Modeling Urban PM 2.5 Concentration by Combining Regression Models and Spectral Unmixing Analysis in a Region of East China

Abstract

Understanding the spatial distribution of PM_2.5 concentration and its contributing environmental variables is critical to develop strategies of addressing adverse effects of the particulate pollution. In this study, a range of meteorological and land use factors were incorporated into a linear regression (LR) model and a logistic model-based regression (LMR) model to simulate the annual and winter PM_2.5 concentrations. The vegetation cover, derived from a linear spectral unmixing analysis (LSUA), and the normalized difference built-up index (NDBI), were found to improve the goodness of fit of the models. The study shows that (1) both the LR and the LMR agree on the predicted spatial patterns of PM_2.5 concentration and (2) the goodness of fit is higher for the models established based on the annual PM_2.5 concentration than that based on the winter PM_2.5. The modeling results show that higher PM_2.5 concentration coincided with the major urban area for the annual average but focused on the suburban and rural areas for the winter. The methods introduced in this study can potentially be applied to similar regions in other developing countries.

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