2004 Progress Report: Emissions Inventory and Process Reconciliation Using Molecular Markers and Hybrid/Inverse Photochemical Modeling with Direct Sensitivity Analysis

EPA Grant Number: R831076
Title: Emissions Inventory and Process Reconciliation Using Molecular Markers and Hybrid/Inverse Photochemical Modeling with Direct Sensitivity Analysis
Investigators: Russell, Armistead G. , Odman, Mehmet Talat , Zheng, M.
Institution: Georgia Institute of Technology
EPA Project Officer: Chung, Serena
Project Period: November 1, 2003 through October 30, 2006 (Extended to September 30, 2008)
Project Period Covered by this Report: November 1, 2003 through October 30, 2004
Project Amount: $449,899
RFA: Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter (PM2.5) (2003) RFA Text |  Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Particulate Matter


The primary objectives of this research project are to:

  1. develop f urther and apply a method for assessing emissions estimates of pollutant precursors and their impact on air quality by reconciling bottom-up and top-down emissions estimates using inverse modeling;
  2. assess and improve the emissions inventory for primary organic particulate matter in the eastern United States with particular focus on the Southeast;
  3. quantify the fraction of primary versus secondary organic aerosol (SOA) and the fractions of SOA that are biogenic and anthropogenic (these results will be compared with results using other methods);
  4. estimate the response of ambient PM2.5 to emission changes by source category;
  5. quantify uncertainties in emissions and source-receptor relationships for the major sources of primary organic matter and precursors to SOA;
  6. assess the information added by using molecular markers in the inverse modeling and using longer periods with more routine measurements;
  7. provide information on the impact of SOA parameters on simulated OC levels;
  8. improve the current methodology by detecting more polar compounds and lower the detection limit for organic tracer analysis by silylation;
  9. optimize the number of species applied in the chemical mass balance model so that only a subset of the important and necessary tracers will be included in the model;
  10. and investigate the sensitivity of the organic tracer-based receptor model technique by comparing the chemical mass modeling results using different number of tracers, different source profiles, and including or excluding more inorganic tracers.

Specific hypotheses to be tested in achieving these research objectives are that

  1. inverse modeling can help identify and quantify emissions inventory errors;
  2. a majority of the organic PM2.5 is primary in origin in both urban and rural areas throughout the Southeast (though SOA may dominate during periods of low visibility in the summer);
  3. most of the SOA in the Southeast is from biogenic emissions;
  4. specific molecular markers, and ratios of specific molecules, can be used to characterize the sources of OC;
  5. and molecular markers increase the certainty of the inverse modeling approach.

The Community Multiscale Air Quality (CMAQ) modeling system, with SAPRC99 (an advanced treatment of SOA formation and gas-particle partitioning), and direct sensitivity analysis will be used. Inverse modeling will be accomplished using ridge regression along with the sensitivities to individual source emissions. After the initial modeling study, where key tracer species and tracer species relations are hypothesized that characterize SOA origins, further organic molecular marker analysis will be conducted to evaluate the findings. Further bottom-up estimates will address biases found from inverse modeling, potentially reconciling differences. Part of the process in testing the inverse modeling, and source-oriented modeling in general, is comparison of the results from the CMAQ modeling with those from receptor modeling using molecular makers. As part of this, we plan to optimize the receptor modeling species being used and conduct an uncertainty analysis of the technique using Monte Carlo analysis. Finally, one of the current limitations of the molecular marker technique is the small amounts of any one organic being present, meaning that longer sampling times often are required. We will address this by assessing silylation for improved quantification.

Progress Summary:

During Year 1 of this project, significant progress has been made on the analysis of past and more current observations, and on the modeling framework. The silylation technique has been developed and is being used, successfully in our laboratory. This has led to increasing our ability to quantify levoglucosan, cholesterol, and other polar compounds to much lower levels than our past studies. Recent measurements have been using this updated procedure. On a second front, we have been diagnosing the use of molecular marker CMB (CMB-MM) for source apportionment. First, we have been identifying the markers that give the most power in applying CMB-MM, which has led to a reduced set of markers that gives very similar results. Second, we have shown the response of applying CMB-MM with alternative profiles. This has shown that the derived fraction of the impact from some sources can vary dramatically depending on the source profiles used, though others are more robust. Third, we have been comparing the CMAQ source apportionment results with CMB. We find that on longer-term averages, they compare relatively well, but day-to-day results have more noise and a lower correlation. This is, in part, the result of the site-based measurements being more sensitive to local sources.

Future Activities:

As discussed in the proposal, in Year 2 of the project, we will perform further analysis of CMB results and comparison with CMAQ. We are now assessing which results are within the method-related uncertainties (i.e., if one propagates uncertainties through both CMB and CMAQ, are the results the same, and if not, what are the likely causes). Associated with this is the identification of the major sources of uncertainty and what might be done. We also are investigating which organic molecules would be best as tracers for SOA formation and how those will be measured.

Journal Articles on this Report : 1 Displayed | Download in RIS Format

Other project views: All 75 publications 40 publications in selected types All 40 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. R831076 (2004)
R831076 (2007)
R831076 (Final)
R829213 (2006)
R829213 (Final)
R830960 (Final)
R832159 (2005)
R832159 (2006)
R832159 (2007)
R832159 (Final)
  • Abstract from PubMed
  • Full-text: ES&T-Full Text HTML
  • Abstract: ES&T-Abstract
  • Other: ES&T-Full Text PDF
  • Supplemental Keywords:

    air quality, organic particulate matter, uncertainty, secondary organic aerosol, SOA, molecular markers, exposure, gas chromatography , emissions, hybrid/inverse PM modeling, emissions inventory analysis, molecular marker/organic tracer analysis,, RFA, Health, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, air toxics, Environmental Chemistry, climate change, Air Pollution Effects, Risk Assessments, Monitoring/Modeling, Environmental Monitoring, Engineering, Chemistry, & Physics, Environmental Engineering, Atmosphere, carbon aerosols, air quality modeling, particle size, atmospheric particulate matter, health effects, aerosol particles, atmospheric particles, mass spectrometry, human health effects, ambient air monitoring, air modeling, air quality models, air sampling, gas chromatography, thermal desorption, carbon particles, air quality model, emissions, molecular markers, direct sensitivity analysis, particulate matter mass, human exposure, ambient particle health effects, particle phase molecular markers, photchemical modeling, aersol particles, particle dispersion, aerosol analyzers

    Progress and Final Reports:

    Original Abstract
  • 2005 Progress Report
  • 2006 Progress Report
  • 2007 Progress Report
  • Final Report