Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study

Assessment of poly(diallyl dimethyl ammonium chloride) and lime for surface water treatment (pond, river, and canal water): seasonal variations and correlation analyses

The present study deals with the assessment of different physicochemical parameters (pH, electrical conductivity (E.C.), turbidity, total dissolved solids (TDS), and dissolved oxygen) in different surface water such as pond, river, and canal water in four different seasons, viz. March, June, September, and December 2023. The research endeavors to assess the impact of a cationic polyelectrolyte, specifically poly(diallyl dimethyl ammonium chloride) (PDADMAC), utilized as a coagulation aid in conjunction with lime for water treatment. Employing a conventional jar test apparatus, turbidity removal from diverse water samples is examined. Furthermore, the samples undergo characterization utilizing X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The study also conducts correlation analyses on various parameters such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity of raw water, polyelectrolyte dosage, and percentage of turbidity removal across different water sources. Utilizing the Statistical Package for Social Science (SPSS) software, these analyses aim to establish robust relationships among initial turbidity, temperature, percentage of turbidity removal, dosage of coagulant aid, electrical conductivity, and total dissolved solids (TDS) in pond water, river water, and canal water. A strong positive correlation could be found between the percentage of turbidity removal and the value of initial turbidity of all surface water. However, a negative correlation could be observed between the polyelectrolyte dosage and raw water's turbidity. By elucidating these correlations, the study contributes to a deeper understanding of the effectiveness of PDADMAC and lime in water treatment processes across diverse environmental conditions. This research enhances our comprehension of surface water treatment methodologies and provides valuable insights for optimizing water treatment strategies to address the challenges posed by varying water sources and seasonal fluctuations.

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

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

Assessing heavy metal and physiochemical pollution load of Danro River and its management using floating bed remediation

River Danro in Garhwa (India) plays a vital role as a significant source of surface water and a crucial tributary of the North Koel River, ultimately joining the Ganga River Basin. Serving both urban-industrial and rural areas, the region faces challenges, including sand mining near Belchampa Ghat. This study aimed to assess physicochemical and heavy metals pollution at nine sampling locations, utilizing the Overall Index of Pollution (OIP), Nemerow Pollution Index (NPI), and Heavy Metal Pollution Index (HPI). OIP values indicated excellent surface water quality (0.71) in non-monsoon and slight pollution (6.28) in monsoon. NPI ranged from 0.10 to 1.74 in non-monsoon and from 0.22 (clean) to 27.15 (heavily polluted) in monsoon. HPI results suggested groundwater contamination, particularly by lead. Principal component analysis (PCA) and geospatial mapping showed similar outcomes, highlighting the influence of adjacent land use on water quality. Recognizing the significance of the Danro River in sustaining life, livelihoods, and economic growth, the study recommends implementing measures like floating bed remediation and regulatory actions for effective river management. The study acknowledges weaknesses in the current practical assessment methods for water contamination. These weaknesses make it difficult to put plans for cleaning up and controlling contamination into action. Because of this, future research on developing new in-place remediation techniques should focus on creating better ways to measure how effective the cleanup is.

Spatial distribution and hydrogeochemical evaluations of groundwater and its suitability for drinking and irrigation purposes in kaligonj upazila of satkhira district of Bangladesh

Groundwater is a significant water resource for drinking and irrigation in Satkhira district, Bangladesh. The depletion of groundwater resources and deterioration in its quality are the results of the confluence of factors such as industrialization, intensive irrigation, and rapid population growth. For this reason, this study focused on the evaluation of tubewell water of six unions of Kaligonj upazila in Satkhira district, which is situated in the coastal southwest part of Bangladesh. Major and trace elemental concentrations were assimilated into positive matrix factorization (PMF) to identify potential sources and their respective contributions. Principal component analysis (PCA) revealed that groundwater salinization and manmade activities were the primary causes of heavy metals in the coastal groundwater. Its average pH value was found to be 7.5, while Dissolved oxygen, Total dissolved solids, salinity, and conductivity, with values ranging from 1.18 to 7.38 mg/L, 0.5-4.88 g/L, 0.4-5%, and 0.95 to 8.56 mS/cm, respectively. The total hardness average value was 561.7 mg/L, classified into the very hard water categories, which is why 90% of the tubewell water samples were unfit for household purposes. All samples had an excessive level of arsenic present. The iron concentration of fifteen (15) samples crossed the standard limit according to WHO 2011 value. Around 63% of the samples were of the Na+-K+-Cl--SO42- type, and about 72% were sodium-potassium and alkali types. 98% of samples were covered in chloride and bicarbonate. The findings showed that 45.83% of the groundwater samples had negative Chloroalkaline index (CAIs), while 54.16% had positive. The permeability index (PI) was an average of 73%, and residual sodium carbonate (RSC) averaged 260.2 mg/L, and the findings clearly showed that 80% of the samples weren't appropriate for irrigation. According to the sodium adsorption ratio (SAR) value, 65% of the samples fell into the unsuitable category. These calculations indicated a high overall salinity hazard in the study area, which may be caused by the intrusion of sea water given that the study area is close to the coastal region. Findings compared to standards revealed that the majority of the samples were deemed unfit for drinking and irrigation purposes. Hence, additional attention must be paid to this area to ensure the availability of drinkable water and to preserve sustainable farming practices.

Review: The application of source analysis methods in tracing urban non-point source pollution: categorization, hotspots, and future prospects

The contribution of urban non-point source (NPS) pollution to surface water pollution has gradually increased, analyzing the sources of urban NPS pollution is of great significance for precisely controlling surface water pollution. A bibliometric analysis of relevant research literature from 2000 to 2021 reveals that the main methods used in the source analysis research of urban NPS pollution include the emission inventory approach, entry-exit mass balance approach, principal component analysis (PCA), positive matrix factorization (PMF) model, etc. These methods are primarily applied in three aspects: source analysis of rainfall-runoff pollution, source analysis of wet weather flow (WWF) pollution in combined sewers, and analysis of the contribution of urban NPS to the surface water pollution load. The application of source analysis methods in urban NPS pollution research has demonstrated an evolution from qualitative to quantitative, and further towards precise quantification. This progression has transitioned from predominantly relying on on-site monitoring to incorporating model simulations and employing mathematical statistical analyses for traceability. This paper reviews the principles, advantages, disadvantages, and the scope of application of these methods. It also aims to address existing problems and analyze potential future development directions, providing valuable references for subsequent related research.

Tracing and source fingerprinting of metals from the southern coastal sediments in Bangladesh

Trace element pollution from anthropogenic sources is increasingly widespread. This pollution in terrestrial environments threatens agricultural crop production, while in aquatic environments, it threatens fish cultivation. The contamination of these crucial food sources raises significant concerns regarding food safety, security, and its potential adverse effects on human health. Coastal areas are particularly vulnerable to heavy metal pollution due to their proximity to industrial and urban centres, as well as their susceptibility to contamination from marine sources. In attempting to identify the sources of heavy metals (As, Cu, Cr, Cd, Fe, Hg, Mn, Ni, Pb, and Zn) and measure their contributions, we collected soil samples from thirty sites along the three coastal districts (Patuakhali, Barguna, and Bhola) in Bangladesh. Using atomic absorption spectroscopy, heavy metal concentrations in soil samples were measured and three receptor models (PMF, PCA-MLR, and UNMIX) were applied to detect their sources. Pairwise correlation analysis of metal concentrations in 30 sites across 3 coastal districts showed all possible patterns, including both significant and insignificant positive and negative relationships between different metals, except for As and Hg which did not display any significant relationships with other metals. The concentrations of Cu, Fe, Mn, Ni, and Zn exceed the US-EPA sediment quality standard. The applied PCA-MLR, PMF, and UNMIX models identified several sources of heavy metal contamination, including (i) mixed anthropogenic and natural activities: contribution of 59%, 37%, and 43%, and (ii) vehicle emissions: contribution of 23%, 26% and 29%. The recognized metal sources should be prioritised to avoid the discharge of poisonous pollutants from anthropogenic factors and any possible future exposure. This study's findings have implications for ongoing monitoring and management of heavy metal contamination in coastal environments to mitigate potential health and ecological impacts and can inform policy development and management strategies.

Analysis of the characteristics of major pollutants discharged from wastewater in China's provinces

In recent years, the discharge of major pollutants in China's wastewater has been decreasing but remains at a high level. Controlling the discharge of pollutants in sewage is of great importance for protecting water quality and maintaining ecological balance. Based on data collected from 31 provinces in China from 2011 to 2020 (except 2018), this study analyzes the spatiotemporal variation emissions of the wastewater pollutants: chemical oxygen demand (COD), ammonia nitrogen (NH3-N), total nitrogen (TN), and total phosphorus (TP). The entropy method was used to evaluate the effectiveness of water pollution control in different provinces. Our results revealed that the total emission per gross domestic product (GDP) for COD, NH3-N, TN and TP in China decreased by 50.7%, 81.9%, 65.4% and 70.8%, respectively. In terms of regional annual emission differences, the Northwest region was the lowest compared with other regions, accounting for 4.87%-6.59% of the national pollutant emissions, and the Central China region was the highest, accounting for 22.4%-26.05% of the national pollutant emissions. The average value of pollutant emissions per unit of GDP decreased year-to-year overall, but Guangxi and Tibet showed a trend of first decreasing and then increasing. The correlation results indicated a significant correlation (0.977) between TN and TP emissions in wastewater in China during 2011-2020. Through clustering and Multidimensional Scaling model (MDS) analysis, Beijing and Shanghai have been performing well in controlling water pollution discharge, while the provinces of Tibet and Guangxi must still increase their efforts in water pollution control. Furthermore, these results demonstrate the experience and achievements of the Chinese government in the treatment of wastewater pollution and provide a useful reference for treatment of wastewater pollution in the world.

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.

The application of chemometrics in metals source of identification in Brunei Bay surface sediment

Brunei Bay is a unique ecosystem which offers a vast biodiversity. This study was carried out to define the source of metals in the surface sediment of Brunei Bay to ensure the bay's health. The secondary data were analysed using chemometrics analysis to verify the possible factors that influence metals distribution in Brunei Bay sediment. Samples were collected several times during 2013 to 2014 using Ponar grab at 16 stations within the bay. Samples were then dried, pre-treated, digested and analysed using Inductively Coupled Plasma Mass Spectrometry (ICPMS) in the laboratory. Overall, the mean concentration of metal, sediment pH and clay fraction were significantly changed during different sampling periods, as the changes were presumed affected by seasonal changes. The Pearson correlation has pointed that metals were dominantly derived by natural input; however, the total organic carbon was proven to be derived by anthropogenic sources. Moreover, the principal component analysis has verified that the distribution of metals in the bay's sediment was dominantly influenced by natural processes. However, the utilization and manipulation of marine resources are slightly affecting the bay's ecosystem which may deteriorate the ecosystem health soon.

Major ion compositions, sources and risk assessment of karst stream under the influence of anthropogenic activities, Guizhou Province, Southwest China

To explore the influence of different types of anthropogenic activity on the rivers, we investigate the major ion composition, sources and risk assessment of the karst stream (Youyu stream and Jinzhong stream), which are heavily influenced by mining activities and urban sewage, respectively. The chemical compositions of the Youyu stream water, which is heavily influenced by mining activities, are dominated by Ca²⁺ and SO₄^2-. However, the chemical compositions of the Jinzhong stream water, which is heavily influenced by urban sewage, are dominated by Ca²⁺ and HCO₃^-. The Ca²⁺, Mg²⁺ and HCO₃^- in Jinzhong stream are mainly derived from rock weathering, while the Youyu stream is affected by acid mine drainage, and sulfuric acid is involved in the weathering process. Ion sources analysis indicates that the Na⁺, K⁺, NO₃^-, and Cl^- in the Jinzhong stream mainly derive from urban sewage discharge; but NO₃^- and Cl^- of the Youyu stream mainly derive from agricultural activities, and Na⁺, K⁺ are mainly from natural sources. The element ratios analysis indicates the ratio of SO₄^2-/Mg²⁺ in Youyu stream (4.61) polluted by coal mine is much higher than that in Jinzhong stream (1.29), and the ratio of (Na⁺+K⁺+Cl^-)/Mg²⁺ in Jinzhong stream (1.81) polluted by urban sewage is higher than Youyu stream (0.64). Moreover, the ratios of NO₃^-/Na⁺, NO₃^-/K⁺, and NO₃^-/Cl^- in the agriculturally polluted Youyu stream were higher than those in the Jinzhong stream. We can identify the impact of human activities on streams by ion ratios (SO₄^2-/Mg²⁺, (Na⁺+K⁺+Cl^-)/Mg²⁺, NO₃^-/Na⁺, NO₃^-/K⁺, and NO₃^-/Cl^-). The health risk assessment shows the HQ_T and HQ_N for children and adults are higher in Jinzhong stream than in Youyu stream and the total HQ value (HQ_T) of children was higher than one at J1 in the Jinzhong stream, which shows that children in Jinzhong stream basin are threatened by non-carcinogenic pollutants. Each HQ value of F^- and NO₃^- for children was higher than 0.1 in the tributaries into Aha Lake, indicating that the children may also be potentially endangered.

Assessment of the spatiotemporal water quality variations in the Middle Route of China's South-to-North Water Diversion Project by multivariate analysis

As an important drinking water source for North China, the Middle Route of China's South-to-North Water Diversion Project (MRP) must provide high-quality water to maintain the health and safety of more than 60 million people. However, different water transfer operation modes may affect the water quality status, and the spatiotemporal characteristics of water quality in the MRP, with high water transfer volumes, remain poorly understood. In this study, the differences in water quality in the MRP between the initial stage (Nov. 2015 to Oct. 2017, low transfer volumes) and the current stage (Nov. 2017 to Oct. 2020, high transfer volumes) were compared, and the spatiotemporal water quality variations in the current stage were evaluated using multivariate statistical methods. For this purpose, approximately 12,528 observations, including the datasets of 12 water quality parameters collected from 29 monitoring sites, were used. The results showed that the water quality status improved significantly during the current stage. Based on principal component analysis (PCA), physical parameters (natural), nutrients, organic matter and microbes (anthropogenic), and heavy metals (natural and anthropogenic) were the key factors influencing water quality variations. Based on hierarchical cluster analysis, 12 months were classified into two groups: the high-flow period (HFP, Jun.-Oct.) and the low-flow period (LFP, Nov.-May). Additionally, 29 sampling sites were grouped into three sections: the Henan section (HN; S1-S16), Hebei section (HB; S17-S24), and Tianjin-Beijing section (TB; S25-S29). From the perspective of water quality regulation, the total nitrogen concentration and permanganate index in the HB and TB sections of the MRP should be considered throughout the year, and the faecal coliform concentrations in these two sections should also be considered during the HFP. The results of this study could be helpful for local administrations to understand and control pollution and better protect the quality of water in the MRP.

Identification of pollutant sources and evaluation of water quality improvement alternatives of a large river

While pollutants are the most important factors for the deterioration of surface water quality, the identification of major pollutant sources for rivers is challenging, especially in areas with diverse land covers and multiple pollutant inputs. This study aims to identify the significant pollutant sources from the tributaries that are affecting the water quality and identify the limiting nutrient for algal growth in the Geum river to provide a management alternative for an improvement of the water quality. The positive matrix factorization (PMF) was applied for pollutant source identification and apportionment of the two major tributaries, Gab-cheon and Miho-cheon. Positive matrix factorization identifies three and two major pollutant sources for Gab-cheon and Miho-cheon, respectively. For Gab-cheon, wastewater treatment plants, urban, and agricultural pollution are identified as major pollutant sources. Furthermore, for Miho-cheon, agricultural and urban pollution were identified as major pollutant sources. Total phosphorus (TP) is also identified as a limiting nutrient for algal growth in the Geum river. Water quality control scenarios were formulated and improvement of water quality in the river locations was simulated and analyzed with the Environmental Fluid Dynamic Code (EFDC). Scenario results were evaluated using a water quality index. The reduction of total phosphorus (TP) from the tributaries has greatly improved the water quality, especially algal bloom in the downstream stations.

Evaluation of geothermal water and assessment of corrosive and scaling potential of water samples in Tulsishyam Geothermal Region, Gujarat, India

The purpose of this study was to evaluate and analyse the water quality in the Tulsishyam region in terms of corrosive and scaling properties. Thermal water samples (TWS), groundwater samples (GWS), and riverwater samples (RWS) were collected and analysed for a variety of geochemical parameters in various locations throughout the Tulsishyam region. The pH of such thermal springs ranges between 7.1 and 7.4, suggesting that they are neutral, and the temperature ranges between 39 and 42 °C. To determine the corrosive and scaling properties of water, various indices such as the Langelier Saturation Index (LSI), the Puckorius Scaling Index (PSI), the Ryznar Stability Index (RSI), and the Larson Index (LaI) are used. According to the indices described previously, all water samples are extremely corrosive to intolerably corrosive with a significant abrasion rate. The chemical composition of the fluids is also investigated in relation to the weathering processes. Sulphate has a significant negative link with chlorine and a weak correlation with bicarbonate, whereas chlorine has a moderate correlation with bicarbonate, according to multivariate analysis. Industrial usage of water samples from the study area should be limited due to the aggressive nature of the water samples.

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R²) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

Water quality assessment of the Karasu River (Turkey) using various indices, multivariate statistics and APCS-MLR model

Determining the water quality status of a river and accurately identifying potential pollution sources threatening the river are pillars in effective control of pollution and sustainable water management. In this study, water quality indices, multivariate statistics and absolute principal component score-multiple linear regression (APCS-MLR) were applied to evaluate the water quality of the Karasu River, the main tributary of the Euphrates River (Turkey). For this, 19 water quality variables were monitored monthly at eight stations along the river during one year. Based on the mean dissolved oxygen (DO), electrical conductivity (EC), nitrate-nitrogen (NO₃-N), orthophosphate-phosphorus (PO₄-P), total phosphorus (TP), ammonium-nitrogen (NH4-N), chemical oxygen demand (COD) and total nitrogen (TN) levels, most stations of the river had "very good" water status according to surface water quality criteria. Spatial cluster analysis (CA) divided eight stations into three regions as clean region, moderate clean region and very clean region. The mean values of Nutrient Pollution Index indicated that the river was "no polluted". Similarly, Water Quality Index and Organic Pollution Index values indicated that the river water quality was between "good" and "excellent". A minimum water quality index (WQI_min) consisted of ten crucial parameters was not significantly different with the WQI based on all the 17 parameters. Discriminant analysis (DA) results showed that water temperature (WT), EC, chlorophyll-a (Chl-a), potassium (K), calcium (Ca), NO₃-N and COD are the variables responsible for temporal changes, while WT, total dissolves solids (TDS), Chl-a, K, magnesium (Mg), Ca, NH₄-N and COD are the variables responsible for spatial changes in the river water quality. Principal component analysis/factor analysis (PCA/FA) identified four potential sources, including anthropogenic, natural, seasonal and phytoplankton. Source apportionment in the APCS-MLR model revealed that seasonal and anthropogenic sources contributed 35.2% and 25.5% to river water quality parameters, respectively, followed by phytoplankton (21.4%) and natural sources (17.9%).

Hydrogeochemistry and quality evaluation of groundwater and its impact on human health in North Tripura, India

Groundwater contamination becomes an alarming threat to the provision of ecosystem services and natural resources. A very high level of groundwater contamination has been observed in the northeastern states particularly in North Tripura district. Therefore, the present study considered the region as a case study to evaluate the hydrogeochemical facies, heavy metal pollution and irrigation indices, and their impact on human health. For the investigation, we have collected a total of 35 groundwater samples from North Tripura district. Hydrogeochemical facies through Piper plot reflect Ca²⁺-Mg²⁺-HCO₃^- and Na⁺-HCO₃^- as dominant water types. Gibbs plot identifies the dominance of rock-water interaction process in groundwater hydrochemistry. Geochemical plots indicate the dominance of silicate weathering, ion exchange and carbonate dissolution processes in groundwater mineralisation. The order of trace metal contaminations follows Fe > As > Zn > Mn > Cu > Pb. Results of heavy metal indices suggest above 80% samples are at high risk due to high Fe contamination. The risk of the heavy metal indices is associated with rising elevation in southern part of North Tripura. Findings of health risk assessment study imply that children face much carcinogenic and non-carcinogenic risks than adults because of unsafe levels of Fe and As. Multivariate statistical tools are applied to unravel interrelationships among all ions and trace metals as well as probable hydrogeochemical processes in groundwater. Results of Wilcox and USSL plots suggest 77% samples meet irrigation suitability criteria. Besides, the analysis suggests a better insight to identify hydrogeochemical processes controlling groundwater chemistry and the suitability of groundwater for irrigation and drinking purposes. The study also suggests treatment and sustainable management of groundwater resources is compulsory to reduce trace metal contaminations before public use.

Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD₅ and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO₄³) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD₅, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO₄^3-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.

Adaptive-weight water quality assessment and human health risk analysis for river water in Hong Kong

The water quality of Hong Kong's four water control zones (Tolo Harbour and Channel, Port Shelter, Victoria Harbour, and Junk Bay) is of vital importance and has attracted much attention. This study aims to more objectively and comprehensively assess the water quality and its health impact based on the four-year monitoring data of 21 parameters collected from four zones. First, physicochemical characteristics of the water system were investigated based on multivariate statistical approaches, including Kruskal-Wallis test, hierarchical cluster analysis, and Mann-Kendall test. Then, water quality levels over space and time and the element sources were analyzed using adaptive-weight water quality index (AWQI) method, and factor analysis, respectively. Finally, the potential harm of trace elements for humankind was identified based on the health risk assessment model. The results revealed that (1) the values of more than half of the water quality parameters exhibited significant interannual changes, and the values of all parameters distinctly varied over space; (2) The water quality status in four water control zones showed a steady and long-term improvement trend from 2016 to 2019; (3) The sources of pollution elements impacting water quality status were related to the comprehensive influence of human activities and natural processes; (4) The carcinogenic risks of all trace elements were negligible or acceptable, while Mn and As may cause noncarcinogenic harm to humankind.

Determining groundwater quality using indices and multivariate statistical techniques: a study of Tosham block, Haryana, India

Water is the most important component for human survival and often the most misused one. The present study deals with the assessment of groundwater quality of Tosham block, Bhiwani District, Haryana, India, and its nearby villages. A total of 23 samples were collected from different groundwater sources and were analysed for 16 different physico-chemical parameters. Correlation coefficients were calculated to identify highly correlated and interrelated water quality parameters. It provides an excellent tool for the prediction of parameter values within the reasonable rank of exactness. A strong correlation was observed between total hardness, magnesium and total dissolved solids, especially between total dissolved solids and electrical conductivity. The obtained results were compared with Indian Standard Drinking water specifications IS: 10500-2012. Concentration of total hardness, iron and chloride was found above the permissible limit in all the samples. Multivariate statistical techniques, namely cluster analysis and principal component analysis, were used to find the relationship between studied parameters. Water quality index (WQI) was also calculated. The WQI of groundwater samples ranged from 125 to 556. The water quality index revealed that out of 23 samples, 13 were found to have poor water quality and 6 samples were of very poor water quality. High value of WQI was found mainly from total dissolved solids, electrical conductivity, total hardness, Mg, Cl and Fe. Groundwater analysis of the studied water samples indicated that there is a need to treat the water before its use for drinking and other domestic activities. Necessary precautions should be taken to prevent the groundwater from being contaminated.

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.

Water quality assessment and pollution threat to safe water supply for three river basins in Malaysia

Pollution in raw water poses increasing threats to safe water supply in many developing countries. Therefore, a comprehensive water quality assessment is essential to provide various stakeholders the information to deal with this problem. This study applies chemometrics to interpret a recent 10-year water quality data from three major river basins (Selangor River basin, Langat River basin, and Klang River basin) frequented by water supply disruptions in Selangor, Malaysia. We present the application of selected chemometrics approaches, namely agglomerative hierarchical cluster analysis, principal component analysis, factor analysis and Man-Kendall trend analysis. The results showed three spatial groups of monitoring stations with similar land use practices and pollution characteristics. Besides spatial differences, periodic variations were observed when similar pollutants exhibited different pollution loads during rainy and dry periods. We found that nitrogen species, total suspended solids, and dissolved solids represented the major pollution loads in the studied basins. The results further confirmed a significant increasing trend in ammonia pollution. Our study demonstrates how ammonia pollutant is likely to pose a threat to water supply and highlights the vulnerability of Selangor's water resource system to water pollution. The results of this study could facilitate decision making towards more holistic strategies, specifically, incorporating ammonia treatment facilities into the conventional water treatment plant will help achieve smooth water supply operations.

APCS-MLR model: A convenient and fast method for quantitative identification of nitrate pollution sources in groundwater

Nitrate (NO₃^-) contamination in groundwater has diverse sources and complicated transformation processes. To effectively control NO₃^- pollution in groundwater systems, quantitative and accurate identification of NO₃^- sources is critical. In this work, we applied hydrochemical characteristics and isotope analysis to determine NO₃^- source apportionment. For the first time, the NO₃^- source contributions were calculated using hydrochemical indicators combined with multivariate statistical model (PCA-APCS-MLR). The results interpret that chemical fertilizers (58.11%) and natural sources (22.69%) were the primary NO₃^- sources in the vegetable cultivation area (VCA) which were rather close to the estimation by Bayesian isotope mixing model (SIAR). In particular, the contributions of chemical fertilizers in the VCA differed by only 3.79% between the two methods. Compared with previous approaches e.g. SIAR, the key advantage of the proposed PCA-APCS-MLR model is that it only requires the hydrochemical indicators which can be easily measured. A series of complicated experiments including measurement of isotope data of NO₃^- in groundwater, monitoring of in-situ pollution source information and calculation of isotopic enrichment factor can be simply avoided. The PCA-APCS-MLR model offers a much more convenient and faster method to determine the contribution rates of NO₃^- pollution sources in groundwater.

Modified DLLME-GC-TQMS determination of pesticide residues in Gomti River, Lucknow, India: ecological risk assessment and multivariate statistical approach

This research article aims to establish an easy and well-defined analytical method for detection and quantification of multiclass pesticides in Gomti river water samples because the increased agricultural activities, industrialization, and urbanization had increased the presence of pesticides in the ecosystem which causes the depletion of water quality making it a global concern. The analytical method, vortex-assisted ultrasonication-based dispersive liquid-liquid microextraction-solidification of floating organic droplets (VAUS-DLLME-SFO) was optimized using one parameter at a time approach which gave the recovery between 69.45 and 114.15%, limit of detection (LOD), and limit of quantification (LOQ) 0.0011-0.0111 µg/L and 0.0033-0.0368 µg/L, respectively, and RSD in the range of 0.75-1.29 which shows sensitivity and accuracy better than earlier reported methods. The data obtained were subjected to measurement uncertainty, risk assessment, and multivariate statistical analysis to establish the robustness of the developed analytical method. The measurement uncertainty found was concluded to be in the acceptable range for analytical results. Furthermore, the real samples were analyzed and the associated value of the risk quotient was found to be less than 1, except for aquatic invertebrates, establishing the fact that the current concentration of pesticides has no such negative threat to flora and fauna. The possible source of pesticides in the Gomti river system was established by multivariate analysis. It was thus concluded that anthropogenic activity is responsible for the variable concentration of pesticides found in the sample.

Analysis of Surface Water Quality in Upstream Province of Vietnamese Mekong Delta Using Multivariate Statistics

The study employed different statistical approaches to assess surface water quality in the upstream region of the Vietnamese Mekong Delta. The dataset included seven parameters (i.e., temperature, pH, total suspended solids (TSS), five-day biological oxygen demand (BOD5), chemical oxygen demand (COD), ammonium nitrogen (NH4+-N) and coliform) at seventy-three locations. Cluster analysis (CA) and principal component analysis (PCA) were applied to analyze spatial variations in surface water quality and recognize the important parameters. The findings revealed that surface water quality was deteriorated by organic matters (high BOD5 and COD), nutrients and microorganisms. Particularly, urban areas were found to be more polluted than the other areas. The PCA results indicated that three potential water pollution sources, including industry, urban and tourism, could explain 87.03% of the total variance. Coliform was identified as the leading latent factor that controls surface water quality in the study area. CA grouped the sampling locations into 11 groups, in which the groups of the baseline monitoring sites and large rivers had better water quality. The results indicated a significant impact of anthropogenic activities (especially, urban and tourism practices) in surface water quality degradation. Moreover, CA suggested that the numbers of the sampling sites could be reduced from 73 to 58 locations, lowering 20.54% of the monitoring cost. Thus, the study recommends scrutinizing the current surface water quality monitoring system to be more economic and urgently implementing appropriate solutions to mitigate coliform pollution in the smaller water bodies.

An Advanced PMF Model Based on Degradation Process for Pollutant Apportionment in Coastal Areas

With increasing stress posed to the marine ecosystem and coastal communities, prevention and control of coastal pollution becomes urgent and important, in which the identification of pollution sources is essential. Currently, the pollutant source apportionment in coastal areas is mainly based on receptor models, such as the positive matrix factorization (PMF) model. Nevertheless, these models still lack consideration of the changes of pollutant behaviors (e.g., the degradation of pollutants) which cause the differences in pollutant compositions. Subsequently, the source apportionment via receptor models only based on the monitoring data may not be consistent with the one in pollution sources. To fill this gap, a pollutant degradation model was firstly developed in this study. Accordingly, the degradation model was inversed to estimate the pollutant concentrations at their emitting sources, based on the monitoring concentration in the coastal area. Finally, the estimated concentrations were fed to the PMF model for pollutant source apportionment, advancing the PMF model with degradation process. To demonstrate the feasibility and accuracy of the developed model, a case study of source appointment was carried out based on the polycyclic aromatic hydrocarbons (PAHs) in the sediments of the Pearl River Estuary. The results indicated the same types of emission source identified by the original and advanced PMF models, which were oil spill, biomass and coal combustion, and traffic emission. Nevertheless, the contributions of sources were significantly varied between the two models. According to the analyses based on emission inventory, the offsets of the results from the original PMF model were −55.4%, 22.7%, and 42.2% for the emission sources of oil spill, biomass and coal combustion, and traffic emission, respectively. Comparatively, the offsets for the advanced PMF model narrowed down to −27.5%, 18.4%, and −4.4%. Therefore, the advanced PMF model is able to provide satisfactory source apportionment for organic pollutants in coastal areas, and thus further provide a scientific basis for marine pollution prevention and control.

Geochemical Modeling Source Provenance, Public Health Exposure, and Evaluating Potentially Harmful Elements in Groundwater: Statistical and Human Health Risk Assessment (HHRA)

Groundwater contamination by potentially harmful elements (PHEs) originating from the weathering of granitic and gneissic rock dissolution poses a public health concern worldwide. This study investigated physicochemical variables and PHEs in the groundwater system and mine water of the Adenzai flood plain region, in Pakistan, emphasizing the fate distribution, source provenance, chemical speciation, and health hazard using the human health risk assessment HHRA-model. The average concentrations of the PHEs, viz., Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe, and Zn 0.23, were 0.27, 0.07, 0.30, 0.07, 0.06, 0.08, 0.68, and 0.23 mg/L, respectively. The average values of chemical species in the groundwater system, viz., H+, OH−, Ni2+, Mn2+, Mn3+, Cr3+, Cr6+, Cu+, Cu2+, Cd2+, Pb2+, Pb4+, Co2+, Co3+, Fe2+, Fe3+, and Zn2+, were 1.0 × 10−4 ± 1.0 × 10−6, 1.0 × 10−4 ± 9.0 × 10−7, 2.0 × 10−1 ± 1.0 × 10−3, 3.0 × 10−1 ± 1.0 × 10−3, 1.0 × 10−22 ± 1.0 × 10−23, 4.0 × 10−6 ± 2.0 × 10−6, 4.0 × 10−11 ± 2.0 × 10−11, 9.0 × 10−3 ± 1.0 × 10−2, 2.0 × 10−1 ± 2.0 × 10−3, 7.0 × 10−2 ± 6.0 × 10−2, 5.0 × 10−2 ± 5.0 × 10−2, 2.0 × 10−2 ± 1.5 × 10−2, 6.0 × 10−2 ± 4.0 × 10−2, 8.0 × 10−31 ± 6.0 × 10−31, 3.0 × 10−1 ± 2.0 × 10−4, 4.0 × 10−10 ± 3.0 × 10−10, and 2.0 × 10−1 ± 1.0 × 10−1. The mineral compositions of PHEs, viz. Ni, were bunsenite, Ni(OH)2, and trevorite; Mn viz., birnessite, bixbyite, hausmannite, manganite, manganosite, pyrolusite, and todorokite; Cr viz., chromite and eskolaite; Cu viz., CuCr2O4, cuprite, delafossite, ferrite-Cu, and tenorite; Cd viz., monteponite; Pb viz, crocoite, litharge, massicot, minium, plattnerite, Co viz., spinel-Co; Fe viz., goethite, hematite, magnetite, wustite, and ferrite-Zn; and Zn viz., zincite, and ZnCr2O4 demarcated undersaturation and supersaturation. However, EC, Ca2+, K+, Na+, HCO3−, Cr, Cd, Pb, Co, and Fe had exceeded the WHO guideline. The Nemerow’s pollution index (NPI) showed that EC, Ca2+, K+, Na+, HCO3−, Mn, Cd, Pb, Co, and Fe had worse water quality. Principal component analysis multilinear regression (PCAMLR) and cluster analysis (CA) revealed that 75% of the groundwater contamination originated from geogenic inputs and 18% mixed geogenic-anthropogenic and 7% anthropogenic sources. The HHRA-model suggested potential non-carcinogenic risks, except for Fe, and substantial carcinogenic risks for evaluated PHEs. The women and infants are extremely exposed to PHEs hazards. The non-carcinogenic and carcinogenic risks in children, males, and females had exceeded their desired level. The HHRA values of PHEs exhibited the following increasing pattern: Co > Cu > Mn > Zn > Fe, and Cd > Pb > Ni > Cr. The higher THI values of PHEs in children and adults suggested that the groundwater consumption in the entire region is unfit for drinking, domestic, and agricultural purposes. Thus, all groundwater sources need immediate remedial measures to secure health safety and public health concerns.

Estimating Point and Nonpoint Source Pollutant Flux by Integrating Various Models, a Case Study of the Lake Hawassa Watershed in Ethiopia’s Rift Valley Basin

Increasing pollutant emissions in the Lake Hawassa watershed (LHW) has led to a severe water quality deterioration. Allocation and quantification of responsible pollutant fluxes are suffering from scarce data. In this study, a combination of various models with monitoring data has been applied to determine the fluxes for Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5), Total Dissolved Solid (TDS), Total Nitrogen (TN), Nitrate and Nitrite-nitrogen (NOx-N), Total Phosphorous (TP) and phosphate (PO4-P). Water, wastewater and stormwater samples were collected and analyzed at eight monitoring stations from rivers and point sources and six monitoring stations of stormwater samples. The flow simulated with soil and water assessment tool (SWAT) could be very well calibrated and validated with gauge data. This flow from SWAT model, measured flow during monitoring and pollutant concentrations were used in FLUX32 to estimate pollutant fluxes of main rivers and point sources in LHW. The formulas provided by Ethiopian Roads Authority and Gumbel’s theory of rainfall frequency analysis was employed to determine the 2-years return period rainfall depth for the City of Hawassa. The integration of HEC-GeoHMS and SCS-CN with the catchment area enabled to determine stormwater pollution load of Hawassa City. The estimated pollutant flux at each monitoring stations showed that the pollutant contribution from the point and nonpoint sources prevailing in the study area, where the maximum fluxes were observed at Tikur-Wuha sub-catchments. This station was located downstream of the two point sources and received flow from the upper streams where agricultural use is predominant. Furthermore, Hawassa city has been identified as a key pollutant load driver, owing to increased impacts from clearly identified point sources and stormwater pollutant flux from major outfalls. Agricultural activities, on the other hand, covers a large portion of the catchment and contributes significant amount to the overall load that reaches the lake. Thus, mitigation measures that are focused on pollutant flux reduction to the lake Hawassa have to target on the urban and agricultural activities.

Multiple stable isotopes and geochemical approaches to elucidate groundwater salinity and contamination in the critical coastal zone: A case from the Bou-areg and Gareb aquifers (North-Eastern Morocco)

Mediterranean areas are characterized by complex hydrogeological systems, where water resources are faced with several issues such as salinity and pollution. Fifty-one water samples were gathered from the Bou-areg coastal and the Gareb aquifers to evaluate the source of water salinity and to reveal the processes of the different sources of pollution using a variety of chemical and isotopic indicators (δ²H-H2O, δ¹⁸O-H2O, δ³⁴S-SO4, and δ¹⁸O-SO4). The results of the hydrochemical analysis of water samples show that the order of dominated elements is Cl^- > HCO₃^- > SO4₂^- > NO₃^- and Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and evidenced extremely high salinity levels (EC up to 22000 μS/cm). All samples exceeded the WHO drinking water guidelines, making them unfit for human consumption. Ion ratio diagrams, isotopic results, and graphical comparing indicate that the mineralization of groundwater in the area, is controlled by carbonate dissolution, evaporite dissolution, ion exchange, and sewage invasion. The return of irrigation water plays a significant role as well in the groundwater recharge and its mineralization by fertilizers mainly. Evaporites (Gypsum), sewage, and fertilizers constitute the main sources of sulfates in the investigated water resources. These scientific results will be an added value for decision-makers to more improve the sustainable management of groundwater in water-stressed regions. The use of chemical and isotopic tracers once again shows their relevance in such zones where systematic monitoring is lacking.

Interpreting the salinization and hydrogeochemical characteristics of groundwater in Dongshan Island, China

The groundwater salinization is a global problem that degrades water quality and endangers sustainable use of water resources, particularly in coastal areas. In this paper, 24 water samples were collected from 12 monitoring wells during the dry (January) and wet (June) seasons for analyzing the salinization and hydrogeochemical characteristics of groundwater in Dongshan Island of China through combined hydrogeochemical and multivariate statistical approaches. Results showed that groundwater in the study area is primarily Cl-Na and followed by Cl-Ca·Mg type in the dry season, Cl-Na and followed by Cl-Ca·Mg and HOC₃·Cl-Na type in the wet season. The groundwater chemistry is predominantly controlled by carbonate, gypsum, and silicate dissolution. However, some areas are strongly influenced by seawater intrusion, sewage infiltration and reverse ion exchange process. Around 40% of water samples from the dry season and 50% from the wet season are at injuriously, highly and severely saline levels while other samples at slightly and moderately saline levels, suggesting that groundwater in the area is partially recharged by seawater. Furthermore, the NO₃^-/Cl^- versus Cl^- diagram and principal component analysis (PCA) indicated nitrate pollution in groundwater that is subjected to anthropogenic activities such as domestic sewage, agricultural and industrial practices, which lead to degradation of groundwater quality in the area. The findings of this study provide helpful insights for understanding the genesis and hydrogeochemical evolution of groundwater in those coastal areas.

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.

Pollution Contribution Response in Governance and Potential Pollution Factors in Licun River

The development of the city results in deterioration of the water quality of the Licun River. As a result, years of governance have been conducted to improve its water quality. In order to clarify the response changes of water quality in the water governance, the governance process is divided into three stages (2000–2007, 2008–2016, 2017–2020) according to different priorities. Spearman’s rank correlation coefficient and the comprehensive pollution index are applied to analyze the variation of water quality response at various stages. In addition, the main pollution contributions with the governance changes were obtained. It is concluded that flood control and incomplete river pollution interception have a limited effect on water quality improvement, with NH3-N (ammonia nitrogen) and COD (chemical oxygen demand) being the main pollution contributions at the first stage. At the second stage, the point source control and sewage treatment facilities significantly improve water quality, and the main pollution contributions are NH3-N and TP (total phosphorus). At the third stage, sewage treatment facilities and supporting pipelines are improved, water sources are replenished, and the main pollution contribution is TN (total nitrogen). For further treatment, the factors affecting pollution are analyzed, including the contradiction of sewage system, point source pollution caused by pipe network problems, shortage of water resources, sludge pollution, and non-point source pollution.

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.

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.

Competitive exchange between divalent metal ions [Cu(II), Zn(II), Ca(II)] and Hg(II) bound to thiols and natural organic matter

Mercuric Hg(II) ion forms exceptionally strong complexes with various organic ligands, particularly thiols and dissolved organic matter (DOM) in natural water. Few studies, however, have experimentally determined whether or not the presence of base cations and transition metal ions, such as Ca(II), Cu(II), and Zn(II), would compete with Hg(II) bound to these ligands, as concentrations of these metal ions are usually orders of magnitude higher than Hg(II) in aquatic systems. Different from previous model predictions, a significant fraction of Hg(II) bound to cysteine (CYS), glutathione (GSH), or DOM was found to be competitively exchanged by Cu(II), but not by Zn(II) or Ca(II). About 20-75% of CYS-bound-Hg(II) [at 2:1 CYS:Hg(II)] and 14-40% of GSH-bound-Hg(II) [at 1:1 GSH:Hg(II)] were exchanged by Cu(II) at concentrations 1-3 orders of magnitude greater than Hg(II). Competitive exchange was also observed between Cu(II) and Hg(II) bound to DOM, albeit to a lower extent, depending on relative abundances of thiol and carboxylate functional groups on DOM and their equilibrium time with Hg(II). When complexed with ethylenediaminetetraacetate (EDTA), most Hg(II) could be exchanged by Cu(II) and Zn(II), as well as Ca(II) at increasing concentrations. These results shed additional light on competitive exchange reactions between Hg(II) and coexisting metal ions and have important implications in Hg(II) chemical speciation and biogeochemical transformation, particularly in contaminated environments containing relatively high concentrations of Hg(II) and metal ions.

Assessment of physicochemical parameters of Vishav stream: an important tributary of river Jhelum, Kashmir Himalaya, India

The deteriorating conditions in stream ecosystems are detrimental for society as far as its health, and development is concerned if the underlying factors continue to operate without regular monitoring. In order to maintain the health of a stream ecosystem, assessment of spatiotemporal changes in its physicochemical attributes and identification of all factors that could alter its hydrological regime is an essential component for managing it. The current 2-year study (October-2017 to September-2019) assessed the physicochemical regime of lower stretches of the Vishav stream, a major left-bank tributary of river Jhelum on a spatiotemporal basis. The physicochemical data was analyzed through linear regression, ANOVA (followed by Duncan's test), multivariate statistical analysis, viz., principal component analysis (PCA) and cluster analysis (CA). Linear regression pointed out the nature and magnitude of the relationship between different physicochemical variables (p < 0.05). PCA showed that WT, pH, EC, NO₃-N, TDS, TH, and DO are the major factors reflecting the water quality of the Vishav stream. The range in water quality parameters of the Vishav stream was found conducive for the inhabitant fishes. Two well-defined clusters were obtained, wherein Cluster-I comprising of Site-III (a downstream pollution prone site) and Cluster-II comprising of Site-II and Site-I (mid- and upstream site respectively) are less prone to human interferences. The present study could serve as baseline information to manage and conserve this precious element of the aquatic ecosphere in terms of better water quality for humans and its inhabitant faunal elements especially fish which play a significant role in the economy of that region.

Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems

This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO42-, total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO3, Mn and total coliform were associated with optimal water pH while SO42-, EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.

Hydrogeochemical processes controlling the groundwater salinity in the coastal aquifers of Southern Tamil Nadu, India

The current study identifies groundwater quality issues and investigates the most important geochemical processes that control seawater intrusion using various ionic ratios, hydrochemical facies evolution, and geochemical modelling. Cl_-/Br ratio is an important indicator to identify the origin of groundwater salinity in coastal aquifers. Nineteen percent of the groundwater samples with Cl^-/Br^- ratio similar to that of Standard Mean Ocean Water (SMOW) are affected by seawater intrusion in the study area. Particularly, nine groundwater samples have high chloride values and are similar to SMOW, and it may derived salinity from seawater sources from the Bay of Bengal due to the over-pumping of production wells in the Uvari zone. Five samples are similar to SMOW, which is due to the presence of salt pan activities. The bivariate plots such as Ca²⁺ + Mg²⁺ vs Cl^-, EC vs Cl^-, and Na⁺/Cl^- ratio indicate that seawater intrusion is the primary source for groundwater salinisation. Evaporation is the dominant process controlling groundwater chemistry, rather than rock-water interaction and precipitation, according to mechanisms controlling groundwater chemistry. Direct ion exchange and converse ion exchange are the critical controlling factors for groundwater salinisation, according to the hydrochemical facies evolution diagram (HFED). The water quality index (WQI) shows that most groundwater belongs to the poor to the marginal category. The saturation indices show that the groundwater samples are saturated with minerals such as dolomite, calcite, aragonite and magnesite. Therefore, these minerals are susceptible to precipitation due to the effective leaching of calcareous minerals from the bedrocks. Compiled hydrogeochemical analysis and multivariate statistical analysis revealed that the Tiruchendur and Uvari zone was affected by the seawater intrusion and led to an increase in the salinity of the groundwater.

The Mechanism and Mediating Effect of the "Perception-Emotion-Behaviour" Chain of Tourists at World Natural Heritage Sites-A Case Study from Bayanbulak, China

The pro-environmental behaviour intentions (PEBIs) of tourists is a popular topic in tourism geography research. Visitors are important stakeholders in the development and conservation of World Natural Heritage sites (WNHs). Based on the perspective of the Mehrabian–Russell (M-R) theory, to advance our understanding of the transmission mechanism and mediation effect of the “perception–emotion–behaviour” chain of visitors at World Natural Heritage sites, we introduced two variables, namely heritage genes perception (HGP) and environmental knowledge perception (EKP), combined with place attachment (PA) and pro-environmental behaviour intentions (PEBIs), and scientifically constructed the conceptual model of the “EHPP model”, consisting of EKP, HGP, PA and PEBIs. Taking the Bayanbulak Heritage Site as an example, the EHPP model was fitted and tested using the structural equation model (SEM). The results show that: (1) the EHPP model is applied to fit the “cognitive–emotional–behaviour intentions” chain of visitors in WNHs and passed the empirical test; (2) there were positive and significant effects of EKP on HGP, and EKP indirectly affects PEBIs via HGP and PA; (3) place dependence (PD) had a significant and positive influence on place identity (PI); and (4) compliance with pro-environmental behaviour intentions (CPEBIs) had a direct positive influence on pro-environmental behaviour intentions (PPEBIs). The findings of this study provide empirical references for stimulating the pro-environmental behaviour intentions of tourists at World Natural Heritage sites.

Assessing the Water Pollution of the Brahmaputra River Using Water Quality Indexes

Water quality is continuously affected by anthropogenic and environmental conditions. A significant issue of the Indian rivers is the massive water pollution, leading to the spreading of different diseases due to its daily use. Therefore, this study investigates three aspects. The first one is testing the hypothesis of the existence of a monotonic trend of the series of eight water parameters of the Brahmaputra River recorded for 17 years at ten hydrological stations. When this hypothesis was rejected, a loess trend was fitted. The second aspect is to assess the water quality using three indicators (WQI)-CCME WQI, British Colombia, and a weighted index. The third aspect is to group the years and the stations in clusters used to determine the regional (spatial) and temporal trend of the WQI series, utilizing a new algorithm. A statistical analysis does not reject the hypothesis of a monotonic trend presence for the spatially distributed data but not for the temporal ones. Hierarchical clustering based on the computed WQIs detected two clusters for the spatially distributed data and two for the temporal-distributed data. The procedure proposed for determining the WQI temporal and regional evolution provided good results in terms of mean absolute error, root mean squared error (RMSE), and mean absolute percentage error (MAPE).

Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed

Stream waters play a crucial role in catering to the world's needs with the required quality of water. Due to the discharges of wastewater from the various point and nonpoint sources, most of the watersheds are contaminated easily. The Upper Green River watershed in Kentucky, USA, is one such watershed that is contaminated over the years due to the runoff from rural areas and agricultural lands and combined sewer overflows (CSOs) from urban areas. Monitoring and characterizing the water quality status of streams in such watersheds has become of great importance, with multivariate statistical techniques such as regression, factor analysis, cluster analysis, and artificial intelligence methods such as artificial neural networks (ANNs). The water quality parameters, namely, fecal coliform (FC), turbidity, pH, and conductivity have been predicted quantitatively using ANNs to understand the water quality status of streams in the Upper Green River watershed elsewhere. In this study, a novel attempt has been made to predict the status of the quality of the Green River water with the predictive capabilities of a few decision tree (DT) algorithms such as classification and regression tree (CART) model, multivariate adaptive regression splines (MARS) model, random forest (RF) model, and extreme learning machine (ELM) model. The RF model's performance is better in predicting FC, turbidity, and pH than CART models in training and testing phases. Relatively, MARS and ELM models did better in testing though the performance is poorer in training. For example, we obtain the RMSE values of 2206, 2532, 1533, and 1969 using RF, CART, MARS, and ELM for FC in testing. A good correlation has been observed between conductivity and temperature, precipitation, and land-use factors for the MARS model. Overall, DT models are helpful in understanding, interpreting the outcomes, and visualizing the results compared with the other models. PRACTITIONER POINTS: The prediction of stream water quality parameters using decision trees is explored. The climate and land use parameters are used as input parameters to the modeling. The DT models of CART, MARS, RF, and ANNs such as ELM are explored to predict stream water quality. The RF model shows stable results compared with CART, MARS, and ELM for the data explored. Apart from the R² value, RMSE and MAE indicate the effectiveness of DTs in prediction.

Temporal trends and source apportionment of water pollution in Honghu Lake, China

Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004-2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004-2011, with better water quality in the wet period than in the dry periods, while the results over 2012-2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting CODMn concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH3-N, TP, and TN decreased by 0.2 mg L-1, 0.039 mg L-1, and 0.37 mg L-1, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to CODMn decreased by 1.17 mg L-1. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L-1 and 887 cfu L-1, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.

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.

Fluoride and nitrate in groundwater of rural habitations of semiarid region of northern Rajasthan, India: a hydrogeochemical, multivariate statistical, and human health risk assessment perspective

In arid and semiarid regions, groundwater is required for the drinking, agriculture, and industrial activities due to scarcity of surface water. Groundwater contaminated with high concentrations of fluoride and nitrate can severely affect human health in these regions. Twenty-eight groundwater samples from rural habitations of Jhunjhunu district, Rajasthan, India, were collected in March 2018 and subjected to analysis for water quality parameters. Fluoride and nitrate concentrations in groundwater varied from 0 to 5.74 mg/L and 10.22-519.64 mg/L, respectively. Nitrate content of about 86% samples and fluoride content of about 54% exceeded the permissible limit of Bureau of Indian Standards (IS:10,500) as well as World Health Organization standards. All groundwater samples belonged to poor to unfit drinking water quality index. Principle component analysis elucidates the anthropogenic contribution to high nitrate concentrations observed in this area. Noncarcinogenic human health risk evaluated from high nitrate and fluoride in drinking water for children, men, and women points to the fact that noncarcinogenic risk is exceeding the allowable limit to human health. The predominating hydrochemical facies in the area is Na⁺-HCO₃^--Cl^- followed by Na⁺-Mg²⁺-HCO₃^--Cl^-. The Gibbs plot and bivariate ionic cross-plots suggest the noncarbonate weathering (rock dominance), evaporation dominance, and ion exchange process to be the predominating geochemical mechanisms governing the evolution of groundwater hydrogeochemistry. Giggenbach diagram shows the immature character, i.e., incomplete equilibration of the groundwater.

Evaluation of Seasonal and Spatial Variations in Water Quality and Identification of Potential Sources of Pollution Using Multivariate Statistical Techniques for Lake Hawassa Watershed, Ethiopia

The magnitude of pollution in Lake Hawassa has been exacerbated by population growth and economic development in the city of Hawassa, which is hydrologically closed and retains pollutants entering it. This study was therefore aimed at examining seasonal and spatial variations in the water quality of Lake Hawassa Watershed (LHW) and identifying possible sources of pollution using multivariate statistical techniques. Water and effluent samples from LHW were collected monthly for analysis of 19 physicochemical parameters during dry and wet seasons at 19 monitoring stations. Multivariate statistical techniques (MVST) were used to investigate the influences of an anthropogenic intervention on the physicochemical characteristics of water quality at monitoring stations. Through cluster analysis (CA), all 19 monitoring stations were spatially grouped into two statistically significant clusters for the dry and wet seasons based on pollution index, which were designated as moderately polluted (MP) and highly polluted (HP). According to the study results, rivers and Lake Hawassa were moderately polluted (MP), while point sources (industry, hospitals and hotels) were found to be highly polluted (HP). Discriminant analysis (DA) was used to identify the most critical parameters to study the spatial variations, and seven significant parameters were extracted (electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), total nitrogen (TN), total phosphorous (TP), sodium ion (Na+), and potassium ion (K+) with the spatial variance to distinguish the pollution condition of the groups obtained using CA. Principal component analysis (PCA) was used to qualitatively determine the potential sources contributing to LHW pollution. In addition, three factors determining pollution levels during the dry and wet season were identified to explain 70.5% and 72.5% of the total variance, respectively. Various sources of pollution are prevalent in the LHW, including urban runoff, industrial discharges, diffused sources from agricultural land use, and livestock. A correlation matrix with seasonal variations was prepared for both seasons using physicochemical parameters. In conclusion, effective management of point and non-point source pollution is imperative to improve domestic, industrial, livestock, and agricultural runoff to reduce pollutants entering the Lake. In this regard, proper municipal and industrial wastewater treatment should be complemented, especially, by stringent management that requires a comprehensive application of technologies such as fertilizer management, ecological ditches, constructed wetlands, and buffer strips. Furthermore, application of indigenous aeration practices such as the use of drop structures at critical locations would help improve water quality in the lake watershed.

Development of a groundwater quality index: GWQI, for the aquifers of the state of Bahia, Brazil using multivariable analyses

This work elaborated a groundwater quality index-GWQI, for the aquifers of the state of Bahia, Brazil, using multivariable analyses. Data from 600 wells located in the four hydrogeological domains: sedimentary, crystalline, karstic, and metasedimentary, were subjected to exploratory statistical analysis, and 22 out of 26 parameters were subjected to multivariable analysis using Statistica (Version 7.0). From the PCA, 5 factors were sufficient to participate in the index, due to sufficient explanation of the cumulative variance. The matrix of factorial loads (for 1-5 factors) indicated 9 parameters related to water quality and 4 hydrological, with factor loads above ± 0.50, to be part of the hierarchical cluster analysis. The dendrogram allowed to choose the 5 parameters related to groundwater quality, to participate in the GWQI (hardness, total residue, sulphate, fluoride and iron). From the multivariable analyses, three parameters from a previous index-NGWQI, were not selected for the GWQI: chloride (belongs to the hardness hierarchical group); pH (insignificant factor load); and nitrate (significant factor load only for 6 factors), also, not a regionalized variable. From the set of communality values (5 factors), the degree of relevance of each parameter was extracted. Based on these values, were determined the relative weights (w_i) for the parameters. Using similar WQI-NSF formulation, a product of quality grades raised to a power, which is the weight of importance of each variable, the GWQI values were calculated. Spatialization of 1369 GWQI values, with the respective colors, on the map of the state of Bahia, revealed good correlation between the groundwater quality and the index quality classification. According to the literature on water quality indexing, the GWQI developed here, using emerging technologies, is a mathematical tool developed as specific index, as it was derived using limits for drinking water. This new index was tailored to represent the quality of the groundwater of the four hydrogeological domains of the state of Bahia. Although it has a regionalized application, its development, using, factor analysis, principal component analysis, and hierarchical cluster analysis, participates of the new trend for WQI development, which uses rational, rather than subjective assessment. The GWQI is a successful index due to its ability to represent the groundwater quality of the state of Bahia, using a single mathematical formulation, the same five parameters, and unique weight for each parameter.

Using multivariate statistical analysis in assessment of surface water quality and identification of heavy metal pollution sources in Sarough watershed, NW of Iran

The Sarough watershed in NW Iran hosts a large amount of mineral occurrences and ore deposits which may be considered as the source of heavy metals in the region. The area has been studied previously; however, the methodology of this paper was less focused on previous studies. This study aimed to assess water quality, determine the spatial distribution pattern, and identify the sources of heavy metals in the main tributaries of Sarough watershed using pollution indexes, multivariate statistical methods, and processing data by geographic information system. Totally, 51 water samples were collected along the main rivers to determine the concentrations of heavy metals by ICP-MS. Regarding the drinking water, agriculture, and freshwater aquatic life guidelines, the rivers were assumed unsafe considering most of toxic elements' content, especially As. The mean values for heavy metal pollution indexes (HPI: 237.32) and metal indexes (MI: 25.37) indicated the intensive heavy metal pollution. The cluster analysis categorized the 51 sampling sites into four clusters with respect to pollution level. The results obtained from the Kruskal-Wallis and multiple comparison tests had the harmony with the results of CA in introducing the most impacted sampling sites and the parameters responsible for water quality degradation. The results of PCA showed the maximum similarity between As, Sb, Se, Fe, and Mn as well as base metals which was attributed to anthropogenic input from mining and mineral processing wastes. Association of Cr and Ni may suggest a lithology source (weathering of metamorphosed ultramafic outcrops). The maps prepared in the GIS system showed the spatial distribution pattern of toxic elements with maximum values nearby mining sites which decreases gradually toward downstream areas. Finally, the results showed that the Sarough River and its tributaries are influenced by high concentrations of heavy metals from the drainages of mining and ore processing sites and naturally occurring metal loadings as well as the geogenic sources such as weathering of geologic formations and hot springs.