Source Attribution of Atmospheric Particulate MatterEPA Grant Number: F07B10436
Title: Source Attribution of Atmospheric Particulate Matter
Investigators: Wagstrom, Kristina M.
Institution: Carnegie Mellon University
EPA Project Officer: Lee, Sonja
Project Period: September 1, 2007 through September 1, 2009
RFA: STAR Graduate Fellowships (2007) RFA Text | Recipients Lists
Research Category: Academic Fellowships , Fellowship - Atmospheric Sciences , Particulate Matter
In order for policy makers to effectively regulate emissions, there needs to be a strong understanding of the relationships between emissions and resulting air pollutant concentrations. Atmospheric pollutant modeling is one of the primary tools employed for this purpose. The objective of this work is to develop, evaluate, and apply modeling techniques that will improve of our understanding of the links between sources and ambient concentrations of air pollutants, focusing on particulate matter.
In order to study these links between sources and ambient pollutant concentrations, an algorithm capable of tracking the contributions of different sources types and source locations to particulate matter concentrations in a three-dimensional atmospheric chemical transport model has been developed. This algorithm will now be applied to study source type contributions to particulate matter concentrations, identify the area of influence of large source regions and study the impact of transport on pollutant concentrations throughout the Eastern United States. Next, modeled predicted particle size distributions will be compared with observed atmospheric size distributions and the algorithm results will be utilized to identify weaknesses in the emissions inputs and atmospheric model. Finally, a climate-sensitive emissions processing system will be developed in order to study both present and future climate scenarios and the changes in predicted particulate matter concentrations due to the potential changes in climate factors.
This work will increase the understanding of the impacts of emissions on particulate matter concentrations by looking at the influence that different sources, whether they are locations or types of sources, have on the particulate matter concentrations in surrounding areas. The evaluation of the predicted size distributions with measured data will allow us to better understand where improvements are needed in the inputs to the model, particularly emissions, and to the model itself. The addition of the climate-sensitive emission processor will help policy makers to better understand the possible impacts of a changing climate on atmospheric particulate matter concentrations.