estimates of the air quality benefits of using natural gas in industrial and transportation...

ORIGINAL PAPER

Estimates of the air quality benefits of using natural gasin industrial and transportation applications in Lima, Peru

Tammy Thompson Æ Yosuke Kimura Æ Cyril Durrenberger Æ Alba Webb ÆAna Isabel Tejela Matias Æ Matt Fraser Æ David T. Allen

Received: 13 July 2007 / Accepted: 21 January 2009 / Published online: 12 February 2009

� Springer-Verlag 2009

Abstract Reductions in air pollutant emission and

ambient concentrations of air pollutants, which would

occur in Lima when natural gas (NG) is substituted for

existing fuels, were estimated. The analysis suggests that

current emission inventories under-predict ambient con-

centrations. Despite the uncertainty in emissions, the

analysis also suggests that significant changes in air pol-

lutant concentrations could be achieved with widespread

replacement of diesel and gasoline fuels with NG.

Keywords Natural gas � Lima � Air pollution �Particulate matter � Sulfur dioxide

Introduction

Lima, which has a population in excess of 7 million, rou-

tinely experiences high concentrations of air pollution. In

the central city, for example, concentrations of particulate

matter (PM) routinely exceed 100 lg/m3. Concentrations

of total suspended PM, measured at five air quality moni-

toring sites in Lima, averaged 185 lg/m3 for the period

between 2001 and 2005. Concentrations of other air pol-

lutants for which measurements are available, including

SO2 and NOx, are also high. These concentrations place the

population of Lima in the top 10% for air pollutant expo-

sures among urban residents in the Americas (Cohen et al.

2006).

The major sources of PM and other air pollutants in

Lima are diesel fuel and gasoline combustion in mobile

sources and fuel combustion by industrial point sources,

especially cement, kiln and foundry operations. Substitut-

ing natural gas (NG) for these more polluting fuels in

Lima has the potential to substantially reduce air pollutant

concentrations. Widespread availability of NG is projected

to increase in Lima due to the development of a NG

pipeline.

Natural Gas fields were discovered in the 1980s in the

area of the Peruvian Amazon known as Camisea. After

this discovery, plans were made to construct a pipeline

that would transport the gas to the coast of Peru where it

could be utilized domestically and sold abroad. August

2004 marked the completion and start-up of the Camisea

NG pipeline. Two pipelines were built, running parallel

for most of the *400-mile journey from the Amazon to

the coast. The two pipelines part near the coast, the

first carrying NG to Lima, the second carrying NG to

the coast for processing then export. The resulting

increase in the availability of NG in Peru offers the

possibility of improving air quality in Peruvian cities by

allowing substitution of NG for more polluting types of

fuels.

The goal of this work was to estimate the improvements

in air quality that could be achieved in Lima through the

widespread substitution of NG for other fuels. There are

multiple types of fuel users in Lima, with the largest users

being vehicles and industrial operations. This paper will

estimate the relative impacts of substituting NG from

various vehicular and industrial sources.

T. Thompson � Y. Kimura � C. Durrenberger � A. Webb �A. I. T. Matias � D. T. Allen (&)

Center for Energy and Environmental Resources,

University of Texas, M/C R7100, 10100 Burnet Road,

Austin, TX 78758, USA

e-mail: [email protected]

M. Fraser

Department of Civil and Environmental Engineering,

Arizona State University, Tempe, AZ, USA

123

Clean Techn Environ Policy (2009) 11:409–423

DOI 10.1007/s10098-009-0199-2

Methodology

The air quality benefits that can be achieved in Lima

through the widespread substitution of NG for other fuels

were estimated in a four-step process:

1. Development of a baseline emission inventory suitable

for estimating ambient air pollutant concentrations in

each of five political jurisdictions. These political

jurisdictions are shown in Fig. 1, and will be referred

to as Norte, Sur, Este, Ciudad and Callao, or North,

South, East, City and Callao.

2. Development of an air quality model for estimating

air pollutant concentrations in each of five political

jurisdictions.

3. Evaluation of the performance of the air quality model

and baseline emission inventory using data collected

by air quality monitors, with adjustment of the baseline

emission inventory.

4. Estimation of the changes in emissions and air

pollutant concentrations that would result from substi-

tuting NG for other, more polluting fuels.

Steps 1 and 2 are described in this section; Steps 3 and 4

are described in Section ‘‘Results and discussion’’.

Baseline emission inventory

In order to model changes in air quality resulting from a

change in emissions, a baseline emissions inventory that

accurately represents current emissions activity must first

be developed. The baseline emissions inventory, described

in this section, was developed from data obtained from

Direccion General de Salud Ambiental (DIGESA 2007).

The baseline emissions inventory divides the Lima area

into the five different geo-political regions shown in Fig. 1.

For each of these regions, emissions inventories were

developed for point source emissions, area source emis-

sions, mobile source emissions, non-road mobile source

emissions, and trash burning emissions. The pollutants

for which emissions were estimated were SO2, NOx, total

suspended particulates (PTS), and particulate matter (PM)

\10 lm in diameter (PM10). Fine PM emissions (PM

\2.5 lm in diameter, PM2.5) were estimated using a PTS/

PM2.5 ratio of 3.5, which was based on ambient measure-

ments made at the monitoring sites in Lima.

Point source emissions

Point sources include industrial and non-industrial sta-

tionary equipment or processes that can be identified by

name and location and are considered significant sources of

air pollution emissions. Point sources are often differenti-

ated into fugitive and non-fugitive (or stack) sources. Stack

emissions are distinct points that are associated with

emissions, such as industrial boiler exhausts. In contrast,

fugitive emissions are associated with multiple small

sources within an industrial facility, such as small leaks

from storage tanks, piping, pumps and valves, and are

therefore generally not directly combustion-related (US

EPA 2007).

The point source emission data for Lima included a

distinction between process and combustion emissions.

Fuel flow rate for Lima’s emission sources and emissions

factors developed by the U.S. Environmental Protection

Agency were used by DIGESA to calculate emissions due

to combustion. There was no documentation describing the

methods used to determine process emissions data for

Lima. No separately identified fugitive emissions data were

included. In addition, DIGESA provided lists of industrial

facilities in Lima, which were compared to the inventory of

point source emissions. In Callao, for example, up to half

of the companies on the list had no emissions data avail-

able. The southern region, Lima Sur, had information for

all five of the point sources contained within it. For the

other three regions, the percentage of industrial facilities

with no data reported was around 25%.

Area sources

Area sources include point sources that do not individually

produce sufficient emissions to be reported as an individual

point source. Collectively, however, the emissions from

many small sources of the same type in an area can be

Fig. 1 Geo-political regions of Lima used in the development of the

emission inventory; locations of the air quality monitoring stations in

Lima are identified by a dot

410 T. Thompson et al.

123

significant and these small sources are aggregated into area

source categories. The principal area sources in urban areas

include cooking, residential combustion and outdoor fires.

Area source data provided by DIGESA includes infor-

mation on bakeries, carpentries, printing, restaurants and

fuel stations. These data were reported by region without

detailed location information.

On-road mobile sources

Emissions from on-road mobile sources result from the use

of cars, trucks and busses traveling on public roadways.

Emissions from on-road mobile sources are calculated

based on estimates of vehicle miles traveled (VMT)

coupled with estimates of the emissions per mile traveled.

These estimates of on-road mobile source emissions

are generally made with models developed by the U.S.

Environmental Protection Agency or the European Envi-

ronment Agency. Alternatively, emissions can be estimated

based on fuel consumption and emissions per unit of fuel

consumed.

DIGESA provided an on-road mobile source emissions

summary for Lima for the year 2000 based on the CORI-

NAIR methodology, which has been used by the European

Environment Agency (CORINAIR/EMEP 2006). Emis-

sions and vehicle kilometers traveled (VKT) data were

reported by vehicle type, age, and fuel type (Lents et al.

2004). No spatial distributions were provided with the

mobile emissions data. Emissions were reported as totals

for Lima. Mobile source emissions are typically distributed

to a roadway network based on the amount of travel on

each road segment by each vehicle type. Since this type of

detailed information was not available for the five areas of

Lima, it was necessary to use other surrogates, such as

population or business density, to distribute the emissions

to each of the five areas of Lima. Assuming the majority of

vehicle use is for commuting, the mobile emissions were

initially distributed using the population of area sources

(assumed to be related to residences and business density)

in each region. The fraction of the total number of area

sources in each region was calculated. Then for each

region, that fraction was multiplied by the total mobile

emissions to arrive at the mobile emissions in each region.

Non-road mobile sources

Non-road mobile sources include a wide variety of internal

combustion engines that are mobile, but that are not

associated with highway vehicles (e.g., construction and

industrial equipment, locomotives and airplanes). Emis-

sions calculation methodologies are as varied as the

categories themselves. For example, when activity data are

available, a model using engine types and landing/takeoff

cycles is used to calculate most aircraft emissions and

actual fuel usage and track mileage are applied to deter-

mine locomotive emissions. Most other non-road mobile

equipment emissions are estimated using EPA’s NON-

ROAD model or similar tools (US EPA 2005).

Total yearly emissions data for non-road mobile sources,

which included only emissions from air traffic and marine

traffic, was provided by DIGESA for the country of Peru.

Population was used to allocate emissions to the five

regions of Lima. In most urban areas in the United States,

emissions from construction equipment dominate the non-

road inventories for PM and NOx (Simon et al. 2008).

Trash incineration

Information on weight of trash produced, and weight of

trash collected was provided by DIGESA. All trash pro-

duced but not collected was assumed burned. EPA

emissions factors were used to calculate emissions from

trash burning. Trash data was provided by region and so

emissions were assigned by region.

Development of the air quality model

Model overview and simplifying assumptions

Air quality models can vary in degree of sophistication

from simple dispersion calculations that can be summa-

rized in a single equation to complex simulations able to

estimate temporal evolution of air pollutant concentrations

in a three dimensional spatial grid (Russel and Dennis

2000). The choice of an appropriate air quality model for

an application depends on the level of detail in the infor-

mation that is sought from the air quality model, the level

of detail in the meteorological and emissions information

that can be provided to the model, and the air quality

monitoring data that can be used to evaluate the perfor-

mance of the model.

For the work described in this report, the goal was to

provide as much information as possible about the spatial

and temporal distribution of air pollutants resulting from

fuel combustion in Lima. However, the level of detail that

could be incorporated into the air quality model was lim-

ited by the lack of three dimensional wind field data, by the

lack of data on monitored pollutant concentrations as a

function of time of day (see next section) and by a limited

spatial distribution of monitors.

Although data were not sufficient for the application of

the most rigorous air quality models, the data that were

available were more than sufficient to run the simplest air

quality models. An emission inventory, described in the

previous section was available. Seasonal average meteo-

rological data were available from government reports and

Estimates of the air quality benefits of using natural gas 411

123

the scientific literature, and seasonal average monitoring

data were available for five geographically distinct sites in

Lima (Silva-Contrina and Montoya-Cabrera 2004).

To utilize these data most effectively, an air quality

model, customized for use in Lima was developed. The

model utilizes a number of meteorological assumptions,

based on the available data. Specifically, because only

seasonal wind speed and direction data were available,

temporally constant, spatially homogeneous wind speeds

and directions were assumed. Because no data were

available on the temporal evolution of vertical mixing

heights and because air quality monitoring data for evalu-

ating the performance of the model were only available for

mid-morning sampling times, a constant mixing height of

500 m was used. The mixing height was based on USEPA

data for Oakland, California, which has similar climate and

topography patterns (US EPA 1991).

Similar types of simplifications were made in the

chemical and physical processes incorporated into the

model. Because no data were available on the concentra-

tion of photochemical oxidants, such as ozone, and on the

chemical composition of some oxidant precursor species,

no attempt was made to follow chemistry. Because of the

limited data on wind speeds and surface characteristics,

deposition of pollutants was neglected.

What the model does incorporate is the horizontal

transport of air pollutants among five sub-regions in Lima,

and the physical dilution that would be expected due to

Fig. 2 Simplified polygons for

each political region: Polygons

are the simplified boundaries

with the name of region polygon

shown as underlined text. The

polygons were used in order to

calculate air flow in/out of each

political region


123

seasonal average winds. The sub-regions are the five

political jurisdictions identified in Fig. 1.

Model formulation

The five sub-regions in Lima (Norte, Sur, Este, Ciudad and

Callao) were treated as five individual box models with

their own emission inventories. Since these five sub-

regions are politically defined regions, the shapes are

complex. The air quality model calculations are greatly

simplified by assuming that boundaries between political

regions can be represented by line segments, thus repre-

senting each political region as a polygon. Figure 2 shows

a map of the polygons used in this work. When these

polygons are defined, gaps between the regions are created.

The gaps between the regions, named ‘‘fill 1’’, ‘‘fill 2’’ and

‘‘fill 3’’, do not correspond to any political regions; no

emissions are assigned to those polygons and species are

simply transported across those polygons.

Once the polygons were defined, the air that flows across

each boundary were calculated using 1. the length of each

side (m), 2. the planetary boundary layer (PBL) height

(assumed to be 500 m above ground level), 3. wind speed

(m/s) and direction (degree, defined counter clockwise,

zero being the vector pointing to east), 4. direction of the

vector normal to each side of the polygon, for which the

mass balance is being performed (degree). Details of the

model formulation have been described by Thompson et al.

(2007).

Results and discussion

Base case analysis

The base case emissions inventory for Lima is summarized

in Table 1. The emissions inventory and the air quality

model were used to estimate air pollutant concentrations,

and these predicted concentrations were compared to

observations. Each of the five sub-regions (Norte, Sur,

Este, Ciudad, and Callao) has one monitor, and the loca-

tions of the monitoring sites are shown in Fig. 1. The

monitors measure concentrations of SO2, NO2, Total Sus-

pended Particulate Matter (PTS), and PM \2.5 lm in

aerodynamic diameter (PM2.5). PM10 concentrations were

Table 1 Emissions by major source category in each of the 5 polit-

ical jurisdictions (Mg/day)

PM PM10 SO2 NOx

Callao

Point and area emissions 30.04 0.00 7.40 5.96

On-road mobile 2.4 2.2 14.7 30.4

Trash burning emissions 0.19 0.06 0.00 0.01

Non-road mobile 0.01 0.07 0.28

Lima Sur


On-road mobile 1.3 1.2 7.8 16.2



Lima Ciudad


On-road mobile 2.8 2.5 17.1 35.5



Lima Este


On-road mobile 1.8 1.7 11.2 23.3



Lima Norte


On-road mobile 2.5 2.2 15.1 31.4



Range of Monitor values from 2000 - 2005

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Fig. 3 Measured concentrations of air pollutants in the five regions

of Lima (Note that molar concentrations of NOx have been converted

to mass equivalents of NO2, to facilitate comparison with emission

inventories)

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Fig. 4 Comparison of observed and predicted concentrations using

the base case emissions


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estimated using a ratio of 1.1 (PTS/PM10) that is based

on the CORINAIR methodology summary (CORINAIR/

EMEP 2006). Monitor measurements were typically made

between 9 and 11 in the morning. Concentrations are

measured and recorded *2–3 times a month at maximum

frequency; however, much of the data in the time period

from 2000 to 2005 is incomplete. All of the data available

for this period were used to calculate an average concen-

tration, and a range of concentrations, for each pollutant for

each site. The average observations and the range of

observations are shown in Fig. 3.

These data were used to evaluate the emissions inven-

tory and the air quality model. Concentrations of SO2 were

used in the initial performance evaluation because SO2 is

the most chemically stable of the gas phase pollutants

measured at the sites, and is not as subject to local depo-

sition as PM. It also has the advantage of not being as

sensitive to emission inventory uncertainties as the other

monitored pollutants. SO2 emission rates are directly

related to the amount of sulfur in fuels and are not as

sensitive as other air pollutant emissions to the operating

conditions of the devices using the fuels. If SO2 concen-

trations predicted by the emission inventory and the air

quality model match concentrations measured at the

monitors, it can be assumed that the performance of the air

quality model is reasonable and that data on fuel use in the

emissions inventory is reasonable.

Figure 4 compares results from the box model with

measured concentrations. In three of the five regions,

agreement between the predicted and measured SO2 con-

centrations is much better than in Lima Ciudad and at the

Este site, where the predicted SO2 concentrations are low

compared to measured concentrations. Two possible

explanations for this discrepancy were examined. The first

possible reason for under-prediction of observations in

Ciudad and Este is the close proximity of the monitors to

a point source with high SO2 emissions. The air quality

model assumes that the emissions are rapidly dispersed

throughout the region, and a local source, that has not

adequately dispersed, could lead to higher than predicted

concentrations. However, none of the point sources of SO2

reported in the emission inventory are near enough to

influence the monitored concentrations in this way. The

second possible explanation for the under-prediction of

SO2 concentrations in Ciudad and Este is close proximity

of the monitor station to a busy roadway. Concentrations

measured within *20 m of a roadway can be a factor of

three or more higher than concentrations a few hundred

meters from the roadway (Zhu et al. 2002). Because of

their proximity to roadways, the Ciudad and Este sites may

be measuring concentrations that are a factor of 2–3 or

more higher than the average concentration in the region,

which is consistent with the level of under-prediction in the

model.

For this work, it will be assumed that the primary reason

for the discrepancy between the predicted and observed

SO2 concentrations at the Ciudad and Este sites is the

proximity of the sites to roadways. The Lima Ciudad and

Este monitors are located directly on or near roadways. The

Ciudad monitor is located within a few meters of a road-

way intersection, and this Ciudad intersection is reported

by DIGESA to have high traffic flow. The Este site (15 m

from a roadway) records concentrations roughly a factor of

two higher than predicted values and the Ciudad site

(directly adjacent to a roadway) records concentrations

roughly a factor of 3–4 higher than predicted values. In

contrast, comparisons between modeled and measured SO2

concentrations (Fig. 4) show better agreement for Norte,

Sur and Callao, which were not as close to heavily traveled

roadways. This suggests that the air quality model provides

reasonable estimates of seasonal and area average con-

centrations, and that the fuel consumption data in the

emission inventory are reasonable. The under prediction at

the Ciudad site, and to a lesser extent the Este site, is likely

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Fig. 5 Model predictions of Lima air pollutant concentrations, using

base case EI with additional area source emissions included, based on

the Mexican NEI

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Fig. 6 Model predictions of Lima air pollutant concentrations, using

base case EI, plus additional emissions from the Mexican NEI, with

new mobile emissions from the Lima Vehicle Activity Study; mobile

emissions concentrated in Ciudad


123

due to these sites sampling near-roadway conditions, rather

than area average concentrations.

Figure 4 indicates, however, serious discrepancies

between observed and predicted values for NO2, PM2.5 and

PTS. If the air quality model provides reasonable estimates

of seasonal average concentrations, and the fuel con-

sumption data in the emission inventory are reasonable,

then the source of these discrepancies is likely to be the

emission factors used in the emission inventory, area

sources and non-road sources that have not been included

in the inventory, or possibly other causes.

Emission factors relate an activity (e.g., amount of fuel

burned) to an emission rate (mass of pollutant emitted per

time). For SO2 emissions, emission factors depend only on

the sulfur content of the fuel and not on the details of

operating patterns. For NO2, PM2.5 and PTS, emissions are

sensitive to the operating characteristics of the emission

source, for example, the way in which an industrial boiler

is operated and the condition of control devices on auto-

mobiles. The emission factors used in developing the

emission inventory were primarily drawn from U.S.

Environmental Protection Agency and European Environ-

ment Agency reports and therefore represent typical

conditions in the United States or Europe. These emission

factors may or may not be appropriate for Lima. Similarly,

area source emissions were based on emission source cat-

egories that are typical of those in the United States or

Europe, but may not correctly represent the emission

source categories in Lima.

To attempt to account for the uncertainties in the

emission inventory, a series of sensitivity studies and

source attribution measurements for PM were performed.

Modified base case analysis

As shown in Fig. 4, ambient concentrations of PM esti-

mated using the base case emissions inventories are

significantly less than PM total and PM2.5 concentrations

measured at the five monitor sites. Because the model

is able to reasonably predict SO2 concentrations, it was

assumed that the under-prediction of PM was due to

uncertainties in emission factors. A series of sensitivity

analyses were run to test some of the assumptions made

when developing the inventories, and to estimate the

quantity and likely source category of emissions that are

missing from the base case inventory.

The first set of sensitivity analyses looked at the possi-

bility of missing area source categories by comparing Lima

data to data in the Mexican National Emissions Inventory

(US EPA 1999). Categories present in the Mexican NEI but

missing in the Lima data were scaled by population and

added to the Lima emissions inventory. Results from this

analysis are shown in Fig. 5. The results indicate that area

sources, accounted for in the Mexican NEI, but absent from

the base case emission inventory for Lima are not sufficient

to explain the under-prediction of PM concentrations.

An additional sensitivity analysis examined the methods

used to calculate the on-road emission inventory. Data

from the ‘‘Lima Vehicle Activity Study’’ (Lents et al.

2004) were used to revise the on road mobile base case

emission inventory. This study, conducted by International

Sustainable Systems Research Center (ISSRC), uses results

from the International Vehicle Emissions Model (IVE)

with fleet and location files developed specifically for Lima

to estimate on road mobile source emissions in Lima.

Emission totals for NOx and PM10 reported by Lents et al.

(2004) were greater than those reported in the original

CORINAIR inventory provided by DIGESA. However,

Table 2 Estimates of emissions under accounted for in inventories

Mg/day or Ton/day PTS PM10 PM2.5

Callao 112.9 67.2 61.6

Lima Sur 651.5 45.2 194.0

Lima Ciudad 296.1 176.3 161.6

Lima Este 289.3 56.1 51.5

Lima Norte 115.4 68.7 63.0

PM emissions added to account for the difference between EI and

monitor observations

Table 3 Distribution of emission source categories in the final base

case emissions inventory

Emissions inventory sector PTS (%) PM10 (%) PM2.5 (%)

Point ? Area 12.65 4.54 9.75

On-road mobile 1.27 4.11 3.01

Trash burning 5.90 6.78 4.96


Area sources from Mexican NEI 2.64 6.42 2.08

Missing emissions 74.85 69.40 71.50

Total with missing emissions 100.00 100.00 100.00

Table 4 Distribution of emission source categories in the final base

case emissions inventory

Emissions inventory sector PTS (%) PM10 (%) PM2.5 (%)

Point ? Area 13 5 10

On-road and non-road mobile 78 82 83

Trash burning 6 7 5

Non-road mobile

Area sources based on Mexican

NEI

3 6 2

Missing emissions 0 0 0

Total with missing emissions 100.00 100.00 100.00


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SO2 emission estimates from the IVE model were lower

than the original SO2 estimates because of lower fuel sulfur

content assumptions. Therefore, for the revised on-road

mobile source emissions inventory, SO2 estimates from the

CORINAIR methodology were retained while NOx and

PM10 emissions were replaced with the revised estimates

based on the IVE model. PM2.5 was estimated from the

average PM10/PM2.5 ratio for motor vehicles in the Mexico

NEI. Additionally, the distribution of vehicle emissions

was revised such that emissions from passenger cars (PCs)

and trucks were distributed to each of the five regions

based on business density while emissions from public

transport (taxis, buses) were distributed 60% to Lima

Ciudad and 10% to each other region. Results of the sen-

sitivity studies are presented in Fig. 6. The agreement

between predicted and observed NOx concentrations is

improved, compared to the base case, however, significant

differences remain for PM concentrations.

There are several possible reasons for the missing PM

emissions. A first is underestimation of emissions from

combustion sources, due to underestimation of emission

factors (emissions per mass of fuel consumed). Lents et al.

(2004) stated that there are fewer cars in Lima with con-

trols than there are in Mexico or Santiago. This means that

emissions factors for combustion in vehicles, based on US,

European or Mexican emission factors, are likely to be

under predicting emissions for Lima. If the missing PM

emissions are from vehicles, then when sources are

switched to NG, these emissions will decrease. Another

possible missing source of PM emissions is fugitive dust.

In the United States, fugitive dust from roadways and from

agricultural operations is estimated to contribute up to half

of all direct fine PM emissions to the atmosphere (Simon

et al. 2008; US EPA 2007). No fugitive dust emissions data

was reported by DIGESA. If the missing PM is dust, the

dust emissions will not decrease when fuel is substituted

with NG. Finally, secondary aerosol, in the form of sul-

fates, nitrates, and oxygenated organics may contribute to

missing PM, especially PM2.5.

In order to examine whether the missing PM emissions

were due to combustion sources, secondary aerosol for-

mation, or fugitive dust, filter samples from Lima were

analyzed for molecular markers of gasoline and diesel fuel

combustion. The methodologies and results are described

in the Appendix. High observed concentrations of alkanes

indicate that the missing emissions are likely due to gas-

oline and diesel combustion sources, therefore the missing

PM sources were assumed to be from on-road and non-road

sources, although other coal, gasoline and diesel combus-

tion sources are also possible sources. Tracers of wood

combustion are present at relatively low concentrations,

indicating that trash (cellulose) and wood combustion,

beyond that described in the emissions inventories, are not

major contributors to missing emissions.

Estimates of the magnitude of the missing PM sources

were made using the air quality model. PM emissions were

added to the inventory so that the predicted concentrations

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200.00

250.00

300.00

CA

LLA

O

LIM

A S

UR

LIM

A C

IUD

AD

LIM

A E

ST

E

LIM

A N

OR

TE

CA

LLA

O

LIM

A S

UR

LIM

A C

IUD

AD

LIM

A E

ST

E

LIM

A N

OR

TE

CA

LLA

O

LIM

A S

UR

LIM

A C

IUD

AD

LIM

A E

ST

E

LIM

A N

OR

TE

5.2 MP01 MPSTP

µg/m

3 Vehicular Emissions

Area Sources

Point Sources

Monitor Data Range

Fig. 7 Estimated source

category contributions to PM

concentrations in Lima

Table 5 Observed vehicle class

distributions in Lima, PeruType of

vehicle

Observed travel,

2003 (%)

Passenger car 52

Taxi 25

Motorcycle 1

Bus 17

Truck 5


123

of both PTS and PM2.5 would be in the range measured by

the monitor. These very preliminary estimates of ‘‘miss-

ing’’ PM emissions, shown in Table 2, are very large

compared to base case vehicular and other combustion

emissions.

Data provided by DIGESA regarding non-road emis-

sions inventories was incomplete and the resulting

emissions were much lower than would be expected. To

provide an estimate of the magnitude of the non-road

emissions, the ratio of non-road to on-road emissions from

0.00

50.00

100.00

150.00

200.00

250.00

300.00

STP5.2 MP2ON2OS

Basecase25% reduction50% reduction100% reductionMinMaxAverage

µg/m

3

Fig. 8 Predicted changes in ambient concentrations associated with replacing various fractions of the taxi and bus fleet with natural gas vehicles

and assuming that all of the missing PM emissions are from on-road vehicles

0.00

50.00

100.00

150.00

200.00

250.00

300.00

STP5.2 MP2ON2OS

Basecase25% reduction50% reduction100% reductionMinMaxAverage

µg/m

3

Fig. 9 Predicted changes in ambient concentrations associated with replacing various fractions of the taxi and bus fleet with natural gas vehicles

and assuming that the missing PM emissions are from both on-road and non-road vehicles


123

Houston, Texas was used. Houston was selected since it is

a port city, and many non-road emissions are associated

with marine vessels. The ratio of non-road to on-road

emissions in Houston was multiplied by the on-road

emissions values for the five regions of Lima to calculate a

revised non-road emissions inventory, shown in Table 3.

If the base case emission inventory for on-road and non-

road vehicles is revised to include these missing PM

emissions, the resulting inventory has source strengths

summarized in Table 4. Comparison between the revised

inventory and the observations is shown in Fig. 7.

Emission and concentration changes associated

with natural gas use

The base case and revised emission inventories were

altered to reflect changes in emissions that would result

from substituting NG use for other, more polluting fuels.

Three scenarios related to transportation and industrial

point source uses of NG were evaluated:

1. Taxis and buses switch to NG as a fuel source.

2. Industrial sources switch to NG as a fuel source.

3. Removal of the most polluting vehicles in the fleet.

Taxis and buses switch to natural gas as a fuel source

Emission reductions were estimated for scenarios in which

25, 50 and 100% of the taxi and bus fleet is converted to NG.

For this assessment, it was assumed that if 100% of the taxi

and bus fleet is converted to NG then 100% of the emissions

of PM and SO2 from these sources would be reduced. Note

that this may overestimate reduction in coarse particle

emissions if emissions from engine lubrication is significant.

Data from the ‘‘Lima Vehicle Activity Study’’ (Lents et al.

2004) were used to establish mobile source emissions esti-

mates. These estimates were based on a total vehicle fleet of

approximately 0.9 million vehicles in the Lima Metropolitan

area traveling *70,000,000 km/day. The overall vehicle

class fraction observed during the study is shown in Table 5.

It is not known with certainty whether the large amount

of ‘‘missing PM emissions’’ in Lima (Table 4) is due to on-

road or non-road emissions, so two scenarios associated

with replacing taxi and bus fleets with NG fueled vehicles

were examined. Figure 8 shows the results of eliminating

various fractions of taxi and bus emissions and assuming

that all missing PM emissions are due to on-road sources.

Figure 9 shows the results of eliminating various fractions

of taxi and bus emissions and assuming that missing PM

emissions are due to on-road and non-road sources in ratios

identical to those predicted in Table 3.

Industrial sources switch to natural gas as a fuel source

For point sources, emission reductions associated with

substituting NG for other fuels were evaluated. This

scenario posed some challenges due to uncertainties in

the emissions inventory. As noted in previous sections, the

point source emission inventory did not always identify the

specific PM sources within each facility. Since emissions

(especially of PM) can come from both fuel combustion and

other sources (for example, activities that create dust), it

was difficult to reliably estimate the extent to which NG use

would reduce emissions, especially of PM. The approach

taken in this work was to assume that fuel related com-

bustion would include sulfur dioxide, PM and NOx

emissions. It was assumed that all point source emissions of

SO2 were related to fuel combustion. If the fuel were

replaced with NG, then the SO2 emissions, and the PM

emissions associated with fuel combustion, would be

eliminated. It was also assumed that the NOx emissions

would not be significantly changed with the combustion of

NG compared to the original fuel, so the NOx emissions

were not modified when the fuel was changed to NG.

0

5000

10000

15000

20000

25000

30000

35000

40000

0 25 50 75 100

Percent of industry converted to natural gas based on total SO2 emissions

PM

EM

ISS

ION

S (

ton

/yea

r)

Callao

South

City

East

North

Fig. 10 Impact of natural gas

substitution in PM Emissions


123

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ble

6T

op

10

po

int

sou

rces

for

PM

emis

sio

ns

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kA

rea

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pan

yP

M,

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10,

SO

2,

NO

xem

issi

on

(to

n/y

ear)

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ssT

ype

of

fuel

use

dT

ype

of

indust

ryD

escr

ipti

on

1S

ur

CE

ME

NT

OS

LIM

AS

.A.

19

14

1P

M

19

14

PM

10

46

44

SO

2

29

55

NO

x

Av

.Ato

con

go

24

40

Vil

lad

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nfo

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aC

emen

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reo

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iffe

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tty

pe

of

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land

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ent

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ican

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ana

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s

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alla

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cess

and

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ket

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rass

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on

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ctu

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of

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pas

ta


123

In the Mexican NEI, emission factors used to calculate

the emissions for distillate fuel combustion and residual

fuel combustion were examined and compared to the

emissions from point sources in Lima. In general, for

combustion of residual fuel, the emissions of SO2 were less

than or equal to the NOx emissions. In this case, the PM

emissions were *20% of the NOx emissions. Therefore,

for cases in the Lima point source inventory where the

emissions of SO2 were less than or equal to the NOx

emissions the PM emissions were reduced by 20% of the

NOx emissions.

For combustion of distillate fuel, the emissions of SO2 in

the Mexican NEI were about ten times the emissions of

NOx. In this case, the PM emissions were *80% of the

NOx emissions. Therefore, for cases in the Lima point

source inventory where the emissions of SO2 were much

greater than the emissions of NOx, the PM emissions were

reduced to 80% of the NOx emissions.

If more specific data on fuel types and sulfur contents

and the amount of fuel consumed were available, more

accurate estimates of the PM reductions could be calcu-

lated. However, the approach used here will provide an

estimate of the amount of point source PM emissions that

can be reduced by conversion of fuels to NG.

Figure 10 shows the impact of converting all of the

point source combustion to NG, using the procedure

described above. Based on the assumptions outlined above,

the use of NG as an alternative fuel produces only a slight

decrease in PM emissions, since the assumed combustion

emissions are only a small portion of the total PM point

source emissions. These estimates have significant uncer-

tainty, however, and more information on the PM sources

from the top ten point source PM emitters, listed in

Table 6, would be very valuable.

Removal of the most polluting vehicles in the fleet

For the final scenario the ‘‘worst’’ vehicles in Lima were

removed using the IVE model as a tool for estimating

emissions changes. For this scenario, *39% of the PC

fleet was replaced with NG vehicles. These vehicles were

‘‘medium no control (US Pre-1972 style) light duty gas-

oline vehicles’’ and ‘‘medium no control (US Pre-1990

style) light duty diesel vehicles’’ as defined the by the

mobile emissions data provided by DIGESA. These are

the vehicles with the highest emissions rates out of all

PCs. These vehicles were replaced with ‘‘medium multi

point fuel injected with three way catalyst and EGR

(US MY 1995–2000) light duty designed to run only on

natural gas’’. Default files assume *74% of PCs are

gasoline, *26% are diesel. Once again, missing PM

emissions are assumed to come from vehicle combustion

sources. Figure 11 shows the results of eliminating high

emitting vehicles under certain assumptions for the

missing PM emissions. The missing PM emissions are

assumed to be 100% from road dust sources (no PM

emission reduction associated with fuel switching), 32%

from on-road sources (using the ratio of non-road to on-

road contributions to PM found in Houston), or 100%

from on-road sources.

0.00

50.00

100.00

150.00

200.00

250.00

300.00

STP5.2 MP)ledom(xON ro )sbo(2ON2OS

Basecase100% Dust32% On-Road Sources100% On-Road SourcesMinMaxAverage

µg/m

3

Fig. 11 Results of substituting highest emitting vehicles with natural gas fueled vehicles


123

Conclusion

Estimated emission reductions and reductions in ambient

concentrations of air pollutants that would occur in Lima

when NG is substituted for existing fuels in industrial and

transportation uses, have been reported. The estimated

changes in air pollutant emissions and concentrations,

associated with the increased NG use, are highly dependent

on assumptions that are made regarding ‘‘missing’’ sources,

especially for PM, in the emission inventory.

Under the assumption that a majority of the ‘‘missing

emissions’’ come from on-road mobile sources, it is clear

that Lima would most benefit from the use of NG as a fuel

source for on-road vehicles. This transition should start

with the category of vehicles with the largest VMT. This is

most likely to be buses and taxis.

Appendix

Identifying sources of PM emissions in Lima

In order to identify emission sources that may be under-

predicted in current emission inventories for Lima, samples

of PM, collected at sites in Lima on filters, were analyzed

for molecular markers of emission sources. Five filter

samples were examined, one from each the five monitoring

stations in Lima. Each filter contains material that was

collected over a 24 h period at a sampling rate of

*1000 m3/h. A summary of the five stations and values

for each station is presented in Table 7.

The samples were analyzed to determine the concentra-

tions of a group of n-alkanes, and other tracers, that are

known to be markers for vehicle exhaust. For this work, the

analysis will focus on the concentration of four n-alkanes:

C27H56, C28H58, C29H60, and C30H62. The ratio of the con-

centration of these n-alkanes to the total concentration of PM

in vehicle exhaust has been measured in Houston, using

methods identical to those described in this work, by Fraser

et al. (2003). The ratios of the concentrations of the molecular

markers to the total PM2.5 concentration, and to the PM2.5

elemental carbon (EC) concentration are listed in Table 8.

Concentrations of the molecular markers in the five

Lima samples are given in Table 9. Concentrations of total

PM2.5 and PM2.5 EC in the Lima samples were then esti-

mated using the ratios listed in Table 7. This yielded four

estimates of the concentrations of PM2.5 and PM2.5 EC, at

each monitoring site, based on the four molecular markers.

The average estimates for each monitoring site, and stan-

dard deviations of the estimates, are reported in Table 10.

This procedure was repeated, using ratios of molecular

marker concentrations to total PM2.5 and PM2.5 EC con-

centrations reported by Schauer et al. (2002). Schauer et al.

(2002) reported these concentration ratios for both catalyst

equipped and non-catalyst equipped gasoline-powered

light-duty motor vehicles. The results are shown in

Table 11.

The estimates of PM concentrations due to gasoline and

diesel exhaust reported in Table 11 represent estimates

based on molecular marker emission ratios derived from

three different vehicle fleets. The first set of Schauer esti-

mates come from a fleet of vehicles without catalyst

enhanced emission controls. The second set of Schauer

estimates come from a fleet of vehicles that have catalyst

Table 7 PM samples

Monitor

location

Date Total collection

time (min)

Air flow rate

(m3/min)

Mass deposited

on filter (g)

Volume

sampled (m3)

PM

(lg/m3)

Lima Norte 7/22/2005 1.515 0.7432 2,759 269.4

Lima Sur 7/21/2005 1,522 1.813 0.2122 2,389 88.8

Callao 7/26/2005 0.1973 2,584 76.4

Lima Ciudad 7/21/2005 1,320 1.068 0.3584 1,409 254.4

Lima Este 7/26/2005 1,460 1.770 0.42 2,803 149.8

Table 8 Concentration ratios of EC and PM2.5 to molecular markers

in vehicle exhaust (10)

Molecular marker PM2.5/Marker EC/Marker

C27H56 11,500 2,100

C28H58 17,600 3,230

C29H60 14,000 2,560

C30H62 15,100 2,750

Table 9 Molecular marker concentrations in Lima PM filter samples

Molecular marker Concentration in Lima samples (lg/m3)

Norte Sur Callao Ciudad Este

C27H56 0.0056 0.0193 0.0141 0.0319 0.0163

C28H58 0.0023 0.0110 0.0081 0.0179 0.0092

C29H60 0.0027 0.0116 0.0088 0.0190 0.0099

C30H62 0.0017 0.0099 0.0075 0.0162 0.0084


123

emission controls. The Fraser data represents a mixed fleet

with an unknown percentage of catalyst-equipped vehicles.

The fleet in Lima is a combination of catalyst and non-

catalyst controlled vehicles. Lima, when compared to

Houston, is likely to have a larger percentage of the vehicle

fleet without catalyst operating controls due to the avail-

ability of leaded gasoline in Lima, which can poison

catalytic converters.

The last two columns in Table 11 report the PM and

PM2.5 ambient concentrations that result from on-road

mobile emissions. On-road emissions values were reported

by DIGESA and then adjusted to account for missing

emissions. This was done by changing the input emissions

values until the output concentrations best matched values

being measured by the monitors in Lima. The average ratio

of reported on-road emissions to the estimated missing

emissions was 30. Therefore the PM and PM2.5 emissions

values were multiplied by a factor of 30 before they were

input into the air quality model. The resulting output

concentrations are reported here.

The results from this analysis show that with the

exception of the Northern region of Lima, the PM observed

in Lima is largely due to diesel and gasoline engine

exhaust. This supports the conclusion that the source of the

‘‘missing’’ PM emissions in Lima is most likely on-road

and non-road gasoline and diesel engines, and therefore,

that use of NG as a transportation fuel would have a large

positive effect on air quality.

Finally, the contributions of cellulose combustion (paper

and vegetation burning) to ambient PM concentrations

were estimated, using a procedure similar to that described

above. In this case, levoglucosan was used as a molecular

Table 10 Estimated concentration of EC and PM2.5 due to diesel and gasoline sources in Lima samples

Molecular marker Norte Sur Callao Ciudad Este

Estimated concentration of EC due to diesel and gasoline sources in Lima samples (lg/m3)

C27H56 11.8 40.5 29.6 67.1 34.2

C28H58 7.3 35.6 26.0 57.8 29.8

C29H60 6.9 29.6 22.4 48.7 25.3

C30H62 4.8 27.1 20.5 44.6 23.2

Avg. ± S.D. 7.7 ± 2.9 33.2 ± 6.0 24.6 ± 4.0 54.5 ± 10.0 28.1 ± 4.9

Estimated concentration of PM2.5 due to diesel and gasoline sources in Lima samples (lg/m3)

C27H56 64.3 222 162 367 187

C28H58 39.9 194 142 316 163

C29H60 37.8 162 123 266 138

C30H62 26.3 148 112 244 127

Avg. ± S.D. 42.1 ± 16.0 182. ± 33.9 135. ± 21.9 298. ± 54.7 154. ± 26.7

Table 11 Comparison of PM2.5 due to diesel and gasoline sources, estimated using source characterizations, to observations and air quality

model calculations

City PM

measured

on filters

(lg/m3)

PM2.5 due to diesel

and gasoline sources

based on Fraser source

characterization (lg/m3)

PM2.5 due to diesel


based on Schauer source

characterization w/o

catalyst PM2.5 (lg/m3)

PM2.5 due to diesel


based on Schauer source

characterization

w/catalyst PM2.5 (lg/m3)

PM2.5 on-road

PM calc by

model (lg/m3)

PM2.5 on-road

PM2.5 calc by

model (lg/m3)

Lima Norte 269 42.1 12.2 69.9 141.6 74.7

Lima Sur 88.8 182 52.1 241 111.7 33.5

Callao 76.4 135 38.7 176 208 104.2

Lima Ciudad 254 298 85.4 399 176.9 49.8

Lima Este 150 154 44.1 203 193 81

Avg. ± S.D. 168. ± 90.5 162. ± 92.4 46.5 ± 26.4 218. ± 120. 94.1 ± 63.3 79.0 ± 53.2

Table 12 Source characterization for wood burning using levoglu-

cosan as a molecular marker (Schauer et al. 2001)

Ratio of Levoglucosan to organic carbon

Pine Oak Eucalyptus Average

0.181 0.166 0.309 0.219


123

marker for cellulose combustion and the ratio of total PM

emissions to levoglucosan concentrations was based on

a study of wood burning by Schauer et al. (2001). The

resulting PM concentrations, attributed to cellulose burning

were on the order of ng/m3. This analysis led to the con-

clusion that cellulose combustion, and therefore trash and

wood burning is not a significant contribution to PM

emissions in Lima.

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Technology transfer network. http://www.epa.gov/ttn/chief/

eiinformation.html. Accessed 22 Jan 2007

Zhu Y, Hinds WC, Kim S, Shen S, Sioutas C (2002) Study of ultrafine

particles near a major highway with heavy-duty diesel traffic.

Atmospheric Environ 36:4323–4335

Table 13 Estimated organic carbon concentrations in Lima that result from cellulose (wood) burning

Norte Sur Callao Ciudad Este

Levoglucosan concentrations (lg/m3) 0.0114 0.0045 0.0018 0.0030 0.0081

Estimated Organic Carbon concentrations in Lima due to cellulose burning

Pine 0.0628 0.0247 0.0097 0.0165 0.0448

Oak 0.0684 0.0269 0.0106 0.0179 0.0488

Eucalyptus 0.0368 0.0145 0.0057 0.0097 0.0263

Average 0.0520 0.0204 0.0080 0.0136 0.0371


123

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