Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD

Abstract

Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…The model obtained similar results to those obtained by the CTM used by van Donkelaar in 2010, but GWR found higher PM 2.5 concentrations in rural areas. Similar results for national PM 2.5 levels were found by You et al [126] with CV-R 2 values of 0.760 and 0.810 for MODIS and MISR, respectively, in China. Additionally, using 3-km resolution MODIS AOD in 2014, You et al [125] confirmed that this GWR approach is useful for estimating large-scale ground-level PM 2.5 distributions in China.…”

“…(1) Model predictability: MLR was commonly used in early studies [17,20,21,[24][25][26][39][40][41]46,47,49,50,54,75], whereas MEM and CTM gradually became the dominant methods and replaced MLR after 2010. However, GWR has developed at a slower pace with a limited number of studies to data, and had moderate performance [32,74,125,126]. Included studies showed that R 2 value of MEM was higher than those of the other three models in the same area [17,87,104,136].…”

“…Studies have shown that compared with CTM, GWR had a higher R 2 value [87]; (2) Similar to MEM, GWR used ground monitored PM 2.5 values for AOD calibration, and it had a better model performance than MLR. The disadvantages are: (1) since model construction depends on ground monitoring data, model performance may be much less reliable in areas lacking ground monitoring data; and (2) to our knowledge, GWR has only been employed in limited PM 2.5 prediction studies with the combination of satellite data [74,87,100,107,125,126,128], so the feasibility of applying it widely in other regions needs to be investigated in further research.…”

A New MODIS C6 Dark Target and Deep Blue Merged Aerosol Product on a 3 km Spatial Grid

“…Therefore, quality assessment of MODIS AOD is crucial for local and global air quality applications. Studies have performed the quality assessment of the MODIS AOD [35,36,41,42,[47][48][49][50] and used it in statistical modeling based on the empirical linear regression, land use regression model, and Geo-graphically Weighted Regression (GWR) model for estimation of PM 2.5 concentrations at regional and global scales [14,[51][52][53][54][55][56][57][58][59][60][61][62][63][64]. These studies found that an accurate estimation of the PM 2.5 concentration depends on the quality of the satellite-retrieved AOD observations.…”

Status update: is smoke on your mind? Using social media to assess smoke exposure

“…We compare two geographically weighted regression (GWR) models that use MODIS AOD and WRF-Chem PM 2.5 with and without the "Percent of Facebook posters" dataset. GWR has previously been used in several different studies to predict surface air quality (Hu et al, 2013;Lassman et al, 2017;Song et al, 2014;You et al, 2016 each surface monitor location, which is then interpolated across the domain. We use the leave-one-out cross-validation (LOOCV) method to test our models, in which the regression coefficients determined at a single monitor are removed from the interpolation scheme.…”