Ground-level ozone (O3) has become a major air pollutant in China following PM2.5, particularly in the southeastern coastal region, where the frequent interaction of typhoons and the subtropical high complicates pollution control. In this paper, spatial autocorrelation and a multiscale geographically weighted regression (MGWR) model were employed to estimate the spatiotemporal heterogeneity and driving mechanisms of O3 in the Southeast Coastal urban agglomerations from 2015 to 2024. Temporally, the annual average O3 concentration exhibited a fluctuating trend of an initial increase, followed by a decrease and a subsequent rebound. A bimodal monthly pattern was observed, with peaks in May–June and August–September and minima in winter. Diurnally, the concentration showed a consistent pattern of being higher in the daytime and lower at night, peaking in the afternoon, driven by solar radiation and temperature. Spatially, O3 exhibited a distinct north–south gradient, with the highest in Jiangsu Province, followed by Shanghai, Zhejiang and Guangdong, and the lowest in Fujian. Significant spatial autocorrelation was detected, with hot spots in the Yangtze River Delta and cold spots in Fujian and adjacent areas. Seasonally, the most severe pollution with the greatest spatial heterogeneity, occurred in summer, contrasting with the uniformly low concentrations in winter. Compared with OLS and GWR, the MGWR demonstrated superior explanatory power. O3 was jointly influenced by precursors, natural factors, and socioeconomic factors, with the influence intensity ranked as follows: NO2 > average elevation > population density > annual precipitation> wind speed > built-up area > proportion of the secondary industry in GDP. Notably, the effects of NO2, annual precipitation, and the proportion of the secondary industry exhibited strong spatial heterogeneity, operating at finer spatial scales. These findings provide scientific support for sustainable air quality management and region-specific O3 control in southeastern coastal China.
Wu et al. (Mon,) studied this question.