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Assessing Local Emission for Air Pollution via Data Experiments
  • Song Xi Chen,
  • Yuru Zhu,
  • Yinshuang Liang
Song Xi Chen
Peking University

Corresponding Author:csx@gsm.pku.edu.cn

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Yuru Zhu
Peking University
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Yinshuang Liang
Zhengzhou University of Technology
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Although air pollution is largely due to anthropogenic emission, the observed pollution levels in a city are confounded by meteorological conditions and regional transportation of pollutants. However, effective air quality management requires measures for local emissions of the city. With a data selection algorithm, we choose calm episodes after strong cleaning processes to measure the growth of three air pollutants (PM2.5, NO2 and SO2) before the arrival of transported pollution. Panel data regression models are used to analyze the episode data from the quasi-experiments to quantify the local emission in three North China cities from March 2013 to February 2019. The study reveals a significant reduction in the average hourly growth rates for PM2.5 and SO2 in 2017-2018 as compared to the levels in 2013 in almost all seasons and cities. However, the local emission with respect to NO2 was little changed for almost all seasons and cities. The study also finds the winter growth rates of PM2.5 in Beijing were comparable to those in the heavy industrialized Tangshan and Baoding, even the PM2.5 hourly growth rates for winter 2018 in Beijing were higher than those in Tangshan and Baoding, revealing Beijing’s substantial emission.