Estimating PM2.5 concentrations in Yangtze River Delta region of China using random forest model and the Top-of-Atmosphere reflectance. (15th October 2020)

• Record Type: Journal Article
• Authors: Yang, Lijuan; Xu, Hanqiu; Yu, Shaode
• Is Part Of: Journal of environmental management. Volume 272(2020)
• Publication Date: 2020-10-15
• Subjects: TOA reflectance -- Random forest model -- PM2.5 estimation -- YRD; Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2020.111061
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Previous studies that have used remote sensing data to estimate the PM2.5 concentrations mainly focused on the retrieval of aerosol optical depth (AOD) with moderate-to-low spatial resolution. However, the complex process of retrieving AOD from satellite Top-of-Atmosphere (TOA) reflectance always generates the missingness of AOD values due to the limitation of AOD retrieval algorithms. This study validated the possibility of using satellite TOA reflectance for estimating PM2.5 concentrations, rather than using conventional AOD products retrieved from remote sensing imageries. Given that the TOA-PM2.5 relationship cannot be accurately expressed by simple linear correlation, we developed a random forest model that integrated satellite TOA reflectance from Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B product, meteorological fields and land-use variables to estimate the ground-level PM2.5 concentrations. The highly-polluted Yangtze River Delta (YRD) region of eastern China was employed as our study case. The results showed that our model performed well with a site-based and a time-based CV R 2 of 0.92 and 0.88, respectively. The derived annual and seasonal distributions of PM2.5 concentrations exhibited high PM2.5 values in northern YRD region (i.e., Jiangsu province) and relatively low values in southern region (i.e., Zhejiang province), which shared a similar distribution trend with the observed PM2.5 concentrations. This study demonstrated the outstanding performance of random forest model using satellite TOA reflectance, and also provided an effective method for remotely sensed PM2.5 estimation in regions where AOD retrievals are unavailable. Highlights: Validated the possibility of using satellite TOA reflectance instead of AOD products for estimating PM2.5 concentrations. The proposed model explained 96% of the variability in PM2.5 concentrations. Illustrated high-coverage PM2.5 concentrations with high accuracy in YRD.