The DOM content of both electrodes did not change significantly when mechanical pressure changed, anodic oxidation can oxidize and decompose macromolecular weight substances into mid-molecular weight and low molecular weight substances. The aim of this research was to investigate zinc removal from submerged arc furnace flue gas wash water with steel slag and polymer treatment. The current treatment for the submerged arc furnace flue gas wash water is treatment with polymer only which aids in the settling of particulate zinc. However, in this research enhanced removal of zinc by also precipitating soluble zinc using steel slag was studied. The zinc removal results were compared with the results using commercial neutralizing agents NaOH, Mg(OH)2, and Ca(OH)2 together with polymer. The precipitation conditions were simulated with MINEQL + software and the calculated results were compared with the results from laboratory jar test experiments. Zinc was removed to less than the target concentration 2 mg/l with steel slag and polymer treatment at pH 9. Additionally, turbidity of the treated water decreased to 20 NTU compared to the initial 860 NTU. However, the amount of steel slag needed in the treatment was significantly higher than the amounts of NaOH and Ca(OH)2. The main zinc removal mechanism of steel slag was precipitation as zinc oxide. Calculated zinc removal was higher than the experimental which indicates that equilibrium was not reached in the precipitation experiments which could be due to relatively short contact time chosen to simulate the actual process conditions at the plant. In many European countries, waste tariff systems are being updated to take account of the consolidation of circular economy objectives. We forecast the impact of a gain-sharing cost-reflective tariff on waste management sustainability. The cost-reflective tariff generates an economic surplus for society, while meeting environmental goals. MEK inhibitor Based on empirical data we developed two scenarios. The baseline scenario formalizes the current Italian tariff scheme, while the intervention scenario includes users’ response to a gainsharing policy. By optimizing the tariff structure, society would benefit from a surplus that could be reached setting an asymmetric regulation based on current waste management efficiency level. Our results will potentially be a basis for directing waste management policy as per tariff design. Groundwater recharge is indispensable for the sustainable management of freshwater resources, especially in the arid regions. Here we address some of the important aspects of groundwater recharge through machine learning algorithms (MLAs). Three MLAs including, SVM, MARS, and RF were validated for higher prediction accuracies in generating groundwater recharge potential maps (GRPMs). Accordingly, soil permeability samples were prepared and are arbitrarily grouped into training (70%) and validation (30%) samples. The GRPMs are generated using sixteen effective factors, such as elevation (denoted using a digital elevation model; DEM), aspect, slope angle, TWI (topographic wetness index), fault density, MRVBF (multiresolution index of valley bottom flatness), rainfall, lithology, land use, drainage density, distance from rivers, distance from faults, annual ETP (evapo-transpiration), minimum temperature, maximum temperature, and rainfall 24-hr. Subsequently, the VI (variables importance) is assessed based on the LASSO algorithm. The GRPMs of three MLAs were validated using the ROC-AUC (receiver operating characteristic-area under curve) and various techniques including true positive rate (TPR), false positive rate (FPR), F-measures, fallout, sensitivity, specificity, true skill statistics (TSS), and corrected classified instances (CCI). Based on the validation, the RF algorithm performed better (AUC = 0.987) than the SVM (AUC = 0.963) and the MARS algorithm (AUC = 0.962). Furthermore, the accuracy of these MLAs are included in excellent class, based on the ROC curve threshold. Our case study shows that the GRPMs are potential guidelines for decision-makers in drafting policies related to the sustainable management of the groundwater resources. Cadmium (Cd) is a toxic heavy metal with unknown biological role. Interactive effect of Enterobacter sp. MN17 and biochar was studied on the growth, physiology and antioxidant defense system of Brassica napus under Cd contaminated soil. A multi-metal tolerant endophytic bacterium, Enterobacter sp. MN17, was able to grow in tryptic soy agar (TSA) medium with up to 160, 200, 300, 700, 160 and 400 μg mL-1 of Cd, Cu, Cr, Pb, Ni and Zn, respectively. Paper and pulp waste biochar was prepared at 450 °C and applied to pots (7 kg soil) at a rate of 1% (w/w), while Cd was spiked at 80 mg kg-1 soil. Application of Enterobacter sp. MN17 and biochar, alone or combined, was found effective in the amelioration of Cd stress. Combined application of Enterobacter sp. MN17 and biochar caused the maximum appraisal in shoot and root length (52.5 and 76.5%), fresh and dry weights of shoot (77.1 and 70.7%) and root (81.2 and 57.9%), photosynthetic and transpiration rate (120.2 and 106.6%), stomatal and sub-stomatal conductance (81.3 and 75.5%), chlorophyll content and relative water content (RWC) (78.4 and 102.9%) than control. Their combined use showed a significant decrease in electrolyte leakage (EL), proline, malondialdehyde (MDA), catalase (CAT), glutathione peroxidase (GPX), glutathione S transferase (GST) and superoxide dismutase (SOD) by 39.3, 39.4, 39.5, 37.0, 39.0 42.1 and 30.8%, respectively, relative to control. Likewise, the combined application of bacterial strain MN17 and biochar reduced Cd in soil by 45.6%, thereby decreasing its uptake in root and shoot by 40.1 and 38.2%, respectively in Cd contaminated soil. The application of biochar supported the maximum colonization of strain MN17 in the rhizosphere soil, root and shoot tissues. These results reflected that inoculation with Enterobacter sp. MN17 could be an effective approach to accelerate biochar-mediated remediation of Cd contaminated soil for sustainable production of crops.