Accurate estimation of surface PM2.5 from satellite aerosol optical depth (AOD) is essential for air quality monitoring and exposure assessment. Yet retrieval-based PM2.5 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 PM2.5 by ~55%, and substantially corrected overestimation of PM2.5-attributable mortality in low-density areas. Results reveal a sharper urban-rural gradient in PM2.5 decline than previously recognized, underscoring persistent exposure disparities. By embedding spatial balance into PM2.5 retrieval and fusion, this study provides a methodologically innovative and scalable pathway to support sustainable development goals on health, climate action, and sustainable cities.
Ding et al. (Sun,) studied this question.