Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

Spatial and temporal evaluation of the physicochemical quality of domestic/industrial water in the Kırklareli Reservoir (Turkish Thrace)

The K?rklareli Reservoir, located in the Meri?-Ergene River Basin, is an important drinking/industrial freshwater resource of the K?rklareli Province. In order to ensure the sustainable use of this important reservoir, its current situation should be examined periodically and evaluated by multivariate analyses. For this reason, water samples were taken between the dates April 2018 and February 2019 at monthly intervals from 3 different stations. The data on the environmental and physicochemical variables (water temperature, dissolved oxygen, pH, salinity, conductivity, total dissolved solids, chlorophyll-a, light permeability, fluoride, chloride, NO2-N, NO3-N, PO4, SO4 and essential/potentially toxic elements) were measured and evaluated according to the classes of surface water quality control regulation of Turkey. The parameters exceeding first-class water quality values (chlorophyll-a, pH, NO2-N, chloride, selenium) were mapped in GIS using the spline integration approach. In addition, the sodium absorbtion ratio, Kelly index values and magnesium ratio, were calculated to evaluate the water quality for agricultural irrigation water standards. The water quality of the reservoir was evaluated using multivariance analyses (Bray?Curtis similarity index, correspondence analyses, Pearson correlation index). As a result, it was emphasized that the use of the GIS approach is a potential useful method for monitoring the sustainable water quality of the K?rklareli Reservoir, which was determined to have an oligomesotrophic character.

A workflow for spatio-seasonal hydro-chemical analysis using multivariate statistical techniques

Multivariate statistical techniques are powerful in data interpretation and pattern recognition, which play a vital role in pollutant source identification for water environment management. Despite of their wide application in hydro-chemical analysis, absence of a comprehensive workflow hinders the practices and further studies. The present study constructed a workflow on the application of multivariate statistical techniques in spatio-seasonal hydro-chemical analysis, which provided a basic guidance for practices and a systematic support to future exploration. Selection of the methods and work paths for spatio-seasonal analysis largely depends on the structure of data set and the requirements of specific tasks. Trial and adjustment could be repeatedly performed to optimize the analysis strategy and identify more underlying patterns. Given a multiscale dataset concerning complex spatio-seasonal variations, temporal or spatial grouping using appropriate methods to reasonably divide the complicated data set contributes to data interpretation and pattern recognition. The upper Yangtze River basin (UYRB, China) was employed for case analysis to demonstrate how the workflow guides an efficient and effective data exploration. Efforts could be made in future works to continually improve the workflow to involve more complicated analysis and techniques and the integrated application in various fields.

The Integration of Multivariate Statistical Approaches, Hyperspectral Reflectance, and Data-Driven Modeling for Assessing the Quality and Suitability of Groundwater for Irrigation

Sustainable agriculture in arid regions necessitates that the quality of groundwater be carefully monitored; otherwise, low-quality irrigation water may cause soil degradation and negatively impact crop productivity. This study aimed to evaluate the quality of groundwater samples collected from the wells in the quaternary aquifer, which are located in the Western Desert (WD) and the Central Nile Delta (CND), by integrating a multivariate analysis, proximal remote sensing data, and data-driven modeling (adaptive neuro-fuzzy inference system (ANFIS) and support vector machine regression (SVMR)). Data on the physiochemical parameters were subjected to multivariate analysis to ease the interpretation of groundwater quality. Then, six irrigation water quality indices (IWQIs) were calculated, and the original spectral reflectance (OSR) of groundwater samples were collected in the 302–1148 nm range, with the optimal spectral wavelength intervals corresponding to each of the six IWQIs determined through correlation coefficients (r). Finally, the performance of both the ANFIS and SVMR models for evaluating the IWQIs was investigated based on effective spectral reflectance bands. From the multivariate analysis, it was concluded that the combination of factor analysis and principal component analysis was found to be advantageous to examining and interpreting the behavior of groundwater quality in both regions, as well as predicting the variables that may impact groundwater quality by illuminating the relationship between physiochemical parameters and the factors or components of both analyses. The analysis of the six IWQIs revealed that the majority of groundwater samples from the CND were highly suitable for irrigation purposes, whereas most of the groundwater from the WD can be used with some limitations to avoid salinity and alkalinity issues in the long term. The high r values between the six IWQIs and OSR were located at wavelength intervals of 302–318, 358–900, and 1074–1148 nm, and the peak value of r for these was relatively flat. Finally, the ANFIS and SVMR both obtained satisfactory degrees of model accuracy for evaluating the IWQIs, but the ANFIS model (R2 = 0.74–1.0) was superior to the SVMR (R2 = 0.01–0.88) in both the training and testing series. Finally, the multivariate analysis was able to easily interpret groundwater quality and ground-based remote sensing on the basis of spectral reflectance bands via the ANFIS model, which could be used as a fast and low-cost onsite tool to estimate the IWQIs of groundwater.

Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI_NSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

Monitoring of pre- and post-monsoon groundwater quality of north-eastern Haryana region using GIS

The present paper is the result of an investigation carried out to analyse the quality of sub-surface water in the districts of Yamunanagar and Ambala of the province of Haryana in India. The investigation was necessitated as the area combines the presence of industrial, commercial and residential units close to each other. A total of 30 groundwater samples were taken each during the months of April and September of 2017 and were appraised for analytical parameters, hydro-geochemical constituents and metal ions. Eight locations were observed to have an abnormal presence of only one element and were, therefore, classified to be falling in the 'Grey-Zone'. Matedi Bus Stand (Sample number - 23) was found to be adversely influenced by the presence of the most of chemical elements and thus was categorized as the 'Red Zone'. For the combined data of post-monsoon and pre-monsoon periods of 2017, the spatial distribution of pH, TDS, TA and TH showed that 100%, 90%, 91.67% and 93.33% of samples, respectively, fall under allowable limits of groundwater quality. Spatial distribution of hydro-chemical elements and metal ions showed that 96.11% of samples for cations, 98.33% for anions and 93.33% for Fe are in the 'allowable' category of groundwater. A comparison of laboratory results with the GIS maps prepared during the study has been found to be in good agreement. The classification of samples pursuance to their hydro-chemical facies indicated that most of the samples fall in Ca²⁺-Mg²⁺- H C O 3 - -Cl^- and Na⁺- H C O 3 - -Cl^- type.

Trophic state and limiting nutrient evaluations using trophic state/level index methods: a case study of Borçka Dam Lake

The relationships between nutrients and the trophic state of Borçka dam reservoir in the Çoruh River Basin, which is located in the Eastern Black Sea region of Turkey, were evaluated using the trophic state index (TSI), trophic level index (TLI), and statistical analysis. The samples data were analyzed for chlorophyll-a (Chl-a), total phosphorus (TP), total nitrogen (TN), and secchi disk (SD). In the evaluation, according to the TSI' TLI and Turkish legislation, the reservoir is assessed as mesotrophic in terms of TP, TN data of the water quality. The measurement results of these parameters are higher at the depth points and lower on the surface. The Chl-a parameter is evaluated mesotrophic with 2013 data at the depth, and oligotrophic with 2016 data. The result of TSI (Chl-a) < TSI (SD), TSI (Chl-a) < TSI (TP), and TSI (TN) indicate non-algal turbidity. At the same time, this was verified by calculating the non-algal turbidity coefficient (Kna). It is observed that there is a low correlation between Chl-a and Kna, however a low correlation with TN, and a high correlation with TP. Non-algal particles in the dam lake may occur due to surface runoff and soil erosion from the agricultural area and human settlements. Regression analysis was conducted to determine the relationship between nutrients and Chl-a. The relationship of Chl-a concentration with TN, TP, and TN/TP concentrations is weak. This supports that there are inorganic suspended solids (non-volatile suspended solids) with high underwater light availability.Graphical abstract.

Multivariate assessment of spatial and temporal variations in irrigation water quality in Lake Uluabat watershed of Turkey

Irrigation water quality has important implications on salinity, ion toxicity, production cost, and crop failures. There is a need for a comprehensive analysis of spatial and temporal dynamics in parameters at a watershed scale. This information is critical for irrigation management in agricultural production. The Lake Uluabat watershed is a significant agricultural area of Turkey, which is studied using monitored water data. Multivariate assessment is performed using cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA), and factor analysis (FA) to evaluate temporal and spatial variations in water quality in the watershed. The data is processed by clustering, reducing data dimensionality, delineating indicator parameters, assessing source identification, and evaluating temporal changes and spatial patterns. The results show that the most representative discriminant parameters had more than 90.98% validity in both temporal and spatial analyses. Runoff rate (Q) and water temperature (WT) were identified in the temporal study, while spatial analysis showed bicarbonate (HCO₃^-), sulfate (SO₄^2-), and boron (B³⁺) as indicators. Salinity, sodicity, boron hazard, and alkalinity affect both spatial and temporal water quality patterns in the watershed. It is observed that continued use of poor-quality irrigation water can adversely affect agriculture and soil health in a watershed. Spatio-temporal relationships in parameters will be useful in sustainable irrigation management and farm planning for improving crop productivity and soil health.

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.

Reducing the dimension of water quality parameters in source water: An assessment through multivariate analysis on the data from 441 supply systems

In this research, multivariate statistical analysis was performed on twenty water quality parameters (WQP) collected on tri-monthly basis (four times/year) from 441 drinking water sources in Newfoundland and Labrador (NL), Canada for 18 years (1999-2016). The WQP included alkalinity (Alk), color (Col), conductivity (Cond), hardness (Hard), pH, total dissolved solids (TDS), turbidity (Turb), bromide (Br), calcium (Ca), chloride (Cl), fluoride (F), potassium (K), sodium (Na), sulfate (SO₄), dissolved organic carbon (DOC), ammonia (NH₃), nitrate (NO₃), Kjeldahl nitrogen (N), total phosphorus (P) and magnesium (Mg). The assessment was conducted on surface water (SWS) and groundwater (GWS) sources separately. In SWS and GWS, number of samples analyzed for each WQP were in the ranges of 3434-6057 and 1915-1919 respectively. Averages of DOC and pH showed increasing trends (SWS: DOC = 0.0722 mg/L/year; pH = 0.0375 units/year; GWS: DOC = 0.0491 mg/L/year; pH = 0.0441 units/year) while the other WQP showed variable characteristics, which could increase treatment cost and deteriorate tap water quality. Strong correlations were observed for Ca-Hard (r = 0.97-0.98), TDS-Cond (r = 0.91-0.99) and Na-Cl (r = 0.87-0.96). In SWS, Alk had stronger correlations with Cond, Hard, pH, TDS, Ca and Mg (r = 0.62-0.94) than GWS (r = 0.56-0.63). Principal Component Analysis revealed separate clusters for DOC-Col, Na-Cl, TDS-Cond, Ca-Alk and Mg-Hard, indicating that these WQP moved together. In SWS and GWS, six principal components were significant (eigenvalue ≥ 1.0), and explained 74.8% and 72.9% of overall variances respectively. In Factor Analysis, six varifactors explained 73.4% and 70.5% of total variances in SWS and GWS respectively. For SWS and GWS, eleven and ten WQP, respectively explained these variances, indicating 45% and 50% data reduction respectively. The findings can assist in controlling water quality through monitoring reduced number of WQP, which is likely to minimize the monitoring cost.

Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques

In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005-2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (-0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO₃, Mg, NO₂ + NO₃, SiO₂ and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO₄ are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO₂ in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin.

Use of water quality index and multivariate statistical methods for the evaluation of water quality of a stream affected by multiple stressors: A case study

The Sürgü Stream, located in the Euphrates River basin of Turkey, is used for drinking water source, agricultural irrigation and rainbow trout production. Therefore, water quality of the stream is of great importance. In this study, multivariate statistical techniques (MSTs) and water quality index (WQI) were applied to assess water quality of the stream affected by multiple stressors such as untreated domestic sewage, effluents from fish farms, agricultural runoff and streambank erosion. For this, 16 water quality parameters at five sites along the stream were monitored monthly during one year. Most of parameters showed significant spatial variations, indicating the influence of anthropogenic activities. All parameters except TN (total nitrogen) showed significant seasonal differences due to high seasonality in WT (water temperature) and water flow. The spatial variations in the WQI were significant (p < 0.05) and the mean WQI values ranged from 87.6 to 95.3, indicating "good" to "excellent" water quality in the stream. Cluster analysis classified five sites into three groups, that is, clean region, low polluted region and very clean region. Stepwise temporal discriminant analysis (DA) identified that pH, WT, Cl^-, SO₄^2-, COD (chemical oxygen demand), TSS (total suspended solids) and Ca²⁺ are the parameters responsible for variations between seasons, and stepwise spatial DA identified that DO (dissolved oxygen), EC (electrical conductivity), NH₄-N, TN (total nitrogen) and TSS are the parameters responsible for variations between the regions. Principal component analysis/factor analysis revealed that the parameters responsible for water quality variations were mainly associated with suspended solids (both natural and anthropogenic), soluble salts (natural) and nutrients and organic matter (anthropogenic).

Hydrogeochemical Characteristics and Groundwater Quality Evaluation Based on Multivariate Statistical Analysis

Hydrogeochemical research and water quality evaluation are an important part of groundwater development and management projects in Dehui City, Jilin Province, China. We collected 217 groundwater samples in the study area and used two multivariate statistical methods, hierarchical cluster analysis and principal component analysis to classify groundwater; combined graphical method, piper diagram, and Gibbs diagram to characterize groundwater chemical types and distinguish the water chemical control mechanism; and fuzzy comprehensive evaluation method to evaluate groundwater quality. Three major categories have been identified. Most of the groundwater in the study area is Ca-HCO3 type water. The water chemistry control mechanism is determined to be based on water-rock interaction and less evaporation. From east to west in the study area, the total dissolved solids (TDS) gradually increased, and water quality gradually deteriorated. In the whole region, 79.26% of the groundwater is suitable for drinking. With Yinma River at the boundary, the water quality in the eastern part is excellent, while that in the southwest is poor. After appropriate treatment, it can be used in industry and agriculture. The excess NO3− is mainly affected by human activities. The unique geological conditions of the Songnen Plain result in an excess amount of Fe3+ and Mn2+ in some areas. This study determined the chemical characteristics of groundwater in the study area and distinguished water quality levels. The results will be helpful for the development and management of groundwater resources.

Chemometrics for environmental monitoring: a review

Environmental monitoring is necessary to ensure the overall health and conservation of an ecosystem. However, ecosystems (e.g. air, water, soil), are complex, involving numerous processes (both native and external), inputs, contaminants, and living organisms. As such, monitoring an environmental system is not a trivial task. The data obtained from natural systems is often multifaceted and convoluted, as a multitude of inputs can be intertwined within the matrix of the information obtained as part of a study. This means that trends and important results can be easily overlooked by conventional and single dimensional data analysis protocols. Recently, chemometric methods have emerged as a powerful method for maximizing the details contained within a chemical data set. Specifically, chemometrics refers to the use of mathematical and statistical analysis methods to evaluate chemical data, beyond univariant analysis. This type of analysis can provide a quantitative description of environmental measurements, while also having the capacity to reveal previously overlooked trends in data sets. Applying chemometrics to environmental data allows us to identify and describe the inter-relationship of environmental drivers, sources of contamination, and their potential impact upon the environment. This review aims to provide a detailed understanding of chemometric techniques, how they are currently used in environmental monitoring, and how these techniques can be used to improve current practices. An enhanced ability to monitor environmental conditions and to predict trends would be greatly beneficial to government and research agencies in their ability to develop environmental policies and analytical procedures.

Hydrogeochemical and statistical analysis of high fluoride groundwater in northern China

Understanding the formation of high fluoride (F^-) groundwater in water-scarce northern China is critical for the sustainable development of the region. This study investigates the effects of F^- enrichment in groundwater from seven typical regions of northern China, including Datong, Guide, Junggar, Yinchuan, Taiyuan, and Tarim basins and the North China Plain. A literature survey of 534 samples of selected regions showed that 45.13% of groundwater F^- exceeded the 1.0 mg/L of Chinese drinking water guideline. Based on the geological background and hydrogeochemical analysis, in Datong and Yinchuan basins and part of the North China Plain, the main types of groundwater are soda water and controlling processes of F^- enrichment are salinization, mineral dissolution, and desorption. In Taiyuan and Guide basins with Cl-Na water type, F^- enrichment is mainly affected by salinization, cation exchange, and evaporation. The hydrogeochemical characteristics of high F^- groundwater in Tarim and Junggar basins reflect the extent of salinization and weathering dissolution of minerals in groundwater. According to PCA, the contribution of salinization and mineral dissolution to F^- enrichment is relatively high. Under the alkaline condition, groundwater with high Cl^-, HCO₃^-, and Na⁺ concentration favors F^- enrichment. Based on HCA, index clustering category I explains the influence of pH and buried depth on F^- enrichment, and category II explains the effect of different ions. It is concluded that F^- enrichment in groundwater is related to hydrogeochemical processes and hydrogeological conditions. The hydrogeochemical and alkaline conditions of groundwater are regulated by mineral dissolution, ion exchange, and evaporation, resulting in different degrees of F^- enrichment.

An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria)

In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of "Wadi Rhumel," and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is "Wadi Rhumel." Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam's water quality.

Multivariate Statistical Approach to Study Spatiotemporal Variations in Water Quality of aHimalayan Urban Fresh Water Lake

Physicochemical parameters determining Dal Lake water quality were evaluated at four different sites during 2016–2017 in four different seasons Spring (April), Summer (July), Autumn (October), and Winter (January). The observed physicochemical values were analyzed by statistical (discriminant analysis) and arithmetic (WQI) methods to ascertain sources and levels of pollution. Discriminant analysis helped to access the contribution of each physicochemical parameter in water quality in the context of sampling sites (spatial) and seasons (temporal) to discriminate pollution loading between sites and as well as seasons. Factors such as temperature, alkalinity, ammoniacal nitrogen, total phosphorous, and orthophosphorous exhibited a strong contribution in the discrimination of sampling sites, while factors such as temperature, alkalinity, hardness, BOD, nitrate nitrogen, and total phosphorous exhibited a strong contribution in the discrimination of sampling seasons. The WQI values for four sampling sites were calculated and indicated that the water at Site I was the most contaminated followed by Site IV, while Site III was the least contaminated. Thus, highlighting that the pressure of anthropogenic activities is subjecting Dal Lake to an unnatural death.

A spatiotemporal analysis of water quality characteristics in the Klip river catchment, South Africa

Understanding the spatial and temporal patterns of water quality is central to its management as it provides information essential to the restoration as well as protection of water resources. The main objectives of this study were (i) to analyze the spatial and temporal trends of water quality and (ii) to identify the critical sources of pollution in the Klip River catchment (KRC). Water samples were collected at 12 sampling points along the Klip River, monthly from February 2016 to January 2017 and analyzed using inductively coupled plasma mass spectrometry (ICP-MS) and spectrophotometry for heavy metals and nutrients, respectively. Multivariate statistical techniques (cluster analysis and discriminant analysis) were used to delineate homogeneous water quality zones and seasons, and principal component analysis was used to identify pollution sources. Comprehensive pollution index (CPI) was also computed to classify the overall pollution of the river. The spatial grouping yielded two homogenous water quality zones namely upstream and downstream. Temporal grouping yielded two clusters, which were attributed to the effects of the El Nino (2015/16 season) and La Nina phenomena (2016/17 season). The CPI revealed that the KRC was critically polluted in the upstream for domestic (162.16-323.28) and aquatic uses (617.70-837.09) in both the 2015/2016 and 2016/2017 seasons. It can be concluded that pollutants, which influence water quality in the KRC in one season and/or location, may not necessarily be the same in the other season or location. Therefore, there is need to develop a water quality management plan in the KRC that targets the most impaired uses, pollutants of priority, and the critically polluted areas.

Residential radon exposure and seasonal variation in the countryside of southeastern Brazil

Poorly ventilated environments such as residences can accumulate radon gas to levels that are harmful to humans and thus produce a public health risk. To assess the risk from natural radiation due to indoor radon exposure, ²²²Rn measurements, using an alpha RAD7 detector, were conducted in Timóteo, Minas Gerais state, southeastern Brazil. Indoor radon concentrations, along with meteorological parameters, were measured every 2 h during both wet and dry seasons in 2017 and 2018. The mean concentration of indoor radon varied between 18.0 and 412.8 Bq m^-3, which corresponded to an effective annual dose of 1.2 and 7.6 mSv y^-1. Average radon concentrations were significantly higher during the winter dry season, and there was a strong positive correlation with humidity in both wet and dry season. Furthermore, concentrations showed an inverse correlation with atmospheric pressure, wind speed, air temperature, and solar radiation. The radon levels are generally above the limits recommended by international standards, meaning that mitigation measures are needed to improve air quality to reduce human exposure and risk. Finally, through the statistical analysis, it was possible to determine the differences and similarities between the sampling points concerning the geology of the place and the geographical location.

Combining punctual and high frequency data for the spatiotemporal assessment of main geochemical processes and dissolved exports in an urban river catchment

The assessment of dissolved loadings and the sources of these elements in urban catchments' rivers is usually measured by punctual sampling or through high frequency sensors. Nevertheless, the combination of both methodologies has been less common even though the information they give is complementary. Major ion (Ca²⁺, Mg²⁺, Na⁺, K⁺, Cl^-, SO₄^2-, and alkalinity), organic matter (expressed as Dissolved Organic Carbon, DOC), and nutrients (NO₃^-, and PO₄^3-) are punctually measured in the Deba river urban catchment (538 km²), in the northern part of the Iberian Peninsula (draining to the Bay of Biscay). Discharge, precipitation, and Electrical Conductivity (EC) are registered with a high frequency (10 min) in three gauging stations. The combination of both methodologies has allowed the assessment of major geochemical processes and the extent of impact of anthropogenic input on major composition of riverine water, as well as its spatial and temporal evolution. Three methodologies for loading estimation have been assessed and the error committed in the temporal aggregation is quantified. Results have shown that, even though carbonates dominate the draining area, the water major ion chemistry is governed by an evaporitic spring in the upper part of the catchment, while anthropogenic input is specially noted downstream of three wastewater treatment plants, in all nutrients and organic matter. The results of the present work illustrate how the combination of two monitoring methodologies allows for a better assessment of the spatial and temporal evolution on the major water quality in an urban catchment.

Assessment of surface water quality status of the Aby Lagoon System in the Western Region of Ghana

The pollution status of the Tendo Lagoon, the upstream section of the Aby Lagoon System, was investigated. The water temperature, pH, turbidity, dissolved oxygen (DO), electrical conductivity (EC), nitrates (NO₃^-), and phosphate (PO₄^3-) levels were evaluated using samples taken during the wet and dry seasons to assess the variabilities in water quality in the area. The water quality data was subjected to paired t-test, One-way ANOVA, Factor Analysis, Cluster analysis (CA) as well as a Water Quality Index (WQI) evaluation using the Canadian Council of Ministers of the Environment (CCME) model. The paired sample t-test confirmed that the surface water quality varies significantly between the wet and dry season samples (p < 0.05) except for phosphate loads which may be contributed largely by year-round municipal waste discharges. The results of the ANOVA showed that the variation of the water quality parameters among sampling stations was not statistically significant except for turbidity which was relatively higher in the upstream sections where the Tano River enters the lagoon. The results of FA indicated that three significant factors–relating to the degradation of organic materials, suspended solids (turbidity) and nutrients- accounted for 73.65% of spatio-temporal variations in the water quality. The CA showed that the eight sampling stations can be grouped into four distinct clusters based on their water quality. The source of pollution in the demarcated sections of the Aby Lagoon was concluded to be largely due to the result of localized anthropogenic inputs of domestic waste and sediments carried from the upstream in the Tano River. An evaluation of the CCME WQI for the sampling stations revealed that all the sampling stations showed marginal water quality relative to the target water quality range recommended by the Water Resources Commission (WRC) of Ghana for domestic use and protection of aquatic life. Monitoring programs were recommended with effective management measures instituted and implemented for the sustainability of the lagoon and the Tano River Basin.

Evaluation of groundwater quality and human health risks from fluoride and nitrate in semi-arid region of northern India

Groundwater quality in the alluvial plains of Punjab has special significance and needs great attention since it is the foremost source of drinking, irrigation and industrial uses. The present research work emphasizes the integrated hydrogeochemical and chemometric statistical approaches to appraise the geochemical processes and source apportionment of the groundwater in the alluvial plains of Jalandhar district, Punjab, India. The human health risk assessment was also performed to quantify the potential non-carcinogenic impacts of nitrate and fluoride on human health through ingestion of groundwater. For this purpose, 41 groundwater samples were collected from different groundwater abstraction units and analysed for pH, electrical conductivity, total dissolved solids, total hardness, total alkalinity and major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, CO₃^2-, SO₄^2-, NO₃^-, F^-, Cl^- and PO₄^3-) using standard protocols. Drinking water quality index and Revelle index showed that groundwater samples fall under poor to unfit water class and salinization along the south-western portion of the study region shows poor water quality. The results of the hazard index (HI_ingestion) show 68% and 46.34% of the groundwater samples have HI > 1 for children and adults. The non-carcinogenic health risk assessment of nitrate (NO₃^-) and fluoride (F^-) on the local population indicated that the children are more vulnerable through direct ingestion of drinking water than adults. Piper diagram and saturation index reveal that Ca²⁺-Mg²⁺-HCO₃^- is the dominant hydrochemical facies and oversaturated with calcite, dolomite and aragonite minerals in the groundwater. Gibbs diagrams, chloro-alkaline indices and scatter plots show that the hydrochemistry of the groundwater is mainly governed by aquifer material interaction such as weathering of silicate, carbonate rock, halite dissolution and cation exchange process. Chemometric statistical techniques revealed that the source identification of parameters such as Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, CO₃^- and F^- is originated from geogenic factors, whereas NO₃^-, SO₄^2-, Cl^- and PO₄^3- are from the anthropogenic origin. Therefore, urgent and efficient measures must be taken to combat groundwater pollution and reduce human health risk in the study area.

Evaluation of water quality in the South-to-North Water Diversion Project of China using the water quality index (WQI) method

The world's longest trans-basin water diversion project, the Middle-Route (MR) of the South-to-North Water Diversion Project of China (SNWDPC), has officially been in operation for over 5 years since December 2014. Its water quality status has always attracted special attention because it is related to the health and safety of more than 58 million people and the integrity of an ecosystem covering more than 155,000 km². This study presented and analysed the spatio-temporal variations and trends of 16 water quality parameters, including pH, water temperature (WT), dissolved oxygen (DO), permanganate index (PI), five-day biochemical oxygen demand (BOD₅), fecal coliform (F. coli), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH₃^-N), sulphate (SO₄^2-), fluoride (F^-), mercury (Hg), arsenic (As), selenium (Se), copper (Cu), and zinc (Zn), which were determined monthly from samples collected at 27 water quality monitoring stations in the MR of the SNWDPC from March 2016 to February 2019. The water quality index (WQI) was used to evaluate the seasonal and spatial water quality changes during the monitoring period, and a new WQI_min model consisting of five crucial parameters, i.e., TP, F. coli, Hg, WT, and DO, was built by using stepwise multiple linear regression analysis. The results demonstrated that the water quality status of the MR of the SNWDPC has been steadily maintained at an "excellent" level during the monitoring period, with an overall average WQI value of 90.39 and twelve seasonal mean WQI values ranging from 87.67 to 91.82. The proposed WQI_min model that uses the selected five key parameters and the weights of those parameters has exhibited excellent performance in the water quality assessment of the project, with the coefficient of determination (R²), Root Mean Square Error (RMSE), and Percentage Error (PE) values of 0.901, 2.21, 1.93%, respectively, showing that the proposed WQI_min model is a useful and efficient tool to evaluate and manage the water quality. For the management department, the risk sources near certain stations with abnormally high values should be carefully inspected and strictly managed to maintain excellent water quality. The potential risks of algae proliferation in this project should be of concern in future research.

Pollution status of the Yellow River tributaries in middle and lower reaches

To determine the water quality status of the primary tributaries in middle and lower reaches of the Yellow River Basin, water collected from the confluence of the ten tributaries and some physical, chemical and biological parameters were analyzed, and then water quality index and health risk were evaluated. Of the ten main tributaries in the middle and lower reaches, only the Qingshui River had water of medium quality in the upper reaches, while all the other tributaries contributed water of poor quality. The Jindi and Dawen rivers in the lower reaches had the poorest water quality, especially the Jindi River. TP, TN, BOD₅, COD, TOC and coliform bacteria exceeded the national criteria by 155%, 1%, 97.5%, 35.5%, 114.2%, and 80%, respectively. Cluster analysis indicated that industrial, agricultural, and domestic sewage, along with industrial waste gas, were the main sources of pollution in these tributaries. An analysis of the bacterial community structure showed that the Jindi River was the most polluted and had the largest species diversity and richness of bacteria. Also, its number of pathogenic microorganisms was much higher than that of other areas, and the bacterial functional genes of related metabolic pathways were significantly enriched. This was in sharp contrast with that of the Qingshui River, which had the best water quality. We suggest more specifics policy should be taken for different tributaries, and poor water quality of Jindi and Dawen River should be further studied to explore the most suitable pollution control methods.

Bacterial community composition and potential pathogens along the Pinheiros River in the southeast of Brazil

The Pinheiros River in São Paulo, Brazil, crosses through the capital city and has its confluence with the River Tiete, which comprises several reservoirs along its course. Although Pinheiros River is considered one of the heaviest polluted rivers in Brazil, little is known about its bacterial composition, their metabolic functions or how these communities are affected by the physicochemical parameters of the river. In this study, we used the 16S rRNA gene Illumina MiSeq sequencing to profile the bacterial community from the water surface at 11 points along the course of the River. Taxonomical composition revealed an abundance of Proteobacteria phyla, followed by Firmicutes and Bacteroidetes, with a total of 233 classified bacterial families and 558 known bacterial genera. Among the 35 potentially pathogenic bacteria identified, Arcobacter was the most predominant genus. The disrupted physicochemical parameters detected in this study may possibly contribute to the composition and distribution of the bacterial community in the Pinheiros River. Predictive functional analysis suggests the River is abundant in motility genes, including bacterial chemotaxis and flagellar assembly. These results provide novel and detailed insights into the bacterial communities and putative function of the surface water in the Pinheiros River.

Influence of Land Use/Land Cover on Surface-Water Quality of Santa Lucía River, Uruguay

Land use/land cover is one of the critical factors that affects surface-water quality at catchment scale. Effective mitigation strategies require an in-depth understanding of the leading causes of water pollution to improve community well-being and ecosystem health. The main aim of this study is to assess the relationship between land use/land cover and biophysical and chemical water-quality parameters in the Santa Lucía catchment (Uruguay, South America). The Santa Lucía river is the primary potable source of the country and, in the last few years, has had eutrophication issues. Several multivariate statistical analyses were adopted to accomplish the specific objectives of this study. The principal component analysis (PCA), coupled with k-means cluster analysis (CA), helped to identify a seasonal variation (fall/winter and spring/summer) of the water quality. The hierarchical cluster analysis (HCA) allowed one to classify the water-quality monitoring stations in three groups in the fall/winter season. The factor analysis (FA) with a rotation of the axis (varimax) was adopted to identify the most significant water-quality variables of the system (turbidity and flow). Finally, another PCA was run to link water-quality variables to the dominant land uses of the watershed. Strong correlations between TP and agriculture-land use, TP and livestock farming, NT and urban areas arose. It was found that these multivariate exploratory tools can provide a proper overview of the water-quality behavior in space and time and the correlations between water-quality variables and land use.

Decrease of both river flow and quality aggravates water crisis in North China: a typical example of the upper Yongding River watershed

Due to unevenly distributed water resources, semi-arid regions are particularly prone to severe water shortage and quality degradation. In this study, based on long-term hydrological database (1935-2015), and the latest available water quality data sets (2011-2016), we analyzed the water crisis and its driving forces in the upper Yongding River watershed, a typical water shortage area in North China. The results showed that human induced excessive water consumption is responsible for the significantly decreased river flow over the past eight decades. Although the capacity of the watershed wastewater treatment has improved, current water quality does not meet the requirements of the national water management goals, because of the excessive nitrogen and COD_Cr (chemical oxygen demand), which mainly come from the wastewater and feedlots discharge. Due to the decreased river flow, current Yongding River is unable to dilute and assimilate pollutions. The analysis of river pollutant load illustrated that more than 60 % of the nitrogen in the river water system is diverted for reservoir storage, and more than 50 % of the COD_Cr and TP are diverted for irrigation, thereby, increasing the risk of reservoirs eutrophication and threatening food safety. Besides, the high Cl^- (388.2 ± 322.5 mg/L) and SO₄^2- (470.6 ± 357.7 mg/L) imply that the upper river water are not suitable for drinking and irrigation purposes, and a potential risk of salinization if the river flow continues to decrease. We conclude that water resources over extraction and quality degradation are the main driving factors of the Yongding River water crisis.

Assessment of nutrient contamination in the waters of the El Fuerte River, southern Gulf of California, Mexico

This study assessed the spatial and temporal nutrient variability in the El Fuerte River basin in northwestern Mexico, considering its effects on the water trophic status as well as the nutrient loading to the Gulf of California. Physicochemical parameters, inorganic species of nitrogen, phosphate, total nitrogen, total phosphorus, and chlorophyll-a (chl-a) were quantified at 16 selected sites along the river in April (dry season) and October (rainy season) 2017. Mean concentrations of nutrients during dry and rainy seasons were 36.3 ± 24.1 and 55.1 ± 74.6 μg L^-1 of total ammonia nitrogen, 3.4 ± 3.6 and 4.5 ± 3.5 μg L^-1 of NO₂^--N, 190.8 ± 256.0 and 163.6 ± 261.0 μg L^-1 of NO₃^--N, 42.4 ± 44.2 and 104.9 ± 76.2 μg L^-1 of PO₄^3--P, 1.0 ± 1.3 and 691 ± 2242 mg L^-1 of TN, 0.06 ± 0.06 and 0.08 ± 0.09 mg L^-1 of TP, and 0.9 ± 0.6 and 2.0 ± 0.9 μg L^-1 of chl-a with significant differences (p < 0.05) between sites and seasons. When waters are transported downstream, nutrient levels are enriched by 4 to 35 times compared to those upstream due to increased population and agriculture downstream, confirming the hypothesis of the study. The calculated TN and TP fluxes were 1.23 × 10⁴ and 3.57 × 10¹ ton year^-1, respectively. Factor analysis indicated that inorganic nitrogen species and phosphorus are the main factors affecting the river water quality. Despite N excess during the rainy season, the river reached mesotrophic waters due to phosphorus limitation. This suggests the need to establish a water quality monitoring program to understand the vulnerability of the river course to changes in its trophic state.

Spatial distribution of heavy metals in the West Dongting Lake floodplain, China

The protection of Dongting Lake is important because it is an overwintering and migration route for many rare and endangered birds of East Asia and Australasia, but an assessment of heavy metal contamination in West Dongting Lake is lacking. A total of 75 sediment samples (five sites × three sediment depths) were collected in West Dongting Lake in January 2017 to assess the spatial distribution and ecological risk of heavy metals in West Dongting Lake. Heavy metal values varied by sediment depth including As, Cd, Zn, and Cu, with the depth giving an indication of recent vs. historical deposition. The major input of Hg, Cu, and Ni may come from continued anthropogenic activities related to regional industrial activities within the Yuan River and Li River, whereas the major sources of Cd pollution may be from agricultural fertilizers.

Assessment of spatial and temporal variations in water quality by the application of multivariate statistical methods in the Kali River, Uttar Pradesh, India

The Kali River is a significant source of surface water as well as the main tributary of River Hindon that flows through major cities of western Uttar Pradesh, India. It flows throughout the urban and industrial regions; hence, it carries various amounts of pollutant. Therefore, a study was conducted to examine spatial-temporal variations in river water quality by determining physicochemical variables and heavy metal concentrations at seventeen sampling stations (S₁-S₁₇) throughout the river stretch. Various physicochemical variables, namely pH, EC, TDS, turbidity, BOD, COD, TH, TA, Ca, Mg, Na, K, HCO₃^-, Cl^-, SO₄^2-, NO₃^-, and PO₄^3- were higher in summer than in winter. The order of mean metal concentrations was Fe > Pb > Mn > Ni > Zn > Cu > Cr > Cd. The relationships among measured physicochemical variables and pollution index were examined. Furthermore, multivariate statistical methods were used to assess spatial-temporal variation in water quality to identify current pollution sources and validate results. Water quality index and comprehensive pollution index indicated that the Kali River was less polluted from S₁ to S₈. However, downstream sampling sites were polluted. Pollution starts from S₉ and drastically increases at and beyond S₁₃ because of effluents from industries and sugar mills in Muzaffarnagar. The study suggests cleaning the downstream region of river to restore human health and flora and fauna in the river ecosystem.

Monitoring Novel Corona Virus (COVID-19) Infections in India by Cluster Analysis

It is a great challenge of identification as well as formation of groups of infectious disease data set. Data mining, a process of uncovering silent characteristics of big data is one of such techniques which have nowadays become more popular for treating massive volume of infectious disease data set. In the current study, we apply cluster analysis, one of the data mining techniques to classify real groups of infectious disease "novel corona virus disease (COVID-19)" data set of different states and union territories (UTs) in India according to their high similarity to each other. The results obtained permit us to have a sense of clusters of affected Indian states and UTs. The main objective of clustering in this study is to optimize monitoring techniques in affected states and UTs in India which will be very valuable to the government, doctors, the police and others involved in understanding seriousness of the spread of novel coronavirus (COVID-19) to improve government policies, decisions, medical facilities (ventilators, testing kits, masks etc.), treatment etc. to reduce number of infected and deceased persons.

Spatio-temporal variability and pollution sources identification of the surface sediments of Shatt Al-Arab River, Southern Iraq

Water draining from heavily industrialized basins introduces significant amounts of pollutants to the rivers water and sediments. Heavy industrial activities in the Shatt Al-Arab basin result in increased pollutant loads to the river's surface sediments. Therefore, it becomes crucial to investigate the influence of anthropogenic activities on both spatial and temporal scales. This study unfolded the extent, sources, and distributions of heavy metals pollution in the sediments of the Shatt Al-Arab River. Extensive samplings were performed during the dry and the wet seasons at 25 stations along the river course for the analysis of 11 heavy metals. The analysis revealed high pollution levels in the river sediments compared to both their historical values and international standards. Statistical analysis techniques such as Principal Component Analysis (PCA) and Factor Analysis (FA) were applied. Statistical analysis showed that all the elements were well represented by four varifactors that explained a cumulative total variance of 74%. PCA/FA indicated that most investigated metals were of anthropogenic origins (i.e., industrial, residential, and agricultural sources). Pollution indices that were applied, such as Contamination Factor (CF) and Nemerow Pollution index (PN), indicated that sediments were: (i) considerably contaminated with Fe and Mo (ii) moderately contaminated with Cr, Zn, Ni, Cu, Pb and Mn and (iii) not contaminated with Co and V. The PN values indicated serious pollution in the river sediments in all sites, even though the pollution was not evenly distributed, i.e., the upstream reaches of the river were more polluted compared to the downstream parts. In contrast to many studies that have reported changes in heavy metals concentrations due to seasonal variations, our data showed no significant relationship between metals concentrations and seasonality. This study addresses several of the major limitations of the current knowledge on this river's pollution sources and analysis, such as the limited number of analyzed pollutants and restricted samplings in the current literature. The findings necessitate the implementation of effective management strategies to control pollution in the river basin.

The Evolution and Sources of Major Ions in Hot Springs in the Triassic Carbonates of Chongqing, China

Thermal groundwater in the Lower and Middle Triassic carbonates in Chongqing, China, is mainly concentrated in anticlines. Hot springs (32.9 to 57 °C) with SO4-Ca type waters and Total Dissolved Solids (TDS) of 1620 to 2929 mg/L emerge in the middle and the plunging ends of the structures. Multivariate methods are used to analyze the hydrochemical characteristics of the waters, and identify the sources of the main dissolved components, providing an insight into the evolution of the environment in which they formed. Hierarchical cluster analysis of compositional data differentiates samples in the study area into three categories: high TDS-high Ca2+ and SO42− water; medium TDS-high Na+ and Cl− water; and low TDS-high HCO3− water. Factor analysis and ion ratio relationships show that Ca2+ and SO42− are mainly derived from the dissolution of gypsum and anhydrite within the geothermal reservoir, with some addition of SO42− from coal-bearing cap rocks. The main source of HCO3−, is in the dissolution of dolomite and CO2 that also promotes the incongruent dissolution of albite and K-feldspar, adding Na+ and K+ to the groundwater. Reverse modelling of the transfers of each phase shows, in three models, that the minerals dissolved decrease progressively—with the exception of halite and albite. Combined with the hydrochemical characteristics of hot water in the same reservoir in the adjacent area (Cl-Na type, TDS of 13.37 g/L), a process of desalination of the hot water can be confirmed, which has not yet reached the ‘freshwater’ stage dominated by HCO3−.

Spatial Forecasting of Dissolved Oxygen Concentration in the Eastern Black Sea Basin, Turkey

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching–learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.

Deterministic and probabilistic health risk assessment techniques to evaluate non-carcinogenic human health risk (NHHR) due to fluoride and nitrate in groundwater of Panipat, Haryana, India

Human interferences have caused groundwater contamination in alluvial aquifers which subsequently affects the health of exposed population. In the present study, 74 groundwater samples from the semi-arid region of Panipat district, falling under Yamuna sub-basin, India was evaluated to know the potential non-carcinogenic human health risk in local adult and child population. The major objective of the present study was to know the non-carcinogenic human health risk due to intake of fluoride and nitrate contaminated water, using two different approaches: deterministic and probabilistic (Monte Carlo simulation). The values of hazard quotient (HQ) determined by deterministic as well as probabilistic approach were nearly identical. The hazard index (HI) value of 40.8% samples was above the unity in case of adults while 69.7% samples indicated HI value greater than unity for children thus indicating children are more prone to non-carcinogenic health risk than the adult population. Sensitivity analysis was performed to identify the influence of the non-carcinogenic human health risk predictor variables for the prediction of risk and concentration factor (CF) was the most influential variable. Multivariate statistical techniques were employed to know the positive and negative relationship of fluoride and nitrate with other parameters. Results of principal component analysis/factor analysis (PCA/FA) indicated that the concentration of fluoride is controlled by the presence of calcium due to their negative correlation in groundwater samples. The hierarchical agglomerative cluster analysis (HCA) also supported the outcome of PCA/FA and both indicated anthropogenic sources of fluoride and nitrate in groundwater.

A new indexing approach for evaluating heavy metal contamination in groundwater

Three indexing methods, namely heavy metal pollution index (HPI), contamination index (C_d) and heavy metal evaluation index (HEI), are commonly used for heavy metal evaluation in groundwater. These methods have several limitations. In HPI, 14 out of 15 groundwater samples collected in the study area of Nalagarh valley, Himachal Pradesh, India qualify for drinking purposes with their values varying between 10.73 and 107.50 (critical limit = 100), while in C_d, the same number of samples (>90%) are rejected as their values (C_d = 1.31-37.87) exceed the critical limit of 3. HEI varies from 10.31 to 46.87 with a mean of 26.06, but since it does not have a defined critical limit, quality assessment depends on worker's discretion. It thus becomes very confusing as to which indexing method to use. To overcome this dilemma, a very simple indexing method called 'heavy metal contamination index (HCI)' has been developed on the basis of assigning weight to each heavy metal parameter. A new classification system with six distinct water classes of different uses too has been proposed considering the regulatory limits, human health risk and toxicity of the violator parameters. Regression analysis confirms that HCI has larger number of significantly correlated key parameters compared to the other three indices. Chemometric techniques confirm that Cr, Cu, Fe, Mn and Zn are derived from lithogenic inputs and As, Cd, Ni and Pb from anthropogenic sources. HCI when integrated with Cluster Analysis gives the best possible results in identifying factors that influence the various water classes.

Spatio-temporal changes in surface water quality and sediment phosphorus content of a large reservoir in Turkey

The Keban Dam Reservoir, located on the Euphrates River, is the second largest reservoir of Turkey. Water quality of this reservoir is of great importance because it is widely used for recreation, aquaculture production, fishing, and irrigation. In this study, discriminant analysis, principal component analysis (PCA), factor analysis (FA) and cluster analysis (CA) were conducted to evaluate the seasonal and spatial variations in surface water quality of the reservoir. Also, total phosphorus (TP) content in sediments, water type and trophic status of the reservoir were determined. For this, 19 water quality variables and TP in sediments were monitored seasonally at 11 sampling stations on the reservoir during one year. Hierarchical CA classified 11 stations into three groups, i.e., upstream (moderate polluted), midstream (low polluted) and downstream (clean) regions. PCA/FA allowed to group the variables responsible for variations in water quality, which are mainly related to mineral dissolution (natural), organic matter and nutrients (anthropogenic), and physical parameters (natural). Discriminant analysis (DA) gave better results for both data reduction and spatio-temporal analysis. Stepwise temporal DA identified eight variables: water temperature (WT), chemical oxygen demand (COD), nitrate nitrogen (NO₃-N), soluble reactive phosphorus (SRP), chlorophyll-a (Chl-a), potassium (K⁺), magnesium (Mg²⁺), and calcium (Ca²⁺), which are the most significant variables responsible for temporal variations in water quality of the reservoir, while stepwise spatial DA identified three variables: K⁺, chloride (Cl^-), and sulphate (SO₄^-2), which are the most significant variables responsible for spatial variations. According to Ontario sediment-quality guidelines, sediments of the reservoir can be considered as unpolluted in terms of mean TP content. The water type of the reservoir was calcium-bicarbonate. According to trophic state index values based on TP and Chl-a, upstream region (moderate polluted) of the reservoir was in the eutrophic status, whereas other regions were in the mesotrophic status.

Eutrophication and heavy metal pollution patterns in the water suppling lakes of China's south-to-north water diversion project

This study used non-supervised machine learning self-organizing maps (SOM) in conjunction with traditional multivariate statistical techniques (e.g., hierarchical cluster analysis, principle component analysis, Pearson's correlation analysis) to investigate spatio-temporal patterns of eutrophication and heavy metal pollution in the water supplying lakes (i.e., the Gao-Bao-Shaobo Lake, GBSL) of the eastern route of China's South-to-North Water Diversion Project (SNWDP-ER). A total of 28 water quality parameters were seasonally monitored at 33 sampling sites in the GBSL during 2016 to 2017 (i.e., 132 water samples were collected in four seasons). The results indicated that: 1) spatially, the western and south-western GBSL was relatively more eutrophic and polluted with heavy metals; and 2) temporally, the lakes suffered from high risks of heavy metal contamination in spring, but eutrophication in summer while water quality in winter was the best among the four seasons. Two main potential sources of pollution and transport routes were identified and discussed based on the pollution patterns. These findings contributed considerably to providing in-depth understanding of water pollution patterns, as well as potential pollution sources in the water-supplying region. Such understanding is crucial for developing pollution control and management strategies for this mega inter-basin water transfer project.

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl^-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.

Water and sediment microbiota diversity in response to temporal variation at the outlet of the Ibrahim River (Lebanon)

Bacterial diversity is an important factor controlling the functioning of aquatic ecosystems. With the critical sensitivity of the microbial community towards chemical and/or physical factors, this study aims to identify for the first time the microbiota of the Lebanese coastal Ibrahim River. Water and sediment samples were collected at the outlet of the river, between May 2016 and April 2017, covering a hydrological year. The main microbiological parameters were tested: total germs, total coliforms, fecal coliforms, Escherichia coli, and Enterococcus. A DNA extraction followed by NGS analysis was applied on both water and sediment samples, in order to investigate the bacterial diversity and the habitat specificity. The link between this microbial composition and seasonal variations was then investigated. Results showed fourteen different bacterial phyla, among which were major microorganisms, including a wide variety of pathogenic and commensal ones, frequently available in the aquatic ecosystem. Most of the detected water microbiota were mostly correlated to other freshwater samples, with the main dominance of 5 common phyla: Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes, and Acidobacteria with the average of 43%, 13%, 16%, 10%, and 4% respectively. Despite this overall similarity, multiple patterns were visible, confirming the influence of the temporal variations and the discharge influence on taxonomic diversity. Sediment samples contained the highest relative abundance of Proteobacteria, Firmicutes, and Bacteroidetes: with an average of 31%, 44%, and 22% respectively. Our study showed that the Ibrahim River outlet has a specific habitat clustering. The among-compartment bacterial community variation, which responded to changing environmental factors, approved the existence of a meaningful temporal heterogeneity.

Shallow Groundwater Quality and Its Controlling Factors in the Su-Xi-Chang Region, Eastern China

Understanding factors influencing groundwater quality is critical to the development of best management practices at the large watershed scale. In this study, the shallow groundwater (10–20 m depth) in the Su-Xi-Chang region, eastern China, was investigated as part of a monitoring program from 2007 to 2008 to analyze the regional groundwater quality as well as the hydrogeochemical processes and their controlling factors. Conventional physicochemical water parameters (pH, turbidity, electrical conductivity, dissolved oxygen, total phosphorus), major cations (Na⁺, Ca²⁺, Mg²⁺ and NH₄⁺) and anions (Cl⁻, NO₃⁻ and SO₄²⁻) were measured. Hydrochemical methods and multivariate statistical methods were applied to analyze the hydrogeochemical signatures, origins, the similarities among the variables and to identify the main pollution sources in the groundwater. The results showed that (1) the concentrations of TDS (224.89–1086.70 mg/L) and turbidity (0.1–18.60 NTU) were higher than the class II groundwater quality standards in China and the WHO drinking water standards, (2) there were extremely high concentrations of ammonia (0.01–32.90 mg/L), with a mean value of 0.72 mg/L and (3) the nitrate concentrations (average value of 22.07 mg/L) exceeded the class III groundwater quality standards. The study also provided evidence that weathering, dissolution of carbonate, halite and silicate and cation exchange were the possible primary hydrogeochemical control mechanisms in the groundwater. The sources of ammonia, total phosphorus, sulfates and nitrates included rock–water interactions and anthropogenic activities. The groundwater administration of pollution sinks and sources, long-term legal frameworks and economic incentives should be improved to optimize watershed scale management in the context of rapid development in China.

Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

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.

Multivariate water quality analysis of Lake Cajititlán, Mexico

Lake Cajititlán is a shallow body of water located in an endorheic basin in western Mexico. This lake receives excess fertilizer runoff from agriculture and approximately 2.3 Hm³ per year of poorly treated wastewater from three municipal treatment plants. Thirteen water quality parameters were monitored at five sampling points within the lake over 9 years. The objective of this work was to characterize the spatial and temporal variations of the water quality and to identify the sources of data variability in order to assess the influence and the impact of different natural and anthropogenic processes. One-way ANOVA tests, principal component analysis (PCA), cluster analysis (CA), and discriminant analysis (DA) were implemented. The one-way ANOVA showed that biochemical oxygen demand and pH present statistically significant spatial variations and that alkalinity, total chloride, conductivity, chemical oxygen demand, total hardness, ammonia, pH, total dissolved solids, and temperature present statistically significant temporal variations. PCA results explained both natural and anthropogenic processes and their relationship with water quality data. The CA results suggested there is no significant spatial variation in the water quality of the lake because of lake mixing caused by wind. The most significant parameters for spatial variations were pH, NO₃^-, and NO₂^-, consistent with the configuration of point and nonpoint sources that affect the lake's water quality. The temporal DA results suggested that conductivity, hardness, NO₂^-, pH, and temperature were the most significant parameters to discriminate between seasons. The temporal behavior of these parameters was associated with the transport pathways of seasonal contaminants.

Chemical and microbiological risk assessment of urban river water quality in Vietnam

The contamination and risk by nutrients (NH₄⁺, NO₂^-, NO₃^- and PO₄^3-), COD, BOD₅, coliform and potentially toxic elements (PTEs) of As, Cd, Ni, Hg, Cu, Pb, Zn and Cr were investigated in urban river (Nhue River), Vietnam during 2010-2017. The extensive results demonstrated that concentrations of these contaminants showed significant spatial and temporal variations. The Nhue River was seriously polluted by NH₄⁺ (0.025-11.28 mg/L), PO₄^3- (0.17-1.72 mg/L), BOD₅ (5.8-179.6 mg/L), COD (1.4-239.8 mg/L) and coliform (1540-326,470 CFU/100 mL); moderately polluted by As (0.2-131.15 μg/L) and Hg (0.11-4.1 μg/L); and slightly polluted by NO₂^- (0.003-0.33 mg/L) and Cd (2.1-18.2 μg/L). The concentrations of NH₄⁺, PO₄^3-, COD, BOD₅ and coliform frequently exceeded both drinking water guidelines and irrigation water standards. Regarding PTEs, As, Cd and Hg concentrations were frequently higher than the regulatory limits. Human health risks of PTEs were evaluated by estimating hazard index (HI) and cancer risk through ingestion and dermal contacts for adults and children. The findings indicated that As was the most important pollutant causing both non-carcinogenic and carcinogenic concerns. The non-carcinogenic risks of As were higher than 1.0 at all sites for both adults (HI = 1.83-7.4) and children (HI = 2.6-10.5), while As posed significant carcinogenic risks for adults (1 × 10^-4-4.96 × 10^-4). A management strategy for controlling wastewater discharge and protecting human health is urgently needed.