PM2.5 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 PM2.5 and O3 from street-scale mobile measurements in Fuzhou, China. A random forest (RF) model was applied to elucidate factors influencing PM2.5-O3 synchronicity. Key findings revealed that street-scale variations in PM2.5 and O3 exhibited multifractality and long-term persistence. Co-directional changes between PM2.5 and O3 peaked at noon, compared to traffic peak hours and midnight. An 800 m threshold was identified for analyzing PM2.5-O3 synchronicity-below this spatial scale, local factors weaken their concordance, while beyond it, the concordance strengthened. RF models showed that PM2.5 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 PM2.5 and O3, whereas high humidity partially mitigated high concentrations of both pollutants but enhanced their synchronicity.
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