Satellite-Based Estimation and Long-Term Trends of PM₂.₅ in Southern Nepal Using AOD and Meteorological Reanalysis

Fine particulate matter (PM₂.₅) poses a significant threat to public health and environmental quality in Nepal’s Tarai and Dune Valley regions. This study characterizes the spatiotemporal variability of PM₂.₅ from 2019 to 2021 by integrating ground-based observations, satellite-derived Aerosol Optical Depth (AOD), gaseous pollutants, and meteorological data. Daily PM₂.₅ measurements from six monitoring stations were analyzed alongside MODIS AOD, TROPOMI CO, NO₂, and SO₂ data, and ERA5 meteorological variables including temperature, relative humidity, and wind components. Correlation and regression analyses revealed strong associations between PM₂.₅ and AOD, CO, and temperature, while relative humidity and wind components exhibited moderate effects. Single linear regression explained 34–38% of PM₂.₅ variability, whereas multiple regression and Random Forest models improved predictive performance (R² ≈ 0.54–0.65, RMSE ≈ 22–26 µg/m³), accurately capturing seasonal and regional differences. Simulated PM₂.₅ concentrations reconstructed long-term trends (2000–2023), highlighting increasing levels in eastern regions (Jhumka) and relatively stable or declining trends in western regions (Dang, Bhimdatta). Seasonal analysis showed the highest concentrations during winter and pre-monsoon periods, and substantial reductions during monsoon months due to rainfall-driven pollutant washout. The results underscore the importance of integrating satellite and ground-based data with statistical modeling to assess historical air quality, identify pollution hotspots, and inform evidence-based mitigation strategies. This framework provides a robust basis for air quality management and public health planning in regions with limited monitoring infrastructure.