A dual-phase air quality monitoring system based on satellite data: Framework and preliminary evaluation

摘要

Nitrogen dioxide (NO2), sulfur dioxide (SO2), and smoke are major pollutants, which are used to evaluate the air quality. This study developed a dual-phase air quality monitoring system to monitor the air quality, which based on the Shuffled Complex Evolution algorithm (SCE-UA), ground-based AQI data and satellite observations of NO2, SO2, and Aerosol Optical Depth (AOD). The system is implemented in two phases: the optimization of model coefficients and the air quality index (AQI) simulation. A comprehensive evaluation system of the air quality was then established. The model coefficients of the AQI regression model are optimized by the SCE-UA algorithm in the optimization phase, and the optimized coefficients are used as the final model coefficients in the AQI simulation phase. The experimental results indicate that the SCE-UA algorithm can effectively optimize the coefficients of the AQI regression model. It provides a promising solution to monitor the air quality through using the satellite observations and optimizing model coefficients.