Search Results (786)

This study was developed to identify the optimal (most cost-effective) strategies to reduce sediment and phosphorus loadings in the Upper Soldier Creek, Kansas, USA, watershed using the Watershed Management Optimization Support Tool (WMOST) suite of programs. Under average precipitation, loading targets for upland total phosphorus (TP) could be met with use of grassed swales for treating urban area runoff and of contouring for agricultural runoff. For a wet year, the same target could be met, but with use of a sand filter with underdrain for the urban runoff. Both annual and daily TP loading targets from Total Maximum Daily Loads (TMDLs) were exceeded in simulations of best management practice (BMP) solutions for 14 alternative future climate scenarios. We expanded the set of BMPs to include stream bank stabilization (physical plus riparian restoration) and two-stage channel designs, but upland loading targets could not be met for either TP or total suspended solids (TSS) under any precipitation conditions. An optimization scenario that simulated the routing of flows in excess of those treated by the upland BMPs to an off-channel treatment wetland allowed TMDLs to be met for an average precipitation year. WMOST can optimize cost-effectiveness of BMPs across multiple scales and climate scenarios. Full article

The effective assessment and improvement of water quality require analysis not only of the main river flowing into the sea but also of its tributaries, which may contribute to significant pollution. This study aimed to evaluate the physicochemical conditions of water in nine streams flowing into the Rega River between 2018 and 2022. It also sought to determine whether the water quality in these tributaries meets the standards defined by EU regulations for inland waters that serve as habitats for fish. The parameters analyzed included water temperature, dissolved oxygen (DO), pH, total suspended solids (TSSs), electrical conductivity (EC), alkalinity, total hardness (TH), biochemical oxygen demand (BOD₅), nitrite nitrogen (NO₂⁻-N), ammonium nitrogen (NH₄⁺-N), and total phosphorus (TP). The results indicated that most indicators met the requirements for waters suitable for salmonid species. Elevated concentrations of NO₂⁻-N observed across all sites were still within acceptable limits for cyprinid species. Among the parameters studied, TSSs was identified as the main factor that downgraded water quality over the study period. Principal component analysis (PCA) showed that the dominant variables influencing water chemistry were NH₄⁺-N, NO₂⁻-N, TP, EC, and chloride (Cl⁻), all of which are associated with anthropogenic sources. Full article

This study analyzed the relationship between zooplankton communities and water quality characteristics, with a focus on total organic carbon (TOC), in 22 reservoirs within the Geum River basin that share similar climatic conditions but exhibit varying levels of pollution. Across all reservoirs, zooplankton community structures showed the highest correlations with TOC, suspended solids (SS), chlorophyll-a (Chl-a), and Secchi depth (SD), with stronger associations observed for rotifers and cladocerans compared to copepods. The classification of zooplankton community composition patterns, followed by an analysis of their associations with TOC concentrations, revealed relatively distinct differences between high-TOC and low-TOC reservoirs, indicating that TOC functions as a key determinant of community composition. Meanwhile, network analysis based on overall water quality characteristics indicated that patterns of water quality similarity among zooplankton-based communities differed somewhat from those based solely on TOC concentrations, suggesting that TOC may exert an independent influence on zooplankton community structure. In high-TOC reservoirs, typical eutrophic characteristics—such as elevated chlorophyll-a, total phosphorus, and suspended solids, along with reduced water transparency—were observed, accompanied by higher zooplankton abundance and a greater proportion of rotifers within the community. In contrast, low-TOC reservoirs, despite exhibiting no marked differences in other water quality variables, showed higher diversity of cladocerans alongside rotifers, further supporting the independent role of TOC in shaping zooplankton community structures. These findings highlight TOC not only as a general indicator of pollution but also as an ecologically significant factor influencing zooplankton community composition and carbon dynamics in reservoir ecosystems. They suggest that TOC should be considered a key variable in future assessments and management of lentic ecosystems. Full article

Industrial wastewater management remains a critical barrier to achieving Sustainable Development Goal 6 (SDG 6) in many developing countries, where regulatory frameworks exist but affordable and scalable treatment solutions are lacking. In Pakistan, the textile sector is a leading polluter, with untreated effluents routinely discharged into rivers and agricultural lands despite stringent National Environmental Quality Standards (NEQS). This study presents a pilot-scale case from Faisalabad’s Khurrianwala industrial zone, where a decentralized, nature-based bioreactor was piloted to bridge the gap between policy and practice. The system integrates four treatment stages—anaerobic digestion (AD), floating treatment wetland (FTW), constructed wetland (CW), and sand filtration (SF)—and was further intensified via nutrient amendment, aeration, and bioaugmentation with three locally isolated bacterial strains (Acinetobacter junii NT-15, Pseudomonas indoloxydans NT-38, and Rhodococcus sp. NT-39). The fully intensified configuration achieved substantial reductions in total dissolved solids (TDS) (46%), total suspended solids (TSS) (51%), chemical oxygen demand (COD) (91%), biochemical oxygen demand (BOD) (94%), nutrients, nitrogen (N), and phosphorus (P) (86%), sulfate (26%), and chloride (41%). It also removed 95% iron (Fe), 87% cadmium (Cd), 57% lead (Pb), and 50% copper (Cu) from the effluent. The bacterial inoculants persist in the system and colonize the plant roots, contributing to stable bioremediation. The treated effluent met the national environmental quality standards (NEQS) discharge limits, confirming the system’s regulatory and ecological viability. This case study demonstrates how nature-based systems, when scientifically intensified, can deliver high-performance wastewater treatment in industrial zones with limited infrastructure—offering a replicable model for sustainable, SDG-aligned pollution control in the Global South. Full article

The quality of water in the Red River is a complex interplay between human-induced changes and inherent natural variables. This research utilized the snapshot sampling approach, garnering water quality data from 45 sampling sites in the Red River and crafting 24 environmental indicators related to land use and inherent natural determinants at the catchment scale. Through Spearman rank correlation and redundancy analyses, relationships among land use, natural variables, and water quality were elucidated. Our variance partitioning revealed differentiated impacts of land use and natural factors on water quality. Pivotal findings indicated superior water quality in the Red River, driven mainly by land use dynamics, which showed a distinct geomorphic gradient. Specific land use attributes, like cropland patch density, grassland’s largest patch index, and urban metrics, were pivotal in explaining variations in parameters such as total nitrogen, ammonia, and temperature. Notably, the configuration of land use had a more profound influence on water quality than merely its components. In terms of natural influences, while topography played a dominant role in shaping water quality, other factors like soil and weather had marginal impacts. Elevation was notably linked with metrics like total phosphorus and suspended solids, whereas precipitation and slope significantly determined electrical conductivity and chlorophyll-a models. In sum, incorporating both land use configurations and natural determinants offers a more comprehensive understanding of water quality disparities in the Red River’s ecosystem. For holistic water quality management, the focus should not only be on the major contributors like croplands and urban areas but also on underemphasized areas like grasslands. Tweaking cropland distribution, recognizing the intertwined nature of land use and natural elements, and tailoring land management based on topographical variations are essential strategies moving forward. Full article

The occurrence of toxic metal(loid)s in surface freshwater is a global concern due to its impacts on human and ecosystem health. Conceptual and quantitative metal(loid) models are needed to assess the impact of metal(loid)s in watersheds affected by acid rock drainage. Few case studies have focused on arid and semiarid headwaters, with scarce hydrological and hydrochemical information. This work reports the use of WASP8 (US EPA) to model Al, Fe, As, Cu, and SO₄²⁻ concentrations in the Upper Elqui River watershed in north–central Chile. Calibrated model performance for total concentrations was “good” (25.9, RRMSE; 0.7, R²-d) to “very good” (0.8–0.9, R²-d). The dissolved concentrations ranged between “acceptable” (56.3, RRMSE), “good” (28.6, RRMSE; 0.7 d), and “very good” (0.9, R²-d). While the model validation achieved mainly “very good” (0.8–0.9, R²-d) predictions for total concentrations, the predicted dissolved concentrations were less accurate for all indicators. Sensitivity analysis showed that the partition coefficient is a sensitive constant for estimating dissolved concentrations, and that integrating sorption and sediment interaction reduces the model error. This work highlights the need for detailed and site-specific information on the reactive and hydrodynamic properties of suspended solids, which directly impact the partition coefficient, sedimentation, and resuspension velocity calibration. Full article

Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an R² of 0.9938 for TN, a mean absolute error (MAE) of 0.0728 for DO, a root mean square error (RMSE) of 0.3881 for total TSS, and a mean absolute percentage error (MAPE) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring. Full article

A core tertiary wastewater reactive filtration technology, where continuously renewed hydrous ferric oxide coated sand is created in an upflow continuous backwash filter, has been adopted in about 100 water resource recovery facilities in several countries. Primarily focused on ultralow phosphorus discharge requirements to address nutrient pollution impacts and harmful algae blooms, the technology has also demonstrated the capacity to address high-efficiency removals of Hg, As, Zn, N, and other pollutants of concern, in addition to water quality needs met by common sand filtration, including total suspended solids. Recent work has demonstrated the capability of an additive iron–ozone catalytic oxidation process to the core reactive filtration technology platform to address micropollutants such as pharmaceuticals. Most recently, direct injection of frangible biochar into the reactive sand filter bed as a consumable reagent demonstrates a novel biochar water treatment technology in a platform that yields dose-dependent carbon negativity. In this work, the reactive filtration technology performance is reviewed from field pilot-scale to full-scale installation scenarios for nutrient removal and recovery applications. We also review the potential of the technology for nutrient recovery with the addition of biochar and micropollutant destructive removal with catalytic oxidation. Research exploration of this reactive filtration technology includes life cycle assessment (LCA) and techno-economic assessment to evaluate the environmental and economic impacts of this advanced water treatment technology. A recent LCA study of a pilot-scale field research and full-scale municipal system with over 2200 inventory elements shows a dose-dependent carbon negativity when biochar is injected into the process stream of reactive filtration. In this study, LCA demonstrates that reactive filtration has the potential as a negative emissions technology with −1.21 kg CO₂e/m³, where the negative contribution from the dosed biochar is −1.53 kg CO₂e/m³. In this biochar water treatment configuration, the system not only effectively removes pollutants from wastewater but also contributes to carbon sequestration and nutrient recovery for agriculture, making it a potentially valuable approach for sustainable water treatment. Full article

Monitoring chlorophyll-a concentration (Chl-a) is essential for assessing aquatic ecosystem health, yet its retrieval using remote sensing remains challenging in turbid coastal waters because of the intricate optical characteristics of these environments. Elevated levels of colored (chromophoric) dissolved organic matter (CDOM) and suspended sediments (aka total suspended solids, TSS) interfere with satellite-based Chl-a estimates, necessitating alternative approaches. One potential solution is machine learning, indirectly including non-Chl-a signals into the models. In this research, we develop machine learning models to predict Chl-a concentrations in the Chesapeake Bay, one of the largest estuaries on North America’s East Coast. Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. Using the entire ocean color datasets acquired by the satellite sensors MODIS-Aqua (>20 years) and VIIRS-SNPP (>10 years), we generated long-term Chl-a estimates covering the entire Chesapeake Bay area. The models achieve a multiplicative absolute error of approximately 1.40, demonstrating reliable performance. The predicted spatiotemporal Chl-a patterns align with known ecological processes in the Chesapeake Bay, particularly those influenced by riverine inputs and seasonal variability. This research emphasizes the potential of machine learning to enhance satellite-based water quality monitoring in optically complex coastal waters, providing valuable insights for ecosystem management and conservation. Full article

Hypoxia in river estuaries poses significant ecological and water safety risks, yet long-term high-frequency monitoring data for comprehensive analysis remain scarce. This study investigates hypoxia dynamics in the Shenzhen River Estuary (southern China) using two-year high-frequency monitoring data. A hybrid anomaly detection method integrating wavelet analysis and temporal information entropy was developed to identify hypoxia events. The drivers of hypoxia were also identified with correlation coefficients and transfer entropy (TE). The results reveal frequent spring–summer hypoxia. Turbidity and total nitrogen (TN) exhibited significant negative correlations and time-lagged effects on dissolved oxygen (DO), where TE reaches a peak of 0.05 with lags of 36 and 72 h, respectively. Wastewater treatment plant (WWTP) loads, particularly suspended solids (SSs), showed a linear negative correlation with estuarine DO. Notably, the 2022 data showed minimal correlations (except SSs) due to high baseline pollution, whereas the post-remediation 2023 data revealed stronger linear linkages (especially r = −0.81 for SSs). The proposed “high-frequency localization–low-frequency assessment” detection method demonstrated robust accuracy in identifying hypoxia events, and mechanistic analysis corroborated the time-lagged pollutant impacts. These findings advance hypoxia identification frameworks and highlight the critical role of Turbidity and SSs in driving estuarine oxygen depletion, offering actionable insights for adaptive water quality management. Full article

The objective of this research was to assess the first flush of rainwater harvested from a fibre–cement roof in southern Brazil. Runoff samples were collected for quantifying pH, total suspended solids, turbidity, conductivity, apparent colour, total coliforms, and Escherichia coli. Statistical methods were employed to describe the data, establish correlations between variables, and assess if the antecedent dry weather periods and rainfall intensity affected water quality. The qualitative characterisation of the first flush was performed using principal component analysis and simple regression analyses. The results show that rainwater runoff can be highly contaminated. Hypothesis tests showed that initial rainfall intensity and antecedent dry weather periods affect the quality of the first flush. Principal component analysis suggested that the most significant variables to characterise the first flush were turbidity and apparent colour. Using first-flush diverters in rainwater harvesting systems does not ensure E. coli removal, but it may reduce the risk of users’ contamination. Practical implications include discussions on the suggested first flush and the consequential impact on the quantity and quality of rainwater harvested. Future studies may consider using the method used in this research to develop guidelines based on more samples across the country. As novelty, one includes a statistically robust qualitative study in a region that lacks research on the quantification and quality of first flush. Such assessment helps to build up Brazilian data for a better understanding of first flush management in rainwater harvesting. Full article

Stormwater runoff from areas with specific industrial, agricultural or logistic land use comprises a significant source of water pollution, yet research on its specific composition remains limited compared to urban stormwater pollution. This review synthesizes findings from different studies to analyze sampling methods, types of pollution parameters and their associated concentration ranges across various non-urban land use types, including industrial and commercial zones, transportation infrastructure (ports, airports, highways, railways) and agricultural areas. Studies differed in sample strategy, investigated phase (water, sediment) and analyzed chemical parameters. The latter can be grouped into sum parameters (e.g., total suspended solids (TSS), chemical oxygen demand (COD)), metals (e.g., nickel, copper, zinc, lead), nutrients (e.g., nitrogen, phosphorus), organic micropollutants (e.g., polycyclic aromatic hydrocarbons (PAH), perfluoroalkyl acids (PFAA)) and microbial contaminants. Results indicate that pollutant loads vary widely depending on land use, with industrial and railway areas showing the highest metal contamination, while agricultural and livestock farming areas exhibit elevated nutrient and microbial concentrations. The heterogeneity of the sampling, analysis and subsequent data processing hindered the statistical condensation of data from different studies. The findings underscore the need for standardized monitoring methods and tailored stormwater treatment strategies to mitigate pollution impact effectively. Full article

This study presents the long-term performance evaluation of a full-scale hybrid membrane bioreactor (MBR)–nanofiltration (NF) system for the treatment of high-strength municipal landfill leachate from the Istanbul–Şile Kömürcüoda facility. Over a 16-month operational period, influent and effluent samples were analyzed for key parameters, including chemical oxygen demand (COD), ammonium nitrogen (NH₄⁺-N), total phosphorus (TP), suspended solids (SS), and temperature. The MBR unit consistently achieved high removal efficiencies for COD and NH₄⁺-N (93.5% and 98.6%, respectively), while the NF stage provided effective polishing, particularly for phosphorus, maintaining a TP removal above 95%. Seasonal analysis revealed that the biological performance peaked during spring, likely due to optimal microbial conditions. To support intelligent control strategies, artificial neural network (ANN) models were developed to predict effluent COD and NH₄⁺-N concentrations using influent and operational parameters. The best-performing ANN models achieved R² values of 0.861 and 0.796, respectively. The model’s robustness was validated through RMSE, MAE, and 95% confidence intervals. Additionally, Principal Component Analysis (PCA) and Random Forest algorithms were employed to determine the parameter importance and nonlinear interactions. The findings demonstrate that the integration of hybrid membrane systems with AI-based modeling can enhance treatment efficiency and forecasting capabilities for landfill leachate management, offering a resilient and data-driven approach to sustainable operation. Full article

Water quality and the culture performance of juvenile rainbow trout (Oncorhynchus mykiss) were evaluated between 2014 and 2017 in a recirculating aquaculture system (RAS) in the Chilean Altiplano. Key parameters such as temperature, total ammonia nitrogen (TAN), nitrates, and dissolved oxygen were monitored, with values ranging from 7 to 21 °C, <0.1 to 0.63 mg/L, 2.0 to 135 mg/L, and 1.8 to 7.5 mg/L, respectively. Additional parameters—including alkalinity, arsenic, chlorine, true color, conductivity, hardness, phosphorus, pH, potassium, suspended solids, and salinity—were also assessed, comparing different points within the system (head tank, culture tanks, and settling tanks). The results showed that water quality remained within acceptable ranges for aquaculture, although fluctuations in pH and low alkalinity levels caused stress in the fish. Despite these challenges, the specific growth rate (SGR) was 1.49, the feed conversion ratio (FCR) was 1.52, and weight gain reached 298.7%, with a survival rate of 96.2%. This study demonstrates that aquaculture in the Altiplano is feasible and can contribute to the sustainable development of aquaculture in the region. Furthermore, it highlights the importance of comprehensive water quality monitoring to optimize RAS performance in challenging environments. Full article

Wastewater treatment plants (WWTPs) are crucial for environmental protection and public health; however, they are among the most energy-intensive facilities in the water sector. This study examines the correlation between energy consumption and treatment efficiency at the Metamorphosis WWTP (MWWTP) in Attica, Greece, during the years 2022 and 2023. By analyzing influent and effluent characteristics, energy consumption patterns, and the removal efficiencies of key pollutants—Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD₅), and Suspended Solids (SS)—this research provides valuable insights into optimizing wastewater treatment operations. The findings reveal that, despite seasonal variations and fluctuations in influent composition, the facility consistently achieved high pollutant removal rates while maintaining stable energy consumption. The influent BOD₅ increased from 992.8 mg L⁻¹ in 2022 to 1122.3 mg L⁻¹ in 2023. COD rose from 1925.4 mg L⁻¹ to 2594.4 mg L⁻¹, SS from 1280.8 mg L⁻¹ to 1421.2 mg L⁻¹, and total phosphorus from 14.2 mg L⁻¹ to 17.0 mg L⁻¹. Effluent concentrations remained consistently low, with BOD₅ at 6.1 mg L⁻¹ in 2022 and 4.7 mg L⁻¹ in 2023; COD at 23.8 mg L⁻¹ and 25.2 mg L⁻¹, respectively; total nitrogen at 20.2 mg L⁻¹ and 16.7 mg L⁻¹; total phosphorus at 2.4 mg L⁻¹ and 2.6 mg L⁻¹; and SS at 2.4 mg L⁻¹ and 3.5 mg L⁻¹. These results indicate removal efficiencies exceeding 90%. Energy consumption remained stable, recorded at 13,044.9 kWh (0.593 kWh m⁻³ influent) in 2022 and 13,126.1 kWh (0.598 kWh m⁻³ influent) in 2023. These results highlight the importance of integrating energy-efficient strategies and renewable energy solutions to enhance wastewater treatment plant (WWTP) sustainability. This study contributes to ongoing efforts to improve energy optimization in wastewater treatment, supporting global initiatives for carbon footprint reduction and advancing the principles of a circular economy. Full article