You are here:
SOURCE APPORTIONMENT RESULTS, UNCERTAINTIES, AND MODELING TOOLS
Citation:
DUVALL, R. M., G. A. NORRIS, AND S. R. MCDOW. SOURCE APPORTIONMENT RESULTS, UNCERTAINTIES, AND MODELING TOOLS. Presented at Krakow Integrated Project, Krakow, POLAND, May 15 - 16, 2006.
Impact/Purpose:
The goal of this task is to develop methods and models to reduce the uncertainty in quantifying local and regional air pollutant source impacts on ambient samples collected in speciated PM, air toxic, and semi-continuous measurement networks. A combination of high resolution sampling, organic and inorganic analytical methods, and models will be developed and evaluated to reduce the uncertainty in source apportionment:
(1) semi-continuous inorganic species sampling
(2) inorganic analysis
(3) organic analysis for medium flow samples
(4) multivariate receptor models for ambient samples
(5) regional and local models
In addition, this task contributes to two additional tasks that have research focused on reducing the uncertainty in source apportionment: Identify Sources of Human Exposure (21176), and NAAQS implementation (21179).
Description:
Advanced multivariate receptor modeling tools are available from the U.S. Environmental Protection Agency (EPA) that use only speciated sample data to identify and quantify sources of air pollution. EPA has developed both EPA Unmix and EPA Positive Matrix Factorization (PMF) and the programs are available to the public as easy to use stand-alone programs. These programs use two different approaches to separate out sources with Unmix using only the species concentration data, while PMF requires a combination of species concentration and uncertainty data. This work presents the following topics: (1) review of the EPA PMF 1.1 and EPA Unmix 5.0 multivariate receptor models, (2) application of these models to PM10 data collected in Krakow, Poland, (3) challenges in U.S. source apportionment, and (4) efforts to reduce uncertainty in source apportionment.