COUPLING CHEMICAL TRANSPORT MODEL SOURCE ATTRIBUTIONS WITH POSITIVE MATRIX FACTORIZATION: APPLICATION TO TWO IMPROVE SITES IMPACTED BY WILDFIRES

Sturtz et. al. 2014

ATMS 790 seminar

Ashley Pierce

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OUTLINE

Background Source Apportionment

Positive Matrix Factorization (PMF)

Chemical Transport Model (CTM)

Hybrid

The study Model comparison and

evaluation Implications

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BACKGROUND

Particulate matter (PM2.5): mixture of small particles and liquid drops

Aerosol: PM suspended in a gas (e.g. air) Volatile Organic Compounds (VOCs): variety of

chemicals (benzene, isoprene) Secondary Organic Aerosols (SOA)

Carbonaceous aerosols – major component of fine particulate mass

IMPROVE: Interagency Monitoring of Protected Visual Environments (1985)

National Ambient Air Quality Standards (NAAQS)

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SOURCE APPORTIONMENT

Source(Anthropogenic combustion, biomass burning,

biogenic emissions from plants)

ReceptorAffected site/organism or measurement area

Source-Receptor relationship: determining the role of meteorology and physical/chemical effects linking source emissions to receptor concentrationsSource profiles: the experimentally determined unique proportion of species concentrations (or speciated PM or speciated aerosol) from a source

Ex. Biomass burning – OC, EC, levoglucosanFeature: A factor profile (source factor) unique proportion of speciated aerosols determined by the PMF analysis

Primary pollutants

Emission

Primary & secondarypollutants

Transport, transformation, & removal processes

Primary & secondarypollutants

Transport, transformation, & removal processes

Meteorology

CTM

PMF

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PARTICULATE CARBON

Fossil carbon: coal, oil, gas fuels Biogenic carbon: biomass burning, meat

cooking, Secondary organic aerosols (SOAs) Upper bound for biomass burning contribution

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http://www.lucci.lu.se/wp1_projects.html

More volatile, lower light absorption

Higher light absorption

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IMPORTANCE

Adversely affect health, contribute to haze, affect radiation balance

Biogenic sources of carbonaceous aerosols 80-100% of fine particulate carbon in rural areas ~50% in some urban areas

Carbonaceous species often largest contributor to haze and PM2.5

Smoke thought to be large contributor (W and SE U.S.) Difficult to apportion smoke from other emissions or

between smoke types >50% of smoke particulate mass can be secondary organic

aerosol (SOA) Similar to SOA composition formed from gases emitted by plant

respiration Biomass burning emissions inventories likely

overestimate PM emissions, underestimate VOC emissions from biomass combustion and biogenic release

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POSITIVE MATRIX FACTORIZATION (PMF)

Multivariate factor analysis Uses matrix of speciated sample data

Source contributions Source profiles

Inputs: Species concentrations () uncertainties () number of sources ()

Interpret source types: Source profile information Wind direction analysis Emission inventories

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PMF DISADVANTAGES

True source profiles not known (no emission info) Requires assumptions that are not always true

Can’t apportion secondary organic aerosols to source types

Factor profiles can have large errors and may correspond to a mixture of source types

Uncertainties in measurements are not always known or well-defined

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PMF ADVANTAGES

Each data point can be weighted individually uncertainty value ()

Data below detection limit Variability in solution estimated by bootstrapping

technique, “re-sampling” of data set Based on observations Don’t need source emissions Less uncertainty than CTM

𝑄=∑𝑖=1

𝑛

∑𝑗=1

𝑚 [ 𝑥 𝑖𝑗−∑𝑘=1

𝑝

𝑔𝑖𝑘 𝑓 𝑘𝑗

𝜎 𝑖𝑗]2

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CHEMICAL TRANSPORT MODEL (CTM)

CAPITA Monte Carlo Lagrangian CTM Direct simulation of atmospheric pollutants Each emitted quantum contains a fixed quantity of

mass for various pollutants based on the source emission rate Individual particles subjected to transport,

transformation, and removal processes 6-day back trajectories of air masses using

meteorological data from the Eta Data Assimilation System (EDAS)

Non-fire emissions: Western Regional Air Partnership (WRAP) 2002 emissions inventory

Biomass burning: MODIS inventory Source profiles compiled from burns

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CTM DISADVANTAGES

Large information requirements Chemical mechanisms are incomplete Large errors and biases

Particularly with wildfires Driven by emissions inventory Overall higher root mean square error (RMSE)

than PMF and Hybrid

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CTM ADVANTAGES

Identification and separation of different source types based on emissions inventory

Primary and secondary carbonaceous fine particles can be identified from source types biomass combustion, biogenic, mobile, area, oil,

point, other

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HYBRID

Source-oriented Measured data used to constrain CTM

Direct incorporation of measured data into model Post-processing of model results

Receptor-oriented CTM results constrain receptor model (PMF)

using Multilinear Engine-2 (ME-2)

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MASS BALANCE

– speciated sample data matrix with dimensions by : number of samples : chemical species measured

- residual for each sample/species (model error)

Want to identify: – amount of mass contributed by each source to

each individual sample – species profile of each source – number of sources

No samples can have a negative source contribution and > 0

𝑥𝑖𝑗=∑𝑘=1

𝑝

𝑔𝑖𝑘 𝑓 𝑘𝑗+ε𝑖𝑗

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THE STUDY

Goal: Distinguish source contributions to total fine

particle carbon Biogenic sources Biomass combustion due to wildfires

Using a receptor-oriented hybrid model

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SITES

Speciated PM2.5 from Monture and Sula Peak Montana

Three year: 2006-2008

Sula

Monture

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SPECIES

Species with 0.2 ≤ S/N < 2.0 were down weighted by factor of 3

Removed species: S/N ratio <0.2 below detection limit missing > 50% samples Mass reconstruction outside IMPROVE limits

8% samples from Monture 25% samples from Sula

Looked at 23 species

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SOURCES

Smallest value of where a change in the ratio of cross-validated to approaches zero

User judgment based on qualitative agreement between the species profiles () and prior knowledge of source profiles from known source types within the model region

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Missoula paper mill & mining

Gold, cobalt andMolybdenum mines

Dry soils,Long-range Transport,Fires?

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COMPARISON

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SEASONS (CTM AND HYBRID)

Winter (Dec Jan Feb) Spring (Mar Apr May)

Summer (Jun Jul Aug) Autumn (Sep Oct Nov)

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MODEL EVALUATION

Root mean square error (RMSE) – measure of the differences between the value predicted by the model and the observed values

Sample standard deviation of the differences between predicted and observed values

Measure of accuracy

Correlation coefficient (R) – measure of strength and direction of linear relationship between two variables The covariance of two variables divided by the product of their standard deviations

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MODEL EVALUATION

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MODEL EVALUATION

PMF γ = 0 CTM γ = 1

Monture γ = 0.83 Sula γ = 0.67

0.67

0.83

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HYBRID DISADVANTAGES

Still unable to distinguish between primary and secondary biomass combustion impacts CTM model predictions were highly correlated

Equation 2 should account for multiplicative bias but does not work with high correlation and no tracer species

Requires experts to run model in current form

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HYBRID ADVANTAGES

Complementary attributes from PMF and CTM Directly applying the CTM predictions to the PMF

model allows for resolution of sources not identified by the PMF alone Biogenic vs. biomass combustion

Theoretically primary and secondary features should be distinguishable

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IMPLICATIONS

Accurate identification of relevant sources and impact on receptors will guide control policy lower costs better results

Ability to better distinguish sources to prove pollution events are due to exceptional events such as wildfires

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REFERENCES

Norris, G., & Vedantham, R. (2008). EPA Positive Matrix Factorization (PMF) 3.0 Fundamentals & user guide.

Paatero, P. (1999). The Multilinear Engine—A Table-Driven, Least Squares Program for Solving Multilinear Problems, Including the n-Way Parallel Factor Analysis Model. Journal of Computational and Graphical Statistics, 8(4), 854-888. doi: 10.1080/10618600.1999.10474853

Polissar, A. V., Hopke, P. K., Paatero, P., Malm, W. C., & Sisler, J. F. (1998). Atmospheric aerosol over Alaska: 2. Elemental composition and sources. Journal of Geophysical Research: Atmospheres, 103(D15), 19045-19057. doi: 10.1029/98JD01212

Ramadan, Z., Eickhout, B., Song, X.-H., Buydens, L. M. C., & Hopke, P. K. (2003). Comparison of Positive Matrix Factorization and Multilinear Engine for the source apportionment of particulate pollutants. Chemometrics and Intelligent Laboratory Systems, 66(1), 15-28. doi: http://dx.doi.org/10.1016/S0169-7439(02)00160-0

Schichtel, B., Fox, D., Patterson, L., & Holden, A. Hybrid Source Apportionment Model: an operational tool to distinguish wildfire emissions from prescribed fire emissions in measurements of PM2.5 for use in visibility and PM regulatory programs.

Schichtel, B. A., & Husar, R. B. (1997). Regional Simulation of Atmospheric Pollutants with the CAPITA Monte Carlo Model. Journal of the Air & Waste Management Association, 47(3), 301-333. doi: 10.1080/10473289.1997.10464449

Schichtel, B. A., & Husar, R. B. (1997). The Monte Carlo Model: PC-Implementation. Retrieved 02/16/15, 2015, from http://capita.wustl.edu/capita/CapitaReports/MonteCarloDescr/mc_pcim0.html

Schichtel, B. A., Malm, W. G., Collett, J. L., Sullivan, A. P., Holden, A. S., Patterson, L. A., . . . Barna, M. G. (2008). Estimating the contribution of smoke to fine particulate matter using a hybrid-receptor model. Paper presented at the Air and Waste Management aerosol and atmospheric optics.

Sturtz, T. M., Schichtel, B. A., & Larson, T. V. (2014). Coupling Chemical Transport Model Source Attributions with Positive Matrix Factorization: Application to Two IMPROVE Sites Impacted by Wildfires. Environmental Science & Technology, 48(19), 11389-11396. doi: 10.1021/es502749r

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EXTRA

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MULTILINEAR ENGINE-2 (ME-2)

performs iterations via a preconditioned conjugate gradient algorithm until convergence to a minimum Q value

Conjugate gradient algorithm: algorithm for the numerical solution of particular systems of linear equations, usually a symmetric, positive-definite matrix

𝑄=∑𝑖=1

𝑛

∑𝑗=1

𝑚 [ 𝑥 𝑖𝑗−∑𝑘=1

𝑝

𝑔𝑖𝑘 𝑓 𝑘𝑗

𝜎 𝑖𝑗]2

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Residuals from Equations 1-2: Equation 1: Standard PMF chemical mass balance

equation

Equation 2: CTM constraint: contributions to total fine particulate carbon predicted by the CTM model sources (total biomass combustion and biogenic emissions)

𝑄=(1−γ )∑𝑖=1

𝑛

∑𝑗=1

𝑚 [ ε𝑖𝑗𝜎 𝑖𝑗 ]2

+(γ )∑𝑖=1

𝑛

∑𝑡=1

𝑣 [ ε𝑖𝑡𝜔𝑖𝑡 ]2

[ ε𝑖 𝑡𝜔𝑖𝑡 ]2

+∑𝑠=1

𝑤

𝑢𝑠+¿∑𝑙=1

𝑏

∑𝑟=1

𝑐

𝑢𝑙𝑟 ¿

𝑥𝑖𝑗=∑𝑘=1

𝑝

𝑔𝑖𝑘 𝑓 𝑘𝑗+ε𝑖𝑗 h𝑊 𝑒𝑟𝑒𝑔𝑖𝑘 , 𝑓 𝑘𝑗>0

: species measurement uncertainty: CTM uncertainty: user-defined weighting parameter for CTM predictions relative to mass balance model

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Prior source profile constraints: Equation 3: Normalized thermal fractions of carbon for each

source. Rescales such that in eq. 1 and 2 represents total fine particle carbon and = mass fraction of species in source relative to total carbon

Equation 4: Secondary feature profile constraint Primary biogenic source consists only of VOCs

Sets all r=1 to c non-carbonaceous species near zero Biomass source: carbon thermal fractions, potassium, nitrate, sulfate,

hydrogen

𝑄=(1−γ )∑𝑖=1

𝑛

∑𝑗=1

𝑚 [ ε𝑖𝑗𝜎 𝑖𝑗 ]2

+(γ )∑𝑖=1

𝑛

∑𝑡=1

𝑣 [ ε𝑖𝑡𝜔𝑖𝑡 ]2

[ ε𝑖 𝑡𝜔𝑖𝑡 ]2

+∑𝑠=1

𝑤

𝑢𝑠+¿∑𝑙=1

𝑏

∑𝑟=1

𝑐

𝑢𝑙𝑟 ¿