Source apportionment of particulate matter based on numerical simulation during a severe pollution period in Tangshan, North China

Jianjun He; Lei Zhang; Zhanyu Yao; Huizheng Che; Sunling Gong; Min Wang; Mengxue Zhao; Boyu Jing

doi:10.1016/j.envpol.2020.115133

Source apportionment of particulate matter based on numerical simulation during a severe pollution period in Tangshan, North China

Environ Pollut. 2020 Nov;266(Pt 3):115133. doi: 10.1016/j.envpol.2020.115133. Epub 2020 Jul 7.

Authors

Jianjun He¹, Lei Zhang², Zhanyu Yao³, Huizheng Che², Sunling Gong², Min Wang⁴, Mengxue Zhao⁴, Boyu Jing⁵

Affiliations

¹ State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin, 300071, PR China. Electronic address: hejianjun@cma.gov.cn.
² State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing, 100081, PR China.
³ Key Laboratory of Cloud Physics of CMA & State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, PR China.
⁴ Policy Research Center for Environment and Economy, Ministry of Ecology and Environment of the People's Republic of China, Beijing, 100029, PR China.
⁵ State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin, 300071, PR China.

PMID: 32693305
DOI: 10.1016/j.envpol.2020.115133

Abstract

Facing serious air pollution problems, the Chinese government has taken numerous measures to prevent and control air pollution. Understanding the sources of pollutants is crucial to the prevention of air pollution. Using numerical simulation method, this study analysed the contributions of the total local emissions and local emissions from different sectors (such as industrial, traffic, resident, agricultural, and power plant emissions) to PM_2.5 concentration, backward trajectory, and potential source regions in Tangshan, a typical heavy industrial city in north China. The impact of multi-scale meteorological conditions on source apportionment was investigated. From October 2016 to March 2017, total local emissions accounted for 46.0% of the near-surface PM_2.5 concentration. In terms of emissions from different sectors, local industrial emissions which accounted for 23.1% of the near-surface PM_2.5 concentration in Tangshan, were the most important pollutant source. Agricultural emissions were the second most important source, accounting for 10.3% of the near-surface PM_2.5 concentration. The contributions of emissions from power plants, traffic, residential sources were 2.0%, 3.0%, and 7.2%, respectively. The contributions of total local emissions and emissions from different sectors depended on multi-scale meteorological conditions, and static weather significantly enhanced the contribution of regional transport to the near-surface PM_2.5 concentration. Eight cluster backward trajectories were identified for Tangshan. The PM_2.5 concentration for the 8 cluster trajectories significantly differed. The near-surface PM_2.5 in urban Tangshan (receptor point) was mainly from the local emissions, and another important potential source region was Tianjin. The results of the source apportionment suggested the importance of joint prevention and control of air pollution in some areas where cities or industrial regions are densely distributed.

Keywords: FLEXPART; Potential source region; Source apportionment; WRF-Chem.

MeSH terms

Air Pollutants / analysis*
Air Pollution / analysis*
China
Cities
Environmental Monitoring
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter