Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.

Attention to physical and mental health is becoming more intensive. In China, factors and mechanisms are now a focus of research. We used dynamic air quality monitoring data and the Chinese General Social Survey (CGSS) to assess the spatial differences and the coupling between subjective and objective air pollution. In addition, a logistic model was used to explore the impact mechanisms of social inequality, air pollution, food safety, and lack of green space on health. The results show that (1) the impact of subjective and objective air pollution on the health level of the population is significant; (2) income inequality, air pollution, food pollution, and travel behavior significantly affect the residents’ health; and (3) environmental health has a significant differentiation mechanism between urban and rural areas. The negative health effects of air pollution and insufficient green space are more significant in cities; food pollution is more likely in rural areas. In terms of socioeconomic inequality, gender, family size, travel, and physical exercise had no significant effect on rural health. Health improvement was higher in the low-income group than in the high-income group. The adverse effect of travel behavior on environmental pollution is conducive to improving health. Therefore, social equality, strictly controlled environmental pollution, exercise, and travel can help narrow the gap between rich and poor, promote urban–rural health equity, and improve human health.

Goaf restoration is an important part of urban space management. With mining of coal resources, appearance of goaf and subsidence areas causes serious geological disasters and environmental and ecological problems, which significantly affect urban safety, development, and construction. Therefore, repair of goafs is crucial. In this study, the goaf of Jingmen Garden Expo Park was taken as an example. Through acquisition of engineering geological condition parameters and data on the goaf combined with the mechanical parameters selected for the site, the deformation mechanism of the overlying strata of the goaf was analyzed, and a numerical model of the goaf was established. On this basis, FLAC(3D) was used for numerical simulation to evaluate the stability of the goaf; the suitability of the site was evaluated and divided, and the ecological restoration model of the goaf in Jingmen Garden Expo Park was studied. The results showed that different degrees of ecological restoration and construction of various facilities and buildings could be carried out in the goaf. Based on the varying degrees of stability in the goaf, an appropriate restoration path is suggested according to the suitability of these different degrees. The green, innovative, and sustainable restoration design of the goaf can be carried out according to these restoration paths in order to establish a green ecological system in Jingmen Garden Expo Park.

PM2.5 is the main source of air pollution in China. The problem of air pollution has become the focus of public opinion and academic research in recent years. This article departs from the traditional single-scale approach and adopts a spatial multiscale perspective. Leveraging annual average PM2.5 concentration data and urban socioeconomic data spanning the period from 2009 to 2018, in conjunction with atmospheric PM2.5 remote sensing inversion datasets, a comprehensive analysis is conducted. This analysis encompasses the utilization of GIS spatial-temporal analysis techniques and geographic detectors. The primary objective of this research is to investigate the spatiotemporal evolution characteristics of PM2.5 in the Yangtze River Basin during the years 2009 to 2018, as well as to elucidate the influencing factors therein. This study is crucial to the joint prevention and control of air pollution. The Results showed that (1) The overall trend in the number of cities with annual average PM2.5 concentrations below 35 μg/m3 (the annual limit value in China) exhibits fluctuating upward dynamics. (2) From 2009 to 2018, the low-value area distribution of the annual average PM2.5 concentration in the Yangtze River Basin was stable, whereas the high-value area showed a trend of “first decreasing, then increasing, and then decreasing.” (3) The spatial and temporal agglomeration effect was evident, showing an “east-hot, west-cold” agglomeration pattern. From 2009 to 2018, the high-value aggregation area expanded to the middle part of the Yangtze River Basin and then continued to the north. The low-value concentration area was concentrated in the western part of the Yangtze River Basin, and the range change trend was not large. (4) While each variable concurrently engages in interactions, they also exhibit varying degrees of influence on the spatiotemporal distribution of PM2.5. Among them, population density and the proportion of urban built-up areas in the index layer of population and urbanization are strongly correlated factors.

As global urbanization accelerates and climate change intensifies, issues related to land use and management have increasingly garnered attention within the international academic community