An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors

Abstract

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.

Comparative evaluation of backpropagation neural network and genetic algorithm-backpropagation neural network models for PM2.5 concentration prediction based on aerosol optical depth, meteorological factors, and air pollutants

“…Determination of the training function: the training functions include the traingd, traingdm, and trainlm algorithms. The trainlm algorithm is the fastest backpropagation algorithm in the MATLAB toolbox 40 . Therefore, trainlm was selected as the network training function in this study.…”

“…The trainlm algorithm is the fastest backpropagation algorithm in the MATLAB toolbox. 40 Therefore, trainlm was selected as the network training function in this study. 3.…”

PM2.5 concentration assessment based on geographical and temporal weighted regression model and MCD19A2 from 2015 to 2020 in Xinjiang, China

“…Li et al [ 13 ] estimated the seasonal spatial distribution of PM 2.5 in Beijing using MCD19A2 data, and the results show that MCD19A2 data have a strong ability to predict the ground PM 2.5 level on a seasonal scale basis. Gu et al [ 14 ] determined the daily concentration of PM 2.5 in Dalian using MCD19A2 data and found that the spatial distribution of AOD and PM 2.5 tended to be consistent, and the PM 2.5 value in industrial areas was high. The above research shows that MCD19A2 is feasible in PM 2.5 inversion.…”

Development of the TOA-Related Models for PM2.5 Prediction Pre- and Post-COVID-19 Outbreak over Yangtze River Delta Region of China

“…Most of the previous studies retrieved the aerosol optical depth (AOD) from satellite remote sensing imageries (e.g., moderate resolution imaging spectroradiometer (MODIS) and ozone monitoring instrument (OMI)) to estimate the PM 2.5 concentrations [4,9,[14][15][16][17][18]. These AOD products with resolution of 1-17.6 km could reveal the overall distribution of PM 2.5 concentrations within an area.…”