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Use of a Process Analysis Tool for Diagnostic Study on Fine Particulate Matter Predictions in the U.S.-Part II: Analysis and Sensitivity Simulations
Citation:
Liu, P., Y. ZHANG, S. YU, AND K. L. SCHERE. Use of a Process Analysis Tool for Diagnostic Study on Fine Particulate Matter Predictions in the U.S.-Part II: Analysis and Sensitivity Simulations. Atmospheric Pollution Research. Turkish National Committee for Air Pollution Research and Control, Izmir, Turkey, 2(1):61-71, (2011).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA′s mission to protect human health and the environment. AMAD′s research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the Nation′s air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
Following the Part I paper that described an application of the U.S. EPA Models-3/Community Multiscale Air Quality (CMAQ) modeling system to the 1999 Southern Oxidants Study episode, this paper presents results from process analysis (PA) using the PA tool embedded in CMAQ and subsequent sensitivity simulations to estimate the impacts of major model uncertainties identified through PA. Aerosol processes and emissions are the most important production processes for PM2.5 and its secondary components, while horizontal and vertical transport and dry deposition contribute to their removal. Cloud processes can contribute the production of PM2.5 and SO42- and the removal of NO3- and NH4+. The model biases between observed and simulated concentrations of PM2.5 and its secondary inorganic components are found to correlate with aerosol processes and dry deposition at all sites from all networks and sometimes with emissions and cloud processes at some sites. Guided with PA results, specific uncertainties examined include the dry deposition of PM2.5 species and its precursors, the emissions of PM2.5 precursors, the cloud processes of SO42-, and the gas-phase oxidation of SO2. Adjusting the most influential processes/factors (i.e., emissions of NH3 and SO2, dry deposition velocity of HNO3, and gas-phase oxidation of SO2 by OH) is found to improve the model overall performance in terms of SO42-, NO3-, and NH4+ predictions.
URLs/Downloads:
Atmospheric Pollution Research
Use of a Process Analysis Tool for Diagnostic Study on Fine Particulate Matter Predictions in the U.S.-Part II: Process Analysis and Sensitivity Simulations (PDF, NA pp, 1157 KB, about PDF)