Assessing spatial distribution and impact factors of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) by combining high-resolution geographical census data, and meteorological data in Hebei province, China

This paper was the first to employ land use regression (LUR) with high-resolution geographical census data for Hebei, one of the most severely polluted regions in China, to evaluate its spatial distribution characteristics of PM_2.5 and NO₂ concentrations and identify influencing factors. To develop the LUR model, PM_2.5 and NO₂ concentrations recorded at 53 sites in Hebei were selected as dependent variables. Independent variables include buffer-related and location-based factors. At first, 169 independent variables were chosen in total. Then pre-processing of bivariate correlation was performed to prevent multicollinearity. Lastly, step-wise regression was processed to identify the impacting factors. Different to other cities which have been studied like Shanghai or Beijing, we find that the results showed that PM2.5 and NO2 concentrations were positively correlated with the industrial pollution sources in a buffer area. NO₂ concentrations displayed significant negative correlations with forestland within the distance of 1 km and from the coastline. This study showed that the introduction of high-resolution geographical data into the LUR model significantly improved the fitting. More importantly, our study identified industries within a 9 km-buffer as important influencing factors in Hebei and was also consistent with empirical observations. It provided data on effective buffers to support future policy-making and designations of residential areas.