County-Based PM2.5 Concentrations’ Prediction and Its Relationship with Urban Landscape Pattern

Abstract

Satellite top-of-atmosphere (TOA) reflectance has been validated as an effective index for estimating PM2.5 concentrations due to its high spatial coverage and relatively high spatial resolution (i.e., 1 km). For this paper, we developed an emsembled random forest (RF) model incorporating satellite top-of-atmosphere (TOA) reflectance with four categories of supplemental parameters to derive the PM2.5 concentrations in the region of the Yangtze River Delta-Fujian (i.e., YRD-FJ) located in east China. The landscape pattern indices at two levels (i.e., type level and overall level) retrieved from 3-year land classification imageries (i.e., 2016, 2018, and 2020) were used to discuss the correlation between county-based PM2.5 values and landscape pattern. We achieved a cross validation R2 of 0.91 (RMSE = 9.06 μg/m3), 0.89 (RMSE = 10.19 μg/m3), and 0.90 (RMSE = 8.02 μg/m3) between the estimated and observed PM2.5 concentrations in 2016, 2018, and 2020, respectively. The PM2.5 distribution retrieved from the RF model showed a trend of a year-on-year decrease with the pattern of “Jiangsu > Shanghai > Zhejiang > Fujian” in the YRD-FJ region. Our results also revealed that the landscape pattern of farmland, water bodies, and construction land exhibited a highly positive relationship with the county-based average PM2.5 values, as the r coefficients reached 0.74 while the forest land was negatively correlated with the county-based PM2.5 (r = 0.84). There was also a significant correlation between the county-based PM2.5 and shrubs (r = 0.53), grass land (r = 0.76), and bare land (r = 0.60) in the YRD-FJ region, respectively. Three landscape pattern indices at an overall level were positively correlated with county-based PM2.5 concentrations (r = 0.80), indicating that the large landscape fragmentation, edge density, and landscape diversity would raise the PM2.5 pollution in the study region.