Water

Drought is one of the most severe natural disasters worldwide, but with a particular emphasis in sub-humid and semi-arid climates. Several indices have been created to appropriately identify drought’s characteristics and variability. The main objectives of this study consisted of analyzing the behavior of different indices applied in northeast Algeria and comparing them across a long-term data set (1961–2014). The SPI and RDI at 9-month time scales were compared to the PDSI and MedPDSI based on 123 rainfall stations and gridded PET data interpolated to all the locations. A principal component analysis (PCA) in S-mode with varimax rotation (RPC) was applied to the monthly values of all indices to analyze the spatiotemporal patterns of droughts. Two principal components were retained, which identified two sub-regions with coherent differences related to their distance from the Mediterranean Sea and the UNEP aridity index. Trends in the RPC scores were assessed using the modified Mann–Kendall (MMK) test and Sen’s slope estimator, which showed a fundamental difference between the two sub-regions. The RPC of all drought indices showed trends of decreases in the frequency and severity of droughts in the northern sub-region, and trends of increases in the frequency and severity of droughts in the southern region, where the climate is mostly semi-arid and arid. Only a few cases were statistically significant, mostly when using the PDSI and MedPDSI for the southern sub-region. The spatial patterns of moderate, severe, and extreme drought occurrences were similar for the SPI and RDI pair of indices based on the probability of rainfall anomalies, and for the Sc-PDSI and MedPDSI pair based on water balance anomalies. The interpretation of the spatial variability of droughts, mainly of the extreme ones, was supported by an analysis of semi-variograms. The novel index MedPDSI compared well with the other indices and showed advantages of performing the soil water balance following the FAO56 dual K_c method with the actual olive evapotranspiration instead of PET, and of better explaining the spatial variability of extreme droughts; in addition, the trends detected were significant for both the northern and southern sub-regions. Full article

Lao PDR, a landlocked country in the lower Mekong River basin of Southeast Asia, has been considered a global biodiversity hotspot with a high level of biological endemism. In recent years, urban development and industrialization have affected the water quality of freshwater ecosystems in Lao PDR. However, the assessment of water quality in the country is primarily focused on a physicochemical method, while the application of a multimetric index (MMI) approach using benthic macroinvertebrates for biomonitoring in rivers and streams has not been established. MMI, based on benthic macroinvertebrates, is a biomonitoring tool that considers the effects of multiple anthropogenic impacts on benthic macroinvertebrate metrics associated with their biological attributes (e.g., taxa richness, composition, pollution tolerance, habits, and functional feeding) and aggregates individual metrics into a single value for assessing the water quality and health conditions of aquatic ecosystems. Here, we developed an MMI based on macroinvertebrate communities collected during 2016–2018 from 10 localities of streams and wadeable rivers in Lao PDR. Of the 54 potential metrics tested, 35 candidate macroinvertebrate metrics representing richness, composition, trophic structure, habit, and tolerance to pollution were selected, while 19 metrics were excluded. Of the 35-candidate metrics, a total of 11 core metrics (Total taxa, EPT taxa, Ephemeroptera taxa, %Diptera, %Plecoptera, %Tolerant, Beck’s biotic index, %Intolerant, Filterers taxa, %Sprawlers, and %Burrowers) were finally selected for the development of MMI based on their sensitivity, redundancy, and easy-to-apply tool for the biomonitoring program. These metrics can be used to distinguish the reference (seven sites) from stressed conditions (seven sites). In addition, the final MMI scores classified 40 sampling sites into four classes of water quality, including excellent (25%), good (10%), fair (60%), and poor (5%), which the conventional physicochemical method could not clearly distinguish. The Lao MMI developed in this study is an effective tool for evaluating the water conditions of sites affected by human activities, particularly agricultural areas, and, thus, is appropriate for use in future studies for assessing the ecological conditions of rivers and streams in the Mekong region. Full article

This study highlights the implementation of an electronic tongue composed of carbon screen-printed electrodes, which were used to discriminate and classify pesticides, such as Curathane, Numetrin, and Nativo in water. Therefore, to verify the capacity and performance of the sensory system, solutions of each of the pesticides at a concentration of 10 ppm were prepared in the laboratory and compared with distilled water. Furthermore, to evaluate the minimum detection limit of the electronic tongue, solutions were prepared at different concentrations: 0.02, 0.04, 0.06, 0.08, 0.1, 0.15, 0.2, and 0.25 ppm, respectively. The analysis and classification of the different categories and concentrations were obtained from the use of pattern recognition and automatic learning methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), k-nearest neighbors (kNN), and naïve Bayes, during this process; the techniques accomplished more than 90% accuracy in pesticide concentrations. Finally, a 100% success rate in classifying the compound types was completely achieved. Full article

Waterflooding is one of the most common reservoir development programs, driving the oil in porous media to the production wells by injecting high-pressure water into the reservoir. In the process of oil development, identifying the underground flow distribution, so as to take measures such as water plugging and profile control for high permeability layers to prevent water channeling, is of great importance for oilfield management. However, influenced by the heterogeneous geophysical properties of porous media, there is strong uncertainty in the underground flow distribution. In this paper, we propose an interpretable recurrent neural network (IRNN) based on the material balance equation, to characterize the flow disequilibrium and to predict the production behaviors. IRNN is constructed using two interpretable modules, where the inflow module aims to compute the total inflow rate from all injectors to each producer, and the drainage module is designed to approximate the fluid change rate among the water drainage volume. On the spatial scale, IRNN takes a self-attention mechanism to focus on the important input signals and to reduce the influence of the redundant information, so as to deal with the mutual interference between the injection–production groups efficiently. On the temporal scale, IRNN employs the recurrent neural network, taking into account the impact of historical injection signals on the current production behavior. In addition, a Gaussian kernel function with boundary constraints is embedded in IRNN to quantitatively characterize the inter-well flow disequilibrium. Through the verification of two synthetic experiments, IRNN outperforms the canonical multilayer perceptron on both the history match and the forecast of productivity, and it effectively reflects the subsurface flow disequilibrium between the injectors and the producers. Full article

Over the past 10 years, urban floods have increased in frequency because of extreme rainfall events and urbanization development. To reduce the losses caused by floods, various urban flood models have been developed to realize urban flood early warning. Using CiteSpace software’s co-citation analysis, this paper reviews the characteristics of different types of urban flood models and summarizes state-of-the-art technologies for flood model development. Artificial intelligence (AI) technology provides an innovative approach to the construction of data-driven models; nevertheless, developing an AI model coupled with flooding processes represents a worthwhile challenge. Big data (such as remote sensing, crowdsourcing geographic, and Internet of Things data), as well as spatial data management and analysis methods, provide critical data and data processing support for model construction, evaluation, and application. The further development of these models and technologies is expected to improve the accuracy and efficiency of urban flood simulations and provide support for the construction of a multi-scale distributed smart flood simulation system. Full article

The imbalance of inorganic nutrients in micro-polluted source water poses a huge threat to aquatic environments and human health. To pretreat micro-polluted source water, some biological reactors have been conducted at lab-scale. However, using phototrophic biofilms to pretreat micro-polluted source water at pilot-scale has yet to be explored, and the effects of light on the practical operation of phototrophic biofilms are poorly understood. In this study, the potential of pretreating micro-polluted source water by phototrophic biofilms was explored. The high light intensity (4500 lx, 60.75 μmol/m²/s) promoted the growth of phototrophic biofilms and the secretion of extracellular polymeric substance. The removal efficiency of inorganic nitrogen and total phosphorus in synthetic micro-polluted water was 56.82% and 40.90%, respectively. When interacting with actual micro-polluted source water, the nutrients in the actual micro-polluted source water were reduced by the stable pilot-scale phototrophic biofilms. The final concentration of effluent nutrients was lower than the Grade II surface water quality standard in China. Therefore, using phototrophic biofilms as a pretreatment facility in water treatment plants is a promising solution to this issue. Full article

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world’s water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade. Full article

Wastewater treatment plants (WWTPs) represent an important input of contaminants in the environment. Therefore, it is critical to continuously monitor the performance of WWTPs to take appropriate action and avoid an influx of contaminants in the environment. In this study, a battery of seven in vitro bioassays covering a selected spectrum of toxicity effects is proposed for quality control of wastewater effluents. The bioassays address mixture toxicity, which is the combined adverse effect of multiple contaminants and can act as an early warning system. The proposed battery was applied to samples from 11 WWTPs of representative technology from the Danube River Basin (DRB). The order of toxic effects in terms of extent of exceedance of effect-based trigger values (EBTs) was PAH (PAH activity) > PXR (xenobiotic metabolism) > ER_α (estrogenic activity) > PPAR_γ > Nrf₂ (oxidative stress) > anti-AR > GR. A mitigation plan for WWTP operators based on EBT exceedance is proposed. This study demonstrates that the proposed effect-based monitoring battery is a complementary tool to the chemical analysis approach. A regular application of such time- and cost-effective bioanalytical tools in the WWTPs of the DRB is proposed to provide a ‘safety net’ for aquatic ecosystems. Full article

The presence of water or the degree of soil saturation has a direct impact on the root development and function in rice. In this regard, a pot investigation was performed to test the response of root traits and yield components of boro (irrigated) rice. Three boro rice varieties named Binadhan-10, Hira-2 and BRRI dhan 29 were grown at four irrigation regimes, viz. continuous flooding (CF), saturation (S), 75% S and 50% S at Bangladesh Agricultural University, Mymensingh, Bangladesh, throughout the boro period of 2020–2021. The study was replicated three times by employing a completely randomized design (CRD) method. The study revealed a drastic decline in root attributes at 75% and 50% S. A significant increase in root number (RN), root length (RL), root volume (RV), total dry matter (TDM) and grain yield (GY) under S condition followed by CF was observed. Binadhan-10 exhibited the largest scores of RN (359.00), RL (1577.83 cm) and RV (8.34 cm³ hill⁻¹) at 80 DAT under S condition. Root attributes and GY were found to be substantially and positively associated in all observations. Binadhan-10 performed best with regard to seed output (26.13 g pot⁻¹) under S condition. S condition increased the yield of Binadhan-10 in CF, 75% S and 50% S by 4.06%, 23.72% and 46.00%, respectively. Full article

Flash floods are typically associated with short, high-intensity and extreme rain-storms, and they are characterized by short response time and severely impact and damage communities in different areas in China. In order to scientifically assess the risks of flash floods, this paper takes Hubei Province as an example to carry out risk assessment. Based on Pearson correlation coefficient and principal component analysis methods, 14 factors were selected from 98 factors to establish a risk assessment model. The confidence coefficient model and multi-factor superposition method were used to determine the weight of each risk factor, and a risk map of Hubei Province was finally constructed. The results show that medium-high risk areas in Huanggang account for 47.00%, and high-risk areas account for 8.70%, with both areas adding up to more than 50%, followed by more than 40% in Shiyan, E’zhou and Xianning, and more than 30% in Huangshi, Yichang, Xiangyang, Jingmen and Suizhou. The risk level distribution is highly consistent with the location and frequency of flash flood disasters, shows high reliability, and can provide data support for flash flood disaster prevention and control. This study used a quantitative method to determine the key factors affecting flash flood disasters and provides a reference and basis for flash flood risk assessment in other provinces in China. Full article

This research proposes a methodology with multi-objective optimization for the placement of Pumps operating As Turbines (PATs), energizing street lighting, devices for monitoring the water network, and charging stations for small electric vehicles such as bikes and scooters. This methodology helps to find the most profitable project for benefiting life quality and energy recovery through pumps operating as turbines, replacing virtual pressure reduction valves to locate the best point for decreasing pressure. PATs are selected by maximizing power recovery and minimizing pressure in the system as well as maximizing recoverable energy. Benefits analyzed include the reduction of carbon dioxide emissions and fuel use, as well as the saving of electricity consumption and benefiting socio-economic impact with street lighting, monitoring, and charging station. It was considered that each PAT proposed by the methodology will supply a street light pole, a station for monitoring the water network, and a charging station; under these established conditions, the return on investment is up to 1.07 at 12 years, with a power generation of 60 kWh per day. Full article

In recent years, increasing attention has been paid to the problem of sewage sludge management and the relevant energy consumption, which represent the main cost items in wastewater treatment plants. Therefore, implementation of technologies that can reduce sludge production and ensure a positive impact on the energy of the entire sewage treatment plant has gained considerable importance in the scientific and technical community. The objective of this study was thus to screen full-scale sludge reduction technologies integrated into both the water line and the sludge line of a municipal sewage treatment plant with a sustainable impact on the overall balance of the plant. The results showed that, within the water line, ultrasound in the recirculation line of the activated sludge allowed for greater reductions in sludge production than the Cannibal and UTN systems, despite the higher energy consumption. CAMBI^TM, BioThelys^TM, Exelys^TM and TurboTec^® enabled the greatest reductions in sludge production among the technologies integrated into the sludge line, and although they required a large amount of energy, this was partially offset by energy recovery in terms of additional biogas production. Full article

Anaerobic digestion, including the gas storage and the CHP unit plays a key role in energy management of water ressource recovery facilities (WRRF). By demand-driven feeding management, a higher degree of utilization of the gas storage tanks can be achieved, which means that a greater decoupling of energy demand and energy generation is taking place. In this context, the predictability of the produced gas volume plays an important role. In this study, a concept was suggested to forecast gas production rates without the need for any analytical substrate characterization. Substrates lead to specific patterns in the course of the gas production rates, which can be fitted using Gaussian functions. As the parameters describing the Gaussian functions have a good correlation to the amount of substrate fed to the digester a prediction of the rates is possible. Within this study a coefficient of determination 82.9% over a period of 5 days was achieved. Full article

The international community has committed to protecting the Earth and its ecosystems, thus ensuring wellbeing, economic growth, and a sustainable environment, by applying 17 sustainable development goals (SDGs), including many related to water. These goals and their indicators can have synergistic, trade-off, or neutral interlinkages. This study measured the interlinkages between 31 SDG indicators directly or indirectly related to water belonging to seven categories: extreme water events, water availability, water quality and waterborne diseases, energy-related water, industry and technology-related water, water governance and management, and ecosystem-related water. All the indicators were paired, resulting in 450 pairs. The interlinkage between water-related indicators globally and in low-income countries (LIC) were determined by Spearman’s rank correlation (ρ), and standardized multilinear regression was applied to identify the dominant drivers of synergistic and trade-off interactions. The finding shows that water quality, waterborne disease, and energy-related water are the most feasible to achieve in SDGs in global and LIC. The local government may take advantage from this study. Moreover, the government should pay attention in developing and providing alternative energy especially in LIC due to some trade-offs appeared with health and social conflict may arise. The interaction between indicators become the main driver of synergy/trade-off over population and GDP in interlinkage water related SDGs. Full article

Predicting the deformation of landslides is significant for landslide early warning. Taking the Shuping landslide in the Three Gorges Reservoir area (TGRA) as a case, the displacement is decomposed into two components by a time series model (TSM). The least squares support vector machine (LSSVM) model optimized by particle swarm optimization (PSO) is selected to predict the landslide displacement prediction based on rainfall and reservoir water level (RWL). Five parameters, including rainfall over the previous month, rainfall over the previous two months, RWL, change in RWL over the previous month and period displacement over the previous half year, are selected as the input variables. The relationships between the five parameters and the landslide displacement are revealed by grey correlation analysis. The PSO-LSSVM model is used to predict the periodic term displacement (PTD), and the least squares method is applied to predict the trend term displacement (TTD). With the same input variables, the back propagation (BP) model and the PSO-SVM model are also developed for comparative analysis. In the PSO-LSSVM model, the R² of three monitoring stations is larger than 0.98, and the MAE values and the RMSE values are the smallest among the three models. The outcomes demonstrate that the PSO-LSSVM model has a high accuracy in predicting landslide displacement. Full article

Similar to most wetlands, the Florida Everglades landscape was altered to promote agriculture and human settlement, significantly altering the natural hydrologic regime. Once former agricultural land located within Everglades National Park (ENP), the Hole-in-the-Donut (HID) wetland restoration program became the first mitigation bank project in Florida. The HID program utilized a restoration technique of complete soil removal to effectively eradicate an invasive plant species. This research investigated the effects of the vegetation and soil removal on the hydrologic conditions of the HID, specifically evapotranspiration and water chemistry. Annual evapotranspiration rates were determined for the region using remotely sensed data and compared to the acres restored over a 15-year period. Groundwater and surface waters were collected from both inside the HID and from adjacent areas within ENP for major cations and anions and total nutrient concentrations. Evapotranspiration rates were found to decrease from a mean of 1083.4 mm year⁻¹ in the year 2000 to 891.6 mm year⁻¹ in 2014 as the restored area increased to 4893 acres. Concentrations of ions and nutrients were lower in groundwater and surface water within the restored areas compared to adjacent areas. We conclude that the lack of soil cover (along with reduced evapotranspiration rates) contributed to the lower ion and nutrient concentrations in the surface water and groundwater within the HID. Full article

Increasing the evaporation zone inside the solar distiller (SD) is a pivotal method for augmenting its freshwater production. Hence, in this work, a newly designed prismatic absorber basin covered by linen wicks was utilized instead of the conventional flat absorber basin to increase the surface area of the vaporization zone in a double-slope solar distiller (DSSD). Meanwhile, for further enhancement of modified DSSD performance, dual parallel spraying nozzles are incorporated underneath the glass cover as a saltwater feed supply to minimize the thickness of the saltwater film on the wick, which enhances the heating process of the wick surface and, consequently, the evaporation and condensation processes are improved. Two double slope distillers, namely a double slope solar distiller with wick prismatic basin and dual parallel spraying nozzles (DSSD-WPB&DPSN) and a traditional double slope solar distiller (TDSSD), are made and tested in the outdoor summer conditions of Tanta, Egypt (31° E and 30.5° N). A comparative energic–exergic-economic analysis of the two proposed solar stills is also conducted, in terms of the cumulative distillation yield, daily energy efficiency, daily exergy efficiency, and cost per liter of distilled yield. The present results show that the cumulative distillation yield of the DSSD-WPB&DPSN was 8.20 kg/m².day, which is higher than that of the TDSSD by 49.64%. Furthermore, the energy and exergy efficiencies were increased by 48.51% and 118.10%, respectively, relative to TDSSD. Additionally, the life cost assessment reveals that the cost per liter of the distilled yield of the DSSD-WPB&DPSN is decreased by 11.13% compared to the TDSSD. Full article

The pumped storage power station plays a vital role in modern power systems, where the key component is the pump turbine. Variable-speed operation can improve the operating efficiency of the pump turbine and increase the operating efficiency under turbine operating conditions and the automatic frequency regulation capability under pump operating conditions, thus obtaining higher efficiency and better stability. However, its operation characteristics are different from many conventional pumped storage units, which makes the study of variable-speed pump turbines more difficult. Therefore, in this paper, 10 representative pressure monitoring points are selected in the model to compare and study the flow characteristics and pressure pulsation characteristics of a variable-speed pump turbine at three speeds (N1-398.57 m/s, N2-412.16 m/s, and N3-428.6 m/s). According to our results, it is shown that the maximum pressure and pressure pulsation are small at low rotational speeds, which means that the unit will maintain better stability during the reduction in rotational speed and reducing the speed will not affect the safety and stability of the equipment. The purpose of this paper is to provide guidance for the safe operation of the unit and to improve the effect of speed in terms of dynamic behavior of variable-speed water pump turbine units. Meanwhile, this study will lay the groundwork for the optimal design of variable-speed pump turbines. Full article

Fluorine pollution of wastewater is a global environmental problem. Capacitive deionization has unique advantages in the defluorination of fluorine-containing wastewater; however, the low electrosorption capacity significantly restricts its further development. To overcome this limitation, nitrogen-doped core-shell mesoporous carbonaceous nanospheres (NMCS) were developed in this study based on structural optimization and polarity enhancement engineering. The maximal electrosorption capacity of NMCS for fluorine reached 13.34 mg g⁻¹, which was 24% higher than that of the undoped counterpart. NMCS also indicated excellent repeatability evidenced by little decrease of electrosorption capacity after 10 adsorption-regeneration cycles. According to material and electrochemical measurements, the doping of nitrogen into NMCS resulted in the improvement of physicochemical properties such as conductivity and wettability, the amelioration of pore structure and the transformation of morphology from yolk-shell to core-shell structure. It not only facilitated ion transportation but also improved the available adsorption sites, and thus led to enhancement of the defluorination performance of NMCS. The above results demonstrated that NMCS would be an excellent electrode material for high-capacity defluorination in CDI systems. Full article