The Ba River in Vietnam has been facing pollution due to waste generation from agricultural and urban areas. This study focuses on evaluating the spatiotemporal variations in river water quality based on physicochemical characteristics and pesticide parameters for different seasons in 2022-2023. The results indicate that the concentrations of most parameters in the rainy season were higher than those in the early-dry and dry seasons due to the non-point sources in agricultural areas. Notably, the analysis of pesticide residue in both the rainy and dry seasons revealed low levels of chlorpyrifos (ethyl), and deltamethrin was detected in the only rainy season. The results from the hierarchical cluster analysis and water quality index show that the water quality at Ben Mong, An Khe, and Ba River Bridges was classified as moderately to highly polluted. These areas should focus on regular water quality monitoring and appropriate pollution source management. PRACTITIONER POINTS: Agriculture activities strongly affected the water quality of the Highland Ba River of Vietnam. Chlorpyrifos and deltamethrin pesticides (0.0074-0.0218 μg/L) were detected in Ba River. Non-point pollution sources significantly influenced water quality in the Ba River. Variations in river water quality mainly depend on seasons and locations. Water quality index values in rainy seasons (26-88) are lower than that in dry season (37-92).

The purpose of the study is to assess the possibilities of using groundwater for water supply in the East European Arctic agglomeration based on an assessment of their quality and health risks. For this purpose, high-precision determinations of the complete macro- and microcomponent composition were carried out in sixty-six water samples taken from wells up to 180 m deep. It was found that in some samples the concentrations of Na+, Fe, B, Ba, Mn and U exceeded WHO standards. The least mineralized young waters are characterized by the processes of dissolution of carbonates with the transition of Ca, Mg, Ba, Sr into water, and the processes of leaching of Fe and Mn by acidic swamp waters from near-surface sediments. Waters of high mineralization, enriched in Na+, Cl-, B, Mo, Cd, Pb, were formed as a result of the dissolution of aluminosilicate rocks over thousands of years and mixing with relics of ancient and modern marine transgressions. An assessment of the average Water Quality Index value of the studied aquifer showed that, in general, the water is of excellent quality. Non-carcinogenic risks were determined primarily by uranium concentrations. The average danger index values for this element for children were 1.22. In adults it was slightly lower and amounted to 0.83. Carcinogenic risks are associated primarily with arsenic concentrations. The average total carcinogenic risk associated with this element was 3.8.10-5, which is acceptable, but samples from two wells showed total carcinogenic risk values above 10-4, which is in the high-risk area. For drinking purposes, it is preferable to use low-mineralized water with a minimum content of toxic elements. If necessary, preliminary aeration of the water is possible, during which precipitation of iron, arsenic and uranium occurs. Due to the typical nature of the problem under consideration for the Arctic regions, the results obtained can be used at other sites in the Subpolar zone.

Regular groundwater quality monitoring in resource-constrained regions present formidable challenges in terms of funding, testing facilities and manpower; necessitating the development of easily implementable monitoring techniques. This study proposes a copula-based risk assessment model utilizing easily measurable indicators (e.g., turbidity, alkalinity, pH, total dissolved solids (TDS), conductivity), to monitor the contaminates in groundwater which are otherwise difficult to measure (i.e., iron, nitrate, sulfate, fluoride, etc.). Preliminary correlation between the indicators and the target contaminates were identified using Pearson coefficient. Best representative univariate distributions for these pairs were selected using the Akaike Information Criterion (AIC), which were used in the formulation of the copula model. Validation against observed data showcased the model\'s high accuracy, supported by consistent Kendall Tau correlation coefficients. Through this model, conditional probabilities of the contaminants not exceeding the permissible limits set by the Bureau of Indian Standards (BIS) were calculated using indicator concentration. Notably, an inverse correlation between iron concentration and conductivity was noted, with the likelihood of iron exceeding BIS limits decreasing from 90 to 50% as conductivity rose from 500 to 2000 micromhos/cm. TDS emerged as a pivotal indicator for nitrate and sulfate concentrations, with the probability of sulfate surpassing 10 mg/l decreasing from 75 to 25% as TDS increased from 250 to 750 mg/l. Likewise, the probability of nitrate exceeding 1 mg/l decreased from 90 to 60% with TDS levels reaching 1500 mg/l. Furthermore, a 63% probability of fluoride concentrations remaining below 1 mg/l was observed at turbidity levels of 0-10 NTU. These findings hold significant implications for policymakers and researchers since the model can provide crucial insights into the risks associated with the contaminates exceeding the permissible limit, facilitating the development of an efficient monitoring and management strategies to ensure safe drinking water access for vulnerable populations.

This study examined the water quality of the Turag River, an important tributary river in Dhaka, Bangladesh in terms of physicochemical characteristics and heavy metal contamination to assess the potential risks to both ecological systems and human health. The majority of the water samples complied with the acceptable limits established by the World Health Organization (WHO) for various parameters including pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), chemical oxygen demand (COD), sodium adsorption ratio (SAR), and magnesium adsorption ratio (MAR), except total hardness (TH). The sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl-), fluoride (F-), nitrate (NO3 -), and sulfate (SO4 2-) levels in the water samples were found to be within acceptable ranges for most cases. Moreover, heavy metals including lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), arsenic (As), selenium (Se), and mercury (Hg) were analyzed and their mean concentrations (μg/L) were found in the order of Fe (244.72 ± 214.35) > Mn (28.93 ± 29.64) > Zn (22.97 ± 10.93) > Cu (8.28 ± 5.99) > Hg (8.23 ± 6.58) > As (1.34 ± 0.39) > Ni (1.20 ± 0.38) > Cr (0.67 ± 0.85) > Pb (0.61 ± 0.72) > Se (0.42 ± 0.48) > Cd (0.13 ± 0.09) which were within the acceptable limit, except Hg. The cumulative effect of all heavy metals was assessed through the heavy metal pollution index (HPI), contamination degree (Cd), and nemerow pollution index (PN). The mean value of HPI (682.38 ± 525.68) crossed the critical index value of 100, indicating an elevated level of pollution. The mean value of Cd (8.763 ± 6.48) indicates a low-moderate-significant level of contamination due to an elevated level of Hg, and for the PN it was found 174.27 ± 146.66, indicating a high level of pollution due to high level of Fe. Ecological risk index (ERI) indicated low levels of risk for Pb, Cd, Cr, Ni, Fe, Mn, As, Se, Cu, and Zn but a significantly high risk for Hg. The water was classified as good to excellent based on its physicochemical properties (pH, EC, TDS, COD, DO, F-, Cl-, NO3 -, and SO4 2-) while it was deemed poor to unsuitable for heavy metals according to the water quality index (WQI). Among the carcinogenic constituents, As poses the greatest carcinogenic risk, particularly for children. The mean value of Cr, Mn, and As in the HQingestion for adult and child, and Cd, Hg for child exceeded the threshold value established by the United States Environmental Protection Agency (USEPA), while the HQdermal values remained below the maximum limit for all heavy metals. The value of HI at all locations exceeds the threshold of 1, as specified by USEPA. Principal component analysis (PCA) and cluster analysis revealed that the presence of heavy metals in the Turag River was mainly attributed to anthropogenic sources, including industrial effluent discharge from neighboring industries, domestic wastewater, and agricultural runoff containing agrochemicals from the surrounding lands.

Fish represent a significant source of nutrients but also cause negative health effects due to their bioaccumulation capacity for pollutants. The aim of this study was to examine the transfer of metals from the water of several rivers (Somes, Tisa, Sasar, Lapus, Lăpusel) to fish (Caras sp) tissue (subcutaneous fat, muscles, liver, intestines, kidneys, gills, brain, and eyes) and to identify and assess the carcinogenic and non-carcinogenic health risks of Arsenic (As), Cadmium (Cd), Nickel (Ni), Manganese (Mn), Cooper (Cu), Lead (Pb), Chromium (Cr) and Zinc (Zn) through the ingestion of fish (muscles and subcutaneous fat tissues). The obtained results indicated that a diet consisting of fish is particularly vulnerable, particularly in children compared to adults. The risk assessment results were below the threshold limit, although the fish samples contained heavy metals, with values exceeding the permitted limits of Fe (4.41-1604 mg/kg), Cr (727-4155 µg/kg), Zn (4.72-147 mg/kg), and Ni (333-2194 µg/kg). The studied surface waters are characterized by low and high degrees of pollution with heavy metals, as indicated by the heavy metal pollution index scores (HPI: 12.4-86.4) and the heavy metal evaluation index scores (HEI: 1.06-17.6). The considerable pollution levels are attributed to the high Mn content (0.61-49.7 µg/kg), which exceeded the limit up to fifty times. A consistent set of physico-chemical analysis (pH, electrical conductivity, total hardness, turbidity, chloride, sulphate, nitrate, nitrite, ammonium, Ca, Mg, Na, K) was analysed in water samples as well. Considering the water quality index scores (WQI: 16.0-25.2), the surface waters exhibited good quality. Microbiological results indicated the presence of Listeria monocytogenes and atypical colonies of coagulase-positive staphylococcus in fish. In contrast, the surface waters from which fish samples were collected were positive for Escherichia coli, and coliform bacteria intestinal Enterococci. Based on the study\'s results, it is recommended to exercise caution in the case of children related to the consumption of fish and using the waters for drinking purposes. This study provides important data of considerable novelty to the riparian population, researchers, and even policy makers on the quality status and potential levels of contamination of river waters, fish and the bioaccumulation of heavy metals in fish that may cause adverse effects on human health if consumed, and similarly the heavy metal pollution degree of waters and the non-carcinogenic risk of heavy metals through ingestion and skin absorption of water in children and adults (the study area is a significant source of fisheries).

This study delves into the environmental impact of inland aquaculture on estuarine ecosystems by examining the water quality of four estuarine streams within the key inland aquaculture zone of South India. In this region, extensive and intensive aquaculture practices are common, posing potential challenges to estuarine health. The research explores the predictive capabilities of the Gaussian elimination method (GEM) and machine learning techniques, specifically multi-linear regression (MLR) and support vector regressor (SVR), in forecasting the water quality index of these streams. Through comprehensive evaluation using performance metrics such as coefficient of determination (R2) and average mean absolute percentage error (MAPE), MLR and SVR demonstrate higher prediction efficiency. Notably, employing key water parameters as inputs in machine learning models is also more effective. Biochemical oxygen demand (BOD) emerges as a critical water parameter, identified by both MLR and SVR, exhibiting high specificity in predicting water quality. This suggests that MLR and SVR, incorporating key water parameters, should be prioritized for future water quality monitoring in intensive aquaculture zones, facilitating timely warnings and interventions to safeguard water quality.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML\'s superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4 years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being HCO 3 - and Mg 2 + . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

Assessing water quality in arid regions is vital due to scarce resources, impacting health and sustainable management.This study examines groundwater quality in Assuit Governorate, Egypt, using Principal Component Analysis, GIS, and Machine Learning Techniques. Data from 217 wells across 12 parameters were analyzed, including TDS, EC, Cl-, Fe++, Ca++, Mg++, Na+, SO4--, Mn++, HCO3-, K+, and pH. The Water Quality Index (WQI) was calculated, and ArcGIS mapped its spatial distribution. Machine learning algorithms, including Ridge Regression, XGBoost, Decision Tree, Random Forest, and K-Nearest Neighbors, were used for predictive analysis. Higher concentrations of Na, K, Ca, Mg, Mn, and Fe were correlated with industrial and densely populated areas. Most samples exhibited excellent or good quality, with a small percentage unsuitable for consumption. Ridge Regression showed the lowest MAPE rates (0.22 % training, 0.26 % in testing). This research highlights the importance of advanced machine learning for sustainable groundwater management in arid regions. Thus, our results could provide valuable assistance to both national and local authorities involved in water management decisions, particularly for water resource managers and decision-makers. This information can aid in the development of regulations aimed at safeguarding and sustainably managing groundwater resources, which are essential for the overall prosperity of the country.