Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

• Published in: Journal of chemistry Vol. 2020; no. 2020; pp. 1 - 7
• Main Authors: Liu, Yun, Lu, Yinan, Jing, Yuqin
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; Hindawi Limited; Wiley
• Subjects: Air monitoring; Air pollution; Algorithms; Analysis; Artificial intelligence; Atmospheric aerosols; Atmospheric radiation; Beer; Bouguer law; Detection; Electromagnetic radiation; Electromagnetism; Energy; Environmental monitoring; Extinction; Laws, regulations and rules; Pixels; Pollution; Pollution monitoring; Radiation (Physics); Remote monitoring; Remote sensing; Satellite remote sensing; Sensors; Simultaneous equations; Time; Turbidity; China
• ISSN: 2090-9063; 2090-9071
• DOI: 10.1155/2020/7390545

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.