Final 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 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:

Confidence in our ability to use both emissions and receptor-oriented models to conduct source apportionment relies on their ability to provide results that are both consistent with each other and with the observations. This is rarely, if ever, achieved in practice, and few studies have ventured to do a comprehensive comparison. This project capitalizes upon recent advances in atmospheric modeling to develop and refine three innovative approaches to source attribution of fine particulate matter (PM2.5): (1) high-order emission-based source apportionment modeling, (2) an Area-of-Influence method for inverting forward concentration-emission relationships in chemical transport models (CTMs) to determine the sources impacting each receptor, and (3) a four-dimensional data assimilation (FDDA) method for using CTMs to improve emissions characterization. All three approaches utilize sensitivity analysis in the CMAQ model by the Decoupled Direct Method in Three-Dimensions (DDM-3D), whose capability was extended to include the non-linear response of PM2.5 to emissions. A tracer module in CMAQ (CMAQ-TR) is utilized for fast, resource-saving way for source apportionment, explaining impacts from 34 sources. Modeling was applied to the Eastern Supersites (ESP01/02) intensives to facilitate rigorous comparison with traditional receptor-oriented methods. While a primary focus of the study is to best exploit the more abundant and detailed data from the Supersites, it is recognized that air quality managers and other stakeholders will seldom have such data at their disposal for conducting source apportionment. We therefore integrated the more generally available observations from routine Speciation Trends Network (STN) and IMPROVE monitoring to identify issues specific to those monitors and their use for model evaluation and source apportionment. Primary objectives of this research are to:

  • Extend a recently developed non-linear source apportionment method for ozone to PM2.5 and apply it to various locations to provide long-term, daily source apportionments (SAs) across the United States.
  • Further develop the Area-of-Influence (AOI) analysis technique to quantitatively show the impacts of local vs. regionally generated PM2.5 by source types and provide information on how future emissions will impact PM2.5 in non-attainment areas.
  • Refine and apply inverse modeling to improve emissions and source apportionment determinations and identify how to use SA methods when profiles change spatially, seasonally and with controls.
  • Inter-compare results from source-apportionment methods (receptor and source-oriented approaches).
  • Identify strengths and limitations of the approaches, focusing on the reasons for disagreement and conditions under which the various approaches tend to agree and disagree most.
  • Quantify uncertainties involved in the application of the various source apportionment methods.
  • Assess the relative strengths of using Supersite data vs. routine monitoring data for source apportionment applications.
  • Provide source apportionment results to air quality managers and epidemiologic researchers.

Summary/Accomplishments (Outputs/Outcomes):

DDM-3D extended to PM2.5 analysis (DDM-3D/PM) provides sensitivity of PM2.5 species to specific pollutants (i.e, SO2, NOx, primary organic carbon [OC], elemental carbon [EC], volatile organic carbon [VOC], etc.) from specific sources, such as area, mobile, point sources. DDM-3D/PM is a resource effective way to estimate sources of primary and secondary PM2.5, and can be utilized to estimate receptor-oriented source impacts through AOI analysis. CMAQ-TR follows only primary PM2.5 emissions but with extended depth. CMAQ-TR is useful especially when areas of concern have many types of sources, such as urban and suburban areas.

Three types of chemical mass balance (CMB) models are commonly used [we refer to them as regular, CMB-MM (i.e., application of the CMB model using organic molecular markers as fitting species in addition to speciated PM2.5 and some trace metals, and CMB-LGO [i.e., CMB extended using speciated PM2.5, trace metals and gas species such as CO and SO2)], and source apportionment results from three CMB models, CMAQ-TR and CMAQ with brute force method were compared together. Those models agree reasonably well for major sources of PM2.5, including mobile sources, biomass burning, but difference in detail, and smaller sources may be totally missed by receptor models. Uncertainties inherent in each type of model are one of the major reasons of differences: uncertainties in source profiles that are used in CMB, biases in emissions inventory and meteorological fields in CMAQ and limitations of methods used (regression analysis in CMB, physicochemical processes in CMAQ). Different trace species (i.e., speciated PM2.5, trace metals and organic molecular markers) also causes disagreement among model results. Reducing uncertainties in each model by improving source profiles and developing more accurate emission estimates improves source impact analysis. Reconciling source impacts from different models can lead to improved source profiles and, in turn, better source contributions. Inverse modeling utilizing DDM-3D/PM can capture biases in emission estimates of the major sources, so they can be reduced.

The current air quality models often fail to simulate equivalent amount of OC as observed in monitoring sites and missing mechanism and/or missing precursors of secondary organic aerosol (SOA) is major reason for it. SOA module in CMAQ version 4.5 with SAPRC99 is updated as part of improving PM2.5 source apportionment by adding SOA from isoprene and sesquiterpene reactions and formation of aged aerosol.

Conclusions:

In this project, three different methods to apportion PM2.5 sources in CMAQ were developed and applied to the southeastern United States and the continental United States. Receptor based models utilized as well and compared with CMAQ results. Biases or errors in receptor based models and in CMAQ were reduced by updating source profiles, reducing biases in emission estimates or reconciling source impact results from both types of models. Secondary organic aerosol simulation in CMAQ was improved with increasing precursor species and parameters, and the approach to aged semi-volatile organic carbon (SVOC) simulation.


Journal Articles on this Report : 24 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 Ding X, Zheng M, Yu L, Zhang X, Weber RJ, Yan B, Russell AG, Edgerton ES, Wang X. Spatial and seasonal trends in biogenic secondary organic aerosol tracers and water-soluble organic carbon in the southeastern United States. Environmental Science & Technology 2008;42(14):5171-5176. R832159 (Final)
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  • Journal Article Habermacher FD, Napelenok SL, Akhtar F, Hu Y, Russell AG. Area of influence (AOI) development: fast generation of receptor-oriented sensitivity fields for use in regional air quality modeling. Environmental Science & Technology 2007;41(11):3997-4003. R832159 (Final)
    R830960 (Final)
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  • Journal Article Hu Y, Napelenok SL, Odman MT, Russell AG. Sensitivity of inverse estimation of 2004 elemental carbon emissions inventory in the United States to the choice of observational networks. Geophysical Research Letters 2009;36(15):L15806 (5 pp.) R832159 (Final)
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  • Journal Article Hu Y, Odman MT, Russell AG. Top-down analysis of the elemental carbon emissions inventory in the United States by inverse modeling using Community Multiscale Air Quality model with decoupled direct method (CMAQ-DDM). Journal of Geophysical Research 2009;114(D24):D24302 (12 pp.). R832159 (Final)
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  • Journal Article Kaynak B, Hu Y, Russell AG. Analysis of NO, NO2, and O3 between model simulations and ground-based, aircraft, and satellite observations. Water, Air, & Soil Pollution 2013;224(9):1-17. R832159 (Final)
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  • Journal Article Lee D, Balachandran S, Pachon J, Shankaran R, Lee S, Mulholland JA, Russell AG. Ensemble-trained PM2.5 source apportionment approach for health studies. Environmental Science & Technology 2009;43(18):7023-7031. R832159 (Final)
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  • Journal Article Lee S, Russell AG. Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results. Atmospheric Environment 2007;41(40):9616-9624. R832159 (Final)
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  • Journal Article Lee S, Russell AG, Baumann K. Source apportionment of fine particulate matter in the southeastern United States. Journal of the Air & Waste Management Association 2007;57(9):1123-1135. R832159 (Final)
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  • Journal Article Lee S, Kim HK, Yan B, Cobb CE, Hennigan C, Nichols S, Chamber M, Edgerton ES, Jansen JJ, Hu Y, Zheng M, Weber RJ, Russell AG. Diagnosis of aged prescribed burning plumes impacting an urban area. Environmental Science & Technology 2008;42(5):1438-1444. R832159 (Final)
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  • Journal Article Liao K-J, Tagaris E, Manomaiphiboon K, Napelenok SL, Woo J-H, He S, Amar P, Russell AG. Sensitivities of ozone and fine particulate matter formation to emissions under the impact of potential future climate change. Environmental Science & Technology 2007;41(24):8355-8361. R832159 (Final)
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  • Journal Article Liao K-J, Tagaris E, Napelenok SL, Manomaiphiboon K, Woo J-H, Amar P, He S, Russell AG. Current and future linked responses of ozone and PM2.5 to emission controls. Environmental Science & Technology 2008;42(13):4670-4675. R832159 (Final)
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  • 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 Marmur A, Mulholland JA, Russell AG. Optimized variable source-profile approach for source apportionment. Atmospheric Environment 2007;41(3):493-505. R832159 (2006)
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  • Journal Article Marmur A, Liu W, Wang Y, Russell AG, Edgerton ES. Evaluation of model simulated atmospheric constituents with observations in the factor projected space: CMAQ simulations of SEARCH measurements. Atmospheric Environment 2009;43(11):1839-1849. R832159 (Final)
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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 Napelenok SL, Habermacher FD, Akhtar F, Hu Y, Russell AG. Area of influence (AOI) sensitivity analysis: application to Atlanta, Georgia. Atmospheric Environment 2007;41(27):5605-5617. R832159 (2006)
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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 Sarnat JA, Marmur A, Klein M, Kim E, Russell AG, Sarnat SE, Mulholland JA, Hopke PK, Tolbert PE. Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environmental Health Perspectives 2008;116(4):459-466. R832159 (Final)
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  • Journal Article Sarnat SE, Klein M, Sarnat JA, Flanders WD, Waller LA, Mulholland JA, Russell AG, Tolbert PE. An examination of exposure measurement error from air pollutant spatial variability in time-series studies. Journal of Exposure Science and Environmental Epidemiology 2010;20(2):135-146. R832159 (Final)
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  • Journal Article Wade KS, Mulholland JA, Marmur A, Russell AG, Hartsell B, Edgerton E, Klein M, Waller L, Peel JL, Tolbert PE. Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia. Journal of the Air & Waste Management Association 2006;56(6):876-888. R832159 (2006)
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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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  • Journal Article Yan B, Zheng M, Hu Y, Ding X, Sullivan AP, Weber RJ, Baek J, Edgerton ES, Russell AG. Roadside, urban, and rural comparison of primary and secondary organic molecular markers in ambient PM2.5. Environmental Science & Technology 2009;43(12):4287-4293. R832159 (Final)
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  • Supplemental Keywords:

    PM2.5, source apportionment, CMAQ, sensitivity analysis, area of influence, uncertainty analysis, DDM-3D/PM, emissions inventory analysis, inverse modeling, chemical mass balance model,, 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

    Relevant Websites:

    http://www.ce.gatech.edu/~trussell/lamda/ Exit

    Progress and Final Reports:

    Original Abstract
  • 2005 Progress Report
  • 2006 Progress Report