Research and Application of Random Forest Method in PM2.5 Remote Sensing Inversion
With the continuous acceleration of urban construction,the problem of urban air quality has become increasingly prominent. Taking Chongqing as the research area and the concentration of PM2.5 in the area as the research object,AOD data with a resolution of 1km are produced by using L2 products of MODIS sensors,and remote sensing inversion is carried out by combining MERRA-2 meteorological data and ground monitoring station data. A model is constructed based on the method of random forest,and the data of training set and verification set are applied to the model to verify the effect of the model in PM2.5 inversion in the research area,and the inversion accuracy of the model is improved by sample equalization. The distribution and agglomeration characteristics of PM2.5 in Chongqing in 2022 are summarized by u-sing the model. The results show that:①The processing method of sample equalization improves the robustness of the model when it encoun-ters highly polluted weather,and makes the fitting correlation coefficient of the model increase from 0. 45 to 0.96. The fitting correlation coeffi-cients between the model and the data of ground monitoring stations are 0.97 and 0. 96,the average absolute errors are 4.54 ug/m and 6.05 ug/m,and the average relative errors are 20.32％ and 26. 76％,which can meet the requirements and tasks of regional air pollution monito-ring. ②In 2022,the air pollution time of PM2.5 in Chongqing showed seasonal changes,with the highest in winter,followed by spring and au-tumn,and the lowest in summer. It is characterized by"high in the west and low in the east"in space.
random forestPM2. 5remote sensing inversionChongqing
NETL
NSTL
万方数据
