Rapid assessment of drivers and air quality effects of regional daily changes in air pollutant emissions based on near-real-time techniques: A case in Jiangsu Province, China

Abstract. Fast and timely estimation of changing air pollutant emissions is critical for understanding the complex sources of air pollution and supporting air quality improvement, while current regional emission inventory was commonly reported with time lag or coarse temporal resolution. Here we developed a near-real-time approach that calculates the daily emissions of anthropogenic air pollutants, and applied this approach for Jiangsu province, a typical developed region in eastern China. We estimated that the annual total anthropogenic emissions of SO₂, NO_X, primary fine particles (PM_2.5), non-methane volatile organic compounds (NMVOCs), and NH₃ were 246, 727, 298, 1186, and 377 Gg, respectively, for Jiangsu in 2022. Compared to the national emission inventory, application of the provincial-level daily emission estimates provided better model performance of PM_2.5 and ozone (O₃) simulation for all the involved months. The NO_X, SO₂, PM_2.5, and NMVOCs emissions in Jiangsu during April–May 2022 (the period of COVID-19 lockdown in Shanghai) were respectively 8 %, 6 %, 6 %, and 10 % smaller than those in the same period of 2023. Transportation and Industry respectively contributed 89 % of NO_X emission reduction and 93 % NMVOCs reduction. Combining with machine learning algorithms, moreover, we revealed that the changing agricultural NH₃ emissions dominated the variability of daily PM_2.5 concentration, and that off-road transportation contributed substantially to variabilities of both PM_2.5 and O₃ levels. The study proved advantages of incorporation of near-real-time data and machine learning techniques on tracking the fast-changing emissions and detecting the sources of varying air quality.