Responses of future air quality to emission controls over North Carolina,Part I: Model evaluation for current-year simulations |
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| Authors: | Xiao-Huan Liu Yang Zhang Kristen M Olsen Wen-Xing Wang Bebhinn A Do George M Bridgers |
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| Institution: | 1. Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA;2. Environment Research Institute, Shandong University, 27 Shanda Nanlu, Jinan, Shandong Province, 250100, PR China;3. Division of Air Quality, North Carolina Department of Environment and Natural Resources, Raleigh, NC 27699, USA;1. Air Quality Forecasting Lab, North Carolina State University, Raleigh, NC 27695, USA;2. Barons Advanced Meteorological Systems, Raleigh, NC, USA;1. SAS Institute Inc., Cary, NC, USA;2. North Carolina State University, Raleigh, NC, USA;1. Space Science Institute, Boulder, CO 80301, USA;2. Jet Propulsion Laboratory/Caltech, Pasadena, CA 91109, USA;1. Department of Geological Sciences, Center for Integrated Earth System Science, The John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, 78712, USA;2. Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia;1. Institute of Meteorology, Free University Berlin, Carl-Heinrich-Becker-Weg 6-10, 12165 Berlin, Germany;2. University of Cologne, Institute for Geophysics and Meteorology, Pohligstraße 3, 50969, Cologne, Germany |
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| Abstract: | The prediction of future air quality and its responses to emission control strategies at national and state levels requires a reliable model that can replicate atmospheric observations. In this work, the Mesoscale Model (MM5) and the Community Multiscale Air Quality Modeling (CMAQ) system are applied at a 4-km horizontal grid resolution for four one-month periods, i.e., January, June, July, and August in 2002 to evaluate model performance and compare with that at 12-km. The evaluation shows skills of MM5/CMAQ that are overall consistent with current model performance. The large cold bias in temperature at 1.5 m is likely due to too cold soil initial temperatures and inappropriate snow treatments. The large overprediction in precipitation in July is due likely to too frequent afternoon convective rainfall and/or an overestimation in the rainfall intensity. The normalized mean biases and errors are ?1.6% to 9.1% and 15.3–18.5% in January and ?18.7% to ?5.7% and 13.9–20.6% in July for max 1-h and 8-h O3 mixing ratios, respectively, and those for 24-h average PM2.5 concentrations are 8.3–25.9% and 27.6–38.5% in January and ?57.8% to ?45.4% and 46.1–59.3% in July. The large underprediction in PM2.5 in summer is attributed mainly to overpredicted precipitation, inaccurate emissions, incomplete treatments for secondary organic aerosols, and model difficulties in resolving complex meteorology and geography. While O3 prediction shows relatively less sensitivity to horizontal grid resolutions, PM2.5 and its secondary components, visibility indices, and dry and wet deposition show a moderate to high sensitivity. These results have important implications for the regulatory applications of MM5/CMAQ for future air quality attainment. |
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