Proactive synergistic control of PM_2.5 and ozone through urban planning: longitudinal data analysis of 274 Chinese cities from 2005 to 2020

Current air pollution control predominantly employs reactive measures like emergency factory closures and production restrictions, which may disrupt socioeconomic activities while lacking sustainability. To address this limitation, we propose proactive urban planning interventions for long-term synergistic PM_2.5 and ozone (O₃) control through the development of the city-level urban form regulation-aided air quality optimization model (CUFR-AQOM). The model utilizes GeoDetector to identify global drivers of air pollution, followed by hybrid stepwise regression and geographic weighted regression to establish localized mapping relationships between air quality and urban form indicators (UFIs). Subsequently, it designs regulatory pathways incorporating regulatable UFI thresholds and air quality optimization targets. Validation across 274 Chinese cities (2005–2020) showed the model’s robust predictive performance, achieving coefficients of determination (R²) of 0.88 and 0.89 for PM_2.5 and O₃, and root mean squared errors (RMSE) of 5.09 μg/m³ and 4.71 μg/m³ for PM_2.5 and O₃, respectively. Projections for the post-regulation scenario revealed a notable reduction in the number of cities exceeding secondary standards for PM_2.5 (from 141 to 94) and O₃ (from 46 to 31), underscoring the effectiveness of UFIs regulation in synergistic PM_2.5 and O₃ control. This study contributes a novel decision-making framework for air quality-friendly urban planning and provides suggestions for tailored regional regulation.