You are here:
Mitigation of severe urban haze pollution by a precision air pollution control approach (2018 CMAS)
Citation:
Yu, S., P. Li, L. Wang, Y. Wu, S. Wang, K. Liu, T. Zhu, Y. Zhang, M. Hu, L. Zeng, X. Zhang, J. Cao, Kiran Alapaty, David-C Wong, Jon Pleim, R. Mathur, D. Rosenfeld, AND J. Seinfeld. Mitigation of severe urban haze pollution by a precision air pollution control approach (2018 CMAS). 2018 CMAS Conference, Chapel Hill, NC, October 22 - 24, 2018.
Impact/Purpose:
A Precision Air Pollution Control Approach (PAPCA) that takes advantage of the predictive power of comprehensive atmospheric chemical transport models is developed to pinpoint the origins of emissions leading to heavy haze events and to optimize emission controls offering effectiveness, practicality, and economic efficiency for significantly mitigating impending severe urban haze pollution in many Chinese metropolitan areas.
Description:
China’s unprecedented urbanization has been accompanied by an increase in the level of air pollution, establishing a long-term control strategy for curbing severe urban haze on a regular basis is urgent. In this work, we propose a new air pollution control strategy, to be implemented when impending meteorological conditions portend a pollution episode. Using a hybrid trajectory-receptor model in conjunction with a state-of-the-art 3-D atmospheric chemical transport model and high PM2.5 concentrations, those emission areas that are predicted to most heavily influence air quality levels in the major urban area are identified. We term this a Precision Air Pollution Control Approach (PAPCA), in that the strategy takes advantage of the predictive power of comprehensive atmospheric chemical transport models, offering effectiveness, practicality, and economic efficiency for significantly mitigating impending severe urban haze pollution. To the best of our knowledge, this paper is first to combine all three components (high PM2.5 concentrations, a hybrid trajectory-receptor model and a comprehensive 3-D air quality model) together to pinpoint the origins of emissions leading to heavy haze events and to optimize emission controls.
