Search Results (336)

Freshwater shallow lakes are vulnerable to global warming, putting entire aquatic ecosystems at risk, but evidence from managed reservoirs remains limited despite the existence of long-term empirical data. Using data from 29 stations on Lake Tisza covering an 18-year period (2007–2024), this study quantifies warming rates, thermal stress patterns and trends in water quality in lacustrine, transitional and riverine zones. Lake areas warmed at a rate of 0.90 °C/decade (p < 0.001), faster than the river/transition areas and even than global averages in shallow lakes. Temperature-critical years now affect 90.4% of lake stations, compared with 59.6% in 2007–2012. A strong negative correlation between temperature and dissolved oxygen was observed along all systems (Spearman’s p; river: −0.83, transition: −0.65, lake: −0.53), indicating thermal-driven deoxygenation risk. At the same time, a water quality index (conductivity, pH, BOD₅, total nitrogen and phosphorus, total coliforms) showed an improvement (lake WQI: 63.7 to 74.3). Principal component analysis explained 85% of its variance, showing spatial gradients of eutrophication and fecal contamination, with lacustrine homogenization suggesting management interventions. Lake Tisza is warming faster than global shallow lake averages, with critical implications for the ecosystem’s function; nonetheless, the coexistence of thermal deterioration with improvements in its WQI reveals the effectiveness of the intermittent discharge system and the need for climate-adapted monitoring frameworks that incorporate thermal vulnerability into water quality assessment for regulated shallow lakes under climate change pressure. Full article

Surface water degradation has intensified due to anthropogenic pressures, especially in urban areas, where unplanned land use compromises the integrity of aquatic ecosystems. This study investigated the relationship between water quality and land use in a Permanent Preservation Area (PPA) within an urban perimeter in Caçador, Santa Catarina, Brazil. Monthly sampling was conducted throughout 2024 at 11 points distributed along urban and rural sections of the river and its tributaries. Physicochemical and microbiological parameters were evaluated, and the Water Quality Index (WQI) established by the National Sanitation Foundation (NSF) was calculated in order to associate the results with the sampling points, complemented by Principal Component Analysis (PCA) to identify multivariate patterns of spatial variability in water quality across the study area. In parallel, the PPA within the urban perimeter was delimited according to current environmental legislation, and land use was classified using ArcGIS and Google Earth Pro. The results revealed greater water quality degradation in urban stretches of the river, particularly at sampling point SP7, which recorded the lowest dissolved oxygen concentration (3.10 mg L⁻¹), alongside elevated values of biochemical oxygen demand (5.23 mg L⁻¹), total phosphorus (2.94 mg L⁻¹), nitrate (18.75 mg L⁻¹), and thermotolerant coliforms (2759.20 MPN 100 mL⁻¹). The WQI ranged from 40.18 (SP7: bad category) to 73.57 (SP1: good category), reflecting a pronounced spatial gradient of water quality degradation associated with increasing urbanization along the river course. Mapping of the PPAs revealed that only 43.72% of the total area was covered by native vegetation, while the remaining 56.28% was occupied by anthropogenic land uses, including miscellaneous use (30.32%), agriculture (9.09%), buildings (6.09%), roads (4.64%), and railway infrastructure (5.81%). PCA accounted for 89.06% of the total data variance and indicated that greater interaction of sampling points with urbanized areas was consistently associated with reduced water quality, thereby demonstrating the direct influence of anthropogenic activities on the environmental parameters assessed throughout the study area. These findings demonstrate that land use patterns directly affect water quality and reinforce the need for riparian forest restoration, expanded sanitation infrastructure, and more sustainable urban planning. Full article

This study investigated the efficiency of macrophyte-based phytoremediation systems using Phragmites karka and Typha latifolia for the treatment of poultry–aquaculture wastewater and its suitability for irrigation reuse. Physicochemical parameters, heavy metals, and water quality indices were analysed using correlation analysis and Principal Component Analysis (PCA). Strong positive correlations were observed among turbidity, nutrients, biochemical oxygen demand (BOD₅), and chemical oxygen demand (COD), while dissolved oxygen (DO) showed significant negative relationships, indicating organic pollution-driven oxygen depletion. Heavy metals exhibited strong intercorrelations, suggesting common anthropogenic sources and similar removal pathways. PCA results revealed that the first three principal components (PCs) explained over 95% of the total variance, with positive values recorded from the first PC highlighting organic load, nutrient enrichment, and metal interactions as dominant factors controlling wastewater quality. The negative values of factor loadings obtained in the second and third PCs confirmed the roles of sedimentation, adsorption, microbial activity, and plant uptake in pollutant removal. Water Quality Index (WQI) values decreased drastically from highly polluted levels (>3000) in raw wastewater to <1.0 after 21 days of treatment, indicating excellent water quality. Sodium Absorption Ratio (SAR) also declined significantly, confirming a low sodicity risk. Both macrophytes demonstrated high treatment efficiency, with Typha latifolia showing slightly improved sodium reduction. Overall, the study highlights macrophyte-based systems as sustainable, cost-effective solutions for wastewater treatment and safe agricultural reuse. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring. Full article

Water pollution poses significant risks to human health and environmental sustainability, highlighting the need for accurate water quality assessment and prediction. This review examines the application of machine learning (ML) in Water Quality Index (WQI) assessments, focusing on WQI formulation, predictive modelling approaches, and explainable artificial intelligence (XAI) techniques. A structured literature review is conducted using major scientific databases, including ScienceDirect, Springer, and other relevant sources, following a systematic study selection process. The review analyzes commonly used water quality parameters and highlights how the deterministic structure of WQI influences machine learning modelling, often leading to high predictive performance that reflects predefined formulations rather than independent pattern learning. A comprehensive comparison of single, hybrid, and ensemble ML models is presented, showing that hybrid approaches generally provide improved robustness and accuracy in complex water quality scenarios. In addition, the role of XAI methods in enhancing model interpretability and supporting transparent decision-making is discussed. Key challenges, including limited generalization, model complexity, and interpretability constraints, are identified, and future research directions are proposed to develop more reliable and practical AI-based water quality monitoring systems. Overall, this review provides insights into the integration of machine learning and WQI, emphasizing the importance of balancing predictive accuracy with interpretability for sustainable water resource management. Full article

Water pollution control for wetland lakes has undergone a fluctuating development process. Effective pollution management requires not only scientific water quality monitoring data but also clear identification of pollution sources within the study area. Accordingly, this study investigated Mudong Lake, the core area of the Huixian Wetland, and conducted water quality monitoring in January 2023 (dry season) and June 2023 (wet season). Based on the Water Quality Index (WQI) assessment results, water quality was better in the wet season than in the dry season. To identify pollution sources, the Absolute Principal Component Score-Multiple Linear Regression (APCS-MLR) model was applied. The results showed that pollution in the dry season was mainly derived from aquaculture and agricultural non-point source pollution, anthropogenic point source pollution, and internal release from sediments, while pollution in the wet season exhibited mixed characteristics, driven by agricultural non-point sources, domestic sewage discharge, and natural factors. Source apportionment analysis indicated that composite pollution sources (domestic sewage and aquaculture wastewater), agricultural non-point source pollution, and other unidentified sources contributed 43.71%, 34.11%, and 22.18% of the total pollution load, respectively. The findings of this study can provide a scientific basis for pollution control, emission reduction, and the targeted management of Mudong Lake. Full article

The aim of this study is to conduct a comprehensive assessment of the water quality of the Sidi Mohammed Ben Taiba (SMBT), one of the largest drinking water reservoirs in northwestern Algeria, by integrating chemical and biological indicators. The assessment combines the Drinking Water Quality Index (DWQI), the Irrigation Water Quality Index (IWQI), the Organic Pollution Index (OPI) and zooplankton-based biological indicators (Zoo-IQ). A total of 23 physicochemical parameters were analyzed and interpreted using multivariate statistical approaches. This study fills an important knowledge gap by evaluating long-term temporal variability (January 2018–May 2025) and recent spatial heterogeneity (June 2023–May 2025), aiming to support sustainable water management. The results indicate that the reservoir water quality is generally suitable for drinking purposes (22.3 < DWQI < 54.0), is deemed excellent for agricultural irrigation (65 < IWQI < 69) and that the reservoir surface waters are slightly polluted to unpolluted (0.3 < OPI < 1.1). However, a deterioration in water quality has been detected in recent years, linked to increasing nutrient concentrations, as confirmed by the TSI–SD index. Despite the early signs of nutrient enrichment, the Zoo-IQ index remained within the moderate to good range, suggesting a certain degree of resilience in the zooplankton community. However, pronounced seasonal fluctuations observed in the Zoo-IQ and species diversity (H′) during periods of environmental stress serve as an early warning signal of emerging problems that may negatively affect water quality indices (WQI, IWQI, OPI). Station S4, located at the confluence of Wadi Belhassen and Wadi Farhat, descending from the Dahra mountain range in Algeria, has been identified as the most sensitive area and a potential hotspot for future pollution. The study provides robust data on the quality of reservoir water, offering a valuable decision-making tool for artificial reservoir managers and contributing to sustainable water management by identifying risk areas and supporting the implementation of preventive measures. Full article

Physicochemical and microbiological parameters are important indicators of drinking water quality. This study assessed the quality of groundwater used for drinking in four regions of Kosovo at 16 locations using an integrated assessment framework that combined physicochemical, microbiological, and Water Quality Index (WQI) approaches. The results reveal substantial spatial variability in water quality. While most physicochemical parameters remained within recommended limits, elevated values of total dissolved solids (up to 2792.5 mg/L), electrical conductivity (up to 2768.5 µS/cm), nitrate (up to 60.75 mg/L), and phosphate (up to 0.875 mg/L) were observed at several locations, indicating localized hydrogeochemical and anthropogenic influences. Dissolved oxygen levels were generally low (0.68–5.49 mg/L), reflecting limited aeration conditions in groundwater systems. Microbiological analysis revealed critical contamination, with Escherichia coli concentrations up to 299.9 CFU/100 mL, and all sampling sites exceeded permissible limits, indicating widespread fecal pollution and rendering the groundwater unsafe for direct consumption. WQI assessment further confirmed this condition, where 93.75% of locations were classified as medium quality using the NSF-WQI method, whereas the WA-WQI method categorized 68.75% of samples as poor and 6.25% as very poor. The novelty of this study lies in the integrated evaluation of hydrogeochemical processes and microbiological contamination using dual WQI methods and multivariate statistical analysis, providing a comprehensive understanding of groundwater degradation pathways. The findings are significant for policymakers, environmental managers, and public health authorities, highlighting the urgent need for groundwater treatment, improved sanitation infrastructure, and sustainable water resource management strategies in vulnerable regions. Full article

This study proposes and evaluates a specialized Recreational Water Quality Index (IR-WQI) designed to prioritize the bathers’ safety and comfort. Focusing on the Năvodari–Mamaia sector (2022–2024), the research investigates how different weighting configurations—prioritizing either microbiological safety or physicochemical stability—affect the accuracy of bathing water assessments. The IR-WQI was tested across four scenarios, comparing the sensitivity of a specialized pH-based “bather-comfort” penalty function against models that include salinity as a weighted constant. Results demonstrate high categorical stability, with 93.3% of monitoring sites maintaining their qualitative classification regardless of the weighting scheme. However, the inclusion of salinity was found to inflate quality scores, potentially masking fecal contamination at vulnerable sites. Scenario 1, which prioritizes microbiological indicators (60% weight) and incorporates a pH filter, provides a transparent and conservative diagnostic tool for coastal managers, thereby supporting sustainable tourism and informed decision-making for beach safety. Full article

This study presents the first watershed-scale assessment of the impact of thermal spring discharges on the hydrochemistry and water quality of the Soummam basin (northeastern Algeria). Fourteen stations were monitored during three campaigns (October 2024, December 2024 and March 2025), combining physicochemical analyses, hydrochemical diagrams, and water quality indices (WQI and IWQI). The results reveal a clear spatial gradient in water composition, from low-mineral Ca-HCO₃/Ca-SO₄ facies in upstream areas to highly mineralized Na-Cl facies associated with thermal springs (Sidi Yahia and Sillal). Electrical conductivity reaches up to 27,359 µS/cm, reflecting intense mineralization driven by evaporite dissolution and deep water–rock interaction. This thermomineral signature propagates downstream through mixing and ion exchange processes, leading to progressive salinity enrichment. Water quality indices highlight significant degradation in thermally influenced zones, with approximately 50% of samples unsuitable for drinking (WQI > 300) and more than 60% classified as highly restricted for irrigation (IWQI < 40). Cluster analysis further confirms the distinction between severely impacted, moderately affected, and relatively preserved waters. Overall, the findings demonstrate that thermal discharges represent a major and persistent driver of salinization, emphasizing the need to incorporate geothermal influences into water resource management strategies in semi-arid environments. Full article

High Andean lagoons in southern Peru have critical hydrological and ecological functions; however, long-term time series integrating trophic, integral quality, and metal contamination metrics to support adaptive management are lacking. A total of 1846 records (2015–2024) from four systems (3100–4600 m a.s.l.) were analyzed using seven indices assessing trophic status (TSItsr, TRIX), general water quality (OWQI, WQIHA, CCME-WQI), and metal contamination (HPI, CD). Temporal trends were assessed using Mann–Kendall and Theil–Sen slope; spatial heterogeneity using Kruskal–Wallis and Dunn–Bonferroni comparisons; controlling factors using distance-based redundancy analysis (999 permutations); and functional typology using Ward’s hierarchical clustering on Z-standardized data. 93% of the series lacked monotonic trends (52/56 lagoon–stratum × index combinations), demonstrating high interannual stability; spatial variance was marked (ε² = 0.73 in CCME-WQI). Distance-based redundancy analysis (db-RDA) explained 24.6% of total variability, with lake identity as the dominant driver (~45%), followed by temporal change (~8%). Four functional archetypes emerged, including a metal-eutrophic hotspot (HPI ≈ 213; CD ≈ 19) and recovering reservoirs with intermediate water quality indicators. Joint thresholds (TSItsr ≥ 60 + HPI ≥ 100) establish early-warning criteria, with Paucarani (HPI = 213) approaching the critical domain where metal-driven stress may facilitate cyanobacterial dominance. Systems show temporal resilience but strong spatial divergence induced by local pressures. The proposed typology and thresholds provide an operational basis for early warnings and prioritization of remediation actions in high-mountain ecosystems subject to increasing anthropogenic stress. Full article

Global freshwater scarcity is a pressing environmental challenge, particularly in Egypt, which depends entirely on the Nile River and its tributaries. Rapid population growth, domestic wastes, agricultural runoff, and rapid industrial expansion exert highly anthropogenic stress on aquatic ecosystems, including Bahr Yusuf and Ibrahimia Canals in Upper Egypt. This study aimed to evaluate the ecological health and sustainability status of the two canals using an integrated multi-metric framework combining physicochemical variables, microbiological indicators, and community structures of zooplankton and benthic fauna. Multivariate statistical analyses (PCA, CCA), and ecological indices, including the water quality index (WQI), microbial assessment index (MAI), Rotifer-Based Index (TSI_Rot) and Hilsenhoff Biotic Index, were applied to determine pollution gradients. The results revealed that Bahr Yusuf suffers from higher pollution levels than the Ibrahimia Canal. Canonical correspondence analysis (CCA) showed that nutrient enrichment and elevated organic load are responsible for over 72% of the variance in zooplankton and benthic invertebrate assemblage in both water bodies. The dominance of pollution-tolerant species, Philodina roseola and B. calyciflorus of zooplankton and Limnodrilus udekemianus, Chironomidae larvae, Melanoides tuberculate and Cleopatra bulimoides of benthic taxa, further indicates a direct increase in organic loading and nutrient enrichment from agricultural and domestic sources. According to the Integrated Water Quality–Biotic Health Index (IWQ-BHI), the downstream stations of Bahr Yusuf are critical risk zones, with scores below 50.0, while the upstream stations of Ibrahimia Canal fell within the “good” category, with scores exceeding 70.0. Overall, both waterbodies are approaching a critical threshold of ecological instability and require urgent, integrated and sustainable management to restore and preserve these vital freshwater ecosystems. Full article

Atmospheric water (AW) is currently recognized as a promising solution to mitigate the global water crisis. Nevertheless, its harvesting techniques should balance three main aspects: energy consumption, yield, and the quality of produced water. Water quality is of the utmost importance, because the potential uses of atmospheric water—and therefore its value—ultimately depend on this characteristic. Currently, existing indices and indicators intended as evaluation tools for different harvesting techniques generally focus on the first two aspects only, overlooking the quality perspective, with the risk of overestimating the performance of systems that require less energy but provide low-quality water. This study fills this knowledge gap by proposing a new evaluation tool, the Atmospheric Water Energy–Quality Index (AWEQI). This index links the energy evaluation of an Atmospheric Water Generator (AWG)—a term referring to all active, passive, or hybrid systems for atmospheric water collection—to the quality of the produced water. The index is constructed through an appropriate reformulation and combination of the Water Energy Transformation (WET) indicator and the Water Quality Index (WQI) to obtain a monotonic function whose values increase with improved performance, both in terms of energy efficiency and water quality. Moreover, based on a literature review, the study presents an analysis of potential AW contaminants and their sources, and proposes two parameter sets to be considered in the WQI calculation. Full article

Sustainable groundwater management in hyper-arid regions requires accurate water quality assessments, yet remote desert environments present major challenges due to data scarcity, high sampling costs, and limited laboratory infrastructure. This study proposes a framework integrating the Water Quality Index (WQI) with Inverse Distance Weighting (IDW)-based spatial data augmentation and machine learning classification for groundwater quality assessment in the Tabelbala region, southwestern Algeria. Three classifiers were evaluated, Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), and trained on an augmented dataset generated from 178 original groundwater samples using IDW interpolation with a sensitivity-optimized 150 m radius, producing 2779 augmented training points. RF achieved the highest predictive accuracy (85.9%), followed by ANNs (84.7%) and SVMs (83.1%), with all models demonstrating excellent discriminative performances (area under the receiver operating characteristic curve > 0.96). Permutation Feature Importance analysis identified total dissolved solids (TDS), sulfates (SO₄²⁻), total hardness (TH), and chlorides (Cl⁻) as the most influential parameters, consistent with World Health Organization (WHO) guidelines. Spatial distribution maps revealed that the majority of groundwater sources exhibited poor to very poor quality, highlighting the urgent need for local water management interventions. The proposed framework offers a replicable decision-support tool for water resource managers in data-scarce arid environments, supporting SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Full article