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A Hybrid Method for Particulate Matter Source Apportionment: Using A Combined Chemical Transport and Receptor Model Approach Yongtao Hu, SivaramanBalachandran, Jorge Pachon,Jaemeen Baek*, Talat Odman, James A. Mulhollandand Armistead G. Russell School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia *Currently at Currently at IIHR-Hydroscience and Engineering, University of Iowa. Iowa City, Iowa 10th Annual CMAS Conference, October 25th, 2010 Georgia Institute of Technology

Objective and Approach • Develop a source-based approach to integrating receptor- and source- oriented modeling of particulate matter • Improve source impact estimates • Extend impact quantification to more sources • Expanded spatial and temporal coverage of source apportionment • Provide estimates of uncertainties for spatial analysis • Approach • CMAQ DDM3D/PM to provide initial source impacts and sensitivities • Use sensitivities to adjust source impacts using CMB-type formulation • Use adjustments and species performance to assess uncertainties • Application • One month simulation over CONUS • STN monitors • Six cities Georgia Institute of Technology

Receptor Oriented Modeling (RM) • RM approaches such as CMB rely on using observed concentrations of the PM composition at a receptor, along with knowledge of the composition of source emissions (source profiles), to solve a species balance equation that estimates the source impacts. For example CMB species balance equations: • Limitations/assumptions/uncertainties • Emission compositions are constant and known (not good for some sources) • No reactions or differential phase changes (not bad for many, but not all, primary compounds) • Most sources are included (typically only about 80% of mass is) • Source compositions are linearly independent of each other (co-linearity can be a problem) • The number of sources is less than or equal to chemical species (limitation) total number of emission sources considered measured concentration of species i RM’s prediction error to be minimized emission fraction of species i in total PM2.5 emitted from source j source j’s impact on the total PM2.5 concentration Georgia Institute of Technology

Source-Oriented Modeling (SM) • SM approaches using chemical transport models (CTMs) follow a first principles approach, tracking the emissions, transport, transformation and loss of chemical species in the atmosphere to simulate ambient concentrations and source impacts. For example using DDM3D derived sensitivities: • Limitations/uncertainties • Emissions estimates, Meteorological inputs, Missing processes and parameter uncertainties • Benefits • Large number of sources, direct link to sources, spatial coverage, non-linear chemistry total number of emission sources that included in CTM calculated sensitivity coefficients of species i’s concentration to emissions from source j Simulated concentration for species i impact from source j’s emissions outside of the domain estimate of source j’s impact on species i’s concentration impact from source j’s emissions prior to the simulation period total emissions of all tracked pollutants emitted from source j Georgia Institute of Technology

A hybrid approach for particulate matter source apportionment: Combining receptor modeling with chemical transport modeling Limited number of sources vs. completeness of source categories SM’s prediction error to be minimized Sensitivities to emissions Sensitivity to BC Sensitivity to IC Constraints from source profiles upgraded to constraints of source-receptor relationship derived from CTM We modify the species balance equations which CMB is based to use outputs of the CTM. Georgia Institute of Technology

Hybrid Approach (continued) The hybrid approach relies on minimizing the differences (c2) between CTM-calculated and observed PM2.5 concentrations (including each PM2.5 component and metals) while considering estimated uncertainties in both the observations and source emission rates: So, where CTM-simulated base case impact of source j on species i to weigh the amount of change in source strengths total number of sources total number of species ratio of adjusted impact from source j to the base case a priori uncertainties Instead of the original CMB solution: Georgia Institute of Technology

Application • 2004 MM5-SMOKE-CMAQ-DDM3D simulation • 36-km grid covering continental United States as well as portions of Canada and Mexico. • Projected VISTAS emissions inventory used as a priori inventory. • First order DDM sensitivity coefficients calculated for 32 separate source categories. • Ambient PM2.5 concentrations apportioned to the 32 separate sources • STN, IMPROVE, SEARCH and ASACA networks • TOT measurements of OC and EC from STN and ASACA converted to TOR equivalences. Georgia Institute of Technology

PM2.5 monitoring networks Georgia Institute of Technology

Hybrid Approach Applied at STN sites • Major PM2.5ions and metals measured: • Use reported detection limits and measurement uncertainties • Obtain metals’ sensitivities to sources: • Split using source specific PM2.5 (unidentified portion) sensitivity coefficients and source profiles of metals for each of the 32 categories assuming that metals remain intact from source to receptor. • Source profiles are assembled from the 84 profiles compiled by Reff et al. 2009 ES&T. The profiles split PM2.5 emissions to the above 42 species. Georgia Institute of Technology

Choice of Гfor Ridge Regression Г=N/J=42/32=1.3125 selected

CMAQ/Hybrid Concentrations Georgia Institute of Technology

Initial/Refined (CMAQ/Hybrid) difference (χ2Ci) between simulated and observed PM2.5 concentrations Georgia Institute of Technology

Initial and Refined PM2.5 source impacts(in percentage) Woodstove Solvent Others Prescribed burn Other combustion Nonroad diesel On-road gasoline Natural gas combustion Mineral product On-road diesel Fuel oil combustion Meat cooking LPG combustion Dust Waste burn Metal product Coal combustion Livestock Biogenic Aircraft Georgia Institute of Technology

Major contributing sources in six cities Georgia Institute of Technology

Initial/Refined (CMAQ/Hybrid) Source Impacts Georgia Institute of Technology

Compare with the CMB Results • CMB apportionment allowed resolution of less than 10 sources while the hybrid method resolved 32, and included total contributions from both primary and secondary paths. • In order to do more specific comparisons, the hybrid results are re-grouped to match up with the CMB categories by • (1) splitting the primary and the secondary contributions from each hybrid category, using the source specific composition profiles and assuming that the primary species are inert and stick together, and • (2) merging the hybrid sub-categories that split to primary and secondary portions to the major categories that match up with the CMB sources. Georgia Institute of Technology

Initial/Refined (CMAQ/Hybrid) Source Impacts Georgia Institute of Technology

Benefits and Future Work • Hybrid Approach Benefits • Completeness of sources • More complete range of sources quantified • First principles’ constraints • Can account for non-linearities and secondary PM sources • Limitations removed, for spatial and temporal applications. • Uncertainty estimation • Ongoing Work • Source apportionment at IMPROVE, ASACA and SEARCH sites. • Simulating full year. • Further uncertainty estimation. • Additional approach for inverse modeling • Optimize source compositions. • Interpolation of source impacts spatially and temporally • Increased resolution Georgia Institute of Technology

Acknowledgements • EPA funding under grants R83362601 and R83386601 • Southern Company and Georgia Power Georgia Institute of Technology

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