Analysis of the spatial characteristics of inhalable particulate matter concentrations under the influence of a three-dimensional landscape pattern in Xi'an, China

Abstract

Atmospheric inhalable particulate matter increases the risks of damage to the respiratory system and cardiovascular diseases among residents, and threatens urban health and sustainable development. In this study, a geographically weighted regression model was constructed using spatial statistical quantification methods to explore the spatial variability characteristics of different influencing factors of the distribution of the atmospheric inhalable particulate matter concentration. Based on a visual simulation and redundancy analysis, the spatial distribution characteristics of the atmospheric inhalable particulate matter concentration in the main urban area of Xi'an were revealed. The results of the study are illustrated as daily average situations, and the selected set of urban landscape metrics performed well at explaining the variation in the atmospheric inhalable particulate matter concentration. Furthermore, the degree of building height variability, the connectivity of green space patches, the number of air-polluting enterprises, and the area of residential land had greater impacts than other factors on the PM2.5 concentration in winter. The degree of building clustering, the shape complexity of the green space patches, the elevation and the distance from a motorway, on the other hand, had some influence on the PM10 concentration in winter. From the perspective of the landscape pattern, it was found that the higher the density and area of green space patches were, the more complex the shape and the higher the connectivity were; additionally, the higher the spatial contiguity was, the lower the concentration of inhalable particulate matter in the atmosphere in winter. The concentrations of inhalable particulate matter in winter increased with the average heights, densities, and volumes of the architectural landscape; and increased with the roughness of the underlying surface, the height variation and building clustering. To the best of our knowledge, this study is the first to apply a geographically weighted regression model to the Guanzhong Basin, a heavily polluted region in China, and provides a scientific basis for urban planning, air pollution control, and public health policy formulation, thus promoting the sustainability of the urban environment.