Local variation of PM2.5 and NO2 concentrations within metropolitan Beijing

Abstract

Studies on long-term effects of air pollutants in China are limited, in part due to a lack of appropriate spatial exposure models. Elsewhere, Land Use Regression (LUR) models have been used extensively to estimate individual-level air pollutant exposure variation within urban areas by combining air pollution measurements with geospatial predictors. Here, LUR models were developed to estimate spatial variation in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations during the heating and non-heating seasons in Beijing, China. Daily routine PM2.5 and NO2 measurements at 35 monitoring stations from October 2012 to December 2013 were collected as dependent variables, with 191 geospatial measures generated as potential independent variables. The PM2.5 LUR model included latitude and forest land (within a 5000m buffer) in the heating season; and latitude, residential land use (5000m) and main road length (100m) in the non-heating season. The NO2 LUR model included latitude, population density (5000m), water bodies (2000m) and road network factors in the heating season; and residential land use (5000m), water bodies (1000m) and road network factors in the non-heating season. Adjusted R2 and cross validation R2 values of all seasonal models were above 0.80, except for the heating season NO2 model (adjusted R2 = 0.87, cross validation R2 = 0.77). Different spatial patterns between NO2 and PM2.5 were evident in Beijing, with a more distinct local pattern for NO2 than PM2.5 and during the non-heating season, when regional pollution was less dominant. These 250m resolution LUR models can be used for health impact assessment or to estimate PM2.5 and NO2 concentrations at the residential addresses of participants in epidemiological cohorts of Beijing.