Integrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning

Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile low-cost sensors exhibit significant data instability. This study integrates PM2.5 data from 320 taxi-mounted mobile low-cost sensors and 52 fixed monitoring stations to address these limitations. By employing the machine learning methods, an appropriate mapping relationship was established between fixed and mobile monitoring concentration. The resulting pollution maps achieved 500-meter spatial and 5-minute temporal resolutions, showing close alignment with fixed monitoring data (+4.35% bias) but significant deviation from raw mobile data (-31.77%). The fused map exhibits the fine-scale spatial variability also observed in the mobile pollution map, while showing the stable temporal variability closer to that of the fixed pollution map (fixed: 1.12 plus or minus 0.73%, mobile: 3.15 plus or minus 2.44%, mapped: 1.01 plus or minus 0.65%). These findings demonstrate the potential of large-scale mobile low-cost sensor networks for high-resolution air quality mapping, supporting targeted urban environmental governance and health risk mitigation.

High-resolution on-road air pollution exposure informed by taxi-based mobile monitoring sensors

Air pollutant exposure exhibits significant spatial and temporal variability, with localized hotspots, particularly in traffic microenvironments, posing health risks to commuters. Although widely used for air quality assessment, fixed-site monitoring stations are limited by sparse distribution, high costs, and maintenance needs, making them less effective in capturing on-road pollution levels. This study utilizes a fleet of 314 taxis equipped with sensors to measure NO\textsubscript{2}, PM\textsubscript{2.5}, and PM\textsubscript{10} concentrations and identify high-exposure hotspots. The findings reveal disparities between mobile and stationary measurements, map the spatiotemporal exposure patterns, and highlight local hotspots. These results demonstrate the potential of mobile monitoring to provide fine-scale, on-road air pollution assessments, offering valuable insights for policymakers to design targeted interventions and protect public health, particularly for sensitive populations.