Exploring the regional pollution characteristics and meteorological formation mechanism of PM2.5 in North China during 2013-2017

Mingge Li; Lili Wang; Jingda Liu; Wenkang Gao; Tao Song; Yang Sun; Liang Li; Xingru Li; Yonghong Wang; Lili Liu; Kaspar R Daellenbach; Pauli J Paasonen; Veli-Matti Kerminen; Markku Kulmala; Yuesi Wang

doi:10.1016/j.envint.2019.105283

Exploring the regional pollution characteristics and meteorological formation mechanism of PM_2.5 in North China during 2013-2017

Environ Int. 2020 Jan:134:105283. doi: 10.1016/j.envint.2019.105283. Epub 2019 Nov 16.

Authors

Mingge Li¹, Lili Wang², Jingda Liu³, Wenkang Gao⁴, Tao Song⁴, Yang Sun⁴, Liang Li⁵, Xingru Li⁶, Yonghong Wang⁷, Lili Liu⁸, Kaspar R Daellenbach⁷, Pauli J Paasonen⁷, Veli-Matti Kerminen⁷, Markku Kulmala⁹, Yuesi Wang¹⁰

Affiliations

¹ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
² State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland. Electronic address: wll@mail.iap.ac.cn.
³ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Department of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China.
⁴ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
⁵ China National Environmental Monitoring Center, Beijing 100012, China.
⁶ Department of Chemistry, Analytical and Testing Center, Capital Normal University, Beijing 100048, China.
⁷ Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland.
⁸ Tianjin Institute of Meteorological Science, Tianjin 300074, China.
⁹ Institute for Atmospheric and Earth System Research / Physics, Faculty of Science, University of Helsinki, Finland; Aerosol and Haze Laboratory, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
¹⁰ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China; Centre for Excellence in Atmospheric Urban Environment, Institute of Urban Environment, Chinese Academy of Science, Xiamen, Fujian 361021, China; University of the Chinese Academy of Sciences, Beijing 100049, China; Department of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China.

PMID: 31743806
DOI: 10.1016/j.envint.2019.105283

Abstract

In the last decade, North China (NC) has been one of the most populated and polluted regions in the world. The regional air pollution has had a serious impact on people's health; thus, all levels of government have implemented various pollution prevention measures since 2013. Based on multi-city in situ environmental and meteorological data, as well as the meteorological reanalysis dataset from 2013 to 2017, regional pollution characteristics and meteorological formation mechanisms were analyzed to provide a more comprehensive understanding of the evolution of PM_2.5 in NC. The domain-averaged PM_2.5 was 79 ± 17 µg m^-3 from 2013 to 2017, with a decreasing rate of 10 μg m^-3 yr^-1. Two automatic computer algorithms were established to identify 6 daily regional pollution types (DRPTs) and 48 persistent regional pollution events (PRPEs) over NC during 2014-2017. The average PM_2.5 concentration for the Large-Region-Pollution type (including the Large-Moderate-Region-Pollution and Large-Severe-Region-Pollution types) was 113 ± 40 µg m^-3, and more than half of Large-Region-Pollution days and PRPEs occurred in winter. The PRPEs in NC mainly developed from the area south of Hebei. The number of Large-Region-Pollution days decreased notably from 2014 to 2017, the annual number of days varying between 194 and 97 days, whereas a slight decline was observed in winter. In addition, the averaged PM_2.5 concentrations and the numbers and durations of the PRPEs decreased. Lamb-Jenkinson weather typing was used to reveal the impact of synoptic circulations on PM_2.5 across NC. Generally, the contributions of the variations in circulation to the reduction in PM_2.5 levels over NC between 2013 and 2017 were 64% and 45% in summer and winter, respectively. The three most highly polluted weather types were types C, S and E, with an average PM_2.5 concentration of 137 ± 40 µg m^-3 in winter. Furthermore, three typical circulation dynamics were categorized in the peak stage of the PRPEs, namely, the southerly airflow pattern, the northerly airflow pattern and anticyclone pattern; the averaged relative humidity, recirculation index, wind speed and boundary layer height were 63%, 0.33, 2.0 m s^-1 and 493 m, respectively. Our results imply that additional emission reduction measures should be implemented under unfavorable meteorological situations to attain ambient air quality standards in the future.

Keywords: Identification of regional pollution type; Meteorological formation mechanism; North China; PM(2.5); Regional pollution events.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants
Air Pollution*
China
Cities
Environmental Monitoring
Particulate Matter
Seasons

Substances

Air Pollutants
Particulate Matter