Influence of urban spatial and socioeconomic parameters on PM_(2.5) at subdistrict level:A land use regression study in Shenzhen,China

摘要

The intraurban distribution of PM_(2.5)concentration is influenced by various spatial,socioeconomic,and meteorological parameters.This study investigated the influence of 37 parameters on monthly average PM_(2.5)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 PM_(2.5)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-bymonth spatial regression shows that RF models for both national stations and all stations show significantly inflated mean values of R^(2)compared with cross-validation results.For MLR models,inflation of both R^(2)and R^(2)CVwas detected when using only national stations and may indicate the restricted ability to predict spatial distribution of PM_(2.5)levels.Inflated within-sample R^(2)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 PM_(2.5)concentrations.