Enhancing PM_2.5 exposure assessment across China with a novel urban–rural balanced estimation framework

Accurate estimation of surface PM_2.5 from satellite aerosol optical depth (AOD) is essential for air quality monitoring and exposure assessment. Yet retrieval-based PM_2.5 datasets are often biased by the uneven distribution of ground stations, especially in rural areas, causing systematic overestimation of concentrations and related health burdens. To address this, we propose a novel urban-rural balanced estimation framework that mitigates structural bias caused by observation imbalance. The framework integrates AOD, reanalysis fields, and ground measurements within a machine learning model guided by data assimilation principles and further stabilizes performance through ensemble averaging across dynamically weighted background-observation settings. Unlike conventional models that rely solely on ground observations, our approach adaptively constrains non-urban estimates using low-weighted reanalysis samples while preserving high fidelity in urban regions. Applied across China during 2015–2021, the framework reduced root mean square error in clean sites from 18.18 to 8.31 µg/m³, lowered national mean PM_2.5 by ~55%, and substantially corrected overestimation of PM_2.5-attributable mortality in low-density areas. Results reveal a sharper urban-rural gradient in PM_2.5 decline than previously recognized, underscoring persistent exposure disparities. By embedding spatial balance into PM_2.5 retrieval and fusion, this study provides a methodologically innovative and scalable pathway to support sustainable development goals on health, climate action, and sustainable cities.