Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Spatial heterogeneity of urban-rural integration and its influencing factors in Shandong province of China.

Based on nighttime light data and statistical data, this study calculated the level of urban-rural integration (URI) of Shandong province, researched spatial heterogeneity of URI levels by local spatial autocorrelation analysis, Geodetector, and geographically weighted regression, and analyzed its influencing factors and spatial heterogeneity. The results concluded that: (1) The spatial pattern of urban-rural integrated level is consistent with the level of regional economic development in Shandong province. The level of URI is higher along the Qingdao-Jinan railway and along the coast, whereas the level is lower in southwest Shandong and northwest Shandong. (2) The cities of Yantai and Weifang are High-High cluster areas of urban integration, and Jining is a Low-Low cluster area. The spatial agglomeration characteristics are not significant in other cities. (3) Among the main factors affecting URI, the explanatory power of the rural population with high school or technical secondary school education or above, the area of urban construction land, and the secondary and tertiary industry GDP to the spatial pattern of URI in Shandong province are 73.58%, 62.08%, and 58.66%, respectively. As the key factors, spatial heterogeneity, such as north-south differences, southwest-to-northeast differences, and east-west differences, is evident.