Assessment of Passive Air Quality Monitoring Networks for PM2.5 and Ozone Using Low-Cost Sensors and Satellite-Derived Aerosol Optical Depth in Densely Populated Urban Areas

Densely populated urban areas experience severe spatial heterogeneity in ambient air pollution, posing substantial challenges for conventional regulatory monitoring networks characterized by limited spatial coverage. This study develops and evaluates an integrated urban air quality assessment framework combining a dense network of low-cost sensors (LCS), passive diffusive samplers, and 1-km resolution satellite-derived Aerosol Optical Depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to map fine particulate matter (PM2.5) and tropospheric ozone (O3) across a complex metropolitan landscape. A multi-tier network comprising 45 calibrated optical and electrochemical sensor nodes alongside 30 passive sampling locations was deployed over a 12-month period across diverse micro-environments, including street canyons, urban background sites, and industrial fringes. Machine learning-based calibration models incorporating ambient relative humidity, temperature, and planetary boundary layer height significantly enhanced sensor precision, yielding strong agreement with Federal Reference Method monitors (PM2.5: R² = 0.87, RMSE = 3.42 µg/m³; O3: R² = 0.83, RMSE = 4.18 ppb). Fusion of in-situ sensor observations with MAIAC AOD via an ensemble spatial regression model resolved microscale pollution gradients, reducing spatial prediction uncertainty by 38% compared to standard spatial interpolation. The findings demonstrate that coupling low-cost surface sensing with satellite remote sensing offers an operationally viable, high-resolution solution for urban exposure assessment, source apportionment, and environmental engineering interventions.