The increasing demand for phosphorus (P) sources and concerns about surface water quality raise the need to explore safe and efficient secondary P fertilizer sources. This study evaluated the effectiveness of a Ca-based recovered nutrient product (RNP) from synthetic swine wastewater using an innovative anaerobic membrane bioreactor (AnMBR) technology. This study aimed to characterize and compare the dissolution, transformations, and potential bioavailability of P in RNP with conventional P fertilizers (monoammonium phosphate; MAP, triple superphosphate; TSP) in selected soils over time by using short-term laboratory incubation studies in Petri dishes. Soil samples sectioned from the point of application were assessed for pH, total P, resin-extractable P, and selected samples by using X-ray absorption near-edge structure spectroscopy. The RNP treatment showed that over 90%, 70%, and 80% of added P remained in the center section in calcareous, neutral, and acid soils, respectively, where the potential plant-available P was greater than the control in all soils and similar to the MAP treatment only in acid soil after 5 weeks of incubation. The hydroxyapatite-like species dominated P speciation in both RNP and RNP-added soils, leading to less solubility. These results underscore the potential of Ca-based RNP as a P source for tested soils, and process modifications could yield a series of viable secondary P sources for agriculture.

Abstract The Nag River, flowing through the highly urbanized core of Nagpur, Maharashtra, serves as the primary drainage system for the city and is critically polluted due to rapid urban development and uncontrolled industrial discharges. While conventional physicochemical assessments exist, they fail to provide the quantitative, spatially resolved source apportionment necessary for targeted remediation. This study integrates WQI with PCA and CA to provide a structured, data-driven assessment of pollution sources along the Nag River, specifically Principal Component Analysis (PCA) and Cluster Analysis (CA) within a spatial framework to provide the first systematic differentiation of pollution sources (e.g. municipal sewage vs. specialized industrial effluent) and link them directly to specific land-use zones along the river’s 17 km urban corridor. The aim was to holistically assess the surface water quality and quantitatively identify, map, and attribute pollution sources along this critical stretch. Nine (9) surface water samples (S1-S9) were systematically collected during the pre-monsoon season (February 2023), covering segments influenced by diverse residential, commercial, and industrial land use. Twenty physicochemical and biological parameters were analyzed, and the reliability of the hydrochemical data was confirmed using the Ionic Balance Error (IBE) validation. WQI values ranged severely from 47.05 (Good) at the upstream baseline (S1) to a maximum of 6440.38 (Unfit for all practical uses) at Yashwant Stadium (S5), confirming chronic heavy pollution. This degradation is primarily attributed to untreated municipal sewage, as indicated by extreme BOD levels up to 216.28 mg/l and non-compliant specialized industrial discharges. PCA identified three primary Varifactors (VFs) explaining 87.935% of the total variance. Varifactor 1 (44.088%) confirmed the overwhelming dominance of untreated municipal sewage (organic load, total dissolved solids, and microbiological parameters). Varifactor 2 (16.666%) was strongly associated with specialized heavy metals (Nickel and Cadmium), indicating a distinct point source industrial effluent. CA successfully categorized sampling sites into four spatial pollution clusters (C1-C4), enabling the identification of high-priority pollution hotspots that correlate directly with land use. The present study integrates the WQI-PCA-CA approach, combined with land use assessment, to provide critical insights to support evidence-based river restoration and sustainable watershed management planning.

Explainable AutoML-driven surface water quality classification with key indicators identification

Access to safe drinking water and decent sanitation is a basic human right, yet most people in developing countries, particularly in Sub-Saharan Africa, lack access to them. The problem of inadequate clean water becomes complicated when biological and chemical agents contaminate available water sources. In Nigeria, bacterial water contamination is common; however, in recent literature, there is a lack of synthesis linking drinking water contamination with the emergence of antibiotic-resistant bacteria (ARB), including how the drinking water ecosystem may contribute to the spread of antibiotic resistance (AR). In addition, the microbiological mechanism that ensures the persistence of ARB in drinking water needs to be fully explored. Thus, this review integrates evidence on bacterial contamination and evaluates the role of drinking water in the dissemination of AR in Nigeria, including the contributions of poor sanitation, industrial effluents, abattoir operations, leachates from dumpsites, agricultural practices, and runoff from farm fields to the bacteriological quality of surface and underground water, and their consequences on human health. Also expanded are the processes leading to the emergence of ARB in water contaminated by sewage from domestic and pharmaceutical sources. Anthropogenic water contamination results in the emergence of ARB carrying transmissible antibiotic resistance genes (ARGs) in drinking water, thus highlighting the need to eliminate bacterial contamination of drinking water sources to protect public health and ensure the sustainability of water resources. Integrating surveillance for AR in environmental and treated water into the national antimicrobial resistance surveillance network is recommended to control the spread and reduce the burden of waterborne antibiotic-resistant bacteria in Nigeria.

Seasonal Dynamics of Groundwater and Surface Water Quality Around a Dumpsite in Dimapur, Nagaland, India: An EWQI and Multivariate Approach

ABSTRACT This study summarises long-term research on the labile fraction of metals (Al, Fe, Cu, Mn, Zn, Pb, Cr, Cd) in surface waters of Ukraine differing in humic substances content and dissolved organic matter composition. The investigations covered the reservoirs of the Dnieper cascade, the Dnipro–Bug and Dniester estuaries, rivers of Polissia, central and southwestern Ukraine, as well as small lakes and urban streams. The selected metals are environmentally relevant due to their toxicity and role in aquatic systems. The proportion of the labile fraction of Al, Fe, Cu, Mn, Zn, and Pb increased with decreasing humic substance concentrations and was highest in waters of the southwestern region and urban areas. Seasonal variability was most pronounced in summer and autumn, reflecting changes in dissolved organic matter composition. In highly anthropogenically impacted waters, elevated labile metal fractions showed limited seasonal variation. Higher labile Fe and Mn concentrations were observed in bottom waters under oxygen-deficient conditions. The applicability of anodic stripping voltammetry and chemiluminescent analysis is discussed, as these sensitive techniques allow direct determination of labile fraction of metals. The results provide a scientific basis for assessing surface water quality under current and future environmental and climatic pressures.

Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization

Surface water quality in Dolisie (southwest of the Republic of Congo) is strongly impacted by human activities, particularly domestic wastewater discharge, agriculture, and livestock farming, leading to progressive degradation of water resources and posing risks to public health as well as domestic and agricultural uses. This study assessed water quality through analysis of physico-chemical and bacteriological parameters at several sites representative of domestic and agricultural areas. Physico-chemical analysis showed that pH ranged from 5.5 (Ninja Lake) to 9.9 (marsh near the Orthodox Church), with a mean of 6.58 ± 1.18. Water temperature varied from 25.36°C (upstream of the Loubomo River) to 28.5°C (Tahiti fish ponds), with an average of 26.83 ± 0.93°C. Electrical conductivity ranged from 115.66 to 315.33 µS/cm (mean 213.65 ± 73.18 µS/cm), while total dissolved solids (TDS) varied from 57 to 210.5 ppm (mean 112.52 ± 46.70 ppm). Three heavy metals were detected: cadmium (0.049–0.070 ppm, mean 0.057 ± 0.005 ppm), copper (0.0217–0.0509 ppm, mean 0.037 ± 0.010 ppm), and zinc (0.0004–0.00311 ppm, mean 0.004 ± 0.008 ppm). Microbiologically, total mesophilic aerobic flora (TMAF) ranged from 1,000 to 6,000 CFU/100mL (mean 3,333 ± 1,670), total coliforms from 69 to 193 CFU/100mL (mean 133 ± 43), and fecal coliforms from 32 to 102 CFU/100mL (mean 64 ± 21), exceeding WHO standards and indicating significant fecal contamination. Fecal streptococci ranged from 30 to 72 CFU/100mL, <i>Staphylococcus</i> spp. from 14 to 97 CFU/100mL, <i>Salmonella</i> from 40 to 110 CFU/100mL, <i>Shigella</i> from 19 to 77 CFU/100mL, and <i>Pseudomonas </i><i>aeruginosa</i> from 15 to 62 CFU/100mL. Correlation analyses revealed significant relationships: temperature correlated with total coliforms (r = 0.69) and fecal coliforms (r = 0.63), electrical conductivity correlated with TDS (r = 0.91), and several bacterial groups showed positive correlations among themselves. Principal component analysis associated the F1 axis (42.24% of variance) with microbiological parameters and the F2 axis (20.7%) with physico-chemical parameters. These findings reveal progressive deterioration of surface water quality in Dolisie and emphasize the urgent need for sustainable management measures, public awareness, and wastewater control to preserve water resources and protect public health.

Accurate and efficient surface water quality monitoring is essential for ecological protection and sustainable development. However, conventional monitoring methods, such as fixed-site observations, often suffer from spatial limitations and overlook crucial auxiliary variables. This study proposes an innovative modeling framework for large-scale river water quality inversion that integrates multi-source data—including Sentinel-2 imagery, meteorological conditions, land use classification, and landscape pattern indices. To improve predictive accuracy, three tree-based machine learning models (Random Forest, XGBoost, and LightGBM) were constructed and further optimized using the Whale Optimization Algorithm (WOA), a nature-inspired metaheuristic technique. Additionally, model interpretability was enhanced using SHAP (Shapley Additive Explanations), enabling a transparent understanding of each variable’s contribution. The framework was applied to the Red River Basin (RRB) to predict six key water quality parameters: dissolved oxygen (DO), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), pH, and permanganate index (CODMn). Results demonstrate that integrating landscape and meteorological variables significantly improves model performance compared to remote sensing alone. The best-performing models achieved R2 values exceeding 0.45 for all parameters (DO: 0.70, NH3-N: 0.46, TP: 0.59, TN: 0.71, pH: 0.83, CODMn: 0.57). Among them, WOA-optimized LightGBM consistently delivered superior performance. The study also confirms the feasibility of applying the models across the entire basin, offering a transferable and interpretable approach to spatiotemporal water quality prediction in other large-scale or data-scarce regions.

ABSTRACT This study investigates the treatment efficiency of Chrysopogon zizanioides (vetiver grass) in improving surface water quality through phytoremediation, complemented by machine learning‐based water quality classification. Water quality index (WQI) and water quality classification (WQC) were anticipated and classified using a machine learning model by filtering used Chrysopogon zizanioides . Surface water samples were collected and monitored at regular intervals every 7 days over a period of 12 weeks to ensure sufficient temporal resolution for tracking dynamic changes in key parameters such as pH, turbidity, hardness, chloride, sulfate, calcium, TDS, TSS, iron, and copper. The results show that the proposed models accurately estimate the WQI and categorize water quality with improved robustness. The study found that the NARNN model predicted WQI values better than the LSTM model. Additionally, the XGBOOST algorithm had a maximum accuracy rate of 97.01%. It also has a 99.23% sensitivity rate, confirming positive detection. With a specificity rating of 97.78%, the system accurately identified negative events. The precision rate was 94.93%, suggesting its ability to predict positive events. Finally, the algorithm's F ‐score of 98.54% indicates its WQC prediction performance. This study integrates ecological restoration, technology, and community‐focused solutions to provide clean water access and sustainable water management to achieve the United Nations Sustainable Development Goals (UN SDGs 6).

Geospatial assessment of surface water quality on integrating entropy - machine learning techniques to predict water contamination in the Paradip area of Mahanadi River Basin, Odisha

Agricultural runoff is a major source of water quality impairments and is prevalent in areas where agricultural operations focus on maintaining global food security. To alleviate downstream impacts, best management practices are used to cultivate food systems and enhance soil nutrient cycling. When runoff events do occur, tracing the impairments often involves complex and costly methods to determine analyte concentrations and forecast mitigation techniques. Fluorescent dissolved organic matter (fDOM) is an innovative approach to understanding parent source materials and carbon signatures from runoff. Fluorescence and absorbance indices can distinguish intensities of the carbon molecular weight, biological activity, and humification that can trace the environmental availability of carbon sources. Comprehensive data sets can be combined using parallel factor analysis (PARAFAC) to determine parent source components. Integrating these analyses can provide real-time high-frequency data to empower policymakers and land managers to make informed decisions aimed at reducing the environmental degradation associated with modern intensive agriculture.

This study evaluates the spatial and seasonal variation of surface water quality in the Can Gio estuary, a core area of the Can Gio Mangrove Biosphere Reserve in Southern Vietnam, using the VN-WQI water quality index. Fourteen monitoring sites were sampled during the dry and rainy seasons, and major physicochemical, nutritional, heavy metal, and microbiological indicators were analyzed according to national standards. The evaluation results showed that the VN-WQI value ranged from 68 to 83 (from moderate to good) in both the weighted VN-WQI and unweighted VN-WQI (the weighted VN-WQI provides a more sensitive indication of organic and nutrient pollution than the unweighted index). The main polluting parameters contributing to water quality degradation were organic and microbiological indicators, particularly BOD5, COD, ammonium, nitrite, phosphate, total coliforms, and Escherichia coli. The data used in this study were derived from secondary monitoring datasets . In particular, the sample collection points near aquaculture areas and human activities (CG5, CG8, CG9, CG10, CG11, and CG12) have a lower VN-WQI index than the rest of the points. The results of these studies contribute to helping the state management agency in charge of the environmental control pollution as well as monitoring surface water quality to protect the biosphere reserve as well as the mangrove ecosystem.

Nutrient fluxes from rivers and groundwater into an urban bay of the New York-New Jersey Harbor Estuary.

Credibility-driven identification of cropland runoff source in surface waters using ANN-XGBoost model ensemble powered by microbial fingerprints.

Comparison of black carbon media for removal of dissolved metals in stormwater.

This study provides a multivariate seasonal assessment of surface water quality for irrigation in western Iraq. Water samples were collected during winter and summer of 2024 from three primary sources—Euphrates River, Lake Habbaniyah, and Lake Tharthar. Key physicochemical parameters were analyzed, including EC, TDS, TH, Na⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, and pH. Multivariate statistical analyses, including Principal Component Analysis (PCA), were conducted to investigate seasonal patterns, assess irrigation suitability, and identify dominant hydrochemical parameters. One-way ANOVA revealed no significant seasonal differences (p > 0.05); however, EC and TDS values showed an increasing trend during summer, particularly in stagnant lakes. Subsequent multivariate analyses, including Pearson correlation and Principal Component Analysis (PCA), identified EC, Na⁺, and TH as the principal hydrochemical drivers, collectively accounting for over 95% of the total variance. The Sodium Adsorption Ratio (SAR) was calculated to quantify the risk of sodicity in irrigation practices. SAR values remained acceptable for Euphrates water (~4.5), but exceeded 5.0 in lake sources, indicating moderate sodicity hazards. A comparative analysis revealed that water from the Euphrates River is the most suitable for irrigation. In contrast, water from the lakes requires dilution or treatment due to elevated salinity and sodium levels. The findings underscore the need for source-specific water management, seasonal monitoring, and SAR-based risk evaluation to maintain soil health and irrigation sustainability in semi-arid regions.

Heavy metal risks and policy analysis on using industrial waste salts for making value-added snow-melting agents.

Refining PAH and PCB bioavailability predictions in industrial sediments using source-fingerprinting, particle size, and bulk carbon, Puget Sound, Washington.

ABSTRACT There is tremendous reduction of the available fresh water sources due to competing uses and uncontrolled pollution leading to global water scarcity. Worldwide, urban areas are well served with improved quality water unlike the rural areas, with a wide gap in the rural developing regions suffering from limited financial resources and other competing needs. Developing regions’ rural population has resorted to using raw surface water, rendering them vulnerable to water-related infections. This study aims to achieving improved drinking water quality using an easy to build horizontal roughing filter (HRF), to be used as a pre-treatment unit for variably turbid surface water before using slow sand filtration (SSF) units. The HRF comprises locally sourced gravel of different sizes filled into different compartments. The system was operated at filtration rates of 0.35, 0.70, 1.10, 1.45, and 2.00 m/h, with a model suspension of varied turbidity level ranging between 300–450 NTU. The results showed a significant reduction of turbidity level to an average range of 8.8–25.02 NTU for the respective filtration rates that conform to the WHO requirements of <50 NTU for subsequent treatment using SSF units. An overall average removal efficiency of 93.68% was observed for the system operation. It was concluded that the HRF system is a suitable alternative to conventional treatment units for use in rural developing regions, for improving the quality of surface water before using SSF as a final purification unit. Compared to conventional systems, the HRF system impresses with its simple design and low costs.