Capacitive deionization (CDI) has emerged as an energy-efficient and sustainable electrochemical technology for removing toxic heavy metal ions from contaminated water. This study investigates the electro-sorptive removal of hexavalent chromium (Cr (VI)) using a continuous, compartmental rectangular-flow-through static CDI system equipped with industrial biochar chitosan-composite electrodes, and develops a Dynamic Langmuir (DL) model to describe the underlying adsorption-desorption kinetics. Experiments were conducted under varying initial concentrations (50,75,100, and 150 ppm), electrode configurations (2,4, and 6 electrodes), and loading rates (single, 0.016 g/cm-2; double, 0.030 g/cm-2; and triple, approx.0.048 g/cm-2). The highest removal efficiency of 33.513
In regions facing water scarcity, optimising existing treatment infrastructure is increasingly important. In the São Paulo Metropolitan Region, low per capita water availability and declining reservoir levels have increased reliance on the São Lourenço water treatment system. The system, however, operates below full capacity because pumps are shut down on peak-tariff hours to cut electricity costs. This study proposes an integrated hydraulic, economic, and environmental framework combining mass balance, tariff-based operational expenditures (OPEX) savings, Net Present Value (NPV) analysis, and comparative Life Cycle Assessment to support decisions on increasing treatment throughput. Economic and environmental dimensions explored sensitivity in discount rate, capital expenditure (CAPEX), pump degradation, coagulant dose, construction allocation and parameters, complemented by Monte Carlo simulation of tariff variation. Two scenarios were compared: operating existing pumps on-peak, and constructing an additional raw-water reservoir to avoid on-peak pumping one business day per week. Both achieved similar maximum treatment throughput (< 1.7
Urban water systems face increasing pressure from rising demand, constrained supply capacity, water losses, and long-term freshwater stress. This study assesses how circular water conservation pathways could influence Auckland’s long-term urban water supply–demand balance by integrating Reduce, Reuse, Recycle, and Recover scenarios into an exploratory System Dynamics Model (SDM). Literature-informed intervention ranges were translated into changes in domestic demand, industrial water intensity, leakage, and alternative usable supply. Conservative, moderate, and ambitious implementation levels were simulated from 2027 to 2050. Under the baseline, the supply–demand ratio falls below 1.0 in 2033 and reaches 0.78 by 2050. All Circular Economy (CE) scenarios improve the long-term balance, although their effectiveness depends on the model pathway and implementation level. At the conservative level, Reduce, Reuse, and Recover delay but do not avoid long-term imbalance, whereas Recycle-Conservative remains slightly above the balance threshold. Moderate and ambitious Reduce, Recycle, and Recover pathways provide larger long-term margins, while Reuse-Ambitious remains close to the threshold and is sensitive to uncertainty. The combined conservative Reduce–Recycle portfolio reaches a supply–demand ratio of 1.29 in 2050 and remains above the balance threshold in all 10,000 simulations under the uncertainty ranges tested. Uncertainty analysis shows that scenarios close to a ratio of 1.0 are more sensitive to changes in demand, supply, intervention effectiveness, and implementation timing. The findings support Auckland’s emphasis on demand efficiency, leakage reduction, and diversification of alternative water sources, while highlighting the need to consider implementation feasibility and uncertainty. The study provides a structured approach for translating CE principles into quantitative urban water-planning scenarios.
Unmanaged floating waste in rivers poses a significant threat to aquatic ecosystems and sustainable water resource management. This study aimed to design and experimentally evaluate a hydromechanical floating waste collector that uses the kinetic energy of flowing water to drive its mechanical waste collection system. The device consisted of a rotating water wheel coupled to a conveyor based collection mechanism, and the effects of blade geometry and number, conveyor type and inclination angle, and guiding arm configuration were experimentally investigated under real river conditions. The optimal configuration, comprising a 12 blade curved water wheel, a modular conveyor inclined at 30°, and a rigid guiding arm, achieved an overall collection efficiency of 83
Short-duration intense rainfall and rapid urbanization have aggravated urban pluvial-flood risk. In hilly cities, pronounced topographic relief and complex flow-concentration pathways cause ponding to be spatially dispersed yet locally concentrated, resulting in marked spatiotemporal differences in water-depth responses among flood-prone sites. This study develops a PSO-CNN-LSTM-Attention surrogate for rapid, 5-min-ahead water-depth prediction at eight fixed flood-prone sites in a representative hilly urban area of Zhuzhou, China. The surrogate integrates particle swarm optimization (PSO), a convolutional neural network (CNN), a long short-term memory network (LSTM), and an attention mechanism, and is trained using outputs from an evaluated MIKE FLOOD 1D–2D model. On the independent test set, the model achieved root mean squared error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2) values of 0.0340 m, 0.0208 m, 0.9158, and 0.9596, respectively, indicating close agreement with MIKE FLOOD simulations under the tested rainfall events. Predicting all time windows at the eight sites required approximately 26.18 s, compared with approximately 1319 s for one complete MIKE FLOOD simulation under the tested configuration. This approximately 50-fold ratio refers only to the online inference stage and excludes the offline costs of hydrodynamic sample generation, model training, cross-validation, and PSO. The proposed surrogate provides an efficient approach for near-real-time water-depth updating at fixed flood-prone sites and supports short-term urban flood-risk assessment in hilly cities.
