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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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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


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


Projected Improvements in PM 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


Vistas organic carbon source apportionment study
VISTAS Organic Carbon Source Apportionment Study PM

  • 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


Vistas ocm source apportionment study

Total Carbon (TC) consists of OCM and EC PM

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


Vistas tc source apportionment modeling

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


Camx psat tc source apportionment modeling

TC Source Apportionment 2009):

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


Model Performance Evaluation for OCM 2009):

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


Comparison of cmb psat tc apportionment
Comparison of CMB & PSAT TC Apportionment 2009):

  • 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


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


TC Diesel Contributions, CMB vs. PSAT for Winter and Summer 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


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


Modern vs. Fossil TC comparisons: Summer14C 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)


Cmb vs psat tc apportionment comparisons
CMB vs. PSAT TC Apportionment Comparisons Summer

  • 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


Summary cmb vs psat tc contributions
Summary CMB vs. PSAT TC Contributions Summer

  • 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)?


Gasoline diesel tc contributions
Gasoline/Diesel TC Contributions Summer

  • 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


Motor vehicle emissions simulator moves
Motor Vehicle Emissions Simulator (MOVES) Summer

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)


Kansas city 2004 2005 vehicle measurement study
Kansas City 2004-2005 Vehicle Measurement Study Summer

  • 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)


Secondary organic aerosol soa
Secondary Organic Aerosol (SOA) Summer

  • 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.


Vistas source apportionment conclusions
VISTAS Source Apportionment Conclusions Summer

  • 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


Acknowledgements
Acknowledgements Summer

  • 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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