrous Oxide Emissions Nitrous oxide has a global warming potential some 310 times greater than carbon dioxide. It is therefore important to analyze N2O emissions in determining the greenhouse gas emissions and climate change impact of agricultural practices. Nitrous oxide emissions can occur in nature through both nitrification and denitrification. In nitrification a group of autotrophic bacteria, typified by Nitrosomonos, convert ammonia to nitrite and a second group of nitrifiers like Nitrobacter then convert the nitrite to nitrate. Nitrous oxide can also be produced through the denitrification process of converting nitrate back to dinitrogen. The microbes exemplifying this process include Pseudomonas stutzeri, Pseudomonas aeruginosa, Paracoccus species, and Rhodobacter sphaeroides. The Intergovernmental Panel on Climate Change (IPCC) has developed a common framework for discussion of greenhouse gas emissions that can be well represented by the schematic of Figure 1, as published by Desjardins et al. This group has used the core IPCC methodology but has proposed some significant revisions to the specific factors used for making the N2O estimates. The revisions concern in particular the emissions from nitrogen-fixing plants like soybean. In this review the consequences of the most recent IPCC recommendations for N2O estimations as adjusted in 1996 are compared to those from the revised version put forward by Agriculture Canada. This review concludes that these revisions are very much a step in the right direction. We concur that the current IPCC recommendations lead to significant overestimates of N2O emissions, especially from leguminous crops. The specific factors from the current IPCC recommendations and from Agriculture Canada are presented in the following two charts.

46

Figure 3.8.1 Schematics of IPCC Methodology for the Estimation of N2O Emissions

Total N 2Oem issions from

agriculture

Direct em issionsof N 2O

from agricultural

Soils

D irect em issionsof N 2O from

anim alproduction

system s

Indirect em issions

of N 2Ofrom agricultural

system s

Em issionsfromother

A .W .M .S

Soilem issions

Em issionsfrom

grazinganim als

Emissionsfrom atm .deposition

of NH 3and NO x

Em issionsfrom N

leaching

Em issionsfrom

hum ansew age

Em issionsfrom

histosols

BiologicalN 2 fixation

CropResidue

Anim alwaste

Syntheticfertilizer

47

Total N2O Emissions from Soybean Agriculture

Direct Soil Emissions

Atmospheric deposition of NH3 and NOX N2O (kg N2O-N)= 0.001a * ∑ N synthetic Fertilizer consumption (kg N)

N leaching and runoffs 1. N2O (kg N2O-N) = 0.00375b * ∑ N synthetic Fertilizer consumption (kg N) (IPCC adjusted by Ag. Can )

2. N2O (kg N2O-N) = 0.0075c * ∑ N synthetic Fertilizer consumption (kg N) (IPCC revised in 1996)

Synthetic nitrogen fertilizer applied N2O (kg N2O-N) = 0.01125d * ∑ N synthetic fertilizer consumption (kg N)

N fixed by N-fixing crops 1. 0 (IPCC adjusted by Ag. Can )

2. N2O (kg N2O-N)= 0.00075e * [Total Seed Yield (kg dry biomass) (IPCC revised in 1996)

N in crop residue returned to soils 1. N2O (kg N2O-N) = 0.00045f Total Seed Yield (pulses & soybeans) (IPCC adjusted by Ag. Can) 2. N2O (kg N2O-N) = 0.00037125g Total Seed Yield (pulses & soybeans) (IPCC revised in 1996)

Note: 1 Adjusted IPCC Methodology, taken from “Regional and National Estimates of the Annual Nitrous Oxide

Emissions from Agroecosystems in Canada using the Revised IPCC Methodology” 2 Revised 1996 IPCC Methodology, taken from “Revised 1996 IPCC Guidelines for National Greenhouse

Gas Inventories: Reference Manual” Without specifically noted, the equation will be the same for both Adjusted IPCC Methodology and Revised 1996 IPCC Methodology

a: 0.001= 0.1 (kg NOx-N and NH3-N/kg N synthetic fertilizer applied to soil) * 0.01 (kg N2O-N/kg of NOx-N and NH3-N)

b: 0.00375 = 0.15 (kg N leaching /runoff /kg of fertilizer) * 0.025 (kg N2O-N/kg N leaching /runoff) c: 0.0075 = 0.3 (kg N leaching /runoff /kg of fertilizer) * 0.025 (kg N2O-N/kg N leaching /runoff) d: 0.01125 = (1-0.1 kg NH3-N and NOx-N/ kg N synthetic fertilizer applied to soil) * 0.0125 (kg N2O-N/kg N) e: 0.00075 = 2 * 0.03 (kg N/kg of dry biomass) * 0.0125 (kg N2O-N/kg N) f: 0.00045 = 2 * 0.02 (kg N/kg of dry biomass) * (1- 0.10 kg N/kg crop-N)* (1 - 0 kg N/kg crop-N) * 0.0125 (kg

N2O-N/kg N), where: 0.02 kg N/kg of dry biomass) is fraction of nitrogen in N-fixing crop, 0.10 kg N/kg crop-N is fraction of crop residue that is removed from the fields as crop, 0 kg N/kg crop-N is fraction of crop residue that is burned rather than left on field

g: 0.00037125 = 2 * 0.03 (kg N/kg of dry biomass) * (1- 0.45 kg N/kg crop-N)* (1 - 0.1 kg N/kg crop-N) * 0.0125 (kg N2O-N/kg N), where:

0.03 kg N/kg of dry biomass) is fraction of nitrogen in N-fixing crop, 0.45 kg N/kg crop-N is fraction of crop residue that is removed from the fields as crop 0.1 kg N/kg crop-N is fraction of crop residue that is burned rather than left on the field.

48

Total N2O Emissions from Canola Agriculture

Direct Soil Emissions

Atmospheric deposition of NH3 and NOX N2O (kg N2O-N)= 0.001a * ∑ N synthetic Fertilizer consumption (kg N)

N leaching and runoffs 1. N2O (kg N2O-N) = 0.00375b * ∑ N synthetic Fertilizer consumption (kg N) (IPCC adjusted by Ag. Can )

2. N2O (kg N2O-N) = 0.0075c * ∑ N synthetic Fertilizer consumption (kg N) (IPCC revised in 1996)

Synthetic nitrogen fertilizer applied N2O (kg N2O-N) = 0.01125d * ∑ N synthetic fertilizer consumption (kg N)

N in crop residue returned to soils 1. N2O (kg N2O-N) = 0.000225f Total Crop Production (IPCC adjusted by Ag. Can) 2. N2O (kg N2O-N) = 0.000185625g Total Crop production (IPCC revised in 1996)

Note: 1 Adjusted IPCC Methodology, taken from “Regional and National Estimates of the Annual Nitrous Oxide

Emissions from Agroecosystems in Canada using the Revised IPCC Methodology” 2 Revised 1996 IPCC Methodology, taken from “Revised 1996 IPCC Guidelines for National Greenhouse

Gas Inventories: Reference Manual” Without specifically noted, the equation will be the same for both Adjusted IPCC Methodology and Revised 1996 IPCC Methodology

a: 0.001= 0.1 (kg NOx-N and NH3-N/kg N synthetic fertilizer applied to soil) * 0.01 (kg N2O-N/kg of NOx-N and NH3-N)

b: 0.00375 = 0.15 (kg N leaching /runoff /kg of fertilizer) * 0.025 (kg N2O-N/kg N leaching /runoff) c: 0.0075 = 0.3 (kg N leaching /runoff /kg of fertilizer) * 0.025 (kg N2O-N/kg N leaching /runoff) d: 0.01125 = (1-0.1 kg NH3-N and NOx-N/ kg N synthetic fertilizer applied to soil) * 0.0125 (kg N2O-N/kg N) f: 0.000225 = 2 * 0.01 (kg N/kg of dry biomass) * (1- 0.10 kg N/kg crop-N)* (1 - 0 kg N/kg crop-N) * 0.0125 (kg

N2O-N/kg N), where: 0.01 kg N/kg of dry biomass) is fraction of nitrogen in non-N-fixing crop, 0.10 kg N/kg crop-N is fraction of crop residue that is removed from the fields as crop, 0 kg N/kg crop-N is fraction of crop residue that is burned rather than left on field

g: 0.000185625 = 2 * 0.015 (kg N/kg of dry biomass) * (1- 0.45 kg N/kg crop-N)* (1 - 0.1 kg N/kg crop-N) * 0.0125 (kg N2O-N/kg N), where:

0.015 kg N/kg of dry biomass) is fraction of nitrogen in non-N-fixing crop, 0.45 kg N/kg crop-N is fraction of crop residue that is removed from the fields as crop, 0.1 kg N/kg crop-N is fraction of crop residue that is burned rather than left on the field.

49

The current “adjusted” methodology of Agriculture and Agri-Food Canada should not be considered the final answer. Actual measurements of nitrous oxide emissions in Canada suggest that even the adjusted estimates are high compared to real values. The following table and commentary is extracted from a publication of R. Lemke52

Table 3.8.1 Experimental Data for N20 Emissions (by R. Lemke)

The very large difference between the 1.25% IPCC estimate versus the experimental value 0.2% for N-conversion to N2O indicates that further adjustments need to be made to the emission estimation methodology. This need is likely to be most pronounced for the semi-arid regions of western Canada. To show the importance of this a sensitivity analysis of GHG emissions due to canola is done with the above experimental value rather than the IPCC estimate. 3.9 Life Cycle Analysis of the Agricultural Production of Canola and Soybean The findings of the agricultural analysis have been integrated into the following two baseline Life Cycle Analyses for canola and soybean respectively. The following conditions apply to the baseline analyses:

52 Lemke, R. No Till, Carbon Sequestration and Nitrous Oxide Emissions. http://www.reducedtillage.ca/No Till Carbon Sequestration Nov 03 DSA.PDF

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• The average Canadian yields for canola and soybean are used. • Field energy requirements are based on the new understanding of

“conventional” till practices (with lower energy input due to the abandonment of the mouldboard plough).

• Fertilizer input requirements for P and K are determined on the basis of sustainability by determining levels needed to offset their rate of removal by the crop harvest.

• No nitrogen fertilizer is used for soybean and the crop is considered to have a net impact of zero on overall soil nitrogen content.

• Saskatchewan guidelines of 67kg/ha are used as the nitrogen fertilizer requirements for canola.

• The Canadian “slow-improvement” scenario is used to determine the energy inputs for fertilizer and, in particular, for urea.

• The “adjusted” estimation method of Agriculture and Agri-Food Canada is used to determine nitrous oxide emissions from the system.

• All figures are expressed in terms of inputs/emissions required to yield one kilogram of either canola or soybean.

Table 3.9.1a LCI Results for 1kg Canola/soybean produced – base case Inputs (g) Canola1 Soybean

Crude oil (resource) 36.6 30.0 Hard coal (resource) 2.5 1.9

Lignite (resource) 0.3 Natural gas (resource) 79.3 3.9

Inert rock 3.1 4.3 Phosphorus minerals 70.0 50.0

1. In this and the following table the canola data has been derived from the analysis

presented earlier with inventory calculations from the GaBi database.

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Table 3.9.1b LCI Results for 1kg Canola/soybean produced – base case

Outputs (mg) Canola Soybean Consumer waste 76.6 123.1 Hazardous waste 102.1 671.7 Radioactive waste 1.4

Ash 3.9 Sludge 1179.9

Ore processing residues 3098.4 1808.9 Overburden 268.4 2567.5 Ammonia 303.7

Carbon dioxide 56725.8 33748.9 Carbon monoxide 262.5 132.4 Hydrogen chloride 1.4 1.0 Hydrogen sulphide 30.0

Nitrogen oxides 660.5 197.6 Nitrous oxide 1196.3 1.8

Steam 1391.5 Sulphur dioxide 314.7 88.1

Ethane 82.5 NMVOC (unspecified) 27.7

Propane 33.1 Methane 795.4

VOC (unspecified) 93.6 Halogenated organic emissions to air 117.3

Exhaust 1698.8 Particles to air 52.0 35.3

Biological oxygen demand (BOD) 1.0 Chemical oxygen demand (COD) 8.8 9.3

Total organic bounded carbon 1.7 5.1 Ammonium / ammonia 160.8

Chloride 38.5 335.3 Fluoride 1.5

Neutral salts 166.8 268.4 Nitrate 554.0

Phosphate 180.1 Potassium 140.2

Sodium 32.8 40.3 Sulphate 2.3

Hydrocarbons to water 1.3 Solids (suspended) 45.9

Boiler ash (unspecified) 3.4 Fly ash (unspecified) 10.4

Gypsum 1.5 Gypsum (FDI) 5.9 waste (inert) 941.0 672.0

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In the next two tables the same results are presented but with a focus on global warming potential from the nitrous oxide, methane and carbon dioxide emissions. Sensitivity analysis is used to gauge the impact on the global warming potential estimates for different scenarios of the following variables:

• Use of the formal IPCC guidelines for N2O emissions from fields for both canola and soybean along with a comparison with a scenario based on the most recent actual measurements of nitrous oxide emissions from Western Canada.

• Different nitrogen fertilizer use and credits for the nitrogen-fixing soybean. • A more optimistic and one more pessimistic scenario of energy

requirements for nitrogen fertilizer. • Lower farm energy consumption through a conservation till regime.

Table 3.9.1 Sensitivity of GHG Emissions to N2O Estimates for 1kg canola Method N2O emission

(kg/ha) N2O emission (kgCO2-eq/kg

canola)

Total kgCO2-eq./kg canola

IPCC adjusted 2.158 0.622 0.696 IPCC 2.47 0.694 0.768

Experimental Data1 0.211 0.171 0.245 1. The Lemke data where the emissions were 0.2% of fertilizer input (see Table 3.8.1) are used. From the Table it can be seen that in the base case using the IPCC adjusted methodology the nitrous oxide emissions from the field are by far the dominating source of greenhouse gases. Unfortunately, this is also the parameter subject to the most debate over its real value and there is a particularly large range of values possible based on the hypotheses put forward. Given the experimental values reported, it should be noted that that the “adjusted” methodology of Agriculture and Agri-Food Canada is likely to be still too high even though it is lower than the estimates of IPCC.

53

Table 3.9.2 Sensitivity of GHG Emssions to N2O Estimates for 1kg Soybean Scenarios Method N2O-N

emission (kg/ha)

N2O emission in kgCO2-eq/kg

soybean produced

Total kgCO2-eq./kg soybean

produced IPCC

adjusted 0.8788 0.16465 0.18514 No fertilizer applied,

nitrogen credit due to fixation IPCC 2.5558 0.47886 0.49934

IPCC adjusted

1.17 0.2192 0.25882 No fertilizer applied, no credit due to

fixation IPCC

2.9153 0.5462 0.58582

IPCC adjusted

1.314 0.2462 0.29526 Fertilizer of 9 kg/ha applied

no credit due to fixation

IPCC 3.093 0.5795 0.62858

All calculations are based on soybean yield of 2.6 tonnes/ha Because the nitrogen-fixing soybean does not require any nitrogen fertilizer, the nitrous oxide emissions from soybean are estimated as only about 35% of the emissions from canola (based on the IPCC adjusted methodology). Nevertheless, the nitrous oxide emissions remain very significant representing some 85% of the total GHG emissions per unit of soybean yield in the base case where no fertilizer is used and no credit is given for reduced fertilizer use in the following year. The importance of these soybean emissions relative to canola becomes all the more important in the overall analysis when it is recognized that the oil production from one kilogram of soybean is substantially lower than from the equivalent amount of canola. The current methodologies for calculating nitrous oxide emissions from nitrogen-fixing crops have a very tenuous foundation and therefore some additional analysis was undertaken to determine the possible influence of more appropriate methodologies. A draft scientific paper derived from this analysis is presented in Appendix A.3. This paper concludes that the current methodologies need to be completely revamped and proposals are made for how to arrive at more appropriate ones. In this scientific paper it can be noted that estimates of emissions based on the combination of two different sets of experimental data lead to annual nitrous oxide emissions about one half of those estimated by the 1996 Revised IPCC methodology. The origin of the actual emissions was quite different from what is proposed in both the revised and adjusted methodologies. But as the IPCC adjusted methodology used above leads to results somewhat less than half of those from the revised IPCC methodology it can be concluded that the base case scenario presents nitrous oxide emissions from soybean that are likely to be at least the right order of magnitude.

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The potential for substantial reductions in effective nitrous oxide emissions from soybean by taking advantage of the nitrogen left in the soil and available to the following crop should not be ignored. It is obviously a very simple process to use less fertilizer in the following year. This leads to about a 30% reduction in emissions. Moreover, this figure is based on a very conservative estimate of the nitrogen credit of 25kg(N)/ha. In the USA this credit is often estimated to be 45kg(N)/ha53. In the other two sensitivity studies undertaken it was found that the importance of the variables was much less than in the case of field emissions.

Table 3.9.3 Sensitivity of GHG Emissions to Conservation and Conventional Till for 1 kg canola produced

Field energy requirements - diesel

consumption

Field Energy kgCO2-eq/kg canola

produced

Total kgCO2-eq./kg Canola

produced Conservational till 37 L/ha (31.4 kg/ha) 0.0282 0.682 Conventional till 54 L/ha (46.5 kg/ha) 0.0418 0.696

This table reflects what is relatively well known in the agricultural industry. The new conservation and no-till regimes are intended for superior soil erosion protection and benefits other than straight energy efficiency.

Table 3.9.4 Sensitivity of GHG Emissions to different estimates of energy requirements for N-fertilizer production (for 1kg canola produced)

Natural gas requirements for N-fertilizer production

Fertilizer kgCO2-eq/ kg canola produced

Total kgCO2-eq./kg canola produced

53 MJ/kg(N) 0.135 0.694 61.9 MJ/kg(N) 0.137 0.696 76.1 MJ/kg(N) 0.140 0.699

While the different scenarios show some substantial differences in energy inputs for nitrogen fertilizer their overall impact on greenhouse gas emissions is relatively small (less than 1%).

53 Green, C.J. and Blackmer, A.M. 1995. Residue decomposition effects on nitrogen availability to corn following corn or soybean. Soil Sci. Soc. Am. J. 59:1065-1070.

55

4. Oilseed Crushing Operations One of the few disadvantages of biodiesel noted in the NREL LCA was that the upstream emissions of volatile organic compounds to be attributed to biodiesel were higher than those from normal diesel. This is largely due to the fugitive emissions of hexane from the solvent extraction process applied to the soybean crushing operations. Because canola has a higher oil content than soybean, the fugitive hexane emissions can even be more serious if the pure solvent extraction process is assumed for canola. The hexane emissions from the vegetable oil extraction industry are considered serious enough in the USA for the country to have recently passed new regulations limiting these emissions. The health risk of high-level exposure to n-hexane is considered to be the potential for irritations, dizziness, headaches and nausea, while long-term exposure could cause permanent nerve damage. The regulation, http://www.epa.gov/ttn/oarpg/t3/fr_notices/vegoil_fr.pdf, falls underneath the EPA’s 40CFR Part 63, National Emission Standards for Hazardous Air Pollutants: Solvent Extraction for Vegetable Oil Production. For soybean the regulation effectively limits hexane emissions in gal/ton of soybean processed to 0.128gal/ton. (In the tables of the regulation this is derived from the product of the hexane content of the typical solvent (64%) by the loss of solvent at 0.2gal/ton representing Maximum Achievable Control Technology (MACT) limits for the solvent.) In SI units this becomes 0.55kg(hexane)/tonne(soybean). For canola the USA regulation allows (0.64)(0.7gal/ton) for existing plants and (0.64)(0.3gal/ton) for new plants, equivalent respectively to 1.9kg(hexane)/tonne(canola seed) and 0.82kg/tonne. The higher numbers for canola reflect the higher oil content of this oilseed and the recognition that the fugitive emission control from older plants would require considerably more capital outlay to achieve reductions than it would for new plants. It should be noted that canola contains more than twice as much oil as soybean, so for the new plants the regulations lead to lower hexane emissions per tonne of oil for canola rather than soybean. There are no such regulations in Canada but the industry must now report the n-hexane emissions to the National Pollutant Release Inventory (NPRI). It would appear that the industry as a whole could meet the USA regulatory emission levels but a few plants would probably exceed them. Here are two tables describing the current emissions in Canada.

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Table 4.0.1 N-Hexane emissions from Canada’s National Pollutant Release Inventory54

Hexane on-site release (tonnes/year)

Facility name City Feedstocks Capacity (tonnes/day feedstock)

1999 2000

ADM Agri-Industries Ltd.

LIoydminster, AB

Canola 2000 100.00 62.00

ADM Agri-Industries Ltd

Windsor, ON Canola & soybean

3000 473.5 343.00

CanAmera Foods – Altona

Altona, MB canola & flax

1000 194.06 211.10

CanAmera Foods – Fort Saskatchewan

Fort Saskatchewan, AB

canola 700 204.90 181.30

CanAmera Foods – Hamilton

Hamilton, ON canola 3000 340.91 371.10

CanAmera Foods – Harrowby

Harrowby, MB canola 1400 102.44 174.30

CanAmera Foods – Nipawin

Nipawin, SK canola 1000 43.29 70.40

Canbra Foods Ltd. - Lethbridge

Lethbridge, AB canola 975 316.40 329.24

Cargill Limited Clavet, SK canola 2000 8.10 150.78 Total 1783.6 1893.22

Table 4.0.2 Hexane Emissions as related to Grain Processed and Oil Produced in 199955 Canola processed (103 tonnes)

Canola Oil produced (103 tonnes)

Soybean processed (103 tonnes)

Soybean oil produced (103 tonnes)

Sunflower and flax processed (103 tonnes)

Sunflower and linseed oil produced (103 tonnes)

Hexane emissions per tonne grain processed (kg/tonne)

Hexane emissions per tonne oil produced (kg/tonne)

2867 1201 1683 311 140 54 0.38 1.14

54 Environment Canada. National Pollutant Release Inventory Data http://www.ec.gc.ca/pdb/npri/npri_online_data_e.cfm. The pertinent data are most easily obtained by a search for n-hexane in the food industry. 55 Canadian Oilseed Processors Association. http://www.reviewcta-examenltc.gc.ca/Submissions-Soumissions/Txt/Canadian Oilseed Processors Association.txt

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4.1 Canadian Canola Crushing Characteristics In Canada, there are actually three extraction processes used for canola. One process is pure solvent extraction as in the USA. However, becoming more common in canola processing is an initial physical extraction phase referred to as expelling, followed by solvent extraction. About 50-60% of the oil can be readily removed in the first phase leading to lower fugitive emissions in the subsequent solvent extraction. The third type of crushing operation is not directly relevant to this study as the pure physical extraction is only used in relatively small volumes by specialty crushers to yield higher-value functional oil products. CanAmera and the Environmental Technology Centre of Environment Canada recently completed a study of options to improve the efficiency of crushing operations. NRC has taken advantage of the good data set on a combined expelling/solvent extraction process to show its influence on reducing the VOC emissions from biodiesel. Because it has recently been made obligatory for companies in Canada to report hexane emissions for the National Pollutant Release Inventory, some comparison between theoretical and actual emissions can be made. That comparison shows that hexane emissions for canola based on this detailed report are relatively high compared to the Canadian average. With the new data the LCA analyses still show that the hexane emissions are a significant source of VOCs as a precursor to summer smog, when compared to diesel. However, it should be noted that the source of the precursors is very different. The VOCs from biodiesel are from the production and not from transport emissions, so are less likely to contribute to urban smog. Figure 4.1 Deleted for Reasons of Business Confidentiality The following table was used to establish the Life Cycle Inventory of the canola crushing stage of the production of biodiesel from vegetable oil.

Table 4.1.1 Input Canola seed (kg) 1000 Steam (140 psig, kg) Deleted for commercial

reasons

Electricity (kWh) deleted Make-up Hexane (kg)1 2 Output Canola oil (kg) 411 Canola meal (kg) 589 (including 8.6 kg oil) Hexane emission (kg) 2 1. Large quantities of hexane are used but close to 98-99% is effectively recycled. LCI data associated with hexane manufacturing is taken from US NREL study

58

In a similar life cycle analysis the Australian Greenhouse Office cited the following table from Ceuterick & Spirinckx, 1999, to establish crushing characteristics. The agreement is reasonably good.

Table 4.1.2 Process inputs and outputs for oil extraction of canola Inputs Unit Value

Oil seeds kg 1000 Electricity kWh 45

Steam (natural gas fired) kg 310 4.2 Soybean Crushing Operations The generic crushing operation of the NREL study (section 5.3 of the report) will be used in this study. It is noted that the new hexane control legislation will lead to significantly lower hexane emissions in the USA than the original NREL data. 56 The one substitution with Canadian data into the NREL model will be for these emissions where the Canadian average 1999 hexane emissions per unit of grain processed, 0.38kg/tonne(oilseed), will be used.

56 The USA legislation clearly makes a distinction between the total solvent loss and the hexane loss which is assumed to a fraction thereof. In other studies it appears that an assumption is made that the solvent is 100% hexane. Care should be taken in interpreting therefore some of this data.

59

Table 4.2a Overall inputs and outputs for soybean crushing (from NREL study except hexane emissions)

per tonne of soybeans

processed1

per tonne of oil produced (no mass

allocation) Inputs

Electricity 69.66 kWh 410.45 kWh Natural gas 266,275 kcal 1,568,858.71 kcal

Energy

Steam 220,020 kcal 1,296,331.52 kcal Soybeans 1,000 kg 5,891.27 kg Hexane 0.38 kg2 2.24 kg

Raw materials

Water 3.28 kg 19.35 kg Outputs

Crude, degummed soybean oil

169.73 kg 1,000 kg Product

Soybean meal 760.06 kg 4,478.19 kg Water vapour 69.28 kg 408.19 kg

air 3.35 kg 19.73 kg Air emissions

Hexane 0.38 kg3 2.24 kg Total waste water 77.70 kg 457.77 kg

Soybean oil 0.85 kg 5.02 kg Triglycerides 0.83 kg 4.91 kg Phosphatides 0.0002 kg 0.00

Unsaponifiable matter

0.01 kg 0.08 kg

Free fatty acids 0.01 kg 0.04 kg

Water emissions

Moisture 76.85 kg 452.75 kg Solid waste Trash metals 0.008 g 0.046 g

1. This column would also give a close approximation to emissions for oil based on mass allocation. The mass allocation associates 18% of the inputs and emissions to the oil, and 82% to the meal. There are some small losses so the actual oil recovered is 16.973%. Therefore the mass allocation figures for oil would be the emissions of this column multiplied by 18/16.973, i.e. 6% higher. 2. The original NREL data had make-up hexane of 2.02kg(Hexane)/tonne (soybean), or 11.90 kg (Hexane)/tonne of oil produced. In the USA, the Maximum Achievable Control Technology Limits for hexane emission is now 0.55 kg(hexane)/tonne for soybean, or 3.25 kg(hexane)/tonne of oil produced. The 0.38kg(hexane)/tonne is based on an average Canadian crushing industry average. 3. The original NREL Hexane emission data is 1.72 kg (Hexane)/tonne (soybean), or 10.15 kg (Hexane)/tonne of oil produced

Table 4.2b Mass Allocation for the generic soybean crushing facility (NREL study)

Mass (%) Soybean oil 18%

Soybean meal 82% Total 100%

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4.3 Life Cycle Inventory of Canola and Soybean Crushing Operations The following table gives reasonably accurate data for the important parameters of crushing operations and allows good estimates of GHG emissions to be made. However, it must be recognized that the table has been purposely left in a form that shows some of the dangers of using more or less complete data sets for LCI. The basis for the canola crushing inventory is good pertinent information but lacking in details. The soybean data is exceptionally complete. The very large difference in wastewater produced is an artifact of these data sets and NOT of the two processes. Secondly, the hexane emissions cannot be considered comparable. While USA results show higher hexane emissions from canola than soybean, the large difference here has been artificially produced by using different data sets. The larger number from canola comes from the analysis of one specific plant while the soybean figure comes from an industry average over canola and soybean crushing operations. The difference is introduced only to show the range of values possible.

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Table 4.1.3 LCI Data for Crushing of 1kg of Canola and of Soybean with mass allocation1

Inputs (g) Canola Soybean Crude oil (resource) 0.5 0.3Natural gas (resource) 24.3 54.6Inert rock 1.0 2.2Water 2.0 8.0 Ouputs (mg) Consumer waste 0.1 0.3Hazardous waste 371.0 834.7Ore processing residues 974.2 2191.8Overburden 84.4 189.9Carbon dioxide 62141.1 70046.8Carbon monoxide 14.5 19.4Hydrogen sulphide 9.4 21.2Nitrogen oxides 161.5 187.6Nitrous oxide 1.6 1.7Steam 44128.4 47781.2Sulphur dioxide 64.3 142.2Benzene 0.2 0Butane (n-butane) 0.6 0Ethane 25.9 58.4Formaldehyde (methanal) 0.5 0Hexane 2000.0 408.9NMVOC (unspecified) 8.7 19.6Pentane (n-pentane) 0.9 0Propane 10.7 23.7Methane 119.4 265.6VOC (unspecified) 2.5 0.5Particles to air 0.7 1.4Biological oxygen demand (BOD) 0.2 0.5Chemical oxygen demand (COD) 1.4 3.2Chloride 12.1 27.2Fluoride 0 1.0Sodium 10.3 23.2Oil (unspecified) 0 916.4Organic Compounds (unspecified 0 918.2Waste water 1643.9 87260.6Steel scrap 8.4

1. Because the approximation has been made that all the seed is used, for each kg of seed 0.411kg ending up in the oil and 0.589kg in the meal, these numbers also represent the emissions for canola oil based on mass allocation. The results here are based on GaBi outputs. Appendix A3 provides comparable data from other sources.

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5. Analyses of Other Biodiesel Feedstocks Yellow grease from used cooking oil and low-grade tallow represent the most economic feedstocks for biodiesel in North America. They are expected to represent the cornerstone feedstock of any substantial development of biodiesel in Canada. The following diagrams represent the processes involved in the production of yellow grease and tallow from rendering operations. Figure 5.1a Yellow Grease Production from Waste Cooking Oil Process description: Partial (3/4) mechanical water removal Remove water by boiling Centrifugation (remove solids) Products Waste Water Wastewater treatment plant City’s WWTP

• Restaurant waste cooking oil (35%- 45% water content) • Petrodiesel is consumed by truck for collecting waste cooking oil from

restaurants Products:

• Yellow Grease Emissions:

• Very low, mainly steam from processing Data source: Rothsay, Canada

Collection of restaurant waste cooking oil

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Figure 5.1b Mass and Energy Balances for Yellow Grease Production from Used Cooking Oil

Energy consumption for Initial water/oil separation: negligible Energy consumption for processing 0.7 kg waste cooking oil, with 14.3% water content: Assume indoor room temperature is 18 0C

• Heating water from 180C to 100 0C: 0.1 kg * 4.186 kJ/kg 0C * (100 – 18)0C = 34 kJ • Water Evaporation: 0.1 kg * 2256 kJ/kg = 225.6 kJ • Heating oil from 18 0C to 100 0C: 0.6 kg * 2.2 kJ/kg 0C * (100-18) 0C = 108.2 kJ

Total energy consumption:

34 + 225.6 + 108.2 = 367.8 kJ With 81% boiler efficiency, the energy required for processing 0.475 kg (40% water content) waste cooking oil is: 367.8 / 0.81 = 454 kJ Note: There is no energy recovery in the current rendering operations

Yellow Grease Tallow

Product. (Rothsay)

Petrodiesel 0.008 liter

Yellow grease 0.6 kg

Natural gas 454 kJ

Wastewater 0.1 kg

Waste cooking oil (14.3% H2O) 0.7 kg

Electricity: 0.059 kWh

Diesel production

Electricity production

Natural gas

d i

Biodiesel

Initial water/oil separation (decantation, mechanical)

Waste cooking oil (40% H2O) 1 kg

Wastewater 0.3 kg

64

Figure 5.2a Tallow Production from Slaughter House Residues Process description: Collecting residues from slaughterhouse Remove water by boiling Centrifugation (remove solids) Products Waste Water Wastewater treatment plant City’s WWTP sludge Animal Feed Product Land Application (e.g. fertilizer or landfill)

• Slaughter house residues (60%- 65% water content) • Petro-diesel is consumed by truck for collecting deadstock, bones & fat and

residues from slaughter house • Heating value of natural gas is 37607 kJ/m3 =35674 Btu/m3, (lower heating value)

Products: • Yellow grease & tallow • Animal meat meal (50% protein)

Emissions: • Low, mainly steam

Data Source: Rothsay

65

Figure 5.2b Mass and Energy Balances for Tallow Production from Slaughter House Residues

Energy consumption for processing 1 kg slaughter-house residues, with 63.7% water content: Assume indoor room temperature is 18 0C

• Heating water from 180C to 100 0C: 0.637 kg * 4.186 kJ/kg 0C * (100 – 18)0C = 218.7 kJ • Water Evaporation: 0.637 kg * 2256 kJ/kg = 1437.1 kJ • Heating oil from 18 0C to 100 0C: 0.363 kg * 2.2 kJ/kg 0C * (100-18) 0C = 65.5 kJ

Total energy consumption:

218.7 kJ + 1437.1 kJ + 65.5 kJ = 1721.3 kJ With 81% boiler efficiency, the energy required for processing 1 kg (63.7% water content) waste cooking oil is: 1721.3 / 0.81 = 2125 kJ Note: There is no heat recovery in current rendering operations.

Yellow Grease Tallow

Product. (Rothsay)

Petrodiesel 0.01 liter

Yellow grease & Tallow 0.10 kg

Natural gas 2125 kJ

Wastewater 0.637 kg

Slaughter-house residues (63.7% H2O)1 kg

Electricity: 0.08 kWh

Petrodiesel production

Electricity production

Natural gas

BIOX

Animal meat meal (50% protein) 0.26 kg

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Table 5.1 LCI of Yellow Grease Production from Used Cooking Oil Inputs and outputs for the production of 1kg of yellow grease - no allocation employed Inputs (g) Crude oil (resource) 11.2Natural gas (resource) 17.9Water 1.5Waste cooking oil (40% H2O) 1666.7 Outputs (mg) Sludge 273.2Ore processing residues 717.5Overburden 62.2Carbon dioxide 13032.2Carbon monoxide 79.2Hydrogen sulphide 6.9Nitrogen oxides 99.8Sulphur dioxide 69.0Ethane 19.1NMVOC (unspecified) 6.4Propane 7.7Methane 207.2VOC (unspecified) 13.0Particles to air 4.7Biological oxygen demand (BOD) 0.2Chemical oxygen demand (COD) 1.0Total organic bounded carbon 0.4Chloride 8.9Potassium 0.1Sodium 7.6Waste water 667877.4

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Table 5.2 LCI of Tallow Production from Slaughterhouse Residues LCI Inputs and Outputs for production of 1kg of tallow - mass allocation employed1 Inputs (g) Crude oil (resource) 23.7Natural gas (resource) 139.5Inert rock 5.6Water 11.4Slaughterhouse residues 2777.8 Outputs (mg) Consumer waste 0.7Sludge 2131.6Ash 1.5Ore processing residues 5597.4Overburden 484.9Carbon dioxide 36066.1Carbon monoxide 178.8Hydrogen sulphide 54.2Nitrogen oxides 253.1Nitrous oxide (laughing gas) 1.4Sulphur dioxide 403.3Ethane 149.0NMVOC (unspecified) 50.0Propane 59.7Methane 923.6VOC (unspecified) 27.2Particles to air 12.2Biological oxygen demand (BOD) 1.2Chemical oxygen demand (COD) 8.2Total organic bounded carbon 3.1Chloride 69.5Fluoride 2.7Phosphate 0.1Potassium 0.8Sodium 59.2Sulphate 0.4

1. One kilogram of slaughterhouse residue produces on average 0.1kg of tallow and 0.26 kg of meal. Therefore the mass allocation associates 0.1/0.26 = 38.5% of the emissions to the tallow and 61.5% to the meal.

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6. Biodiesel Production In biological processes the oils and fats used to store energy take the form of a triglyceride. The triglyceride can be understood as a three-carbon backbone attached by three carboxyl groups (COO) to three carbon chains of about 16 to 18 carbons atoms each. This oil or grease could be burned in diesel motors under warm conditions but the oils would tend to gum up motors and produce high levels of exhaust emissions. Detaching the long carbon chains from the backbone and substituting a methyl group for it can overcome these problems. The process to achieve this is called transesterification and it is the core technology of most biodiesel production methods today. In summary, the methanol is made to react with the triglycerides to produce methyl esters (biodiesel) and glycerol. The conversion can be very efficient with close to one unit of biodiesel for each unit of oil input. The reaction is: C3H5(OOCR)3 + 3CH3OH 3RCOOCH3 +C3H5(OH)3 Triglyceride Methanol Methyl ester Glycerol The three separate methyl ester chains now constitute the oxygenated biodiesel. The high conversion efficiency can be explained when it is recognized that the three methyl esters actually have the same total mass as the triglyceride except for the addition of four H. In another perspective the methanol required for the reaction has almost the same mass as the byproduct, glycerol, 96 and 92 respectively. Yields for biodiesel can be as high as 99%. All of this is more simply said than done. Actual processes developed depend on either an acid and/or base catalysts and problems can emerge if the feedstock oil or grease has a large number of detached chains, i.e. free fatty acids. These problems have been overcome in certain technologies. The BIOX process has been chosen as one of two processes to be examined in more detail in this LCA for three reasons:

• Biox Corporation has provided relatively detailed information on mass and energy balances for their process;

• The process is among the more versatile ones that can treat efficiently high free fatty acid content in the feedstock;

• It is a new Canadian process developed originally by Professor David Boocock of the University of Toronto that has attracted considerable attention.

6.1 BIOX Process Summary The key to the BIOX process is the use of a co-solvent, oxolane, that allows both the acid and base hydrolysis reactions to proceed quickly and completely. The oxolane is almost totally recycled in the course of the reactions. The free fatty acids are first converted to the methyl ester by the combination of oxolane, sulphuric acid and methanol. Once completed a second phase reaction converts

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the triglycerides to methyl esters through the use of sodium hydroxide. After neutralization the final products are the methyl ester, and anhydrous glycerine containing a sodium sulphate-sodium bisulphate residue. The mass and energy balances are shown in the following schematic.

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Table 6.1 Mass and Energy Balances for the BIOX Process

Note: The data was simulated for a feedstock with 15% free fatty acid concentration in mass

Condensate (116 0C) 1.369 kg

Biodiesel 1kg

Water 0.021 kg

Sodium bisulphate 0.026 kg

Sodium Sulfate 0.019 kg

Saturated steam (116 0C) 0.162 kg

Fuel storage and distribution

Recycled

BIOX PLANT

Natural gas boiler (83%

Feedstock (15% FFA in mass) 0.999 kg

Oxolane Negligible

Methanol 0.1 kg

NaOH 0.018 kg

H2SO4 0.032 kg

Saturated steam(165 0C) 1.532 kg

Electricity 0.02 kWh

Methanol production

NaOH production (GaBi)

H2SO4 production (GaBi)

Electricity generation

Yellow Grease or Tallow

N2 g 0.001 kg

Nitrogen production

Steam energy content: • Steam supply: 1.532 kg * 2761.8 kJ/kg (enthalpy of saturated steam at 165 0C) = 4231.0776 kJ • Steam return: 0.162 kg * 2700.4 kJ/kg (enthalpy of saturated steam at 116 0C) = 437.4648 kJ • Condensate: 1.369 kg * 486.16 kJ/kg (enthalpy of condensate at 116 0C) = 665.55304 kJ

Steam energy consumption = steam supply – steam return – condensate = 3128 kJ

Glycerol 99.7% pure 0.089 kg Market

Waste

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6.2 The Lurgi Process The Lurgi process is the second process described here for comparative purposes. Lurgi provided information on its process through a public Web site so a reasonable energy and mass balance can also be obtained. The process is shown as a two-step reactor. Most of the glycerin is recovered after the first stage where a rectifying column leads to separation of the excess methanol and crude glycerin. The methyl ester output of the second stage is purified to some extent of residual glycerin and methanol by a wash column. Figure 6.2.1 Simplified Lurgi Process Diagram Lurgi has also provided a summary of energy and mass requirements on its biodiesel section of the Web that corresponds to the schematic given below.

Table 6.2.1 First Order Energy and Mass Requirements for the Lurgi Process Input Requirement/tonne Biodiesel Feedstock 1,000 kg vegetable oil Steam Requirement 415 kg Electricity 12 kWh Methanol 96 kg Catalyst 5 kg Hydrochloric acid (37%) 10 kg Caustic soda (50%) 1.5 kg Nitrogen 1 NM3 Process water 20 kg

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1. This table includes biodiesel drying but not the glycerin refinement. It is important to note that this breakdown of energy and mass requirements is not comparable to the Biox example for two reasons:

• Additional steps and energy would be required if the feedstock contained any significant free fatty acid (beyond 0.1%).

• The glycerin requires further processing to attain the anhydrous state of the Biox process output.

Lurgi talks of a pretreatment for deacidification and degumming stage if the FFA is above 0.1%. The requirements at 15% FFA are not specified so only a very approximate number for steam requirements of 330MJ/tonne can be assumed here. The purification process to go from 80% up to 99% for the glycerin would logically proceed by distillation, so this can be estimated at about 850MJ/tonne. The steam requirement for the core process was stated on the Web at 415kg/tonne, so this would constitute about 1135MJ/tonne. With the necessary processes included within the boundary of the system to achieve more comparable final products with the BIOX process, there is a significant increase in energy requirements from 1135 MJ to 2315 MJ per tonne of biodiesel for the Lurgi process. This shows clearly the need to have comparable system boundaries before comparing different systems. From this analysis the Biox process is still more energy intensive because of its steam consumption, about 3128MJ steam per tonne of biodiesel for Biox relative to about 2315MJ for Lurgi. Caution is still required in interpreting these results as our means of estimating the full Lurgi energy requirements are limited. In conclusion, the Biox process appears to be a good conservative choice in determining the full requirements for a biodiesel production plant. 6.3 LCI Results for Biodiesel Production

Table 6.3.1a LCI for BIOX Biodiesel Conversion Process with no mass allocation Inputs (g) for 1kg of biodiesel production Crude oil (resource) 2.7Hard coal (resource) 4.0Lignite (resource) 9.6Natural gas (resource) 85.5Copper ore (0.14%) 9.5Inert rock 94.8Lead - zinc ore (4.6%-0.6%) 11.2Limestone (calcium carbonate) 0.7Nickel ore (1.6%) 0.9Sodium chloride (rock salt) 15.8Zinc - copper ore (4.07%-2.59%) 5.4Zinc - lead - copper ore (12%-3%-2%) 1.2

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Table 6.3.1b LCI for BIOX Biodiesel Conversion Process with no mass allocation Outputs (mg) for 1 kg biodiesel production Industrial waste for municipal disposal 5.0Inert chemical waste 177.3Slag 83.9Sludge 1529.0Radioactive ore processing residues 50.5Ash 20.0Ore processing residues 15701.4Carbon dioxide 240117.1Carbon monoxide 59.8Hydrogen chloride 0.5Hydrogen sulphide 32.8Nitrogen oxides 604.3Nitrous oxide 6.2Steam 219686.8Sulphur dioxide 419.4Butane (n-butane) 2.0Ethane 90.2Formaldehyde (methanal) 1.9NMVOC (unspecified) 46.1Pentane (n-pentane) 3.1Propane 37.2Methane 463.1VOC (unspecified) 29.6Particles to air 12.9Biological oxygen demand (BOD) 0.9Chemical oxygen demand (COD) 95.0Total organic bounded carbon 3.1Iron 16.5Chloride 835.6Chlorine (dissolved) 1.2Fluoride 7.1Sulphate 187.7Particles to water 40.3Boiler ash (unspecified) 158.1Fly ash (unspecified) 491.2Gypsum 6.8Gypsum (FDI) 270.3Slag 245.4Slag (Iron plate production) 102.6Sodium sulphate 19000.0Sodium bisulphite 26000.0

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Table 6.3.2a LCI for Lurgi1 Biodiesel Conversion Process with no mass allocation Inputs (g) for 1kg biodiesel production Crude oil (resource) 0.3Hard coal (resource) 0.2Lignite (resource) 0.5Natural gas (resource) 39.1Inert rock 6.5Sodium chloride (rock salt) 0.8Catalyst 5.01. It is important to recognize that the two LCI tables, 6.3.2a and b, for the Lurgi process are more approximate estimates than for the Biox process. The Lurgi data presented here include both the core requirements (given by Lurgi on the Web) and our rudimentary estimates for the distillation of the glycerine and for additional deacidification and degumming of free fatty acids.

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Table 6.3.2b LCI for Lurgi Biodiesel Conversion Process with no mass allocation Outputs (mg) for 1kg biodiesel production Inert chemical waste 8.9Sewage sludge 2.0Slag 4.4Sludge 605.0Radioactive ore processing residues 2.6Ash 1.4Ore processing residues 1696.5Overburden 4988.4Treatment residue (mineral) 8.5Carbon dioxide 100899.5Carbon monoxide 23.6Hydrogen sulphide 15.1Nitrogen oxides 260.4Nitrous oxide 2.6Steam 74388.3Sulphur dioxide 102.7Butane (n-butane) 0.9Ethane 41.7NMVOC (unspecified) 14.9Pentane (n-pentane) 1.4Propane 17.2Methane 194.9VOC (unspecified) 1.5Particles to air 1.4Biological oxygen demand (BOD) 0.3Chemical oxygen demand (COD) 7.0Total organic bounded carbon 2.4Iron 0.9Calcium 4.5Chloride 60.3Fluoride 1.0Sodium 25.7Sulphate 8.0Particles to water 2.0Boiler ash (unspecified) 8.4Cooling water 94030.6Fly ash (unspecified) 26.1Gypsum 0.4Gypsum (FDI) 14.7Glycerine 120000.0

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In the transesterification process there are two valuable products produced, the biodiesel and the glycerine. In the Biox process the glycerine is produced in anhydrous form with small residual quantities (99.7% pure). The energy requirements of a distillation process to convert the crude glycerine to a more purified form have been estimated to make the Lurgi output more comparable. As described in the early chapters, there are at least three ways of proceeding with an LCA with co-products such as this, mass allocation, value or economic allocation and by system expansion. Mass allocation simply assigns the environmental emissions to the two co-products according to their relative mass outputs. In the system expansion for glycerine, it is supposed the additional output of glycerine from the biodiesel process displaces another production source on the margin and this source has been typically identified as synthetic glycerine production. Ahmed et al considered this issue in an often-referenced paper from 1994.57 From the Handbook of Petrochemical and Processes, Shreve’s Chemical Process Industries book and a publication by Pittinger et al58, Ahmed et al were able to identify the four materials used to produce synthetic glycerine and give values of their embodied primary energy as reproduced in the following table. The actual process of producing the glycerine from these materials is exothermic, so the process itself consumes minimal energy.

Table 6.3.3 Synthetic Glycerine Production according to Ahmed et al Raw Material Input (kg/kg

glycerine) Energy content (MJ/kg)

Glycerine Energy Input (MJ/kg)

Propylene 0.62 19.94 12.36 Chlorine 2.00 12.37 24.73 NaCl 0.45 1.38 0.62 NaOH 0.45 26.21 11.80 Total 49.51 These appear to be fairly well established numbers. The only problem appears to be in deriving the GHG emissions because this depends on the type of energy source used. However, Scharmer and Gosse59 have developed a different dataset for a biodiesel LCI in Europe. Note that the embodied energy estimate here is some four times larger at 209MJ/kg for glycerine. 57 Ahmed, I., Decker, J. and Morris, D. 1994. How much Energy does it Take to Make a Gallon of Soydiesel, Institute for Local Self-Reliance. http://www.eere.energy.gov/afdc/pdfs/3229.pdf 58 Pittinger, C.A., Sellers, J.S., Janzen, D.C., Koch, D.G. Rothgeb, T.M., and Hunnicutt, M.L. 1993. Environmental Life-Cycle Inventory of Detergent-Grad Surfactant Sourcing and Production, Journal of the American Oil Chemists’ Society, vol. 70(1): 1-15. 59 Scharmer, K. and Gosse, G. 1996. Ecological Impact of Biodiesel Production and Use in Europe. http://www.blt.bmlf.gv.at/vero/liquid_biofuels_newsletter/Liquid_biofuels_Newsletter-07_e.pdf.

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Table 6.3.4 Synthetic Glycerine Energy Content according to Scharmer and Gosse

It is preferable to develop a system expansion where the biodiesel process is credited for the glycerine it produces rather than assigning environmental emissions on a mass basis. However, with such divergent results the system expansion tends to lose credibility. Moreover the results are far from being negligible. In the following table the GHG emissions from the Biox process are calculated with and without mass allocation for the glycerine produced and then using the system expansion. If the system expansion were accurate, it would mean that the biodiesel transesterification process is actually highly efficient at producing glycerine and its production can totally offset the entire energy requirements for both the biodiesel and glycerine.

Table 6.3.5 GHG Emission Estimates for the Biox Biodiesel Production Without allocation Mass allocation for

glycerine System expansion1

kgCO2-eq./kg biodiesel

0.2518 0.2312 -0.3109

1. In the system expansion the co-product of glycerine is credited this displacing the need for the production of an equivalent amount of synthetic glycerine. The raw materials of Ahmed are used in combination with the most conservative GHG emissions numbers that are available for their production. Through this process the GHG emission requirement to produce 1kg of synthetic glycerine was 3.9635

Table 6.3.6 GHG Emission Estimates for the Lurgi Biodiesel Process1 Without allocation Mass allocation System expansion kgCO2-eq./kg biodiesel

0.1058 0.0945 -0.4604

1. An estimate of the energy for the distillation and bleaching of the glycerine has been included in the analysis.

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7. Downstream Emissions of Diesel and Biodiesel The chemical emissions from diesel combustion within a motor are dependent on the state of diesel motor technology used, the characteristics of the fuel, the efficiency of the motor and the driving characteristics. For a given technology, fuel, and driving conditions (as reflected in a specific transient test) the efficiency is determined so that the emissions become directly proportional to the mass of fuel consumed. Different driving cycles will lead to some differences in emissions, especially for particulate matter (PM), but in this analysis this will be considered a secondary trait that can be captured by some reasonable extrapolation of data. For instance, it can be assumed that constant speed driving on a highway would produce slightly lower PM values per unit mass consumed as compared to a bus route characterized by many accelerations and decelerations. There will be two main sources of information for the analyses in this report. The first source is a 2002 EPA study that undertook the compilation and analysis of all the previous data in biodiesel and diesel exhaust emissions. It is prudent to exploit such a large compilation of data because the data scatter from different experiments is substantial. The actual trends in emission performance become clearer through the averaging process. The core of the data relates back to diesel technologies that respond to the performance standards set from 1984 through 1997. From 1984 through 1997 there has been substantial progress in the diesel technology. This has meant that the absolute emissions from diesel and biodiesel blends have dropped substantially. However, the EPA study was able to determine that there was some consistency in the impact of biodiesel blends in a relative sense, i.e that a 20% blend of biodiesel would tend to reduce hydrocarbon emissions for the different technologies by about 21%. The second source of key information came from the Biobus study undertaken in collaboration with the Societé de transport de Montréal (STM). The study included chassis testing as well as some actual performance data from buses on selected Montreal routes. The tests used a specific transient cycle, that of the US EPA Heavy Duty Engine Transient Test Code of US Federal Regulations (CFR) 40, Part 86 – Protection of Environment, Sections 86-1337-90 and 86.1337-96. These tests provide a good representation of urban bus performance, including the bus routes of the city of Montreal where experience was gained with the buses running on 5 to 20% biodiesel blends. Use of the data from the Biobus project, offers a number of advantages:

• The data comes from a highly qualified team at the Environmental Technology Centre in Ottawa, Canada;

• The data analysis was strong thanks to good technical and logistics committee;

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• Two different diesel technologies (electronic and mechanical injection) and three different sources of biodiesel, soy, yellow grease and tallow were used in separate trials;

• The data from the electronic injection system are from a diesel engine designed to meet 1998 performance standards for which little to no data was available in the EPA study;

• This data from more modern engines allowed more accurate determination of the absolute emission levels now and into the near future.

The only disadvantage to use of the Biobus results is that they still remain related to two only particular motors and to one type of petrol diesel. There must be some care in generalizing the results from the report. 7.1 The Challenge of Data Accuracy It can be anticipated that biodiesel will essentially be used in blends of from 1-20% by volume. The LCA must relate to this type of blend yet eventually be able to distinguish the specific impact of the biodiesel, which means that the differences in emissions between the pure diesel and the blend become the determining factor in the LCA. This constitutes a serious challenge. There may be reasonable emission values for pure diesel and for diesel-biodiesel blends. However, the differences between the two are small enough that the percent error in the difference calculation can be large. This partly explains the substantial scatter in data sets but there is also a more fundamental reason; emission differences can be highly dependent on characteristics of the diesel as well as the biodiesel, the diesel injection and combustion-control technology as well as the duty cycle of the vehicle. This will be discussed more fully in a separate section. However, to begin it is important to establish a framework that makes the analysis as tractable as possible. 7.2 The LCA Framework for Exhaust Emission Analysis The functional unit of this analysis is the energy output of the diesel for a duty cycle typified by the EPA Heavy Duty Engine Transient Test transient cycle. The energy output is most often given in brake horsepower-hour (bhp-h). To retain SI (metric) consistency the energy output in this study will be given in megajoules (MJ) where 1 bhp-h = 2.685MJ. For the heavy-duty motor, the vast majority of data60 supports the conclusion that the efficiencies of the conversion of diesel and biodiesel energy through

60 Some trials of light-duty vehicles undertaken by Professor Barry Hertz of the University of Saskatchewan appear to show a higher efficiency due to the biodiesel. This is attributed to the superior lubricity characteristics of the biodiesel. This efficiency improvement due to lubricity can only be expected where the original diesel used as the benchmark is particularly poor in its lubricity characteristics – which appears to be the case for the diesel available in Saskatchewan at the time of the studies. However, once the lubricity of the benchmark is adequate no significant improvements in efficiency should be expected from the biodiesel. The improved lubricity of biodiesel is confirmed by technical studies: Munson, Jason W., Hertz, Barry P., Dalai, Ajay K.,

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combustion are equivalent, so that the specific energy output per unit mass fuel is expressed as

Eout = (effH)(HHV) or Eout = (effL)(LHV) (7.2.1) where LHV and HHV are respectively the lower and higher heating value of the diesel or the biodiesel in MJ/kg, and eff is independent of whether the fuel is diesel or a biodiesel-diesel blend.

As the HHV for diesel is typically 1.078 times higher than the LHV,effL = 1.078effH The Biobus project undertook studies with diesel (No.2) and biodiesel-diesel mixtures from 5 and 20% by volume. The effH was found to be between 0.318 and 0.324. With the combined field and laboratory studies there was no systematic variation of this efficiency with biodiesel content at these concentrations. In the subsequent analyses a value of effH = 0.31861 is taken for both diesel (No. 2) and biodiesel when emission conversions of g/kg to g/MJ are required. In the NREL study (Section 3.1.2) effL for a diesel bus is given at 0.358, so effH becomes 0.332. This is in good agreement with the Biodiesel study results. Overall, the Genius model uses effH of 0.37 for heavy-duty diesel but it is meant to cover all fleet operations including extensive highway operations where the efficiency can be expected to be somewhat higher. To calculate biodiesel emissions, the following process has been used. For a given biodiesel blend the part of the blend emissions to be attributed to the diesel in g/MJout is calculated based on the percentage diesel by mass in the blend, its HHV, its effH and the emissions that have been measured in trials with 100% diesel. The emissions of biodiesel are calculated on a marginal basis. Generally, it would first be necessary to determine the difference in energy output of the blend and the biodiesel to calculate, based on the HHV of the biodiesel and its percentage content in the blend, the effective effH of the biodiesel in the blend. However, the vast majority of trials indicate that this efficiency is the same at that for diesel (except when the diesel is seriously deficient in lubricity or in cetane

Reaney, Martin J. SAE Technical Paper Series, 1999-01-3590, Lubricity Survey of Low-Level Biodiesel Fuel Additives Using the "Munson ROCLE" Bench Test. 61 Throughout our analysis of the Biobus data there was only one small mistake that we identified. In the table giving the thermodynamic and mechanical efficiencies, p. 60, the conversion from brakehorsepower-hour to metric units was based on a conversion factor of 735.5W/bhp. This is associated with a so-called metric horsepower unit sometimes used. However, throughout the bulk of the report the correct conversion value of 746W/bhp was used. This means that the engine efficiency values given on the Biobus report, page 60 are in error of 0.4%. The efficiency reported as 31.8% for diesel in electronic injection would actually be 32.2%. 20% biodiesel blends with electronic injection were in the range of 32.0 to 32.3%. The efficiency of 31.8% used in some parts of this study is slightly lower than average values for electronic injection but well representative of a mix of mechanical and electronic injection engines.

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number) and therefore the energy output of the biodiesel in simply calculated based on its HHV and the diesel effH. The specific emissions of biodiesel (per unit energy output) are calculated by considering the specific emission of the blend to be the sum of the specific emissions of diesel times it fractional contribution to the energy in the blend (both of these known quantities) plus the specific emissions of the biodiesel multiplied by its fractional contribution to energy. Note that the proportions of biodiesel to diesel are expressed typically in volumetric terms, i.e. 20% biodiesel is 20% by volume, but given the differences in density of biodiesel and diesel this becomes close to a 21% mass proportion. This approach has the advantage of showing clearly the influence of the biodiesel. It is very important to note that this normalization process gives the impact per unit mass or per unit energy input of biodiesel in the blends but this cannot be extrapolated to estimate the emissions of a vehicle running on 100% biodiesel. The mathematical basis for these arguments is presented below. The specific emissions of a blend of biodiesel and diesel are expressed as:

emT(g/MJ) = emdFEId + embFEIb (7.2.2a) emb(g/MJ) = [emT – emdFEId]/FEIb (7.2.2b) Where emT = specific emissions of the total blend in g/MJ(output),

emd = specific emissions of pure diesel in g/MJ(output) emb = specific emissions of the biodiesel and FEId and FEIb are respectively the fractional energy input of the diesel and biodiesel to the blend.

The impact on emissions of developing one MJ output using biodiesel rather than diesel would be:

Ib(g/MJ) = emb – emd (7.2.3) For a blend of biodiesel and diesel the specific energy output of the blend per unit mass of fuel is calculated as:

Eot = effH{(fmb)(HHVb) +(1- fmb)(HHVd} (7.2.4) Where fmb = mass fraction of biodiesel in the blend. This means that the fractional energy input to the blend from diesel and biodiesel are respectively: FEId = (1- fmb)(HHVd)/{(fmb)(HHVb) +(1- fmb)(HHVd} FEIb = (fmb)(HHVb)/ {(fmb)(HHVb) +(1- fmb)(HHVd} = 1 - FEId

For certain types of emissions, e.g. total hydrocarbons, the percentage emission reduction for a biodiesel blend can exceed the actual percentage of the biodiesel in the blend. This means that the biodiesel is actually promoting the cleaner

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burning of the diesel fuel. Under these conditions, the allocated biodiesel emissions can become negative. This is obviously a function of the biodiesel fraction. As the biodiesel fraction increases, the emissions attributed to the biodiesel must increase. This points out that the environmental benefits of biodiesel are a function of its blending fraction. This also explains why this study’s results of the emissions per megajoule output of biodiesel cannot be directly used to determine emissions of neat or 100% biodiesel.

The emissions attributed to biodiesel in terms of g/MJ can readily be converted to emissions per unit mass of biodiesel used:

emb(g/kgb) = emb(g/MJ)(effH)(HHVb) (7.2.5) The typical energy requirement for one kilometer of urban bus travel in Montreal allows values given as a function of output energy to be converted to a function of kilometers of travel along an urban bus route. This conversion will not be widely used in this analysis as it yields values more dependent on specific driving conditions than values normalized with respect to energy. However the conversion is simple. The Biobus project lists the fuel consumption as 65L/100km and the energy output per liter as 0.2314bhp.h/L. The conversion factor from output energy to kilometers becomes:

(65L/100km)(bhp.h/0.2314L)(2.685MJ/1bhp.h) = 7.54MJ(output)/km.

As a comparison, the Genius model uses an energy requirement per kilometer that factors in the lower requirements for highway travel to yield 5.82MJ/km, some 77% of that for the urban bus route. 7.3 Integration of EPA relative emission data for biodiesel blends into framework EPA emission data and that obtained from the Biobus project can be compared directly by expressing the Biobus data in the same format as the EPA study, i.e. the % emission reduction for a given biodiesel blend ratio. However, it is also important to consider absolute values. To this end, the EPA relative emission estimates for a 20% blend are multiplied by the absolute exhaust emissions of the base Biobus diesel to obtain a data set that can generate a second set of brake specific emission numbers that can be associated with biodiesel. A comparison of the results from the two sets of data allows an assessment of the range of values that may be anticipated for emissions with biodiesel blends. This study also considers the differences in biodiesel and diesel emissions for the older and newer technologies of the mechanical and electronic injection systems respectively. 7.4 Preliminary Analysis of Parameters Likely to Affect Exhaust Emissions The interpretation of the data sets is greatly facilitated by some background information on the physical processes that are involved in producing the

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emissions. All the forms of biodiesel considered in this report are various blends of methyl esters. This consist of straight hydrocarbon chains (fatty acids), mainly of length C16 and C18, to which a methyl ester group OOCH3 is bonded to one of the end carbon atoms. There are up to three unsaturated bonds in the chain depending on the source of the biodiesel. The dominant mono-unsaturated fatty acid is oleic acid with an 18-carbon chain. The predominant double and triple unsaturated chains are also composed of 18 carbon atoms and are referred to as linoleic and linolenic acid respectively. Soybean oil is typified by a high content of linoleic acid. Canola has a much higher content of oleic acid. Yellow grease derived from waste cooking oil has about 20% animal-based hydrocarbon chains and some 80% or more from vegetable oils. Because vegetable oils with high levels of polyunsaturated bonds tend to be unstable and oxidize when used in cooking, the vegetable oils for cooking are largely comprised of oils like canola with a high percentage of monounsaturated chains. Furthermore, the cooking oils are often partially hydrolyzed to reduce the linolenic and linoleic acid content.

Table 7.4.1 Approximate composition of canola, soy oils and yellow grease Oil % Saturated

fatty acids C16:0, C18:0

% Oleic acid C18:1 *YG includes 2% 16:0

% Linoleic acid C18:2

% Linolenic acid C18:3

Soybean 15 25 52 8 Canola 7 60 23 10 Yellow Grease

30 58* 10 2

Tallow from rendered animals has a ratio of unsaturated to saturated fatty acids from 1.6:1 to 1:1, i.e. the saturated fatty acids would constitute 32 to 50% of the tallow. 7.5 Chemical Differences between Biodiesel and Diesel no.2 and their Impact on Emissions The properties of diesel from different sources are quite varied. Nevertheless there are still substantive differences between the vast majority of diesel formulations and biodiesel. 7.5.1 Biodiesel as a Methyl Ester The methyl ester ending to the hydrocarbon chain has a number of consequences. It lowers the energy density of the biodiesel relative to diesel. In simple terms the additional oxygen of the ester adds very little to the energy content but does add to the overall mass of the chain so that the energy density decreases. This can mean a drop of from 8-12% of the biodiesel energy.

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The ester adds a hydrophilic end to an otherwise hydrophobic molecule. This makes biodiesel a superior solvent and lubricity agent. This chemical difference also contributes to higher viscosity and a lower compressibility. Both of these can affect injection characteristics and atomization. Specifically with some types of injection system there is a significant advance in the injection of 1-2 degrees of crank angle for 100% biodiesel that is still discernable for a 20% blend. Figure 7.5.1 Injection Pressure for Biodiesel, Diesel and 20% Biodiesel Blends, according to Tat and Van Gerpen62

This all adds up to one very advantageous feature, the enhanced lubricity that will reduce engine wear, and some potentially negative features through changes to timing and combustion characteristics. The enhanced solvency of biodiesel must be taken into account in vehicle operations. Normal diesel will coat out slightly onto the fuel tank and the biodiesel will tend to dissolve this and end up producing blockage in the filters. This problem was encountered in the Biobus study but simple corrective measures were found, like avoiding changing back and forth between the use of pure diesel and biodiesel bIends. The ester also introduces chemically-bonded oxygen into the interaction and so the fuel is referred to as an oxygenate. The implications of the oxygenate have

62Tat, M.E. and Van Gerpen, J.H. 2003. Measurement of Biodiesel Speed of Sound and Its Impact on Injection Timing. NREL/SR-510-31462 Report

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been questioned by authors such as Knothe et al63 who make the case that the carbon and oxygen are likely to remain bonded and a simple decarboxylation takes place during the combustion process. However, more recent arguments by authors such as Kitamura et al64 suggest that even attached to the carbon, the oxygen can play a useful role in suppressing the formation of some of the aromatic structures, precursors to soot formation, during the pyrolysis phase of the combustion. The authors cite the previous work of Murayama et al, “The results showed that the presence of atomic bonded oxygen reduces the formation of unsaturated hydrocarbons such as ethylene and benzene during the fuel pyrolysis process, which leads to the reduction of PAH formation and ultimately soot formation.” They then go on to present further experimental and theoretical model analysis to support their hypotheses. However, it is important to recognize that the straight chain nature of biodiesel is probably a more important factor in its enhanced combustion and the reduced biodiesel emissions of carbon monoxide, soot, total hydrocarbons (THC) and polycyclic aromatic hydrocarbons in blends. This leads naturally into the next topic. 7.5.2 Biodiesel’s Straight Hydrocarbon Chains, their Unsaturated Bonds and their Relationship to Combustion Characteristics Biodiesel’s composition of virtually 100% of straight-chain fatty acid is not found in petrol diesel. The latter has more of a branched structure and a 15-45% aromatics content. Diesel from light oils tend to have less aromatics while diesel derived from heavy oils have greater branched structure and more aromatics. The consequences of the straight chain relative to the branched, aromatic structure are given below. The aromatics and branched structure have a higher energy content than the straight-chain structure. The difference in aromatic content among diesels is readily apparent in both energy content and density. This explains, in particular, the difference between the average energy content of Canadian oil used in the Genius model, 45.8MJ/kg HHV, versus the 43.5MJ/kg of the diesel oil from off-shore oil used in the Biobus project. The former is influenced strongly by the heavy, aromatic oils from the West while the latter is a lighter oil, mainly from Venezuela. The higher energy content can be accompanied by higher flame temperature. This can give slightly improved thermodynamic efficiency, but in diesel engines it tends to produce the temperature regimes necessary for thermal nitric oxide formation.

63 Knothe, Gerhard, Dunn Robert O. and Bagby Marvin O. 1996. Biodiesel: The Use of Vegetable Oils and Their Derivatives as Alterative Diesel Fuels, http://www.biodiesel.org/resources/reportsdatabase/reports/gen/19961201_gen-162.pdf 64Kitamura, T., Ito, T., Senda, J. and Fujimoto, H. Detailed Chemical Kinetic Modeling of Diesel Spray with Oxygenated Fuels, http://comb.doshisha.ac.jp/03-Projects/2001-01-1262.pdf

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Figure 7.5.2.1 Correlation of Aromatic Content and Density in Diesel Fuels according to the EPA65

Figure 7.5.2.2 Correlation of Cetane Number and Aromatic Content of Diesel Fuels according to the EPA

65 This and the following two figures are from EPA420-R-03-002, Feb. 2003, The Effect of Cetane Number Increase Due to Additives on NOx Emissions from Heavy-Duty Highway Engines, Final Technical Report http://www.epa.gov/otaq/models/analysis/r03002.pdf

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The cetane number is determined by the auto-ignition characteristics of the fuel and more specifically a higher cetane number produces a reduced delay in ignition and generally superior performance and reduced NOx emissions. A cetane number of from 40-55 is generally appropriate for most diesel engines operating now. Longer or straight chain produces a much better cetane number than shorter, branched or aromatic compounds. Short-chain fuels or ones with high aromatic content like the synthetic heavy oils can require additives or enhanced hydrogenation to produce an adequate cetane value. From the graph below it can be seen that a five-point increase in cetane number, with no other modifications, can decrease NOx emissions from 1.6 to 2.7%. It should be noted that the incoming generation of diesel engine with exhaust gas recirculation (EGR) is not expected to show this dependency of NOx emissions on the cetane number. Figure 7.5.2.3 NOx Reduction Due to Cetane Enhancers Effective at 1% Concentration or Less according to the EPA

Because of their long, straight chains biodiesel fuels have generally superior cetane numbers but this can be compromised to some extent by the content in unsaturated bonds, and especially of linolenic and linoleic acid. Knothe et al66 have recently published experimental data the relationship of cetane number to the fatty acid content.

Table 7.5.2.1 Cetane Number of Fatty Acid Methyl Esters Stearic (18:0) Oleic (18.1) Linoleic (18:2) Cetane Number 101.0 59.3 38.2

66Knothe, G., Matheaus, A.C. and Ryan III, T.W. 2003. Cetane numbers of branched and straight-chain fatty esters determined in an ignition quality tester, Fuel, 82(8):971-975

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It can be anticipated that linolenic’s cetane value would be again considerably lower than linoleic.67 N-hexadecane (16:0) has the defined cetane number of 100 so it can be seen from the cetane value of methyl stearate that the methyl ester attachment to the hydrocarbon does not dramatically reduce the cetane number. Also the single unsaturated bond in oleic acid still gives the related methyl ester a superior cetane number to most diesel. However, the cetane number for polyunsaturated linoleic and linolenic are typical of highly-branched alkanes or high aromatic content oils. This substantial cetane number reduction for the polyunsaturated compounds is reflective of profound changes in the combustion characteristics. A graph from the 2003 doctoral thesis of M.E. Tat shows persuasively the scope of these changes. Figure 7.5.2.4 Heat Release as a function of Crank Angle for Biodiesel and Diesel Fuels according to Tat68

The combustion processes for soybean, yellow grease and diesel fuels given above can be related to the brake specific NOx emissions. The case has been 67 Van Gerpen gives a cetane number of 23 for methyl linlenate, http://www.gwu.edu/~combenv/essci2003/VanGerpenCombustion Institute_Oct 29 2003.pdf 68 This and Figure 6.4.2.6 come from the doctoral thesis of Mustafa Ertunc Tat, Investigation of oxides of nitrogen from bio-fueled engines, prepared for Iowa State University, http://www.me.iastate.edu/biodiesel/Technical Papers/Mustafa Intro.pdf Unfortunately, the results of this thesis are not highly predictive as many of the results are related to the type of distributor injection system with which many of the tests were conducted. Nevertheless the study provides valuable information and insight into the diesel combustion processes.

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made that a significant portion of the NOx emission increases sometimes measured is associated with the timing advance. This is undoubtedly true but the potential impact of the olefin content of biodiesel, especially from soybean, on the combustion itself should not be neglected. The olefin content is simply another way of talking about the unsaturated carbon bonds and they are most important in methyl esters of linoleic and linolenic acid. This is dismissed in certain circles so let us consider this more closely. In combustion the most significant source of NOx is the attainment of very high temperatures at which the vital dissociation of oxygen can take place as a precursor to the formation of NO. It is important therefore to identify those features that would tend to produce the highest temperatures. There are usually two combustion phases identified in a diesel engine The pre-mix combustion is said to reflect isolated auto-ignition before the second phase of flame diffusion emerges and provides more uniform burning. These phases are marked by two peaks in the typical heat release curves as seen in Figure 7.5.2.4. Many authors claim that the fuel-rich nature of the pre-mix combustion makes it unconducive to being the source of NOx. The argument is not entirely satisfactory. If the pre-mix combustion is initiated early enough after injection so the turbulence is still typified by very high Reynolds numbers then the pre-mix combustion will occur along sheets of very high shear. The shear will separate full-rich from fuel-lean air and within the shear zone there will naturally be a thin zone of near-stoichiometric conditions. The three-dimensionality of the turbulence becomes very important at this point as the high shear sheet is maintained by steady replacement of fluid at the boundary by flow along shear sheet parallel to the local primary vorticity vector.69 This means that the oxygen and hydrocarbon are naturally being renewed within the sheet around the stoichiometric point. Very high temperatures and conditions on either side of the boundary can lead to both prompt and thermal nitrogen precursors. It should be recognized that the apparent temperature of the radiation from soot emissions during pre-mix is not a good indicator of the high temperatures of the shear zone. This radiation is actually a result of the very high turnover of fluid at the shear

69 The structure of these shear sheets is described more fully by Rollefson, J.P., On Kolmogoroff’s theory of turbulence and intermittency, Canadian Journal of Physics, vol. 56, 1978 p. 1426-1441. “From the atmospheric frontal system in the middle latitudes to the stirring of a cup of tea, sharp vorticity gradients in the form of sheets are produced and maintained by two basic characteristics of the flow, its three dimensionality and the ratio of viscous and inertial time scales. The latter characteristic is just one way of understanding the Reynolds number, since if tv =L2/v and ti = L/U then tv/ti = UL/v = Re (where v is taken as the kinematic viscosity). This means that the inertial motion can replace those elements of fluid subject to strong viscous forces before these forces can act for a substantial time. The three dimensionality of the problem gives the degree of freedom necessary for these inertial forces to maintain the discontinuities. This can be seen clearly in the teacup problem where the basic rotational flow about the vertical axis together with the frictional forces at the bottom surface induces a secondary rotational flow inward at the lower surface, upward in the middle, then downward at the outer surface. This produces a high wavenumber dissipation fed directly from the flow as a whole, as the fluid subjected to strong dissipative forces is constantly renewed.”

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boundary. This fluid that moves from the high shear to low shear zone is transferring the heat into the core of the fuel-rich mixture and this is causing pyrolytic reactions and the radiation. The above argument does not lead to any general prediction on how and where NOx is formed in diesel combustion. Indeed, there is unlikely to be a unique reason for NOx formation under different diesel conditions. Nevertheless, pre-mix combustion conditions do warrant more study. Some NOx will definitely be associated with the diffusion flame, some may be associated with the pre-mix. It may be that the certain pre-mix conditions become critical to establishing the necessary conditions for high temperatures in the early part of the flame diffusion. Why different reasons for actual NOx emissions may need to be employed under different conditions emerges when Figure 7.5.2.4 is compared to the following figure from Van Gerpen.70 Figure 7.5.2.5 Heat Release versus Crank Angle for Diesel and Soybean Biodiesel according to Van Gerpen

It is to be noted that both these curves produced significant increases in NOx from biodiesel as compared to diesel. Yet the curves are quite dissimilar. The former has a much higher pre-mix peak for biodiesel while the opposite is true for the latter. The very high pre-mix peak and its timing just before top dead center (TDC) is likely to be a critical factor for NOx production from biodiesel in the former. The high heat value in the flame diffusion regime near TDC is likely to be 70Van Gerpen, Jon H. 2003. Combustion and Emissions from Biodiesel Fuels, http://www.gwu.edu/~combenv/essci2003/VanGerpenCombustion Institute_Oct 29 2003.pdf

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the cause in the latter. The early timing of the combustion relative to the TDC is important in both. However this is not the whole story. From Figure 7.5.2.4 it can be seen that yellow grease and soybean biodiesel show a similar ignition advance with respect to the diesel. This can be related, as explained earlier, mainly to the specifics of the injection process and the compressibility and viscosity of the biodiesels. As the compressibility values (as reflected in the inverse of the isentropic bulk modulus of the two biodiesels) are similar the initiation point of ignition is almost the same. However, the two biodiesel processes differ significantly afterwards. The slope of the heat release for the soybean-based biodiesel is the steepest and it progresses to considerably higher values than that for yellow grease. For the case represented by Figure 7.2.5.4 it is the combination of the extremely fast and high heat production combined with its timing in the engine compression cycle prior to Top Dead Centre that is likely to be generating high temperatures that should be reflected in higher NOx emissions. While the peak of the heat production from pre-mix diesel is even higher, the slope of the curve is considerably less and the timing is such that it takes place after TDC. It is interesting to note that in spite of the very significant differences in the form of the heat release, the NOx emissions from yellow grease biodiesel and diesel are almost identical. However the soybean NOx emissions are some 14% higher as seen in the following figure from the doctoral thesis by M. E. Tat. Figure 7.4.2.6 Brake Specific NOx Emissions from Biodiesel and Diesel Fuels according to Tat

Unfortunately, the exact reason cannot yet be identified for why the soybean-based biodiesel has such different characteristics, but the most likely influence is soybean’s high content of polyunsaturated bonds relative to that of yellow

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grease. For one thing, the break up of unsaturated bonds releases more energy than the alkane bonds. It is known from more classical studies of diesel that the olefin content, i.e unsaturated bond content can lead to higher NOx emissions. In blends of 20% of soybean biodiesel the impact on NOx should be considerably reduced but nevertheless detectable. This appears to be the case for Biobus exhaust emission data to be presented later in the report. The influence of the base diesel characteristics also must be considered. The temperatures attained by high-aromatic-content diesel are already significantly higher than those with lower content. The combination of soybean biodiesel may exacerbate the problem. It is tempting to want to add biodiesel to improve the cetane number of such aromatic diesels but this requires more study. There may need to be fuel adjustments made to transform the initial combustion pattern of the soy-based biodiesel. The most obvious solutions are either to hydrolyze partially the soybean oil and thus reduce the linolenic and linoleic acid content, or to find additives that attack the problem. The first approach would tend to cause other problems associated with cold-temperature crystallization due to the much higher melting point of the saturated compounds. Some industrial companies are claiming to use the appropriate additives with biodiesel to address the NOx problem.71 Other approaches are also being studied.72 Reference has been made to the other significant property dependent on the hydrocarbon chains of biodiesel and diesel. The unsaturated bonds currently play a significant role in improving the cold flow characteristics of the long-chain biodiesels. A very large fraction of totally saturated straight chains of length 18 would have serious impacts on cold flow filtering characteristics. The cold flow properties must be taken into account. The impact in Canada can be controlled by the use of low percentage blends (1-5% biodiesel) or possibly by the use of new additives.73 At these low mix ratios it will still be very important to have the proper logistical approach for the transport and mixing of the biodiesel and diesel and adequate temperatures to avoid microcrystalline formation. Biodiesel blends of 10-20% should probably be limited to summer operations and/or fleet 71 See, for instance, the information given by the company Distribution Drive, http://www.distributiondrive.com/FAQ.html. 72 McCormick, R. L. and Herring, A. M. 2000-01873 Chemical Modification of Biodiesel by Oxidative Cleavage. Strengthening Award; Grant 2001-35504-10177; $144,132; 2 Years “We have developed a strategy for overcoming these problems by oxidative cleavage of the carbon-carbon double bonds in the fatty acid chains. The products of this process include biodiesel with improved emissions, cold flow properties, and oxidative stability, as well as several high value co-products. A two-step approach has been developed based on preliminary results. In step 1 catalytic oxidation of double bonds to produce epoxides, under acidic conditions where the epoxides are rapidly converted to diols, is performed. In step 2 the diols are catalytically cleaved with oxygen or air over a complex oxide catalyst. Development of reaction conditions and catalysts that maximize yields is the objective of this project.” 73 See for example the information from Power Service Products, http://www.powerservice.com/arcticexpress_biodiesel_antigel.asp.

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operations like urban bus companies that have heated garages etc. to alleviate cold weather conditions. To provide a conclusion to this section it should be noted that the research in this field is very intensive around the world. Solutions are sought where there can be the simultaneous reduction of both NOx and particulate matter (PM). This appears achievable but it remains challenging as it is easy to slip into a situation where you reduce one at the expense of increasing the other. This next graphic presents recent results from Akihama et al in this field.74 The equivalence ratio is the ratio of the actual local fuel mix to that of a stoichiometric mix of fuel and oxygen. Note that the NO production is typified by the high temperatures and lower values of the equivalence ratio while soot is produced with higher fuel concentrations and lower temperatures. The ideal, if this theory proves valid, is to keep within the open regions. Figure 7.4.2.7 Equivalence Ratio and Temperature Influence on NO and Soot Formation according to Akihama et al

This research into NOx formation is important to the future of biodiesel as, of all the exhaust emissions, the future standards for NOx are expected to be the most difficult to meet with new diesel motors. If biodiesel causes even minor increases in emissions, they still may be important. This is why this report devotes so much consideration to this fundamental issue.

74 Akihama, K., Takatori, Y., Inagaki, K., Sasaki, S. and Dean, A.M. 2001. Mechanism of the Smokeless Rich Diesel Combustion by Reducing Temperature, Copyright © 2001 Society of Automotive Engineers, Inc. http://www.mines.edu/Academic/chemeng/faculty/amdean/pubs/ref52.pdf

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7.5 The EPA Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions75 as Interpreted in the light of this Report’s Preliminary Analysis The primary findings of the EPA study are summarized in the following figure and table extracted from the report. Figure 7.5.1 Average exhaust emission impacts of biodiesel for heavy-duty highway engines, EPA results

75 The EPA Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, EPA420-P-0-001, October 2002, http://www.nbb.org/resources/reportsdatabase/reports/gen/20021001_gen-323.pdf

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Figure 7.5.2 Exhaust emission impacts of 20%vol biodiesel for soybean-based biodiesel added to an average base fuel, EPA results

The average base diesel fuel in the EPA study has a specific gravity of 0.85 and a net energy content (LHV) of 129,500BTU/gal. With 1.078 used as a multiplying factor for converting LHV to HHV this represents 45.7MJ/kg. This is in good agreement with Genius model’s average value give for Canada as 45.8MJ/kg. Beyond these average numbers the comprehensive analysis recognizes the difference that the base diesel fuel can have in the biodiesel emissions. This is reflected in a series of figures from Figure IV.b.4-1 through Figure IV.B.4-4 where biodiesel blend emission differences for clean and average base diesel are compared. A clean base fuel is defined as having all the following properties:

• Total cetane number is greater than 52, • Total aromatics content is less than 25%vol, • Specific gravity is less than 0.84.

A fuel that meets the California requirements for diesel is also considered clean. All of the above conditions would tend to produce lower exhaust emissions so, as a first hypothesis it could be considered difficult for the biodiesel to produce the same relative performance change with them. In other words, if biodiesel is a cleaner burning than normal diesel, the differences will be less marked when compared to a cleaner diesel. Another explanation for the reduced improvement of biodiesel is that the high cetane value of the biodiesel can have little positive influence with a base fuel having such a good cetane value. When the data is separated to show the influence of the diesel base, the differences between the clean and average base data do emerge. Typically the biodiesel emission improvements decrease by some 3-6 percentage points for the 20% blend. Here is, for instance, the figure for particulate matter reductions.

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Figure 7.5.3 Base fuel effects on particulate matter exhaust emissions, EPA results

Some of the largest differences between diesel and biodiesel emissions occur for total hydrocarbon emissions. Figure 7.5.4 Base fuel effects on hydrocarbon exhaust emissions, EPA results

In the case of NOx, the biodiesel emissions are on average worse than diesel and this difference is accentuated with the clean fuel data.

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Figure 7.5.5 Base fuel effects for NOx exhaust emissions, EPA results

Certainly, the higher cetane value of the clean fuel can be expected to play a role in the higher NOx values but other studies show that the lower aromatic content of the clean fuel is unlikely to be one of the criteria that had a great bearing in increasing the biodiesel NOx emissions. In Table 10 from a study by McCormick et al76 results are given for a 20% blend of biodiesel with a diesel of very low aromatic content, 10%. The base level of NOx emissions for the low aromatic fuel is 4.478g/bhp-h. The B-20 blend for yellow grease fuel has emissions levels of 4.586 and that of soybean, 4.606g/bhp-h. The percent increases relative to this base are respectively 2.4 and 2.9%. This is much more in line with the 2% value predicted for the average fuel compared to the 5% increase predicted for the clean base fuel in the EPA study. Because of the preponderance of data from soybean biodiesel the average data from the comprehensive analysis is representative of that type of biodiesel. The EPA study does, however, consider the impact that the different feedstock for biodiesel may have on emissions. Below are some figures relating to emission differences for soybean, rapeseed (canola) and animal-based biodiesels for NOx, particulate matter (PM) and carbon monoxide, CO.

76McCormick, Robert L., Alvarez Javier R. Graboski, Michael S., Tyson, K. Shaine and Vertin, Keith. 2002. Fuel Additive and Blending Approaches to reducing NOx Emissions from Biodiesel, SAE Technical Paper Series 2002-01-1658

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Figure 7.5.6 Biodiesel source effects for NOx exhaust emissions, EPA results

Figure 7.5.7 Biodiesel source effects for particulate matter exhaust emissions, EPA results

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Figure 7.5.8 Biodiesel source effects for carbon monoxide exhaust emissions, EPA results

Suffice it to say that the results of the EPA study given immediately above are consistent with the preliminary analysis of emissions given in this report. The soybean-based biodiesels show poorer performance in emission reductions. It is worth emphasizing, however, that solutions to these problems are emerging and this performance can be expected to improve, at least for diesel motors without EGR. The final figure from the EPA study is important in terms of the greenhouse gas emissions analysis. Essentially it shows that the impact of 20% biodiesel on exhaust emissions of carbon dioxide is neutral relative to diesel (less than 0.5% change).

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Figure 7.5.9 Biodiesel impacts on CO2 exhaust emissions, EPA results

7.6 Interpretation of Biobus Emissions Data from Different Sources of Biodiesel This study’s analysis and that of EPA both arrived at the conclusion that the base diesel can influence the relative emission difference of the biodiesel blend. Some of the characteristics that should be considered for the diesel are its density, energy content, cetane number, sulphur content and aromatic content. While the aromatic content of the diesel in the Biobus study has not been specified it can be readily inferred from its low specific gravity of 0.837 and from its low energy content of 43.5MJ/kg that it is relatively low in aromatics. The diesel has a sulphur content of 504 ppm. The cetane index of 42.4 was provided instead of the cetane number. Under the particular circumstances it should be a reasonable proxy for the cetane number. Most of these diesel characteristics make it an excellent blending material for biodiesel. The cetane value is good but low enough to be positively influenced by the biodiesel high value of from 52 to 56.6. The combination of low energy content (relative to other diesels) and its low density when combined with the high density of the biodiesel leads to only a very small difference in energy input per unit volume of fuel. This suggests that no significant modification to the engine should be necessary to adjust for the biodiesel blend unless injection problems were to emerge because of the changes in compressibility and viscosity of the biodiesel. The power and energy outputs were reduced by less than 2% in all cases of the 20% biodiesel blend used with electronic injection.

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In summary, the biodiesel emission results from the Biobus trial can be anticipated to be among the better ones recorded and it should be no surprise that some improvements exceed the average values of the EPA study. 7.6.1 Biobus NOX Emissions From the preliminary discussion it should be anticipated that NOx emissions from soy-based biodiesel should be distinguishable from those from yellow grease and animal tallow. This is indeed the case for the Biobus study. The latter two (for the 20% blend) are significantly different form the soybean biodiesel at the confidence level of 99.5%. In the context of the EPA study the increase of 1.7% for the NOx emissions of soy-based biodiesel is entirely consistent with the 2% average value suggested by the EPA overview study. The actual reductions in NOx emissions of 3 and 5% respectively for yellow grease and tallow-based in the Biobus study appear somewhat unusual in the context of the EPA study, but it can be seen as entirely reasonable in the context of this report’s preliminary analysis. That NOx conditions emissions need not necessarily rise in all cases is an important result because by far the most difficult standard to attain over the next few years for diesel engines will be the one associated with NOx emissions. Figure 7.6.1 Biobus NOx Emissions

7.6.2 Biobus Polycyclic aromatic Hydrocarbon (PAH) Emissions If the influence of the differences in polyunsaturated bond structure is as important as hypothesized above, it should be most obvious in the PAH emissions. The scission produced about the unsaturated bond structure should lead to short chain precursors to aromatic bonds. These should tend to persist through to the exhaust emissions. The results of the Biobus study at 20% blend tend to support this hypothesis.

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Figure 7.6.2 Biobus Polycyclic aromatic Hydrocarbon (PAH) Emissions

7.6.3 Biobus Other Exhaust Emissions The impacts of the biodiesel feedstock on other emission are less apparent. There can be counterbalancing factors that enter into play. However the trends in most cases are similar to those found in the EPA study. Here is a summary of dependency of the emissions on feedstock from the Biobus study. The only trend in the table that is not in line with the EPA study is the reduction in particulate emissions with the soy-based biodiesel marginally superior to the two others. Figure 7.6.3 Biobus Exhaust Emissions with Electronic Fuel Injection

7.7 Specific Life Cycle Analysis Parameters to be Used in this Study It has been shown above that the Biobus study represents a good set of data where the combination of biodiesel and diesel produce superior results. The electronic injection data sets show less scatter than the mechanical injection

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systems and are therefore more reliable. They are also more representative of current technology. The biodiesel emissions are derived from experimental measurements of a 20% blend level (rather than 5-10%) in order to better distinguish the impact of biodiesel and reduce errors. The average relative values from the EPA study will be used in conjunction with the absolute diesel emissions from the Biobus study to provide some range of values that can be expected. The Biobus results on biobiesel blend emissions and performance have been used in this study because of the extensive testing undertaken to ensure their accuracy. However, one characteristic of diesel base stock used in the Biobus research does not match up well with its counterpart for “average” Canadian diesel with a significant heavy oil component. The diesel used in Biobus was from off-shore light crude and with a low aromatic content it had a higher heating value (HHV) of 43.5MJ/kg. The HHV for the average diesel in Canada in the Genius model is 45.8MJ/kg, i.e. a 5% difference. To be able to use in a consistent manner the Biobus results the HHV of 43.5MJ/kg has been extensively used in this study. However, it must be noted that this tends to overestimate the greenhouse gas emissions from both the upstream and exhaust emissions by the same 5% when values are given in terms of specific emissions per unit energy output instead of per unit mass. For instance Biobus diesel exhaust emissions for CO2 were determined to be 221g/MJout (see Table 7.8.1) whereas diesel emissions based on the carbon fraction of average Canadian diesel, 0.858 and the HHV of 45.8MJ/kg would lead to emissions of 213g/MJout.77 In the tables the results are all expressed for an HHV of 43.5MJ/kg, but the way in which it tends to overestimate GHG emissions by about 5% from some diesel sources should be recognized. This also impacts on the calculation of the GHG benefits of biodiesel. While the emission differences between the different feedstocks have been shown to be significant in certain cases, on the scope of the total LCA the differences will be typically small. The yellow grease values will be generally used. The specific cases will include the NOx emissions from soybean biodiesel where an additional sensitivity analysis will show the impact of the higher emissions. 7.7.1 Biodiesel’s Carbon Dioxide Emissions of Biological Origin The biodiesel and glycerin co-products result from the reaction of methanol (typically of non-biological origin) and of the triglyceride of biological origin. If one were to tag the carbon atoms in the biodiesel methyl ester, there would be the one carbon in the final methyl group attached to the carboxyl group that would be of non-biological origin. As the length of the preponderant carbon chain to which the methyl group is attached is 18 carbon atoms, the ratio of organic carbon to

77 This number is the product of the carbon fraction and the ratio of the atomic weights of carbon dioxide to carbon divided by the product of the HHV and the conversion efficiency of chemical to mechanical energy in the diesel (effH): [(0.858)(44)/12]/[(45.8)(0.322)] = 0.213kg.

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the total carbon content in biodiesel is 18/19. In the NREL study this kind of differentiation of the organic and non-organic carbon content of the methyl ester was included in calculating the greenhouse gas impact of biodiesel. That is to say that a fraction of the biodiesel exhaust emissions of carbon dioxide were included in the GHG emissions for biodiesel. However, further analysis might then be warranted to take account of the 100% organic carbon content of the glycerin co-product. Another perspective on the process is to consider that as part of the total net balance of the reaction, the organic carbon content of the triglyceride exactly matches the carbon content of the biodiesel, and the inorganic content of the methanol exactly matches the carbon content of the glycerin co-product. This understanding leads to a simpler analysis where the organic origin of the glycerin’s carbon need not enter into the analyses. For this LCA study the base case attributes the biodiesel with the organic carbon and therefore sets to zero the GHG emissions of carbon dioxide. A sensitivity analysis also shows the impact of associating 1/19 of the exhaust emissions to non-biological (and therefore non-renewable) sources. 7.8 Biodiesel Exhaust Emissions The analyses developed in section 7.2.1 allow the exhaust emissions specific to the biodiesel to be differentiated from diesel emissions in a biodiesel-diesel blend. These analyses, and their derived equations, are used to produce the following tables of biodiesel emissions. Some biodiesel emissions, e.g. carbon monoxide, become negative within a blend. This happens when the relative decrease in blend emissions exceeds the relative portion of the biodiesel in the blend. It means that the biodiesel is promoting cleaner burning of the diesel itself.

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Table 7.8.1 Tail-pipe emissions attributable1 to biodiesel for electronic injection engine (g/MJ)2 Tail-pipe emissions3

Animal fat based

Used cooking oil based

Vegetable oil based

Average of the Biobus B100

EPA - soybean based

STM Petrodiesel

CO -0.0628 -0.141 -0.101 -0.102 0.100 0.229 CO24 229.3 216.6 232.8 226.2 220.7 220.7 CO25 theoretical

222.1 217.8 225.0 221.6 221.5 221.5

Total hydrocarbon

-0.035 -0.017 -0.006 -0.019 -0.0055 0.063

NOx 1.503 1.357 1.919 1.593 1.946 1.768 PM 0.0090 0.0043 0.002 0.0051 0.0075 0.015 PM2.5 0.0147 0.0028 0.0032 0.0069 unavailable 0.013 SO4 0.0035 0.012 0.006 0.007 unavailable 0.05 Polycyclic Aromatic Hydrocarbon

-1.98E-05

-7.75E-06 1.72E-05 -3.45E-06

unavailable 0.000036

1. All data, except when otherwise specified, are based on the formulae of 7.2.1-5 and are derived from emissions from the Biobus B20 experimental data. Where EPA data is used the specific emissions are derived by calculating the specific emissions of the 20% blend from the product of the EPA relative emission estimates with the absolute exhaust emissions of the base Biobus diesel. 2. Because 1 bhp.h = 2.685MJ the specific emissions per brake horsepower-hour can be calculated as 2.685 times the specific emissions per megajoule output. 3. All Biobus emission measurements were made at the output of the vehicle’s exhaust system after the exhaust passed through the catalytic converter, the same converter being used for both engine types on the test facility. 4. These are the total emissions of carbon dioxide and are considered of biological origin. Therefore when total GHG emissions for biodiesel are calculated these emissions will be set to zero (except in the sensitivity analysis identified in section 7.7.1. 5.The theoretical CO2 values were determined from the carbon content of the biodiesel, its higher heating value and the efficiency of the motor (effH) to give some appreciation of the errors in the approach taken. The relative error between the two approaches yield values in error by 0.5-3% which is considered very satisfactory considering the results are derived from slight differences of diesel and 20% biodiesel-diesel blends. 6. The HHVs for biodiesel derived from animal fat, used cooking oil and vegetable oil were respectively 39.98, 39.9 and 39.8 MJ/kg. The efficiencies of the electronic injection engine (effH) were between 0.320-.324 and on average 0.318 for the mechanical injection. These numbers allow the conversion factor for calculating the emissions per unit kilogram of biodiesel from the emissions per unit energy output in MJ according to equation 7.2.5 as 12.85, i.e 1g/MJ is equivalent to 12.85g/kg(biodiesel) for electronic injection and 12.69g/kg(biodiesel) for mechanical injection.

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Table 7.8.2 Tail-pipe emissions attributable to biodiesel for mechanical injection engine (g/MJ) Tail-pipe emissions

Animal fat based

Used cooking oil based

Vegetable oil based

Average of the Biobus B100

EPA - soybean based

STM Petrodiesel

CO 0.0377 -0.105 -0.098 -0.055 0.161 0.373 CO2 212.3 201.5 213.3 209.03 223.35 223.3 CO2 theoretical

221.2 217.2 223.5 220.6 224.3 224.3

Total hydrocarbon

0.027 0.046 0.027 0.033 -0.0044 0.073

NOx 2.510 2.764 2.876 2.717 3.001 2.723 PM -0.0053 -0.0002 -0.021 -0.0088 0.017 0.036 PM2.5 unavailable -0.0034 -0.0057 -0.0046 unavailable 0.032 SO4 unavailable 0.018 0.010 0.014 unavailable 0.048 Polycyclic Aromatic Hydrocarbon

4.47E-05 6.38E-05

2.56E-05 4.47E-05

unavailable 4.47E-05

Note: The footnotes of Table 7.8.1 apply as well to the above table To provide some perspective on the above tables the actual emission of the 20% blends (without specifically separating out the impact of the biodiesel) are given in the two following tables. The tables point out two things. The net emissions of biodiesel are very favourable in a number of cases. However, because the biodiesel will tend to be used as a blend of some 2-20% with diesel, its overall impact on air quality in cities will be limited.

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Table 7.8.3 Specific Exhaust Emissions of 20%vol Biodiesel-Diesel Blends a. for electronic injection engine (g/MJ) Tail-pipe emissions (g/MJ)

Animal fat based

Used cooking oil based

Vegetable oil based

Average biodiesel blend for of the Biobus

EPA - soybean based

STM Petrodiesel (100% diesel)

CO 0.172 0.157 0.165 0.165 0.204 0.229 CO2 222.39 219.92 223.07 221.79 220.7 220.7 Total hydrocarbon

0.044 0.047 0.050 0.047 0.050 0.063

NOx 1.717 1.688 1.798 1.734 1.803 1.768 PM 0.014 0.013 0.012 0.013 0.013 0.015 PM2.5 0.013 0.011 0.011 0.012 0.013 SO4 0.041 0.043 0.041 0.042 0.05 Polycyclic Aromatic Hydrocarbon

2.5E-5 2.7E-5 3.2E-05

2.8E-5 0.000036

b. for mechanical injection engine (g/MJ) Tail-pipe emissions (g/MJ)

Animal fat based

Used cooking oil based

Vegetable oil based

Average of the Biobus

EPA - soybean based

STM Petrodiesel

CO 0.308 0.280 0.281 0.290 0.332 0.373 CO2 211.16 219.05 221.35 217.19 223.3 223.3 Total hydrocarbon

0.064 0.067 0.064 0.065 0.058 0.073

NOx 2.682 2.731 2.753 2.722 2.777 2.723 PM 0.028 0.029 0.025 0.027 0.032 0.036 PM2.5 0.025 0.024 0.025 0.032 SO4 0.042 0.041 0.042 0.048 Polycyclic Aromatic Hydrocarbon

4.8E-5 4.1E-5 4.5E-5 4.47E-05

Note: All data, otherwise specified, are for 20% biodiesel fuel from Biobus B20 experimental data. EPA data is obtained by multiplying the EPA relative emission estimates for a 20% blend with the absolute exhaust emissions of the base Biobus diesel.

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8. Life Cycle Analysis of Entire Biodiesel Cycle In the Genius model there are only a certain number of emission outputs given. Fortunately these are the most relevant ones for this study. Results for the total biodiesel emissions will be given for the same set of variables so the comparisons can be meaningful. In the first tables are the total emissions through all stages of diesel and biodiesel production according to the different sources of for the two processes. Included within the biodiesel emissions are the same estimates for storage, distribution and dispensing as used for diesel. In the first tables a simple mass allocation system has been used to account for the co-products of glycerin and protein meal.

Table 8.0.1 LCI Results for Diesel and Biodiesel with mass allocation

Inputs (g/MJout) Diesel

Biodiesel Yellow grease

Biodiesel Tallow

Biodiesel Soybean

Biodiesel Canola

Crude oil 72.3 1.4 2.3 3.3 3.6 Hard coal 0.3 0.3 0.4 0.5 Lignite 0.7 0.7 0.7 0.7 Natural gas 7.5 16.3 10.4 13.7 Inert rock 6.9 7.3 7.4 7.2 Sodium chloride 1.1 1.1 1.1 1.1 Phosphorus minerals 3.9 5.1 Waste cooking oil (40% water) 120.5 Fat (Slaughterhouse residues) 200.8 Emissions (mg/MJout) Carbon dioxide 290203.5 24311.1 25976.5 32616.5 34037.5 Carbon monoxide 729.1 -96.3 -89.1 -83.6 -78.9 Nitrogen oxides1 2356.5 1467.9 1479.0 1506.4 1548.2 Nitrous oxide 3.8 0.7 0.8 55.93 145.95 Sulphuric acid 50.0 12.0 12.0 12.0 12.0 Methane 787.8 62.5 114.3 91.0 119.6 Particles to air 39.6 11.9 12.4 15.4 16.3 NMHC2 147.9 7.3 24.7 85.3 171.9

1. While NOx exhaust emissions from biodiesel are similar to those from diesel, the net life cycle impact on NOx emissions from biodiesel is very advantageous.

2. In the non-methane hydrocarbon emissions tow scenarios were introduced to account for the different estimates of hexane emitted during the crushing operations. Hexane emissions of 2kg/(tonne canola oilseed), very similar to NREL input for soybean in its study, and the calculated Canadian average emissions from data of Environment Canada’s National Pollutant Release Inventory of 0.38/(tonne oilseed). The two different estimates should not be associated specifically with canola and soybean. The numbers were introduced in this way only to reflect the range of values possible. Note that the higher hexane estimate reproduces the results of NREL where biodiesel production with high hexane emissions can lead to NMHC emissions higher than diesel. But with Canadian average data the biodiesel production becomes advantageous to that of diesel in this category as well.

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Figure 8.0.1 GHG Emissions for Biodiesel, Base Cases with Mass Allocation

Yellow Grease Production from Used Cooking Oil T = 1.27

Tallow Production from Animal Residue T=4.04

Agricultural Production Soybean T=20.1 Diesel=1.62 Fertilizer=0.92 Field N2O=17.1

Agricultural Production Canola T=50.3 Field N2O = 36.1 Fertilizer = 11.08 Diesel=3.02

Soybean Crushing T=7.16

Canola Crushing T=6.37

Biodiesel Production Biox Process T=19.76

Biodiesel Distribution T = 5.5 (canola) T = 4.83 (all others)

Biodiesel from Used Cooking Oil Grand Total=25.86

Total Biodiesel from Animal Tallow GT=28.63

Total Biodiesel from Soybean GT=51.9

Total Biodiesel from Canola GT=81.8

Exhaust Gas Emission Supplement1 T=11.4

All values in g(CO2-eq)/MJout

1.To be added to each total above if NREL process of tagging inorganic carbon due to methanol in biodiesel is to be used.

T = Total emissions for stage GT = Grand total emissions for biodiesel for the feedstock

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Table 8.0.2 GHG Emissions for the Life Cycle of Canadian Diesel and Biodiesel Diesel Diesel Blend1,2 for

central-western Canada

Diesel onshore

Diesel offshore Diesel heavy

g(CO2-eq.)/MJ 307.92 283.71 290.82 321.01 Biodiesel (g(CO2-eq.)/MJ)

Yellow Grease

biodiesel3,4

Tallow biodiesel

Canola biodiesel

Soy biodiesel7

Without allocation 27.73 42.19 176.83 189.12 With mass allocation (for glycerin and protein meal) 25.86 28.63 81.8 51.9 With system expansion for glycerine and protein byproducts5 -216

-406.97

-610.88 114.27 -179.4 NA

With system expansion for Meat and Bone Meal (MBM) as energy source8 -355

1. Diesel blend is 27% onshore light, 10% offshore and 63% heavy. 2. The diesel emissions are based on the sum of upstream emissions according to the

Genius model, version 2.3a, but with exhaust emissions based on the Biobus results for electronic injection engine and a diesel with HHV= 43.5MJ/kg rather than the more typical Canadian value (from Genius) of HHV=45.8MJ/kg.

3. All biodiesel results are based on the Biox process for biodiesel production. 4. Biodiesel exhaust emissions here are considered to be of biologic origin and are not

counted. This is consistent within the overall LCA if the byproduct glycerin displaces a carbon of non-biological origin, as it would do for synthetic glycerin. However, if the byproduct glycerin is displacing glycerin of biological origin then the accounting of the non-biological carbon atom in each methyl ester chain would be appropriate. This would mean 11.4g should be added to all biodiesel emissions due to the biodiesel exhaust emissions. This latter number is derived from the good approximation that the methyl ester biodiesel is mainly an 18-carbon fatty acid of biological origin with the extra carbon of the carboxylic group coming from the methanol used in the biodiesel production reaction. Therefore 1/19 of the biodiesel exhaust emissions are not of biological origin.

5. The system expansion explanation warrants a separate section given below. 6. If the glycerine was of such poor quality that its most practical use would be as an

energy source to displace some of the natural gas required for rendering as shown in Figure 5.1b, the system expansion result would be 2.5g(CO2eq/MJ).

7. This system expansion result is actually for an increase in canola production combined with a reduction in soybean such that no net protein is produced. The system expansion also takes account of the glycerine offset. The upper value is based on a calculation based on the IPCC adjusted methodology for nitrous oxide emission for canola and soybean while the lower value is based on the same N2O estimate for soybean but with N2O from canola based on experimental measurements by Lemke (see Table 3.8.1).

8. Soybean emissions are derived from the base case of no fertilizer input nor fertilizer credit for the crop with the IPCC adjusted methodology.

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9. This is appropriate for MBM possibly contaminated by BSE prions. See bottom of 8.1.1 for an explanation.

8.1 Biodiesel System Expansion Results The system expansion is employed in two instances, to account for the glycerin co-product and to account for the protein displacement from the oilseed meal. It is the purity of the anhydrous glycerin (99.7%) from the Biox process that allows for it to be considered as a reasonable facsimile for synthetic glycerin. In the NREL study the glycerin was much less pure and the main co-product was listed as soapstock rather than glycerin. Synthetic glycerin production using raw materials such as propylene, chlorine, and sodium hydroxide is energy intensive so if it is being displaced (as it actually is to some extent by biodiesel production in Europe) then the GHG emission credits for displacing the need for this production are substantial (see Table 6.3.5). In the case of yellow grease from used cooking this credit for this displaced glycerin production is actually larger than all other GHG emissions in the process so the biodiesel has a net negative value of –21g(CO2eq)/MJ for its GHG emissions. If the co-product was poorer quality soapstock and it was used only as an energy source to replace natural gas in the rendering industry, the net GHG emissions become 2.5g(CO2eq)/MJ. The case for the oilseeds system expansion may at first be surprising as the net GHG impacts is higher than in all other scenarios. However the results are understandable. In this system expansion scenario the agricultural sector responds to the demand for more biodiesel production (but with no additional demands for protein meal) by shifting some production from soybean to canola. In Europe this type of situation has actually emerged as the demand for rapeseed (canola is a rapeseed with low erucic acid content) has risen spectacularly. With a production of 3.36 additional kilograms of canola oilseed and 2.25 less kilograms of soybean there is no net production of protein meal but there is an increase of one kilogram of oil (see Section 1.6 and equation 1.6.1). It is the consequence of this transfer of production from soybean to canola that is being measured in the system expansion. For this reason the system expansion should not be directly associated with canola, and definitely not with soybean, because it relates to an overall oilseeds production shift. The large difference emerges because the soybean is a nitrogen fixing plant requiring no nitrogen fertilizer, with consequently low nitrous oxide emissions, while the canola does require substantial nitrogen fertilizer. With current estimation methods, and, in particular, that of the IPCC as adjusted by scientists of Agriculture and Agri-Food Canada, the nitrous oxide emissions from canola become very large relative to those from soybean. Just how sensitive these nitrous oxide estimates are reflected in Table 8.0.2. The net GHG emissions for the oilseed system with data for N2O emissions based on the IPCC adjusted estimation drops from 184 to 40.4g(CO2-eq)/MJ when the nitrous oxide emissions are based on the measured values by Lemke (Table 3.8.1).

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In the case of biodiesel derived from the tallow of slaughterhouse residues, the system expansion results are even more sensitive to the canola nitrous oxide emissions in relative terms. There is a production ratio of 1.3:1of protein meal to oil in the tallow process itself so in the system expansion there must be an important decrease of meal within the system to compensate for this. A drop in soybean production of 5.38kg and an increase in canola of 2.22kg accomplish this. This can be seen from the equation developed earlier:

Ero = 10Ert-5.38Est+2.22Ect (1.6.2) The sensitivity to the GHG emissions from canola and soybean becomes extremely high. It should be remembered that Est and Ect represent total emissions for production of the meal and the oil. These GHG emissions are recorded in the Table 8.0.2 as biodiesel emissions without mass allocation. The very large drop in emissions due to the much reduced need for soybean (and the consequent much lower nitrous oxide emissions) is the principal reason for what may be considered a very unexpected result. Note however that this based on the “adjusted” IPCC methodology and this estimate is likely to be high relative to actual measurements. Even with a considerable reduction in the estimate of N2O emissions due to soybean agriculture, the overall tendency will be to have negative values for this system expansion. MBM is best used as an energy source when the MBM has any significant risk of being contaminated by BSE prions. If the MBM displaces coal use in the cement industry (as it most likely would in Canada) then the net GHG reductions are still very substantial. These LCA results point out the very significant environmental benefits that come from ensuring that all dead animals or parts thereof are treated by the rendering industry. The inappropriate disposal of animal carcasses on land has serious health risks, leads to further methane emissions and precludes the possibility of these major GHG reductions available through the use of the byproducts. 8.1.1 Biodiesel System Expansion from Feedstock where Bovine Spongiform Encephalopathy (BSE) is a Significant Risk Factor The system expansion for tallow given in the previous section was predicated on the use of the byproduct meat and bone meal (MBM) as a protein supplement. However, in a recent proposal78 published by the Canadian Food Inspection Agency (CFIA) MBM use for animal feed will not be allowed where there is some significant risk of transmission of the faulty prions responsible for BSE (or similar diseases like scrapie in sheep and chronic waste disease in cervids) to susceptible animals. This means that the MBM from downers and dead stock from a variety of animals excluding swine and horses must not be used for animal feed or fertilizer. The interdiction of animal feed use also applies to MBM 78 The CFIA proposal is provided on the Web site: http://www.inspection.gc.ca/english/anima/feebet/rumin/docinfoe.shtml

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derived from the specific parts of cattle where 99.8% of the infectivity of BSE is concentrated. These parts are referred to as “Specified Risk Material” or SRM which includes:

“(a) the skull, brain, trigeminal ganglia, eyes, tonsils, spinal cord and dorsal root ganglia of cattle aged 30 months or older; and (b) the distal ileum of cattle of all ages.

In practical terms, the tissues to be removed would most likely include the entire small intestine from all animals and vertebral column from those over 30 months as it is difficult to remove the distal ileum and spinal cord completely and consistently from each carcass.”

The actual tallow from the SRM, dead stock and downers can still be used for all applications including as an animal feed supplement when it is protein-free, as confirmed by containing less than 0.15% of insoluble material. The tallow could be used for biodiesel as well. From the experience in Europe the most environmentally sure and economical application of this MBM from BSE risk material appears to be as an energy source in the cement industry. This material becomes Agricultural Waste Derived Fuel (AWDF) and in France some 350-400 kilotonnes of the MBM is exploited in this manner. Its fuel value is on the order of 15-20MJ/kg with an average given by one source79 as 19MJ/kg. More specifically, the advantages of the cement application for the MBM are:

• It displaces fossil fuel and therefore contributes to lower GHG emissions. • Extremely high temperatures (well over 1000C) are achieved in the kiln

and it is likely one of the few processes that actually destroys completely the prion.

The big disadvantage is that the economic value of the energy from MBM is much less than its value as a protein and phosphate additive (if it were prion-free). The system expansion based on the use of MBM for energy use still gives interesting results from a perspective of reducing GHG emissions. To achieve a net output of biodiesel with no other net gains the new energy from the MBM would be offset by a concomitant reduction in energy requirements for coal in the cement manufacturing. The system expansion for the glycerin production would remain as before. Approximate values for this system expansion where MBM is used strictly for energy has the 1kg of MBM displace the need of combusting 19/26 kg of 79 This information comes from a permit application in Europe by Castle Cement of the Heidelberg Cement Group, see http://www.castlecement.co.uk/documents/AWDF PPC variation Oct 2003 Submitted to EA.pdf

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bituminous coal with characteristics of 62% carbon content, 26MJ/kg (HHV), 2.254kg(CO2)/kg coal of direct emissions and 0.086kg/kg (upstream emissions).80 This leads to a net reduction of GHG emissions of some 355g(CO2-eq)/MJout of biodiesel. 8.2 The Role of Biodiesel in the Deadstock and Rendering Industry In this LCA study the system expansion was always directed at achieving the same designated functional unit of 1MJ of energy derived from different sources of biodiesel and delivered to the crank of a heavy-duty diesel engine. As noted in chapter 1 the LCA framework for the system expansion was for a “technologically-whole system”. The system is adjusted so that the impact measurement is from the comparison of a system where one additional functional unit is delivered and nothing else. This study shows the great care is required even with these limited objectives in order to interpret properly the results. It should also be clearly understood what the limitations are. An analysis of wider scope would be required in order to compare various scenarios where economic and environmental objectives go well beyond the delivery of a functional unit. There remains therefore much more analysis beyond the strict LCA to appreciate the potential impact of biodiesel in a larger context that includes the economics of the deadstock collection and the rendering industry. An introduction to the topic can still be provided based on the research done in the preparation of the LCA. The production of biodiesel does not address the fundamental problem which is the high lost value associated with the meat and bone meal that must be burned rather than used for protein because of the risk of BSE. For biodiesel to be economic without substantial subsidies in the market it must be derived from low-cost feedstock. The value of the tallow as a feedstock for diesel is far from being adequate to make up for the loss of revenue from the MBM. However, biodiesel production can play a useful, albeit indirect, role in the overall industry. With similar levels of meat production from livestock the reduced production of protein meal from the MBM has to be made up of other protein sources. As MBM constitutes a very well-rounded mix of the amino acids necessary for animals, this will most likely come from a combination of various vegetable stocks. However the most prominent of the feedstocks is likely to be soybean with its good ratio of protein to oil. Nevertheless, this means additional oil production that must be absorbed by some market. Already the Canadian oil crushing industry is at a point where it could produce more protein meal if it had 80 The higher heating value comes from a report prepared for the Cement Association of Canada by Nyboer and Tu of the Canadian Industrial End-Use Data and Analysis Centre, http://www.cieedac.sfu.ca/CIEEDACweb/pubarticles/Industry - Cement/cement report Final 2003 _2001 data_.pdf while the direct emissions come from Canada’s Greenhouse Gas Inventory, 1990-2001, http://www.ec.gc.ca/pdb/ghg/1990_01_report/annex7_e.cfm - t37, and the upstream emissions from the Genius model (based on a 26MJ/kg HHV).

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better oil markets. The added protein demand will only compound this problem. Biodiesel might be considered as the application for the soy oil market. However, the low price at which the soybean oil would have to be sold for biodiesel to be economic would mean that the price of protein meal would have to rise very significantly to offset the lost revenues from the oil. There could, however, be industrial collaboration to encourage the more economic tallow and yellow grease production to go towards biodiesel and not towards any feed supplement applications so that the soy oil market would be less depressed. In whatever scenario that establishes proper controls to limit the BSE threat, there are increased costs for livestock production and conversion that must be offset by a combination of reduced production and eventually higher prices to the consumer. In summary, biodiesel could play a useful but limited role in helping stabilize the oil demand and supply that will be upset to some extent by the additional vegetable protein requirements for livestock. All this being said, as part of a total system the environmental benefits of exploiting effectively the possibly contaminated MBM as an energy source and the tallow as biodiesel are very substantial as can see by the large net negative GHG emissions fro this option. 8.3 Summary Conclusion of the Life Cycle Analysis The greenhouse gas emission benefits per unit mass or per unit energy delivered by biodiesel are excellent. The challenge will be to find the low-cost feedstocks for the biodiesel to displace 1-5% of current diesel requirements in order to have some significant impact on overall GHG emissions in Canada.

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9. References Ahmed, I., Decker, J. and Morris, D. 1994. How much Energy does it Take to Make a Gallon of Soydiesel, Institute for Local Self-Reliance. http://www.eere.energy.gov/afdc/pdfs/3229.pdf Agriculture and Agri-Food Canada. Canadian Fertilizer Consumption, Shipments and Trade. Tables 1.4 and 1.8. http://www.agr.gc.ca/policy/cdnfert/text99-00.pdf. Akihama, K., Takatori, Y., Inagaki, K., Sasaki, S. and Dean, A.M. 2001. Mechanism of the Smokeless Rich Diesel Combustion by Reducing Temperature, Copyright © 2001 Society of Automotive Engineers, Inc. http://www.mines.edu/Academic/chemeng/faculty/amdean/pubs/ref52.pdf ASAE. 2000. Agricultural machinery management data. ASAE Standards 2000: EP496.2 p 344-349 and D297.4 p 213-220. American Society of Agricultural Engineers. http://waterhome.brc.tamus.edu/NRCSdata/technote/497_4.pdf Beer, T., Grant, T., Morgan, G., Lapszewicz, J., Anyon, P., Edwards, J., Nelson, P., Watson, H. and Williams, D. 2004. Comparison of Transport Fuels Final Report (EV45A/2/F3C) to the Australian Greenhouse Office on the Stage 2 study of Life-cycle Emissions Analysis of Alternative Fuels for Heavy Vehicles. http://www.greenhouse.gov.au/transport/comparison/ Biobus Project Committee Members. 2003. Biobus - Biodiesel Demonstration and Assessment with the Société de transport de Montréal. Final Report. http://www.stcum.qc.ca/English/info/a-biobus-final.pdf Bohner, H. 2003. Head-to-Head Comparison of Soybean Inoculants in 2002. http://www.gov.on.ca/OMAFRA/english/crops/field/news/croptalk/2003/ct_0103a8.htm Bohner, H. 2003. What About Yield Drag on Roundup Ready Soybean? http://www.gov.on.ca/OMAFRA/english/crops/field/news/croptalk/2003/ct_0303a9.htm Brophy, L.S. and Heichel, G.H. 1989. Nitrogen release from roots of alfalfa and soybean grown in sand culture. Plant and Soil 116: 77-84. Canada’s National Climate Change Process (NCCP). Greenhouse Gas Emissions and the Canadian Fertilizer Industry. 2002. http://www.nccp.ca/NCCP/national_stakeholders/pdf/1_d_fertilizer_industry_overview_e.pdf Canadian Fertilizer Institute. Nutrient Uptake and Removal by Field Crops. Eastern Canada. 2001. http://www.cfi.ca/uploaddocuments/d160%2BNU%5FE%5F01%2Epdf

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Canadian Food Inspection Agency. 2004. Information Paper, Development of Specific Regulatory Enhancements to Canada's BSE Feed Controls http://www.inspection.gc.ca/english/anima/feebet/rumin/docinfoe.shtml Canadian Oilseed Processors Association. 2000. http://www.reviewcta-examenltc.gc.ca/Submissions-Soumissions/Txt/Canadian Oilseed Processors Association.txt Canola Council of Canada. http://www.canola-council.org/, Castle Cement Company. Permit Application. Use of Agricultural Waste Derived Fuel (AWDF) as a fuel for kiln 7. http://www.castlecement.co.uk/documents/AWDF PPC variation Oct 2003 Submitted to EA.pdf Delucchi, M. A. and Lipman, T. A Lifecycle Emission Model (LEM): Lifecycle Emissions From Transportation Fuels, Motor Vehicles, Transportation Modes, Electricity Use, heating and Cooking Fuels and Materials, UCD-ITS-RR-03-17D, http://www.its.ucdavis.edu/faculty/delucchi.htm. Desjardins, R.L, Janzen, H.H., Lemke, R. 2002. Regional and National Estimates of the Annual Nitrous Oxide Emissions from Agroecosystems in Canada using the Revised IPCC Methodology. Agriculture Canada. Dyer, J.A. and Desjardins, R.L. Simulated Farm Fieldwork, Energy Consumption and Related Greenhouse Gas Emissions in Canada. Biosystems Engineering. 85(4):503-513. Dyer, J.A. and Desjardins, R.L. 2003a. The Impact of Farm Machinery Management on the Greenhouse Gas Emissions from Canadian Agriculture. Journal of Sustainable Agriculture. 20(3):59-74 Dyer, J.A. and Desjardins, R.L. 2003b Analysis of trends in CO2 emissions from fossil fuel use for farm fieldwork related to harvesting annual crops and hay, changing tillage practices and reduced summerfallow practices in Canada. Journal of Sustainable Agriculture. 25(3):1044-1046. Environmental Protection Agency. 2002. A Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, EPA Draft Technical Report, October 2002 http://www.epa.gov/otaq/models/analysis/biodsl/p02001.pdf Environmental Protection Agency. 2002. The EPA Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, EPA420-P-0-001, October 2002, http://www.nbb.org/resources/reportsdatabase/reports/gen/20021001_gen-323.pdf

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Appendices Appendix A1 Comparison of Upstream Emissions for Canadian and USA Diesel The following tables provide both an overview and a breakdown of upstream emissions for diesel in Canada and the USA for 500ppm sulphur content. The USA data based on the NREL study are first provided and the Canadian results based on the Genius model are then given. NREL Data – petrodiesel (500ppm)

NREL Upstream Overview Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Domestic Foreign CO2 (not including other pollutants) 443.1968 534.4871

CH4 0.859716 1.413972N2O 0.052901 0.02244 CO 0.368782 0.430242NOx 1.214151 1.16867

Hydrocarbons (except methane) 0.16157 0.186001SOx 4.142979 4.426159

CFCs+HFCs PM (unspecified) 0.727155 0.750804

The operations are listed in reverse order from the last stage to the first. NREL Fuel storage and distribution and dispensing

petrol diesel (500ppm) g/kg diesel CO2 - not including other pollutants 18.259

CH4 0.020819 N2O 0.001252 CO 0.039881 NOx 0.127928

Hydrocarbons (except methane) 0.008311 SOx 0.056308

CFCs+HFCs PM (unspecified) 0.034011

NREL Refinery

petrol diesel (500ppm) g/kg diesel CO2 - not including other pollutants 360.415

CH4 0.30982 N2O 0.00728 CO 0.25025 NOx 0.753419

Hydrocarbons (except methane) 0.00272 SOx 2.554930

CFCs+HFCs PM 0.458895

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NREL Crude oil transport to refinery

petrol diesel (500ppm) g/kg crude oil domestic Foreign

CO2 - not including other pollutants 17.8928 76.6231 CH4 0.029077 0.0473333N2O 0.000369 0.0009081CO 0.011651 0.0171105NOx 0.072804 0.113323

Hydrocarbons (except methane) 0.001129 0.0033896SOx 0.121741 0.894921

CFCs+HFCs PM (unspecified) 0.054249 0.0978977

NREL Domestic crude oil extraction (including CH4 and CO2 leaks and flares) petrol diesel (500ppm) g/kg crude oil

Onshore Offshore Onshore advanced DomesticCO2 - not including other pollutants 38.93 8.99 -1.29 46.63

CH4 0.21 0.019 0.27 0.50 N2O 0.00056 0.00022 0.04354 0.044 CO 0.028 0.0088 0.030 0.067 NOx 0.10 0.010 0.15 0.26

Hydrocarbons (except methane) 0.11073 0.01510 0.02357 0.14941 SOx 0.26 0.0009 1.144 1.41

CFCs+HFCs PM (unspecified) 0.14 0 0.05 0.18

Domestic includes 69% onshore, 20% offshore and 11% onshore advanced

NREL Foreign crude oil extraction (including CH4 and CO2 leaks and flares) petrol diesel (500ppm) g/kg crude oil

Onshore Offshore Heavy Foreign CO2 - not including other pollutants 66.31 13.23 -0.35 79.19

CH4 0.459 0.503 0.074 1.036 N2O 0.0006 0.0002 0.012 0.013 CO 0.095 0.020 0.008 0.123 NOx 0.12 0.012 0.04 0.174

Hydrocarbons (except methane) 0.14559 0.01956 0.00643 0.17158 SOx 0.56 0.047 0.312 0.92

CFCs+HFCs PM (unspecified) 0.15 0 0.013 0.16

Foreign includes 77% onshore, 20% offshore and 3% onshore advanced

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Canadian Upstream Overview Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 (not including other pollutants) 561.73 643.06 988.35 836.29

CH4 8.42 9.06 11.78 10.59 N2O 0.038 0.041 0.055 0.049

CFCs+HFCs 4.46E-05 4.49E-05 4.58E-05 4.54E-05 CO 3.40 4.38 8.55 6.71 NOx 3.99 5.11 9.88 7.78

VOC-Ozone weighted 0.95 1.01 1.26 1.15 SOx 1.29 1.38 1.76 1.59 PM 0.235 0.265 0.394 0.337

Canadian Fuel dispensing Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 - not including other pollutants 5.059 5.061 5.069 5.065

CH4 0.00974 0.00975 0.00982 0.00979 N2O 0.000323 0.000324 0.000324 0.000324CO 0.00174 0.00176 0.00186 0.00182 NOx 0.00825 0.00827 0.00839 0.00834

VOC, Ozone-Weighted based on Sheet F factors 0.002279 0.002281 0.002287 0.002284SOx 0.019739 0.019741 0.01975 0.019746

CFCs+HFCs 6.55E-09 6.55E-09 6.58E-09 6.57E-09 PM 0.001321 0.001322 0.001325 0.001324

Canadian Fuel storage and distribution Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 - not including other pollutants 53.75142 54.484 57.59404 56.22438

CH4 0.111547 0.117315 0.141803 0.131018N2O 0.002322 0.002351 0.002475 0.00242 CO 0.392571 0.401407 0.438915 0.422396NOx 0.532474 0.542577 0.585471 0.56658

VOC, Ozone-Weighted based on Sheet F factors 0.049654 0.050182 0.052425 0.051437SOx 0.067506 0.068308 0.07171 0.070212

CFCs+HFCs 4.21E-05 4.21E-05 4.21E-05 4.21E-05 PM 0.072539 0.072812 0.073971 0.073461

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Canadian Refinery Emissions Genius Model

Results for Buses – petrol diesel (500ppm) g/kg diesel Year 2005 Onshore Offshore Heavy Central

CO2 - not including other pollutants 250.3882 254.3904 271.381 263.8983CH4 0.605681 0.637193 0.770972 0.712055N2O 0.026905 0.027064 0.027741 0.027443CO 0.496653 0.544922 0.749838 0.659593NOx 0.570769 0.625968 0.860301 0.7571

VOC, Ozone-Weighted based on Sheet F factors 0.331357 0.334243 0.346494 0.341098SOx 0.693045 0.697423 0.716009 0.707824

CFCs+HFCs 4.09E-07 4.2E-07 4.68E-07 4.47E-07 PM 0.077909 0.079401 0.085735 0.082946

Canadian Oil transport to refinery Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 - not including other pollutants 7.79074 7.81286 7.906764 7.865409

CH4 0.049189 0.049363 0.050102 0.049777N2O 0.00042 0.000421 0.000425 0.000423CO 0.012653 0.01292 0.014053 0.013554NOx 0.0248 0.025105 0.0264 0.02583

VOC, Ozone-Weighted based on Sheet F factors 0.101352 0.101368 0.101436 0.101406SOx 0.023863 0.023887 0.02399 0.023945

CFCs+HFCs 1.16E-06 1.16E-06 1.16E-06 1.16E-06 PM 0.005448 0.005456 0.005491 0.005476

Canadian Oil Extraction, Processing before refinery Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 - not including other pollutants 207.4917 284.0686 609.1596 465.9893

CH4 1.628347 2.231288 4.790947 3.663672N2O 0.008258 0.011311 0.02427 0.018563CO 2.49632 3.419878 7.340647 5.613938NOx 2.85543 3.911572 8.395198 6.420607

VOC, Ozone-Weighted based on Sheet F factors 0.14943 0.204645 0.43905 0.335818SOx 0.226794 0.310561 0.666177 0.509563

CFCs+HFCs 9.27E-07 1.14E-06 2.04E-06 1.64E-06 PM 0.077695 0.106244 0.227441 0.174066

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Canadian CH4 and CO2 leaks and flares Genius Model

Results for Buses – petrol diesel (500ppm) g/kg diesel Year 2005 Onshore Offshore Heavy Central

CO2 - not including other pollutants 37.24454 37.24454 37.24454 37.24454CH4 6.019597 6.019597 6.019597 6.019597N2O 0 0 0 0 CO 0 0 0 0 NOx 0 0 0 0

VOC, Ozone-Weighted based on Sheet F factors 0.317099 0.317099 0.317099 0.317099SOx 0.2629 0.2629 0.2629 0.2629

CFCs+HFCs 0 0 0 0 PM 0 0 0 0

Central Canada: (27% onshore, 10% offshore, 63% heavy) The last table provides the estimates of downstream emissions from the Genius model.

Canadian Vehicle Operation Genius Model Results for Buses – petrol diesel (500ppm) g/kg diesel

Year 2005 Onshore Offshore Heavy Central CO2 (not including other pollutants) 3757.7 3757.7 3757.7 3757.7

CH4 0.245 0.245 0.245 0.245 N2O 0.1655 0.1655 0.1655 0.1655

CFCs+HFCs 0.0041 0.0041 0.0041 0.0041 CO 41.908 41.908 41.908 41.908 NOx 51.905 51.905 51.905 51.905

VOC-Ozone weighted 3.948 3.948 3.948 3.948 SOx 1.480 1.480 1.480 1.480 PM 1.668 1.668 1.668 1.668

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Appendix A2 Opportunity for Refining Agricultural Fieldwork Data for Life Cycle Analyses of Soybean and Canola Production in Canada The recent paper by Robert “Bobby” Grisso et al, Predicting Tractor Fuel Consumption, offers an excellent opportunity to update and further validate Canadian models and estimates of agricultural fieldwork and of their greenhouse gas impacts. A reassuring feature of the Grisso study is that one of the pillar stones for making estimates of fieldwork has been confirmed to be on relatively solid ground. The pillar stone is the data analysis of agricultural machinery energy requirements found in the standard D947.4 March 1999 of the American Society of Agricultural Engineers. These data were based on quite historic information and it is reassuring to note its continued pertinence, in spite of the 4.8% decrease in average annual specific volumetric fuel consumption (SVFC) determined by Grisso over the past twenty years. Below is produced Figure 2 from the Grisso paper. It shows the curve for SVFC from the standard and the new curve of Grisso based on the latest diesel engines. Figure A2.1 (Figure 2 of Grisso et al)

The overall lower consumption due to the advances in technology can be detected (between the dotted line representing the standard formula and the new curve marked by a triangle) but there remains a reasonable fit between the two curves. Grisso et al also found some very simple mathematical formulae that provide an excellent fit with data. For instance, the fuel use as a function of the ratio of the equivalent PTO power to the rated PTO of the machinery was found to be well represented by a linear relationship with an added constant. These lead to simplified expressions for SVFC or its inverse, the SVFE (Specific Volume Fuel Efficiency). Another interesting conclusion of the Grisso paper is the average

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14% decrease in efficiency between the power train and the drawbar at full throttle. (The data at less than full throttle have much more scatter.) All of this bodes well for Canadian model, the Farm Fieldwork and Fossil Fuel Energy and Emissions (F4E2) model. Dyer and Desjardins have used this model to calculate overall fieldwork energy requirements in Canada. There latest paper shows an agreement within 5% of their aggregate results with the data of the 1996 Farm Energy Use Surveys (FEUS). A2.2 The F4E2 Model The model is described in some detail in other publications. In summary, it is based on the agricultural machinery management data of the American Society of Agricultural Engineers as given in the standard D947.4 March 1999. In the F4E2 process initial analyses are performed to find a balance between the time available for time-critical operations and the kind of equipment necessary to achieve them within these constraints. This would, for instance, determine that most western Canadian farmers can accomplish key operations with the use of two tractors, one of high power output and a second of medium power. Then more detailed analyses are performed to determine the work at the level of the power train for each of the field operations. Finally the diesel consumption is calculated based on the use of the most suitable equipment available and the efficiency of its operation based on the ratio of the power requirement to the designated power of the equipment using the ASAE D947.4 equation: FC = 2.64X + 3.91 – 0.203(738X + 173)0.5 (1a) Where FC = specific fuel consumption in L/kWh, with the energy in kWh taken at

the power take off (PTO). X = is the ratio of PTO power required by an operation to that maximum available from the PTO.

The ASAE notes that this equation assumes a fuel consumption 15% higher than typical Nebraska Tractor Test performance to reflect loss of efficiency under field conditions. Note that this equation is the one used to calculate the dotted curve presented in Grisso’s Figure 2. As the specific gravity of diesel is typically 0.84-0.85, equation (1) can readily be adapted to give a measure of fuel consumption, FC, in g/kWh: FC = 850(2.64X + 3.91 – 0.203(738X + 173)0.5 ) (1b) From the new analysis of Grisso et al, it is possible for Dyer and Desjardins to apply the slightly revised equations as a refinement to the model. The model is of modular design so changes can be made in one specific domain like this and its repercussions estimated at the end.

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A2.3 Needs for New Surveys and Analyses One of the intriguing questions that must be asked is the degree to which the equation 1a should be applied to farm operations. This equation is based on tractor data set to full throttle, i.e. no account has been taken of the possibility of gearing up when the power requirements are considerably below what the engine can deliver. The gearing up, equivalent to working in overdrive, is known to significantly reduce fuel consumption and this has been confirmed in the Grisso study. In this study it was found that the gear up adjustment led to a 13% average improvement in specific volume fuel efficiency (SVFE) compared to the full throttle result at 75% of nominal drawbar load and a 21% improvement at 50% nominal load. If farmers could, and did, regularly gear up regularly for low-power operations then the curve 1a would need to be changed significantly. In the following table is presented results from the new Grisso equation for fuel consumption normalized with respect to the consumption at the nominal power output, with and without the gear up.

Table A2.3 Comparison of SVFE for Gear Up and Full Throttle 75% Power 50% Power Normalized SVFE full throttle 0.91 0.77 Normalized SVFE gear up 1.03 0.93 This table shows that the drop off in efficiency is much less pronounced with the appropriate gear being chosen to ensure a better match of power output potential with power requirements. The difference is 12 percentage points at 75% power and 16 points at 50% power. There are a number of operations in which the farmer would logically choose not to gear up. There may be certain rpm requirements for some of the equipment being driven or there may enough variations in power requirements that the farmer would prefer the lower gear. However, if the good fit in data from the F4E2 model and the survey reflects the farmer’s propensity to operate almost entirely at full throttle, then there appears to be an opportunity for fuel economy that is being missed. However, it must be recognized that the economic impetus to strive for the higher fuel efficiency is low. In the case of Saskatchewan canola grower, even with conventional tillage, the fuel only represents 5% of total costs. Even a 20% improvement in fuel efficiency means only a 1% improvement in overall cost efficiency. Secondly, the increasing farm size is undoubtedly forcing a heavier emphasis on speed rather than efficiency. It is extremely important to get the crop in and harvest it whenever that is possible and if that means running at a higher but less

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efficient speed, so be it.81 In summary, the ever-increasing time constraints on farm operations may be leading to reduced fuel efficiency even as engine efficiency shows some improvement. A2.4 Recommendations It is recommended that a limited survey be conducted to determine the common practice of Canadian farmers in this regard. The survey should include assessment of possible additional transport requirements. It is also recommended that the results of ongoing field energy studies in Western Canada be compared directly with F4E2 model predictions. This would allow more precise models of fieldwork to be integrated into F4E2 and help identify the cause of the 5% lower aggregate number of field energy requirements of F4E2 as compared to FEUS results for 1996. Finally, it is recommended that the refined model be used to provide a model of operations that can be used at the level of life cycle analysis of agricultural practices for products like canola and soybean. The format for presenting the information from the model would be similar to the table below:

Table A2.4a Diesel Fuel Consumption (FC) for Field Activities in g/ha and g/(kg(yield)/ha)1 Field Activity FC East 41ha

sandy (g/ha) FC East 41ha clay loam (g/ha)

FC West 162ha sandy (g/ha)

FC West 162ha clay loam (g/ha)

Primary Tillage (moldboard) NA NA NA Primary Tillage (chisel plow) Secondary Tillage (harrow after primary tillage)

Secondary Tillage (harrow with no primary tillage)

Seeding after harrow Seeding in no till Fertilization Pesticide Control Harvesting Oilseed transport to farm granary Total for Conventional Tillage Total for Minimum Tillage Total for No Tillage

81 Neil McLaughlin of Agriculture and Agri-Food Canada notes that speed of operation need not amount to less efficiency: “Implement draft increases a small amount at higher ground speed which would contribute to lower fuel efficiency in litres per unit area. However, higher tillage speeds provide more effective tillage due to more effective break up of the soil by impact. This means that the same degree of tillage can be performed with fewer operations. Also, tractors for higher speed tillage have a higher power to weight ratio, which means that there are lower losses due to tractor rolling resistance.

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1. The latter dependency on yield would only be introduced it appeared a significant factor in the harvesting and transport phases. Certainly for a logical standpoint certain energy requirements are proportional to yield rather than area.

For certain variables the model would be run to determine the sensitivity of the energy consumption to this variable and present it as a multiplying factor of the base cases. This would allow the update of tables that are still being used for LCA analysis such as this one cited in the Australian LCA study of biodiesel. This update would be useful as the average energy requirements in Canada are now considered to be considerably less than some of these historical estimates.

Table A2.4b Fuel use (liter/hectare) Fuel Rapeseed production in Europe (L/ha)

Ploughing 20.3 Harrowing 8.3

Seed bed preparation 12 Sowing 4.9

Fertilizer application 7.6 Harvesting 17

Total 70.1 Data source: Australian Greenhouse Office, 2001 cited Ceuterick & Spirinckx, 1997

References for A2 ASAE. 2000. Agricultural machinery management data. ASAE Standards 2000: EP496.2 p 344-349 and D297.4 p 213-220. American Society of Agricultural Engineers. http://waterhome.brc.tamus.edu/NRCSdata/technote/497_4.pdf Dyer, J.A. and Desjardins, R.L. Simulated Farm Fieldwork, Energy Consumption and Related Greenhouse Gas Emissions in Canada. Biosystems Engineering. 85(4):503-513. Dyer, J.A. and Desjardins, R.L. 2003a. The Impact of Farm Machinery Management on the Greenhouse Gas Emissions from Canadian Agriculture. Journal of Sustainable Agriculture. 20(3):59-74 Dyer, J.A. and Desjardins, R.L. 2003b Analysis of trends in CO2 emissions from fossil fuel use for farm fieldwork related to harvesting annual crops and hay, changing tillage practices and reduced summerfallow practices in Canada. Journal of Sustainable Agriculture. 25(3):1044-1046. Grisso, R.D., Kocher, M.F. and Vaughan, D.H. 2004. Predicting tractor fuel consumption. Applied Engineering in Agriculture 20(5):553-561.also on R. Grisso’s Web site.

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Weidema, B.P. and M.J.G. Meeusen, editors, Agricultural data for life cycle assessments, The Hague, Agricultural Economics Research Institute (LEI), 1999 Report 2.00.01; ISBN 90-5242-563-9 http://www.lcacenter.org/library/pdf/2_00_01_1.pdf

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Appendix A3 Data from other sources for vegetable oil extraction and refining Data source: Australian Greenhouse Office, 2001 cited Ceuterick & Spirinckx, 1999 Process inputs and outputs for oil extraction of canola

Inputs Unit Value Oil seeds kg 1000 Electricity kWh 45 Steam (natural gas fired) kg 310 Hexane kg 1.5 Outputs

Crude oil kg 399 Seed cake kg 598 Solid waste kg 3 Hexane to air kg 1.5

Process inputs and outputs for refining the canola oil

Inputs Unit Value Crude oil kg 1000 Electricity kWh 10 Steam (natural gas fired) kg 80 Outputs Refined oil kg 975 Solid waste kg 25

Data Source: Levelton, 2002 Requirements for soybean crushers

US average (The data used in the model)

Lurgi plants

Original unit Metric unit Original unit Metric unit Electricity 68

kWh/tonne 68 kWh/tonne

9 kWh/tonne

9 kWh/tonne

Natural gas

770,000 BTU/tonne

812.35 MJ/tonne

550,000 BTU/tonne

580.25 MJ/tonne

Hexane 0.98 USG/tonne

3.71 liter/tonne

0.22 USG/tonne

0.83liter/tonne1

1. The specific gravity of hexane is 0.66, so 3.71 L = 2.45 kg, 0.83L = 0.55 kg. Note: The US average is extracted from the US Census information and been verified by a number of references

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Data Source: (Levelton, 2002) Energy requirements for canola crushing

Original unit Metric unit Electricity 55 kWh/tonne 198 MJ/tonne Natural gas 1,000,000 BTU/tonne 1.055 MJ/tonne Hexane 0.735 USG/tonne 2.78 liter/tonne

Energy requirements were estimated based on data from two equipment manufacturers for rapeseed plants. The hexane losses have been assumed to be 75% of those for soybean processing

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Appendix A4 Towards a Better Estimation Methodology for Nitrous Oxide Emissions from Nitrogen-Fixing Crops

Jim Rollefson

The methodology proposed under the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories [i] for assessing direct nitrous oxide emissions from nitrogen-fixing crops does not stand up to close scrutiny. Neither the results themselves nor the framework within which the estimates are made can be justified by experimental data. This paper analyzes the inconsistencies in the methodology and its impact on results. Proposals are then made for more accurate estimation techniques. The specific case for nitrogen-fixing soybean will be developed to illustrate the impact on nitrous oxide emission estimates. It will be shown that the nitrogen fixing term in the IPCC methodology for estimating nitrous oxide emissions should be completely revamped. The IPCC Methodology for Nitrogen-Fixing Crops and its Inconsistencies The equation for the N2O direct emission estimates for nitrogen-fixing crops based on IPCC 1996 can be written as:

N2O (kg N/a) = (FBN +FCR)(EF1) (1) where EF1 = emission factor that estimates the fraction of the nitrogen input that becomes nitrogen in nitrous oxide emissions in kg N2O-N/kg N input, most often estimated as 0.0125; FBN = N fixed by crop (kg N/a) annually; FCR = N in crop residues returned to soil (kg N/a) annually. The last two factors are to be estimated by the following formulae: FBN = (2)(CropBF)(FracNCRBF) (2) FCR = (2)(CropBF)(FracNCRBF)(1-FracR)(1-FracBURN) (3) where 2 = Default value representing an estimate of the ratio of the mass of biomass residue to seed yield, both taken dry; CropBF = Seed yield (kg/a) (dry);

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FracNCRBF = fraction nitrogen in N-fixing crop per unit mass (dry), with a default value provided as 0.03kg/kg; FracBURN = fraction of crop residue that is burned rather than left on field; FracR = fraction of the crop residue that is removed from the field as crop with units given as kg N/kg N crop.

The Equation for Nitrogen in Crop Residues Returned to the Soil To begin, it should be stated that the last definition used in the equation is enigmatic. If its units were taken literally it would mean that FracR is better defined as the fraction of nitrogen in the crop residue that is removed during harvesting. But there is already a factor, FracNCRBF, introduced in Equation (3) to convert biomass mass into nitrogen biomass. In the IPCC guidelines a default value of 0.45 is given for FracR and it is stated in the text that: “Residue remaining on the field may not always total 55% of total crop biomass.” This suggests that the default value for FracR is referring to biomass and not the nitrogen content. So the introduction of the nitrogen in the units for FracR appears to be spurious. That being said the default value is totally out of line for crops like soybean grown for the oil seed. In modern agricultural practices FracR is close to 0 for bean and soybean as grain crops. Farmers retain the soybean residue to help avoid erosion and maintain the organic carbon content of the soil. The next question that emerges from the equations above is whether calculations are to be based on fractions of total biomass (as the quote above suggests) or of biomass that is residual after seed yield? The original source of Table 4-17 of the IPPC Guidelines is Strehler and Stutzle [ii] and they clearly state that 1:2.1 is the ratio of main product (grain) to byproduct (straw) for soybean and bean. So in the case of soybean, the default factor of 2 could be replaced by 2.1 to convert a known value of crop seed yield into an estimate of above-ground residue mass. The ratio of 2.1:1 has been confirmed in more recent experiments by Morrison et al [iii]. They have determined the Harvest Index for a sample of soybean cultivars developed over the last twenty-five years to be between 0.29 to 0.34, with an average of 0.32. The index refers to the ratio of the dry seed yield of the soybean crop to the total plant dry biomass above ground including seed mass. The average value of 0.32 for the Harvest Index leads to an estimate of the ratio of biomass above-ground residue to seed yield of about 2.1:1. The Harvest Index as provided by Morrison et al has been chosen here because special attention was paid by them to ensure the senescent leaf biomass was taken into account. Many other authors report an apparent Harvest Index that does not include the senescent material and this can lead to significant overestimates of the index which results, in turn, in serious underestimates of total biomass, especially for a crop like soybean.

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The accuracy for the estimate of nitrogen input as written in equation (3) now depends on two other factors:

• A reasonable estimate of FracNCRBF, • An additional term to account for the root and root-derived biomass.

The default value given by IPCC for FracNCRBF equal to 0.03 could be acceptable if the soybean crop is strictly grown for forage purposes. At the beginning of the seed fill stage (R5) Vasilas et al show nitrogen concentrations of the above-ground soybean biomass in the range of 0.030 to 0.034. However, when the plant has proceeded to maturity at the R7-8 stage the oilseed has drawn down heavily from the reserves of nitrogen elsewhere in the plant. Green and Blackmer [iv] and Koutroubas et al report respectively the nitrogen content of soybean biomass residue at R8 to be 0.01, and 0.01 to 0.011 for the latter’s inoculated soybean crop with no fertilizer input. The original reference that was used for setting the soybean ratio of above ground biomass residue to seed yield, Strehler and Stutzle, also provided an indication of nitrogen content through its raw protein values. They gave raw protein percentages ranging from 4 to 7.4% for soybean straw having a dry matter content of 81.6 to 89.1%. The percentage of nitrogen in the dry straw can therefore be estimated as about 1% when the factor of 6.25 is used to convert from nitrogen to raw or crude protein. In summary, a more representative default value for FracNCRBF for soybean grown for the oilseed is 0.01 rather than 0.03. The determination of soybean root biomass is a difficult undertaking. Extending out from the soybean taproot and other roots of significant diameter is a fine array of roots that are difficult to separate from soil structure. This means most estimates of soybean root biomass should have a caveat attached that indicates that a portion of the root mass is not captured in most experimental data.. Goss et al measured a recoverable root biomass of 367kg/ha associated with a total biomass above ground (not including senescent leaves) of 6054kg/ha. The yield of oilseed (dry) was 3049kg/ha so if the ratio of 1:2.1 applies to this crop an estimate of total residue biomass, including the contribution of senescent leaves, would be 6403kg/ha. The roots would therefore increase the estimate of total biomass residue by about 5.7%. Goss et al also measured the nitrogen content of the root relative to its total biomass as 0.79%. This appears consistent with FracNCRBF of 0.01 for the above-ground residue. This would mean that the total N contribution of the roots to the residue would be on the order of 5%. However Goss et al were keenly aware of the caveat above and expressed concern over their capability of retrieving the entire root mass when they determined that the N contribution by the soybean roots to the soil would be only 2.5kg/ha according to their measurements. The missing soil nitrogen of concern to Goss et al is actually mainly found in the N rhizodeposition. Mayer et al [v] have followed in the tracks of other authors in using this term to apply to the nitrogen released from the roots in the form of root

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exudates, sloughed off cells, cell lysates, decomposed root material, etc. In the experiments of Mayer et al the percentage of the rhizodeposition that can be qualified as the micro-root structure was 7%, 16% and 31% for the nitrogen-fixing crops of, respectively, lupin, faba bean and pea. So the missing N is not only a consequence of the fine root structure. Account must be taken of all root exudates and sloughed materials. Mayer et al were able to trace back this rhizodeposition nitrogen to the plant through its use of the 15N isotope labeling method. The rhizodeposition constitutes a much more important source of soil nitrogen than the macro-root mass. The results of Mayer et al show the percentage contribution of the rhizodeposition relative to the total below-ground plant nitrogen as 78%, 82% and 85% for faba bean, pea and white lupin respectively. Similar results can be expected with soybean and this will be approximated here by a value of 80%.82 This means that there is an additional nitrogen contribution from soybean to the soil that is often not taken into account and it is of the order of magnitude of 20% of the residual biomass nitrogen. Note that this added factor will impact on many of the previous estimates of soil nitrogen balance after a soybean crop. The previously assumed negative N balances will be reduced or even converted into modest positive gains. The question becomes how to treat this additional source of nitrogen found in the rhizodeposition within the contest of the nitrous oxide estimation methodologies. Because rhizodeposition is a form of biologically-derived mass and it is not unique to the nitrogen-fixing crops, the simplest way to integrate it into the equations might be to add its biomass and nitrogen fraction to the biomass residue term. However there is a good reason for differentiating this rhizodeposition from the biomass when nitrous oxide emissions estimates are to

82 Rhizodeposition of soybean was measured by Brophy and Heichel. They found that the leachate gathered periodically from around the soybean root and nodules had a nitrogen content, summed over the growing period, equal to 10.4% of the total N content of the plant. They used sterile sand as the root medium to avoid the complexities associated with possible chemical nitrogen bonding and of nitrate formation. The nitrogen around the roots was gathered fourteen times over the course of the soybean growing period by adding fresh nutrient solution and letting it leach through the sand. For each leachate sample the soil was flushed three times in less than 30 seconds to avoid anoxic conditions in the soil. Interestingly, some 93% of the nitrogen recovered was in an “other-N” category of insoluble peptides, cell debris etc. and not ammonium or amino acids. This is consistent with the results of Mayer et al. Unfortunately, the number of 10.4% given above cannot be used quantitatively to describe net N input to soil after harvest due to rhizodeposition. The repeated leaching operation constitutes a serious aberration of field conditions. It could not be expected that this kind of intensive leaching would occur some fourteen times over the course of the growing period. It might happen once or twice, but even then cation exchange complexes, biological immobilization etc. would make the flushing operation less efficient in extracting nitrogen. Given the intensity of various transformations in the rhizosphere it could be expected that a significant portion of the nitrogen removed by leaching in the experiment would have normally been reintegrated into the soybean plant. As an aside, this result does help explain how there can be significant transfer of fixed nitrogen from plants like soybean or clover to neighbouring plants and bacteria.

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be made. These exudates and sloughed off material is in the form that lends itself to very rapid biological transformation, including those transformations that lead to nitrous oxide emissions. It will be shown that the timing of these transformations can affect significantly the probability of nitrous oxide emissions. The macro-root structure will typically degrade on a time scale intermediate to the above-ground residue and the rhizodeposition. The former difference will depend on the extent to which above-ground material is worked into the soil. As a starting approach the macro-root structure will be grouped with the above-ground biomass to increase the ratio of biomass residue to grain yield from the previous 2.1 value to 2.2. As the rhizodeposition term is some four times the macro-root term the root contribution an additional term based on crop yield is 0.4. This is small relative to the 2.2 term for biomass residue but the former’s effects on nitrous oxide emissions can be far more important than a direct proportionality would imply. Therefore, it is necessary to include a separate emission factor for it in a revised equation (1). The revised equation (1), not including a possible FBF term, can therefore be written as follows:

N2O (kg N/a) = (FCR)(EF1) + (FRD)EFRD (4) where FCR = (2.2)(CropBF)(FracNCRBF)(1-FracR)(1-FracBURN) (5)

• A normal default value of FracR for crops like soybean and bean is 0 (when not used as a forage crop);

• A normal default value of FracNCRFB is 0.01; • FracR is defined as fraction of the crop residue that is removed from

the field with units given as kg (dry removed residue)/kg (total dry biomass residue).

• The factor to convert oilseed yield into biomass residue is 2.2 to include macro-root biomass.

FRD = (0.4)(CropBF)(FracNCRBF) (6) The Equation for Estimating the Nitrogen Fixed from the Air It must be stated to initiate the analysis that the rationale for IPCC equation (2) to estimate the nitrogen fixed by the crop is difficult to fathom. The equation has been seen to be an approximation of the above-ground crop residue and its nitrogen content. The link between this quantity and the total nitrogen fixed by the crop is tenuous at best. The majority of the nitrogen fixed by crops like soybean ends up in the seed. Indeed, up to 83% of the above-ground nitrogen of the soybean plant can be in the seed according to the results of Koutroubas et al [vi]. So, in equation (2) it is being assumed that the portion of nitrogen remaining in the residue just matches the portion of the total nitrogen that is derived from atmospheric fixation. However, experimental data does not support this assumed relationship. Studies show a wide variation in the percentage of the total nitrogen

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in a soybean plant that comes from nitrogen fixation. The percentage is highest when the available nitrogen in the soil is low and when there is soil with good aeration characteristics. The former relationship is well established from many studies but it becomes most evident in laboratory experiments where nitrate irrigation levels can be tightly controlled. Wanek and Arndt [vii] show variation in the portion of nitrogen derived from air (%Ndfa) in soybean to extend over the entire range of 0 to 90% when nitrate levels in the water are varied from 0.25 to 25mM. In field trials, Goss et al measures %Ndfa at 61% without any addition of nitrogen fertilizer. The variation of %Ndfa over various soil nitrogen levels fitted the equation:

%Ndfa = 68 – 0.26x where x is the soil N content at planting (mineral N + N fertilizer input in kg N ha-1.

Gentry et al [viii] estimate %Ndfa in their field experiment with soybean to 58%, in line with the results of Goss et al [ix]. However, with soils of higher organic content, Harper [x] estimates %Ndfa in the range of 25-50% for the Midwest. These results indicate the extent to which soybean draws down on existing reserves of nitrogen in the soil before developing and exploiting the symbiotic relationship with the bradyrhizobia. This is further reflected in poor nodule development until the available nitrate is depleted. Vasilas et al [xi] find a range of %Ndfa from 51 to 82% in their field trials. An interesting element in their study is the significant difference in these values between two different soil types and locations. The high values of %Ndfa were associated with a loamy sand soil near Georgetown whereas the low values were associated with a silt loam near Newark. This is almost surely a reflection of better nodule development under soils with better aeration characteristics. This is also well known to result in superior yields for the soybean where coarser loam soils outperform fine-structure soils like clay. In summary, the best linkage that can be made between the amount of nitrogen fixed by soybean and what equation (2) actually estimates (the nitrogen content of the above-ground biomass residue) is that both are some portion of the nitrogen content of the total biomass of the plant. However, that these portions should bear any real relationship to one another is not obvious. Furthermore, equation (2) does not logically follow from a basic understanding of the processes in play nor do its results relate well to experimental data. The point will not be belabored because a more fundamental debate has to be undertaken: should there be any additional direct term at all that relates the nitrogen fixed to the estimate of nitrous oxide emissions. Presumably the FBN

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term has been introduced to mimic the artificial fertilizer input term. It could be argued that the nitrogen fixation is a highly significant input and that, in an analogy based on the leaky pipe hypothesis, a certain amount of this nitrogen is lost to N2O as the various biological and chemical reactions proceed. However, this belies another finding that is becoming increasingly clear. It is not so much the total nitrogen input that may lead to nitrous oxide emissions, rather it is the surplus nitrogen that cannot be taken up in a timely manner by the plants that may lead to the GHG emissions. An important aspect of the symbiotic relationship between rhizobial bacteria and leguminous crops is that a mutually beneficial system is set up which is controlled by the inherent feedback loop between the nitrogen-fixing bacteria and the plant. The bacteria function at a high level due to the plant’s carbohydrate input and the plant responds in turn to the nitrogen input of the bradyrhizobia. If there is no strong plant input then the nitrogen-fixing activity rapidly decreases. Under such conditions there is unlikely to be any important nitrous oxide emissions directly related to the nitrogen fixation. There may, however, be limited nitrous oxide emissions associated with some sharp discontinuity in the relationship, for instance, when a nitrogen-fixing crop like alfalfa is harvested during a period of vigorous plant growth and nitrogen fixation. The recent experimental data of Rochette et al [xii] supports the interpretation given above. An examination of their nitrous oxide flux data shows small peaks in emissions after each cutting of the alfalfa. There is also a small N2O emission increase after the soybean harvest in the loam soil. However, there is one major peak of N2O emissions after the harvest of soybean on a clay soil when the harvest happens to coincide with a heavy rainfall. It appears to the combination of all three factors, the disruption in the plant-bradyrhizobial relationship, the heavy rainfall and the clay soil that leads to the peak. The soil and ammonium levels remain relatively low during this event so it is likely due to the nodular breakdown with only limited availability of oxygen in the soil. In the following year with the same clay soil there is a much smaller nitrous oxide peak in September that follows a period of several days of rain rather than the harvest date itself. It should be recognized that most soybean harvests are undertaken after the oilseed filling period. At this point the remainder of the plant biomass is rapidly proceeding to senescence after it has transferred much of its nitrogen content to the seed. The nodules will then decay rapidly due to the lack of plant carbohydrate input and this will be independent of the harvest itself. Nitrogen fixation and its Potential to Enhance Nitrous Oxide Emissions It is worthwhile to classify two distinct ways in which the nitrogen fixation can contribute to N2O emissions. The first way is through the significant augmentation in the biomass, the nitrogen content and the yield due to the rhizobial contribution of nitrogen to the plant. This is apparent in comparisons of nodulating and non-nodulating varieties of soybean. For instance, Gentry et al report in their Table 3

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grain yields over two times greater for nodulating versus non-nodulating soybean varietals. There are also large differences in the above-ground N accumulation and in the N stover estimates. Because they measure N accumulation in the late R6 growth stage instead of later, their results will tend to underestimate both the N accumulation and N stover. It can be anticipated that differences between nodulating and non-nodulating varietals would be even more important if more inclusive measurement techniques had been employed. Nevertheless their results leave no doubt of the major impact that the symbiotic relationship between the bradyrhizobia and the plant has on the nitrogen content of the residual biomass returned to the soil and which can therefore lead to nitrous oxide emissions. However, this type of impact is already largely accounted for in the estimation techniques through the biomass residue term. There is therefore no need to add another biofixation term in the N2O estimation techniques to account for this phenomenon. Direct nitrogen release from the nodules and the bacteria and surrounding root system may be a second way in which biofixation contributes to nitrous oxide emissions. However, this appears to be mainly accounted for in the rhizodeposition term introduced earlier. It is still worth examining the potential differences due to the biofixation. At the very least one could expect some qualitative differences in the nitrogen-fixing rhizodeposition due to the very intense biological transformations taking place in and about the nodules and roots. In this environment there are a number of sources and sinks of the various forms of nitrogen and the level of the transformations taking place would be most apparent when there is a major disruption to the system, e.g. the harvest of a growing crop like alfalfa. Indeed Brophy and Heichel [xiii] show a very high nitrogen leachate peak after the first harvest of alfalfa and this can be linked to nitrous oxide emissions from studies like that of Rochette et al. The research results of Gentry et al warrant further attention to help gain perspective on the influence of these disruptive events. In their experiments most of the differences in nitrate formation ensuing from the crops of nodulating soybean, non-nodulating soybean and maize could be attributed to:

• The degree to which the soybean residue breaks down more rapidly than maize residue,

• The major difference in biomass residue, and rhizodeposition, between nodulating and non-nodulating soybean.

However, there is one feature that appears to be related specifically to the nitrogen-fixing process, or more precisely, its sudden interruption at the end of the growing year. Gentry et al have measured total inorganic nitrogen at three distances from the centre line of where soybeans were sown in a row: 5 cm, 19 cm and 38 cm, with the last measurement being at the midpoint between soybean rows. In both maize and non-nodulating soybean there is no significant difference between these measurements in the fall season. However, the difference between the 5-cm measurement and the other two is significant over

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October, November and extending into April of the next year. The difference over the fall season is the order of 10-12 kg(N)/ha. The most logical explanation for this difference is that it is associated with the disruption of processes associated with the rhizodeposition as well as, or including, the breakdown of the nodule structure. At the R8 stage, not only is there a smaller sink for the nitrogen fixed by the bradyrhizobia, there is a stress on these bacteria as the feedback of sustaining carbohydrates from the plant is cut off. The emphasis here is put on the timing. In spring, the non-nodulating soybean crop of Gentry et al also shows a higher nitrate level close to the roots before this can be taken up by plant growth. In this case there is a relatively rapid transformation of some biological nitrogen in the rhizodeposition and roots to an inorganic form, but the transformation is not as rapid as that of the main portion of the nitrogen-fixing soybean rhizodeposition and its nodules. Only a rough approximation can be made of the overall impact on the average inorganic nitrogen level in the field associated with this soybean biomaterial as there are only the course measurements at 5, 19cm and 38cm. If the higher levels measured at 5 cm were assumed representative of some integrated value out to 9.5cm (one quarter the distance to the midpoint between rows) then the total augmentation of the additional inorganic nitrogen would be an average 2.5-3kg(N)/ha. This is not a large quantity but it may well be significant from a perspective of nitrous oxide emissions. The probability of conversion of this additional nitrate to nitrous oxide emissions into the thawing period of the soil in spring is much higher than at other times. This is because there is a relatively high probability that the denitrification process can be interrupted several times over the freeze-thaw cycles and this can cause emissions of the obligate intermediate product of nitrous oxide to soar. In summary, there appear to be some features of the biofixation process that can lead to higher nitrous oxide emissions. How to capture this becomes the question. The hypothesis put forward here is that it is mainly through the sudden disruption of certain equilibria set up that the nitrogen-fixing systems distinguish themselves above and beyond the higher nitrogen input to the soil from the biomass, including rhizodeposition. The latter differences in inputs can be accounted for directly without reference to biofixation in the residue terms. The simplest and most logical approach to account for some dependency of the nitrous oxide emissions to biofixation appears to be to allow for the value of the EFRD introduced in the last section to be made a function of whether the plant is nitrogen fixing or not. This is part of a more general conclusion from this review. Better estimates for nitrous oxides emissions will inevitably depend on converting the emission factors into probability functions based on such variables as timing of fertilizer application, climatic probability of extended freeze-thaw cycles, soil type, crop type, biomass residue integration into soil, etc. Admittedly, this probability

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function will not be based on some simple regression formula for each variable. There is already enough evidence to suggest that the simplistic formulation of universal regression formulae will be of limited value. A variable may contribute to higher emissions in one case and to lower ones with another set of conditions. The approach given above has a major advantage. The nitrous oxide emissions that are measured over the course of a season are to be related deterministically to the variables such as saturating precipitation events, spring freeze-thaw cycles etc. The averages over several seasons are a product of these functions with the probability functions that give, for instance, the probability of the different numbers of freeze-thaw events in spring. Each scientific paper should then be able to contribute more methodically to the understanding of the phenomena over a wide range of variables. As example of how this would be applied in practice will be given at the end of the following section after some data on freeze-thaw cycles have been introduced. Analysis of Nitrous Oxide Emissions from Soybean Crops The two principal references for this section are by Wagner-Riddle and Thurtell [xiv] and by Rochette et al. Both studies include the analysis of a number of variables that influence the nitrous oxide emissions as well as the emissions themselves. This greatly facilitates their interpretation. Furthermore, the two studies are complementary. Wagner-Riddle and Thurtell use micrometeorological flux techniques that allow the measurements to be continued over the winter and into the critical thaw-freeze cycles in spring. Rochette et al do not cover this period in the year but have considerable data on various variables over the growing season that are interesting to examine in more detail. The origin of the nitrous oxide emissions is understood to be as follows. Both denitrification and nitrification can lead to nitrous oxide emissions. In the absence of oxygen certain bacteria, dominated by organotrophs like Pseudomonas or Alcaligenes in the soil, have the capacity to convert from oxidative processes dependent on oxygen to one that exploits a chain of reactions involving the descending redox potential of NO3

-, NO2-, NO and N2O to yield an end product of

N2. This is the basis of denitrication. Specific species of bacteria can have a number of the required enzymes so some can exploit a series of these redox changes. In other cases the intermediate product must be transferred to other bacteria to pursue the reaction chain. Interestingly, the bradyrhizobia, responsible for nitrogen fixation, can exploit the denitrification pathway in the presence of nitrate and in the absence of oxygen. Nitrous oxide is an obligate intermediate in the denitrification process so, for instance, if there is an experimental procedure with high flushing of the soil medium, N2O measurements can be produced which are totally anomalous in terms of predicting net N2O emissions under more normal conditions. Under steady conditions net N2O emissions can be extremely low as the vast majority of nitrous oxide produced is reduced in turn to yield nitrogen.

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Any sudden environmental changes that constitute an interruption of the smooth transition from one enzymatic reaction to another can cause a burst of nitrous oxide emissions. This is most often associated with sudden changes in oxygen levels (at the macro or micrometeorological scale and often provoked by varying water levels) or changes in temperature, carbon availability etc. In contrast, nitrous oxide is not a normal byproduct of the nitrification process where autotrophic bacteria, typified by Nitrosomonos, oxidize ammonia to nitrite, and nitrite oxidizers like Nitrobacter convert nitrite to nitrate. Normal in this case refers to a situation when oxygen levels accessible to the bacteria remain above a certain threshold. If oxygen supply becomes sufficiently low there is a transition period when the bacteria resort to oxidizing the outstanding hydroxylamine (the intermediate between ammonia and nitrite) by the available nitrite to yield nitrous oxide. Under ammonia shock with low diffusion of oxygen into a micro-site, the autotrophic bacteria can end up initiating nitrification, so depleting the oxygen, stopping nitrification with a small burst of nitrous oxide emissions, then reinitiating the nitrification process once oxygen levels pass the critical threshold, only to repeat the cycle again. This explains some of the apparent sustained emissions of N2O during nitrification. The description given above allows a simple explanation of one of the principal features of nitrous oxide emissions. These emissions tend to be highly intermittent and are not well predicted by actual rates of either nitrification or denitrification. It is the changes in the conditions setting these rates that impact net nitrous oxide emissions. Freeze-thaw cycles are the principal phenomenon studied by Wagner and Thurtell. These cycles clearly constitute the kind of repeated disruptive process under marginal conditions that can yield high nitrous oxide emissions. Moreover, the input to the soil of significant nitrogen, readily transformed biologically, from nitrogen-fixing crops like soybean, can set the stage for these emissions. The first feature of note is the high seasonal variability in these emissions. If there are only a couple of freeze-thaw events then the emissions will be low. However, multiple cycles lead to higher emissions. Nitrous oxide emissions in 1995 after a soybean crop the previous year were only 0.2kg(N)/ha with two cycles. With five cycles the emissions rose to 1.2kg(N) in 1995 after a soybean crop. The level of soybean emissions relative to most of the other crops except grass is low. As could be expected, fields with high levels of biomass or manure integrated into the soil in autumn produce the highest results. The fall nitrate and ammonia levels after the soybean crop are relatively low (3-6g(N)/kg soil) compared to other crops like canola and maize. It would appear that most of the soybean nitrogen has remained in organic form or, once released, has been

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rapidly immobilized by bacteria. This helps explain the low values of N2O emissions found in this study. However, one must be careful not to generalize this result. In the study by Gentry et al the fall soil nitrate levels following the soybean crop are substantially higher (about double) those of the maize crop. In this case there is a more significant potential for nitrous oxide emissions from soybean during the intermittent denitrification conditions of the freeze-thaw conditions. The results of Wagner-Riddle and Thurtell indicate that, whatever the crop, the potential for additional nitrous oxide emissions for areas prone to multiple freeze-thaw cycles should be considered where measurements (like those of Rochette et al) are restricted to the growing season and fall. Based on the same results, it is clear that the both ordinary crops and nitrogen-fixing crops can produce these peaks of nitrous oxide emissions in early spring. There does not appear to be any need to introduce a specific term related to biofixation per se to account for them. Finally, the results suggest the need for emission formulae to depend on factors like residue integration and climatic effects in determining the probability of N2O emissions from crops. A universal factor like 0.015 is very unlikely to produce good results. To reinforce the basic understanding of the nitrous oxide emission given earlier in this section, it is worthwhile to initiate analysis of the results of Rochette et al with some of the non-events in nitrous oxide emissions, i.e. where some might anticipate substantial nitrous oxide emissions due to high levels of nitrification or denitrification but where these emissions don’t occur. This is best seen in the data for soil ammonia and nitrate levels for the Harlaka loam site in 2002. In May there are peak levels of ammonium nitrogen of the order of 20-25kg/ha that can be related to the breakdown of some of the biological nitrogen. The timothy crop is readily able to take this ammonium up so very little of it is nitrified to nitrate. For alfalfa there is some uptake of the ammonium but there is some nitrification as soil nitrate-N levels stabilize in the range of 5-10kg/ha. At this early stage in the growth cycle of soybean there is little initial capacity for the plant to exploit the ammonium so much of it is nitrified. This leads to a very high peak of soil nitrate-N in mid-June of some 30kg/ha. By July the soybean crop has taken in much of this nitrate and soil nitrate-N levels drop to 5kg/ha. Throughout all of this period the nitrous oxide emissions from the soybean crop are virtually nil. The porous soil combined with only moderate rains have ensured that there is readily available oxygen in the surface soil. Consequently, there are no nitrous oxide emissions. There are small peaks in the nitrous oxide emissions in the alfalfa field following the disruption of the hay cutting.

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There is only one small peak of nitrous oxide emissions from this soybean crop about the time of the final harvest. The total N2O-N emissions over the period of late April through the end of October are given as 0.71kg/ha. This cannot be described as a typical result as there was an extended period of only very light rain over most of August and early September that led to an unusually low yield. In 2002 the Harlaka clay site as compared to the loam site showed a similar evolution of soil nitrate levels over June and July but some low level peaks of nitrous oxide emissions from the soybean crop are now evident. They are well correlated with the rain events. The less porous soil has led to local denitrification events that are transient in nature and therefore likely to produce the nitrous oxide. As the drought conditions become established the nitrous oxide emissions drop to virtually nil on this soil as well. A small final peak of N2O emissions occurs when the plant senescence and some rains combine just before final harvest to create the conditions propitious to these emissions. Total N2O-N emissions for this soil over the same period as before are 1.65kg/ha. Over the 2001 summer and fall the strong dependence of the nitrous oxide emissions on soil porosity is equally apparent. Very low values of total N2O emissions from the soybean crop comparable to baseline crop of perennial timothy crop are measured for both the sandy loam site, (N2O-N of 0.46kg/ha) and the loam site (N20-N of 0.66kg/ha including the small peak at harvest time). In contrast, the Harlaka clay site in 2001 produced a huge nitrous oxide peak coincident with its harvest and extending on at significant levels until the end of October. A substantial rain of some 45mm at harvest set up the ideal conditions for this type of emission event. The measured N20-N emissions over the period of July 1 through to the end of October are given as 3.08kg/ha. However this total includes the impact of the initial nitrous oxide emission reading that was very high for all three crops, with the soybean being the lowest. In the case of the soybean field this initial peak is associated with the biodegradation of the residue from the crop of orchardgrass grown on the site over the previous three years. The impact of this initial peak on the total is about 0.36kg/ha and so, for the purposes of this paper, the N2O-N emissions value due to soybean has been adjusted downwards to 2.7kg/ha. Even at this reduced level the impact of the non-porous soil can be recognized as highly significant. In 2001 the emissions from the clay soil are some four to six times higher than loam and sandy loam soils respectively. Because of the very sudden rise of the nitrous oxide emissions around the harvest time the rhizodeposition term is the most likely cause of the emissions. The N2O-N emissions of 2.7kg/ha are largely associated with this one fall event so the approximation is made in this particular instance that the first term in equation (4) can be ignored, i.e. EF1 = 0, so Equation (4) becomes: N2O (kg N/a) = (FRD)EFRD

Where

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FRD = (0.4)(CropBF)(FracNCRBF) (6) EFRD = (N2O (kg N/a))/[(0.4)(CropBF)(FracNCRBF)] With FracNCRBF = New default value of 0.01 and the experimental result of CropBF = 3123kg/ha, EFRD = 0.22

This is an entirely reasonable estimate under the specific conditions of this crop. A simplification has been made here in that the rhizodeposition term’s impact would also be felt during any freeze-thaw cycles in early spring. Given the results of Wagner-Riddle and Thurtell the EFRD could rise to 0.3 or more. This raises the technical issue of how the experimental measurements of emissions are applied to different crops under rotation. When nitrogen fertilizer input is the dominant factor in determining nitrous oxide emissions it is simple to assign the emissions to the crop for which it is intended. In the case of fall emissions and freeze-thaw cycles it again is relatively simple to assign the emissions to the previous crop. But the majority of the emissions due to above-ground residue will emerge in the following spring and summer. For instance at the Harlaka clay site the early July emissions of 2001 are most probably associated with the previous crop of orchardgrass. Furthermore, the 2001 crop residues at the Harlaka clay site are the dominant factor in emissions at least through May and June of 2002. It will be necessary to set protocols that determine a proper assignment of the emissions to the crops. This is especially important in nitrogen-fixing crops where nitrogen fertilizer inputs are minimal. The experiments of Rochette et al clearly show how strongly dependent EFRD is on such factors as soil porosity and water content. EFRD is essentially zero for the sandy loam soil at the Chapais site. This only reemphasizes the point made earlier that progress in estimation techniques will only be made when the emission factors are considered as probability functions determined by climatic, crop and soil variables as well as farm practices. To get reasonable aggregate estimates for a crop like soybean, it is necessary to know the percentage of crops grown in different soil types. Then climate probabilities would have to be introduced to account for freeze-thaw cycles and saturated soil conditions during harvest or early spring. For instance, it may be found that the conditions south of Quebec City are such that one year out of four the EFRD for soybean grown in clay soils is 0.24, the other three years the value is 0.12 to give an aggregate value of 0.15. For the sandy loam soils the aggregate would be much less. While the analysis above leads to a significant reformulation of the framework for estimating nitrous oxide emissions, some may still want to consider how the emission measurements relate to the 1996 IPCC methodology. In order to assess this, the data from the papers of Rochette et al and of Wagner-Riddle and Thurtell must be combined because only together do they constitute a set of data over one full year. The latter paper covers the winter and spring thaw period while the former covers the growing season and fall.

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For the N20-N emissions there are only two data points associated with soybean and the freeze-thaw cycles, 1.2kg/ha and 0.2kg/ha. The large range in these numbers reflects the variability in the number of these cycles in a season. As no probability function is yet available to determine what may constitute representative conditions, this analysis has simply taken the average of N2O-N emissions of 0.7kg/ha for the winter and early spring measurements and added this to the summer and fall measurements made by Rochette et al. In the following table are the measurements of Rochette et al with the additional average term from Wagner-Riddle and Thurtell to account for the early spring emissions. The IPCC estimate is based directly on the IPCC methodology using the equations (2) and (3) with FracR and FracBURN put to zero and FracNCRBF = 0.03 (the default value).

Table 1 Comparison of Measured and Estimated (IPCC Methodology) Nitrous Oxide Emissions for Eastern Canada Soil type IPCC

Estimate1 N2O-N kg/ha

Measured Summer and Fall Emissions N2O-N kg/ha

Annual Emission Measurement (est.)2 N2O-N kg/ha

Fraction of measured to estimated emissions

Sandy loam 4.70 0.46 1.16 0.25 Loam 3.32 0.66 1.36 0.41 Loam 2.66 0.71 1.41 0.53 Clay 4.92 2.703 3.40 0.69 Clay 4.78 1.65 2.35 0.49 Average 0.47

1. These values are twice the term referred to by Rochette et al as the IPCC residue term because the IPCC estimation methodology makes the fixation term equal to the residue term and the total is simply twice the latter when FracR and FracBURN are zero.

2. Annual measurements are taken as the fall and summer emissions measured by Rochette et al plus the average winter and early spring emissions after soybean crops as measured by Wagner-Riddle and Thurtell.

3. This number is lower than the 3.08 given by Rochette et al to account for the impact of one early summer emission peak of N2O associated with the residues of the previous crop.

Because the measured values are on average about one half of the IPCC estimates, and the term that accounts for emissions from nitrogen fixation is one half of the total estimate, it would be tempting to advocate the status quo but with the elimination of the fixation term. This would be shortsighted. The IPCC methodology used above is based on an erroneous value of the nitrogen fraction of the biomass residue (0.03 versus 0.01) and it does not address the issues raised below.

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It is worthwhile to show how one of the obvious determining variables, the number of freeze-thaw cycles, can be integrated into a more methodical framework of analysis. The results of Wagner-Riddle and Thurtell indicate that the number of freeze-thaw cycles in spring strongly influences the nitrous oxide emissions at this time of year. As it is a denitrification process that leads to the nitrous oxide emissions, another key variable is the soil nitrate content combined with the organic material that may be very readily converted to nitrate in early spring. For soybean this available nitrogen was earlier proposed to be a function of the rhizobial deposition term estimated from the seed yield by the equation given earlier as:

FRD = (0.4)(CropBF)(FracNCRBF)

Wagner-Riddle and Thurtell have communicated that the soybean yields were respectively XX and XX for the years preceding the measured N2O emissions during the freeze-thaw cycles. The equation above allows conversion of these numbers into estimates of nitrogen readily available for denitrification in late fall and early spring as XX and XX. As a first approximation the fraction of readily available nitrogen converted to nitrous oxide during the freeze-thaw events is considered to be a simple function of the number of such cycles and is denoted by EFFT. The experimental data provide two points in a table of EFFT versus number of events. To illustrate the approach other values of EFFT have been hypothesized over a range of freeze-thaw cycle values and an equally hypothesized probability function given for the long-term probability of such numbers occurring. The next table shows how this leads to the determination of an average nitrous oxide emission estimate for a climatic region.

Table 2 Illustration of Methodology to Estimate Nitrous Oxide Emissions Due to Thaw-Freeze Cycles Number of Thaw-Freeze Cycles

Probability of occurrence (hypothesized)

Emission Factor EFFT

Probability*EF Product

0 0.05 1 0.10 2 0.20 XX1 3 0.25 4 0.20 5 0.10 XX1 6 0.05 7 0.05

Sum 1. These two emission factors are the only ones that are based on experimental

data.

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This case is particularly simple in that the crop yield is likely to be entirely independent of both the probability function and the emission factor itself. The experimental values of the crop yield are still needed to estimate EFFT But the eventual average nitrous oxide emissions due to the thaw-freeze cycles for this region would be the product of the average crop yield with the sum of the probability and emission factor products. In the course of time it may be determined that certain features of a given thaw-freeze cycle can be related to more or less emissions. This would constitute the next phase in the refinement of the emission model. The total nitrous oxide emissions from a crop would be estimated from the sum of the various contributions to it, e.g. N2O bloom events at soybean harvest coincident with saturated soil conditions, the crop residue contributions from the following early summer emissions related to major rain events and the characteristics of the soil, the excess nitrogen from fertilizer that leads first to some nitrification losses, then to denitrification losses etc. In each experiment emissions would be related to specific experimental conditions and average values would only emerge through combining these with the various probability factors for the determining events. Discussion It is no secret that the revised 1996 IPPC methodology for estimating nitrous oxide emissions gives only very approximate values. Given the complexity of the phenomena involved and the lack of obvious paths to more accurate estimates, there has been a tendency to make do with the simplistic formulations. However, the use of such a simplistic framework is considered by the author to be insidious in its impact. Presumably, one of the primary uses of such estimation methods is to measure progress towards lowering greenhouse gas emissions. With the perspective of the 1996 methodology, one would have to assume that lower fertilizer use is almost the unique answer to lower GHG emissions. This is far from being the case. Farm practices can make a very significant difference. The choice of appropriate crop and crop rotation sequences for the soils and climate can make substantial differences. In the case of crops like soybean, the 1996 estimation techniques make it appear inevitable that nitrogen fixation will lead to substantial nitrous oxide emissions. But there is no good experimental evidence to support this. In fact it is quite the contrary as can be concluded from the interpretation of results presented in this paper. If one were to prepare a map of nitrous oxide emissions due to soybean for Eastern Canada according to the 1996 methodology the results would be uniquely dependent on the average grain yield in each region. This would be misleading. There is actually a good correlation of crop yield with soil and climatic

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type. Soybeans prosper in coarse loam soil and therefore produce the best yields in this soil, yet the probability of nitrous oxide emissions from these soils is lower than from more marginal clay soil regions. Below is a graph derived from data of soybean seed variety trials over the years 2001-2003 [xv].

Table 3 Soybean Yield as a Function of Soil and Crop Heat Units

Crop Heat Units/Soil 2400 ±100

2650±150

2800±100

3100±200 clay

3100±200 loam

3400±100 clay

3400±100 loam

Soybean Yield ((t/ha) 2.90 2.83 3.23 2.35 3.40 2.53 3.50 For the crop heat units of 3100±200 the soybean crop yield is seen to be 45% higher in the loam soil as opposed to the clay soil. According to the 1996 methodology this means nitrous oxide emissions per unit area are 34% higher whereas the emissions might actually be closer to 45% less. The impact of the climate must also be taken into account. If soybeans are planted in a Mediterranean-type climate with rains predominant in spring with little fall rain then nitrous oxide emissions through the fall are likely to be very low, even with a clay soil. In Canada consideration must be given to probability of multiple freeze-thaw cycles as well. There are two points to be made from this. Any mapping of nitrous oxide emissions from soybean based on the 1996 IPCC methodology is likely to be largely erroneous. Secondly, the estimates are misleading in terms of identifying strategies for GHG emissions reduction. For instance, with the current methodology one would tend to target reduction strategies for soybean grown in loam soils where the highest yields are obtained on overage. However, a much more appropriate strategy would be to discourage soybean crops in marginal regions where soybean is grown in clay soils with higher probabilities for fall rain and/or freeze-thaw cycles. The methodology, with emission factors based on probability functions, could take into account the impact of other farm practices as well and give some quantitative relation between them and nitrous oxide emissions. For instance, the integration of biomass residue from nitrogen-fixing plants (and others) will tend to increase nitrous oxide emissions. Conclusions This paper proposes a new framework for the estimation of nitrous oxide emissions from nitrogen-fixing crops, and, in particular, for soybean. It proposes elimination of the 1996 IPCC term, (FBN)(EF1), representing the contribution of nitrogen biofixation to the nitrous oxide emissions. It recognizes that the rhizodeposition, consisting of exudates and sloughed off material of the live root system, can play a very important role in nitrous oxide emissions, especially from nitrogen-fixing plants. It recognizes that the characteristics of the rhizodeposition

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are such that an N2O emission factor in the form of a probability function dependent on the climate and soil type should be specifically associated with the rhizodeposition. Experimental evidence indicates that adjustment of this term related to rhizodeposition can explain the key features of the N2O emissions from nitrogen-fixing plants. In particular, it helps explain the very large peaks of N2O emissions that can emerge near soybean harvest time under wet conditions with clay soil. This paper concludes that the 1996 IPCC default values given for many of the factors in calculating the nitrous oxide emissions from nitrogen-fixing plants are far from representative. New default values are proposed which are based on experimental evidence. It is recognized that the proposals made in this paper are only the first steps towards a sound estimation methodology for nitrous oxide emissions. Nevertheless, the framework on which it is built is more logically tied to the actual physical phenomena that determine nitrous oxide emissions. The framework can serve as the basis for more fruitful scientific investigation of the phenomena. Furthermore, it provides a basis upon which strategies for GHG emission reductions involving agricultural practices can be related quantitatively to these emission reductions. The framework should allow more accurate estimates to be made and it is anticipated that aggregate emissions for land areas such as Canada’s should be significantly reduced. Nevertheless, emissions from certain regions can be anticipated to increase, mainly due to the soil type and climatic regime.

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References [i] Intergovernmental Panel on Climate Change. Revised 1996 IPCC Guidelines for national Greenhouse Gas Inventories: Reference Manual, http://www.ipcc-nggip.iges.or.jp/public/gl/invs6.htm [ii] Strehler, A. and Stutzle, W. 1987. Biomass Residues. Chapter 4 of Biomass edited by Hall, D.O. and Overend, R.P. [iii] Morrison, Malcolm.J., Voldeng, Harvey C., and Cober, Elroy R. 1999. Physiological Changes from 58 Years of Genetic Improvement of Short-Season Soybean Cultivars in Canada, Agron. J. 91:685-689. [iv] Green, C.J. and Blackmer, A.M. 1995. Residue decomposition effects on nitrogen availability to corn following corn or soybean. Soil Sci. Soc. Am. J. 59:1065-1070. [v] Mayer, Jochen, Buegger, Franz, Jensen, Erik Steen, Schloter, Michael, Hess, Jurgen. 2003. Estimating N rhizodepositon of grain legumes using a 15N in situ stem labeling method. Soil Biology &Biochemistry 35:21-28. [vi] Koutroubas, S.D., Papakosta, D.K., Gagianas, A.A. 1998. The importance of early dry matter and nitrogen accumulation in soybean yield. European Journal of Agronomy 8, 1-10. [vii] Wanek, Wolfgang and Arndt, Stefan K., 2002. Difference in δ15N signatures between nodulated roots and shoots of soybean is indicative of the contribution of symbiotic N2 to plant N, Journal of Experimental Botany, Vol. 53, No. 371: 1109-1118. [viii] Gentry, L.E., Below, F.E., David, M.B., Bergerou, J.A. 2001. Source of the soybean N credit in maize production. Plant and Soil 236: 175-184. [ix] Goss, M.J., A. de Varennes, Smith, P.S. and Ferguson, J.A., 2002. N2 fixation by soybeans grown with different levels of mineral nitrogen, and the fertilizer replacement value for a following crop. Canadian Journal of Soil Science, 82: 139-145. [x] Harper, J.E. 1987 Nitrogen metabolism. In Soybean: Improvement, Production and Uses, 2nd edn. Ed. Wilcox Jr, pp 487-533, Agronomy Monograph. [xi] Vasilas, B.L., Nelson, R.L., Fuhrmann,J.J. and Evans T.A. 1995. Relationship of Nitrogen Utilization Patterns with Soybean Yield and Seed-Fill Period, Crop Science 35:809-813. [xii] Rochette, Philippe, Angers, Denis A., Bélanger, Gilles, Chantigny, Martin H., Prévost, Danielle and Lévesque, Gabriel. 2004. Emissions of N2O from Alfalfa and Soybean Crops in Eastern Canada. Soil Sci. Soc. Am. J. 68:493-506. [xiii] Brophy, Laura S., Heichel, G.H. 1989. Nitrogen release from roots of alfalfa and soybean grown in sand culture. Plant and Soil 116: 77-84. [xiv] Wagner-Riddle, C., Thurtell, G.W. 1998. Nitrous oxide emissions from agricultural fields furing winter and spring thaw as affected by management practices. Nutrient Cycling in Agroecosystems 52: 151-163. [xv] Ontario Oil & Protein Seed Crop Committee (OOPSCC). Ontario Soybean Variety Trials for 2001-2003. http://www.oopscc.org/2004rpt.pdf.

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PART II ASSESSING THE SUSTAINABITY OF BIODIESEL IN CANADA

Albert Chan Executive Summary Further to performing an LCA to assess the environmental performance of biodiesel, a systemic view of how biodiesel may impact the transportation sector in the longer term is also taken. An initial assessment of the potential of biodiesel to achieve sustainability objectives in Canada has focused on an analysis of the air quality impact and the relationship between supply and demand of feedstocks for the production of biodiesel. While a comparison between petroleum diesel and biodiesel via a Life Cycle Analysis suggests there are definite GHG advantages and reduction in most CAC emissions, a systems analysis based on projected penetration scenarios in Canada reveals that the impact on air quality and GHG reduction is not likely to be of great magnitude, and can only be part of a total solution for improving urban air quality. This is due to the relatively small part diesel fuel represents in the vehicle transportation sector, and the relatively low blends of biodiesel that are considered suitable for Canadian conditions. As further studies are being planned using more recent data and analysis techniques, a question of interest has emerged: to what extent must diesel fuel penetrate the light duty vehicle market before noticeable environmental benefits derived from the use of biodiesel will be realized in the overall transportation system? A preliminary analysis on the supply and demand of biodiesel feedstocks reveals that biodiesel has a role to play as a valuable alternate market (for yellow grease and tallow) and could possibly have a price stabilizing effect for the rendering sector. Under the hypothetical scenario where ruminant bone meal were to be eliminated from circulation and vegetable protein meal was used to substitute for it, then the excess oil resulting from extra oil seed crushing can be readily absorbed by a modest penetration of biodiesel in the vehicle transportation sector. A systems analysis framework has been set up whereby additional aspects of the potential system-wide impact of biodiesel in the longer term can be analyzed, and useful insights gained on how best to capitalize on biodiesel’s environmental advantages in Canada. 1. Issues of Sustainability As Life Cycle Assessment considers the stages in the life cycle of a technology at a given point in time and assesses the environmental impacts per functional unit, it does not address directly certain issues of sustainable development, such as scale and time frame. It is well-suited to highlight the most important areas to focus research for product and process redesign for improvement. The general perception about sustainable development encompasses not only the

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environmental dimension but also the economic and social dimensions as well. Sustainability is a concept that applies to a system as a whole. It is concerned with the persistence of harmonious relationships between human activities and the environment into the future. In order to properly assess the sustainable performance of a technology, it is necessary to include the dynamic aspect by considering historical trends and making projections into the future over a period of 10, 20 or 50 years. It is also sometimes helpful to assess performance against certain targets of sustainability with a time reference, such as the Kyoto target for greenhouse gas emissions. In assessing one or more technologies, such as the use of biodiesel as an alternate transportation fuel, it is necessary to embed the technologies in a whole system in such a way that the evolution of the system over the long term can be evaluated, with and without penetration of the targeted technologies. It is of interest to determine not only that a new technology is “cleaner” than the technology that it is intended to displace, but also the extent to which the technology must penetrate it if it is to deliver certain desired property of sustainability. A suitably designed dynamic systems model will help to provide useful insights to meet the challenge. 2. Analysing the Sustainability of Biodiesel in Canada To better understand the role biodiesel can play as an alternate transportation fuel in the context of achieving sustainability objectives in Canada, there are very many different aspects throughout the life cycle of biodiesel, as detailed in the LCA reported previously, that can be chosen as a focus. The analysis reported here has focused on two specific aspects:

1. The potential impact of the use of biodiesel on urban air quality. 2. The potential future demand of biodiesel under different market

penetration scenarios, and the ways in which this demand can be met by a supply of the various types of feedstocks.

An NRC research team, headed by Dr. Weimin Jiang, has developed a comprehensive set of capabilities to model urban air quality by considering emission data, mechanisms of chemical reactions in the atmosphere and other contributing factors such as topography, land use, and meteorological conditions. This research has been applied not only to assess alternative process and emission control technologies, but also to evaluate the impact of emissions from different fuels and vehicle technologies to deal with ground level ozone and air toxics. As there has been extensive data and experience accumulated based on case studies already conducted on the specific case of the Lower Fraser Valley (LFV) in British Columbia, it was decided that the analysis of the potential impact of biodiesel on air quality would capitalize on the available data and expertise. In consultation with the air quality modelling group, the NRC project team carried out a case study based on the LFV for the biodiesel sustainability investigation.

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Through an earlier project on analysing the sustainability of bioethanol, and contacts previously made with the Office of Energy Efficiency, NRCan and a mutual collaborator, Robbert Associates of Ottawa, the NRC project team was introduced to a systems model, called the Canadian Transportation Energy and Emissions Model (CanTEEM). It was decided that the analysis on the sustainability of biodiesel be carried out on a systems model that would be built upon CanTEEM by adding the appropriate details to meet the above objectives. Robbert Associates was subsequently contracted to make the necessary adaptations to CanTEEM, and to consult with the NRC team in setting up and running the appropriate scenarios in the analysis. 3. Adaptation of CanTEEM 3.1 CanTEEM Background The current version of the biodiesel systems model has been built as an extension of another model, called CanTEEM, developed in 2001 by the Office of Energy Efficiency at Natural Resources Canada. Natural Resources Canada is involved in assessing energy demand and greenhouse gas emissions arising from the Canadian transportation sector. With the objective to develop a modelling framework to examine the future impacts of new and existing technologies and fuels in a Canadian context out to the year 2050, the Office of Energy Efficiency at NRCan commissioned the development of the Canadian Transportation Energy and Emissions Model (CanTEEM). It is designed to provide information to help guide current and future policies and programs in the realm of reducing energy intensity of the transportation vehicle stocks in Canada (for both passenger and freight transport) and fuel system as well as the mitigation of greenhouse gas emissions. The modes of transport represented include air, rail (intercity and urban transit), marine, and road (passenger motor vehicles, trucks, and buses). In addition to the use of fuels in the vehicle stocks, the production and transportation of fuels and feedstocks, and the associated greenhouse gas emissions will be modelled for a suite of fuels including motor gasoline, diesel fuel, ethanol and ethanol blends, electricity, and hydrogen. Vehicle stocks will be represented by year-built, size (or weight), and engine type (gasoline, diesel, natural gas, propane, E85/E100, electric, fuel cells), permitting the simulation of fuel substitution possibilities within modes and fuel efficiency gains associated with improved engine performance and the use of lighter vehicles. As well, CanTEEM was designed with sufficient structure to simulate modal substitution possibilities for both freight and passenger transportation. The Canadian Transportation Energy and Emissions Model (CanTEEM) has been developed as an analytical tool for assessing the potential energy and GHG effects of existing or proposed climate change measures and policy options. By

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casting the transportation sector within the entire Canadian economy, it examines a wide variety of technologies and fuel pathways to explore the impacts on energy supply and emission profiles in the transportation sector for the longer term. Built on 25 years of historical data, CanTEEM models energy use and GHG emissions for the transportation and other sectors of the Canadian economy at a provincial and national level. Based on historical trends, it tracks progress in the areas of vehicle use, energy use and emissions, and projects both energy use and GHG emissions under various scenarios. With a maximum time frame to the year 2050, the model tracks the effect of demographic changes and economic growth in Canada, and can simulate energy use and GHG emissions, including electricity generation and fossil fuel production, in order to explore when and how a possible transition to new technologies and future fuel options and systems, such as a hydrogen economy, may occur. This capability is especially important when considering complex and multidimensional transportation issues such as the potential impacts of: new and advanced energy technologies, the increased use of renewable energy sources, and the potential energy savings and emission reductions through improved energy efficiency. Multiple policies and measures can also be forecast at the same time to estimate the combined effects. The model simulates alternate future patterns of fuel use in, and emissions from, the stock of transportation vehicles and in the fuel production system. The starting point is the present stocks of vehicles and fuel production capacity; future scenarios reflect explicit assumptions about the adoption of new vehicle technologies and deployment of new fuel supply options. The model assures coherency between the population, their requirement for transportation service, the stocks of vehicles required to deliver that service, the fuel used in the vehicle stocks, the production of fuels, and the emissions from the production and use of fuels. The model does not represent explicitly stocks of resources or reserves, nor does it represent exploration. It is assumed that reserves are sufficient for the specified levels of production required. Resource constraints can be simulated by varying the rate of feedstock production. 3.2. Scope of CanTEEM At the top level, CanTEEM comprises of the following modules:

• Population and economy • Food production • Fuel production • Fuel use • Emissions

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The population and economy module tracks the change in population by considering factors such as immigration, emigration, fertility, mortality, internal mobility between provinces/regions. As well, the formation of family units, and the number of members in a family are modelled. This in turn drives the number of vehicles per family unit, and the distance driven on personal use vehicles. The economy is represented by the Gross Domestic Product indices, which drive the volume of freight transport, including intercity and intracity freight. The food production module models the production of crops and animals for food, the land use requirement (crop land, pasture land, etc.), animal feed required and the emission resulting thereof. The fuel production module includes crude oil extraction and refining, petroleum products, coal, uranium, natural gas, hydrogen, for transportation and electricity generation. Stages of the fuel cycle considered include leaks and flares, fuel transport and dispensing, where the associated energy use and emissions are tracked. A special emphasis is placed on fuels with a potential biomass source, such as biomass-derived ethanol. For crop biomass production, the manufacture and application of fertilizers are considered in addition to the energy use. The landuse requirements are also tracked, together with the emissions from soil. The fuel use module places an emphasis on the use of fuel for road transport, while other modes such as rail and air are also considered, but with less detail. Fuel use in the residential, commercial and industrial sectors is represented at a high aggregate level. Transportation service demand is modelled through families, trips, modes of transport, passenger kilometers on the one hand, and Gross Domestic Product (GDP), freight, modes of transport, intercity and intracity volumes on the other hand. For road transport, vehicles are grouped into the personal use and commercial use categories. Vehicle stocks are represented by year-built, size (or weight), and engine type (gasoline, diesel, natural gas, propane, E85/E100, electric, fuel cells etc.). Vehicles are modelled for the engine efficiency and the vehicle kilometers travelled, and the associated tail pipe emissions tracked, based on the fuel type/blend used. Figure B01 illustrates the scope of the model for personal use vehicle fuel use and emissions. The emissions module consolidates the emissions from all the other modules. In addition to emissions from road transport, emissions from rail and air transport, and from other sectors (residential, commercial, industrial), life cycle emissions from fuel production and electricity generation, and from soil in agriculture are summed up to yield the total GHG emissions. 3.3. Details Incorporated into CanTEEM for the Biodiesel Analysis In order to adapt CanTEEM to facilitate the systemic analysis of sustainability of biodiesel in Canada, certain details pertaining to the life cycle stages of biodiesel

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were incorporated into selected modules of CanTEEM. Four feedstock sources of biodiesel are considered: soybean, canola, waste oil (yellow grease) and animal fats (tallow). For road vehicles (both personal use and commercial use), the portion of the vehicle stock that uses diesel fuel becomes a focus. The distances travelled for these vehicles, the fuel mixes, and the corresponding distances travelled, and the associated emissions are modelled. To facilitate the analysis of biodiesel’s impact on urban air quality, another focus is placed on urban transit buses, where the passenger kilometers travelled, the fuel mixes used and the emissions are tracked. In addition to GHG emissions, ground level emission of Criteria Air Contaminants (CAC), including CO, PM, NOx and VOC’s are also considered. Special provisions are made to allow the modelling of the air quality implications in the Lower Fraser Valley airshed. Next, the modelling of these specific aspects in the biodiesel systems model is briefly described, supplemented by a set of whatIf? ™ process diagrams presented in the appendix to illustrate the approach and scope of CanTEEM. whatIf? is the computer-based systems modelling tool developed by Robbert Associates83, and is the software platform upon which the CanTEEM model is constructed. The model for fuel production in CanTEEM is augmented to allow for the various feedstocks for biodiesel production. The scope of this module is illustrated in Figures B02-1 and B02-2. The model to determine the amount of animal fats available for a potential source of biodiesel feedstock (shown in Figure B03) takes into account the animal stock fed, the average animal life, the mass of animal at slaughter, the animal mortality fraction, rendering of dead stock, the rendering yield for bone meal and animal fats. Animals are categorized into cattle, hogs, sheep/lamb, poultry, horses and fish. The model to determine the amount of waste (or recycled) oil available (shown in Figure B04) assumes that a fraction of the oil consumed is recycled, and all recycled oil is vegetable-based. The model to facilitate the analysis of urban transit buses using a biodiesel blended fuel is shown in Figure B05. The model to estimate the ground level emissions for the Lower Fraser Valley is shown in Figures B06-1 and B06-2. The total emissions for the province of British Columbia is spatially allocated to the Fraser Valley region by considering the population, the stock of vehicles, the amount of travel, and the economic activities as represented by the commercial vehicle traffic. Allowance is made to simulate the concentrated growth of an urban area, such as the Fraser Valley, with respect to the region or province in which it is located, as well as the relative 83 www.robbert.ca

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trip propensity between the various trip types, such as commuter, non-commuter and intercity for the specific area. 4. Formulation of Scenario 1 As discussed in the LCA section of this report, due to the potential feedstock supply and the cold flow properties of biodiesel, only low percentage blends (1-5% biodiesel) can be expected to have potential wide applicability in Canada. For urban bus fleets with heated garages in the winter, the blending may be extended to 5-10%. Thus in setting up scenarios to represent possible future penetration of biodiesel into the transportation fuel market, these limitations are taken into account. Another consideration is a key finding from the Bio-bus study84, that it is not recommended that a diesel engine be switched back and forth between regular diesel and biodiesel blends, as there may be a risk of clogging up the filter due to biodiesel’s strong-solvent characteristics. As a result, the scenarios set up to analyze the potential demand of biodiesel are based on a modest usage of B2 for transport trucks and B5 for urban buses in one case, and B5 for transport trucks and B10 for urban buses in the other case. The penetration of biodiesel into the diesel market is assumed to be gradual over a 10-year period from 2005 to 2015. This is represented by 0% of all diesel-kilometers travelled using the target blend (2%, 5% or 10%, as the case may be) in 2005, rising linearly to 100% of all diesel-kilometers travelled using the target blend in 2015. It should be noted that there is an underlying profile for the “diesel-kilometers travelled” in question, reflecting the trend projections of demographics such as population and GDP growths and the evolution of the specific classes of vehicles in the vehicle stock, plus any modal substitution that might be taking place over the period. For example, the historical trends for freight transport in the recent decade suggest a gradual shift from rail to trucks in Canada, resulting in an increase in the diesel-kilometers travelled for freight, even without accounting for any underlying growth in economic activities (see Figure 4.1). The trend projections of population and GDP are displayed in Figures 4.2 and 4.3 respectively. With the above considerations in mind, a first hypothetical scenario, with 2 sub-cases, was set up for simulation using the systems model: Scenario 1: Assume that demand of biodiesel feedstock will be supplied by yellow grease (50%) and tallow (50%). Case A: Biodesel will penetrate the diesel market over a 10-year period from 2005 to 2015, with the target blends being B2 for transport trucks and B5 for urban buses.

84 Biodiesel Demonstration and Assessment with the Société de transport de Montréal, Final Report, 2003

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Case B: Biodesel will penetrate the diesel market over a 10-year period from 2005 to 2015, with the target blends being B5 for transport trucks and B10 for urban buses. Figure 4.4 depicts the demand on biodiesel feedstocks under this hypothetical scenario.

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Figure 4.1: Inter-City Freight Traffic -- Rail and Truck

Truck

Rail

Figure 4.2: Trend Projection of Population in Canada

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Figure 4.3: Trend Projection of Gross Domestic Product

Figure 4.4: Biodiesel Feedstock Demand – Cases A and B

Case B

Case A

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5. Assessing the Air Quality Impact of Biodiesel 5.1 Approach In order to analyse the impact of transportation emissions on urban air quality, mobile sources, primarily from road vehicles, are the main area of interest. For mobile sources, ground level emissions of Criteria Air Contaminants (CAC) are broken down into four major categories: nitrogen oxides (NOx), carbon monoxide (CO), particulate matter (PM) and hydrocarbons (HCs). Of these four categories, NOx, PM and HCs have the most detrimental effect on air quality. LCA results described in another part of this report show that on average, biodiesel reduces tailpipe emissions of PM, CO and HCs, while there could be a small increase in NOx when using biodiesel fuel. This study attempts to assess the potential effects of biodiesel on air quality under various penetration scenarios. A case study based on a specific geographical area and over a specific period of time has been set up to illustrate this approach. The geographical and meteorological data, as well as the emission inventory used are for the following episode in history:

Lower Fraser Valley Region – July 31, 1993 to August 8, 1993. The Lower Fraser Valley (LFV) is located in British Columbia, and is centered on the major city of Vancouver. Because of the high population, large industrial sector, the natural mountainous barrier and the seasonal weather, the LFV is known to have air quality problems. Extensive analyses have been performed by the NRC air quality modelling group on this geographical area, and a suite of modelling software had been set up to model the air quality for this region in particular. The episode was during the year 1993, from July 31st at 7:00am to August 8th at 7:00am, encompassing 8 full days or 192 hours. The emissions inventory for the LFV is taken from 1993 data, and all meteorology data is for the specific 8-day period. The biodiesel emissions data used to estimate the air quality effects, is either derived from other published biodiesel studies, or projected by the systems model described earlier. This systems model, based on the adapted CanTEEM, estimates at an aggregate level the emission of CO2 and CAC under various scenarios of biodiesel market penetration. The emissions for the province of British Columbia are spatially distributed to the Lower Fraser Valley airshed. The air quality modelling software is then applied to estimate the impact on a range of air quality measures, such as ground level ozone formation and particulate concentration. The reference case used for the comparison is where petro-diesel is used without any blending with biodiesel. Figure 5.1 shows the flow of information for evaluating the air quality effects of biodiesel fuel.

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The main input required to evaluate biodiesel is either a vehicle miles travelled (VMT) inventory or an emissions inventory. The VMT data gives the distance travelled for each particular vehicle type (light-duty gasoline vehicles, light-duty diesel vehicles, heavy-duty gasoline vehicles, buses, motorcycles, etc.) in a given county, broken down by speed and by the roadway type on which the distance is travelled. Typically, this data is given in millions of miles per year. In the latter case, emissions of HCs, NOx, CO, PM, SO2, etc. in units of tonnes per year for each vehicle type in each county of a state or province, are included in the inventory.

If VMT data is provided, it is necessary to calculate the total emissions, which is done by multiplying the VMT data by the appropriate emission factors, which have the units of grams of pollutant per mile. There are emission factors for each pollutant – CO, HCs, PM and NOx – and for each vehicle type. These emission factors vary depending on the speed that the vehicle is travelling, whether it is running in a cold-start, hot-start or hot stabilized mode, the vehicle age, and various other vehicle characteristics, as well as meteorological properties such as temperature and humidity. The dependence of the emission factors on weather conditions dictates the requirement for meteorological data in generating the necessary emission factors for each vehicle type, which are multiplied by the corresponding VMT, resulting in emissions of CO, PM, NOx and HCs in tonnes per year per county. Generating the total emissions for the major pollutants is straightforward, especially for CO, as there is little additional processing required. However, NOx, PM and HC emissions are further broken down into more specific components. The breakdown of the overall emissions into specific or grouped components is referred to as speciation, and is detailed in the speciation profiles for both HC and PM emissions. When a fuel is burned, the exhaust is made up of a unique speciation of HCs and PM, as different fuels will have different speciation profiles (e.g., biodiesel has a different speciation profile than petro-diesel fuel). These different speciation profiles are important because certain HCs are more reactive in the atmosphere than others, meaning they have higher ozone forming potentials. NOx emissions are somewhat simpler than PM and HC emissions, as there are only two components that NOx is speciated into: nitrogen oxide (NO) and nitrogen dioxide (NO2). Once the emission files are produced, this data is input into the air quality model to evaluate various air quality measures needed to assess the impacts of biodiesel fuel. These measures include the ground level ozone (O3) and particulate matter concentrations. A comparison of these values to the corresponding values produced from diesel use can provide a basis to estimate the air quality effects of biodiesel in relation to petro-diesel fuel. Additionally, the concentrations can be compared to a threshold value in terms of how high the

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predicted concentration is above the threshold, or how many times the threshold is exceeded during the episode. Meteorological data is significant when evaluating air quality measures since the chemical reactions occurring in the atmosphere are affected by temperature, the amount of sunlight, etc. Furthermore, wind speed and direction are used to determine the physical transport of the pollutants. For example, ground level ozone concentrations are often highest in the suburbs surrounding a major urban centre because of the physical transport of the pollutants and the reaction time required for ground level ozone to form.

A previously conducted case study for the Lower Fraser Valley has already included data for diesel vehicles that are using straight petro-diesel fuel. In the present case study, the reference for comparison of biodiesel effects is based on the case where only petro-diesel fuel is used in diesel vehicles. Accordingly, the biodiesel cases incorporate the emission changes resulting from biodiesel use in the LFV domain. The general approach for incorporating biodiesel emissions into the model is as follows:

1. Through research, determine the overall percent change in regulated emissions when using biodiesel as a replacement for conventional diesel fuel.

2. Determine the change in speciation profile for biodiesel use for PM and HC emissions for the different diesel vehicle types.

3. Apply the percent change for biodiesel use to conventional diesel emissions and use the new speciation profiles to determine the effect on air quality.

4. Incorporate penetration of biodiesel and the use of a variety of feedstocks (i.e. yellow grease, animal fats, soybeans, etc.) to estimate future effects.

A detailed description of this approach is presented in [Smyth85].

85 Steve Smyth, “Evaluating the Sustainable Performance of Biodiesel Fuel Focusing on the Air Quality Impacts Due to Vehicular Emissions,” NRC/STO Project Report, 2004

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5.2 Analysis As B20 is the blend that has undergone the most study and with the most data available, an initial analysis was conducted for B20 using average emissions data for biodiesel and applied to the LFV domain. This first step involves setting up a procedure to pass the emissions data through a series of air quality modelling programs, schematically represented in Figure 5.2 (For more details, refer to [Smyth85]). Subsequently, other biodiesel market penetration scenarios have been formulated and simulated using the systems model. The output emissions from these scenarios are then spatially and temporally distributed to the LFV domain. An analogous software procedure was set up to run the emissions data through the same series of air quality modelling programs to project the impact on air quality measures, such as ozone concentration. Output from one of those scenarios set up for the systems model was then selected to test out this software procedure.

Figure 5.1 – Flow of Information for Biodiesel Analysis

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5.2.1 Analysis using Average Emissions Data for B20 A technical analysis of the effect of biodiesel on exhaust emissions from diesel-powered vehicles has been published in a US EPA draft report [EPA86]. It analyzes pre-existing, publicly available data from various emissions test programs to investigate these effects. In addition, the investigation made use of statistical regression analysis to correlate biodiesel concentration in conventional diesel fuel with changes in regulated and unregulated pollutants. Unregulated pollutants include individual HC emissions, such as formaldehyde, acetaldehyde, 1,3-butadiene, etc. The majority of the data obtained for the analysis was collected on heavy-duty highway engines, which formed the basis for the report. The average effects of biodiesel use on regulated emissions from a heavy-duty highway engine are shown in Figure 5.3. As can be seen from the figure, ‘neat’ biodiesel (B100) gives approximately a 48% reduction in PM and CO emissions, a 67% reduction in hydrocarbon emissions and a 10% increase in NOx emissions.

86 The EPA Comprehensive Analysis of Biodiesel Impacts on Exhaust Emissions, EPA420-P-0-001, October 2002, http://www.nbb.org/resources/reportsdatabase/reports/gen/20021001_gen-323.pdf

Figure 5.2 – Air Quality Modelling Program Flow

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Figure 5.3 – Avg. Emission Impacts of Biodiesel Use in Heavy Duty Engines (U.S. EPA, 2002)

It should be noted that this data is a numerical average of all biodiesel produced from various feedstock types, including soybean, canola/rapeseed and animal fats. Furthermore, the graph was generated by the development of a correlation between biodiesel concentration and percentage change in emissions. In this part of the investigation, the base case assumes that all diesel vehicles use petro-diesel fuel and the biodiesel case assumes that all diesel vehicles use B20 fuel. For the biodiesel case, emissions are reduced accordingly, and the new biodiesel HC and PM speciation profiles are used. The mobile emissions are then combined with emissions from other sources including area, point, non-road mobile and biogenic sources. This “complete” emission file is input into the air quality model and certain air quality parameters are compared to the base case in order to evaluate the impact of biodiesel fuel. The air quality parameters investigated include: ground level ozone, primary and secondary anthropogenic organics, elemental carbon, total sulfates, particulate number concentration and visibility. From the comparison, no noticeable change was detected in any air quality measures as a result of using biodiesel. An analysis of the emission profile for the Lower Fraser Valley previously completed provided some information as to why this might be the case. This analysis is summarized in Table 5.1. The table shows that mobile on-road sources make up a relatively small portion of each pollutant, except for CO and NOx, when comparing them to the total emissions from all sources.

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Source Type HC* NOx CO SOx PM10 PM2.5 All Sources [tons/yr] 13,162 1,295 8,697 881 700 352 Biogenic [tons/yr] 7,582 27 - - - - Point [tons/yr] 189 296 400 689 212 129 Area [tons/yr] 4,538 32 208 22 183 90 Mobile Offroad [tons/yr] 199 452 1,452 141 77 71 Fugitive Dust [tons/yr] - - - - 195 36 Mobile Onroad [tons/yr] 654 488 6,636 30 33 26 Mobile Onroad [% of total] 4.97 % 37.69

% 76.31

% 3.38 % 4.68 % 7.27 % * Includes methane and ethane

Table 5.1 – Comparison of Emissions by Source Type

Another question that emerges relates to the relative magnitude of the contribution of diesel vehicles to the emissions from mobile on-road sources. Hence a comparison between gasoline and diesel vehicles was made for the LFV domain to determine the percentages of each fuel type. For the LFV, the mobile inventory is given in terms of VMT, and Table 5.2 shows a breakdown of gasoline and diesel VMT in the inventory.

VMT (106 miles/yr) % of Total Gasoline Vehicles

68,416.74 92.7 %

Diesel Vehicles 5,353.58 7.3 % Total 74,770.32 100.0 %

Table 5.2 – Gasoline and Diesel Vehicle VMT Comparison for LFV

From this comparison, it can be seen that diesel vehicles make up a very small percentage of the total VMT for the LFV case study. Due to the small fraction of diesel vehicles, when a decrease of 20% is applied to the diesel inventory, there is only a 1.46% change in the total mobile inventory. Such a small change in the total inventory is not likely to produce recognizable effects in the air quality model. Another notable point is that VMT percentage does not translate directly into emissions percentage. For example, diesel vehicles emit much more particulates than gasoline vehicles, and even though they may account for less than 10% of the total vehicle miles travelled, they may emit more than 2/3 of the total particulates. Table 5.3 shows the breakdown of gasoline and diesel vehicle emissions in tonnes per year for the various pollutants for the LFV domain.

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Vehicle Type HCs NOx CO SO2 PM10 PM2.5

Gasoline % 94.9 % 36.8 % 97.8 % 51.6 % 9.8 % 16.4 % Diesel % 5.1 % 63.2 % 2.2 % 48.4 % 90.2 % 83.6 %

Table 5.3 – Gasoline and Diesel Emission Comparison for LFV

From this comparison, it can be seen that diesel vehicle emissions make up a very small percentage of the total HC and CO emissions. The portion of emissions of SO2 and NOx from diesel vehicles is larger, and as expected, a very high percentage of particulate matter emissions originate from diesel vehicle. However, because of the small portion of diesel vehicles in use, any changes to diesel vehicle emissions are almost completely hidden in the total emissions inventory, even for PM emissions. The following examples help to illustrate this point:

B20 use results in a 12% reduction in the PM10 emissions from diesel vehicles. Since diesel vehicles emit 90.2% of the total mobile on-road PM10 emissions, this results in a 10.8% reduction in total mobile on-road PM10 emissions.

However, mobile on-road sources emit only 4.68% of all PM10 in the LFV domain. This means that an original 12% reduction of PM10 from diesel vehicles shows up only as a 0.51% reduction in total PM10 emissions.

Likewise, a 20.1% decrease in diesel HC emissions results in a 0.051% decrease in total HC emissions. A 2.0% increase in diesel NOx results in a 0.5% increase in the total NOx emissions, and a 12.3% decrease in CO emissions results in a 0.2% decrease in total CO.

For B100, the percent changes in emissions from diesel vehicles result in the following changes in total emissions:

• HC: 67.4% decrease results in a 0.17% decrease in total HC emissions

• NOx: 10.3% increase results in a 2.4% increase in total NOx emissions

• PM: 47.2% decrease results in a 2.0% decrease in total PM10 emissions

• CO: 48.1% decrease results in a 0.81% decrease in total CO emissions.

Since diesel vehicles make up such a small portion of the vehicle fleet, in conjunction with the fact that mobile on-road sources make up small portions of each of the pollutant categories, excepting NOx and CO, the emission effects on air quality that result from biodiesel use are too small to be significantly recognizable. 5.2.2 Analysis Using Emissions Output from Systems Model

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The emissions output data from Scenario 1 described earlier form the basis of this part of the analysis. This is the case where B2 and B5 are assumed to gradually penetrate the diesel market over a 10-year period from 2005 to 2015. The year 2020 was chosen as the year of comparison between the use of petro-diesel and biodiesel, since another project had required the projection of area, point, non-road mobile sources of emissions and biogenics for that year in the LFV domain. The emissions output for the province of British Columbia is spatially distributed to the LFV. A comparison of the two cases is shown in Table 5.4.

Pollutant Future Base

Case [tonnes/yr]

Future Biodiesel Case [tonnes/yr]

Percentage Change in Emissions [%]

VOC 17,458.8 17,435.2 -0.135 CO 227,925 227,848 -0.0337 NOx 35,100.7 35,502.3 +1.13 SO2 1,199.51 1161.43 -3.28

PM2.5 210.294 208.335 -0.940 PM10 258.662 256.489 -0.847

Table 5.4 – Mobile Emissions Analysis for 2020 Base Case and Biodiesel Case for LFV

The software needed to allocate the emissions spatially and temporally is set up and tested on the LFV domain. The emissions data must also be set in the proper format for input to the air quality modelling software. An additional capability to allow for potential growth of an urban area (i.e., urban sprawl) over the period the systems model is simulating (e.g., to 2020, or 2050) is also included. For a detailed description of the conversion mechanism set up for this part of the analysis, see [Smyth]. 5.3 Results Again, the changes in the different forms of emissions between the base case (for petro-diesel) and the biodiesel case are very small, even smaller than in the previous comparison, using B20. This is not unexpected due to the use of B2 (for transport trucks) and B5 (for urban buses) in the current scenario being compared. As a result, the net impact on air quality is too small to be significantly recognizable, suggesting that biodiesel can only be a small part of a total solution for improving urban air quality. However, a notable achievement is that a computer-based approach has been developed and tested for projecting the potential urban air quality impact when biodiesel is allowed to penetrate the diesel market. This same approach is more generally applicable to analysing the

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impact on other airsheds, of other blends of biodiesel (or even other biofuels for that matter), as well as other penetration scenarios. The current version of the CanTEEM models only the transportation emissions in Canada, but many of the urban areas in Canada today are in close proximity to the US. When the air quality of an airshed is considered, it will be incomplete to model just the mobile emissions arising from the Canadian portion of the locale. For example, the Lower Fraser Valley is affected by emissions just across the border in Washington State; similarly for the Windsor-Quebec City corridor (emissions from the Ohio Valley) and for Atlantic Canada (emissions from the US east coast). There is another effect that is particular to the LFV. Due to the geographical nature of the location, biogenic emissions dominate in terms of VOC emissions. Since VOC emissions are a major factor in the formation of ground level ozone, the potential effect of biodiesel on air quality may not be as apparent. The LFV has been chosen due to the ready availability of data. If another urban setting is chosen as the modelling domain for the comparison, it is reasonable to expect that urban air quality effects of biodiesel would be more recognizable. It is noted that the Bio-bus experience showed that in a corridor of high density diesel traffic such as in Montreal, the use of biodiesel could lead to a noticeable reduction in particulate matter. It is noted that at the present time, the use of diesel fuel in the transportation sector in Canada (and the US) is relatively small compared to the use of gasoline. It is also possible that if diesel vehicles made up a larger portion of the current vehicular fleet, then the change in emissions resulting from biodiesel use would have a more noticeable effect on air quality when modelling emissions from all sources. However, there is at the least some consideration given to increasing the proportion of diesel-fueled personal use vehicles, following the trend in Europe. If this type of scenario is modelled, it is likely that the potential air quality benefits of biodiesel would be more apparent. This investigation is currently near the top of the list of future research topics. Since the time the preliminary assessment was conducted (started over two years ago), more recent data have become available and newer analysis techniques have been developed. These could permit a more rigorous approach to the scenario analysis, and further studies are being planned at the time of preparing this report. 6. Biodesel Supply-Demand Analysis 6.1 Background On the supply side of biodiesel feedstock, as there have been price pressures on yellow grease and tallow in a highly fluctuating market, it is perceived that a more

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stable demand for these feedstocks for biodiesel production will help to stabilize the rendering industry. As well, there are emerging public health and environmental concerns associated with the evaporating demand for deadstock (road kill and dead farm animals) by the rendering sector. A more stable demand of feedstock arising from a biodiesel industry can help alleviate the deadstock disposal problems resulting from a collapsing deadstock collection industry [OMAFRA87]. Yet another emerging health concern is related to the recent occurrence of mad cow disease (BSE) in the cattle industry. One control measure that has been proposed is to take the bone meal of ruminants out of circulation. A ramification of this scenario is that the corresponding protein in animal feed will have to be replaced, possibly by soy meal. While there has been a surplus of crushing capacity in the oilseed crushing industry due to a shortage of demand for oil, the potential impact of this scenario is of interest to a number of stakeholders. As well, the calorific value of the displaced bone meal could potentially become an energy source to the cement industry, as is the case in Europe. With the above considerations in mind, in addition to an analysis performed on Scenario 1 described earlier, a second hypothetical scenario, with the same two sub-cases as in Scenario 1, was also set up for simulation using the systems model: Scenario 2: Assume that all ruminant bone meal is eliminated, and replaced by soy meal for animal feed. The vegetable oil resulting from the additional crushing of soybean is compared against the demand of feedstock under the two cases: Case A: Biodesel will penetrate the diesel market over a 10-year period from 2005 to 2015, with the target blends being B2 for transport trucks and B5 for urban buses. Case B: Biodesel will penetrate the diesel market over a 10-year period from 2005 to 2015, with the target blends being B5 for transport trucks and B10 for urban buses. 6.2 Analysis and Results Scenario 1 With the assumption that the demand of biodiesel under the first scenario being fulfilled by feedstocks made up of 50% yellow grease and 50% tallow, Figure 4.4 shows the resulting demand on yellow grease or tallow (same for both). Whereas Figure 4.4 depicts the demand on biodiesel feedstocks under Cases A and B, Figure 6.1 tracks the corresponding reduction in demand for diesel fuel under the two cases.

87 The Implications of a Biodiesel Industry in Ontario: A Report to the Inter-Ministry Task Group on Biodiesel, OMAFRA Report, 2002

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Figure 6.2 presents a comparison of the available supply of yellow grease plus tallow against the demand on these as feedstocks of biodiesel under the two cases. The supply curve is based on a projected growth of the economy, including a trend projection of demographics such as the population in Canada (Figure 4.2) and the growth in GDP (Figure 4.3), which is expected to lead to a gradual increase in the consumption of oil and the number of animals raised in farms, which in turn will lead to an increase in the supply of waste oil and animal fats. It should be kept in mind that at the present time, there is a healthy demand of yellow grease and tallow for the Canadian export market, and only a fraction of the supply of these feedstocks is actually available to be economically applied to the production of biodiesel. However, it is evident from Figure 6.2 that even under a modest penetration of biodiesel in the diesel transportation fuel scenario (as in Scenario 1A), there is sufficient demand on these feedstocks to absorb a fraction of the total supply that is not destined for the export market. Under the higher penetration scenario (Scenario 1B), Figure 6.2 also shows that the supply of feedstocks from yellow grease plus tallow will indeed not keep up with demand starting from about year 2035, even if 100% of feedstocks derived from these sources are applied to the production of biodiesel. A potential biodiesel industry can develop a valuable alternate market for biodiesel feedstocks under certain conditions, such as an increased demand for vegetable protein meal. Figure 6.3 shows a comparison of the net GHG emissions between using petro-diesel (business as usual case) and using biodiesel under Scenario 1B. The calculations for GHG emissions have taken into account the fuel cycle emissions for petro-diesel and the credit for the bio-based carbon in the biodiesel. It can be seen that there is no noticeable difference in the net GHG emissions. This is probably due to the same reason presented earlier in the air quality comparison. The fairly small part diesel fuel plays in the big picture of the vehicle transportation sector, and the relatively low blends that are being considered for Canadian use, combine to obscure any impact that biodiesel may have on the system as a whole. This appears to be true for the impact both on air quality and on GHG emissions. A question of interest is to what extent must diesel vehicles penetrate the Canadian transportation market before any expected environmental benefits of biodiesel will become significant enough to make a real difference? Further extension and investigation using the biodiesel systems model are being planned, and it is anticipated that they will provide more insight on this question. Scenario 2 This scenario is set up to provide some insight under the hypothetical situation that all ruminant bone meal was to be eliminated from circulation, ostensibly under a threat of the spread of mad cow disease. Figure 6.4 shows the following projections for soybean in Canada:

a) The historical production volume and the trend projection into the future;

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b) The demand on soybean feedstock if all biodiesel required under Cases A and B was to be produced from soybean;

c) The additional oil that will become available as feedstock for biodiesel as a result of replacing the protein content in ruminant bone meal eliminated from circulation.

It should be kept in mind that this scenario is not meant as a prediction that soybean will become a major feedstock for biodiesel in Canada. It is meant only to provide some useful insight as to the role soybean may play as a feedstock in a potential biodiesel industry. While it is perceived that there is currently a surplus capacity for oil seed crushing due to a shortage of demand, under the hypothetical scenario where extra oil seeds need to be crushed to replace the protein content lost through removing all ruminant bone meal from circulation, Curve (c) above shows that the extra oil produced as a result constitutes only a fraction of the demand on feedstock arising from biodiesel use under either Case A or Case B (Curves (b) above). There should be little difficulty in absorbing the extra oil even under a modest penetration scenario for biodiesel. As well, the extra soybean crushing needed to replace the lost protein represents a small fraction of the total soybean produced, and could conceivably be derived from existing surplus oil crushing capacity and not from additional growing of soybean. Following the practice in Europe, another idea that has been proposed under this hypothetical scenario is the potential to apply the ruminant bone meal to the cement industry as an energy source to replace non-electrical energy. This presents an opportunity to reduce the energy demand from other sources. Figure 6.5 depicts the current non-electrical energy use in the cement industry and the trend projection into the future. The green curve provides a view of how the displaced ruminant bone meal may potentially help to reduce the total energy use in that industry.

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Figure 6.1: Petro-diesel Demand – Cases A and B

Trend Projection Case A

Case B

Figure 6.2: Biodiesel Feedstock – Supply vs Demand

Demand – Case A

Demand – Case B

Supply – Yellow Grease plus Tallow

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Figure 6.3: Net GHG Emissions – Petro-diesel vs Biodiesel

Biodiesel – Case B Trend Projection

Figure 6.4: Scenario 2 – Feed Replacement Scenario

Soybean Production

Biodiesel Demand – Case B

Biodiesel Demand – Case A

Soybean for Feed Replacement

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Figure 6.5: Scenario 2 – Potential Reduction on Energy Use in Cement Industry

Trend Projection

Available Energy from Displaced Bone Meal

Apply Displaced Energy to Cement Industry

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7. Summary An initial assessment of the longer term potential of biodiesel in Canada to achieve sustainability objectives has focused on an analysis of the air quality impact and the relationship between supply and demand of feedstocks for the production of biodiesel. While a comparison between petroleum diesel and biodiesel via a Life Cycle Analysis suggests there are definite GHG advantages and reduction in most CAC emissions, a systems analysis based on projected penetration scenarios for the Lower Fraser Valley reveals that the impact on air quality is not likely to be of great magnitude, and can only be part of a total solution for improving urban air quality in Canada. This is due to the relatively small part diesel fuel represents in the vehicle transportation sector, and the relatively low blends of biodiesel that are considered suitable for Canadian conditions. The preliminary assessment conducted was based on emission inventory data available two years ago and analysis techniques used at the time. More recent data and modern techniques could lead to a more rigorous approach to the scenario analysis. As these updates are being planned, a question of interest has emerged: to what extent must diesel fuel penetrate the light duty vehicle market before noticeable environmental benefits derived from the use of biodiesel will be realized in the overall transportation system? A preliminary analysis on the supply and demand of biodiesel feedstocks reveals that biodiesel has a role to play as a valuable alternate market (for yellow grease and tallow) and could possibly have a price stabilizing effect for the rendering sector. Under the hypothetical scenario where ruminant bone meal were to be eliminated from circulation and vegetable protein meal was used to substitute for it, then the excess oil resulting from extra oil seed crushing can be readily absorbed by a modest penetration of biodiesel in the vehicle transportation sector. A systems analysis framework has been set up whereby additional aspects of the potential system-wide impact of biodiesel in the longer term can be analyzed, and useful insights gained on how best to capitalize on biodiesel’s environmental advantages in Canada.

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APPENDIX B This appendix presents a set of whatIf? process diagrams to illustrate the scope of modules in CanTEEM adapted for the biodiesel systems analysis.

Figure B01

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Figure B02-1

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Figure B02-2

(continued)

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Figure B03

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Figure B04

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Figure B05

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Figure B06-1

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Figure B06-2

(continued)