Influence of Urban Spatial Structure on the Spatial Distribution of Gaseous Pollutants

Abstract

The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June).