Improvements in Emissions Inventories using Semi-Continuous Monitoring Data and Concentrations Field AnalysisEPA Grant Number: R834557
Title: Improvements in Emissions Inventories using Semi-Continuous Monitoring Data and Concentrations Field Analysis
Investigators: Schauer, James J. , Turner, Jay R. , deFoy, Benjamin
Institution: University of Wisconsin - Madison , Saint Louis University - Main Campus , Washington University
EPA Project Officer: Chung, Serena
Project Period: June 1, 2010 through May 30, 2013 (Extended to May 30, 2014)
Project Amount: $499,777
RFA: Novel Approaches to Improving Air Pollution Emissions Information (2009) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air
This project will focus on a three city study using yearlong datasets from St. Louis, Milwaukee, and Los Angeles. Emissions inventory data will be evaluated and improved for fine particle elemental carbon, ultrafine particle number concentrations and fine particle organic carbon using data from the EPA funded St. Louis Supersite; speciated mercury compounds in the Milwaukee region using data from an EPA STAR Project; and fine particle carbonaceous particulate matter and associated precursor gases in Los Angeles. Concentration Field Analysis (CFA) will be used to map emissions sources and identify unknown or poorly identified source regions using stochastic backward and forward particle trajectories with a temporal resolution finer than one hour and spatial resolution finer than 5 km during the yearlong study periods. The integration of high quality monitoring data with multiple 3D modeling approaches will assess existing emissions inventories and improve the understanding and representation of the temporal distribution of emissions, spatial distributions of emissions, missing sources, and inaccurate emissions estimates for point sources, mobile sources and area sources.
The goal of this project is to couple high-resolution meteorological modeling with existing high time resolution atmospheric pollutant data sets to assess and improve emissions inventories.
CFA will be used to identify probabilistic source regions from the measurements independent of the emissions inventory data. Forward Lagrangian modeling will then be used to evaluate individual transport events. Cluster analysis will link yearlong trends with the hour-long episodes to assess the statistical relevance of the conclusions. Uncertainties due to the simulation of vertical dispersion will be constrained by comparing measurements and particle transport with forward Eulerian models. This combined modeling approach was demonstrated by members of the project team as part of the NSF funded MILAGRO Study in Mexico City. The methods were evaluated with CO and SO2 measurements and were applied to speciated mercury data, ATOFMS data and AMS data as well as biomass burning impacts, urban outflow estimates and large point source emissions.
The project will demonstrate the integration of CFA with multiple modeling approaches to develop a new strategy to assess, diagnose, and improve emissions inventories. Direct improvements will be obtained for important atmospheric pollutants that historically have had poor emissions data including fine particle primary and secondary organic carbon, fine particle elemental carbon, ultra fine particle number and speciated atmospheric mercury. The close link between meteorological simulations, particle trajectories, grid modeling and existing data analysis will enable cross-evaluation of the conclusions. Yearlong analyses will improve the statistical relevance of the results. In addition, integration of measurements with meteorological analyses will help improve understanding of complex atmospheric transport such as lake effects, basin flows and urban heat islands.