Impact of weather and emission changes on NO2 concentrations in China during 2014-2019.

Nitrogen dioxide (NO2) is one of the most important air pollutants that highly affect the formation of secondary fine particles and tropospheric ozone. In this study based on hourly NO2 observations from June 2014 to May 2019 and a regional air quality model (WRF-CMAQ), we comprehensively analyzed the spatiotemporal variations of NO2 concentrations throughout China and in 12 urban agglomerations (UAs) and quantitatively showed the anthropogenic and meteorological factors controlling the interannual variations (IAVs). The ground observations and tropospheric columns show that high NO2 concentrations are predominantly concentrated in UAs such as Beijing-Tianjin-Hebei (BTH), the Shandong Peninsula (SP), the Central Plain (CP), Central Shaanxi (CS), and the Yangtze River Delta (YRD). For different UAs, the NO2 IAVs are different. The NO2 increased first and then decreased in 2016 or 2017 in BTH, YRD, CS, and Cheng-Yu, and decreased from 2014 to 2019 in Harbin-Changchun, CP, SP, Northern Slope of Tianshan Mountain, and Beibu-Gulf, while increased slightly in the Pearl River Delta (PRD) and Hohhot-Baotou-Erdos-Yulin (HBEY). The NO2 IAVs were primarily dominated by emission changes. The net wintertime decreases of NO2 in BTH, Yangtze River Middle-Reach, and PRD were mostly contributed by emission reductions from 2014 to 2018, and the significant increase in the wintertime in HBEY was also dominated by emission changes (93%). Weather conditions also have an important effect on the NO2 IAVS. In BTH and HBEY, the increases of NO2 in winter of 2016 are mainly attributed to the unfavorable weather conditions and for the significant decreases in the winter of 2017, the favorable weather conditions also play a very important role. This study provides a basic understanding on the current situation of NO2 pollution and are helpful for policymakers as well as those interested in the study of tropospheric ozone changes in China and downwind areas.