High spatiotemporal resolution mapping of PM2.5 concentrations under a pollution scene assumption

Accurate individual exposure assessment to particulates in complex urban environments requires maps of PM2.5 concentration at high spatiotemporal resolution. Previous empirical researches of PM2.5 mapping usually have ignored the contextual influences of associated factors on pollution variation. This study presents a new thinking about spatial prediction of PM2.5 pollution based on the pollution scene assumption. Methodologically, pollution scenes are areas exert contextual influences on the spatiotemporal variety of air pollution and can be expressed by urban microenvironment dependence and temporal nonstationarity. Taking Changsha, China as a case, a two-stage modelling strategy of geographically weighted regression kriging (GWRK) was developed to validate the assumption based on a high-density sampling campaign and a fine-scale, manually interpreted urban microenvironment map. Our results confirm the potential existence of urban air pollution scene. PM2.5 concentration varies between urban microenvironments; pollution scene based GWRK is effective for high resolution mapping of PM2.5 concentration at the hourly scale and depicts more detailed spatial variations than traditional GWR in this study. This assumption and modelling strategy provide a promising way for mapping urban air pollution at high resolution which will further benefits works on exposure assessment and risk avoidance at fine scales.

Automated summary

This study proposes a new method for spatial prediction of PM2.5 pollution based on the pollution scene assumption, and validates the potential effectiveness of this assumption through geographically weighted regression kriging. The research finds that PM2.5 concentration varies between urban microenvironments, and pollution scene-based GWRK is effective for high-resolution mapping.