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PM Modeling and Source Apportionment

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. With Special Thanks to:. Eric Edgerton, Ben Hartsell and John Jansen

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PM Modeling and Source Apportionment

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  1. 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 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… Georgia Institute of Technology

  3. 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

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

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

  6. 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

  7. 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

  8. Use AQ Models to Address Issues: Assess 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

  9. 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

  10. 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…

  11. 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.

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

  13. 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

  14. 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

  15. 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 Georgia Institute of Technology

  16. 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 Receptor Model 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

  17. 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

  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. 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…

  20. 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

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

  22. 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 Georgia Institute of Technology

  23. 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

  24. Species of PM 2.5 in JST MODEL(CMAQ) OBS 29.42 (mg/m3) 22.53 (mg/m3) January 2002 July 2001 28.07 (mg/m3) 13.28 (mg/m3) Winter problem largely nitrate + ammonium Georgia Institute of Technology

  25. SAMI: URM Georgia Institute of Technology

  26. 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

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

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

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

  30. 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 Georgia Institute of Technology

  31. Predicted vs. Estimated in Organic Aerosol in Pittsburgh (Pandis and co-workers)Primary and Secondary OA Predicted [g/m3] Estimated [g/m3] • EC Tracer Method (Cabada et al., 2003) See also 4D4, 5D2… Georgia Institute of Technology

  32. 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 Georgia Institute of Technology

  33. 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 Georgia Institute of Technology

  34. Emissions “Diffusion” On-road OC Emissions 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. (~) Nonroad OC Emissions Georgia Institute of Technology

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

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

  37. EC Mean Fractional Error X Georgia Institute of Technology

  38. 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 Now getting sufficient data Evaluation Application Computational implementation Mathematics Science (chemistry/physics) Georgia Institute of Technology

  39. 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 Now getting sufficient data: Holes will get filled Evaluation Application Computational implementation Mathematics Science (chemistry/physics) Georgia Institute of Technology

  40. 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 Georgia Institute of Technology

  41. Source apportionment of PM 2.5 in JST CMAQ CMB 24.42 (mg/m3) 22.53 (mg/m3) January 2002 July 2001 28.07 (mg/m3) 13.28 (mg/m3) Georgia Institute of Technology

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

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

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

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

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

  47. 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 Georgia Institute of Technology

  48. Results: SO4-2 Georgia Institute of Technology

  49. 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 Georgia Institute of Technology

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

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