Spatiotemporal correlations of PM_2.5 and O₃ variations: A street-scale perspective on synergistic regulation

PM_2.5 and O₃ are major pollutants affecting air quality and posing serious health risks in China. While many studies focus on their control at urban and regional scales, their co-regulation at the street level-closely tied to traffic emissions and commuting patterns-remains unexplored. This study addressed the gap by using nonlinear statistical methods to analyze the spatiotemporal evolution of PM_2.5 and O₃ from street-scale mobile measurements in Fuzhou, China. A random forest (RF) model was applied to elucidate factors influencing PM_2.5-O₃ synchronicity. Key findings revealed that street-scale variations in PM_2.5 and O₃ exhibited multifractality and long-term persistence. Co-directional changes between PM_2.5 and O₃ peaked at noon, compared to traffic peak hours and midnight. An 800 m threshold was identified for analyzing PM_2.5-O₃ synchronicity-below this spatial scale, local factors weaken their concordance, while beyond it, the concordance strengthened. RF models showed that PM_2.5 was primarily influenced by precursor substances in winter and meteorological conditions in summer, while O₃ was consistently affected by meteorological conditions across both seasons. Road traffic and construction disrupted the co-directional changes of PM_2.5 and O₃, whereas high humidity partially mitigated high concentrations of both pollutants but enhanced their synchronicity.