Simulating the cumulative effects of potential open-pit mining and climate change on streamflow and water quality in a mountainous watershed

The effect of heavy precipitation on the leaching of heavy metals from tropical coastal legacy tailings

The continued growth in demand for mineral resources has led to a large amount of mining wastes, which is a major challenge in the context of carbon neutrality and climate change. In this study, runoff migration, batch leaching, and column experiments were used to investigate the short-, medium-, and long-term leaching of heavy metals from legacy tailings, respectively; the cumulative metal release kinetic equations were established, and the long-term effects of tailings leaching were verified by HYDRUS-1D. In runoff migration experiments, surface dissolution of tailings and the co-migration of adsorbed soil particles by erosion were the main carriers in the early stages of leachate formation (Mn ∼ 65 mg/L and SO42- up to 2697.2 mg/L). Batch leaching tests showed that the concentration of heavy metals in soil leached by acid rain were 0.1 ∼ 22.0 μg/L for Cr, 0.7 ∼ 26.0 μg/L for Cu, 4.8 ∼ 5646.0 μg/L for Mn, 0.3 ∼ 232.4 μg/L for Ni, and 1.3 ∼ 448.0 μg/L for Zn. The results of column experiments indicated that some soluble components and metals with high mobility showed a significant decreasing trend at cumulative L/S ≤ 2. Additionally, the metals have higher leaching rates under TCLP conditions, as shown by Mn > Co > Zn > Cd > Ni > Cu > Pb > Cr. The fitting results of Langmuir equation were closer to the cumulative release of metals in the real case, and the release amounts of Mn, Zn, Co, and Ni were higher with 55, 5.84, 2.66, and 2.51 mg/kg, respectively. The water flow within tailings affects the spatial distribution of metals, which mainly exist in relatively stable chemical fractions (F3 + F4 + F5 > 90 %) after leaching. Numerical simulation verified that Mn in leachate has reached 8 mg/L at a scale of up to 100 years. The research results are expected to provide technical basis for realizing the resource utilization of tailings in the future.

Predicting Daily Streamflow in a Cold Climate Using a Novel Data Mining Technique: Radial M5 Model Tree

In this study, the viability of radial M5 model tree (RM5Tree) is investigated in prediction and estimation of daily streamflow in a cold climate. The RM5Tree model is compared with the M5 model tree (M5Tree), artificial neural networks (ANN), radial basis function neural networks (RBFNN), and multivariate adaptive regression spline (MARS) using data of two stations from Sweden. The accuracy of the methods is assessed based on root mean square errors (RMSE), mean absolute errors (MAE), mean absolute percentage errors (MAPE), and Nash Sutcliffe Efficiency (NSE) and the methods are graphically compared using time variation and scatter graphs. The benchmark results show that the RM5Tree offers better accuracy in predicting daily streamflow compared to other four models by respectively improving the accuracy of M5Tree with respect to RMSE, MAE, MAPE, and NSE by 26.5, 17.9, 5.9, and 10.9%. The RM5Tree also acts better than the M5Tree, ANN, RBFNN, and MARS in estimating streamflow of downstream station using only upstream data.