Estimation of ground-level PM2.5 concentration using MODIS AOD and corrected regression model over Beijing, China

Abstract

To establish a new model for estimating ground-level PM 2.5 concentration over Beijing, China, the relationship between aerosol optical depth (AOD) and ground-level PM 2.5 concentration was derived and analysed firstly. Boundary layer height (BLH) and relative humidity (RH) were shown to be two major factors influencing the relationship between AOD and ground-level PM 2.5 concentration. Thus, they are used to correct MODIS AOD to enhance the correlation between MODIS AOD and PM 2.5. When using corrected MODIS AOD for modelling, the correlation between MODIS AOD and PM 2.5 was improved significantly. Then, normalized difference vegetation index (NDVI), surface temperature (ST) and surface wind speed (SPD) were introduced as auxiliary variables to further improve the performance of the corrected regression model. The seasonal and annual average distribution of PM 2.5 concentration over Beijing from 2014 to 2016 were mapped for intuitively analysing. Those can be regarded as important references for monitoring the ground-level PM 2.5 concentration distribution. It is obviously that the PM 2.5 concentration distribution over Beijing revealed the trend of "southeast high and northwest low", and showed a significant decrease in annual average PM 2.5 concentration from 2014 to 2016.

Estimation of the PM2.5 and PM10 Mass Concentration over Land from FY-4A Aerosol Optical Depth Data

“…Additionally, it employs the improved model to estimate hourly PM2.5 and PM10 concentrations in China. The research conducted by Xu et al showed that the quality of the model to estimate PM2.5 could be improved by introducing NDVI into the regression model [34]. The CV of the IGTWR model employed in the present study indicated that the model showed an excellent estimation ability of PM2.5 and PM10 concentrations.…”

“…The impact of the spatial resolution and sampling frequency of the AOD on PM 2.5 prediction was then assessed. Xu et al [34] introduced NDVI into the corrected regression model to map the seasonal and annual mean distribution of PM 2.5 concentrations in Beijing from 2014 to 2016, and the quality of the corrected regression model was improved significantly.…”

The spatiotemporal heterogeneity of the relationship between PM2.5 concentrations and the surface urban heat island effect in Beijing, China

“…There have been a number of domestic and international researchers using a variety of methods to quantify the relationship between PM 2.5 concentrations and AOD (Li et al, 2017). The common linear statistical regression model (Li et al, 2017; Xu and Zhang, 2020) is a commonly used point-surface fusion PM 2.5 inversion method. According to the high correlation between PM 2.5 concentration and AOD, we set up a linear regression model between the Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth and ground PM 2.5 concentration to retrieve the spatial distribution of PM 2.5 concentrations in Beijing.…”

Estimating the Volcanic Ash Plume Course, PM_2.5 Emissions, and Environmental Impacts Based on the December 4, 2021, Mount Semeru Eruption

“…The PM 2.5 distribution and mapping analysis was followed current methods (Xu & Zhang 2020. Bai et al 2021.The sources of PM 2.5 were Aerosol Optical Depth (AOD) data produced by MODIS.…”