A Satellite Remote Sensing Estimation Method of PM2.5 Concentration in Polluted Weather

Abstract

A method for satellite remote sensing estimation of PM2.5 concentration under polluted weather, which uses satellite aerosol optical depth data and corresponding ground PM2.5 concentration data to establish a data set, and completes sample learning and data testing based on gradient boosting regression tree learning method. The accuracy of the test results is verified, and the parameters of the gradient boosting regression tree are adjusted to meet the accuracy requirements. The final calculation model of the regression tree can be effectively used for PM2.5 concentration estimation in polluted weather, and the results are more accurate. The estimation speed is faster, which can supplement the insufficiency of traditional methods for PM2.5 concentration estimation in polluted weather, and provide more accurate data support for air pollution prevention and control.

Description

technical field

[0001] The invention relates to the technical field of remote sensing, in particular to a satellite remote sensing estimation method for PM2.5 concentration in polluted weather. Background technique

[0002] With the sustained and rapid development of my country's industrialization and urbanization, people's living standards have risen sharply, and more and more environmental problems have also emerged. In recent years, large-scale continuous smog has occurred in China many times. The smog is mainly composed of PM2.5, which can enter the lungs. PM2.5 refers to particles with an aerodynamic diameter of less than 2.5 μm. Compared with PM10, PM2.5 has a smaller particle size and can be absorbed in the atmosphere Long-term stay and long-distance transmission have a greater impact on the quality of the air environment; PM2.5 is easy to attach to various toxic and harmful substances (such as persistent organic pollutants, heavy metals, various pathogenic bacteria, ...

Images