Tailings ponds are a main heavy metal pollution source in mining areas. In this study, the geo-accumulation index ($I_{geo}$) and the potential ecological risk index (RI) are used to evaluate the environmental impact of Hongtou Mountain (HTM) tailings pond on the surrounding area. Farmland soil, surface water, and sediment samples in the Hun River around the HTM tailings pond were collected. Heavy metal contents in the samples were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results show that Cu, Zn, and Cd content in the farmland soil and sediment around the lower reaches of the Hun River (HTM tailings pond section) are obviously higher than the upper reaches. The $I_{geo}$ values show that the farmland soil near the outlet of the tailings pond is the most polluted area. Cu was classified as moderate–strongly pollution, Zn was moderately pollution, and Cd was strongly pollution. Cd is the major pollutant in farmland soil, the monomial ecological risk ($E_r^i$) for Cd is a very high potential ecological risk. The potential ecological risk of sediment in the dry season is more serious than in the raining season. In the dry season, the $I_{geo}$ index shows strong pollution for Cu and Cd at the confluence of the Hun River and the tributary from the HTM tailings pond, and a moderate–strongly pollution for Zn. Whereas, the $E_r^i$ index shows that the monomial ecological risk for Zn at H3 is low, and Cu is moderate. The potential ecological risk at H3 is high, and Cd is the main source of the ecological risk around the HTM tailings pond.

After the first industrial revolution, urbanization level worldwide has increased rapidly. As the largest developing country in the world, China has witnessed a rapid improvement in its urbanization level in recent years. Nevertheless, the quality of urbanization has not been improved simultaneously. The relationship between the level and the quality of urbanization has thus become a hot topic for researchers. By introducing the concept and model of decoupling in the field of resources and environment into the analysis of urbanization level and quality, this study evaluated the relationship between urbanization level and urbanization quality of 285 prefecture-level cities in China from 2005 to 2014. It was found that: (1) The urbanization level and urbanization quality in China are unbalanced because the former is growing in a faster rate than the latter. The average urbanization level of China has increased by 27.40% from 42.99% in 2005 to 54.77% in 2014, while the increase of urbanization quality, however, is much slower with only 11.21% for the same period. It can be concluded that China has paid more attention to urbanization level than urbanization quality. (2) From 2005 to 2014, the relationship between China’s urbanization level and quality showed a total of eight decoupling states, of which the main ones were strong negative decoupling (non-ideal state) and growth negative decoupling (close to ideal state), accounting for 38.32% and 33.49% of the total number of samples in China, respectively. (3) The change of urbanization level and urbanization quality in China can be divided into two stages: for the first stage from 2005 to 2010, with rapid improvement in urbanization level, and the other from 2011 to 2014, with rapid improvement in urbanization quality. (4) Spatially, the areas with significant decoupling between urbanization level and urbanization quality are mainly distributed in underdeveloped areas such as the west; and the decoupling presents the spatial pattern of the highest in the west, the second in the middle, and the lowest in the east.

In recent years, China’s urbanization rate has been increasing rapidly, reaching 59.58% in 2018. Urbanization drives rural-to-urban migration, and inevitably promotes urban sprawl. With the development of remote sensing and geographic information technologies, the monitoring technology for urban sprawl has been constantly innovated. In particular, the emergence of night light data has greatly promoted monitoring research of large-scale and long-time-series urban sprawl. In this paper, the urban sprawl in China in 1992, 1997, 2002, 2007, 2012, and 2017 was identified via night light data, and the Artificial Neural Network-Cellular Automata-Markov (ANN-CA-Markov) model was developed to simulate the future urban sprawl in China. The results show that the suitability of urban sprawl based on the ANN model is as high as 0.864, indicating that the ANN model is very suitable for the simulation of urban sprawl. The Kappa coefficient of simulation results was 0.78, indicating that the ANN-CA-Markov model has a high simulation accuracy on urban sprawl. In the future, the hotspot areas of urban sprawl in China will change over time. Although the urban sprawl in the Beijing-Tianjin-Hebei region, the Yangtze River delta, and the Pearl River delta will still be considerable, the urban sprawl in the Chengdu-Chongqing city cluster, the Guanzhong Plain city cluster, the central plains city cluster, and the middle reaches of the Yangtze River will be more prominent. Overall, China’s urban sprawl will be concentrated in the east of Hu’s line in the future.

Developing countries account for about 86.5% of the world’s population and are experiencing rapid urbanization. Globally, the increase in the urban population is generally accompanied by the expansion of the latter and construction lands, as well as the reduction in the rural population and rural construction lands. However, with the rapid development of urbanization in China, the rural population has decreased, while the proportion of rural construction lands has increased, resulting in a significant waste of land resources. In order to quantitatively characterize the degree of deviation between the permanent rural population and rural construction lands based on the 2009–2016 demographic data and land survey data in China, we comprehensively used the decoupling model and the coordination degree model to analyze the temporal change characteristics, spatial distribution law, and the degree of deviation of rural construction land areas and the number of rural permanent residents. Firstly, according to the decoupling model, the type of decoupling between the area of rural construction lands and the number of rural permanent residents at the national scale was strongly negative. Secondly, according to the coordination degree model, the coordination type between rural construction land areas and the rural resident population was uncoordinated; at the provincial scale, the coordination system involved one city and one district (Beijing and the Tibet Autonomous Region) and the basic coordination of two cities (Tianjin and Shanghai). Xinjiang and Qinghai belonged to the reconcilable type, and the other 25 provinces belonged to the uncoordinated type. Finally, according to the comprehensive measurement model, the number of rural permanent residents and rural construction lands showed two types of decoupling: highly strong negative decoupling incoordination and moderately and weakly strong negative decoupling incoordination.

Global climate change results in an increased risk of high urban temperatures, making it crucial to conduct a comprehensive assessment of the high-temperature risk of urban areas. Based on the data of 194 meteorological stations in China from 1986 to 2015 and statistical yearbooks and statistical bulletins from 2015, we used GIS technology and mathematical statistics to evaluate high-temperature spatial and temporal characteristics, high-temperature risk, and high-temperature vulnerability of 31 cities across China. Over the past 30 years, most Chinese cities experienced 5–8 significant oscillation cycles of high-temperature days. A 15-year interval analysis of high-temperature characteristics found that 87% of the cities had an average of 5.44 more high-temperature days in the 15-year period from 2001 to 2015 compared to the period from 1986 to 2000. We developed five high-temperature risk levels and six vulnerability levels. Against the background of a warming climate, we discuss risk mitigation strategies and the importance of early warning systems.