Mining and industrial activities are the primary sources of soil pollution in the open-pit coal mine. The concentrations of PAHs in 11 sampling sites in the industrial district and 9 sampling sites with different land use types in Pingshuo open-pit coal mine, China, were measured to investigate the distributions of PAHs and possible sources in soil profiles (0–100 cm). In the topsoil layer (0–20 cm), concentrations of 16 PAHs ranged from 2.15 to 33.51 mg kg⁻¹, with a mean value of 11.93 mg kg⁻¹. PAHs were more variable in the middle soil layer (20–50 cm), ranging from 0.199 to 36.888 mg kg⁻¹ with the average value of 9.21 mg kg⁻¹. Comparable extreme concentrations were detected from the samples in the subsoil layer (50–100 cm). Compared with those in topsoil, the average concentrations of the most individual PAH species were less in the middle soil and subsoil layers. The concentrations of most of the individual PAHs in the topsoil were higher than those in the middle soil and subsoil. Distribution patterns of PAHs in the three soil layers correlated well with each other. The high concentration hotspots were concentrated around the old coal washery, reaching about 30 mg kg⁻¹. The average concentrations of PAHs with different rings in the industrial district exceeded those in the other land use types. However, only the concentrations of 5-ring PAHs in topsoil showed difference with different land use types (P < 0.05). Diagnostic ratios and the total index showed that petroleum combustion was the main source of PAHs.

This study focuses on the issue of supply security in Chinese tin resources, conducting a comprehensive analysis encompassing supply security, demand conditions, market risk, and geopolitical stability. The study employs the AHP-Entropy Weight method to calculate the comprehensive weights of various indicators, revealing the sustainable supply capability of the resources and the challenges they confront. The research unveils that the domestic supply potential and the international commercial extraction environment are pivotal influences on the sustainable supply capability of tin resources. Firstly, since 2010, China has started to import net tin ores and concentrates, leading to a substantial increase in external dependency. However, China’s tin resource reserves have gradually decreased, posing challenges to the stability of the tin resource supply. Secondly, the stability of supply countries and the security of import channels critically affect the stability of the tin resource supply chain. A comprehensive analysis of the outcomes indicates a consistent decline in China’s sustainable tin resource supply capability since 2010, primarily due to the deterioration of resource supply conditions, growth in demand, and the escalation of geopolitical risks. Thus, the evolving foreign extraction environment, domestic resource supply potential, and the alteration in the global tin resource extraction landscape require significant attention.

The steel industry in the Belt and Road region holds significant potential for development, and the systematic evaluation of its sustainable development capacity serves as a crucial foundation for improving the investment environment in the steel industry. This study focuses on the driving forces, pressures, current status, impacts, and policy responses of the steel industry, constructing an evaluation model for the sustainable development of the steel industry in the Belt and Road region. Using this model, the sustainable development capacity of the steel industry (SCSI) in 65 countries along the Belt and Road is assessed. The results indicate the following: (1) The SCSI index in the Belt and Road region has significantly increased from 18.050 in 2000 to 22.873 in 2021. (2) Since 2005, the factors influencing the spatial differentiation of SCSI in the Belt and Road region have generally followed the pathway of “industrial infrastructure → innovation environment → global governance capacity → per capita GDP,” with domestic economic level and domestic demand exerting a substantial promoting effect on the steel industry. (3) The regional differences in SCSI within the Belt and Road region are the combined result of multiple factors, with significant composite influences from indicators such as per capita GDP, population size, industrial infrastructure, and innovation environment. To further enhance the sustainable development of the steel industry along the Belt and Road, it is necessary for each country to formulate appropriate development plans based on local conditions, emphasizing strengthened international cooperation, optimized international investment environment, enhanced policy support, and improved technological level in the steel industry.

The specification of environmental extrema is a persisting problem, especially in soil with spatial heterogeneity owing to anthropogenic activities. Using a geographic detector, a Bayesian spatial model, and a copula-based spatial model, methods of identification of extremal regions in a mine area were compared. The results are as follows. (1) All of the heavy metals in anthropogenic soil, including As, Cd, Cr, Hg, and Ni, had a weak random spatial heterogeneity, but Cd and As exhibited strong stratification spatial heterogeneity (q = 0.21** a nd 0 .11*, respectively). (2) The Cr, Hg, and Ni predictions are very similar for both models (the improvements in the mean absolute percentage error (MAPE) and R2 are 5.88% at most and 3.29%, respectively). The copula-based spatial model outperformed the Gaussian spatial model in the predictions of Cd (MAPE: 12.12%; R2: 16.67%) and As (MAPE: 4.16%; R2: 7.89%). (3) Based on the comparison with the Gaussian spatial model using a Bayesian process, the identification of the extremal regions using the copula-based spatial model had a higher accuracy for the extreme samples. In general, the prediction obtained using the copula-based model revealed the probability of exceeding a certain threshold at a location. Moreover, it uses the copulas fitting of the samples’ spatial heterogeneity obtained through maximum likelihood estimation, rather than variogram fitting, resulting in the random spatial heterogeneity summing to a nugget, which preserves more information about the samples. Thus, we conclude that the copula-based spatial model can be used to predict the heavy metal concentrations in soil with weak random spatial heterogeneity but strong stratification spatial heterogeneity.