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Published May 25, 2018 | Published + Supplemental Material
Journal Article Open

Mitigation of severe urban haze pollution by a precision air pollution control approach


Severe and persistent haze pollution involving fine particulate matter (PM_(2.5)) concentrations reaching unprecedentedly high levels across many cities in China poses a serious threat to human health. Although mandatory temporary cessation of most urban and surrounding emission sources is an effective, but costly, short-term measure to abate air pollution, development of long-term crisis response measures remains a challenge, especially for curbing severe urban haze events on a regular basis. Here we introduce and evaluate a novel precision air pollution control approach (PAPCA) to mitigate severe urban haze events. The approach involves combining predictions of high PM_(2.5) concentrations, with a hybrid trajectory-receptor model and a comprehensive 3-D atmospheric model, to pinpoint the origins of emissions leading to such events and to optimize emission controls. Results of the PAPCA application to five severe haze episodes in major urban areas in China suggest that this strategy has the potential to significantly mitigate severe urban haze by decreasing PM_(2.5) peak concentrations by more than 60% from above 300 μg m^(−3) to below 100 μg m^(−3), while requiring ~30% to 70% less emission controls as compared to complete emission reductions. The PAPCA strategy has the potential to tackle effectively severe urban haze pollution events with economic efficiency.

Additional Information

© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received: 10 April 2018; Accepted: 08 May 2018; Published: 25 May 2018. Shaocai Yu, Pengfei Li and Liqiang Wang contributed equally to this work. This work was partially supported by the Department of Science and Technology of China (No. 2016YFC0202702; No. 2014BAC22B06; No. 2014BAC21B01) and National Natural Science Foundation of China (No. 21577126). This work was also supported by the Joint NSFC-ISF Research Program (No. 41561144004), jointly funded by the National Natural Science Foundation of China and the Israel Science Foundation. Part of this work was also supported by the "Zhejiang 1000 Talent Plan" and Research Center for Air Pollution and Health in Zhejiang University. The views expressed in this paper are those of the author(s) and do not necessarily represent those of the U.S. EPA. Author Contributions: S.Y., K.A. and J.H.S. initiated the project and designed the experiments; S.Y., P.L., L.W., Y.W. and J.H.S. wrote the main manuscript. S.Y., P.L., L.W., Y.W., S.W., K.L., T.Z., Y.Z., M.H., L.Z., X.Z., J.C., K.A., D.W., J.P., R.M., D.R. and J.H.S. contributed to the interpretation and to manuscript preparation. The authors declare no competing interests.

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Published - s41598-018-26344-1.pdf

Supplemental Material - 41598_2018_26344_MOESM1_ESM.pdf


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