Atmospheric Emissions

Emission inventories are a fundamental input into air quality modeling and analyses. The Harvard-China Project recruited postdocs from Tsinghua University to develop comprehensive bottom-up emission inventories for its interdisciplinary initiatives and to apply them in analyses of emission trends, uncertainties, and control policies. It now collaborates with some of these research alumni back in China, led by ZHAO Yu and WANG Haikun of the Nanjing University School of Environment, to improve and apply emission inventories from urban to national scales for a variety of research objectives.

Click "More Publications" below to see a full list of publications supported by the Harvard-China Project in this research area. 
 
Without accurate emission profiles of relevant chemical species located in a sufficiently fine spatial distribution, it is impossible to simulate atmospheric concentrations and fluxes accurately. This is an essential ingredient to conduct simulations that can be confirmed by observations and used for analysis of pollution control options. In general, the emissions of each species by sector and region are calculated based on activity levels (energy consumption or industrial production level), unabated emission factors, and the removal efficiencies of applicable emission control technologies. The unabated emission factors are expressed as the mass of emitted pollutant per unit fuel combusted, or per unit industrial production, prior to any emission control.
 
Also contributing to emissions research as recent visiting scholars at the Project have been WANG Shuxiao (Tsinghua University) and YANG Qing (Huazhong University of Science and Technology). The emissions research, complemented by other Project research on atmospheric transport and chemistry and atmospheric field observation, also serve as a central element of China Project’s integrated cost-and-benefit assessments of emission control and energy policies in China.

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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).

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