Development of a high-resolution PM_2.5 model in Taiwan using integrated low-cost air quality sensors and satellite...

Air pollution modeling generally suffers from the limited and uneven distribution of fixed air quality monitoring (AQM) stations. Low-cost sensors have emerged to fill these monitoring gaps; however, few studies have integrated low-cost sensor data with satellite-based measurements.

We aimed to develop a daily 250-m resolution fine particulate matter (PM_2.5) estimation model in Taiwan by incorporating low-cost sensor data with aerosol optical depth (AOD), meteorological variables, and land-use data.

PM_2.5 measurements from 16,795 sensors and 74 AQMs collected between 2019 and 2021 were used. Multi-Angle Implementation of Atmospheric Correction (MAIAC) satellite AOD data were obtained. A highly efficient LightGBM model was applied to correct bias in low-cost sensor measurements, accounting for distance to AQMs and meteorological variables. Subsequently, a second LightGBM model was employed to develop the PM_2.5 estimation model. Model performance was evaluated using the overall, temporal, spatial 10-fold, and rolling-origin cross-validation (CV). SHapley Additive exPlanations (SHAP) summary plots and accumulative local effect plots were used to interpret feature contributions RESULTS: The overall, temporal, spatial 10-fold, and rolling-origin CV coefficient of determination (R²; root mean square error [RMSE]) were 0.98 (1.23 μg/m³), 0.74 (4.62 μg/m³), 0.93 (2.48 μg/m³), and 0.61 (5.77 μg/m³), respectively. The ten most important predictors were: day of the year, u- and v-wind components, relative humidity, year, total precipitation, surface pressure, boundary layer height, AOD, and temperature. Higher PM_2.5 estimates were observed in the western region compared to the east, with the southwestern region being the most polluted.

This efficient model provides PM_2.5 estimates that can be used to identify hotspots, abnormal events, and potential emission sources. The ultra-high-resolution estimates can also inform design of healthier walking routes for general public. Furthermore, the model enables the definition of short- and long-term exposure metrics and supports back-extrapolate historical PM_2.5 exposures in epidemiological studies.

A highly efficient LightGBM model integrating low-cost sensor data with satellite-based measurement was developed and validated. The model demonstrated excellent performance, with a 10-fold CV R² (RMSE) of 0.98 (1.23 μg/m³). By integrating Internet of Things (IoT), this approach mitigates the monitoring gaps, facilitates the identification of pollution hotspots, abnormal events, and sources, and enhances exposure assessment in epidemiological research.