Applied Water Science最新文献

The citrus processing industry generates enormous amounts of wastes: solid residues from orange juice production process and mandarin wastewater from canned mandarin segments processing. These wastes are notably rich in high added-value bioactive compounds, such as polyphenols. Previous studies have explored extraction and concentration methods to recover and concentrate polyphenols from citrus waste. However, the high concentration of other compounds such as sugars and pectins in orange and mandarin concentrates, has prompted further studies on polyphenol purification using an adsorption/desorption process. The MN200 non-ionic resin was selected. First, different resin dosages were tested to recover polyphenols from model solutions simulating orange and mandarin wastewater. The best results were obtained with the resin concentration range of 20–30 g·L^− 1. The equilibrium data fitted well the Temkin isotherm, while the adsorption kinetics were best described by the pseudo-second-order model. Secondly, polyphenol purification was performed from real mandarin and orange concentrate solutions. Polyphenols, sugars and pectin recoveries were 81.9%, 5.4% and 3.5%, respectively, for mandarin solution; and 64.5%, 3.6% and 2.9%, respectively, for orange solution, at a resin concentration of 20 g·L^− 1. Hence, the solution obtained after the adsorption step could be used as a pectin concentrate with a great potential in the food industry. On the other hand, the solution obtained after desorption, enriched in polyphenols, could have a potential application in the pharmaceutical and cosmetic industries.

This study evaluated the efficiency of reduction facilities installed in agricultural drainage channels in Gyeongsangnam-do, South Korea. The stormwater management model was employed to evaluate the efficiency of the reduction facilities, and to enhance the simulation capability of the rainfall-runoff model, topographic information of the target region was obtained through field surveys. Unmanned aerial vehicles were used to analyze land-use patterns and drainage-flow structures and to acquire topographic information of areas with dense networks of agricultural drainage channels. Precise spatial information was applied to the model by overlapping the current land-use characteristics and digital topographic maps. For the simulation, we considered 10 scenarios for the installation of the reduction facilities in the channels (SR1–SR10), with different biological oxygen demands, total nitrogen concentrations, and total installation costs. Among all the scenarios concerning the reduction efficiency, the simultaneous application of SR4, SR8, and SR10 yielded the best results. SR7 was the most suitable scenario when prioritizing installation costs, with the total cost being USD 775,144. When considering both reduction efficiency and installation costs, SR3 and SR7 were the most suitable scenarios. Our study presents an effective method for selecting the location of pollutant reduction facilities in agricultural drainage channels to reduce the nonpoint source pollution load in these channels. Notably, our study can serve as a foundation for policymakers and planners to mitigate environmental pollution caused by agricultural activities.

Subsidy standards play a pivotal role in sustainable agricultural water management by incentivizing farmers to adopt water-saving technologies and improve irrigation efficiency. Existing studies generally determine subsidy standards based on either the additional costs of adopting water-saving technologies or the environmental benefits generated, but few consider both dimensions within an integrated framework. To address this gap, this study develops a novel analytical framework that estimates optimal subsidy standards from a shadow price perspective, thereby internalizing both economic and environmental externalities. Using panel data from 86 cities across the Yellow River Basin between 2010 and 2020, we uncover pronounced spatial and temporal disparities in agricultural water-saving subsidy standards. More than 55% of the cities exhibited shadow-price-based subsidy levels exceeding 5 CNY/m³, with the highest reaching 12.57 CNY/m³, while provincial-level subsidies during the same period remained within the range of 0.1–3.93 CNY/m³. Taking Zhangye City as an example, its estimated subsidy standard averaged 1.97 CNY/m³-approximately 1.5 times that of downstream regions-and displayed a fluctuating yet upward trend. Results further indicate that incorporating both additional costs and environmental benefits yields consistently higher subsidy estimates than approaches relying solely on one dimension. These findings reveal the heterogeneity and complexity of agricultural water-saving subsidies, reflecting variations in local economic structures, environmental constraints, and water resource endowments across the Yellow River Basin.

Artificial Intelligence (AI) offers significant potential to transform wastewater treatment by enhancing reliability, affordability, and sustainability. However, the adoption of AI in rural wastewater management remains limited due to unique challenges, including constrained resources, fragmented infrastructure, and variable water quality. These issues significantly impede the effectiveness of wastewater treatment, intensifying environmental pollution and public health threats in rural communities. This review systematically analyzes literature published between 2006 and 2024 on AI-driven wastewater monitoring and management, emphasizing machine learning (ML) and deep learning (DL) techniques tailored for urban and rural contexts. Relevant peer-reviewed studies were identified using targeted keyword searches across ScienceDirect and Elsevier databases, prioritizing comprehensive methodology and transparent reporting. Findings demonstrate that existing AI approaches predominantly address urban wastewater systems by optimizing chemical usage, energy efficiency, and operational effectiveness. Conversely, rural systems continue to face barriers such as data scarcity, incompatible infrastructure, and limited interpretability of ML and DL models, hindering AI implementation. To bridge these critical gaps, this paper recommends a modular, interpretable AI framework incorporating hierarchical input decomposition, adaptive data augmentation, and real-time monitoring strategies tailored explicitly to rural conditions. Furthermore, future research directions are also proposed to advance energy efficient, cost-effective, and privacy-preserving federated learning methodologies. Enhancing interpretability, addressing rural-specific data challenges, and promoting collaborative policy frameworks with active community participation are essential steps. Ultimately, scalable AI interventions emphasizing adaptive, interpretable strategies are urgently needed to mitigate environmental risks, safeguard public health, and promote sustainable wastewater infrastructure in rural communities.

The northeastern region of Ethiopia faces significant water scarcity challenges, including drought, repeated crop failures, food insecurity, and famine. To address this issue, water harvesting has emerged as a highly viable approach. This study aimed to identify potential sites for water harvesting practices in the moisture-stressed areas of the North Wollo Zone, Ethiopia. The site selection process considered various factors, including topography (slope), hydrology (rainfall, drainage density, and runoff), soil (texture and depth), agronomy (land use and cover), and socioeconomic factors (proximity to roads). An Analytical Hierarchical Process (AHP) and weighted overlay analysis were employed as the geospatial-based multicriteria decision-making methods. The results showed that less than 1% (13.8 km²) of the study area was highly suitable, while 39.3% (4,802.6 km²) was classified as moderately suitable for water harvesting practices. These moderately suitable areas present promising opportunities for installing water harvesting structures to benefit local communities. However, a significant portion of the study area, 60.2% (7,348.7 km²), was only marginally suitable. Verification of existing water harvesting structures revealed that 74% (28 out of 38) were located in moderately suitable areas, while the remaining 26% were in marginally suitable areas, indicating the community’s adaptive use of available land. The findings highlight that integrating geospatial and multicriteria approaches can effectively guide sustainable water resource planning in drought-prone regions. Future studies should incorporate additional socioeconomic parameters and higher-resolution datasets to refine the identification of suitable water harvesting sites and support evidence-based watershed management strategies.

Safe and energy-efficient alternatives to chemical disinfection are urgently needed to address the environmental and health risks associated with chlorination and its by-products. This study demonstrates the effective inactivation of pathogenic microorganisms in drinking water and wastewater using strong electric fields and microsecond pulsed discharges. A 50 kV pulse system with an asymmetrical pin-to-plane reactor was developed, incorporating a mobile fluoroplastic nozzle on the energized electrode to expand the ionization and discharge zone. Experiments operated in a soft spark-discharge mode (~ 20 kV, 1 µF). Substantial microbial reductions were achieved: in wastewater, total coliforms decreased from 3.7 × 10⁷ to 7.2 × 10⁴ CFU per 100 mL and fecal coliforms from 2.6 × 10⁷ to 1.53 × 10⁵ CFU per 100 mL; in drinking water, Escherichia coli was fully eliminated and total microbial load declined from 146 to 15 CFU mL⁻¹. These outcomes correspond to > 2 log₁₀ reduction in wastewater and complete pathogen removal in drinking water. Equivalent-circuit analysis revealed higher per-pulse energy transfer in wastewater (≈ 1.88 J) than in drinking water (≈ 2.10 J), attributed to differences in electrical resistance and capacitance. Microbial inactivation arises from synergistic physical, chemical, and mechanical processes generated during pulsed discharge. The results highlight high-voltage pulsed discharge as a promising, chemical-free, and environmentally responsible alternative to chlorination for water treatment.

Water security, the fundamental guarantee for socioeconomic development, is the basic prerequisite for ensuring humans have access to sufficient and safe water resources. In this study, a comprehensive water security evaluation system was constructed from four dimensions: water quantity, water quality, water pollution, and flood disasters. A system-dynamics simulation model for water security was developed. By coupling the Shared Socioeconomic Pathways and Representative Concentration Pathways, predictions were made about the future development trends of China’s water security. The results indicate that China’s water security situation shows significant volatility. The total water supply peaked in 2025. The compliance rate of drinking-water sources reached 98% and stabilized in 2034. The chemical oxygen demand (COD) emissions of industrial and domestic sewage peaked in 2020 and 2034 respectively, and the direct economic losses caused by flood disasters increased cyclically. In future scenario simulations, the water security situation will reach its optimal state under the SSP1-RCP2.6 scenario, while it will be most severe under the SSP5-RCP8.5 scenario. By 2052, the proportion of provinces with an “excellent” level in the water-quantity and water-quality subsystems will reach 52% and 77.4% respectively, mostly concentrated in economically developed regions. The balance between water supply and demand is the primary factor driving changes in water security. These results highlight the necessity of researching the stress factors and stress mechanisms influencing water security in the context of climate change.

Smart irrigation systems utilising the Internet of Things (IoT) technology and sensory systems have emerged as a revolutionary approach to modernising agriculture and addressing sustainability challenges. The worldwide agricultural sector continues to struggle with two major problems, which include water resource depletion and high operational expenses because of outdated irrigation systems. The research investigates the immediate requirement for budget-friendly precision agriculture solutions that can serve small to medium farmers operating in limited resource areas. The paper describes the development of an affordable IoT-based smart irrigation system that underwent experimental testing. The system uses an ESP32 microcontroller as its core component while incorporating a capacitive soil moisture sensor for precise measurements, a DHT11 sensor for environmental data collection, and the Blynk IoT platform for live monitoring and distant system operation. The system begins irrigation when soil moisture reaches 20% and stops irrigation when moisture levels reach 80%. The system achieved operational success through field tests, which showed it used 25–35% less water than fixed-schedule irrigation systems while reducing operational expenses. The research proves that this affordable design can be duplicated and shows both technical and financial viability, which makes it an effective solution for sustainable farming practices.

Accurate water quality prediction is essential for water resource protection. For the complex water quality change mechanism is difficult to grasp, the water quality time series of long-term and short-term information affect each other and lead to poor prediction accuracy, this study proposes a hybrid prediction model based on quadratic decomposition, the chaos sparrow search optimization algorithm (CSSOA), and the combination of long short-term memory neural network (LSTM). The original data is first decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain several subsequences, and then the features in the high frequency subsequences are further extracted using variation mode decomposition (VMD) to further extract the features in the high frequency subsequences. Finally, the CSSOA-optimized LSTM is applied to predict each sequence, and the prediction results of each sequence are summed to yield the final prediction results. Experimental results show that the proposed model achieves significant improvements compared to benchmark models. For example, on the Gui River dataset (Dataset A), the mean square error (MSE) is reduced by 89% compared to the single LSTM model (0.005 vs. 0.046). On the Xun River dataset (Dataset B), the MSE is 0.003, which is 83% lower than the single LSTM model (0.018). Meanwhile, the results of extreme value and Diebold-Mariano (DM) test as well as multi-step prediction similarly confirm the superiority of the proposed model. So, the combined prediction method proposed in this study has enhanced prediction generalizability and accuracy, and it can be used to predict water quality in complex water environments.