Improving Particulate Matter Source Apportionment for Health Studies: A Trained Receptor Modeling Approach with Sensitivity, Uncertainty and Spatial AnalysesEPA Grant Number: R833866
Title: Improving Particulate Matter Source Apportionment for Health Studies: A Trained Receptor Modeling Approach with Sensitivity, Uncertainty and Spatial Analyses
Investigators: Russell, Armistead G. , Sarnat, Stefanie Ebelt , Marmur, Amit , Mulholland, James , Sarnat, Jeremy , Klein, Mitchel , Tolbert, Paige
Institution: Georgia Institute of Technology , Emory University
EPA Project Officer: Chung, Serena
Project Period: December 1, 2008 through November 30, 2012 (Extended to November 30, 2013)
Project Amount: $899,956
RFA: Innovative Approaches to Particulate Matter Health, Composition, and Source Questions (2007) RFA Text | Recipients Lists
Research Category: Health Effects , Particulate Matter , Air
A flexible and extensible approach to conducting particulate matter (PM) source apportionment (SA) analyses for health investigations and air quality management is proposed for development. The proposed work takes advantage of the unique air quality and health data available in the study regions (Atlanta and St. Louis) and the extensive SA and epidemiologic analyses already conducted and underway. Several limitations and fundamental problems associated with various SA approaches have been identified in previous work by this research team. Here, the goal is to develop a SA modeling approach that can be generally applied for epidemiologic assessment and advanced air quality management, and that will provide quantitative estimates of sensitivities and uncertainties propagated from SA inputs to health association assessments. The method utilizes generally available techniques, and would be readily applied by other groups and agencies.
A “trained” receptor-based SA approach will be developed using ensemble results from a variety of methods. A reference SA time series of source impacts will be compiled from application of multiple methods over a one-year time period, and then used to optimize a chemical mass balance SA approach. Characterization of temporal source variability will be assessed to develop a filter to retain the original temporal characteristics. Uncertainties and sensitivities will be quantified in the processing. The procedure will then be applied to a long term (10-yr) daily data set from Atlanta, as well as to data sets from an Atlanta STN monitor to demonstrate applicability to widely available data, a rural monitor to assess urban-to-rural differences, and to St. Louis data to demonstrate applicability to other cities. Output will be used in time-series epidemiologic analyses of acute health effects in both Atlanta and St. Louis.
An approach for conducting PM source apportionment will be developed, tested, and applied that directly addresses limitations in current SA methods, in particular variability, biases, and intensive resource requirements. Uncertainties in SA results and sensitivities to SA inputs will be used to assess uncertainties and sensitivities in epidemiologic results. Epidemiologic analyses using the improved approach over an extended period will have increased power and resolution over past studies. This research will provide a more stable and accurate SA methodology for use by other air quality and health researchers as well as environmental policy makers. Further, the development of a year-long, national-level, emissions model-based, daily SA, and an ensemble SA will also be useful to researchers and planners.