Particulate Matter Prediction and Source Attribution for U.S. Air Quality Management in a Changing World

Donald J. Wuebbles, University of Illinois at Urbana-Champaign

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Xin-Zhong Liang, University of Maryland


There is increasing demand of credible information on local climate changes for decision makers to assess risks and to identify risk management strategies. In response to the pressing needs for such information, the Universities of Illinois and Maryland have established a program to develop an integrated Climate and Air Quality Modeling System, for which Dr. Liang is the lead developer. The objectives of this study are to better understand how global changes in climate and emissions will affect the U.S. pollution, focusing on particulate matter and ozone, project their future trends, quantify key source attributions, and thus provide actionable information for U.S. environmental planners and decision makers to design effective dynamic management strategies, including local controls, domestic regulations and international policies, to sustain air quality improvements in a changing world. We will apply a state-of-the-science dynamic prediction system that couples global climate-chemical transport models with regional climate-air quality models over North America to determine the individual and combined impacts of global climate and emissions changes on U.S. air quality from the present to 2050 under multiple scenarios. The integration of the most advanced modeling system, most updated emissions treatment, multi-scale processes representation, and multi climate-emission scenarios assessment will improve the predictive capability and result in more reliable projection of future changes in particular matter, ozone and related pollutants as well as their global and regional sources. As the core component of our dynamic prediction system, the regional climate-air quality models, CWRF and CMAQ, needs continuous development to coupling them. The coupling and testing of CWRF and CMAQ are physically comprehensive and computationally intensive.


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