Analysis of influencing factors on predicted PM2.5 concentrations and pollution control measures
To analyze the influence of meteorological fields on the predicted concentration of PM2.5 in atmospheric environmental impact assessments,this study utilized NASA MODIS aerosol optical depth(AOD)1 km grid remote sensing data,combined with ground PM2.5 monitoring data and meteorological data to construct an ordinary least squares(OLS)model and a random forest regression prediction model,obtaining the PM2.5 concentration in the main urban area of Taiyuan during non-heating seasons.The results show that before supplementary meteorological elements,the PM2.5-AOD fitting R2 was below 0.2 for all three seasons,indicating a low fitting degree.After correcting for meteorological factors,the fitting R2 increased to above 0.6.The fitting accuracy of the random forest model was higher than that of the OLS model.The influence of meteorological fields on the predicted concentration of PM2.5 in atmospheric environmental impact assessments is significant.
NETL
NSTL
万方数据
