Monitoring Trends of CO, NO2, SO2, and O3 Pollutants Using Time-Series Sentinel-5 Images Based on Google Earth Engine

Air pollution (AP) is a significant risk factor for public health, and its impact becoming increasingly concerning in developing countries where it causing growing number of health issues. It therefore essential to map monitor AP sources order facilitate local action against them. This study aims at assessing the suitability Sentinel-5 products based on Google Earth Engine (GEE) air pollutants, including CO, NO2, SO2, O3 Arak city, Iran from 2018 2019. Our process involved feeding satellite images cloud-free GEE platform that identified pollutant-affected areas monthly, seasonally, annually. By coding JavaScript language GEE, four parameters were obtained. Following that, with clouds filtered by defining cloud filters, average maps extracted filters both years. The employed model, which solely used products, was tested assessed using ground data collected Environmental Organization Central Province. findings revealed annual estimated RMSE 0.13, 2.58, 4.62, 2.36, respectively, year 2018. 2019 also calculated 0.17, 2.41, 4.31, 4.6, respectively. results demonstrated seasonal 0.09, 5.39, 0.70, 7.81 O3, Seasonal 0.12, 4.99, 1.33, 1.27 this combined automated-based approaches, such as can perform better than traditional approaches (e.g., measuring stations) mapping monitoring since they are capable providing spatially distributed sufficiently accurate.