Evaluation of water quality in the Chillán River (Central Chile) using physicochemical parameters and a modified water quality index

Development of soft computing-based models for forecasting water quality index of Lorestan Province, Iran

The Water Quality Index (WQI) is widely used as a classification indicator and essential parameter for water resources management projects. WQI combines several physical and chemical parameters into a single metric to measure the status of Water Quality. This study explores the application of five soft computing techniques, including Gene Expression Programming, Gaussian Process, Reduced Error Pruning Tree (REPt), Artificial Neural Network with FireFly (ANN-FFA), and combinations of Reduced Error Pruning Tree with bagging. These models aim to predict the WQI of Khorramabad, Biranshahr, and Alashtar sub-watersheds in Lorestan province, Iran. The dataset consists of 124 observations, with input variables being sulfate (SO4), total dissolved solids (TDS), the potential of Hydrogen (pH), chloride (Cl), electrical conductivity (EC), Potassium (K), bicarbonate (HCO), magnesium (Mg), sodium (Na), and calcium (Ca), and WQI as the output variable. For model creation (train subset) and model validation (test subset), the data were split into two subsets (train and test) in a ratio of 70:30. The performance evaluation parameters values of training and testing stages of various models indicate that the ANN-FFA based data-driven model performs better than the other modeling techniques applied with the values of coefficient of correlation 0.9990 & 0.9989; coefficient of determination 0.9612 & 0.9980; root mean square error 0.3036 & 0.3340; Nash-Sutcliffe error 0.9980 & 0.9979; and Mean average percentage error 0.7259% & 0.7969% for the train and test subsets, respectively. Taylor diagram results also suggest that ANN-FFA is the best-performing model, followed by the GEP model. This study introduces a novel model for predicting WQI using advanced soft computing models that have not been previously applied in this study area, highlighting its novelty and relevance. The proposed model significantly enhances predictive accuracy and efficiency, offering real-time, cost-effective WQI predictions that outperform traditional methods in handling complex, nonlinear environmental data.

Water quality assessment methods of the highland Andean rivers: A scoping systematic review

Water is a resource that influences sustainable development in different ways in social, economic, and environmental aspects, being the Andes the major provider of this resource. However, they have been affected mainly by anthropogenic activities due to the proximity of settlements in the watersheds, so they tend to have more significant contamination, and their evaluation is essential to mitigate problems for those who consume them. However, despite being a fundamental resource and one of the main contributors of water, it is not so studied, so the present study aims to determine the studies based on the water quality of the high mountain rivers of the Andes by using a PRISMA methodology with the scoping review extension, based on search techniques, inclusion and exclusion criteria, and monitoring tables, in order to maintain a line of research attached to the objective of the study. After using the methodology, ten articles were obtained, which were analyzed after a bibliometric analysis to determine features of interest, such as countries in which the studies were carried out, years of publication, methodologies used, and authors' consensus. High Andean rivers' importance, the need for more studies within these areas, and the lack of suitable indexes for these unique ecosystems are highlighted.

Long-term (2003-2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The degradation of water quality induced by the construction of large-scale hydraulic projects is one of the primary public concerns; however, it is rarely addressed with long-term field observation data. Here, we reported the long-term (2003-2021) trends, seasonal patterns, and overall condition of water quality of the Three Gorges Reservoir (TGR) with an enhanced water quality index (WQI). Specifically, to emphasize the importance of the biological role in water quality assessment, chlorophyll-a (Chla) was incorporated into WQI, and then a novel workflow using machine learning approach based on Random Forest (RF) model was constructed to develop a minimal water quality index (WQImin). The enhanced WQI indicated an overall "good" water quality condition, exhibiting a gradually improving trend subsequent to the reservoir impoundment in 2003. Meanwhile, the assessment revealed that the water quality has discernible seasonal patterns, characterized by poorer conditions in the spring and summer seasons. Furthermore, the RF model identified Chla, dissolved oxygen (DO), ammonium nitrogen (NH4-N), water temperature (WT), pH, and total nitrogen (TN) as key parameters for the WQImin, with Chla emerging as the most important factor in determining WQImin in our study. Moreover, weighted WQImin models exhibited improved performance in estimating WQI. Our study emphasizes the importance of biological parameters in water quality assessment, and introduces a systematic workflow to facilitate the development of WQImin for accurate and cost-efficient water quality assessment. Furthermore, our study makes a substantial contribution to the advancement of knowledge regarding long-term trends and seasonal patterns in water quality of large reservoirs, which provides a foundational basis for guiding water quality management practices for reservoirs worldwide.

Evaluation of groundwater quality by adopting a multivariate statistical approach and indexing of water quality in Sagar Island, West Bengal, India

In the vicinity of the coast, predominantly groundwater is the sole reliable resource for potable purposes as the surface water sources are highly saline and unfit for human consumption. However, the groundwater in Sagar Island is highly vulnerable to saltwater intrusion. The majority of drinking water comes from government-owned hand pump-equipped tube wells. But during the summer season, many of these tube wells yield significantly less water. Hence, in the current scenario, water quality assessment has become important to the quantity available. Total of 31 samples of deep tube wells (groundwater) are collected at variegated locations during pre-monsoon season throughout Sagar, and then, the physical and chemical quality parameters of these water samples are analysed. Furthermore, a multivariate statistical technique is executed with the aid of the SPSS program. The hydro-chemical parameters that are taken into account for the quality analysis are pH, salinity, electrical conductivity (EC), total dissolved solids (TDS), total hardness, aluminium, arsenic, bi-carbonate, cadmium, iron, chloride, copper, chromium, cobalt, lead, magnesium, manganese, nickel, potassium, sulphate, zinc, and sodium. Then, the analysed data evaluates the water quality index (WQI). Five components are identified through the principal component analysis (PCA) technique, and 82.642% total variance is found. The outcomes of the quality assessment study illustrate that about 54.84% of collected samples come in the "excellent" water quality class when calculated by the "weighted arithmetic WQI method," and 90.32% of collected groundwater samples come in the "good" water quality class when computed using the "modified weighted arithmetic WQI method." This study helps for the interpretation of WQI to assess groundwater quality.

Reversing the damage: ecological restoration of polluted water bodies affected by pollutants due to anthropogenic activities

Aquatic ecosystems provide a large number of cultural, regulating, and supporting services to humans and play a pivotal role in sustaining freshwater-dependent ecosystems. However, an increase in human population coupled with economic growth in the last few decades has severely affected their functioning and ecological health. This has led to an increase in concentrations of pollutants originating from anthropogenic activities such as heavy metals, plastics, semi-volatile organic compounds, and endocrine disruptors. These pollutants provoke deleterious impacts on aquatic biodiversity and affect the water quality and functioning. In this paper, we discuss the sources and impacts of such pollutants as well as restoration techniques for reducing their impact on aquatic ecosystems. Several physical and chemical ecological restoration techniques, such as dredging, sediment capping, water diversion, adsorption, aeration, and flushing, can be employed to improve the water quality of water bodies. Additionally, biological techniques such as phytoremediation, phycoremediation, the use of biomembranes, and the construction of ecological floating beds can be employed to increase the population of aquatic organisms and improve the overall ecological health of aquatic ecosystems. Restoration techniques can effectively reduce the concentrations of suspended solids and dissolved phosphorus and increase the levels of dissolved oxygen. The restoration techniques for improving the ecological health of water bodies should not be limited to simply improving the water quality but should also focus on improving the biological processes and ecosystem functioning since it is essential to mitigate the adverse effects of pollutants and restore the vital ecosystem services provided by water bodies for future generations.

Performance evaluation of rural water environment governance based on AHP: a case study of the Beitang River Basin

Rural water environment governance in China still lacks a systematic and comprehensive assessment protocol to help analyze and improve such governance performance. The Analytic Hierarchy Process (AHP) method was employed in this study to build a governance assessment system that integrates ecological conditions, water pollution control, and public satisfaction. To cover these topics, the assessment system is composed of an indicator layer that is customized to rural water environment governance in China. The Beitang River, located in the rural region of Hangzhou, Zhejiang, China, was presented as a case study. Field investigation provided raw data for this assessment. A questionnaire survey was conducted to interview local residents on the governance performance. An additional survey with executives who played major roles in the governance was performed to reconstruct a water environment assessment on the Beitang River prior to the governance, in order to highlight the effects of the governance through contrast. The results showed consistency in the questionnaire survey and the assessment system. The AHP assessment system was able to reflect the improvement in the water quality, river ecology, and residential welfare after the governance, and suggested limits and future directions in the following upgrade programs for the river basin.

An integrated risk assessment framework using information theory-based coupling methods for basin-scale water quality management: A case study in the Danjiangkou Reservoir Basin, China

As the second largest reservoir in China, the Danjiangkou Reservoir (DJKR) serves as the water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the currently longest (1273 km) inter-basin water diversion project in the world, for more than eight years. The water quality status of the DJKR basin has been receiving worldwide attention because it is related to the health and safety of >100 million people and the integrity of an ecosystem covering >92,500 km2. In this study, basin-scale water quality sampling campaigns were conducted monthly at 47 monitoring sites in river systems of the DJKRB from the year 2020 to 2022, covering nine water quality indicators, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), total nitrogen (TN), and fluoride (F-). The water quality index (WQI) and multivariate statistical techniques were introduced to comprehensively evaluate water quality status and understand the corresponding driving factors of water quality variations. An integrated risk assessment framework simultaneously considered intra and inter-regional factors using information theory-based and the SPA (Set-Pair Analysis) methods were proposed for basin-scale water quality management. The results showed that the water quality of the DJKR and its tributaries stably maintained a "good" status, with all the average WQIs >60 of river systems during the monitoring period. The spatial variations of all WQIs in the basin showed significantly different (Kruskal-Wallis tests, P < 0.01), while no seasonal differences were found. The increase in built-up land use and agricultural water consumption revealed the highest contributions (Mantel's r > 0.5, P < 0.05) to the rise of nutrient loadings of all river systems, showing the intensive anthropogenic activities can eclipse the power of natural processes on water quality variations to some extent. The risks of specific sub-basins that may cause water quality degradation on the MRSNWDPC were effectively quantified and identified into five classifications based on transfer entropy and the SPA methods. This study provides an informative risk assessment framework that was relatively easy to be applied by professionals and non-experts for basin-scale water quality management, thus providing a valuable and reliable reference for the administrative department to conduct effective pollution control in the future.

Method for screening water physicochemical parameters to calculate water quality index based on these parameters' correlation with water microbiota

Water quality index (WQI) plays a crucial role in guiding water resource management. However, WQI calculation methods are not uniform, especially the selection of water parameters and the weighting given to each water parameter (P_i). To optimize WQI calculation, 132 water samples from seven rivers and from Chaohu Lake (33 sampling sites in Chaohu Lake Basin) in four seasons were collected, and the water parameters and microbiota composition were analyzed using high-throughput sequencing of 16 S rDNA. The correlation coefficient R² between water parameters and microbiota composition using redundancy analysis with the Monte Carlo method were calculated, and the water parameters that significantly correlated with the microbiota composition were selected to calculate WQI_min. The results showed that TP, COD, DO, and Chl a correlated significantly with water microbiota composition. WQI_b calculated by substituting R² for P_i was more consistent with the similarity between the microbiota compositions. WQI_minb calculated using TP, COD, and DO was consistent with WQI_b. The results of WQI_b and WQI_minb were more consistent than those of WQI and WQI_min. These results imply that using R² instead of P_i could help obtain a more stable WQI_b that could better reflect the biological characteristics of the Chaohu Lake Basin.

Effects of rainfall characteristics on runoff quality parameters within an industrial sector in Tennessee, USA

The relationship between rainfall characteristics and pollutant discharge has rarely been investigated in industrial sectors. To address this need, we investigated the pollutant concentrations of surface runoff and the correlation between pollutant discharge and rainfall characteristics using the self-reported stormwater quality data collected under the Tennessee Multi-Sector Permit program for two industrial facilities in West Tennessee. The variation of certain stormwater quality parameters over this period was utilized as an indicator to evaluate the effectiveness of control measures implemented at these two facilities. Furthermore, the Water Quality Index (WQI) as an indicator to assess the temporal changes in stormwater quality at industrial facilities was determined using the Weighted Sum (WSM) and Canadian Council of Ministers of the Environment (CCME) methods. The principal component analysis (PCA) and Pearson correlation coefficient were utilized to understand the correlation between runoff quality parameters, rainfall characteristics, and the sources of pollutants. The results demonstrated lower WQI indices using the WSM method compared to the CCME method. The data analysis revealed that 93.1%, 100%, 86.2%, and 48.3% of Al, Mg, Cu, and Fe experienced a concentration greater than the benchmark level, respectively. There was a significant relationship between Total suspended solids (TSS) and Al, Chemical Oxygen Demand (COD), Fe, oil and grease (O&G), and Zn concentrations. As a result, TSS could be a priority pollutant for designing various best management practices (BMPs) and low impact developments (LIDs). The result of the PCA and Pearson correlation coefficient showed that Al concentration made a significant correlation with the rainfall depth and rainfall duration. This analysis also illustrated that biochemical oxygen demand (BOD₅), COD, and O&G concentrations were highly correlated with antecedent dry days (ADDs). However, pH was more related to rainfall depth and rainfall intensity. This study informs both regulatory agencies and industry stakeholders regarding the importance of evaluating self-reported stormwater quality data.

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.

Using Canadian Water Quality Index method to evaluate the spatio-variation of water quality and the impacts of quality parameters: a case study of Amasya's surface water (Northern Turkey)

In this study, the spatial variation of water quality in Yeşilırmak River passing through Amasya was investigated using the Canadian Water Quality Index (CWQI). For this aim, the measured 15 parameters in 3-month periods between the years 2008 and 2015 were used at 11 sample points from the Yeşilırmak River and its tributaries. The calculated CWQI scores using parameters of pH, Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), ammonia, ammonium, nitrite, nitrate, phosphate, iron, copper, zinc, potassium, sulfate, sulfite and chlorine range from 33 to 64. These scores indicate that the surface waters in the studied area are poor to marginal in quality. The effect of each parameter on the CWQI scores by excluding each parameter, one by one, considering the water quality of the Yeşilırmak River was investigated using the Hierarchical Cluster Analysis (HCA) method. It was determined that the presence and/or absence of the parameters, which caused an increase or decrease in CWQI scores, were ammonia, phosphate, COD, sulfide, iron, ammonium, nitrite, DO. On the other hand, the parameters having positive effects on CWQI are nitrate, chlorine and potassium. The HCA statistical analysis method is suitable for interpreting complex water quality datasets and understanding time/location dependent changes in water quality. HCA can be used effectively to group parameters in river water quality monitoring programs.

Evaluation of surface water quality in basins of the Chilean Altiplano-Puna and implications for water treatment and monitoring

Water quality characterization and assessment are key to protecting human health and ecosystems, especially in arid areas such as northern Chile, where water resources are scarce and rich in pollutants. The objective of this study was to review and assess available official water quality data in the Chilean Altiplano-Puna basins for a 10-year period (2008-2018), including water treatment systems. Within the 43,600 km² of Chilean Altiplano-Puna territory, only 16 official water quality monitoring stations had up-to-date data, and the sampling frequency was less than 3 per year. Most of the water samples collected at the evaluated stations exceeded the drinking and irrigation water Chilean standards for arsenic, boron, and electrical conductivity. Moreover, the characteristics of the Altiplano-Puna affect water quality inside and beyond the area, limiting water usage throughout the Altiplano-Puna basins. Drinking water treatment plants exist in urban and rural settlements; however, the drinking water supply in rural locations is limited due to the lack of adequate treatment and continuity of service. Wastewater treatment plants operate in some urban locations but rarely exist in rural locations. Limited data impede the proper assessment of water quality and thus the evaluation of the need for treatment systems. As such, the implementation of public policies that prioritize water with appropriate quantity and quality for local communities and ecosystems is imperative.

Spatiotemporal variation evaluation of water quality in middle and lower Han River, China

As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world’s largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQI_min) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQI_min model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R²) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.

Application of remodeled water quality indices for the appraisal of water quality in a Himalayan lake

Natural and anthropogenic pollution influence the general hydrochemistry of freshwater sources. Effective management strategies need an accurate evaluation of the water quality parameters, and inferences extracted from the data should be based on the most appropriate statistical methods. Conventional water quality indices (WQI) being related to a large number of water quality parameters results in significant variability and analytical costs. The focus of this study was to develop a remodeled water quality index (WQI_min) based on the localized trends in water quality and demonstrate it to understand water quality variations of Dal Lake (a freshwater lake in the Himalayan region). Spatio-temporal changes and trends of 14 water quality parameters were investigated that were arbitrated from the samples collected at 11 sampling locations during the water quality monitoring across the Dal Lake from September 2017 to August 2020. The results signify that the general mean WQI value was 81.9, and seasonal average WQI values ranges from 79.44 to 84.55. The water quality showed seasonal variance, with lowest values in summer, succeeded by autumn and winter, and highest in spring. Moreover, the results from stepwise multiple regression analysis indicated that the WQI_min significantly correlates with six water quality parameters (ammonia, dissolved oxygen, chemical oxygen demand, temperature, turbidity, and nitrate) in Dal Lake. The WQI_min model predicted the water quality of the Dal Lake with a coefficient of determination (R²) value of 0.96, root mean square error (RMSE) value of 4.1, and percentage error (PE) of 5.3%. The developed WQI_min model can be applied as a cost-effective and efficacious approach to determine the water quality of fresh surface water bodies.

Environmental damage caused by coal combustion residue disposal: A critical review of risk assessment methodologies

Coal combustion generates almost 40% of world's electricity. However, it also produces 1.1 billion tons of coal combustion residues (CCR) annually, half of which end up in landfills. Although current regulations require proper lining and monitoring programs, the ubiquitous old, abandoned landfills are often not lined nor included in these programs. In addition, the total number of coal ash disposal sites and their status in the world is unknown. Therefore, this article reviews the environmental damage caused by CCR and three commonly used risk assessment methodologies: leaching assessment, groundwater assessment, and toxicity testing. Leaching methods are usually the first step in coal ash risk assessment, however, a large number of methods with different parameters make a comparison of data difficult. Groundwater pollution is commonly detected near coal ash disposal sites, but other anthropogenic activities may also exist nearby. Therefore, multivariate statistical methods and isotope traces should be used to differentiate between different sources of pollution. So far, both stable (δ¹⁸O, δD, δ¹¹B, δ³⁴S, δ⁷Li) and radiogenic (⁸⁷Sr/⁸⁶Sr, ²⁰⁶Pb/²⁰⁷Pb) isotopes have been successfully used as coal ash pollution tracers. Coal ash also negatively affects biota, reduces the diversity of organisms, affects children's health, and increases the risk for developing various diseases. Toxicity studies are great for early screening of coal ash safety; however, they provide no insights into mechanisms causing the adverse effects. Future directions are also proposed, such as the development of new 'low-level' detection methods for coal ash pollution and sustainable and selective method for recovery of critical elements.

Simple Prediction of an Ecosystem-Specific Water Quality Index and the Water Quality Classification of a Highly Polluted River through Supervised Machine Learning

Water quality indices (WQIs) are used for the simple assessment and classification of the water quality of surface water sources. However, considerable time, financial resources, and effort are required to measure the parameters used for their calculation. Prediction of WQIs through supervised machine learning is a useful and simple approach to reduce the cost of the analysis through the development of predictive models with a reduced number of water quality parameters. In this study, regression and classification machine-learning models were developed to estimate the ecosystem-specific WQI previously developed for the Santiago-Guadalajara River (SGR-WQI), which involves the measurement of 17 water quality parameters. The best subset selection method was employed to reduce the number of significant parameters required for the SGR-WQI prediction. The multiple linear regression model using 12 parameters displayed a residual square error (RSE) of 3.262, similar to that of the multiple linear regression model using 17 parameters (RSE = 3.255), which translates into significant savings for WQI estimation. Additionally, the generalized additive model not only displayed an adjusted R2 of 0.9992, which is the best fit of all the models evaluated, but also fitted the rating curves of each parameter developed for the original algorithm for the SGR-WQI calculation with great accuracy. Regarding the classification models, an overall proportion of 93% and 86% of data were correctly classified using the logistic regression model with 17 and 12 parameters, respectively, while the linear discriminant functions using 12 parameters correctly classified an overall proportion of 84%. The models evaluated were found to be efficient in predicting the SGR-WQI with a reduced number of parameters as complementary tools to extend the current water quality monitoring program of the Santiago-Guadalajara River.

Assessing the factors influencing water quality using environment water quality index and partial least squares structural equation model in the Ebinur Lake Watershed, Xinjiang, China

Surface water quality deterioration is commonly associated with environmental changes and human activities. Although some research has been carried out to evaluate the relationship between various influencing factors and water quality, there is still very little scientific understanding on how to accurately define the key factors of water quality deterioration. This study aims to quantify the impact of environmental factors and land use land cover (LULC) changes on water quality in the Ebinur Lake Watershed, Xinjiang, China. A total of 20 water parameters were used to calculate the Environment Water Quality Index (CWQI). Meanwhile, the partial least squares-structural equation model (PLS-SEM) was used to quantify the impact of eleven factors influencing water quality in the watershed. About 33.3% of the monitoring points that located mostly in the downstream region with dominant anthropogenic activities were detected as poor quality. There were no obvious temporal changes in water quality from 2016 to 2019. The PLS-SEM simulation shows that the latent variable "land use/cover types" (path coefficient =  - 0.600) and "Environmental factor" (path coefficient =  - 0.313) are two major factors affected water quality in the Ebinur Lake Watershed, with a strong explanatory power to water quality change (R² = 0.727). In the latent variable "Environmental factors", the "NDVI" and "night light brightness value" have a great influence on water quality, with the weights of 0.451 and 0.427, respectively. Correspondingly, the "farmland" and "forest land" within the latent variable of "Land use/cover type" have a considerable impact water quality, with the weights of 0.361 and - 0.340, respectively. In conclusion, the influence of anthropogenic activities on surface water quality of the Ebinur Lake Watershed is greater than that of environmental factors. Compared with the traditional multivariate statistical method, PLS-SEM provides a new insight for quantifying the complex relationship between different influencing factors and water quality.

Analysis of spatio-temporal variations of river water quality and construction of a novel cost-effective assessment model: a case study in Hong Kong

Assessment of river water quality has been attracting a great deal of attention because of its important implications for the living environment of human beings and aquatic organisms. River water quality is commonly assessed using dozens of different water quality parameters. However, different parameters may contain redundant information, which could lead to the waste of monitoring efforts. Thus, this study constructed a novel cost-effective assessment model of river water quality using the 1-year monitoring data collected from 23 sampling stations in the water control zone of Tolo Harbour and Channel in Hong Kong. First, the spatio-temporal variations of water quality parameters and the overall status of river water quality were analyzed based on all 19 parameters using Kruskal-Wallis test, hierarchical cluster analysis, and the water quality index (WQI). The results indicated that most water quality parameters and overall water quality status varied significantly over space, but did not exhibit obvious seasonal differences; and 99.27% of water samples were identified to be in good or excellent status of overall WQI. Then, using principal component analysis (PCA)/factor analysis (FA) and Pearson's correlation analysis, eight parameters, including 5-day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), ammonia-nitrogen (NH₃-N), nitrate-nitrogen (NO₃-N), chlorophyll-a (Chl-a), fluoride (F^-), total suspended solids (TSS), and arsenic (As), were verified to be responsible for the greatest contributions to water quality, the assessment of overall water quality status. These eight crucial parameters were further employed to establish six cost-effective water quality assessment models. Using the overall WQI as the benchmark, the results of linear regression analysis demonstrated that the cost-effective model constructed based on BOD₅, COD, NH₃-N, NO₃-N, F^-, TSS, and As were the optimal water quality assessment model, which can achieve the most reliable results with reduced parameters.

Construction and Application of a Water Quality Risk Sensitive Area Identification System in the Wudongde Reservoir

Numerous water quality risks exist during the initial water storage stage in reservoirs; however, little water quality data is available for this stage. Taking the Wudongde Reservoir as an example, we proposed a water quality risk sensitive area identification system for the initial impoundment stage comprising three modules: water quality assessment, water quality similarity clustering analysis, and sensitive area identification. Temporal and spatial variation in the water quality of the whole reservoir was analyzed, combined with a comprehensive evaluation using the Canadian Council of Ministers of the Environment Water Quality Index. A water quality similar clustering module was used to form similar clusters for monitoring sections in the reservoir area. The water quality risk sensitive areas were then identified and verified through a prototype test. The reservoir water quality was primarily excellent to good, although that of the Madian and Longchuan Rivers was poor. Through cluster analysis, the Madian River and tributaries of the Longchuan River were identified as sensitive areas, and the causes of water quality risk were analyzed. Based on these findings, we suggested focus areas for water environmental protection measures, providing a basis for the protection and restoration of the reservoir water environment.

Surface Water Quality Assessment and Contamination Source Identification Using Multivariate Statistical Techniques: A Case Study of the Nanxi River in the Taihu Watershed, China

Understanding the spatiotemporal patterns of water quality is crucial because it provides essential information for water pollution control. The spatiotemporal variations in water quality for the Nanxi River in the Taihu watershed of China were evaluated by a water quality index (WQI) and multivariate statistical techniques; additionally, the potential sources of contamination were identified. The data set included 22 water quality parameters collected during the monitoring period from 2015 to 2020 for 14 monitoring stations. WQI assessment revealed that approximately 85% of monitoring stations were classified as “medium-low” water quality, and most showed continuous improvement in water quality. Cluster analysis divided the 14 monitoring stations into three clusters (low contamination, medium contamination and high contamination). Discriminant analysis identified pH, petroleum, volatile phenol, chemical oxygen demand, total phosphorus, F, S, fecal coliform, SO4, Cl, NO3-N, total hardness, NO2-N and NH3 as important parameters affecting spatial variations. Factor analysis identified four potential contamination source types: nutrient, organics, feces and oil. This study demonstrated the usefulness of multivariate statistical techniques in assessing large data sets, identifying contamination source types, and better understanding spatiotemporal variations in water quality to restore and protect water resources.

Investigation of water quality and its spatial distribution in the Kor River basin, Fars province, Iran

As one of the largest rivers in the southwest of Iran, the Kor River plays an important role in local economy and ecosystem. However, the rapid development of industry has caused significant pollution in this river in recent years. Despite of a number of studies reported on this river regarding water pollution, few have conducted a comprehensive investigation of a wide range of water quality parameters to map the current pollution status. This study focuses on 21 water quality parameters around the industrial centers of the Kor River basin with samples taken from 25 stations. With the measured parameters, the interpolation maps of each parameter were determined using the Kriging method, and the water quality was quantified using the Water Quality Index (WQI) method. The results showed that the WQI values were between 28 and 73, showing more pollution around the factories than in the upstream areas. The results of the principal component analysis (PCA) indicated that BOD, COD, NO₃^-, and coliforms were the most important parameters among the 21 parameters affecting the water quality. Linear regression results suggested that the best parameters for determining coliforms and WQI values were BOD, and Cr, PO₄^3-, Hg and Zn levels, respectively, with R² greater than 0.87. These results can also simplify the prediction of coliforms and WQI using only a few parameters. We further found that flatter regions generally had more pollution, primarily due to pollutant accumulation as a result of water stagnation.

Multivariate statistics and entropy theory for irrigation water quality and entropy-weighted index development in a subtropical urban river, Bangladesh

Currently, a well-developed combination of irrigation water quality index (IWQIs) and entropy water quality index (EWQIs) for surface water appraisal in a polluted subtropical urban river is very scarce in the literature. To close this gap, we developed IWQIs by establishing statistics-based weights of variables recommended by FAO 29 standard value using the National Sanitation Foundation Water Quality Index (NSFWQI) compared with the proposed EWQIs based on information entropy in the Dhaleshwari River, Bangladesh. Fifty surface water samples were collected from five sampling locations during the dry and wet seasons and analyzed for sixteen variables. Principal component analysis (PCA), factor analysis (FA), Moran's spatial autocorrelation, and random forest (RF) model were employed in the datasets. Weights were allocated for primary variables to compute IWQI-1, 2 and EWQI-1, 2, respectively. The resultant IWQIs showed a similar trend with EWQIs and revealed poor to good quality water, with IWQI-1 for the dry season and IWQI-2 for the wet season is further suggested. The entropy theory recognized that Mg²⁺, Cr, TDS, and Cl^- for the dry season and Cd, Cr, Cl-, and SO₄^2- for the wet season are the major contaminants that affect irrigation water quality. The primary input variables were lessened to ultimately shortlisted ten variables, which revealed good performance in demonstrating water quality status since weights have come effectively from PCA than FA. The results of the RF model depict NO₃^-, Mg²⁺, and Cr as the most predominant variables influencing surface water quality. A significant dispersed pattern was detected for IWQImin-3 in the wet season (Moran's I>0). Overall, both IWQIs and EWQIs will generate water quality control cost-effective, completely objective to establish a scientific basis of sustainable water management in the study basin.

Assessing the influence of horticultural farming on selected water quality parameters in Maumau stream, a tributary of Nairobi River, Kenya

This study aimed to determine the levels of contamination in Maumau stream as a result of horticultural activities in its vicinity. The stream was purposefully delineated into three blocks including upstream, midstream and downstream, where water samples were collected and analyzed for physicochemical attributes. Standard analytical procedures for water analysis were followed in laboratory analysis and the collected data was analyzed using Genstat software. Analyzed parameters include total dissolved solids (TDS), salinity, total suspended solids (TSS), sulphates (SO₃⁻³), phosphates (PO₃⁻³), nitrates (NO₃^-), fluoride (Fl⁻), turbidity, chloride (Cl⁻), magnesium (Mg⁺²), sodium (Na⁺), potassium (K⁺) and zinc (Zn⁺²). The results were presented in tables and a graph against WHO standards. All measured parameters showed significant differences (p=<0.001) among their means across the sampling sites and control. The pH did not show a clear trend from upstream through midstream to downstream. The concentrations of chloride decreased down the stream with control, midstream and downstream showing no statistical significance. Means of fluoride, magnesium, phosphates, sulphates, total soluble solids and zinc increased down the course of the stream. Increasing concentrations of the physicochemical parameters down the stream was attributed majorly to release and addition of agrochemicals to the stream from the nearby farms. A lucid knowledge of the nexus between land use and water quality was recommended as a prime management implication. In conclusion, the water quality of Maumau stream is being degraded by horticultural activities along the stream. Key policy actions including river pegging should be adopted to protect the water quality.

•

There was linear relationship between land use and water quality of the stream.

•

Contamination down the stream was due to agrochemicals from nearby farms.

•

Recommended policy actions include river pegging to protect the water quality.

Effects of COVID-19 lockdown and unlock on health of Bhutan-India-Bangladesh trans-boundary rivers

The COVID-19 pandemic significantly destructs the rhythm of global modern human civilization but worldwide lockdown radically recovers the health of the total environment. The Himalayan trans-boundary rivers provide huge provisional, regulatory and cultural ecosystem services to millions of people throughout the year but in the recent years the water quality is being deteriorated due to multiple reasons. In the last decade, India-Bangladesh political relationship has been slightly broken down due to water sharing and environmental flow of rivers.The COVID-19 lockdown offered a great scope to execute the comparative study among pre, lockdown and unlock phase. The research attempts to investigate the spatiotemporal water quality of trans-boundary rivers through WAWQI and irrigation water quality indices such as Sodium absorption ratio, Soluble sodium percentage, Potential salinity, Magnesium hazard and Kelly's index considering eighteen water quality parameters (pH, EC, TDS, TSS, Ca²⁺, Mg²⁺, Na²⁺, K⁺, F-, Cl-, NO3-, SO₄²-, PO 4 3 -, DO, T, TUR, COD and BOD). The result shows the strong positive correlation between EC and TDS during three phases. Significant reduction of BOD, COD and TUR has been noticed almost 70% stations during lockdown compared with prelockdown while augmentation of DO has been recorded around 40% stations. WQI of most of the stations shows around 80% improvement of water quality during lockdown period. Moreover, worst kind of WQI was found in the Mathabhanga-Churni river followed by Mahananda. During lockdown, the striking results show that SAR and MH were significantly amplified in most of the stations due to agricultural run-off.

Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas

Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

Water Quality Assessment and Potential Source Contribution Using Multivariate Statistical Techniques in Jinwi River Watershed, South Korea

To investigate the effects of rapid urbanization on water pollution, the water quality, daily unit area pollutant load, water quality score, and real-time water quality index for the Jinwi River watershed were assessed. The contribution of known pollution sources was identified using multivariate statistical analysis and absolute principal component score-multiple linear regression. The water quality data were collected during the dry and wet seasons to compare the pollution characteristics with varying precipitation levels and flow rates. The highest level of urbanization is present in the upstream areas of the Hwangguji and Osan Streams. Most of the water quality parameter values were the highest in the downstream areas after the polluted rivers merged. The results showed a dilution effect with a lower pollution level in the wet season. Conversely, the daily unit area pollutant load was higher in the rainy season, indicating that the pollutants increased as the flow rate increased. A cluster analysis identified that the downstream water quality parameters are quite different from the upstream values. Upstream is an urban area with relatively high organic matter and nutrient loads. The upstream sewage treatment facilities were the main pollution sources. This study provides basic data for policymakers in urban water quality management.

Development of an Adaptive Model for the Rate of Steel Corrosion in a Recirculating Water System

The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling for corrosion rate prediction suffer from adaptability and accuracy problems. To address these problems, the water quality analysis data of the circulating water in the field were subjected to data preprocessing and water quality index calculation to perform feature engineering, followed by modeling using a machine learning method that integrates the adaptive immune genetic algorithm and random forest (RF) algorithm and can intelligently select the water quality parameters to be used as the input variables for the RF modeling. Finally, the method was validated using an industrial example, and the results indicate that the method is capable of removing interference variables and is suitable for carbon steel corrosion rate prediction based on water quality models. The proposed method provides a basis for water quality management and real-time decision-making by circulating water field personnel.

Assessing the Water Quality of Lake Hawassa Ethiopia-Trophic State and Suitability for Anthropogenic Uses-Applying Common Water Quality Indices

The rapid growth of urbanization, industrialization and poor wastewater management practices have led to an intense water quality impediment in Lake Hawassa Watershed. This study has intended to engage the different water quality indices to categorize the suitability of the water quality of Lake Hawassa Watershed for anthropogenic uses and identify the trophic state of Lake Hawassa. Analysis of physicochemical water quality parameters at selected sites and periods was conducted throughout May 2020 to January 2021 to assess the present status of the Lake Watershed. In total, 19 monitoring sites and 21 physicochemical parameters were selected and analyzed in a laboratory. The Canadian council of ministries of the environment (CCME WQI) and weighted arithmetic (WA WQI) water quality indices have been used to cluster the water quality of Lake Hawassa Watershed and the Carlson trophic state index (TSI) has been employed to identify the trophic state of Lake Hawassa. The water quality is generally categorized as unsuitable for drinking, aquatic life and recreational purposes and it is excellent to unsuitable for irrigation depending on the sampling location and the applied indices. Specifically, in WA WQI, rivers were excellent for agricultural uses and Lake Hawassa was good for agricultural uses. However, the CCME WQI findings showed rivers were good for irrigation but lake Hawassa was marginal for agricultural use. Point sources were impaired for all envisioned purposes. The overall category of Lake Hawassa falls under a eutrophic state since the average TSI was 65.4 and the lake is phosphorous-deficient, having TN:TP of 31.1. The monitored point sources indicate that the city of Hawassa and its numerous industrial discharges are key polluters, requiring a fast and consequent set-up of an efficient wastewater infrastructure, accompanied by a rigorous monitoring of large point sources (e.g., industry, hospitals and hotels). In spite of the various efforts, the recovery of Lake Hawassa may take a long time as it is hydrologically closed. Therefore, to ensure safe drinking water supply, a central supply system according to World Health organization (WHO) standards also for the fringe inhabitants still using lake water is imperative. Introducing riparian buffer zones of vegetation and grasses can support the direct pollution alleviation measures and is helpful to reduce the dispersed pollution coming from the population using latrines. Additionally, integrating aeration systems like pumping atmospheric air into the bottom of the lake using solar energy panels or diffusers are effective mitigation measures that will improve the water quality of the lake. In parallel, the implementation and efficiency control of measures requires coordinated environmental monitoring with dedicated development targets.

Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea

This study assessed spatial and temporal variations of water quality to identify and quantify possible pollution sources affecting the Yeongsan River using multivariate statistical techniques (MSTs) and water quality index (WQI) values. A 15 year dataset of 11 water quality variables was used, covering 16 monitoring sites. The nutrient regime, organic matter, suspended solids, ionic contents, algal growth, and total coliform bacteria (TCB) were affected by the summer monsoon and the construction of weirs. Regression analysis showed that the algal growth was more highly regulated by total phosphorus (TP; R² = 0.37) than total nitrogen (TN, R² = 0.25) and TN/TP (R² = 0.01) ratios in the river after weir construction and indicated that the river is a P-limited system. After constructing the weirs, the mean TN/TP ratio in the river was about 40, meaning it is a P-limited system. Cluster analysis was used to classify the sampling sites into highly, moderately, and less polluted sites based on water quality features. Stepwise discriminant analysis showed that pH, dissolved oxygen (DO), TN, biological oxygen demand (BOD), chemical oxygen demand (COD), chlorophyll-a (CHL-a), and TCB are the spatially discriminating parameters, while pH, water temperature, DO, electrical conductivity, total suspended solids, and COD are the most significant for discriminating among the three seasons. The Pearson network analysis showed that nutrients flow with organic matter in the river, while CHL-a showed the highest correlation with COD (r = 0.85), followed by TP (r = 0.49) and TN (r = 0.49). Average WQI values ranged from 55 to 141, indicating poor to unsuitable water quality in the river. The Mann-Kendall test showed increasing trends in COD and CHL-a but decreasing trends for TP, TN, and BOD due to impoundment effects. The principal component analysis combined with factor analysis and positive matrix factorization (PMF) showed that two sewage treatment plants, agricultural activities, and livestock farming adversely impacted river water quality. The PMF model returned greater R² values for BOD (0.92), COD (0.87), TP (0.93), TN (0.91), CHL-a (0.93), and TCB (0.83), indicating reliable apportionment results. Our results suggest that MSTs and WQI can be effectively used for the simple interpretation of large-scale datasets to determine pollution sources and their spatiotemporal variations. The outcomes of our study may aid policymakers in managing the Yeongsan River.

Assessing the fluvial system resilience of the river Bacchiglione to point sources of pollution in Northeast Italy: a novel Water Resilience Index (WRI) approach

Modelling and evaluating the resilience of environmental systems has recently raised significant interest among both practitioners and researchers. However, it has not yet been used to measure the absorption and recovery capacities of a river subject to varying levels of pollution due to natural and anthropic sources of contamination within the basin. Fast worldwide population growth and climate change are contributing to an increased degradation status in surface water bodies and to a decreased efficiency of their natural self-purification processes. Decision-makers are, therefore, more and more encouraged to implement alternative management strategies focussed on improving the system resilience to current and future perturbations. To this end, a novel Water Resilience Index (WRI), based on different quality parameters, was developed, and it is here proposed to estimate the ability of the river Bacchiglione, located in Northeast Italy, absorb continuous and unpredictable changes due to potential effects of point sources of pollution, that is, urban and industrial wastewater, and still maintain its vital functions. This new index is integrated in a mathematical model, which represents the river as an influence diagram where the nodes are the gauged stations and the arcs are the fluvial reaches among the stations, to identify the river reaches in need of resilience improvement. In addition, in order to simplify the analytical procedure and lower the costs and times of the monitoring activities, a principal component analysis is also used, as it is able to reduce the number of the water quality parameters to be collected from the sampling stations, distributed along the main river, and thus to calculate a minimum WRI. The good agreement between the results obtained by both the original and minimum WRI shows the effectiveness of the proposed methodology. This approach could be applied to all basins with the same issues, and not just in the Italian case study here analysed, as it might be a valid tool to plan interventions and mitigation actions, protecting the resource from pollution risks and achieving environmental quality and Sustainable Development Goals both in the water bodies and their surrounding territories. In addition, this strategy could be integrated in the existing models supporting local decision-makers and administrators, aiming at increasing the resilience of urban and rural areas to pollution phenomena and facilitating the development of effective policies to reduce the impacts of global change on water quality.

Enteric Viruses and Pepper Mild Mottle Virus Show Significant Correlation in Select Mid-Atlantic Agricultural Waters

Enteric viruses (EVs) are the largest contributors to foodborne illnesses and outbreaks globally. Their ability to persist in the environment, coupled with the challenges experienced in environmental monitoring, creates a critical aperture through which agricultural crops may become contaminated. This study involved a 17-month investigation of select human EVs and viral indicators in nontraditional irrigation water sources (surface and reclaimed waters) in the Mid-Atlantic region of the United States. Real-time quantitative PCR was used for detection of Aichi virus, hepatitis A virus, and norovirus genotypes I and II (GI and GII, respectively). Pepper mild mottle virus (PMMoV), a common viral indicator of human fecal contamination, was also evaluated, along with atmospheric (air and water temperature, cloud cover, and precipitation 24 h, 7 days, and 14 days prior to sample collection) and physicochemical (dissolved oxygen, pH, salinity, and turbidity) data, to determine whether there were any associations between EVs and measured parameters. EVs were detected more frequently in reclaimed waters (32% [n = 22]) than in surface waters (4% [n = 49]), similar to PMMoV detection frequency in surface (33% [n = 42]) and reclaimed (67% [n = 21]) waters. Our data show a significant correlation between EV and PMMoV (R² = 0.628, P < 0.05) detection levels in reclaimed water samples but not in surface water samples (R² = 0.476, P = 0.78). Water salinity significantly affected the detection of both EVs and PMMoV (P < 0.05), as demonstrated by logistic regression analyses. These results provide relevant insights into the extent and degree of association between human (pathogenic) EVs and water quality data in Mid-Atlantic surface and reclaimed waters, as potential sources for agricultural irrigation. IMPORTANCE Microbiological analysis of agricultural waters is fundamental to ensure microbial food safety. The highly variable nature of nontraditional sources of irrigation water makes them particularly difficult to test for the presence of viruses. Multiple characteristics influence viral persistence in a water source, as well as affecting the recovery and detection methods that are employed. Testing for a suite of viruses in water samples is often too costly and labor-intensive, making identification of suitable indicators for viral pathogen contamination necessary. The results from this study address two critical data gaps, namely, EV prevalence in surface and reclaimed waters of the Mid-Atlantic region of the United States and subsequent evaluation of physicochemical and atmospheric parameters used to inform the potential for the use of indicators of viral contamination.

Analysis of the Water Quality of the Ishim River within the Akmola Region (Kazakhstan) Using Hydrochemical Indicators

For the first time in scientific literature, this work addresses the current situation of the Ishim River water quality in the Akmola Region (Northern Kazakhstan). This work uses environmental monitoring techniques to analyze the current state of surface waters in the river. The content of main ions, biogenic and inorganic ions, heavy metals, organic impurities in seasonal and annual dynamics have been studied. Results show that, despite the tightening of requirements for wastewater discharge into the Ishim River basin, a number of water quality indicators did not fulfill the regulatory requirements for surface water bodies during 2013–2019. It has been identified that the greatest pollution in the Ishim River is brought by enterprises of the Karaganda-Temirtau technogenic region, located in the upper reaches of the river. Future water quality monitoring is needed and should include increasing the number of sampling locations and the sampling frequency in order to characterize the spatial and temporal variability of hydrochemical parameters and allow a comprehensive monitoring of legally fixed water quality parameters/indicators.

Surface water quality assessment of Skardu springs using Water Quality Index

Natural springs which originate from hilly areas of Skardu in Pakistan make their route downward and are utilized by the public as they passes from residential areas. Due to weathering processes in mountainous regions, these springs can be the source of various trace elements and pollutants. Keeping in mind the same concept, ten mostly used freshwater springs were selected to evaluate their drinking water quality in the Skardu region. Three samples of water from each spring (start/mouth, 100 m away from the mouth, and 200 m away from mouth) were collected and analyzed for water quality via Water Quality Index (WQI). The main parameters of spring water were recorded in the laboratory as electrical conductivity (EC), total dissolved solids (TDS), residual sodium carbonate (RSC), and sodium adsorption ratio (SAR), and secondary parameters, i.e., Kelley's ratio (KR), permeability index (PI), and WQI, were derived. Besides these parameters, heavy metal pollutants (Cd, Cr, Mn, and Cu) were also determined from the water samples. The results showed that the assessed parameters TDS, SSP, KR, and PI were found within the safer limits of drinking water as prescribed by the Pak-EPA and WHO. However, among trace elements, only Cd (0.03 mg L-1) was found above the permissible limits of 0.01 mg L-1 as given by the GOP-EPA (2008) and WHO (1996) at Shigri Bala spring 200 m away. Similarly, at 2 ft from the mouth of Chumig S1 spring, its concentrations were recorded 0.03 mg L-1, and at Benazir Chowk spring (100 m away), it was found 0.02 mg L-1. In general, the Water WQI demonstrates that springs have good water quality. Our findings are useful for the environmental protection managers and citizens of the Skardu concerned with the water quality of the springs.

Determination of Changes in the Quality of Surface Water in the River—Reservoir System

Assessing the changing parameters of water quality at different points in the river–reservoir system can help prevent river pollution and implement remedial policies. It is also crucial in modeling water resources. Multivariate statistical analysis is useful for the analysis of changes in surface water quality. It helps to identify indicators that may be responsible for the eutrophication process of a reservoir. Additionally, the analysis of the water quality profile and the water quality index (WQI) is useful in assessing water pollution. These tools can support and verify the results of a multivariate statistical analysis. In this study, changes in water quality parameters of the Turawa reservoir (TR), and the Mała Panew river at the point below the Turawa reservoir (bTR) and above the Turawa reservoir (aTR), were analyzed. The analyzed period was from 2019 to 2020 (360 samples were analyzed). It was found that TN, NO2-N, and NO3-N decreased after passing through the Turawa reservoir. Nevertheless, principal component analysis (PCA) and redundancy analysis (RDA) showed that NO2-N and NO3-N contribute to the observed variability of the water quality in the river-reservoir system. PCA showed that pH and PO4-P had a lower impact on the water quality in the reservoir than nitrogen compounds. Additionally, RDA proved that the values of the NO3-N and NO2-N indicators obtained the highest values at the aTR point, PO4-P at the bTR, and pH at the TR. This allows the conclusion that the Turawa reservoir reduced the concentration of NO2-N and NO3-N in comparison with the concentration of these compounds flowing into the reservoir. PCA and RDA showed that both parameters (NO2-N and NO3-N) may be responsible for the eutrophication process of the Turawa reservoir. The analysis of short-term changes in water quality data may reveal additional sources of water pollution. High temperatures and alkaline reaction may cause the release of nitrogen and phosphorus compounds from sediments, which indicates an increased concentration of TP, PO4-P, and Norg in the waters at the TR point, and TP, PO4-P, and NH4-N concentrations at the bTR point. The water quality profile combined with PCA and RDA allows more effective monitoring for the needs of water management in the reservoir catchment area. The analyzed WQI for water below the reservoir (bTR) was lower than that of the reservoir water (TR), which indicates an improvement in water after passing through the reservoir.

Comparison between water quality indices in watersheds of the Southern Bahia (Brazil) with different land use

The present study evaluated the influence of land use and occupation on water quality indices (WQI); the WQI developed by the National Sanitation Foundation (NSF), the WQI adapted by the Environmental Company of the São Paulo State (CETESB), WQI proposed by Bascarón and the Canadian Council of Ministers of the Environment (CCME) WQI, obtained for watersheds located in the Eastern Water Planning and Management Region (BA). The study also analyzed the divergences and similarities of these WQI methods. Water quality data were obtained from the Monitoring Program (Monitora) of Environment and Water Resources Institute of Bahia (INEMA), covering the period from 2008 to 2015, at thirteen (13) sampling sites, with quarterly collections, as well as land use and occupation data. The influence of land use and occupation on water quality indices was assessed by principal component analysis (PCA). The PCA showed that urban and agricultural/pasture areas were influencing factors on water quality variables, such as total phosphorus, biochemical oxygen demand, total nitrogen, turbidity total residues and consequently lower WQI values in the Cachoeira watershed. Among the tested methods to evaluate the water quality of watersheds in the study area, the most similar were the NSF WQI, CETESB WQI, and Objective Bascarón WQI.

A framework for assessing the adequacy of Water Quality Index - Quantifying parameter sensitivity and uncertainties in missing values distribution

Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQI_ADJUSTED) to handle missing values, and capitalise on the remaining available information for the development of a WQI. A Sub-WQI was also developed to address local water quality conditions. WQI results (weighted and non-weighted) developed using different parameter optimisation methods, namely Multivariate Linear Regression and Principal Component Analysis were compared. To build upon the current framework, a new procedure was developed to assess the adequacy of WQI based on the sensitivity analysis of parameters and uncertainties associated with each parameter's missing values distribution. The number of observations needed for the development of a robust WQI was optimised with respect to user-defined acceptable change in WQI, based on Monte Carlo probabilistic simulation. The Johor River Basin (JRB), Malaysia is used as a case-study for the application of this new framework. The JRB serves as an important resource for Johor, one of the most populous state in Malaysia, and Singapore, a country south of Johor. WQI_MLR performed better in explaining the general water quality than WQI_PCA for weighted water quality parameters. Optimisation of sampling frequency revealed that around 130 samples will be required if a 2% change in WQI can be tolerated. The results (specific to the JRB) also revealed that total coliform is the most sensitivity parameter to missing values, and the distribution of sensitive parameters are similar for both WQI_NON-ADJUSTED and WQI_ADJUSTED.

Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt

Limited water resources are one of the major challenges facing Egypt during the current stage. The agricultural drainage water is an important water resource which can be reused for agriculture. Thus, the current study aims to assess the quality of drainage water for irrigation purpose through monitoring and predicting its suitability for irrigation. The chemical composition of Bahr El-Baqr water drain, especially salinity, as well as ions are mainly involved in calculating indicators of water suitability for irrigation, i.e., Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO^-3, Cl^-, and SO₄^2-. Further analysis was carried out to evaluate the irrigation water quality index (IWQI) through integrated approaches and artificial neural network (ANN) model. Further, ARIMA models were developed to forecast IWQI of Bahr El-Baqr drain in Egypt. The results indicated that the computed IWQI values ranged between 46 and 81. Around 11% of the samples were classified as excellent water, while 89% of the samples were categorized as good water. The results of IWQI showed a standard deviation of 8.59 with a mean of 62.25, indicating that IWQI varied by 13.79% from the average. ANN model showed much higher prediction accuracy in IWQI modeling with R² value greater than 0.98 during training, testing and validation. A relatively good correlation was obtained, between the actual and forecasted IWQI based on the Akaike information criterion (AIC); the best fit models were ARIMA (1,0) (0,0) without seasonality. The determination coefficient (R²) of ARIMA models was 0.23. Accordingly, 23% of IWQI variability could be explained by different model parameters. These findings will support the water resources managers and decision-makers to manage the irrigation water resources that can be implemented in the future.

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

Assessing Land-Cover Effects on Stream Water Quality in Metropolitan Areas Using the Water Quality Index

This study evaluated the influence of different land-cover types on the overall water quality of streams in urban areas. To ensure national applicability of the results, this study encompassed ten major metropolitan areas in South Korea. Using cluster analysis, watersheds were classified into three land-cover types: Urban-dominated (URB), agriculture-dominated (AGR), and forest-dominated (FOR). For each land-cover type, factor analysis (FA) was used to ensure simple and feasible parameter selection for developing the minimum water quality index (WQImin). The chemical oxygen demand, fecal coliform (total coliform for FOR), and total nitrogen (nitrate-nitrogen for URB) were selected as key parameters for all land-cover types. Our results suggest that WQImin can minimize bias in water quality assessment by reducing redundancy among correlated parameters, resulting in better differentiation of pollution levels. Furthermore, the dominant land-cover type of watersheds, not only affects the level and causes of pollution, but also influences temporal patterns, including the long-term trends and seasonality, of stream water quality in urban areas in South Korea.

Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment.

Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality-a case study of Kanavi Halla Sub-Basin, Belagavi, India

Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser-Meyer-Olkin and Bartlett's test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation.

Developmental instability is associated with estrogenic endocrine disruption in the Chilean native fish species, Trichomycterus areolatus

Endocrine disrupting compounds (EDCs) are widespread contaminants that alter the normal functioning of the endocrine system. While they cause dysfunctions in essential biological processes, it is unclear whether EDCs also impact developmental stability. In the present study, we investigated the occurrence of estrogenic endocrine disrupting compounds in a small watershed of south-central Chile impacted by anthropogenic activities. Then, we assessed their relationship with internal levels of estrogenic active compounds and fluctuating asymmetry (FA), a proxy of developmental stability in organisms with bilateral symmetry, in a native fish species (Trichomycterus areolatus). Yeast estrogenic screen assays were performed to measure estrogenic activity in river sediments and in male fish tissues collected from 17 sites along the Chillán watershed, and geometric morphometrics used to estimate fluctuating asymmetry based on the shapes of 248 fish skulls. Estrogenic activity was detected both in sediments and male fish tissues at concentrations of up to 1005 ng and 83 ng 17β-estradiol equivalent/kg dw, respectively. No significant correlation was found between the two. However, fish tissue estrogenicity, water temperature and dissolved oxygen explained >80% of the FA population variation. By showing a significant relationship between estrogenic activity and FA of T. areolatus, our results indicate that developmental stability can be altered by estrogenic endocrine disruption, and that FA can be a useful indicator of sub-lethal stress in T. areolatus populations.

Assessment of the water quality of a subtropical lake using the NSF-WQI and a newly proposed ecosystem specific water quality index

A Water Quality Index (WQI) is a formulation that enables the estimation of the overall quality of a water body based on significant parameters. One example of this is the well-known and widely accepted NSF-WQI, which is frequently used to assess chemical, physical, and microbiologic features of waterbodies in temperate latitudes. In this work, a well-structured method, completely based on multivariate statistical methods and historical data distributions, was used to develop an ecosystem specific water quality index (ES-WQI). Lake Cajititlán, a subtropical Mexican lake located in Tlajomulco de Zúñiga, was selected as a case of study because it is an endorheic shallow lake that shows signs of high levels of eutrophication due to anthropogenic contamination. As a result of the contamination, and its sensibility to changes in the water level, it undergoes important changes in its water features, such as turbidity and intense green color, and experiences massive events of fish mortality. The proposed ES-WQI describes the changes in water quality over the year well and correlates with the capability of the lake to support aquatic life, as the lowest estimated values coincide with the biggest events of massive fish mortality in the lake. Furthermore, the ES-WQI clearly differentiates between typical cyclic behaviors and actual deteriorating trends and is capable of tracking incremental changes all over the range of the possible concentration values of the water quality parameters.

Water quality evaluation of a lacustrine water body in the Mediterranean based on different water quality index (WQI) methodologies

The relative response of seven different Water Quality Indices (WQIs) was comparatively tested in an artificial Mediterranean lake, with the ultimate goal of the study to function as a first step in investigating the applicability of the examined WQIs in Mediterranean lacustrine water bodies. The following WQIs were examined: Prati's Index of Pollution, Bhargava's Index, Oregon WQI, Dinius' Second Index, Weighted Arithmetic Index, Canadian Council of Ministers of the Environment (CCME) WQI and National Sanitation Foundation (NSF) WQI, using physicochemical parameters measured in an artificial lake on a monthly basis from June 2004 to May 2005. The results showed that, on the basis of their comparative response, the examined indices are grouped in three categories. Specifically, NSF and Bhargava indices classified the examined water body in higher quality classes, Prati's and Dinius in medium, whereas CCME and Oregon categorized it in lower quality classes. WQIs were proved to be extremely useful in water quality evaluation. Most of them were found to be adequate to represent water quality conditions and trends in a lacustrine water body in the Mediterranean and, therefore, they present high potential to be adopted at European Union level and assist in Water Framework Directive (WFD) implementation.

Groundwater Quality Assessment for Sustainable Drinking and Irrigation

Identification and management of the groundwater quality are of utmost importance for maintaining freshwater resources in arid and semi-arid areas, which is essential for sustainable development. Based on the quality of the groundwater in various areas, local policymakers and water resource managers can allocate the usage of resources for either drinking or agricultural purposes. This research aims to identify suitable areas of water pumping for drinking and agricultural harvest in the Tabriz aquifer, located in East Azerbaijan province, northwest Iran. A groundwater compatibility study was conducted by analyzing Electrical conductivity (EC), total dissolved solids (TDS), Chloride (Cl), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Sulfate (SO4), Total hardness (TH), Bicarbonate (HCO3), pH, carbonate (CO3), the and Sodium Adsorption Ratio (SAR) obtained from 39 wells in the time period from 2003 to 2014. The Water Quality Index (WQI) and irrigation water quality (IWQ) index are respectively utilized due to their high importance in identifying the quality of water resources for irrigation and drinking purposes. The WQI index zoning for drinking classified water as excellent, good, or poor. The study concludes that most drinking water harvested for urban and rural areas is ‘excellent water’ or ‘good water’. The IWQ index average for the study area is reported to be in the range of 25.9 to 34.55. The results further revealed that about 37 percent (296 km2) of groundwater has high compatibility, and 63 percent of the study area (495 km2) has average compatibility for agricultural purposes. The trend of IWQ and WQI indexes demonstrates that groundwater quality has been declining over time.

Long-Term Water Quality Patterns of a Flow Regulated Tropical Lowland River

Floodplain ecosystems in Africa are under threat due to direct anthropogenic pressure and climate change. The lower Phongolo River and associated floodplain is South Africa’s largest inland floodplain ecosystem and has been regulated by the Pongolapoort Dam since the 1970s. The last controlled flood release from the dam occurred in December 2014, after which a severe drought occurred and only a base flow was released. The central aims of this study were to determine the historic and present water quality state of the middle and lower Phongolo River and assess the possible effects of the most recent drought may have had. Historic water quality data (1970s to present) were obtained from monitoring stations within the Phongolo River catchment to assess the long-term water quality patterns. Using multivariate statistical analyses as well as the Physicochemical Driver Assessment Index (PAI), a water quality index developed for South African riverine ecosystems, various in situ and chemical water variables were analysed. Key findings included that the water quality of the middle and lower Phongolo River has degraded since the 1970s, due to increased salinity and nutrient inputs from surrounding irrigation schemes. The Pongolapoort Dam appears to be trapping nutrient-rich sediments leading to nutrient-depleted water entering the lower Phongolo River. The nutrient levels increase again as the river flows through the downstream floodplain through input from nutrient rich soils and fertilizers. The drought did not have any significant effect on water quality as the PAI remained similar to pre-drought conditions.