Influence of urban spatial and socioeconomic parameters on PM2.5 at subdistrict level: A land use regression study in Shenzhen, China
Min Shao , Liyue Zeng , Jian Hang , Xuemei Wang , Maofa Ge , Yujing Mu , Jianmin Chen , Zifa Wang
DOI:10.1016/j.jes.2021.12.002
Received May 31, 2021,Revised , Accepted December 08, 2021, Available online December 21, 2021
Volume 34,2022,Pages 485-502
The intraurban distribution of PM2.5 concentration is influenced by various spatial, socioeconomic, and meteorological parameters. This study investigated the influence of 37 parameters on monthly average PM2.5 concentration at the subdistrict level with Pearson correlation analysis and land-use regression (LUR) using data from a subdistrict-level air pollution monitoring network in Shenzhen, China. Performance of LUR models is evaluated with leave-one-out-cross-validation (LOOCV) and holdout cross-validation (holdout CV). Pearson correlation analysis revealed that Normalized Difference Built-up Index, artificial land fraction, land surface temperature, and point-of-interest (POI) numbers of factories and industrial parks are significantly positively correlated with monthly average PM2.5 concentrations, while Normalized Difference Vegetation Index and Green View Factor show significant negative correlations. For the sparse national stations, robust LUR modelling may rely on a priori assumptions in direction of influence during the predictor selection process. The month-by-month spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R2 compared with cross-validation results. For MLR models, inflation of both R2 and R2CV was detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM2.5 levels. Inflated within-sample R2 also exist in the spatiotemporal LUR models developed with only national stations, although not as significant as spatial LUR models. Our results suggest that a denser subdistrict level air pollutant monitoring network may improve the accuracy and robustness in intraurban spatial/spatiotemporal prediction of PM2.5 concentrations.
