Abstract Due to severe water pollution, gradual shortage of energy resources, and increasing demand for food, the environment related to the water,food, and energy nexus has caused serious damage and pollution and has had a significant impact on economic development.“ to ”The intensification of water pollution, the gradual shortage of energy resources, and the increasing demand for food have caused serious damage and pollution to the natural environment and also had a significant impact on economic development.

Abstract Water Supply issues a formal retraction in relation to the above article by Liyu Lu. The publisher issues this retraction due to concerns relating to inappropriate references. The journal did not receive a satisfactory response from the authors to these concerns and as such the Editors-in-Chief no longer have confidence in the integrity of the article. We apologize for any confusion this may have caused.

Abstract Water level prediction is an essential factor for the safe operation of pumping stations. However, due to the complex nonlinear relationship between the water level of the front pool of the pumping station and the influencing factors, the prediction of the water level is still inaccurate and untimely. Backpropagation (BP) neural network, improved particle swarm optimization-BP neural network (PSO-BP), support vector machine regression (SVR), and improved PSO-SVR were used to construct 4-h and 8-h ahead prediction models for pumping station prestation water levels. Mean absolute error, mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R correlation coefficient were used as prediction evaluation metrics. This method is applied in the Baliwan Pumping Station, the highest pumping station in the South-to-North Water Diversion Eastern Route Project (SNWDERP). The results showed that the MSE, RMSE, and MAPE of the improved PSO-BP model were smaller than other models, whereas the R correlation coefficient was larger, confirming its high prediction accuracy. All models had higher prediction accuracy 4 h ahead than 8 h ahead. Combining the time-phased water level prediction method and hybrid machine learning can enhance the water level prediction accuracy of a pumping station pre-station.

Abstract This research article presents a data-driven approach for detecting bursts in water distribution networks (WDNs). The framework uses spatiotemporal information from monitoring pressure and unsupervised learning model. This approach employs three stages: (1) benchmark dataset acquisition, (2) spatiotemporal information analysis, and (3) burst detection model construction. First, the benchmark datasets were the normal dataset initially obtained by the clustering algorithm. Second, spatiotemporal information features are extracted from multimoment time windows from multiple sensors, including the distance and shape features. Third, burst detection was performed based on the isolation forest technique. A WDN is used to evaluate the performance of the method. Results show that the method can effectively detect the burst.

Abstract Based on the monthly monitoring data of water quality monitoring stations in the Xiang River Basin from 1990 to 2020, the Mann–Kendall test was used to analyze dissolved DO, BOD5, CODMn, TP, NH3-N,Cd, As, and Cr6+. The changing trend of nine indicators, including the stepwise regression method, was used to determine the cross city driving force of each water quality index, and the contribution rate (weight) of each driving force was obtained by principal component analysis. The research results show that (1) agriculture in Yongzhou is the main driving force, and its contribution rate is 67.2%; (2) urbanization has a greater impact on the driving process of the water environment in the Xiang River Basin, and its contribution rate is as follows: Changsha (83%) > Hengyang (80.7%) > Pingxiang (63.7%) > Chenzhou (60.9%) > Xiangtan (57.4%) > Zhuzhou (50%) > Loudi (48.5%); (3) the urbanization of Zhuzhou City and Loudi City's urban sewage discharge not only has an impact on the city's water environment, but also drives the water environment in the downstream Xiangtan area. The research results can provide a basis and reference for the study of water environmental governance in the basin.

Abstract Traditional studies on the carrying capacity of groundwater resources mainly focus on a comprehensive evaluation, and this paper provides quantitative prediction and early warning on the carrying capacity of groundwater resources, which is important for groundwater management and protection. This study constructed an early warning indicator system for the carrying capacity of groundwater resources, established an early warning method on the carrying capacity of groundwater resources, determined the early warning grades of Henan Province and various cities from 2023 to 2025, and analyzed the spatial distribution pattern. The results showed that the early warning grades of Henan Province from 2023 to 2025 were mild warnings. In the subsystems, the early warning grades of the groundwater resources and economic society systems were mild warnings, while the early warning grades of the ecological environment system were moderate warnings. The early warning grades of the carrying capacity of groundwater resources in all cities were basically mild or moderate warnings, showing a downward trend. This study establishes an early warning method on the carrying capacity of groundwater resources that includes the selection of early warning indicators, prediction of indicators, and determination of the early warning grades which can be a reference for other regions.

Abstract Intermittent water supply (IWS) is a frequent operation approach in developing countries. A number of factors contribute to IWS, such as leakage and water shortage, leading to inequitable water distribution. A way to reduce the inequity in IWS systems is by planning or adapting the network for equitable water distribution, which requires software capable of describing IWS conditions, including not only pressurized flow but also free-surface. In this paper, a new index for equity analysis is presented and compared with the existing indices available in scientific literature. The comparison is based on the EPA-SWMM modelling framework and standard IWS operations, including pipe emptying and filling, have been considered. Results reveal that EPA-SWMM has great potential in simulating IWS systems, especially in the application for equity analyses, as it provides a more realistic description of the pipe filling and emptying operations compared to standard water distribution modelling software. Furthermore, a novel equation called volumetric coefficient was developed which has a significant advantage in identifying the nodes with a higher impact on network equity, and ensuring the fair distribution of supply along the distribution network, considering the relevance of nodal demand, which none of the other indexes in the literature were addressed.

Abstract Water erosion is a serious challenge in Algeria, because it affects ecosystems, contributes to soil degradation, and leads to the silting of dams; moreover, this process is complex and needs costly field equipment and trip reconnaissance. The main objective of this paper is to evaluate the sensitivity of the Revised Universal Soil Loss Equation (RUSLE) model to rainfall erosivity in the Ksob watershed in Algeria, using six empirical formulas of rainfall erosivity. The RUSLE model with the Geographic Information System (GIS) produces highly variable specific soil loss ranging from 11.35 to 22.85 t ha−1 year−1 throughout the watershed, depending on the erosivity R factor. To validate the results, the soil loss is compared with the silting volume data of the Ksob dam, which is located at the outflow of the basin. The best result is obtained using the Diodato R factor formula with a relative error of 21% and a specific soil loss of 11.35 t ha−1 year−1. Moreover, over 81% of the watershed area is exposed to low erosion (<20 t ha−1 year−1), and less than 5% are affected by high erosion (>50 t ha−1 year−1).

Abstract Chlorination disinfection in water treatments may be highly destructive to microplastics (MPs). Herein, low- and high-dose (concentration–time values at 75 and 9,600 mg min L−1, respectively) chlorination processes were used to simulate short-term chlorination in drinking water treatment plants and long-term residual chlorine reaction in drinking water supply systems, respectively. Both chlorination processes induced modifications to polyethylene (PE), polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs, varying in polymer types and sizes. Oxidized and chlorinated bonds were detected, and destructed surfaces with increased specific surface area and reduced hydrophobicity were observed. As a result, the sorption capacity of all MPs was weakened, e.g., low-dose chlorination (pH 7) depressed the sorption of ciprofloxacin by 6.5 μm PE (Kf from 0.140 to 0.128 L g−1). The sinking behavior of PET, PS, and PVC MPs was enhanced, e.g., the sinking ratio of 200 μm PET increased by ∼30% after low-dose chlorination (pH 7). By contrast, PE tended to float after high-dose chlorination. Furthermore, chlorination of MPs generated various products, which were the degraded fragments from the MP skeleton. In general, chlorination disinfection reduces the potential of MPs as transport vectors of organic contaminants.

Abstract Arsenic is naturally found in the rocks of the Pleistocene and Holocene ages in the groundwater aquifer media. The district Ballia has many reports which are focused on arsenic contamination in its different blocks. This article is devoted to a thorough study of hydrogeochemical characteristics and the source of origin of arsenic in the groundwater system of the Ballia district. The relationship between this hydrogeologically present arsenic and various cations and anions including some physicochemical parameters are discussed here. These observations have been done using ICP MS and ion chromatographic techniques. The relationship study has been done by using a correlation matrix which is further proven by the dendrogram. Different types of loading plots of various principal components have also been studied with the help of principal component analysis. The nature and quality of groundwater are studied with the help of a piper plot. This research article is a complete and detailed study of the nature of groundwater in district Ballia. It also enlightens the source of arsenic contamination in the study area.