Assessing local emission for air pollution via data experiments

Abstract

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 in three North China cities. 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 significant reductions in the average hourly growth rates from 5.9 to 11.1 μg/m3 to 2.9–4.5 μg/m3 for PM2.5, 2.2–8.9 μg/m3 to 0.4–2.5 μg/m3 for SO2 from 2013 to 2018, respectively, mounting to 44–70% and 57–82% reductions in the two pollutants in the three cities. However, the hourly growth rate for NO2 was less changed with the annual decrease ranging from −9.4% to 27.9% over the 2013 level in 2018. The study also finds the growth rates of PM2.5 and NO2 in Beijing were comparable to those in the heavy industrialized Tangshan and Baoding, revealing Beijing's substantial emission despite its very low profile on SO2.