Assessment of water quality and identification of priority areas for intervention in Guanabara Bay basin, Rio de Janeiro, Brazil, using nonparametric and multivariate statistical methods.

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3-), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3-, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3- concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system.

New insights on the hydrochemistry, geothermometry, and isotopic characteristics of the hydrothermal groundwater of the SASS basin: case study of the Jérid geothermal field, Southern Tunisia.

Chemical and isotopic indicators were used to recognize the origin of hydrothermal groundwater, to assess the mineralization processes and groundwater quality, to identify the source of solutes and the likely mixing with cold, and elucidate the fluid geothermometry in the Jérid field of Southern Tunisia. The results show that the geothermal groundwater is neutral to slightly alkaline. They are characterized by SO4-Cl-Na-Ca water type. The dissolution of evaporates and pyrite-bearing rocks is the dominant mineralization process. The groundwater quality index indicates that the majority of samples are very hard and belong to poor to unsuitable for drinking classes. Applications and calculations of hydrogeochemical parameters, including SAR, %Na, PI, Kr, and MAR, showed that the majority of samples are unsuitable for agricultural practices. The human health risk was assessed based on hazard quotient and total hazard index through ingestion and dermal contact with iron-rich groundwater. The consumption of CI groundwaters does not present non-carcinogenic risk to adults and children. The δ18 O and δ2H signatures indicate that the geothermal groundwater was recharged by ocean precipitation during cold and wet paleoclimatic periods. The slight enrichment of oxygen-18 and deuterium contents suggests a limited mixing effect between geothermal water and cold groundwater within the same aquifer. This mixing effect is confirmed by the Na-K-Mg and the chloride-enthalpy diagrams. The K-Mg and SiO2 geothermometers provided fairly reliable reservoir temperature values, ranging between 69.6 and 99 °C. Calculated geothermal potential values, varying between 469 and 16987 kWth, which allow several applications such as domestic and agricultural heating.

Comparative analysis of water quality index and river classification in Kereh River, Penang, Malaysia: Impact of untreated swine wastewater from Kampung Selamat pig farms.

The Kereh River in Penang, Malaysia, has faced severe pollution for over 40 years due to untreated wastewater from swine farms in Kampung Selamat, discharged via stormwater drains. Despite official claims that all 77 swine farms treat their wastewater to meet regulatory standards, local non-governmental organizations and villagers have challenged this, though their concerns lack scientific backing. This study evaluates the river's water quality by analyzing samples from upstream (US), midstream (MS), and downstream (DS), and from Parit Cina-Parit Besar, a conduit for untreated swine wastewater. Fourteen parameters were measured against Malaysia's National Water Quality Standards (NWQS). Significant differences were found in six parameters: ammonium nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), total suspended solids (TSS), and oil and grease (OG). While Dunn's post hoc pairwise comparison showed no significant differences among river segments, mean values indicated increased pollution downstream, particularly after the convergence with untreated swine wastewater. River classification worsened, with water quality index dropping from 69.88 ± 11.37 score (Class III) US to 38.49 ± 12.74 and 50.44 ± 3.14 scores (Class IV) MS and downstream, respectively. A significant positive correlation between E. coli and AN (r = 0.71, p < 0.01) suggests a common point source pollutant, particularly the untreated swine wastewater. The river exhibits low oxygen levels and high organic matter and nutrient concentrations, especially MS and downstream, highlighting substantial ecological and public health risks. Effective enforcement of waste treatment regulations and enhanced monitoring are crucial for mitigating pollution and restoring the river's ecosystem. Collaboration between authorities and pig farmers is essential to improve water quality and maintain the river's ecological balance. PRACTITIONER POINTS: Severe Kereh River pollution: Untreated swine wastewater from Kampung Selamat pig farms, primarily via Parit Cina-Parit Besar, has degraded the river for over 40 years. Regulatory non-compliance: Despite official claims, untreated swine wastewater continues to pollute the river, challenging regulatory standards. Significant pollution indicators: Elevated levels of AN, BOD, COD, DO, TSS, OG, and E. coli signal severe pollution midstream and downstream. Water quality index drop: WQI scores classify midstream and downstream sections as polluted, indicating worsening conditions downstream. Urgent need for action: Enforcing regulations, improving wastewater treatment, and relocating pig farms are crucial for restoring the Kereh River.

Evaluation of groundwater quality in communities near Sokoban Wood Village.

Groundwater is vital for drinking, agriculture, and domestic use in Sokoban Wood Village, Ghana, but concerns exist about its quality. This study assessed the suitability of 20 groundwater samples for domestic purposes. The study was carried out in 2023. We collected samples from boreholes and hand-dug wells using standard methods, analyzing them for various physicochemical parameters (pH, electrical conductivity, turbidity, nitrates, fluorides, and heavy metals). The microbiological analysis assessed fecal coliforms and E. Coli to identify microbial contamination. Established methodologies were used to evaluate potential health risks (carcinogenic and non-carcinogenic) associated with heavy metals. The Water Quality Index (WQI), Hazard Potential Index (HPI), and Heavy Metal Evaluation Index (HEI) provided a comprehensive water quality evaluation. The results revealed that the water fell below the recommended WHO pH range for drinking water. While most other parameters and heavy metals fell within WHO guidelines, 25 % of the samples contained fecal coliforms and E. Coli, indicating ongoing microbial contamination. The overall cancer risk was low for all age groups. Although some parameters met WHO standards, the WQI classified 20 % of the samples as not of good quality. Despite this, the HPI and HEI (-4.62 and 0.001) suggested generally good water quality based on heavy metal content. In conclusion, despite some positive indicators, acidic water and microbial contamination raise concerns. Regular monitoring and potential treatment measures are crucial to ensure safe drinking water for the Sokoban Wood Village community.

Comprehensive approach integrating water quality index and toxic element analysis for environmental and health risk assessment enhanced by simulation techniques.

Due to water shortages and the potential impact of Ethiopia's new dam on the Nile River, Egypt is seeking new water resources. This study assesses the drinking water quality and associated risks from potentially toxic elements (PTEs) in the Quaternary aquifer (QA) in Beni-Suef, Egypt. Using a comprehensive approach, including PHREEQC geochemical modeling, ionic ratios, multivariate statistical analyses, and the integrated weight water quality index (WQI), the study evaluated the sources of ion contamination and the mixing of Nile water with QA. Various indices, such as the Heavy Metal Pollution Index (HPI), ecological Risk Index (RI), Hazard Quotient (HQ), and Hazard Index (HI), were used to assess ecological and health risks. Monte Carlo simulations provided probabilistic assessments of non-carcinogenic risks for adults and children. GIS tools were used to map risk indices, identifying the most deteriorated locations for sustainable management. The hydrochemical analysis revealed water facies including Na-Cl, Ca-Mg-HCO3, and mixed types, influenced by carbonate dissolution, ion exchange, and silicate weathering. Contamination sources, particularly in the north and south, were linked to agricultural activities, irrigation return flow, municipal waste, and evaporation. The WQI indicated that 10.14% of samples were extremely poor, 21.7% were poor, 26% were medium, and 42% were good to excellent. PTE contamination varied, with HPI values indicating good water quality in the central area in 53.6% of the collected samples (HPI < 30), but contamination in the north and south is high (HPI > 51). Ecological Risk Index values were below the threshold in 100% of samples (RI < 30), confirming water safety regarding PTEs. In comparison, for hazard index (HI) through oral/ingestion, adults exhibited HI values ranging from 0.012 to 2.16, while children showed higher values, ranging from 0.045 to 8.25. However, the hazard index for oral/ingestion exceeded safe limits in the north and south (HI oral > 1), posing non-carcinogenic risks. Monte Carlo simulations revealed significant risks from oral exposure to manganese (HQ oral > 1), particularly in El-Wasta and El-Fashn, necessitating further treatment and management.

Groundwater quality evaluation of the weathering mantle in crystalline basement aquifer system, southern Brazil.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

Heavy metal concentrations in drinking water sources in two mining districts in Ghana.

- In Ghana, mining of minerals at small-scale and large-scale is widespread across many districts, leading to significant heavy metal pollution in the environment. In this study, the concentrations of iron (Fe), manganese (Mn), arsenic (As), and mercury (Hg) in the different drinking water samples collected from households, institutions, water points and surface water in two mining districts namely the Wassa East and Asutifi North were analyzed. The water types collected included boreholes, wells, piped water into yards, public standpipes, rainwater, sachet water, and surface water. The results indicated that the levels of Fe and Mn were higher than As and Hg in all the drinking water samples. The levels of As and Hg in drinking water from households and institutions were higher in the Wassa East district compared to the Asutifi North district. However, the metal levels at water points were similar in both districts. In surface water, Fe levels were higher in the Wassa East district compared to the Asutifi North district with median values of 1243 µg/L and 860 µg/L for the Wassa East and Asutifi North districts, respectively. In contrast, the Mn levels were higher in the Asutifi North district than the Wassa East district with median values of 9.5 µg/L and 90 µg/L for Wassa East and Asutifi North districts, respectively. All the metals (Mn, As, and Hg) studied except Fe were within the recommended WHO level. The Heavy Metal Pollution Index (HPI) values for the different water types in households, institutions and water points were all below the critical limit of 100. The Water Quality Index (WQI) indicates that the boreholes, piped water into yards, and public standpipes in both districts were classified as excellent or good, making them suitable for drinking. However, the wells and surface water in both districts were classified as very poor and unfit for drinking, respectively.

Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach.

Monitoring of groundwater (GW) resources in coastal areas is vital for human needs, agriculture, ecosystems, securing water supply, biodiversity, and environmental sustainability. Although the utilization of water quality index (WQI) models has proven effective in monitoring GW resources, it has faced substantial criticism due to its inconsistent outcomes, prompting the need for more reliable assessment methods. Therefore, this study addressed this concern by employing the data-driven root mean squared (RMS) models to evaluate groundwater quality (GWQ) in the coastal Bhola district near the Bay of Bengal, Bangladesh. To enhance the reliability of the RMS-WQI model, the research incorporated the extreme gradient boosting (XGBoost) machine learning (ML) algorithm. For the assessment of GWQ, the study utilized eleven crucial indicators, including turbidity (TURB), electric conductivity (EC), pH, total dissolved solids (TDS), nitrate (NO3 -), ammonium (NH4 +), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and iron (Fe). In terms of the GW indicators, concentration of K, Ca and Mg exceeded the guideline limit in the collected GW samples. The computed RMS-WQI scores ranged from 54.3 to 72.1, with an average of 65.2, categorizing all sampling sites' GWQ as "fair." In terms of model reliability, XGBoost demonstrated exceptional sensitivity (R2 = 0.97) in predicting GWQ accurately. Furthermore, the RMS-WQI model exhibited minimal uncertainty (<1 %) in predicting WQI scores. These findings implied the efficacy of the RMS-WQI model in accurately assessing GWQ in coastal areas, that would ultimately assist regional environmental managers and strategic planners for effective monitoring and sustainable management of coastal GW resources.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China.

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3-72.0 and 56.4-67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Investigation of groundwater and its seasonal variation in a rural region in Natore, Bangladesh.

In Bangladesh, groundwater is the most widely used source of drinking water for rural communities. However, the groundwater quality is degraded by natural contaminants and anthropogenic pollution. Groundwater is a reliable and sustainable source of safe water for irrigation and domestic purposes, especially during the dry season. The water quality assessment data for the study area was not found in the literature. This study aims to assess groundwater quality and seasonal variation in a rural area of five unions of Bagatipara Upazila, Natore, and its suitability for drinking purposes by measuring the Water Quality Index (WQI). The groundwater of five unions, namely Dayarampur, Bagatipara, Faguardiar, Jamnagar, and Pacca, has been selected for investigation. The electrical conductivity (EC), color, and turbidity exceeded the ECR guidelines. EC showed a positive correlation with total dissolved solids (TDS), total solids (TS), and turbidity. On the other hand, dissolved oxygen (DO), hardness, chloride, carbon-di-oxide (CO2), and iron (Fe) concentrations varied based on the location of the sampling points. The bacteriological parameters TC and E. coli were found in most of the samples, which indicate the potential sources of contaminants such as septic tank leakage and inadequate waste disposal systems. The groundwater quality was found not to be influenced by seasonal variation except by pH, DO, and CO2. The Water Quality Index (WQI) spatial mapping demonstrated that during the post-monsoon period, the water quality of the central part of Bagatipara upazila was in 'good' condition, which was in Bagatipara, Fagurdiar, and Pacca unions, whereas during the pre-monsoon season, the 'good' condition was found very limited to Fagurdiar union only. The study revealed that the groundwater of Bagtipara Upazila is not suitable for drinking water due to the presence of TC and E. Coli as well as 'poor' to 'unsuitable' conditions in most of the areas based on WQI.

Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management.

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as 'unsuitable' for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.

Water quality evaluation based on water quality index and multiple linear regression: A research on Hanyuan Lake in southern Sichuan Province, China.

This study aims to understand the changes in the water quality of Hanyuan Lake and to show these changes over time. In this study, monthly sampling was conducted at three sampling sites in Hanyuan Lake, and water samples were measured for water quality indicators in the laboratory according to the methods specified in the Environmental Quality Standards for Surface Water (GB3838-2002). Based on the monitoring data from January to December 2019, the WQI comprehensive evaluation method was used to conduct multiple linear stepwise regression analysis, extract key indicators, and establish the WQImin model. The results show that according to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, and the grade is excellent. The overall water quality of Hanyuan Lake is excellent, and most of the water quality indexes reach the Class I standard in the Environmental Quality Standards for Surface Water (GB3838-2002). WQImin1 (R2 = 0.86, p < 0.001, PE = 4.28) as the best WQImin model. In this study, a model with fewer parameters was established by multiple linear regression method, which is conducive to better monitoring of water quality at monitoring stations while saving costs. PRACTITIONER POINTS: According to the WQI comprehensive evaluation method, the WQI values of Hanyuan Lake are all above 90, the rating is excellent. From January 2019 to September 2020, the monthly change trend of each section is roughly the same, showing a trend of first decreasing, then rising, then decreasing, and finally rising and flattening. The WQImin model was developed to completely describe the change in the water body.

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Impact of industrial hotspots on Tamla nala and Nunia nala confluence - a tributary of the Damodar river.

Clean water is essential for drinking, household use, and agriculture. Researchers studied 39 sites near Tamla nala and Nunia nala channels in Durgapur and Asansol City (West Bengal) to assess the deterioration level of water due to industrial discharge. During the first phase out of three, the researchers conducted a spatial representation of various physicochemical parameters, such as temperature, pH, Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness (TH), Electrical Conductivity (EC), Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), significant anions such as chloride (Cl-), nitrate (NO3-), phosphate (PO4-3), sulfate (SO42-), cyanide (CN-1) and fluoride (F-), as well as heavy metals/metalloids such as lead (Pb), cadmium (Cd), chromium (Cr), iron (Fe), copper (Cu), nickel (Ni), mercury (Hg) and arsenic (As). As observed the parameters at various sites along the stream exceeded threshold limits majorly due to industrial discharge: highest pH, TDS, TH, EC, Cl-, SO42- at site 26; Fe at site 1, TSS, COD, CN- at site 33, 31, 2 respectively; Cd, Ni, Cu at site 19; Hg and Pb at site 3 and As at site 20. Contaminated areas were marked in red and secure areas in green. Additionally, the HMPI (Heavy metal pollution index) was estimated for eight locations to understand the impact of heavy metal pollution in the second phase of the study. An extremely high HMPI indicates heightened toxicity and health risks for both residents and outsiders. The Canadian Water Quality Index (1.0) was calculated for eight sites in the third phase based on seventeen parameters. The resulting WQI value was below 44, indicating poor water quality at the sites. Due to the poor quality and critical heavy metal toxicity, the authors recommended continuous monitoring, strict regulation enforcement, increased treatment capacity, Zero Liquid Discharge implementation, and raising awareness among residents.

Heavy metal contamination and environmental risk assessment: a case study of surface water in the Bahr Mouse stream, East Nile Delta, Egypt.

Water, as an indispensable constituent of life, serves as the primary source of sustenance for all living things on Earth. The contamination of surface water with heavy metals poses a significant global health risk to humans, animals, and plants. Sharkiya Governorate, situated in the East Nile Delta region of Egypt, is particularly susceptible to surface water pollution due to various industrial, agricultural, and urban activities. The Bahr Mouse Stream, crucial for providing potable water and supporting irrigation activities in Sharkiya Governorate, caters to a population of approximately 7.7 million inhabitants. Unfortunately, this vital water source is exposed to many illegal encroachments that may cause pollution and deteriorate the water resource quality. In a comprehensive study conducted over two consecutive seasons (2019-2020), a total of 38 surface water samples were taken to assess the quantity of heavy metals in surface water destined for human consumption and other applications, supported by indices and statistics. The assessment utilized flame atomic absorption spectrophotometry to determine the concentration of key heavy metals including iron (Fe), manganese (Mn), cadmium (Cd), copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), cobalt (Co), and chromium (Cr). The calculated mean value of the Water Quality Index (WQI) was found to be 39.1 during the winter season and 28.05 during the summer season. This value suggests that the surface water maintains good quality and is suitable for drinking purposes. Furthermore, the analysis indicated that the concentrations of heavy metals in the study area were below the recommended limits set by the World Health Organization and fell within the safe threshold prescribed by Egyptian legislation. Despite the identification of localized instances of illegal activities in certain areas, such as unauthorized discharges, the findings affirm that the Bahr Mouse stream is devoid of heavy metal pollution. This underscores the importance of continued vigilance and regulatory enforcement to preserve the integrity of these vital water resources.

Tracing the land use specific impacts on groundwater quality: a chemometric, information entropy WQI and health risk assessment study.

Understanding the nexus of land use and water quality can potentially underline the influences within the groundwater management. The study envisages land use-specific qualitative assessment of the groundwater resources in Ghaziabad district, in western Uttar Pradesh, India. For encountering the relative impacts of land use on the groundwater quality, chemometric analysis has been employed to apportion the pollution sources. The integration of quality parameters, in the information entropy index modeling, has segregated the quality classes and visualized the seasonal suitability trends as per potability standards along with non-carcinogenic health hazard risk assessment (HHRA). The qualitative assessment of the groundwater resources, along with spatial distribution, has deciphered a polluting impact, specifically in western and south western parts of district, and observed the linkages with direct and indirect discharges/seepages from densely populated residential and industrial land use types localized in urbanized areas. Statistically significant annual and seasonal variations have been found exclusively for EC, Mg2+, F-, Cd, Cr(total), Ni, and Pb which inferred variable concentrations, whereas land use types showed a non-significant variation within groundwater quality. Chemometric-based source apportioning and hierarchical cluster analysis (HCA) have derived salinization and enrichment of dissolved salts, arising from mixed sources and contributes to metal pollution, i.e., mainly from anthropogenic sources. Information EWQI derived poor to extremely poor category represented degraded potability specifically for fewer sites located within western and southern parts on the Yamuna-Hindon flood plains for limited sites of residential, industrial, and agricultural in an urbanized region. However, majority of the samples fall under excellent to good groundwater quality, recommendable in the north and north-eastern (peri-urban) regions. Non-carcinogenic HHRA has shown that majority of the samples categorized under unsafe value for hazard index (HI > 1), for females and children and thus, presumed probable health hazard risk from metal groundwater pollution in south-western part, eastern, and northern regions.

Pollution sources apportionment and suitability assessment of Lah River, Ethiopia: Conjunctive application of multivariate statistical analysis and water quality index.

This study aimed to assess spatiotemporal water quality variation and its suitability for irrigation and domestic purposes in Lah River using the irrigation water quality index (IWQI) and the weighted arithmetic water quality index (WAWQI). The IWQI analysis result showed that the sodium absorption ratio, residual sodium carbonate, potential salinity, Kelly index, magnesium ratio, sodium percentage, and permeability index were found to be 1.07 mEq/L, -0.43 mEq/L, 0.8 mEq/L, 0.78 mEq/L, 43.01%, 42.95%, and 63.46%, respectively. The IWQIs revealed that the water quality of the river was appropriate for agricultural use during the dry season. Furthermore, the calculated WAWQI of the river water ranged from 123.13 to 394.72 during the wet season, indicating the high pollution levels in the Lah River and incompatibility for drinking purposes. On the other hand, the principal component analysis identified two pollution sources during the wet season and three during the dry season. In addition, the positive matrix prioritization model predicted the pollution source's contribution quite well with a signal-to-noise ratio of >2 and a residual error between -3 and 3 for both seasons. This study suggests that water quality of Lah River is degrading periodically necessitating proper pollution management.

Spatiotemporal pattern of coastal water pollution and its driving factors: implications for improving water environment along Hainan Island, China.

In the context of human activities and climate change, the gradual degradation of coastal water quality seriously threatens the balance of coastal and marine ecosystems. However, the spatiotemporal patterns of coastal water quality and its driving factors were still not well understood. Based on 31 water quality parameters from 2015 to 2020, a new approach of optimizing water quality index (WQI) model was proposed to quantitatively assess the spatial and temporal water quality along tropical Hainan Island, China. In addition, pollution sources were further identified by factor analysis and the effects of pollution source on water quality was finally quantitatively in our study. The results showed that the average water quality was moderate. Water quality at 86.36% of the monitoring stations was good while 13.53% of the monitoring stations has bad or very bad water quality. Besides, the coastal water quality had spatial and seasonal variation, along Hainan Island, China. The water quality at "bad" level was mainly appeared in the coastal waters along large cities (Haikou and Sanya) and some aquaculture regions. Seasonally, the average water quality in March, October and November was worse than in other months. Factor analysis revealed that water quality in this region was mostly affected by urbanization, planting and breeding factor, industrial factor, and they played the different role in different coastal zones. Waters at 10.23% of monitoring stations were at the greatest risk of deterioration due to severe pressure from environmental factors. Our study has significant important references for improving water quality and managing coastal water environment.

The impact of the COVID-19 pandemic on the seawater quality of Ha Long Bay, Vietnam.

Ha Long Bay is a UNESCO World Heritage Site in Vietnam with unique natural scenery. Development and socio-economic activity impact its water quality. In the context of the Vietnam National Standards, historical follow-up data taken over a five-year period (2016 to 2020), for twenty-eight widely dispersed sampling sites, has been used to carry out a temporal assessment of seawater quality utilizing a Water Quality Index (WQI) method. The analysis shows that the seawater quality is generally acceptable over this period. However, the calculated WQI values for the "pandemic year" of 2020 compared to the data for 2016 to 2019, demonstrate a significantly lesser impact for the bay overall and, more specifically, for seventeen individual sites. Ten sites remain unaffected, and one site shows a significantly higher impact. This study demonstrates how the occurrence of the pandemic in 2020 may be exploited for the interrogation of anthropogenic impacts around the bay.

Identification of groundwater pollution sources and health risk assessment in the Fengshui mining area of Central Shandong, China.

The crux of groundwater protection lies in a profound understanding of the sources of pollutants and their impacts on human health. This study selected 47 groundwater samples from the Fengshui mining area in central Shandong Province, China, employing advanced hydrogeochemical techniques, positive matrix factorization (PMF), and Monte Carlo analysis methods, aimed at unveiling the characteristics, origins, and health risks of water pollutants. The results indicated that the majority of samples exhibited a slightly alkaline nature. Notably, the concentrations of fluoride (F-) and nitrate (NO3-) exceeded China's safety standards in 40.43% and 23.40% of the samples, respectively. Moreover, a water quality index (WQI) below 50 was observed in approximately 68.09% of the sites, suggesting that the water quality in these areas generally met acceptable levels. However, regions with higher WQI values were predominantly located in the northern and southern parts of the mining area. PMF analysis revealed that regional geological and industrial activities were the primary factors affecting water quality, followed by mining discharges, fundamental geological and agricultural processes, and leachate enrichment activities. The health risk assessment highlighted the heightened sensitivity of the youth demographic to fluoride, with a more pronounced non-carcinogenic risk compared to nitrate, affecting about 31.89% of the youth population. Hence, it is imperative for local authorities and relevant departments to take prompt actions to remediate groundwater contamination to minimize public health risks.