C OUPLING C HEMICAL T RANSPORT M ODEL S OURCE A TTRIBUTIONS WITH P OSITIVE M ATRIX F ACTORIZATION : A PPLICATION TO TWO IMPROVE SITES IMPACTED BY WILDFIRES Sturtz et. al ATMS 790 seminar Ashley Pierce

O UTLINE Background Source Apportionment Positive Matrix Factorization (PMF) Chemical Transport Model (CTM) Hybrid The study Model comparison and evaluation Implications 2

B ACKGROUND Particulate matter (PM 2.5 ): 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) 3

S OURCE A PPORTIONMENT 4 Source (Anthropogenic combustion, biomass burning, biogenic emissions from plants) Receptor Affected site/organism or measurement area Source-Receptor relationship: determining the role of meteorology and physical/chemical effects linking source emissions to receptor concentrations Source profiles: the experimentally determined unique proportion of species concentrations (or speciated PM or speciated aerosol) from a source Ex. Biomass burning – OC, EC, levoglucosan Feature: A factor profile (source factor) unique proportion of speciated aerosols determined by the PMF analysis Primary pollutants Emission Primary & secondary pollutants Transport, transformation, & removal processes Primary & secondary pollutants Transport, transformation, & removal processes

P ARTICULATE CARBON Fossil carbon: coal, oil, gas fuels Biogenic carbon: biomass burning, meat cooking, Secondary organic aerosols (SOAs) Upper bound for biomass burning contribution 5

6 More volatile, lower light absorption Higher light absorption

I MPORTANCE Adversely affect health, contribute to haze, affect radiation balance Biogenic sources of carbonaceous aerosols % of fine particulate carbon in rural areas ~50% in some urban areas Carbonaceous species often largest contributor to haze and PM 2.5 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 7

P OSITIVE M ATRIX F ACTORIZATION (PMF) 8

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 9

PMF ADVANTAGES 10

C HEMICAL T RANSPORT M ODEL (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 11

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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 13

CTM A DVANTAGES 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 14

H YBRID 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) 15

MASS BALANCE 16

T HE S TUDY Goal: Distinguish source contributions to total fine particle carbon Biogenic sources Biomass combustion due to wildfires Using a receptor-oriented hybrid model 17

S ITES Speciated PM 2.5 from Monture and Sula Peak Montana Three year: Sula Monture

S PECIES 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 19

S OURCES 20

21 Missoula paper mill & mining Gold, cobalt and Molybdenum mines Dry soils, Long-range Transport, Fires?

C OMPARISON 22

S EASONS (CTM AND H YBRID ) Winter (Dec Jan Feb) Spring (Mar Apr May) Summer (Jun Jul Aug) Autumn (Sep Oct Nov) 23

M ODEL EVALUATION 24

M ODEL EVALUATION 25

M ODEL E VALUATION PMF γ = 0 CTM γ = 1 Monture γ = 0.83 Sula γ =

H YBRID D ISADVANTAGES 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 27

H YBRID A DVANTAGES 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 28

I MPLICATIONS 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 29

R EFERENCES 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), doi: / 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), doi: /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), doi: (02) http://dx.doi.org/ /S (02) 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), doi: / Schichtel, B. A., & Husar, R. B. (1997). The Monte Carlo Model: PC-Implementation. Retrieved 02/16/15, 2015, from 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), doi: /es502749r 30

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M ULTILINEAR E NGINE -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 32

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