Detecting spatiotemporal dynamics of PM2.5 emission data in China using DMSP-OLS nighttime stable light data

Haze pollution in China is getting worse with the rapid growth of economy and urbanization rate, which is harmful to human health and closely related with China's sustainable development. In response to PM2.5 pollution problem in China, this study first analyzed the correlation between the intercalibrated Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) sensor nighttime stable light (NSL) data and statistical PM2.5 emissions at the provincial level from 1992 to 2012 respectively and the results demonstrated that there was a positive correlation between the intercalibrated DMSP-OLS NSL data and PM2.5 emissions. Then linear regression analysis was proposed to simulate spatiotemporal dynamics of PM2.5 emission at the 1 km resolution level in China by the intercalibrated DMSP-OLS NSL data and PM2.5 emission. Spatiotemporal dynamics of PM2.5 emission were analyzed from national scale down to regional and urban agglomeration scales. The results clearly showed that the variations of PM2.5 emissions in different regions and urban agglomerations were different and large. The high growth type and high grade of PM2.5 emissions were mainly located in the Eastern region, Central region, Shandong Peninsula and Beijing-Tianjin-Tangshan, with significant lower concentrations in the Western region, Northeastern region, Sichuan-Chongqing and Middle south of Liaoning. Considering the spatial and temporal patterns of PM2.5 emissions between the four economic regions, the mitigation strategies for Eastern and Central China should mainly focus on the industry structure adjustments, while Western and Northern China should pay more attention to the optimizations of regional energy structures and improvements of energy efficiencies. The results of this study is not only beneficial to understand accurately the regional discrepancies of spatiotemporal PM2.5 emission dynamics, but also helpful for proposing mitigation policies in air pollution control and providing scientific support for regional sustainable development. •There is a positive correlation between the DMSP-OLS data and PM2.5emission data.•We simulated spatial distribution of PM2.5 emissions at 1 km resolution from 1992 to 2012.•We analyzed the spatiotemporal dynamics of PM2.5 emissions from multiple scales.