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Resolving the sources of PM 2.5 in Georgia using emission and receptor based models

Resolving the sources of PM 2.5 in Georgia using emission and receptor based models. Amit Marmur, Di Tian, Byeong-Uk Kim, James Boylan 6 th Annual CMAS Conference, October 1-3, 2007. Overview. PM 2.5 non-attainment areas in Georgia CMAQ based PM 2.5 sensitivity analysis

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Resolving the sources of PM 2.5 in Georgia using emission and receptor based models

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  1. Resolving the sources of PM2.5 in Georgia using emission and receptor based models Amit Marmur, Di Tian, Byeong-Uk Kim, James Boylan 6th Annual CMAS Conference, October 1-3, 2007

  2. Overview • PM2.5 non-attainment areas in Georgia • CMAQ based PM2.5 sensitivity analysis • PM2.5 source-apportionment using a receptor based model, Positive Matrix Factorization (PMF) • Comparison between PMF and CMAQ source-apportionment results • Policy implications

  3. Background: PM2.5 Attainment Status in Georgia • Atlanta, Macon, Floyd county, Chattanooga are in non-attainment of the annual PM2.5 NAAQS (15 g/m3): • Designation for the new daily standard (35 g/m3) not yet finalized, but no “new” non-attainment areas expected Annual PM2.5 non-attainment areas

  4. CMAQ based sensitivity analysis • In preparation for the PM2.5 SIP, GA-EPD conducted a PM2.5 sensitivity analysis (see 2005 & 2006 CMAS conference presentations) • Based on this previous analysis, the following controls were considered: • SO2 controls at major power plants • Controls of primary carbon emissions (EC/OC) • (ammonia) • Controls of NOx and anthropogenic VOCs controls had a negligible effect on PM2.5 levels

  5. Chemistry Source Impacts Air Quality Meteorology Receptor vs. Emissions-Based Models Emissions Inventory Source-compositions Receptor (monitor) Emissions-based Model (3D Air-quality Model) Receptor Model (e.g., PMF, CMB) (e.g., CMAQ, CAMx)

  6. PM2.5 source-apportionment using receptor models Purpose: Identify the main contributors (sources) to measured concentrations of pollutants at a receptor site. Required input: Speciated ambient measurements, along with “knowledge” of “typical” emissions composition 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) n – total number of sources ei – error term to be minimized (to obtain best fit) Sj are the unknowns (Ci, fi,j, n – required input)

  7. The PMF receptor model • Positive Matrix Factorization (PMF) is the most commonly applied factor analytical technique in recent years • Enhanced factor analysis, including constraints to prevent negative source contributions • Developed by Dr. Pentti Paatero at the University of Helsinki in Finland • In 2005, EPA released the EPA-PMF 1.1 model

  8. Why apply PMF in SIP development? Why compare with CMAQ? • Identify the major sources affecting monitors at the non-attainment area via ambient data, rather than emissions inventories • Evaluate/expand findings from CMAQ sensitivity analysis • Often impractical to quantify all sources via a CMAQ-based sensitivity analysis (time/resources) • Improve emissions inventories • Analyze events leading to high daily PM2.5 concentrations (emissions vs. meteorology) • Quantify impacts of local sources (e.g., FS#8 monitor) • Evaluate model performance for SOA

  9. PMF-based PM2.5 source-contributions Based on STN data, 2003-04

  10. What we’ve learnt so far… • CMAQ sensitivity modeling suggests control of primary carbon (PC) emissions for PM2.5 SIP • Receptor modeling suggests mobile sources and biomass burning as major sources of PC PM2.5 • Other major components of PM2.5 are: • Sulfate: modeling capabilities and control strategies (CAIR) are fairly mature • SOA: understanding of formation mechanism and modeling capabilities are not as well developed • Most available evidence suggest majority of SOA is of biogenic origin (yields, C14 analyses), though some studies suggest anthropogenic origin (Sullivan & Weber, WSOC studies)

  11. What needs further investigation… • How good is our understanding of PM2.5 impacts from mobile-sources, biomass burning? • How do CMAQ and PMF compare? • How does that affect policy development? • Can PMF assist in CMAQ-SOA evaluation? • Can PMF assist in understanding CMAQ’s (high) unspecified/crustal concentrations

  12. Modeling methodology • CMAQ4.5 w/SOA mods*: annual brute-force runs based on the VISTAS 2002 G2 “actual” inventory (alga12km domain), to quantify the impacts of: • Mobile sources (on and off road) • Fires (Rx, wild, agricultural, land clearing, residential) • PMF analysis for 2002-2005 using speciated PM2.5 data from eight STN sites in Georgia • Analyses done for each site separately and using one combined dataset for all sites; fairly similar results • Comparison of source/factor contributions for • Mobile sources • Fires/ biomass burning • Crustals/Soil • SOA Tracked directly by CMAQ * - Morris et al., Atm. Env., 40, 4960-4972, 2006.

  13. CMAQ-based quarterly PM2.5 contributions Jan-Mar Apr-Jun Jul-Sep Oct-Dec Mobile sources Scale of0.0- 4.0 Fires Crustals SOA g/m3

  14. Comparison b/w CMAQ and PMF:Monthly averages, Atlanta STN site Fires Mobile sources Crustals/Soil SOA

  15. Comparison b/w CMAQ and PMF:Daily contributions, Atlanta STN site Mobile, R=0.66 Fires, R=0.23 Jan-Mar, R=0.47/0.67Apr-Dec, R=0.19/0.37 SOA, R=0.56 Road, R=0.66Soil, R=-0.11

  16. Correlations between sources and species:Atlanta STN site Highlighted values: R0.5

  17. Temporal variability in fire emissionsVISTAS G2 actual emissions 3/23/02 EC (g/s)

  18. Temporal variability in fire emissionsVISTAS G2 actual emissions 3/24/02 EC (g/s)

  19. Temporal variability in fire emissionsVISTAS G2 actual emissions 3/25/02 EC (g/s)

  20. Temporal variability in fire emissionsVISTAS G2 actual emissions 3/26/02 EC (g/s)

  21. Contributions from various biomass-burning sources at the Atlanta SEARCH site * * PMF analysis by Kim&Hopke, Atm Env 38, 3349-3362, 2004

  22. Contributions from various biomass-burning sources at the Atlanta SEARCH siteUsing EPA 2001 Rx burning emissions, instead of VISTAS 2002 (see Tian et al., CMAS 2006 presentation for details) * * PMF analysis by Kim&Hopke, Atm Env 38, 3349-3362, 2004

  23. Contributions from various biomass-burning sources at the Atlanta SEARCH siteUsing EPA 2001 Rx burning emissions, instead of VISTAS 2002 (see Tian et al., CMAS 2006 presentation for details) * * Marmur et al., Atm Env 40, 2533-2551, 2006

  24. Summary and future work • Moderate agreement b/w CMAQ and PMF estimates for mobile sources and SOA PM2.5 • Poor agreement for biomass-burning • CMAQ crustals overestimated, temporal variability suggest resuspended road dust • There are “issues” with any modeling approach: • PMF • Measurement uncertainties and limitations • Fixed source compositions • Temporal variability over-estimated • Point measruement / Impacts of local sources • Models-3 (CMAQ) • Uncertainties in emission rates • Temporal variability in emissions under-represented • Meteorology/mixing • Volume average

  25. Summary and future work • Future work • PMF • Use of organic markers data • Markers for SOA (oxidation products) • Investigation of spatial representativeness • Models-3 (CMAQ) • Investigation into Rx burning emissions (and others) • Detailed temporal variability in fire emissions • Soil dust emissions as a function of wind speed, moisture; increased “near-source” removal of particles • Detailed mobile-sources activity • Policy implications • Regulatory needs precedes scientific understanding

  26. Contact Information Amit Marmur, Ph.D.Georgia Dept. of Natural Resources4244 International Parkway, Suite 120Atlanta, GA 30354amit_marmur@dnr.state.ga.us 404-363-7072

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