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

Abstract

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.

Diversified Integration and Integrated Application of Ideological and Political Teaching Resources Based on Artificial Intelligence Model Algorithm

“…Although some moral education resources do not have the actual conditions for the implementation of moral education, they can fully stimulate their moral education potential through development and utilization. Finally, the different forms and manifestations of moral education resources provide favorable conditions for their integration and application, and various forms of moral education resources help to form multiple moral education implementation strategies [9]. The diversification of moral education resources is helpful for the integration of moral education resources from various perspectives and provides the basis and premise for the further application of moral education resources.…”

Air Pollution: A Review of Its Impacts on Health and Ecosystems, and Analytical Techniques for Their Measurement and Modeling

“…Existing models for surface-level air pollution rely on extensive landuse datasets, which are often locally restricted and temporally static (Scheibenreif et al, 2021). Another limitation is the timeconsuming nature of monitoring air pollution data using current algorithms, resulting in low monitoring efficiency and accuracy (Liu et al, 2020). Additionally, "using sensors for air pollution monitoring requires highly durable sensors that can operate for long periods under severe conditions such as high humidity, solar radiation, and dust" (Kendler and Zuck, 2020).…”