In order to understand the heavy metal pollution status and ecological effect in aquatic environment around copper mine areas, seven heavy metals (Cd, Cd, Cr, Cu, Hg, Zn, the Ni, and Pb) in aquatic environments in seven representative copper mine regions were selected from the literature in 2005–2013 for ecological risk assessment by using potential ecological risk index, geoaccumulation index, nemerow index and species sensitivity distribution method (Potential Affected Fraction (PAF) and Multi-Substance PAF (MSPAF)). The results of sediment ecological risk analysis showed that Cd, Cu and Pb were the main pollutants in sediments. The results of species sensitivity distribution analysis showed that the HC5 values (Hazardous Concentration for 5% of species) of seven heavy metals were different with order Zn > Cr > Cd > Pb > Cu > Ni > Hg. The MSPAF of seven copper mines in the following order with species sensitivity distribution method was as follows: Dabaoshan (99%) = Dahongshan (99%) = Baiyin (99%) > Dexing (97%) > Jinchuan (92%) > Tongling (39%) > Daye (24%). This study analyzes the impact of copper mining on the aquatic environment, and the results of this study will be great value for the comprehensive pollution governance of mining.

Green approaches are much more appreciable during the present scenario. Soil amendments are frequently applied for improving water use efficiency, reducing soil salinity in arid and semi-arid areas, controlling the secondary salinization of cultivated soils, and increasing the carbon sequestration capacity of soils. Thus, lab soil column simulation experiments and field experiments were carried out to evaluate these functions of two separate amendments, polyacrylamide-based super absorbent polymer (SAP) and corn straw biochar at different application rates. The simulation experiments showed that both SAP and biochar inhibited the accumulation of soil salinity, with a reduced rate of 9.7–26.3% and 13.5–37.2%, respectively, dependent on the amendment application rates. The field experiments found different salt inhibition effects of the two amendments with growth stages of maize. Soil salinization was inhibited in the pre-germination and early jointing stages by SAP, but throughout the whole growth period by biochar. Both soil amendments reduced soil electrical conductivity, and biochar increased the soil contents of Ca²⁺, Mg²⁺, and K⁺. Our observations demonstrated that application of biochar and SAP played important roles in increasing soil fertility and inhibiting soil salt accumulation. It provided an effective method to potentially mitigate the environmental crisis and promote sustainable development in agriculture.

In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: “Excellent”, “Good”, “Medium”, “Poor”, or “Unacceptable”. Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, COD_Mn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking.