Machine learning-based global air quality index development using remote sensing and ground-based stations

Abstract

Air pollution refers to the presence of hazardous substances in the air that has adverse effects on health, causing millions premature deaths annually. Ground-based stations can provide accurate measurements for monitoring air pollution. However, the spatial coverage of air pollution measurements is limited by the number of measurement instruments available in specific hotspot areas. Satellite remote sensing can reduce spatial uncertainty; however, the measurement results are mostly limited to the upper atmosphere with high measurement sensitivity. To better represent surface conditions, this study aims to model the Air Quality Index for pollutants CO, NO2, SO2, PM2.5, and PM10 in the global region using remotely sensed data. To support this study, 425 data points from air pollution stations distributed globally are combined using Machine Learning Linear Regression methods. Furthermore, socioeconomic and environmental data are combined with air pollution satellite to form Multiple models. According to the results of this study, the Multiple Models are more accurate than the single models, showing that the addition of socioeconomic and environmental data can enhance accuracy. The results of this study are expected to help regions without air pollution monitoring stations to estimate the air quality index using satellite data, in turn, preventing air pollution disasters.