Distribution and Determinants of Correlation between PM2.5 and O3 in China Mainland: Dynamitic simil-Hu Lines

TL;DR

This study analyzes the spatial and temporal correlation patterns between PM2.5 and O3 in China, introducing dynamic similar Hu lines and identifying key natural and anthropogenic factors influencing these patterns.

Contribution

It proposes the concept of dynamic similar Hu lines to replace fixed divisions and quantitatively explores factors affecting PM2.5 and O3 correlation patterns using advanced spatial analysis methods.

Findings

Regions with dense populations and higher GDP tend to have positive PM2.5-O3 correlation.

Humidity, temperature, and precipitation are key factors influencing correlation distribution.

Correlation patterns vary significantly across different geographical and socio-economic contexts.

Abstract

In recent years, China has made great efforts to control air pollution. During the governance process, it is found that fine particulate matter (PM2.5) and ozone (O3) change in the same trend among some areas and the opposite in others, which brings some difficulties to take measures in a planned way. Therefore, this study adopted multi-year and large-scale air quality data to explore the distribution of correlation between PM2.5 and O3, and proposed a concept called dynamic similar hu lines to replace the single fixed division in the previous research. Furthermore, this study discussed the causes of distribution patterns quantitatively with geographical detector and random forest. The causes included natural factors and anthropogenic factors. And these factors could be divided into three parts according to the characteristics of spatial distribution: broadly changing with longitude, changing with latitude, and having local characteristics. Overall, regions with relatively more densely population, higher GDP, lower altitude, higher humidity, higher atmospheric pressure, higher surface temperature, less sunshine hours and more accumulated precipitation often corresponds to positive correlation coefficient between PM2.5 and O3, no matter in which season. The parts with opposite conditions that mentioned above are essentially negative correlation coefficient. And what's more, humidity, global surface temperature, air temperature and accumulated precipitation are four decisive factors to form the distribution of correlation between PM2.5 and O3. In general, collaborative governance of atmospheric pollutants should consider particular time and space background and also be based on the local actual socio-economic situations, geography and geomorphology, climate and meteorology and other comprehensive factors.