Globally, the urban heat island (UHI) effect is a major problem which leads to urban residents suffering from adverse urban ecological environments and serious health risks. Understanding the impacts of urban landscape features on the thermal environment has been an important focus across various fields of research. The purpose of this study is to analyze the impacts of urban heat source–sink landscape patterns on urban heat islands, using the fast-growing Zhengzhou City in central China as the case study. Landsat data (captured in 1996, 2006, and 2014), various geospatial approaches, and correlation analysis were applied to facilitate the analysis. Based on the contributions of the urban landscape to land surface temperature (LST), we empirically identified the heat sources and heat sinks. Then, the composition and configurations of heat source and sink landscapes were estimated by a series of spatial metrics at the landscape and class levels. The results showed that the overall mean land surface temperature (LST) in the study area increased by 2.72 °C from 1996 to 2014. This observed increasing trend in overall mean LST is consistent with the process of rapid urbanization in the study area, which was evidenced by the dramatic increase in impervious surfaces and the substantial loss in vegetation cover. Generally, as observed, landscape composition has a stronger influence on LST than does landscape configuration. For heat sources, the proportion, size, aggregation, and density of patches have positive effects on LST, while adjusting the spatial distribution and abundance of urban landscape are effective ways to control the UHI effects. In contrast, the percentage, size, density, and aggregation of heat sink patches have negative effects on LST. Additionally, the effects of increasing total patch edges and shape complexity should be considered when mitigating the UHI effect. These findings are beneficial for furthering our understanding of how urban landscape patterns affect UHI, and they can help optimize urban landscape patterns to alleviate the UHI effect and enhance sustainable development in the study area.

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO₂ Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

During the progress of urbanization in China, a large number of natural landscapes have been replaced by impervious surfaces. The strong interference from human activities has led to the intensification of urban heat island (UHI) effects and has had a negative influence on the health of residents. Zhengzhou, as a new representative city of rapid urbanization, can be used as a case study for UHI. This study built an inversion model of the land surface temperature (LST) of Zhengzhou in 1996, 2000, 2006, 2010 and 2014. On this foundation, the four indicators of land use/land cover (LULC), density of the population, urban construction, and industrial development were chosen to establish a quantitative analysis model between them and the LST. The conclusions were as follows: (1) From 1996–2014, the average LST in Zhengzhou increased by 2.939 °C, and the standard deviation decreased from 4.08 to 2.64. (2) Since 2006, the development zone far from the center of city has become a new urban high temperature zone. The distribution characteristics of the UHI have changed from “centralization in downtown” to “downtown and suburban distribution”. (3) Construction land and vegetation had the most significant impacts on the UHI effect. The construction land was positively correlated with LST, and the vegetation showed the opposite effect. The population density, urban construction and industrial development have strong driving effects on the UHI effect, of which the driving force of industrial development is the most intense.

Fine particulate matter (PM_2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM_2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM_2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM_2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM_2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM_2.5 pollution in the study area, the PM_2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM_2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM_2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM_2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM_2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM_2.5, while elevation makes a strong negative impact on PM_2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM_2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM_2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM_2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

Background: China is considered as the largest and most rapidly urbanizing nation in the world. However, possible changes of urban thermal environment and comfort under the rapid urbanization in China still remain poorly understood at a national scale. Methods: Based on the data collected from 180 cities in 1990, 2005, and 2015 in China, the spatiotemporal patterns of urban thermal environment and comfort in summer and their relationships with urbanization variables were investigated in this study. Results: Our results indicate that urban thermal environment has changed greatly during the 25 years. Furthermore, the changes of urban climate in different regions are inconsistent. The Physiological Equivalent Temperature (PET) at most cities (81%) in China increased from 1990 to 2015, which suggested that urban thermal comfort in China was also deteriorating during the 25 years. However, while the PET of some cities in China began to decrease from 2005 to 2015, there were still 33% of cities that had positive trends,which mainly located in North region. Urbanization resulted in a significant influence on urban climate. Compared to southern cities, northern cities were more sensitive to urbanization impact. The most important contribution to increasing of PET for urbanization variables is gross domestic product, followed by urban population. The analysis results reveal changing patterns of urban thermal comfort in China during summer season. It can help urban government and managers improve urban thermal environment and comfort.