Xue, WH; Zhang, J; Zhong, C; Ji, DY; Huang, W (2020). Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China. SCIENCE OF THE TOTAL ENVIRONMENT, 712, 134577.

The PM2.5 concentration is an important evaluation index for the global Sustainable Development Goals (SDGs) for its negative impacts on human health. Last decade, several fine particulate pollution episodes occurred in the vast area of China. In response to this, the Chinese government has stepped up efforts to tackle air pollution. In this paper, the temporal trends of PM2.5 and the quantitative potential impact of environmental governance on PM2.5 are analyzed for China. Due to the lack of historical records, a two-stage model was used to estimate the historical PM2.5 concentrations, combined with the newly released satellite-based aerosol optical depth (AOD) product (MODIS Collection 6.1) and other data. The estimated PM2.5 concentrations showed strong consistency with the surface observations. Furthermore, significant seasonal variations existed in the PM2.5 concentrations and the temporal trends were captured, especially in city clusters. Then eight major city clusters were selected as typical samples. All the city clusters showed decrease trends in recent years, with PM2.5 concentrations in these regions decreased by 0.269-1.604 mu g m 3 year 1. From 2006 to 2017, the annual PM2.5 concentrations decreased by 7.83%-26.35% in the major city clusters among China. Technological innovation and environmental governance play an important role in the decrease of PM2.5. In order to quantify the influence of governance, environmental regulation intensity and synergy were applied as the indicators of the internal governance and co-governance in each city cluster. In most city clusters, PM2.5 concentrations were significantly negatively correlated with regional internal governance and co-governance (R = -0.596 to-0.930, p < 0.05), and the effect on PM2.5 lasted for several years. However, 1- to 2-year lagged effect was found for governance, which means that the regulatory measures should be enhanced to decrease PM2.5 in the future to achieve the SDGs in China. (C) 2019 Elsevier B.V. All rights reserved.