Land use changes affect the surface radiative budget and energy balance by changing the surface albedo, which generates radiative forcing, impacting the regional and global climate. To estimate the effect of land use changes on the surface albedo and climate change in a mountainous area with complex terrain, we obtained MODIS data, identified the spatial–temporal characteristics of the surface albedo caused by land use changes, and then calculated the radiative forcing based on solar radiative data and the surface albedo in the Qinling-Daba mountains from 2000 to 2015. The correlation between the land use changes and the radiative forcing was analyzed to explore the climate effects caused by land use changes on a kilometer-grid scale in the Qinling-Daba mountains. Our results show that the primarily land use changes were a decrease in the cultivated land area and an increase in the construction land area, as well as other conversions between six land use types from 2000 to 2015. The land use changes led to significant changes in the surface albedo. Meanwhile, the radiative forcing caused by the land use had different magnitudes, strengths, and occurrence ranges, resulting in both warming and cooling climate change effects.

Rapid urbanization aggravates issues related to protection and optimization of the ecological environment. Constructing an ecological network system, including ecological values in planning, and using landscape effects efficiently are important for adjusting regional ecological space and promoting local sustainable development. Land use data from eight time points between 1980 and 2020 in the Zhengzhou Metropolitan Area were used to identify the local ecological sources, corridors and nodes and to identify an ecological network with high structural integrity. The study used the FLUS, MSPA, MCR, and gravity models, hydrological analysis, and network structure evaluation by applying tools such as ArcGIS, Guidos Toolbox and Conefor. The results indicated that: (1) among the nine major ecological sources, those in the Yellow River Basin connected the large−scale sources in the east and west of the network, and the rest were located in the northeast, southeast and southwest of the research area, semi−enclosing the main urban area of Zhengzhou. (2) There were 163 least−cost paths and 58 ecological corridors, mainly distributed along the Yellow River Basin. (3) There were 70 ecological nodes, divided into 10 strategic, 27 natural ecological and 33 artificial environment nodes, distributed in key locations such as the core of each source and the intersection of corridors. (4) The ecological network included all the landscape elements in the research area and connected the main ecological substrates in a semi−enclosing network structure with one horizontal and two vertical corridors and four clusters.

Urban cold island effects have become increasingly relevant with accelerating climate change. However, the relationship between such effects and their causal variables remains unclear. In the present study, we analyzed the relationship between blue-green space variables and land surface temperature (LST) and park cooling intensity (PCI) in central Zhengzhou City using a random forest regression model. Cool urban areas corresponded to the location of blue-green spaces. The average temperatures of these spaces were 2 °C and 1 °C lower than those of the built-up areas and the full study region, respectively. Blue-green spaces also had a maximum temperature that was 8 °C lower than those of the built-up areas and the study region. The three primary variables determining LST were blue space proportion and area and vegetation cover, whereas the three variables determining PCI were blue-green space width, vegetation cover, and patch density. At a width of 140 m, blue-green spaces caused a PCI peak, which further improved at 310 m. The proportion of blue space had a stepwise effect on PCI. A vegetation coverage of 56% represented the lower threshold of LST and the higher threshold of PCI. These results reflect a nonlinear relationship between blue-green variables and urban cold islands. In conclusion, the study provides data that could inform the efficient use of blue-green spaces in urban construction and renewal.

Land surface temperature (LST) variations are very complex in mountainous areas owing to highly heterogeneous terrain and varied environment, which complicates the surface urban heat island (SUHI) in mountain cities. Previous studies on the urban heat island (UHI) effect mostly focus on the flat terrain areas; there are few studies on the UHI effect in mountainous areas, especially on the influence of elevation on the SUHI effect. To determine the SUHI in the Qinling–Daba mountains (China), MODIS LST data were first preprocessed and converted to the same elevations (1500 m, 2000 m, 2500 m, 3000 m, and 3500 m) using a digital elevation model and the random forest method. Then, the average LSTs in urban land, rural land, and cultivated land were calculated separately based on the ranges of the invariable urban, rural, and cultivated areas during 2010–2018, and the urban, rural, and cultivated land LST difference were estimated for the same elevations. Results showed that the accuracy of LST estimated using the random forest method is very high (R² ≥ 0.9) at elevations of 1500 m, 2000 m, 2500 m, 3000 m and 3500 m. The difference in urban, rural, and cultivated lands’ LST has a trend of decrease with increasing elevation, meaning that the SUHI weakens at higher elevations. The average LST of urban areas is 0.52–0.59 °C (0.42–0.57 °C) higher than that of rural and cultivated areas at an elevation of 1500 m (2000 m). The average LST of urban areas is 0.10–1.25 °C lower than that of rural and cultivated areas at elevations of 2500 m, 3000 m, and 3500 m, indicating absence of the SUHI at those elevations.

Although the cultivation of food crops in farmland heavily contaminated by heavy metals is prohibited in China, vegetables can still be planted on a small-scale due to their short growth cycles and flexible sale models, posing a significant threat to local consumers. In this study, a pot culture experiment was conducted to investigate the feasibility of safe production through the in-situ stabilization of heavy metals in heavily contaminated soil. The remediation efficiency of wheat straw biochar and N-doped biochar, the growth of spinach, the heavy metal accumulation in spinach, and potential health risks were also explored. The results indicated that both biochar and N-doped biochar significantly affected the soil pH, cation exchange capacity, organic matter, available phosphorus, available potassium, alkaline nitrogen content, and spinach biomass, but the trends were variable. Additionally, the diethylenetriaminepentaacetic-extractable Pb, Cd, Cu, Zn, and Ni concentrations decreased 9.23%, 7.54%, 5.95, 7.44%, and 16.33% with biochar, and 10.46%, 12.91%, 21.98%, 12.62%, and 12.24% with N-doped biochar, respectively. Furthermore, N-doped biochar significantly reduced the accumulation of Pb, Cd, and Ni in spinach by 35.50%, 33.25%, and 30.31%, respectively. Health risk assessment revealed that the non-carcinogenic risk index for adults and children decreased from 17.0 and 54.8 to 16.3 and 52.5 with biochar and 11.8 and 38.2 with N-doped biochar, respectively, but remained significantly higher than the acceptable range (1.0). The carcinogenic risk assessment revealed that the risk posed by Cd in spinach exceeded the acceptable value (10⁻⁴) for both adults and children across all treatments. These results may imply that biochar and N-doped biochar cannot achieve the safe production of vegetables in soil heavily contaminated by heavy metals through in-situ stabilization.

The Qinba Mountain range is a typical climate-sensitive and ecologically fragile region. Monitoring of vegetation dynamics is crucial for ecological protection and achieving sustainable development goals. Various mutation-detection methods, along with slope analysis, hot-spot analysis, and residual analysis, were used to examine changes in the Normalized Difference Vegetation Index (NDVI) during the growing and non-growing seasons over 41 years and to distinguish the relative effects of the drivers. This revealed four key findings. (1) NDVI increased at 0.02 decade⁻¹, with mutation points in 2006 for growing-season NDVI and 2007 for non-growing-season NDVI. (2) The trend in NDVI changed markedly at the mutation point. After the mutation point, NDVI was impacted more by human activity than by climate change. The hot and cold spots of the NDVI trend rate change in location and range in the growing season; in the non-growing season, it shows an obvious north–south distribution. (3) The spatial patterns in the effects of the drivers changed at this point. In the growing season, before this point, climate change and human activity collectively enhanced NDVI in ca. 81.3% of the region; after the mutation point, this value declined to 59.9% of the area, and human activity became the dominant driver in the area formerly dominated by both factors in combination. In the non-growing season, after the mutation point, the areas where both factors promoted vegetation growth decreased by 12.6% and those where climate change alone promoted it decreased by 11.1%, whereas the area affected only by human activity increased by 11.6%. (4) Before this point, human activity contributed >60% to the change in NDVI in the western Qinling region, with climate change contributing >60% in the other areas. After this point, human activity exerted a stronger influence than climate change, contributing >60% to enhancing vegetation growth and >80% reducing it. These findings provide a scientific basis for protecting the Qinba Mountain ecosystem and are essential for achieving sustainable development goals.