1. Georgia Institute of Technology PM Modeling and Source Apportionment Amit Marmur, Dan Cohan, Helena Park, Jeameen Baek, Sangil Lee, Mei Zhang, Jim Boylan, Katie Wade,Jim Mulholland, …, and Armistead (Ted) Russell Georgia Institute of Technology

2. With Special Thanks to: Eric Edgerton, Ben Hartsell and John Jansen –for making the required observations possible as part of SEARCH Southeastern Aerosol Research and Characterization study –Discussions and additional analyses Mike Kleeman –Additional source apportionment calculations (see also, 1PE11) Phil Hopke Paige Tolbert and the Emory crew –As part of ARIES, SOPHIA, and follow on studies NIEHS, US EPA, FHWA, Southern Company, SAMI –Financial assistance And more…

3. Georgia Institute of Technology 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

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

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

6. Georgia Institute of Technology 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

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

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

9. Georgia Institute of Technology 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?

10. Georgia Institute of Technology 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 *On slide 10, the talk starts…

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

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

13. Georgia Institute of Technology 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

14. Georgia Institute of Technology Receptor Models Obsserved Air Quality C i (t) Source Impacts S j (t) C i - ambient concentration of specie i (  g/m 3 ) f i,j - fraction of specie i in emissions from source j S j - contribution (source-strength) of source j (  g/m 3 )

15. Georgia Institute of Technology Receptor Models Strengths –Results tied to observed air quality Reproduce observations reasonably well, but… –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

16. Georgia Institute of Technology Hybrid: Inverse Model Approach* Emissions (E ij (x,t)) C i (x,t), F ij(x,t), & S j (x,t) Air Quality Model + DDM-3D Receptor Model Observations taken from routine measurement networks or special field studies New emissions: E ij (x,t) Other Inputs INPUTS 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

17. Georgia Institute of Technology 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: SEARCH 1 + ASACA 2 –Southeast… Atlanta focus –Daily, speciated, PM 2.5 since 1999 1. Edgerton et al., 4C1; 2. Butler et al., 2001

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

19. Georgia Institute of Technology 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? *On slide 10, the talk starts…

20. Georgia Institute of Technology Source Apportionment Results Hopke and co-workers (Kim et al., 2003; 2004) for Jefferson Street SEARCH site (see, also 1PE4…) SourcePMF 2PMF8ME2CMB-MM* Sec. Sulf.56625628 Diesel151119 Gasol.5153 Soil/dust1322 Wood Smoke116310 Nitr.-rich7895 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

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

22. Georgia Institute of Technology Receptor Models Approaches do not give “same” source apportionment results… yet –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

23. Georgia Institute of Technology 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)

24. Georgia Institute of Technology Species of PM 2.5 in JST January 2002 July 2001 MODEL(CMAQ) OBS 29.42 (  g/m 3 ) 22.53 (  g/m 3 ) 28.07 (  g/m 3 ) 13.28 (  g/m 3 ) Winter problem largely nitrate + ammonium

25. Georgia Institute of Technology SAMI: URM

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

27. Georgia Institute of Technology Mean Fractional Error: Combined Studies Plot by J. Boylan

28. Georgia Institute of Technology VISTAS PM Modeling Performance Modeling conducted by ENVIRON, UC-Riverside. Plot by J. Boylan

29. Georgia Institute of Technology January 2002 July 2001 Species of PM 2.5 (OBS:Left column, MODEL(CMAQ): right column) OBS MODEL (CMAQ) Too much simulated nitrate and soil dust in winter

30. Georgia Institute of Technology Performance PM Performance (Seignuer et al., 2003; see also 6C2) –Errors from recent studies using CMAQ, REMSAD Organic carbon: 50-140% error Nitrate: 50-2000% error –Understand the reason for much of the error in nitrate Deposition, heterogeneous reaction Ammonia emissions still rather uncertain –OC more difficult Understand part –Heteorgenous paths not included More complex mixture Primary/precursor emissions more uncertain Nitrate

31. Georgia Institute of Technology Predicted vs. Estimated in Organic Aerosol in Pittsburgh (Pandis and co-workers) Primary and Secondary OA Predicted [  g/m 3 ] Estimated [  g/m 3 ] EC Tracer Method (Cabada et al., 2003) See also 4D4, 5D2…

32. Georgia Institute of Technology Limitations on Model Performance The are (should be) real limits on model performance expectations –Spatial variability in concentrations –Spatial, temporal and compositional “diffusion” of emissions –Met model removal of fine scale (temporal and spatial) fluctuations (Rao and co-workers) –Stochastic, poorly captured, events (wildfires, traffic jams, upsets, etc.) –Uncertainty in process descriptions and other inputs Heterogeneous formation routes

33. Georgia Institute of Technology Spatial Variability Spatial correlation vs. temporal correlation (Wade et al., 2004) –Power to distinguish health associations in temporal health studies –Sulfate uniform, EC loses correlation rapidly Data withholding using ASACA data: –Interpolate from three other stations, compare to obs. –EC: Norm. Error=0.6 TC: 0.2! –Sulfate: NE = 0.12 EC Sulfate

34. Georgia Institute of Technology Emissions “Diffusion” Dial Variation of ATL emissions Default profile (black) vs. plane/engine dependent operations (red) Chemical dilution: assume source X has same emissions composition, independent of location, etc. (~) On-road OC Emissions Nonroad OC Emissions

35. Georgia Institute of Technology Wildfire and Prescribed burn 3.4 19 Black: estimates based on fire records Red: estimates based on satellite images (Ito and Penner, 2004) 32 56 51 Capturing stochastic events using satellites:

36. Georgia Institute of Technology Sulfate Mean Fractional Error X Spatial variability limit?

37. Georgia Institute of Technology EC Mean Fractional Error X

38. Georgia Institute of Technology How Good Are They? All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission Science (chemistry/physics) Mathematics Computational implementation Evaluation Application Now getting sufficient data

39. Georgia Institute of Technology How Good Are They? All evidence suggests that they describe the processes most affecting the evolution of ozone and (if equipped) particulate matter (o.k., many components of PM) after pollutant emission Science (chemistry/physics) Mathematics Computational implementation Evaluation Application Now getting sufficient data: Holes will get filled

40. Georgia Institute of Technology Emissions-based Model Performance Some species well captured –Sulfate, ammonium, EC(?) “Routine” modeling has performance issues –Multiple causes Species dependent –OC tends to be a little low Heterogeneous formation? (or emissions or meteorology) Some “research-detail” modeling appears to capture observed levels relatively well –Finer temporal variation captured as well Real limits on performance –Data with-holding and statistical analysis suggests model performance may be limited due to spatial variability (5PC5) Longer term averages look reasonable for most species –Nitrate high This is not an evaluation of source-apportionment accuracy –But it is an indication of how well one might do

41. Georgia Institute of Technology Source apportionment of PM 2.5 in JST CMAQ CMB 24.42 (  g/m 3 ) 22.53 (  g/m 3 ) 28.07 (  g/m 3 ) 13.28 (  g/m 3 ) January 2002 July 2001

42. Georgia Institute of Technology Source apportionment of PM 2.5 (CMB:Left column, CMAQ: right column) January 2002 July 2001 CMB CMAQ

43. Georgia Institute of Technology Note. CMB data are missing on July 1, 2, 5, 11, 22, 24, and 28. Source apportionment of PM 2.5 in JST (July 2001) CMB with MM CMAQ (12 km) CMAQ (36 km) (CMB: 1 st column, CMAQ (12km): 2 nd column, CMAQ (36km): 3 rd column) Reasonable agreement…

44. Georgia Institute of Technology Source apportionment of PM 2.5 in JST (Jan 2001) CMB with MM CMAQ (12 km) CMAQ (36 km) (CMB: 1 st column, CMAQ (12km): 2 nd column, CMAQ (36km): 3 rd column) Remarkable agreement… most others not

45. Georgia Institute of Technology CMAQ vs. CMB* Primary PM Source Fractions More variation than I would expect in emissions and large volume average *Not using molecular markers

46. Georgia Institute of Technology California (Kleeman: see 1PE11)

47. Georgia Institute of Technology EBM Application: Site Representativeness Compare observations to each other and to model results to help assess site representativeness –Grid model provides volume-averaged concentrations Desired for health study Assessed representativeness of Jefferson Street site used in epidemiological studies –Found it better correlated with simulations for most species than other Atlanta sites

48. Georgia Institute of Technology Results: SO 4 -2 JSTFTMSDTUCMAQ Mean (  g/m3)4.864.334.274.144.77 Correlation (R)0.730.540.440.491.00 RMSE2.303.023.413.31-

49. Georgia Institute of Technology Emissions-Based Models EBM’s can provide additional information –Coverage (chemical, spatial and temporal) Intelligent interpolator –Source contributions Relatively little day-to-day variation in source fractions from EBM –Reflects inventory –May not be capturing sub-grid(?... Not really grid) scale effects Inventory is spatially and temporally averaged May inhibit use for health studies Agreement between EBM and CBM good, at times, less so at others –Longer term averages look reasonable: Applicable for control strategy guidance, with care –understand limitations –Not apparent which is best

50. Georgia Institute of Technology Getting back to Health Association Application: What’s Best? Air Qual. Data Air Quality Model SA Health Endpoints Source-Health Associations Data Air Quality Model SA Species- Health Associations

51. Georgia Institute of Technology Or? Air Quality Model C(x,t), S(x,t) Health Endpoints Data Understanding Of AQM & Obs. Limitations Observd Air Quality C(x,t) C(x,t), S(x,t) Source/Species Health Associations

52. Georgia Institute of Technology Summary Application of PM Source apportionment models in health studies more demanding than traditional “attainment-type” modeling –New (and relatively unexplored) set of issues Receptor models do not, yet, give same results –Nor do they agree with emissions-based model results (that’s o.k. for now) –Need a way to better quantify uncertainty –If results driven by a single species, little is gained, for epi application Receptor models (probably) lead to excess variability for application in health studies –Representativeness error –Not yet clear if model application, itself, decreases or increases representativeness error over directly using observations Emissions-based models –Likely underestimate variability (too tied to minimally varying inventory) –Performance is spotty Groups actively trying to reconcile differences –Focus on emissions, range of observations, applying different models –Hybrid approaches?

53. Georgia Institute of Technology Acknowledgements Staff and students in the Air Resources Engineering Center of Georgia Tech SEARCH, Emory, Clarkson, UC Davis teams. SAMI GA DNR Georgia Power US EPA NIEHS Georgia Tech

54. Georgia Institute of Technology Effect of Grid Resolution (4x too big)

55. Georgia Institute of Technology Performance MetricsEquation Mean Bias (  g/m 3 ) Mean Error (  g/m 3 ) Mean Normalized Bias (%) (-100% to +  ) Mean Normalized Error (%) (0% to +  ) Normalized Mean Bias (%) (-100% to +  ) Normalized Mean Error (%) (0% to +  ) Mean Fractional Bias (%) (-200% to +200%) Mean Fractional Error (%) (0% to +200%)