Atmospheric Chemistry and Transport Modeling

A primary tool used in our research on atmospheric chemistry is the GEOS-Chem chemical transport model, and a nested-grid version of the model focused on East Asia developed by WANG Yuxuan as a student affiliate of the Harvard-China Project (now at Tsinghua University and University of Houston) with Project Chair Michael B. McELROY.

Validated by agreement of modeled concentrations with measurements made by ground stations, aircraft, and satellites, nested-grid models can differentiate air transport mechanisms for regions of China on a finer scale than previously possible. They capture the effects of sources far outside of the target domain and also seasonally changing meteorology—such as cold fronts in winter and monsoonal patterns in summer—on regional air quality. 
 
Using this model and other analytical approaches, researchers have studied the transport and secondary chemistry of diverse air pollutants and the effectiveness of control policies on concentrations. Applied in inverse mode—in which atmospheric concentrations observed by ground stations, aircraft, and satellites are use to derive optimized emissions—such models also provide independent checks on bottom-up emission inventories of nitrogen oxides (NOX), carbon dioxide (CO2), carbon monoxide (CO), and other pollutants and greenhouse gases.
 
The China Project's atmospheric research is committed to building observationally validated, fundamental research on the physical and chemical dimensions of China’s atmospheric environment, from urban to global scales. In addition to the model-based research described here, it includes field observations described here and bottom-up emissions research described here. It is also a core component of a Project-wide interdisciplinary framework now being applied to evaluation of national GHG and pollution control policies, described here.

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Acknowledgment: Some of the papers cited here are based on work supported by the National Science Foundation under Grants No. ATM-1019134 or ATM-0635548 (indicated by acknowledgments in the papers). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation (NSF).