Faculty of Science and Technology

Abstract: The Community Multiscale Air Quality (CMAQ) model has evolved as a comprehensive Eulerian air pollution modeling system over the past decade. CMAQ is designed as a multi-scale, multi-pollutant system, incorporating the representation of current scientific knowledge of the interactions among atmospheric dynamical, physical and chemical processes on spatial scales ranging from urban to hemispheric and temporal scales varying from seconds to annual cycles, within a single model. The ability to simulate the distributions and trends of a variety of pollutants (including ozone, particulate matter, airborne toxic pollutants, and acid/nutrient deposition) facilitates the use of the modeling system for a variety of research and regulatory (e.g., in designing multi-pollutant control strategies) applications. The first version of the modeling system was publicly released in 2000. Since then newer versions of the modeling system incorporating improvements in scientific algorithms as well as extending the capability of the modeling system to address emerging air pollution related environmental issues have been developed and made available on a 2-3 year development cycle. This talk will briefly review the motivation for development of the CMAQ system, its formulation that lends itself to modularity and extensibility, trace its scientific evolution through discussion of model applications to address various air pollution issues, and outline plans for model development over the next 3-5 years.

Dr. Rohit Mathur is currently the Associate Director for Science of the Atmospheric Modeling Division in the National Exposure Research Laboratory, U.S. Environmental Protection Agency. His research deals with the development of methods to represent the physical and chemical behavior of atmospheric pollutants in comprehensive modeling frameworks. Through a multidisciplinary approach involving physical, numerical and computational modeling, his work has focused on continually enhancing the science in air quality models through exploring the development of novel new modeling methodologies.

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