A Harvard-China Project Research Seminar with Meng Gao, Assistant Professor, Department of Geography, Hong Kong Baptist University; Alumnus (Postdoctoral Fellow) and Associate, Harvard-China Project
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Monitoring and modeling/predicting air pollution are crucial to understanding the links between emissions and air pollution levels, to supporting air quality management, and to reducing human exposure. Yet, current monitoring networks and modeling capabilities are unfortunately inadequate to understand the physical and chemical processes above ground, and to support attribution of sources. Vertical observations of atmospheric composition would be essential to reduce uncertainties, and to advance diagnostic understanding and prediction of air pollution. In this talk, three major issues of air quality research in China will be exemplified: (1) current observation networks provide only partial view of air pollution, and this can lead to misleading air quality management actions; (2) satellite retrievals of air pollutants are widely used in air pollution studies, such as health risk assessment, but too often users do not acknowledge that they have large uncertainties, which can be reduced with measurements of vertical profiles; (3) air quality modeling and forecasting require vertical observational constraints.
Meng Gao is an Assistant Professor in the Department of Geography, Hong Kong Baptist University; Alumnus (Postdoctoral Fellow) and Associate, Harvard-China Project. He earned a B.Sc degree in atmospheric physics from Nanjing University of Information Science and Technology and an M.Sc and Ph.D in chemical engineering from the University of Iowa. Dr. Gao Meng’s research focuses on air pollution in highly polluted regions (China and India) and its interactions with health and climate. He uses a coupled meteorology-chemistry model to investigate in detail the chemical and physical processes leading to severe particulate matter and ozone pollution in Asia. He has demonstrated that aerosol interactions with radiation and clouds contribute in important ways to intensification of aerosol enhancements. He has shown how the assimilation of PM2.5 in winter haze periods can improve model predictions and that these improved predictions can reduce significantly the uncertainties in estimates of health impacts and aerosol radiative forcing. He has also shown how ocean temperature in autumn can be used effectively to predict the severity of Indian winter haze, which can help guide pollution control planning at least a season in advance.
Sponsored by Harvard-China Project, Harvard Paulson School of Engineering and Applied Sciences.