China’s rapid urbanization and industrialization process has triggered serious air pollution. As a main air pollutant, PM2.5 is affected not only by meteorological conditions, but also by land use in urban area. The impacts of urban landscape on PM2.5 become more complicated from a three-dimensional (3D) and land function zone point of view. Taking the urban area of Nanchang city, China, as a case and, on the basis of the identification of urban land function zones, this study firstly constructed a three-dimensional landscape index system to express the characteristics of 3D landscape pattern. Then, the land-use regression (LUR) model was applied to simulate PM2.5 distribution with high precision, and a geographically weighted regression model was established. The results are as follows: (1) the constructed 3D landscape indices could reflect the 3D characteristics of urban landscape, and the overall 3D landscape indices of different urban land function zones were significantly different; (2) the effects of 3D landscape spatial pattern on PM2.5 varied significantly with land function zone type; (3) the effects of 3D characteristics of landscapes on PM2.5 in different land function zones are expressed in different ways and exhibit a significant spatial heterogeneity. This study provides a new idea for reducing air pollution by optimizing the urban landscape pattern.

/pdf/three-dimensional-landscape-pattern-characteristics-of-land-1v69ilq7.pdf

Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China

PM2.5 pollution exposure assessment necessitates spatial distribution of PM2.5 concentration; however, sparse ground monitoring sites cannot meet the requirement. The Land Use Regression (LUR) model has been applied to simulate pollutants’ concentration successfully. This study took meteorological factors into account to develop a LUR model to predict winter mean PM2.5 concentrations in the Greater Bay Area. Five predictor variables were left in the final LUR model: BLH, V wind speed, percentage of forest area within the 4000-m buffer, density of all roads within the 1500-m buffer. These were negatively associated with PM2.5 concentration, and a percentage of buildings area within the 5000-m buffer were positively associated with PM2.5 concentration. The model adjusted R2 and the leave-one-out-cross-validation (LOOCV) R2 of PM2.5 LUR models were 0.756 and 0.717, respectively. The model would provide acceptable measurements for air pollution exposure of some acute disease for sensitive groups.

Applying LUR model to estimate spatial variation of PM2.5 in the Greater Bay Area, China

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Applying LUR model to estimate spatial variation of PM2.5 in the Greater Bay Area, China