Urban parks can effectively reduce surface temperatures, which is an important strategic approach to reducing the urban heat island effect. Quantifying the cooling effect of urban parks and identifying their main internal influencing factors is important for improving the urban thermal environment, achieving maximum cooling benefits, and improving urban sustainability. In this study, we extracted data frobut this is often unrealisticm 28 urban parks in Zhengzhou, China. We combined multivariate data, such as Landsat 8 data, to retrieve the land surface temperature (LST), extract the park interior landscape, and quantify the cooling effect using three cooling indices: park cooling distance (L_∆max), temperature difference magnitude (∆T_max), and temperature gradient (G_temp). Furthermore, the relationship between the internal landscape characteristics of the park and the average LST and cooling indices of the park was analyzed. The results showed that different buffer ranges affect the LST-distance fitting results of urban parks, and a 300-m buffer zone is the optimal fitting interval. However, specific parks should be analyzed to select the optimal buffer range and reduce the cooling index calculation errors. Additionally, the mean values of LST, ∆T_max, L_∆max, and G_temp for the 28 parks in Zhengzhou were 34.11, 3.22 °C, 194.02 m, and 1.78 °C/hm, respectively. Park perimeter (PP), park area, internal green area (GA), and landscape shape index (LSI) were both significantly correlated with ∆T_max and the main factors associated with maintaining a low LST in parks. L∆_max was mainly affected by the GA, LSI, and perimeter-area ratio, whereas G_temp was positively correlated with PP. Finally, the threshold value of efficiency for parks in Zhengzhou was 0.83 ha, and comprehensive parks showed optimal cooling in every aspect.

The acceleration of global urbanization has increased the frequency of the urban heat island (UHI) effect and heatwaves, which seriously endanger human health. We used Shenzhen as a case study to examine the daytime and nighttime differences in UHI intensity (UHII), considering different local climate zones (LCZs) and sea–land breezes. The diurnal UHII was >3 °C for 52% of the study period, whereas the nocturnal UHII was >3 °C for only 26% of the study period. The average diurnal and nocturnal building-type UHII values were 2.77 and 1.11 °C higher than those of the natural type, respectively. Sea breezes alleviated the UHI effect with a linear correlation coefficient of −0.68601 between them. Moreover, diurnal and nocturnal UHII showed differences across different gradients, which can help guide urban planning.

The urban development boundary (UDB) is a core issue in territorial spatial planning and remains a research focus in urban development. However, in the past, land use simulations of coastal cities have had some limitations. For example, new urban land outside the research boundary could not be effectively simulated, resulting in a large gap between the simulation results and reality. In this study, an artificial neural network (ANN) cellular automata (CA) model based on a new perspective, the Offshore Island Connection Line (collectively, OICL-ANN-CA), was developed to address this problem. We first proposed the delineation principles and methods of OICL and applied it to the Jinpu New Area of Dalian City, Liaoning Province, China. Based on the conversion probability and land use simulation results obtained by ANN and CA, this study validated the UDB simulations for 2000–2020 in the Jinpu New Area and predicted the UDB for 2020–2035 under three scenarios: historical inertia development, ecological security protection, and ecological and economic balance. The results indicate that, compared with the traditional perspective model (the ANN-CA model based on the sea–land boundary), the OICL-ANN-CA model exhibited better simulation accuracy (the figure of merit was approximately 35% higher) and effectively simulated new urban land outside the traditional boundary. This simulation of urban land expansion is more consistent with recent development in the study area. In addition, the predicted results of UDB in 2035 also demonstrate the benefits of this model for detecting reclaimed land. This study illustrates that the OICL-ANN-CA model is a more capable method for capturing changes in new coastal urban land and can produce realistic simulations. It provides a reference for delimiting the UDB and defining the research boundary for the Jinpu New Area and other fast-growing coastal cities in China.

Global warming has led to the enhancement of diurnal (daytime-nighttime) compound heat waves, which can severely affect the population’s physical health and productive life; this is particularly the case for vulnerable populations that are more susceptible to psychological and physiological harm with the future normalization of heatwave scenarios. In this study, we attributed the changes in population exposure under diurnal compound high temperatures (HTs) and extremely high temperatures (EHTs) to the influence of climatic (exposure duration) and population factors and their combined influence to determine the relationship between exposure duration and the changes in exposure of vulnerable populations. Diurnal compound HT and EHT covered a land area of 4,016,800 and 1,984,200 km2, respectively. The heat-exposed area spread step-like in all directions, centering on the 24-h high-exposure area with a strong gradient effect. The area of prolonged exposure shifted from the Yellow-Huai-Huai Plain across the geographical boundary between the south and the north to the middle and lower reaches of the Yangtze River Plain. Meanwhile, the average maximum value of vulnerable population exposure reached 186,800 and 93,100 person-h, with high values in Zhengzhou, Chongqing, Wuhan, Chengdu, Beijing, and other cities. Changes in vulnerable population exposure were dominated first by demographic factors and then by climatic factors, with the dominance of climatic factors evident in areas of prolonged exposure. Under prolonged exposure to daytime-nighttime compound EHT, changes in the exposure of child and older populations were dominated by climatic factors with a contribution rate of 87.16% and 87.43%, respectively. Therefore, suitable cooling measures should be proposed for vulnerable populations under prolonged exposure, focusing more on the physical and mental health of children and older adults exposed to compound EHT.

Nighttime light images are valuable indicators of regional economic development, and nighttime light data are now widely used in town monitoring and evaluation studies. Using the nighttime light data acquired through Luojia1-01 and the geographic information system spatial analysis method, this study analyzed the spatial vitality pattern of 402 characteristic towns in six geographic divisions of China. The average DN (Digital Number) value of Guzhen, having the highest vitality level, was 0.05665221, whereas that of Xin’an, having the lowest vitality level, was 0.00000186. A total of 89.5% of towns have a low level of vitality. The regional differences were significant; high vitality towns are concentrated in economically developed coastal areas, mainly in two large regions of east China and south central. The average lighting densities of the towns in east China and south central were 0.004838 and 0.003190, respectively. The lighting density of the towns in west central was low, and the vitality intensity was generally low. A spatially significant positive correlation of small-town vitality was observed, and “high–high” agglomeration was primarily distributed in the Yangtze River Delta, Pearl River Delta, and Fujian coastal areas in east and south China. The towns with high vitality intensity had similarities in their geographical location, convenient transportation conditions, and profound historical heritage or cultural accumulation along with many industrial enterprises. This research empirically demonstrates the feasibility of using the 130-m-high resolution of the nighttime lighting data of Luojia1-01 to evaluate the vitality at the town scale, and the vitality evaluation focuses on the spatial attributes of the town, which is meaningful to guide the development of the town in each region given the vast area of China and the large differences in the development of different regions.