Use of a process analysis tool for diagnostic study on fine particulate matter predictions in the U.S.–Part II: Analyses and sensitivity simulations

Developmental Biology

Yang Zhang³  Ping Liu¹  Kenneth L. Schere²  Shaocai Yu² 

[1] School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, China$$Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695$$;Atmospheric Modeling and Analysis Division, the U.S. Environmental Protection Agency, Research Triangle Park, NC$$;Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695$$

关键词: Fine Particulate Matter;    CMAQ;    Process Analysis;    Sensitivity Study;    1999 SOS;   

DOI  :  10.5094/APR.2011.008

学科分类：农业科学（综合）

来源: Dokuz Eylul Universitesi * Department of Environmental Engineering

【 摘 要 】

Following the Part I paper that describes 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 PM_2.5 and its secondary components, while horizontal and vertical transport and dry deposition contribute to their removal. Cloud processes can contribute the production of PM_2.5 and SO₄^2– and the removal of NO₃^– and NH₄⁺. The model biases between observed and simulated concentrations of PM_2.5 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 PM_2.5 species and its precursors, the emissions of PM_2.5 precursors, the cloud processes of SO₄^2–, 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 SO₄^2–, NO₃^–, and NH₄⁺ predictions.

【 授权许可】

Unknown   

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