Accurate prediction of dissolved oxygen time series is important for improving the water environment and aiding water resource management. In this study, four stand-alone models including multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN) and random forest (RF), and four hybrid models based on wavelet transform (WT) including WT-MLR, WT-SVM, WT-ANN and WT-RF were used to predict the daily dissolved oxygen (DO) at 1-5-day lead times in the Dongjiang River Basin, China. To make the prediction robust, the maximal information coefficient (MIC) was used to capture comprehensive information between DO and explanatory variables. The 5-fold cross validation grid search approach was used to optimize parameters of machine learning tools. Two types of frameworks of WT direct framework (i.e., only the explanatory variables were decomposed) and multicomponent framework (i.e., both explanatory variables and target variables were decomposed) were used to construct hybrid models. The results show that MIC extracts four optimal explanatory variables previous DO, water temperature, air temperature and air pressure. Four evaluation parameters including correlation coefficient (R), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE) and root mean square error (RMSE) indicate that the prediction accuracy decreases as the lead time changes from 1 to 5 days. In terms of the stand-alone models, MLR model outperforms the other three models with higher NSE values of 0.616-0.921, and lower RMSE values of 0.503-1.111. With regard to the hybrid models, WT-ANN and WT-MLR models exhibit higher performance, and multicomponent framework performs better than direct framework in all hybrid models. In general, the multicomponent framework of WT can improve the prediction accuracy of stand-alone models at a certain degree, while the direct framework shows no obvious advantage.The Action Plan for Water Pollution Prevention and Control (i.e., the “10-Point Water Plan”) is a regulation formulated by China to prevent and control water pollution and ensure China’s water safety. To test the policy effect of the “10-Point Water Plan”, we use data from 269 cities for the period from 2012 to 2017 to examine whether the implementation of the plan can help reduce the intensity of industrial water pollution. The results show that the industrial water pollution intensity in Central and Western China is significantly higher than that in other regions, and the implementation of the “10-Point Water Plan” significantly reduces industrial water pollution intensity in China. We further find that upgrading industrial structures and technological innovation are effective ways to ameliorate the intensity of industrial water pollution. In terms of spatial heterogeneity, the impact of the “10-Point Water Plan” on reducing industrial water pollution is smaller in areas with high environmental regulation intensity than in areas with low environmental regulation intensity. Pitavastatin inhibitor We also find a strong inhibitory effect of environmental regulations on industrial water pollution intensity in areas with low environmental regulation intensity. Our findings support the positive policy effect of the “10-Point Water Plan” and provide significant policy implications for water pollution prevention and control actions in China and other countries.Floods often significantly impact human lives, properties, and activities. Prioritizing areas in a region for mitigation based on flood probability is essential for reducing losses. In this study, two game theory (GT) algorithms – Borda and Condorcet – were used to determine the areas in the Tajan watershed, Iran that were most likely to flood, and two machine learning models – random forest (RF), and artificial neural network (ANN) – were used to model flood probability (the probability of flooding). Twelve independent variables (slope, aspect, elevation, topographic position index (TPI), topographic wetness index (TWI), terrain ruggedness index (TRI), land use, soil, lithology, rainfall, drainage density, and distance to river) and 263 locations of flooding were used to model and prepare flood-probability maps. The RF model was more accurate (AUC = 0.949) than the ANN model (AUC = 0.888). Frequency ratio (FR) was calculated for all factors to determine which had the most influence on flood probability. The values of twelve factors that affect flood probability were estimated for each sub-watershed. Then, game-theory algorithms were used to prioritize sub-watersheds in terms of flood probability. A pairwise comparison matrix revealed that the sub-watersheds most likely to flood. The Condorcet algorithm selected sub-watersheds 1, 2, 4, 5, and 11 and the Borda algorithm selected sub-watersheds 2, 4, 5, 20 and 11. Both models predicted that most of the watershed has very low flood probability and a very small portion has a high probability for flooding. The quantitative analysis and characterization of the watersheds from the perspective of flood hazard can support decision making, planning, and investment in mitigation measures.Acid mine drainage (AMD) with toxic arsenic (As) is commonly generated from the tailings storage facilities (TSFs) of sulfide mines due to the presence of As-bearing sulfide minerals (e.g., arsenopyrite, realgar, orpiment, etc.). To suppress As contamination to the nearby environments, As immobilization by Ca-Fe-AsO4 compounds is considered one of the most promising techniques; however, this technique is only applicable when As concentration is high enough (>1 g/L). To immobilize As from wastewater with low As concentration (~10 mg/L), this study investigated a two-step process consisting of concentration of dilute As solution by sorption/desorption using schwertmannite (Fe8O8(OH)8-2x(SO4)x; where (1 ≤ x ≤ 1.75)) and formation of Ca-Fe-AsO4 compounds. Arsenic sorption tests indicated that As(V) was well adsorbed onto schwertmannite at pH 3 (Qmax = 116.3 mg/g), but its sorption was limited at pH 13 (Qmax = 16.1 mg/g). A dilute As solution (~11.2 mg/L As) could be concentrated by sorption with large volume of dilute As solution at pH 3 followed by desorption with small volume of eluent of which pH was 13.