The Spatiotemporal Variation of Pm2.5-O3 Association and its Influencing Factors Across China: Dynamic Simil-Hu Lines

Abstract

In recent years, PM2.5 and O3 composite airborne pollution has become one of the most severe environment issues in China. To get a better understanding and tackle these problems, we employed multi-year data to explore the spatiotemporal variation of the PM2.5–O3 relationship in China and investigated its major driving factors. Firstly, interesting patterns were found that named dynamic Simil-Hu lines, which presented a combined effect of natural and anthropogenic influences, were closely related to the spatial patterns of PM2.5-O3 association across seasons. Furthermore, regions with lower altitudes, higher humidity, higher atmospheric pressure, higher temperature, fewer sunshine hours, more accumulated precipitation, denser population and higher GDP often show positive PM2.5-O3 associations, regardless of seasonal variations. Amongst these factors, humidity, temperature and precipitation were dominant factors. This research suggests that the collaborative governance of composite atmospheric pollution should be implemented dynamically, in consideration of geographical locations, meteorological conditions and socioeconomic conditions.

Keywords: PM2.5 and O3, correlation analysis, geographical detector, Random Forest, collaborative governance of atmospheric pollutants