Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis

1. Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis U.S. EPA STAR PM Source Apportionment Progress Review Workshop July 19, 2005 Jeameen Baek, Amit Marmur, Dan Cohan, Helena Park, Sangil Lee, Jim Boylan, Katie Wade,Jim Mulholland, …, Talat Odman, Mei Zheng and Armistead (Ted) Russell Georgia Institute of Technology Georgia Institute of Technology

2. Outline • Format: Provide overview (minimal details) with some suggestive results • Not yet definitive… comments desired. • Objectives • Detailed vs. overarching • Direct sensitivity analysis for source apportionment • Results for Atlanta • Integrated source/receptor-based approach • Preliminary results • Source apportionment of PM2.5 • Comparison between CMAQ and receptor models • Future activities Georgia Institute of Technology

3. Proposal Objectives • Extend ozone source apportionment method to particulate matter. • Inter-compare results from a variety of source-apportionment methods, including both receptor and source-oriented approaches. • Identify strengths and limitations of the approaches in the applications, focusing on the reasons for disagreement and under what conditions the various approaches tend to agree and disagree most. • Quantify uncertainties involved in the application of the various source apportionment methods. • Further develop and assess the Area-of-Influence (AOI) analysis technique, and compare the results to those obtained using PSCF. • Assess the relative strengths of using Supersite level data vs. routine monitoring data for source apportionment applications. • Provide source apportionment results to health effects researchers. Georgia Institute of Technology

4. Overarching Objective • Improve our ability to accurately identify how current and future sources impact particulate matter • For use in air quality management and health effects assessments • Spatial and temporal completeness • Compositional and size distribution detail • Quantified uncertainties • Preferably not overly burdensome and can be conducted by various communities Georgia Institute of Technology

5. Activities to Date • Implemented DDM for PM source apportionment • Inverse Modeling for identifying PM emission biases • Preliminary results • Added organic carbon (and other) PM source tracers • Comparison of SA approaches • Compared CMAQ, CMB-reg, CMB-MM, CMB-LGO, PMF-2, PMF-8, PMF-PM+gases • Improving SA analysis via environment-specific measurements • Prescribed forest emissions • Analyzed for OC, EC, metals, organic species, ions • Freeway, 500 m away, forest (all summer) (analysis underway) • Also measured water soluble OC • Provided SA results to health effects researchers • Preliminary analysis conducted Georgia Institute of Technology

6. Chemistry Source Impacts Air Quality Meteorology Receptor vs. Source-oriented Model Source-compositions (F) Receptor model C=f(F,S) Receptor (monitor) Source-oriented Model (3D Air-quality Model) Receptor Model Georgia Institute of Technology

7. Source-Oriented Source Apportionment • Use first-principles, model to follow the emissions, transport, transformation and fate of contaminants • Typical air quality models include CMAQ, CAMX, URM, UAM, EUMAC,… • Identify source impacts by • Removing simulated source (brute force) • Instrument model to calculate impacts directly • First and higher-order sensitivity analysis (e.g., DDM) • Can also use a receptor-oriented sensitivity approach (adjoint method) Georgia Institute of Technology

8. Emissions, Initial Conditions, Boundary Conditions, etc. ∆ (e.g., Atlanta Emissions) Air Quality Model Air Quality Model Sensitivities Concentrations ∆ Check scientific understanding Extend beyond observations Forecasting and prediction Atmospheric response Control strategies Source apportionment

9. Model Parameters (P) State Variables: Sensitivity Parameters: Inputs (P) Model Sensitivity analysis • Given a system, find how the state (concentrations) responds to incremental changes in the input and model parameters: If Pj are emission, Sij are the sensitivities/responses to emission changes, i.e., sensitivity of ozone to Atlanta NOx emissions Georgia Institute of Technology

10. Advection Diffusion Chemistry Emissions Sensitivity Analysis with Decoupled Direct Method (DDM) • Define first order sensitivities as • Take derivatives of • Solve sensitivity equations simultaneously Georgia Institute of Technology

11. FAQS Model Application Domain 36-km 12-km 4-km PM-SA applied within 12 km domain Georgia Institute of Technology

12. Atlanta PM2.5 Source Apportionment July, 2001 Georgia Institute of Technology

13. Inverse Modeling Source Apportionment and Inventory Analysis • Integrated observations and emissions-based air quality modeling to identify biases in emissions inventories • Three-dimensional AQM (CMAQ model), direct sensitivity analysis (DDM-3D), receptor model (ridge regression) • The AQM provides concentration fields • DDM-3D provides sensitivity fields, i.e., how simulated concentrations vary as emissions are adjusted • Sensitivity field provides a chemically-evolved source fingerprint • Ridge regression model, using predicted and observed concentrations, as well as modeled sensitivities, determines optimal adjustment to emissions to derive emission scaling factors Georgia Institute of Technology

14. Hybrid: Inverse Model Approach* INPUTS Emissions (Eij(x,t)) Ci(x,t), Fij(x,t), & Sj(x,t) Air Quality Model + DDM-3D Other Inputs Inverse Model: Minimize differences New emissions: Eij(x,t) Observations taken from routine measurement networks or special field studies Main assumption in the formulation: A major source for the discrepancy between predictions and observations are the emission estimates *Other, probably better, hybrid approaches exist Georgia Institute of Technology

15. P W N M S G Application • CMAQ w/ DDM • Continental US, 36 km domain • 12 km in SE to come • July 2001 & January 2002 • AQS, IMPROVE, ASACA, SEARCH, Supersite data • Divided US in to six regions Georgia Institute of Technology

16. Regionally-Specific Emissions Scaling Factors Weekday Weekend Elemental Carbon SO2 (g) Pacific; Mountain; Midwest; Northeast; Southeast; Georgia Georgia Institute of Technology

17. Comparison of SA Approaches • Wish to compare/contract/dissect various source apportionment methods • Have a “proponent” of each method apply approach as well as they knew how, and compare results • CMB-regular, CMB-molecular marker, CMB-Lipshitz Global Optimizer, Positive Matrix Factorization (2 & 8 C; gas phase), CMAQ • Apply models to same data/periods with extensive monitoring • July, 2001 & January 2002 • Eastern Supersite coordinated intensive periods • Additional data for PMF methods • SEARCH and ASACA data • Identify problems and how they might impact results • Uncertainty analysis Georgia Institute of Technology

18. rural suburban urban Yorkville (YRK) North Birmingham (BHM) Jefferson Street (JST) Centreville (CTR) Oak Grove (OAK) Outlying Landing Field #8 (OLF) Gulfport (GFP) Pensacola (PNS) SEARCH & ASACA SEARCH Funding from EPRI, Southern Company ASACA Georgia Institute of Technology

19. Many Methods, Many Answers Atlanta, July 17, 2001 Georgia Institute of Technology

20. Daily Variation: PMF vs. CMB-LGO PMF Georgia Institute of Technology

21. Mass contributions to PM2.5:Comparison of CMB-MM and CMAQ Averaged contribution over the eight SEARCH stations for July 2001 and January 2002 • Average across months and locations of source contributions looks pretty good, but… r = 0.74CMB = 1.04 * CMAQ Georgia Institute of Technology

22. Monthly contributions in SEARCH stations for July 2001 and January 2002 Disaggregatedsome: not so good • If we look at the results by individual stations, not quite so good… and further r = 0.39 Georgia Institute of Technology

23. Daily average mass contributions to PM2.5 in July 2001 CMB-MM and CMAQ (left to right) Georgia Institute of Technology

24. Area-of-Influence (AOI) Sensitivity of A-NO3 to NO2 • Invert DDM fields to identify how a specific amount of emissions will impact a receptor sight • DDM is source-oriented • Sometimes want a receptor-oriented impact (e.g., specific monitor) • Approach • Calculate forward sensitivities • Interpolate between “sources” to provide sensitivity field coverage • Invert interpolated field to derive receptor-oriented sensitivity Field of sensitivities to point emissions Interpolation Exact Interpolated sensitivity Inversion AOI Comp. vs. exact Georgia Institute of Technology

25. Other Activities • Field Measurements • Prescribed burning (separate contract) • New source profiles: significantly different than current • Highway, urban, rural • Highway almost solely gasoline-fueled vehicles • Metals, EC/OC, organics, water soluble, ions • Plans to go back out in winter, include diesel-laden highway • Uncertainty analysis • Monte Carlo and other methods • Continued collaborations with Emory’s Rollins School of Public Health • Use SA results for epidemiologic analyses • Lots of interesting issues • Definitely more involved than traditional use in AQ management Georgia Institute of Technology

26. Proposal Objectives • Extend ozone source apportionment method to particulate matter. • Done (though some improvements possible) • Inter-compare results from a variety of source-apportionment • Initial results of interest • Identify strengths and limitations of the approaches • Results suggestive • Quantify uncertainties of the various methods. • Applied MC, expert elicitation, etc.: more to come • Further develop and assess the Area-of-Influence (AOI) • Initial AOI’s completed (similar to adjoint sensitivity field) • Assess the relative strengths of using Supersite level data vs. routine monitoring data for source apportionment applications. • Underway • Provide source apportionment results to health effects researchers. • Initial results provided to Emory colleagues Georgia Institute of Technology

27. Questions? • As I say to my students… results from all of the approaches are wrong, but we need to find out how wrong, when most wrong, and how should we not use them. Georgia Institute of Technology

28. Genesis • (How) Can we use “air quality models” to help identify associations between PM sources and health impacts? • Species vs. sources • E.g., Laden et al., 2000 Georgia Institute of Technology

29. Epidemiology • Identify associations between air quality metrics and health endpoints: Health endpoints Statistical Analysis (e.g. time series) Sulfate Association Georgia Institute of Technology

30. Association between CVD Visits and Air Quality Georgia Institute of Technology (See Tolbert et al., 9C2)

31. Issues • May not be measuring the species primarily impacting health • Observations limited to subset of compounds present • Many species are correlated • Inhibits correctly isolating impacts of a species/primary actors • Inhibits identifying the important source(s) • Observations have errors • Traditional: Measurement is not perfect • Representativeness (is this an error? Yes, in an epi-sense) • Observations are sparse • Limited spatially and temporally • Multiple pollutants may combine to impact health • Statistical models can have trouble identifying such phenomena • Ultimately want how a source impacts health • We control sources Georgia Institute of Technology

32. Health Endpoints Statistical Analysis Use AQ Models to Address Issues: Link Sources to Impacts Data Air Quality Model Source Impacts S(x,t) Association between Source Impact and Health Endpoints Georgia Institute of Technology

33. Use AQ Models to Address Issues: Address Errors, Provide Increased Coverage Data Air Quality Model Air Quality C(x,t) Health Endpoints Site Representative? Association between Concentrations and Health Endpoints Monitored Air Quality Ci(x,t) Georgia Institute of Technology

34. But! • Model errors are largely unknown • Can assess performance (?), but that is but part of the concern • Perfect performance not expected • Spatial variability • Errors • … • Trading one set of problems for another? • Are the results any more useful? Georgia Institute of Technology

35. PM Modeling and Source Apportionment* • What types of models are out there? • How well do these models work? • Reproducing species concentrations • Quantifying source impacts • For what can we use them? • What are the issues to address? • How can we reconcile results? • Between simulations and observations • Between models Georgia Institute of Technology *On slide 10, the talk starts…

36. Emissions- Based Hybrid Receptor CMB FA Lag. Eulerian (grid) PMF Molec. Mark. Norm. Source Specific* “Mixed PM” UNMIX PM (Source Apportionment) Models(those capable of providing some type of information as to how specific sources impact air quality) PM Models Georgia Institute of Technology *Kleeman et al. See 1E1.

37. Chemistry Source-based Models Air Quality Model Emissions Meteorology Georgia Institute of Technology

38. Source-based Models • Strengths • Direct link between sources and air quality • Provides spatial, temporal and chemical coverage • Weaknesses • Result accuracy limited by input data accuracy (meteorology, emissions…) • Resource intensive Georgia Institute of Technology

39. Receptor Models Obsserved Air Quality Ci(t) Source Impacts Sj(t) Ci - ambient concentration of specie i (g/m3) fi,j - fraction of specie i in emissions from source j Sj - contribution (source-strength) of source j (g/m3) Georgia Institute of Technology

40. Receptor Models • Strengths • Results tied to observed air quality • Less resource intensive (provided data is available) • Weaknesses • Data dependent (accuracy, availability, quantity, etc.) • Monitor • Source characteristics • Not apparent how to calculate uncertainties • Do not add “coverage” directly Georgia Institute of Technology

41. Source Apportionment Application • So, we have these tools… how well do they work? • Approach • Apply to similar data sets • Compare results • Try to understand differences • Primary data set: • SEARCH1 + ASACA2 • Southeast… Atlanta focus • Daily, speciated, PM2.5 since 1999 Georgia Institute of Technology 1. Edgerton et al., 4C1; 2. Butler et al., 2001

42. rural suburban urban Yorkville (YRK) North Birmingham (BHM) Jefferson Street (JST) Centreville (CTR) Oak Grove (OAK) Outlying Landing Field #8 (OLF) Gulfport (GFP) Pensacola (PNS) SEARCH & ASACA SEARCH ASACA Georgia Institute of Technology

43. Questions • How consistent are the source apportionment results from various models? • How well do the emissions-based models perform? • How representative is a site? • What are the issues related to applying source apportionment models in health assessment research? • How can we reconcile results? Georgia Institute of Technology *On slide 10, the talk starts…

44. Source Apportionment Results • Hopke and co-workers (Kim et al., 2003; 2004) for Jefferson Street SEARCH site (see, also 1PE4…) Average Source Contribution }22 • Notes: • CMB-MM from Zheng et al., 2002 for different periods, given for comparison • Averaged results do not reflect day-to-day variations Georgia Institute of Technology

45. Daily Variation LGO-CMB: see Marmur et al., 6C1 PMF: See Liu et al., 5PC7 Georgia Institute of Technology

46. Receptor Models • Approaches do not give “same” source apportionment results • Relative daily contributions vary • Important for associations with health studies • Introduces additional uncertainty • Long term averages more similar • More robust for attainment planning • Using receptor-model results directly in epidemiological analysis has problem(s) • Results often driven by one species (e.g., EC for DPM), so might as well use EC, and not introduce additional uncertainty • No good way to quantify uncertainty Georgia Institute of Technology

47. Emissions-based Model (EBM)Source Apportionment • Southeast: Models 3 • DDM-3D sensitivity/source apportionment tool • Modeled 3 years • Application to health studies • Provides additional chemical, spatial and temporal information • Allows receptor model testing • Concentrate on July 01/Jan 02 ESP periods • Compare CMAQ with molecular marker CMB • California: CIT (Kleeman) • But first… model performance comments • CAMX-PM (Pandis), URM (SAMI), CMAQ (VISTAS) Georgia Institute of Technology

48. SAMI: URM Georgia Institute of Technology

49. Performance Sulfate EPI OC FAQS* Simulated a bit low: Analyses suggests SOA low VISTAS *Fall Line Air Quality Study, Epi: 3-year modeling, VISTAS: UCR/ENVIRON Georgia Institute of Technology

50. Checklist • Improved inventories • Meat cooking, forest fires • DDM-SA: Done • Applied CMAQ for July 2001, January 2002 • Initial evaluation completed • Also applied for 1999-2001 • Inverse Modeling: First set, done • Added tracers: Done • Environment specific observations • Analyzed for OC, EC, metals, organic species, ions • Prescribed forest emissions • Freeway, 500 m away, forest (all summer) (analysis underway) • Also measured water soluble OC Georgia Institute of Technology