The Use of Source Apportionment for Air Quality Management and Health Assessments

Abstract

Receptor modeling is the application of data analysis methods to elicit information on the sources of air pollutants. Typically, it employs methods of solving the mixture resolution problem using chemical composition data for airborne particulate matter (PM) samples. In such cases, the outcome is the identification of the pollution source types and estimates of the contribution of each source type to the observed concentrations. Receptor modeling also involves efforts to identify the locations of the sources through the use of local meteorology or ensembles of air parcel back trajectories. Compositional data were collected in a number of monitoring programs. The U.S. Environmental Protection Agency deployed a network of urban airborne PM samplers to provide PM_2.5 composition data for urban centers across the United States. In addition, advanced monitoring methods were deployed at “supersites.” These data show the differences in composition in different part of the country and were also used to identify and apportion the particle sources. These results were used to (1)develop effective and efficient air quality management plans and (2) refine emission inventories for input into deterministic models to predict changes in air quality as the result of the implementation of various management plans. The apportionments also serve as exposure estimates for health effects models to identify those components of the PM that are most closely related to observed adverse health effects. Although current regulations target total airborne mass concentrations, such health effects results might result in targeting those sources that are most likely linked to adverse health effects and thus produce the maximum health benefit.

The work presented here was supported in part by the Lake Michigan Air Directors Consortium under contract PO5780807 to Clarkson University, the U.S. Environmental Protection Agency through Science to Achieve Results (STAR) grant RD83107801, and the New York State Energy Research and Development Authority (NYSERDA) through contract 8650. Although the research described in this article has been funded wholly or in part by the U.S. EPA, it has not been subjected to the agency's required peer and policy review and therefore does not necessarily reflect the views of the agency and no official endorsement should be inferred. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model used in this publication.