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Ralph Morris and Jaegun Jung, ENVIRON Intl. Corp. Eric Fujita, Desert Research Institute

Assessment of the Sources of Organic Carbon at Monitoring Sites in the Southeastern United States using Receptor and Deterministic Models. Ralph Morris and Jaegun Jung, ENVIRON Intl. Corp. Eric Fujita, Desert Research Institute Patricia Brewer, National Park Service 2009 CMAS Conference

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Ralph Morris and Jaegun Jung, ENVIRON Intl. Corp. Eric Fujita, Desert Research Institute

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  1. Assessment of the Sources of Organic Carbon at Monitoring Sites in the Southeastern United States using Receptor and Deterministic Models Ralph Morris and Jaegun Jung, ENVIRON Intl. Corp. Eric Fujita, Desert Research Institute Patricia Brewer, National Park Service 2009 CMAS Conference October 19-21, 2009 Chapel Hill, North Carolina

  2. Organic Carbon Mass (OCM) is an Important Component of Total PM2.5 Mass and Visibility Impairment in the Southeastern U.S. • Time series of annual PM2.5 at Great Smoky Mountains NP 1988-2006 • OCM second highest PM2.5 component to Ammonium Sulfate

  3. Projected Improvements in PM2.5 Mass and Visibility Impairment in Southeastern U.S. primarily due to Reductions in Ammonium Sulfate • Estimated percent change in particle extinction from 2000-2005 to 2018 for Worst 20% days at VISTAS Class I areas

  4. VISTAS Organic Carbon Source Apportionment Study • Visibility Improvement State and Tribal Association of the Southeast (VISTAS) undertook a multi-pronged study to understand the source of OCM in the southeastern U.S. • Enhanced PM monitoring at 5 sites • Organic Tracers • 14C dating • Receptor OCM/EC source apportionment modeling • Chemical Mass Balance (CMB) and PMF • Deterministic OCM/EC source apportionment modeling • Particulate Source Apportionment Technology (PSAT) in CAMx

  5. Total Carbon (TC) consists of OCM and EC Most of TC is OCM Primary emitted and secondarily formed in the atmosphere (SOA) Anthropogenic and biogenic sources Past CMB studies identified three largest components as: Vegetative Burning Mobile Sources Unexplained Carbon Unexplained Carbon presumed to be secondary in origin Large seasonal and spatial variability in the TC Five monitoring sites with enhanced measurements 4 Class I areas plus Raleigh, NC (Millbrook) VISTAS OCM Source Apportionment Study

  6. CMB Receptor TC SA Modeling for 2004/2005 (Fujita et al., 2009): Gasoline Vehicle Exhaust Diesel Vehicle Exhaust Hardwood Combustion Softwood Combustion Meat Cooking Vegetative Detritus Unexplained Carbon (UC) CAMx/PSAT TC SA Modeling for 2002 (Morris et al., 2009): Gasoline Combustion Diesel Combustion Biomass Burning Other Point Sources Other Area Sources Anthropogenic SOA (SOAA) Biogenic SOA (SOAB) VISTAS TC Source Apportionment Modeling

  7. TC Source Apportionment SMOKE emissions modeling to separate TC source categories CAMx photochemical grid model Particulate Source Apportionment Technology (PSAT) to obtain TC source contributions for primary EC and OCM emissions Standard model output to obtain SOAA and SOAB contributions Model performance evaluation VISTAS 2002 36 km Continental U.S. Database CMAQ and CAMx CAMx PSAT TC Source Apportionment Modeling

  8. Model Performance Evaluation for OCM Monthly Fractional Bias (FB) for OCM shows large underprediction bias OCM underprediction bias greatest for urban-oriented STN network and during summer Identification of the source of OCM underprediction bias one of objectives of VISTAS TC source apportionment study

  9. Comparison of CMB & PSAT TC Apportionment • Convert CAMx/PSAT OCM into OC using source-specific OCM/OC ratios • e.g., 1.4 for gasoline and 2.2 for SOA • Combined OC with EC to make TC • Compare seasonal average PSAT & CMB TC • Map PSAT and CMB source categories: CMB UC split between modern (UCm) and fossil (UCf) Carbon using 14C data

  10. TC Gasoline Contributions, CMB vs. PSAT for Winter and Summer PSAT gasoline contributions much lower than CMB Variability in PSAT 24-hour gasoline TC contributions shown Largest difference at suburban MILL site CMB gasoline TC ~5 times greater than PSAT Gasoline Winter Gasoline Summer

  11. TC Diesel Contributions, CMB vs. PSAT for Winter and Summer PSAT seasonal average always lower than CMB PSAT 24-hour variability overlaps with CMB goodness of fit On average CMB Diesel TC contributions factor of ~2 greater than PSAT Diesel Winter Diesel Summer

  12. TC Vegetative Burning Contributions, CMB vs. PSAT Winter and Summer Comparable seasonal average TC contributions from fires Lots of variability in the 24-hour PSAT Vegetative Burning TC contributions Fires Winter Fires Winter

  13. Modern vs. Fossil TC comparisons: 14C vs. CMB vs. PSAT for Mammoth Cave, KY CMB and PSAT frequently overstating the fraction of Fossil Carbon CMB best fit with 14C data if assume UC is modern (i.e., SOAB)

  14. CMB vs. PSAT TC Apportionment Comparisons • Gasoline: CMB TC ~5 times greater than PSAT • Diesel: CMB TC ~2 times greater than PSAT • Fires: CMB and PSAT TC comparable • Other Area: CMB and PSAT comparable • Other Point: No comparable source category in CMB • Both CMB w/ 14C and PSAT estimate that SOA is dominated by SOAB • Exception is suburban Millbrook site that has some higher SOAA • Several confounding aspects to the comparison: • CMB frequently overstates amount of fossil carbon • 36 km grid cell size in CAMx PSAT diluting TC signal at MILL • PSAT point source has no counterpart in CMB • Maybe partially embedded in gasoline or diesel CMB contributions

  15. Summary CMB vs. PSAT TC Contributions • 5-Site and 4-Site average CMB vs. PSAT TC contributions • Why CMB gasoline (~5x) and diesel (~2x) greater than CAMx/PSAT? • Why CMB/14C SOAB (~1.5-2x) greater than CAMx/PSAT? • Why does CMB not attribute TC to stationary sources (points)?

  16. Gasoline/Diesel TC Contributions • CAMx/PSAT gasoline and diesel TC emissions • MOBILE6 on-road mobile sources • LDGV dominate gasoline • HDDT large component of diesel • NONROAD non-road mobile source emissions • Large component of diesel • Locomotive, marine vessels and airplanes separately • EPA’s MOBILE6 and NONROAD being replaced by new EPA/OTAQ MOVES model • Preliminary MOVES vs. MOBILE6 comparisons just becoming available

  17. Motor Vehicle Emissions Simulator (MOVES) MOVES estimating 2.5-3.0 times more PM2.5 emissions from on-road mobile sources than MOBILE6 for three test cities (Source: Beardsley and Dolce, 2009)

  18. Kansas City 2004-2005 Vehicle Measurement Study • KC motor vehicle measurements used in MOVES • Also found high emission levels of Semi-Volatile Organic Compounds (SVOC) from LDGV • SVOC compounds not typically collected in vehicle exhaust VOC measurement studies • e.g., alkanes with 12 carbons or more, PAH compounds • SVOC emissions from LDGV 1.5 times the TC emissions • SVOC can condense to form an SOAA that would increase amount of TC from LDGVs • Unclear where condensed LDGV SVOC emissions would be in the CMB source apportionment (gasoline and/or UC)

  19. Secondary Organic Aerosol (SOA) • SOA an area of current research and development • Significant progress over last 5 years • MEGAN biogenic emissions model • CMAQ SOAmods (2005), CAMx V4.5 (2008) and CMAQ V4.7 (2008) • Added SOAB from isoprene and sesquiterpene and other processes not treated in previous versions • Several researchers are attributing more SOAA to aromatic VOC precursors (e.g., Toluene) than in current models • e.g., UofWI, NOAA, Kleindienst, etc.

  20. VISTAS Source Apportionment Conclusions • Comparison of CMB and CAMx/PSAT TC source apportionment provides insight into both methods and identifies areas for further research to improve our OCM modeling capability • Current emission inventories underestimate particulate Carbon emissions from gasoline and diesel combustion • New MOVES on-road and non-road mobile source emissions factor model will make up much of the shortfall • KC vehicle study SVOC emissions may also help with gasoline OCM and/or SOAA shortfall • CMB gasoline contribution may also be overstated • Where are the stationary source TC contributions in the CMB analysis? • SOA due to biogenic emissions is an area of current research • Implementation of SOA basis set treatment in CAMx will allow more flexibility in treating SOA from SVOC emissions and biogenic VOCs

  21. Acknowledgements • Acknowledge Dr. Eric Fujita’s colleagues at Desert Research Institute who performed sampling and CMB/PMF modeling • David Campbell, Johann Engelbrecht and Barbara Zielinska • Acknowledge Woods Hole Oceanographic who made 14C measurements that were documented by Roger Tanner of TVA • This study was sponsored by VISTAS and acknowledge John Hornback and Ron Methier of SESARM for their support

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