AUTHOR=Kong Shaojie , Wang Teng , Li Fei , Yan Jingjing , Qu Zhiguang TITLE=Unraveling spatiotemporal patterns and multiple driving factors of surface ozone across China and its urban agglomerations management strategies JOURNAL=Frontiers in Ecology and Evolution VOLUME=Volume 11 - 2023 YEAR=2023 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2023.1103503 DOI=10.3389/fevo.2023.1103503 ISSN=2296-701X ABSTRACT=

Since State Council launched the Action Plan for Air Pollution Prevention and Control in 2013, national concentration of fine particulate matter (PM2.5) has continued to decline in China, while surface ozone (O3) pollution shows an obvious rise. To identity hot regions and develop targeted policy, the spatiotemporal O3 variation and its population-weighted exposure features were analyzed in 337 cities across China, using autocorrelation analysis and grid exposure calculation. In the identified hot urban agglomerations, the correlation analysis and geographic weighted regression model (GWR) were used to study related meteorological factors and socioeconomic driving factors. O3 pollution and its human exposure were found to have significant spatial aggregation characteristics, showing a need for regional management policy. Beijing-Tianjin-Hebei Urban Agglomeration (BTH-UA), Central Plains Urban Agglomeration (CP-UA), and Yangtze River Delta Urban Agglomeration (YRD-UA) were identified as hot regions where O3 concentration exceeded 160 μg·m−3, exceedance rate was over 20% and population-weighted exposure risk was relatively high. Correlation analysis in the hot regions indicated high surface temperature, low relative humidity, and low wind speed were positive to O3 increase. Further, GWR results revealed that O3 in the majority of cities was positively related with population density (PD), the per capita GDP (Per_GDP), industrial soot emissions (ISE), industrial SO2 emissions (ISO2), and average annual concentration of inhaled fine particulate matter (PM10), and negatively related with total land area of administrative region (Administration) and area of green land (Green). From the regional driving factor difference, the targeted UA management policy was provided.