Evaluation of Regional Scale Receptor Modeling

EPA Grant Number: R832156
Title: Evaluation of Regional Scale Receptor Modeling
Investigators: Lowenthal, Douglas H. , Chen, Lung-Wen Antony , Koracin, Darko , Watson, John L.
Institution: Desert Research Institute
EPA Project Officer: Chung, Serena
Project Period: January 1, 2005 through December 31, 2007 (Extended to December 31, 2009)
Project Amount: $436,687
RFA: Source Apportionment of Particulate Matter (2004) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air

Objective:

Assess and provide guidance for using multivariate and trajectory-based receptor models for regional source apportionment with the following tasks: 1) document model evolution, theoretical foundations, data requirements, and operational assumptions; 2) critically review previous model applications, including interpretation and evaluation of the results; 3) apply receptor models to data simulated with an air quality model for two eastern IMPROVE sites; 4) determine and document the assumptions and operational procedures necessary to reproduce the known regional contributions to sulfate and PM; 5) perform a “blind” test on a second simulated data set using guidance from (4); 6) apply models to IMPROVE data at these sites to determine regional contributions to particulate matter; and 7) finalize guidance for systematic application and validation of these models in future regional-scale applications.

Approach:

Conduct a comprehensive literature review of the historical development and prior applications of the Absolute Principal Component Scores, Positive Matrix Factorization, UNMIX, residence time analysis, and back and forward Trajectory Mass Balance Regression receptor models. For each model, define data requirements, operating assumptions, and alleged improvements over previous approaches. Critically evaluate prior results with respect to methods, assumptions, and validation. Assess the degree to which these receptor models have been able to infer source contributions in the absence of source emissions data. Evaluate the models by applying them to simulated aerosol concentration data at Brigantine National Wildlife Refuge, NJ and Great Smoky Mountains National Park, TN generated with a detailed air quality model (CMAQ) from National Emissions Inventory data, category-specific source profiles and meteorology for ambient scenarios simulated with MM5. Establish guidance for reproducing known regional source contributions. Evaluate this guidance using a second synthetic data set in a blind trial. Apply the receptor models to existing ambient IMPROVE data from these sites and compare source apportionment results with those obtained from synthetic data.

Expected Results:

This work will assess the ability of multivariate and trajectory-based receptor models to produce accurate and precise regional-scale source apportionment. It will provide systematic guidance for future application and evaluation of these models for regional-scale air quality problems.

Publications and Presentations:

Publications have been submitted on this project: View all 10 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 6 journal articles for this project

Supplemental Keywords:

multivariate receptor modeling, air pollution, regional haze, IMPROVE network, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Atmospheric Sciences, Environmental Engineering, atmospheric dispersion models, atmospheric measurements, model-based analysis, area of influence analysis, source receptor based methods, chemical characteristics, environmental measurement, source apportionment, emissions monitoring, air quality models, airborne particulate matter, air sampling, air quality model, analytical chemistry, particulate matter mass, modeling studies, aerosol analyzers, atmospheric chemistry, chemical speciation sampling, real-time monitoring

Progress and Final Reports:

  • 2005 Progress Report
  • 2006 Progress Report
  • 2007 Progress Report
  • 2008 Progress Report
  • Final Report