Applied Water Science最新文献

To achieve sustainable conservation and management of wetlands, this study investigates the influence of water balance dynamics in Baiyangdian (BYD) on the cyclical evolution of wetland landscapes from 1980 to 2020, as well as elucidates the driving role of key hydrological processes in wetland degradation. To accomplish this, a “process analysis–driver identification–uncertainty assessment” research framework was established. This framework facilitates a systematic investigation into how hydrological processes induce changes in landscape patterns and enables a quantitative evaluation of uncertainties associated with water balance states. This approach enhances our understanding of the mechanisms regulating water in wetlands and the factors contributing to their degradation. The results show that between 1980 and 2020, the wetland landscape underwent staged changes of contraction, recovery, decline, and stabilization, with the dominant mudflat gradually transitioning into marsh ecosystems. PLS-SEM analysis revealed that wetland landscape patterns were predominantly influenced by water balance dynamics: Recharge factors significantly promoted lake storage variation increases and wetland expansion, thereby enhancing landscape indices, while discharge factors suppressed lake storage variation, leading to wetland contraction and diminished landscape indices. Notably, Inflow emerged as the most substantial positive driver. Across distinct phases, both recharge and discharge factors exhibited marked uncertainties, with the uncertainty in lake storage variation reaching its maximum when (:Inflow) levels were elevated alongside reduced inputs from precipitation and evapotranspiration. The cyclical responses of wetland landscapes offer a foundation for elucidating uncertainties in hydrological driver interactions and establish critical linkages between water balance dynamics and wetland landscape evolution. These findings highlight the necessity of integrating basin management with multi-source hydrological replenishment strategies in water resource allocation and wetland conservation efforts, thereby ensuring the long-term sustainability and ecological integrity of wetland ecosystems.

This study evaluated heavy metal contamination in agricultural soils irrigated with untreated industrial wastewater from two major industrial zones in Lahore, Pakistan. Soil samples from cucumber (S1), wheat (S2), green chili (S3), potato (S4), ladyfinger (S5), and tomato (S6) field were collected from six industrial sites. Heavy metals including cadmium (Cd), chromium (Cr), lead (Pb), and nickel (Ni) were analyzed using atomic absorption spectroscopy (AAS). Various indices such as the contamination factor (CF), potential contamination index (PCI), geo-accumulation index (Igeo), potential ecological risk index (PERI), and human health risk assessment (HHRA) were employed to assess pollution levels and associated health risks. Results showed that most heavy metal concentrations were within permissible limits set by the European Union (EU) and World Health Organization (WHO), except at certain sites. For instance, Cd at Site 4 was 0.085 mg/kg (above EU/WHO guideline of 0.05 mg/kg), Cr exceeded the limit (0.10 mg/kg) at all sites except Site 1, and Pb levels were higher than (0.1 mg/kg) at all sites except Sites 1 and 4. Ni concentrations surpassed guidelines (0.14 mg/kg) at all sites except Sites 1 and 2. Among all elements, Cr exhibited the highest contamination factor. The PCI results also indicated that Cr and Ni posed significant contamination potential. All soil samples exhibited PERI values exceeding 600, indicating a very high ecological risk. HHRA analysis showed that children were more vulnerable than adults to all four heavy metals, as per USEPA guidelines. This study provides a comprehensive, multi-index assessment of industrial wastewater-induced soil pollution and its implications for human health. Unlike previous research that focused on individual contaminants, this work integrates ecological and human health risk assessments, offering novel insights for urban environmental protection and sustainable agricultural practices.

The simplification of algorithms for predicting natural events, particularly in the context of environmental and agricultural applications, has gained significant attention due to the need for reliable, efficient, and adaptable models. This study aims to assess the performance of various simplified algorithms for predicting daily reference evapotranspiration (ET_O) across diverse climatic conditions. A comparative analysis of multiple models, including combination-based, mass transfer-based, radiation-based, and temperature-based approaches, was conducted to evaluate their precision and adaptability in different environmental settings. Among the combination-based models, PME and ResNet₄ exhibited outstanding performance, with standardized index (SI) values consistently below 0.1 and generalized predictive index (GPI) values under 5% across all climatic conditions, making them ideal for applications in regions with diverse environmental characteristics. Other models such as LSSVR₄ and ANF-PSO₄ demonstrated moderate effectiveness in arid climates but struggled in more humid conditions, highlighting the need for further model refinement in extreme environments. The mass transfer-based models, including A-LSTM₃ and ResNet₃, showed strong performance in very dry climates, although their precision decreased in humid regions, indicating the sensitivity of these models to changes in moisture availability. Radiation-based models such as A-LSTM₂ and ResNet₂ performed well in humid and semidry conditions, while LSSVR₂ and ANF-PSO₂ were most effective in dry climates. Temperature-based models, particularly LSSVR₁ and ANF-PSO₁, demonstrated remarkable stability across all climates, with low GPI values, making them well-suited for temperature-sensitive environments. Overall, PME emerged as the most reliable model, offering consistent high performance across all climates. The findings of this study emphasize the importance of selecting and calibrating models based on climatic variability, ensuring accurate predictions for sustainable environmental management and agricultural planning.

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.