Modification of the Water Quality Index (WQI) Process for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method: A Review

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36 mg/L and 44.72 mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

Balancing aquaculture and estuarine ecosystems: machine learning-based water quality indices for effective management

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.

A critical analysis of parameter choices in water quality assessment

The determination of water quality heavily depends on the selection of parameters recorded from water samples for the water quality index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine the parameter set for four main reasons: reducing cost and uncertainty, addressing the eclipsing problem, and enhancing the performance of models predicting the WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies in this area. Such reviews are essential to assess the validity of these objectives and to demonstrate the effectiveness of data-driven methods in achieving these goals. This paper sets out with two primary aims: first, to provide a review of the existing literature on methods for selecting parameters. Second, it seeks to delineate and evaluate the four principal motivations for parameter selection identified in the literature. This manuscript categorizes existing studies into two methodological groups for refining parameters: one focuses on preserving information within the dataset, and another ensures consistent prediction using the full set of parameters. It characterizes each group and evaluates how effectively each approach meets the four predefined objectives. The study presents that the minimal WQI approach, common to both categories, is the only approach that has successfully reduced recording costs. Nonetheless, it notes that simply reducing the number of parameters does not guarantee cost savings. Furthermore, the group of studies classified as preserving information within the dataset has demonstrated potential to decrease the eclipsing problem, whereas studies in the consistent prediction group have not been able to mitigate this issue. Additionally, since data-driven approaches still rely on the initial parameters chosen by experts, they do not eliminate the need for expert judgment. The study further points out that the WQI formula is a straightforward and expedient tool for assessing water quality. Consequently, the paper argues that employing machine learning solely to reduce the number of parameters to enhance WQI prediction is not a standalone solution. Rather, this objective should be integrated with a more comprehensive set of research goals. The critical analysis of research objectives and the characterization of previous studies lay the groundwork for future research. This groundwork will enable subsequent studies to evaluate how their proposed methods can effectively achieve these objectives.

Levels of Potentially Toxic and Essential Elements in Water and Estimation of Human Health Risks in a River Located at the Interface of Brazilian Savanna and Amazon Biomes (Tocantins River)

The Tocantins-Araguaia basin is one of South America's largest river systems, across three Brazilian states (Maranhão, Tocantins, and Pará), within the Legal Amazon region. Despite draining extensive Cerrado savanna and rainforest ecosystems, it has suffered significant degradation, notably in the past 40 years. Human activities, including agricultural expansion, deforestation, and the introduction of non-native species, have worsened the environmental damage, which is alarming since many residents and villages along the middle Tocantins River rely on it for water supply, recreation, and fishing. This study assessed the concentration of potentially toxic and essential elements in water samples from four sampling sites distributed along the middle Tocantins River. The monitoring occurred throughout 2023, involving the measurement of parameters both on-site and in the laboratory. Water quality and its health implications were evaluated using the Weighted Arithmetic Water Quality Index (WAWQI), the Water Quality Index (WQI), and the health risk assessment index. The levels of aluminum, copper, iron, magnesium, and selenium exceeded legal standards. Seasonal fluctuations indicate a complex dynamic influenced by climatic or seasonal factors, with February showing the highest values. Site P1, located in urban areas, exhibited elevated mean concentrations for conductivity, total dissolved solids (TDS), and chlorophyll, indicating the need for continuous monitoring. The nitrogen concentrations at P1 raise concerns regarding drinking water quality, which is a concern for the region's residents who use untreated river water. Despite seasonal variations in element concentrations, the overall WAWQI categorized all sections as "Excellent," and the WQI rated as "Good." Human health risk assessments detected no risks, but continuous monitoring and interventions are crucial for sustained water quality improvement.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural 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.

Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping

Groundwater is an excellent alternative to freshwater for drinking, irrigation, and developing arid regions. Agricultural, commercial, industrial, residential, and municipal activities may affect groundwater quantity and quality. Therefore, we aimed to use advanced methods/techniques to monitor the piezometric levels and collect groundwater samples to test their physicochemical and biological characteristics. Our results using software programs showed two main types of groundwater: the most prevalent was the Na-Cl type, which accounts for 94% of the groundwater samples, whereas the Mg-Cl type was found in 6% of samples only. In general, the hydraulic gradient values, ranging from medium to low, could be attributed to the slow movement of groundwater. Salinity distribution in groundwater maps varied between 238 and 1350 mg L-1. Although lower salinity values were observed in northwestern wells, higher values were recorded in southern ones. The collected seventeen water samples exhibited brackish characteristics and were subjected to microbial growth monitoring. Sample WD12 had the lowest total bacterial count (TBC) of 4.8 ± 0.9 colony forming unit (CFU mg L-1), while WD14 had the highest TBC (7.5 ± 0.5 CFU mg L-1). None of the tested water samples, however, contained pathogenic microorganisms. In conclusion, the current simulation models for groundwater drawdown of the Quaternary aquifer system predict a considerable drawdown of water levels over the next 10, 20, and 30 years with the continuous development of the region.

Reliable water quality prediction and parametric analysis using explainable AI models

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.

Emerging paradigms into bioremediation approaches for nuclear contaminant removal: From challenge to solution

The release of radionuclides, including Cesium-137 (137Cs), Strontium-90 (90Sr), Uranium-238 (238U), Plutonium-239 (239Pu), Iodine-131 (131I), etc., from nuclear contamination presents profound threats to both the environment and human health. Traditional remediation methods, reliant on physical and chemical interventions, often prove economically burdensome and logistically unfeasible for large-scale restoration efforts. In response to these challenges, bioremediation has emerged as a remarkably efficient, environmentally sustainable, and cost-effective solution. This innovative approach harnesses the power of microorganisms, plants, and biological agents to transmute radioactive materials into less hazardous forms. For instance, consider the remarkable capability demonstrated by Fontinalis antipyretica, a water moss, which can accumulate uranium at levels as high as 4979 mg/kg, significantly exceeding concentrations found in the surrounding water. This review takes an extensive dive into the world of bioremediation for nuclear contaminant removal, exploring sources of radionuclides, the ingenious resistance mechanisms employed by plants against these harmful elements, and the fascinating dynamics of biological adsorption efficiency. It also addresses limitations and challenges, emphasizing the need for further research and implementation to expedite restoration and mitigate nuclear pollution's adverse effects.

Integrated water quality dynamics in Wadi Hanifah: Physical, chemical, and biological perspectives

The Wadi Hanifah, a crucial aquatic ecosystem, has unfavorable consequences from natural occurrences and human activities. Recognizing the critical need for sustainable water management, this study provides an in-depth evaluation of wadi water quality. A comprehensive assessment was conducted, analyzing physical properties (temperature, pH, electrical conductivity, turbidity, color, and odor), chemical constituents (nitrogen compounds, ion concentrations, heavy metals), and bacterial diversity. The study found significant temperature fluctuations, particularly in sun-exposed or stagnant water areas. The water exhibited slight alkalinity and variable electrical conductivity and turbidity, indicating differing pollution levels. High ammonia and heavy metal concentrations suggested organic and industrial contamination, respectively. In addition, the prevalent fecal-indicator bacteria pointed to possible sewage or agricultural runoff. The research highlights the complex interplay of natural and anthropogenic factors affecting Wadi Hanifah's water quality. It emphasizes the need for location-specific environmental management strategies focusing on pollution control and conservation to safeguard the wadi's ecological health. This study provides vital insights for effective water resource management in Wadi Hanifah, serving as a model for similar ecosystems.

A new evaluation framework for the assessment of wastewater heat recovery potential coupled with wastewater reuse

The integration of wastewater heat recovery (WWHR) and wastewater reuse offers a numerous advantage, making its application possible in various sectors. Nevertheless, this concept faced challenges to the identification of appropriate location. Existing research lacks comprehensive evaluation methods that encompass a various factor for effective decision-making. This study introduces a new evaluation framework that involves different aspects, including thermal energy potential and spatial distribution analysis. The novelty of this research lies in its unique focus on the combination of WWHR and wastewater reuse. Moreover, it introduces a structured evaluation framework that considers multiple criteria and expert opinions, enhancing decision-making precision. Multi-criteria decision analysis (MCDA) was applied to select assessment criteria, which were categorized into three aspects: water-energy supplier, water-energy consumers, and water-energy station. The relative importance of criteria was determined using the analytical hierarchical process (AHP). The results of the AHP highlight significance of factors: treated wastewater flow rate; treated wastewater temperature; water-energy supply distance, and type of water-energy consumer. These factors were assigned weight values of 0.297, 0.186, 0.123, and 0.096, respectively. It is emphasizing their influence in the decision-making process that potential locations depend on the water-energy supplier and water-energy consumer as supply and demand sources.

Assessment of seasonal water quality and land use land cover change in Subarnarekha watershed of Ranchi stretch in Jharkhand

In this paper, the assessment of seasonal water quality and land use land cover change in the Subarnarekha watershed in Ranchi stretch was analysed. Agricultural runoff along with climate change adds to the pollution risk to the Subarnarekha River of Ranchi stretch in Jharkhand. Water quality indicators, like acidity alkalinity (ALK), total dissolved solids (TDS), hardness (H), dissolved oxygen (DO), biochemical oxygen demand (BOD), chlorides (CL-), electrical conductivity (EC), salinity (SAL), resistivity (RES) and pH, were assessed as per the standard method. During monsoon season, acidity, alkalinity, hardness, chlorides, salinity, pH and DO decreased, whereas EC, TDS, BOD and resistivity increased in comparison to pre-monsoon season. In post-monsoon, chloride problem was observed very high. Hardness was least in monsoon and maximum in post-monsoon season. EC and BOD increased in monsoon season in comparison to other seasons. Statistical analysis like HCA (hierarchical cluster analysis) and PCA (principal component analysis) also confirmed the problem of TDS, EC, chloride and hardness in the area. WQI (water quality index) analysis showed that the water quality was poor to unsuitable on all the sampling points throughout the study area in all seasons. LULC (land use land cover) and NDWI (normalized difference water index) analysis had also concluded that due to high rate of urbanization, the area has undergone a massive change in terms of forest cover and water bodies. The need for afforestation, forest protection and wetland protection can be clearly seen from the result of this study.

Fluoride Contamination in Groundwater of Community Tube Wells, Source Distribution, Associated Health Risk Exposure, and Suitability Analysis for Drinking from Arid Zone

Fluoride (F−) pollution in potable groundwater (GW) is a serious environmental concern in Pakistan with substantial human health hazard reports. The research on F− pollution in GW resources in Sindh Province is still incomplete. To explore the realistic conditions, the present research aimed to investigate the GW quality of community tube wells concerning F− contamination in Tharparkar, Sindh, Pakistan. A total of 53 samples were collected and examined for F−, along with other physicochemical parameters. The F− values observed varied from 0.2–4.2 mg/L, with a mean value of 1.63 mg/L. Among the 53 samples, 46% had F− levels that were higher than the World Health Organization’s (WHO) recommended limit (1.5 mg/L). The water type of the studied region was Ca-HCO3 type, which can be attributed to fresh recharged water. The interaction of rock–water contact controls the hydrochemistry of GW. The GW resources of the research zone were highly saturated with fluorite minerals. Human health risk calculation outcomes exposed that 21 samples showed high HQ values for children and 7 samples showed high values for adults in the research zone. Children are at high risk in the study area from drinking F−-contaminated GW. WQI results showed that 31 samples were not suitable for drinking.

Fuzzy-based global water quality assessment and water quality cells identification using satellite data

The water environmental impact assessment and management programs increasingly rely on accurate and quantitative estimates of water quality parameters through remote sensing, owing to the limitation of the time-consuming field-based approaches. Numerous studies have utilised the remote-derived water-quality products and existing water quality index WQI models, but they are typically site-specific and yield significant errors for the accurate assessment and monitoring of coastal and inland water bodies. This study presents a generalized WQI model that incorporates a flexible number of parameters, simplifying them to produce comprehensive water quality index values with the fuzzy logic approach. To derive these index values, three major water quality parameters such as Chl, TSS and aCDOM443 were estimated using new remote-sensing models, and the corresponding indices Trophic State Index (TSI), Total Suspended Solids Index (TSSI) and CDOM Index (CI) were produced by a generalized index model. Finally, WQI products were derived based on the Mamdani-based Fuzzy Inference System (FIS) and individual contribution of the water quality parameters to WQI was analysed to establish 'Water Quality Cells' WQcells, which are represented by the dominant WQ parameter. The new models were tested on MODIS-Aqua and Sentinel-3 OLCI data in different regional and global oceanic waters. Further, a time series analysis was performed in regional coastal oceanic waters (along the Indian coast) to study the seasonal variations of individual water quality parameters and WQI over the period from 2011 to 2020. The results demonstrated that the FIS is efficient in handling the parameters with varying units and their relative importance. The water quality cells were identified in the bloom-dominated (Arabian Sea), TSS-dominated (Point Calimere, India and Yangtze River estuary, China) and CDOM-dominated (South Carolina coast, USA) regions. The time series analysis revealed that the water quality of the Indian coast exhibits cyclic seasonal variations due to the annual occurrence of the south-west and north-east monsoons. These results are critical for monitoring and assessing the quality of surface waters in coastal and inland environments and enabling water resources managers to formulate and implement management plans for a variety of water bodies cost-effectively.

A comprehensive review of water quality indices for lotic and lentic ecosystems

Freshwater resources play a pivotal role in sustaining life and meeting various domestic, agricultural, economic, and industrial demands. As such, there is a significant need to monitor the water quality of these resources. Water quality index (WQI) models have gradually gained popularity since their maiden introduction in the 1960s for evaluating and classifying the water quality of aquatic ecosystems. WQIs transform complex water quality data into a single dimensionless number to enable accessible communication of the water quality status of water resource ecosystems. To screen relevant articles, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed to include or exclude articles. A total of 17 peer-reviewed articles were used in the final paper synthesis. Among the reviewed WQIs, only the Canadian Council for Ministers of the Environment (CCME) index, Irish water quality index (IEWQI) and Hahn index were used to assess both lotic and lentic ecosystems. Furthermore, the CCME index is the only exception from rigidity because it does not specify parameters to select. Except for the West-Java WQI and the IEWQI, none of the reviewed WQI performed sensitivity and uncertainty analysis to improve the acceptability and reliability of the WQI. It has been proven that all stages of WQI development have a level of uncertainty which can be determined using statistical and machine learning tools. Extreme gradient boosting (XGB) has been reported as an effective machine learning tool to deal with uncertainties during parameter selection, the establishment of parameter weights, and determining accurate classification schemes. Considering the IEWQI model architecture and its effectiveness in coastal and transitional waters, this review recommends that future research in lotic or lentic ecosystems focus on addressing the underlying uncertainty issues associated with the WQI model in addition to the use of machine learning techniques to improve the predictive accuracy and robustness and increase the domain of application.

Water quality assessment of Remeți watercourse, Maramureș, Romania, located in a NATURA 2000 protected area subjected to anthropic pressure

The study assessed the evolution of water indicators of Remeți water body that is located in the Remeți locality in the Upper Tisa, a Natura 2000 protected area. Thus, electric conductivity, dissolved oxygen, oxygen saturation, temperature, pH, turbidity, ammonium concentration (NH₄⁺), nitrates (NO₃^-), nitrites (NO₂^-), orthophosphate (PO₄^3-), dissolved Fe, Mn, water hardness, alkalinity (A) and chloride were measured over the January (I)-October (X) 2021 period. This water course was subjected to anthropic pressure, being polluted with nutrients such as ammonium and orthophosphate ions, iron and manganese. The concentrations of other metals were either low (Al, Ba, Li, Ga, Rb, Ni, Sr, Zn, Cu, Ti) or below the detection limit (Pb, Cd). The study was performed over a period of 8 months, namely January 2021-October 2021, covering the 4 seasons, in order to establish their influence on the level of water quality indicators. Exceeded turbidity values and high concentrations of ammonium, orthophosphate and dissolved iron were found, these being generally higher in the summer-autumn months. Dissolved oxygen concentrations were low in the summer-autumn months. Based on the values of the physico-chemical indicators, two types of water quality indices WA-WQI (weighted arithmetic water quality indices) and CCME-WQI (Canadian Council of Ministers of the Environment water quality indices) were calculated to evaluate the global water quality and its evolution over the seasons with a single value. WA-WQI values varied in the range of 78.56-761.63, with a tendency to increase in autumn, showing an intensified tendency of global water quality deterioration due to an increase in ammonium, turbidity, iron and orthophosphates in autumn months while CCME-WQI values were between 39.6 and 68.9, being fair in winter-spring months and marginal / bad in summer and autumn months. The results of this study are advantageous in identifying the level of pollution of Remeți water course, being a signal for local authorities in taking the necessary measures to reduce the pollution around it, for a better human health and conservation of the ecosystems hosted in the protected area.

A systematic and comparative study of Water Quality Index (WQI) for groundwater quality analysis and assessment

Water is essential for human survival. Its quality must be maintained to prevent any potential health problems. Pollution and contamination are likely causes of the water quality decline. This may occur if the world's rapidly expanding population and industrial facilities fail to clean their effluent correctly. The Water Quality Index, often known as the WQI, is the indicator most frequently used to characterize surface water quality. This study emphasizes several WQI models that could be of use to us in determining the level of water quality available in the various areas. We have tried to cover multiple essential procedures and their corresponding mathematical counterparts. In this article, we also examine the applications of index models in different types of water bodies, such as lakes, rivers, surface water, and groundwater. The level of contamination in water due to pollution directly affects the overall quality of water. A pollution index is a valuable tool for measuring the level of pollution. Concerning this, we have discussed two approaches, namely, the Overall Index of Pollution and Nemerrow's Pollution Index, which demonstrate the most effective technique to evaluate the water standard. Examining the similarities and differences between these approaches may offer researchers a suitable starting point to delve further into assessing water quality.

A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques

The impact of land use on water quality is becoming a global concern due to the increasing demand for freshwater. This study aimed to assess the effects of land use and land cover (LULC) on the surface water quality of the Buriganga, Dhaleshwari, Meghna, and Padma river system in Bangladesh. To determine the state of water, water samples were collected from twelve locations in the Buriganga, Dhaleshwari, Meghna, and Padma rivers during the winter season of 2015 and collected samples were analysed for seven water quality indicators: pH, temperature (Temp.), conductivity (Cond.), dissolved oxygen (DO), biological oxygen demand (BOD), nitrate nitrogen (NO₃-N), and soluble reactive phosphorus (SRP) for assessing water quality (WQ). Additionally, same-period satellite imagery (Landsat-8) was utilised to classify the LULC using the object-based image analysis (OBIA) technique. The overall accuracy assessment and kappa co-efficient value of post-classified images were 92% and 0.89, respectively. In this research, the root mean squared water quality index (RMS-WQI) model was used to determine the WQ status, and satellite imagery was utilised to classify LULC types. Most of the WQs were found within the ECR guideline level for surface water. The RMS-WQI result showed that the "fair" status of water quality found in all sampling sites ranges from 66.50 to 79.08, and the water quality is satisfactory. Four types of LULC were categorised in the study area mainly comprised of agricultural land (37.33%), followed by built-up area (24.76%), vegetation (9.5%), and water bodies (28.41%). Finally, the Principal component analysis (PCA) techniques were used to find out significant WQ indicators and the correlation matrix revealed that WQ had a substantial positive correlation with agricultural land (r = 0.68, P < 0.01) and a significant negative association with the built-up area (r =  - 0.94, P < 0.01). To the best of the authors' knowledge, this is the first attempt in Bangladesh to assess the impact of LULC on the water quality along the longitudinal gradient of a vast river system. Hence, we believe that the findings of this study can support planners and environmentalists to plan and design landscapes and protect the river environment.

Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation

Water quality assessment is critical to better recognise the importance of water in human society. In this study, a new framework to predict long-term water quality is proposed by using Bayesian-optimised machine learning methods and key pollution indicators collected from monitoring stations in the Pearl River Estuary, Guangdong, China. The optimised stacked generalisation (SG-op) model achieved the best performance with the highest accuracy (0.992) and Kappa coefficient (0.987). Feature importance of the prediction model was consistent with key pollution indicators. The Spearman rank correlation coefficient was used to determine the significance level of the variation trends of different pollution indicators. The results show that the total phosphorus (TOP), dissolved oxygen (DO), chemical oxygen demand (COD), and petroleum (PET) among the key pollution indicators were on an upward trend in the study area. This framework can be applied to efficiently predict future water quality and to provide technical support for emergency pollution control.

Health Risk Assessment of Trace Metals in Bottled Water Purchased from Various Retail Stores in Pretoria, South Africa

Bottled water is one of the fastest growing commercial products in both developing and developed countries owing to the believe that it is safe and pure. In South Africa, over the years, there has been an increase in the sale of bottled water due to the perceived notion that water supplied by the government may not be safe for human consumption. This study investigated the concentrations of trace metals and the physicochemical properties of bottled water purchased from various supermarkets (registered and unregistered) in Pretoria with a view to determining the health risk that may be associated with the levels of trace metals resulting from the consumption of the bottled water. Twelve commonly available different brands of bottled water were purchased and analysed for trace-metal content using inductively coupled plasma mass spectrometry (ICP-MS). The water samples were also analysed for various physicochemical parameters. The health risk was assessed using the target hazard quotient (THQ). For all the bottled water, the highest concentration of all the elements was recorded for Fe. The values reported for Cr, Ni and Pb were above the limit recommended by World Health Organization. The pH values ranged from 4.67 to 7.26. Three of the samples had pH values in the acidic region below the permissible standard of 6.8-8.0 set by the International Bottled Water Association (IBWA). The target hazard quotient calculated for the water samples showed a minimum risk for Pb, Cr and Ni. The study showed the need to adhere to a strict compliance standard considering the fact that South Africa has rich natural mineral elements, which may have played a role in the high levels of trace metals reported from some of the water samples.

Spatiotemporal assessment of groundwater quality in the Central Ganga Plain, India, using multivariate statistical tools

In the present study, the water quality status of the Sadar block in Pratapgarh district of Uttar Pradesh, India, was calculated by Water Pollution Index (WPI) using 15 groundwater quality parameters (pH, turbidity, EC (electric conductivity), TDS (total dissolved solids), salinity, total hardness, Ca²⁺, K⁺, Mg²⁺, Na⁺, Cl^-, F^-, SO₄^2-, HCO₃^-, and NO₃^-) at 40 different study locations. It is found that 10%, 25%, and 65%, of samples, fall under the good, moderately polluted, and highly polluted category, respectively. Irrigation suitability of groundwater has also been evaluated by using irrigation suitability parameters, i.e., Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Kelly Ratio (KR), Percentage Sodium (% Na), and Magnesium Hazards (MH). Based on the analysis, it was determined that most of the samples were suitable for irrigation in the area. Irrigation practices are found to be unsuitable for Kelly's ratio of 37.5%, MH 60%, and RSC 55% samples. WPI and Pearson's correlation matrix (PCA) have suggested a significant linear relationship among the water quality parameters based on the test results. The Gibbs plot indicates that the majority of samples fall under the "evaporation dominance" category while the rest fall under the "rock weathering dominance" suggesting that the complex geochemical mechanisms are active in the study area. This study would be very helpful to government authorities and it would aid them to suggest alternate drinking water resources in affected areas.

Indices and models of surface water quality assessment: Review and perspectives

Many technologies have been designed to monitor, evaluate, and improve surface water quality, as high-quality water is essential for human activities including agriculture, livestock, and industry. As such, in this study, we investigated water quality indices (WQIs), trophic status indices (TSIs), and heavy metal indices (HMIs) for assessing surface water quality. Based on these indices, we summarised and compared water assessment models using expert system (ES) and machine learning (ML) methods. We also discussed the current status and future perspectives of water quality management. The results of our analyses showed that assessment indices can be used in three aspects of surface water quality assessment: WQIs are aggregated from multiple parameters and commonly used in surface water quality classification; TSIs are calculated from the concentrations of different nutrients required for algae and bacteria, and employed to evaluate the eutrophication levels of lakes and reservoirs; HMIs are mainly applied for human health risk assessment and the analysis of correlation of heavy metal sources. ES- and ML-based assessment models have been developed to efficiently generate assessment indices and predict water quality status based on big data obtained from new techniques. By implementing dynamic monitoring and analysis of water quality, we designed a next-generation water quality management system based on the above indices and assessment models, which shows promise for improving the accuracy of water quality assessment.

Evaluating the Performance of Water Quality Indices: Application in Surface Water of Lake Union, Washington State-USA

Water quality indices (WQIs) are practical and versatile instruments for assessing, organizing, and disseminating information about the overall quality status of surface water bodies. The use of these indices may be beneficial in evaluating aquatic system water quality. The CCME (Canadian Council of Ministers of the Environment) and NSF (National Science Foundation) WQIs were used for the assessment of surface water (depth = 1 m) in Lake Union, Washington State. These WQIs were used in surface water at Lake Union, Seattle. The modified versions of the applied WQIs incorporate a varied number of the investigated parameters. The two WQIs were implemented utilizing specialized, publicly accessible software tools. A comparison of their performance is offered, along with a qualitative assessment of their appropriateness for describing the quality of a surface water body. Practical conclusions were generated and addressed based on the applicability and disadvantages of the evaluated indexes. When compared to the CCME-WQI, it is found that the NSF-WQI is a more robust index that yields a categorization stricter than CCME-WQI.

Combinations of multivariate statistical analysis and analytical hierarchical process for indexing surface water quality under arid conditions

Novel methods for water quality indexing increase insight into the fitness of water bodies for different uses. We hypothesized that integrating multivariate statistical analysis (MSA) with the analytical hierarchical process (AHP) may provide a reliable estimation of water quality status. Hence, twenty water samples from canals and drains in the northern Nile Delta, Egypt were collected during summer, autumn, winter, and spring and analyzed. Data were subjected to MSA, including correlation analysis, principal component analysis (PCA), and hierarchal cluster analysis (HCA). The AHP was applied to derive weights of parameters implied in developing water quality indices for irrigation (IWQI) and fish farming (FFWQI). Human health risks due to exposure to potentially toxic elements (PTEs) via dermal contact were also considered. The average concentrations of water constituents were acceptable for irrigation, except sodium adsorption ratio (SAR) and Cl^-. The dissolved oxygen, total dissolved solids, Cl^-, NO₂-N, NO₃-N, NH₃, and PTEs (except Zn) did not meet standard limits for fish production. The MSA revealed that water contamination resulted from human activities (agriculture, industry, and domestic wastes) and hydrochemical processes. The PCA indicated that SAR, Cu, and pH could adequately represent water quality for irrigation, while temperature, NO₂-N, Cr, and Zn could reflect fish farming requirements. The AHP provided consistent weights for the original and shortlisted parameters. The water quality varied from good to poor for irrigation and from excellent to low for fish farming. The minimum IWQI could adequately represent the IWQI (R² = 0.83) and thus reduce the time, effort, and cost for monitoring water quality. However, the minimum FFWQI showed moderate consistency (R² = 0.51) with FFWQI, implying that increasing the sampling size is essential for better performance. The hazard quotient of all PTEs was below 1.0 for both adults and children, indicating a safe limit. The potential cancer risk was acceptable (1.36E-06) for adults and safe (8.03E-07) for children. Results of this work would be a start point for efficient quality control programs in arid regions.

Occurrence and Fate of Triclosan and Triclocarban in Selected Wastewater Systems across Durban Metropolis, KwaZulu-Natal, South Africa

Triclosan (TCS) and triclocarban (TCC) are antimicrobial agents that have been used in personal care and consumer products in the past decades. In this study, influent, effluent, and sludge samples collected in selected wastewater treatment plants across the Durban metropolis were qualitatively and quantitatively investigated. It was revealed that the concentration of TCS ranged from 1.906 to 73.462 µg/L, from 1.732 to 6.980 µg/L, and from 0.138 to 2.455 µg/kg in influent, effluent, and sludge samples, respectively. The concentrations of TCC were found to be between 0.320 and 45.261 µg/L, <LOQ−1.103 µg/L, and from 0.107 to 8.827 µg/kg in the influent, effluent, and sludge samples, respectively. Higher concentrations of TCS as compared with TCC were observed in the aqueous samples. However, the concentrations of TCC in the sludge samples were significantly higher than the level of TCS. More water solubility of TCS could be responsible for the observed trend in the influent and effluent samples, while the trend observed in the sludge could be due to the more hydrophobicity character of TCC. The results of this study indicated that substantial amounts of TCS and TCC are been removed during the treatment process which could be a major reason for the decline in the levels recorded in the effluent samples, therefore, reducing the amount of the TCS and TCC that would eventually end up in the surface rivers. Qualitative analyses of the samples indicated the presence of caffeine, tert-butylhydroquinone, chloroxylenol, phenol, 4-(1,1,3,3-tetramethyl butyl), and dimethyl-bisphenol A. Further investigative ecological risk assessment studies are crucial due to the potential threat the contaminants may pose to aquatic lives and humans.

A Systematic Review of the Applications of Multi-Criteria Decision Aid Methods (1977–2022)

Multicriteria methods have gained traction in academia and industry practices for effective decision-making. This systematic review investigates and presents an overview of multi-criteria approaches research conducted over forty-four years. The Web of Science (WoS) and Scopus databases were searched for papers on multi-criteria methods with titles, abstracts, keywords, and articles from January 1977 to 29 April 2022. Using the R Bibliometrix tool, the bibliographic data was evaluated. According to this bibliometric analysis, in 131 countries over the past forty-four years, 33,201 authors have written 23,494 documents on multi-criteria methods. This area’s scientific output increases by 14.18 percent every year. China has the highest percentage of publications at 18.50 percent, followed by India at 10.62 percent and Iran at 7.75 percent. Islamic Azad University has the most publications with 504, followed by Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. Expert Systems With Applications, Sustainability, and the Journal of Cleaner Production are the top journals, accounting for over 4.67 percent of all indexed works. In addition, E. Zavadskas and J. Wang have the most papers in the multi-criteria approaches sector. AHP, followed by TOPSIS, VIKOR, PROMETHEE, and ANP, is the most popular multi-criteria decision-making method among the ten nations with the most publications in this field. The bibliometric literature review method enables researchers to investigate the multi-criteria research area in greater depth than the conventional literature review method. It allows a vast dataset of bibliographic records to be statistically and systematically evaluated, producing insightful insights. This bibliometric study is helpful because it provides an overview of the issue of multi-criteria techniques from the past forty-four years, allowing other academics to use this research as a starting point for their studies.

Treated wastewater suitability for reuse in comparison to groundwater and surface water in a peri-urban area: Implications for water quality management

The climate change and population growth led to a severe water shortage that limits fresh water availability in some areas of Togo. Thus, the farmers of Adjougba District have no other choice than using treated wastewater as an alternative for irrigated agriculture. The purpose of this study is to compare the suitability of three types of water for uses with identification of the reliable parameters in the assessment of water suitability for irrigation and domestic purposes. The raw water quality parameters, water quality indices (WQIs) and water suitability indicators for irrigation purpose (WSI-IPs) were applied for the comparison while statistical analysis and, with some experts' consensus were used to identify reliable parameters. The results suggested that the treated wastewater is more suitable than groundwater for irrigation purpose. Treated wastewater constitutes a viable fertilizer supply and is placed like surface water from permissible to excellent classes according to WSI-IPs values. The sodium absorption ratio (SAR), electrical conductivity (EC), residual sodium carbonate (RSC), Cl^- and faecal coliforms (FC) are the most reliable parameters in the detection of water suitability for irrigation purpose. EC, DO, pH, turbidity or TSS, COD or COD_Mn, hardness, FC, NO₃^-, national sanitation foundation's water quality index (NSFWQI), and overall index of pollution (OPI) are the most reliable in the detection of water suitability for domestic use. The reliable parameters identified in this study are potential candidates for the development of a single water quality index for both irrigation and domestic uses in Adjougba District. However further study will be necessary for the identification of reliable parameters and the development of a water quality index at the country scale.

Mathematical Description of the Increase in Selectivity of an Anion-Exchange Membrane Due to Its Modification with a Perfluorosulfonated Ionomer

In this paper, we simulate the changes in the structure and transport properties of an anion-exchange membrane (CJMA-7, Hefei Chemjoy Polymer Materials Co. Ltd., China) caused by its modification with a perfluorosulfonated ionomer (PFSI). The modification was made in several stages and included keeping the membrane at a low temperature, applying a PFSI solution on its surface, and, subsequently, drying it at an elevated temperature. We applied the known microheterogeneous model with some new amendments to simulate each stage of the membrane modification. It has been shown that the PFSI film formed on the membrane-substrate does not affect significantly its properties due to the small thickness of the film (≈4 µm) and similar properties of the film and substrate. The main effect is caused by the fact that PFSI material “clogs” the macropores of the CJMA-7 membrane, thereby, blocking the transport of coions through the membrane. In this case, the membrane microporous gel phase, which exhibits a high selectivity to counterions, remains the primary pathway for both counterions and coions. Due to the above modification of the CJMA-7 membrane, the coion (Na⁺) transport number in the membrane equilibrated with 1 M NaCl solution decreased from 0.11 to 0.03. Thus, the modified membrane became comparable in its transport characteristics with more expensive IEMs available on the market.

Priority Pollutants Monitoring and Water Quality Assessment in the Siret River Basin, Romania

The Integrated Water Resources Management regulations aim to ensure a good status of surface water quality and its sustainable use. Water quality monitoring of various water users supports the identification of pollution sources and their environmental impacts. The priority pollutants generated by wastewater discharges from municipal, industrial wastewater treatment plants or agricultural areas are of great interest due to their eco-toxicological effects and bio-accumulative properties. The aim of this study was to monitor the priority organic and inorganic pollutants from the Siret River basin, in Romania, with the purpose of assessing the surface water quality status and evaluating it by the Water Quality Index (WAWQI) method. The monitoring of inorganic priority pollutants (e.g., As, Cd, Hg, Ni, Pb) and organic priority pollutants (e.g., Naphthalene, Anthracene, Phenanthrene, Fluoranthene, Benzo(a)anthracene, Benzo(b)fluoranthene, Benzo(k)fluoranthene, Benzo(a)pyrene, Benzo(ghi)perylene, Indeno(1,2,3-cd)pyrene, α, β, and γ-Hexachlorocyclohexane, and Di-2-ethyl-hexyl-phthalate) was conducted within the Siret River basin, during the period 2015–2020. With this purpose, 21 sampling points (18 river sections and 3 lakes) were considered to assess the water quality. The results of this study proved that the water quality within the Siret River basin is generally classified in the 2nd or 3rd class. The spatial distribution of the water quality index values, using ARCGIS, also highlighted the fact that the water quality is mostly unsuitable for drinking water supplies, being influenced by the quality of its main tributaries, as well as by the effluent of wastewater treatment plants.

Various Natural and Anthropogenic Factors Responsible for Water Quality Degradation: A Review

Recognition of sustainability issues around water resource consumption is gaining traction under global warming and land utilization complexities. These concerns increase the challenge of gaining an appropriate comprehension of the anthropogenic activities and natural processes, as well as how they influence the quality of surface water and groundwater systems. The characteristics of water resources cause difficulties in the comprehensive assessment regarding the source types, pathways, and pollutants behaviors. As the behavior and prediction of widely known contaminants in the water resources remain challenging, some new issues have developed regarding heavy metal pollutants. The main aim of this review is to focus on certain essential pollutants’ discharge from anthropogenic activities categorized based on land-use sectors such as industrial applications (solid/liquid wastes, chemical compounds, mining activities, spills, and leaks), urban development (municipal wastes, land use practices, and others), and agricultural practices (pesticides and fertilizers). Further, important pollutants released from natural processes classified based on climate change, natural disasters, geological factors, soil/matrix, and hyporheic exchange in the aquatic environment, are also discussed. Moreover, this study addresses the major inorganic substances (nitrogen, fluoride, and heavy metals concentrations). This study also emphasizes the necessity of transdisciplinary research and cross-border communication to achieve sustainable water quality using sound science, adaptable legislation, and management systems.