A land use regression application into assessing spatial variation of intra-urban fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations in City of Shanghai, China.

Intra-urban assessment of air pollution exposure has become a priority study while international attention was attracted to PM 2.5 pollution in China in recent years. Land Use Regression (LUR), which has previously been proved to be a feasible way to describe the relationship between land use and air pollution level in European and American cities, was employed in this paper to explain the correlations and spatial variations in Shanghai, China. PM 2.5 and NO 2 concentrations at 35–45 monitoring locations were selected as dependent variables, and a total of 44 built environmental factors were extracted as independent variables. Only five factors showed significant explanatory value for both PM 2.5 and NO 2 models: longitude, distance from monitors to the ocean, highway intensity, waterbody area, and industrial land area for PM 2.5 model; residential area, distance to the coast, industrial area, urban district, and highway intensity for NO 2 model. Respectively, both PM 2.5 and NO 2 showed anti-correlation with coastal proximity (an indicator of clean air dilution) and correlation with highway and industrial intensity (source indicators). NO 2 also showed significant correlation with local indicators of population density (residential intensity and urban classification), while PM 2.5 showed significant correlation with regional dilution (longitude as a indicator of distance from polluted neighbors and local water features). Both adjusted R squared values were strong with PM 2.5 (0.88) being higher than NO 2 (0.62). The LUR was then used to produce continuous concentration fields for NO 2 and PM 2.5 to illustrate the features and, potentially, for use by future studies. Comparison to PM 2.5 studies in New York and Beijing show that Shanghai PM 2.5 pollutant distribution was more sensitive to geographic location and proximity to neighboring regions. [ABSTRACT FROM AUTHOR]