Spatiotemporal Distribution Characteristics and Multi-Factor Analysis of Near-Surface PM2.5 Concentration in Local-Scale Urban Areas

Abstract

Near-surface PM2.5 concentrations have been greatly exacerbated by urban land expansion and dense urban traffic. This study aims to clarify the effects of multiple factors on near-surface PM2.5 concentrations from three perspectives of background climatic variables, urban morphology variables, and traffic-related emission intensity. First, two case areas covering multiple local blocks were selected to conduct mobile measurements under different climatic conditions. The observed meteorological parameters and PM2.5 concentration were obtained through GIS-based imaging. These interpolation results of air temperature and relative humidity reveal highly spatiotemporal diversity, which is greatly influenced by artificial heat emissions and spatial morphology characteristics in local areas. The PM2.5 concentration on measurement days also varies considerably from the lowest value of 44~56 μg/m3 in October to about 500 μg/m3 in December in Harbin winter and ranges between about 5 μg/m3 and 50 μg/m3 in Guangzhou summer. The correlation analysis reveals that both the climatic conditions and urban morphology characteristics are significantly correlated with local PM2.5 concentration. Especially for Guangzhou summer, the PM2.5 concentration was positively correlated with the street traffic emission source intensity with correlation coefficient reaching about 0.79. Multivariate nonlinear formulas were applied to fit the association between these factors and PM2.5 concentration with higher determined coefficients. And optimization strategies are thus suggested to improve the urban air quality in local-scale areas. This attribution analysis contributes to environmentally friendly urban construction.