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