A Hybrid Method for Particulate Matter Source Apportionment

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

Objective and Approach • Develop a source-based approach to integrating receptorand 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 DDM 3 D/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 Georgia Institute of Technology – Six cities

Receptor Oriented Modeling (RM) ØRM 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: 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 PM 2. 5 emitted from source j ØLimitations/assumptions/uncertainties source j’s impact on the total PM 2. 5 concentration • 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) Georgia Institute of Technology

Source-Oriented Modeling (SM) ØSM 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 DDM 3 D derived sensitivities: total number of emission sources that included in CTM calculated sensitivity coefficients of species i’s Simulated concentration for concentration to emissions from source j species i impact from source j’s emissions outside of the domain estimate of source j’s impact on total emissions of all tracked pollutants species i’s concentration emitted from source j impact from source j’s emissions prior to the simulation period ØLimitations/uncertainties ØEmissions estimates, Meteorological inputs, Missing processes and parameter uncertainties ØBenefits ØLarge number of sources, direct link to sources, spatial coverage, non-linear chemistry 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 Sensitivity to to emissions IC BC 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 (c 2) between CTM -calculated and observed PM 2. 5 concentrations (including each PM 2. 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 a priori uncertainties Instead of the original CMB solution: Georgia Institute of Technology ratio of adjusted impact from source j to the base case

Application Ø 2004 MM 5 -SMOKE-CMAQ-DDM 3 D 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. ØFirst order DDM sensitivity coefficients calculated for 32 separate source categories. Ø Ambient PM 2. 5 concentrations apportioned to the 32 separate sources Ø STN, IMPROVE, SEARCH and ASACA networks • TOT measurements of OC and EC from STN and ASACA converted to TOR equivalences. Georgia Institute of Technology

PM 2. 5 monitoring networks Georgia Institute of Technology

Hybrid Approach Applied at STN sites ØMajor PM 2. 5 ions and metals measured: ØUse reported detection limits and measurement uncertainties ØObtain metals’ sensitivities to sources: • Split using source specific PM 2. 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 PM 2. 5 emissions to the above 42 species. Georgia Institute of Technology

Choice of Г for Ridge Regression Г=N/J=42/32=1. 3125 selected

CMAQ/Hybrid Concentrations Georgia Institute of Technology

Initial/Refined (CMAQ/Hybrid) difference (χ2 Ci) between simulated and observed PM 2. 5 concentrations Georgia Institute of Technology

Initial and Refined PM 2. 5 source impacts (in percentage) Woodstove Solvent Others Prescribed burn Other combustion Nonroad diesel On-road gasoline Natural gas combustion On-road diesel Meat cooking Dust Waste burn Metal product Mineral product Fuel oil combustion LPG combustion 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 ØCMB apportionment allowed resolution of less than 10 sources while the hybrid method resolved 32, and included total contributions from both primary and secondary paths. ØIn order to do more specific comparisons, the hybrid results are regrouped 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 R 83362601 and R 83386601 • Southern Company and Georgia Power Georgia Institute of Technology