Air Pollution Sources Identification Precisely Based on Remotely Sensed Aerosol and Glowworm Swarm Optimization

摘要

In this paper, we developed a novel method to identify air pollution sources based on remotely sensed aerosol data and Glowworm Swarm Optimization (GSO). In practice, it is usually to identify the air pollution sources to certain industries, such as transportation, power plants, biomass burning, and et.al. To our knowledge, the problem of locating and quantifying the pollution to the specified factories is faced for the first time. In this study, the aerosol retrieved from remotely sensed image and GIS were used to locate and quantify the pollution to each enterprise in the study area based on an improved Glowworm Swarm Optimization and meteorological condition. As a result, the gross and intensity of every enterprise in the study area were achieved. Therefore, the polluting contribution of each factory could be listed, and the most polluting factories could be found. Some experiments were carried out to validate the method, and the Key monitoring factories by local authority was ferreted out accurately.