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Particulate matter (PM) pollution has caused several environmental problems and damaged human health. To understand the different driving forces of PM2.5 and PM10, we investigated the spatial distribution of PM2.5, PM10, and the ratio of PM2.5 to PM10 (PM2.5/PM10), and simulated how they respond to socioeconomic, natural conditions and pollutant transmission in 336 cities across Chinese mainland in 2017. The results indicated that 35.4% and 49.7% of cities in Chinese mainland satisfied the national level II standard for PM2.5 (35 μg/m3) and PM10 (70 μg/m3), respectively. The average PM2.5/PM10 was 57.5 ± 9.4%, which is a relatively high value across the world. Global spatial regression results revealed that the transport of pollutants emitted from neighboring regions was the most important factor for local PM concentrations, while population density was the dominant local variable. The influence of socioeconomic factors and the neighboring pollution level on PM2.5 was greater than that on PM10, while the effect of precipitation was greater on PM10. Cluster analysis revealed that 336 Chinese cities could be classified into three groups. The regions with a high PM concentration and a high level of social economy were included in Group 1 (G1). Group 2 (G2) was predominantly observed in southern China, while Group 3 (G3) was seen in western China. Furthermore, population density significantly affected the PM in G2 and PM2.5 in G3, and PM levels in G1 and G3 had a sensitive response to the variation in precipitation, especially PM10. [ABSTRACT FROM AUTHOR]

Precipitation is an essential climate variable in the hydrologic cycle. Its abnormal change would have a serious impact on the social economy, ecological development and life safety. In recent decades, many studies about extreme precipitation have been performed on spatio-temporal variation patterns under global changes; little research has been conducted on the regionality and persistence, which tend to be more destructive. This study defines extreme precipitation events by percentile method, then applies the spatio-temporal scanning model (STSM) and the local spatial autocorrelation model (LSAM) to explore the spatio-temporal aggregation characteristics of extreme precipitation, taking China in July as a case. The study result showed that the STSM with the LSAM can effectively detect the spatio-temporal accumulation areas. The extreme precipitation events of China in July 2016 have a significant spatio-temporal aggregation characteristic. From the spatial perspective, China's summer extreme precipitation spatio-temporal clusters are mainly distributed in eastern China and northern China, such as Dongting Lake plain, the Circum-Bohai Sea region, Gansu, and Xinjiang. From the temporal perspective, the spatio-temporal clusters of extreme precipitation are mainly distributed in July, and its occurrence was delayed with an increase in latitude, except for in Xinjiang, where extreme precipitation events often take place earlier and persist longer. [ABSTRACT FROM AUTHOR]