2007 Progress Report: Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis

EPA Grant Number: R832159
Title: Integrated Source/Receptor-Based Methods for Source Apportionment and Area of Influence Analysis
Investigators: Russell, Armistead G. , Odman, Mehmet Talat
Institution: Georgia Institute of Technology
EPA Project Officer: Chung, Serena
Project Period: December 27, 2004 through December 26, 2007
Project Period Covered by this Report: December 27, 2006 through December 26, 2007
Project Amount: $444,899
RFA: Source Apportionment of Particulate Matter (2004) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air

Objective:

  • Assess the impact of emission categories on PM2.5 by reconciling the receptor and air quality modeling methods.
  • Identify the reasons of the discrepancy of sources of PM2.5 apportioned from the air quality model with those from the receptor model.
  • Improve the emission inventory for each source category via inverse modeling.
  • Apply Area of Interest (AOI) analysis to quantify the influence of any source’s emissions on the air quality at the receptor’s location

Progress Summary:

The primary goal of this project is to improve the emission inventories via reconciling receptor-based and emission-based air quality models and inverse modeling. The emission based method selected is the Community Multiscale Air Quality (CMAQ) model, as part of EPA MODELS-3, and the receptor-based method used is the Chemical Mass Balance models with organic tracers as Molecular Markers (CMB-MM). A ridge regression analysis is performed to decrease discrepancies between CMAQ, CMB-MM and other two CMB models, which are the CMB model using traditional markers (regular) and the CMB Lipshitz Global Optimizer approach. This year, new CMAQ runs and measurements were conducted for the Atlanta area for two periods: June and July 2005 and January 2006. Specific characteristics of aerosol close to highway were obtained through these new episodes.

Future Activities:

Future activities include inverse modeling of emissions followed by  source apportionment of PM2.5. Further, we are extending our description of SOA formation.  We are also using multiple receptor modeling approaches, and comparing their results.   A second order sensitivity approach for aerosols is being developed.


Journal Articles on this Report : 6 Displayed | Download in RIS Format

Other project views: All 50 publications 24 publications in selected types All 24 journal articles
Type Citation Project Document Sources
Journal Article Marmur A, Unal A, Mulholland JA, Russell AG. Optimization-based source apportionment of PM2.5 incorporating gas-to-particle ratios. Environmental Science & Technology 2005;39(9):3245-3254. R832159 (2005)
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  • Journal Article Marmur A, Park S-K, Mulholland JA, Tolbert PE, Russell AG. Source apportionment of PM2.5 in the southeastern United States using receptor and emissions-based models:conceptual differences and implications for time-series health studies. Atmospheric Environment 2006;40(14):2533-2551. R832159 (2005)
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  • Journal Article Napelenok SL, Cohan DS, Hu Y, Russell AG. Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment 2006;40(32):6112-6121. R832159 (2005)
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  • Journal Article Park S-K, Marmur A, Kim SB, Tian D, Hu Y, McMurry PH, Russell AG. Evaluation of fine particle number concentrations in CMAQ. Aerosol Science and Technology 2006;40(11):985-996. R832159 (2005)
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  • Journal Article Park S-K, Cobb CE, Wade K, Mulholland J, Hu Y, Russell AG. Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations. Atmospheric Environment 2006;40(Suppl 2):563-573. R832159 (2005)
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  • Journal Article Yan B, Zheng M, Hu YT, Lee S, Kim HK, Russell AG. Organic composition of carbonaceous aerosols in an aged prescribed fire plume. Atmospheric Chemistry and Physics 2008;8(21):6381-6394. R832159 (2007)
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  • Supplemental Keywords:

    Source apportionment, inverse modeling, direct sensitivity analysis,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, climate change, Air Pollution Effects, Monitoring/Modeling, Environmental Monitoring, Atmospheric Sciences, Environmental Engineering, Atmosphere, particulate organic carbon, atmospheric dispersion models, atmospheric measurements, model-based analysis, source receptor based methods, area of influence analysis, source apportionment, chemical characteristics, emissions monitoring, environmental measurement, airborne particulate matter, air quality models, air quality model, air sampling, speciation, particulate matter mass, analytical chemistry, aersol particles, modeling studies, real-time monitoring, aerosol analyzers, chemical speciation sampling, particle size measurement

    Progress and Final Reports:

    Original Abstract
  • 2005 Progress Report
  • 2006 Progress Report
  • Final Report