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

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.

Climate and Land Use Influences on Bacteria Levels in Stormwater

The influence of climatic variables and land use on fecal coliform (FC) levels in stormwater collected from outfalls throughout southern Vancouver Island between 1995 and 2011 are examined through statistical analyses, Fourier analysis, Multiple Linear Regression (LR) and Multivariate Logistic Regression (MLR). Kendall’s τ-b demonstrated that FC levels were significantly and positively correlated with the amount of residential area within a drainage catchment generating the runoff, and that FC levels were location dependent. Climatic variables of temperature and antecedent dry period length were significantly and positively correlated with FC levels at both the sampling location level and across the region overall. Precipitation and flowrates were negatively correlated with FC levels. Fourier analysis showed that monthly FC levels shared the same 12 month cycle (peaking in July) as precipitation and temperature. MLR modelling was applied by aggregating the LogFC data by order of magnitude. The MLR model shows that the data are subject to different influences depending on the season and as well, the month of the year. The land use and climate analyses suggest that future climate change impact studies attempted on nearshore bacterial water quality should be conducted at the urban catchment scale.

Variations in the Distribution of Chl-a and Simulation Using a Multiple Regression Model

Chlorophyll a (Chl-a) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl-a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl-a in the bay was investigated, and a statistical model to relate the Chl-a concentration to key driving variables was also developed. The monthly Chl-a concentration in Meiliang Bay changed from 2.6 to 330.0 μg/L, and the monthly mean Chl-a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl-a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl-a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH (r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO3--N; r = -0.28, p < 0.01), dissolved oxygen (DO; r = -0.12, p < 0.01), and Secchi depth (ln(SD); r = -0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl-a concentrations was established (R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl-a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

Allocation of weights using factor analysis for development of a novel water quality index

Water quality assessment and monitoring is one of the most important aspects for ensuring a good environmental health. A Water Quality Index (WQI) is one of the most frequently used tools for assessing overall quality of water resources. This study uses Factor Analysis (FA) for one of the most significant steps in WQI development - weight determination. Factor analysis has been applied to the water quality parameters shortlisted from Principal Component Analysis in the study area and it grouped the parameters into different sets of loadings. Each loading contained a group of parameters contributing to the overall variance addressed by that loading. Weights were allocated to each loading as well as to individual parameters within each loading. For final aggregation, a hybrid method was followed; where weighted harmonic means were estimated for the parameters within each loading and weighted arithmetic mean was estimated from the results of harmonic mean. The use of multivariate statistical technique reduces the subjectivity in the development of the final WQI and makes the current study a useful step in future for the development of a Ganga Water Quality Index (GWQI). In addition to this, the developed methodology can also be used for developing WQI for any water body depending on the availability of historical data.

Application of Escherichia coli antibiotic resistance patterns for contamination source identification in watershed

Spatial correlation of pollution of the water resource in Taipei, Taiwan, were examined by analyzing the antibiotic resistance patterns (ARPs) of 96 Escherichia coli colonies, which were isolated from 7 sampling sites in 3 river sections. The ARPs were the growth patterns of isolated E. coli colonies in the medium with seven kinds of antibiotics, including ampicillin, chlortetracycline, erythromycin, oxytetracycline, streptomycin, tetracycline, and salinomycin of different concentrations. The results showed that the survival rate of E. coli decreased with increasing concentration of antibiotics; however, various ARPs under different antibiotics of different concentrations significantly increased both the useful information and complexities. Hierarchical cluster analysis (HCA) and two-stage principal component analysis (PCA) were applied to analyze the spatial correlations and interrelations of distinct ARPs among sampling sites in this study. It was found that the seven sampling sites can be categorized into three groups which may represent three possible pollution characteristics.

Metagenome of a polluted river reveals a reservoir of metabolic and antibiotic resistance genes

BACKGROUND

Yamuna, a major tributary of Ganga, which flows through the national capital region of Delhi, is among the major polluted rivers in India. The accumulation of various effluents, toxic chemicals, heavy metals, and increased organic load in the Yamuna directly affects the organisms that thrive inside or around this river. It also makes it an ideal site for studying the impact of pollution on the river microflora, which are sentinels of the water quality.

RESULTS

In this study, the microbial community structure and functional diversity of the Yamuna river water was assessed from the New Delhi region. The community structure of Yamuna during pre-monsoon (June) was found to be significantly different from the post-monsoon (November) time, with Acinetobacter being the most abundant genus during June, and Aeromonas during November. The functional characterization revealed the higher abundance of Methyl-accepting chemotaxis protein in the river water, which could be important for the microbial chemosensory adaptation in the environment. A higher abundance of genes related to nitrogen and sulfur metabolism, metal tolerance, and xenobiotic degradation, and complete degradation pathways of aromatic compounds such as toluene, xylene, benzene and phenol were identified. Further, the results showed the presence of a pool of antibiotic resistance genes in the bacterial microbiome in the Yamuna alongside a large number of broad-spectrum antibiotics, such as carbapenemases and metallo-β-lactamases. Efflux mechanism of resistance was found to dominate among these microbes conferring multi-drug resistance. The Principal Coordinate Analysis of the taxonomic composition of the Yamuna River water with publicly available freshwater and sewage datasets revealed significant differences in the two Yamuna samples and a greater resemblance of pre-monsoon Yamuna sample to sewage sample owing to the higher pollution levels in Yamuna in the pre-monsoon time.

CONCLUSION

The metagenomic study of the Yamuna river provides the first insights on the bacterial microbiome composition of this large polluted river, and also helps to understand the dynamics in the community structure and functions due to seasonal variations. The presence of antibiotic resistance genes and functional insights on the metabolic potential of a polluted river microbiome are likely to have several applications in health, biotechnology and bioremediation.

Chemical and physicochemical characterization of effluents from the tanning and textile industries in Bangladesh with multivariate statistical approach

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz., Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz., F^-, Cl^-, NO₂^-, NO₃^-, and SO₄^2- were measured by ion chromatograph. The physicochemical parameters viz., temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl^-, SO₄^2-, and Cr concentrations while textile dyeing effluents have higher pH, NO₂^-, and NO₃^- concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

Meltwater hydrochemistry at four glacial catchments in the headwater of Indus River

Glacier runoff shows significant change under global warming in the headwater region of the Indus River with great impact on its highly populated downstream area, but the hydrochemistry characteristics of meltwater and the changing mechanism remain unclear in this region. In this study, runoff water samples were collected during May and June, 2015, from four glacial catchments in the Upper Indus Basin to investigate general characteristics and daytime dynamics of meltwater runoff together with sediment and chemical contents. Results showed that glacier runoff in the studied area had an alkaline pH and much higher sediment yields than the local average of the non-glacier areas. The carbonate-dominated geological feature in the four catchments resulted in single chemical facies of Ca-HCO₃. The dominant process determining the glacier runoff chemistry was rock-water interaction, with less soluble minerals and less intensive evaporate weathering in the Passu and Gulmit catchments than the B&B and Hinarchi catchments. Comparing the investigated catchments, the larger glacier with longer flow path exhibited higher runoff but lower melting rate, higher SSC resulting from higher erosive power of flow, and higher solute concentrations as a consequence of more intensive contact of meltwater with rock minerals along the longer flow path. For individual catchments, a negative correlation between TDS and flow rate (R² = 0.26~0.53) and changing trends of ion ratios with flow rate demonstrated that under intensive melting conditions, rock-water interactions were reduced, resulting in dilution of solutes. Overall, the general chemical characteristics of the investigated glacier runoff indicated geological control, whereas individual glacier illustrated hydrological control on the daytime dynamics of glacier runoff chemistry. The presence of glacier terminal lake and agriculture land can significantly alter the hydrochemistry of downstream runoff.

Multivariate Analyses and Human Health Assessments of Heavy Metals for Surface Water Quality in the Xiangjiang River Basin, China

Pollution by heavy metals in river water is becoming a major subject of global drinking water concern, and the Xiangjiang River is one of the most heavily polluted rivers in China. Water samples were collected from 17 sites spanning the entire Xiangjiang watershed from 2005 to 2016 to investigate spatial-temporal distributions and potential human health risks related to 8 metal pollutants (As, Cd, Hg, Cr, Cu, Pb, Zn, and Se). The results of spatial-temporal distribution analyses proved that most metals were below the guideline limits the majority of the time. However, the hazard index and carcinogenic risk analyses indicated that As and Cr were associated with a potential risk of cancer, although noncarcinogenic heavy metals in general and carcinogenic risk declined year by year. A nonparametric seasonal Mann-Kendall's test revealed that there were notable decreasing trends in As, Cd, Zn, Cu, Cr, and Pb for most sites, whereas Se and Hg significantly increased in some areas over the targeted 12 yr. The results of principal component analysis agreed with those of dual hierarchical cluster analysis in the identification of pollution sources, the results of which are as follows: 1) As, Cd, Pb, Hg, and Zn were mainly derived from anthropogenic activities and the smelting industry; 2) Cr and Cu mainly originated from agricultural or industrial activities; and 3) Se was predominantly from natural erosion. The present study will be conducive to optimizing the distribution of water monitoring stations and drafting remediation strategies pertaining to the protection of public health in metal-polluted areas. Environ Toxicol Chem 2019;38:1645-1657. © 2019 SETAC.

Water quality trend assessment in Jakarta: A rapidly growing Asian megacity

Megacities are facing serious water pollution problems due to urbanization, rapid population growth and economic development. Water is an essential resource for human activities and socio-economic development and water quality in urban settings has important implications for human and environmental health. Urbanization and lack of sewerage has left the water in Jakarta, Indonesia in a heavily polluted condition. Rigorous assessment of urban water quality is necessary to understand the factors controlling water quality conditions. We use trend analysis to assess the current water quality conditions in Jakarta, focusing on Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), and Total Suspended Solids (TSS). In most monitoring stations analyzed, BOD and TSS concentrations have decreased over time, but from large starting concentrations. DO in most monitoring stations has increased. Although Jakarta’s water quality has shown some improvement, it remains heavily impaired. The average value of BOD is low in upper stream stations compared to middle and lower stream stations. BOD and TSS trends of some water quality stations in middle and lower streams show increasing trends. Cluster analysis results suggest three groups for BOD and TSS, and four groups for DO. Understanding water quality conditions and factors that control water quality suggest strategies for improving water quality given current trends in climate, population growth and urban development. Results from this study suggest research directions and management strategies to address water quality challenges.

Water Quality and Macroinvertebrate Community in Dryland Streams: The Case of the Tehuacán-Cuicatlán Biosphere Reserve (México) Facing Climate Change

The Tehuacán-Cuicatlán Biosphere Reserve (TCBR), the southernmost semi-arid zone of North America, includes two dryland streams, the Río Salado (RS) and Río Grande (RG); it is surrounded by high vegetation diversity, a cacti diversification center, and the densest columnar cacti forest worldwide. However, no scientific knowledge is currently available on these dryland streams. We evaluated water quality, its relationship with the local geological characteristics, land uses, and the composition of aquatic macroinvertebrates (AM), analyzing their bioindicator potential. These results were discussed in relation to climate change predictions. The RS showed higher mineralization, salinity, hardness, water and air temperature, and low water quality index (WQI), relative to the RG. A discriminant analysis showed spatial (mineralization, salinity, and hardness in the RS) and temporal patterns (higher nitrogen compounds and temperature in the rainy season). The RS showed a lower AM diversity (40 taxa) compared to the RG (73 taxa); Ephemeroptera-Plecoptera-Trichoptera reached higher values in the RG. A co-inertia analysis identified five groups of sites with different AM assemblages and water quality characteristics. Climate change predictions for the TCBR suggest increased aridity, higher temperature, and lower rainfall, leading to reduced river flow and increased salinity and mineralization. These could alter habitat features and connectivity, with loss of AM diversity, highlighting the vulnerability of these unique ecosystems to climate change.

Anthropogenic activity-induced water quality degradation in the Loktak lake, a Ramsar site in the Indo-Burma biodiversity hotspot

Wetland contributes to human well-being and poverty alleviation. The increase in human population leads to more demand for water and degradation of the water bodies around the globe, resulting in scarcity of water. The aim of the present study was to assess the impact of anthropogenic activity on the water quality of the Loktak lake. Water samples were collected seasonally, namely, monsoon, post monsoon, winter and pre-monsoon, during 2013-2014 from 10 sites. For each water sample, 20 physicochemical parameters were analysed using the American Public Health Association method. Furthermore, 11 significant parameter values were used to develop the water quality index (WQI). The result shows high concentrations of nitrite (5.45-11.83 mg/l) and nitrate (93.67-177.75 mg/l) in rivers which is beyond the permissible limit and higher compared to the Loktak. Highest turbidity was observed at Langthabal with 21 NTU, which is above the permissible limit. The WQI of the Loktak ranged from 64 to 77, while for rivers they ranged from 53 to 95, which indicates that the water is in a very poor state. The WQI values of rivers are higher compared with those of the lake, and it was identified that water from the rivers is a major reason for increase in pollution in the lake water. The study suggests the need for long-term monitoring of the lake aquatic ecosystem and identification of pollution sites for proper management of the lake water. The WQI is an important tool to enable the public and decision makers to evaluate the water quality of the Loktak lake.

Identifying effects of pipe material, hydraulic condition, and water composition on elemental accumulation in pipe corrosion scales

Identification of the accumulation mechanism of major elements on pipe surface is essential to investigate the development of corrosion scales and co-occurrence of trace inorganic contaminants. In this study, corrosion scale samples were collected from old, corroded iron pipes made of different materials and exposed to different water qualities and operation conditions. Elemental composition of these scales was determined by energy dispersive X-ray spectroscopy (EDS). Cumulative occurrence analysis, Q-style hierarchical cluster analysis (CA), and principal component analysis (PCA) were conducted to ascertain major elements typical for corrosion scales and to estimate the dominant influencing factor to each elemental constituent. The major elements in the examined scales are Fe, C, Zn, Si, Ca, Al, and S in the descending prevalence. Their occurrences are influenced by an interactive effect. Pipe material imposes a significant effect on the accumulation of Fe, Zn, and Ca in corrosion scales; water composition can account for the presence of Si, Al, and S in this study; hydraulic condition is identified as the primary factor influencing the occurrence of C and Ca. Q-style CA and PCA are verified practicable for data interpretation and identification of dominant factors influencing scale characteristics.

Assessment of the Bulgarian Wastewater Treatment Plants' Impact on the Receiving Water Bodies

Deterioration of water quality is a major problem world widely according to many international non-governmental organizations (NGO). As one of the European Union (EU) countries, Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to utilize ecotoxicological (Microtox^®, Phytotoxkit F^TM, Daphtoxkit F^TM), instrumental (to determine pH, electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen (N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well as advanced chemometric methods (partial least squares-discriminant analysis (PLS-DA)) in data evaluation to comprehensively assess wastewater treatment plants' (WWTPs) effluents and surface waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the physicochemical parameters gave excellent discrimination between WWTP effluents and surface waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and Se). The classification model based on ecotoxicological data identifies the plant test endpoints as having a greater impact on the classification model efficiency than bacterial, or crustaceans' endpoints studied.

Mapping human health risk from exposure to potential toxic metal contamination in groundwater of Lower Dir, Pakistan: Application of multivariate and geographical information system

The study aimed to assess the physicochemical parameters (pH, electrical conductivity (EC), total dissolve solid (TDS), oxidation reduction potential (ORP), Temperature) and potential toxic metals (PTMs), including Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe and Zn in the groundwater of Lower Dir, Pakistan. Furthermore, the pollution sources and spatial distribution pattern of PTMs were also investigated via principal component analysis (PCA) and geographic information system (GIS) application to understand the changing behaviors of PTMs in groundwater. The average concentrations of physicochemical parameters such as pH, EC, TDS, ORP and Temperature were 7.1, 418 μS/cm, 251 mg/L, 193 mV and 25.7 ^○C, while the concentrations of PTMs; Ni, Mn, Cr, Cu, Cd, Pb, Co, Fe and Zn were 0.25, 0.34, 0.09, 0.29, 0.10, 0.08, 0.10, 0.83 and 0.25  mg/L, respectively. Among the selected metals, Mn, Cr, Cd, Pb, Co and Fe were exceeded the WHO guidelines and their percentage contribution were 43%, 57%, 45%, 70%, 70% and 62%, respectively. The increasing order of PTMs were; Pb > Co > Fe > Cr > Cd > Mn > Cu > Ni > Zn in the study area. PCA represented three significant factors, which explained 76% variability in the groundwater. Whereas, clustering analysis (CA) grouped groundwater into three distinct clusters less polluted (C1), moderate polluted (C2) and highly polluted (C3). Human health risk assessment was carried out to check the suitability of groundwater for drinking and domestic uses. The HQ and HRIs values of Cd were >1, suggested that the groundwater sources are unfit for drinking and domestic purposes and may be caused potential health risk after long term ingestion.

Quality Indicators and Possible Ecological Risks of Heavy Metals in the Sediments of three Semi-closed East Mediterranean Gulfs

Pollution with copper (Cu), lead (Pb), zinc (Zn), chromium (Cr), and nickel (Ni) heavy metals of the surface sediments collected from three semi-closed East Mediterranean Gulfs, namely Kavala, Strymonikos, and Ierissos Gulfs, North Aegean Sea, Greece, was investigated to evaluate potential benthic ecological risks. The mean concentrations of the studied metals decrease according to the order: Zn > Pb > Cr > Ni > Cu (176.50, 166.23, 127.41, 43.12, and 33.64 mg kg⁻¹ dry weight). Quality indicators and possible ecological risks for metals in surface sediments were evaluated at 60 sampling sites of these three gulfs using the contamination factor (CF), the contamination degree (CD), the pollution load index (PLI), the geoaccumulation index (I_geo), the potential risk factor (PRF_i), and the potential ecological risk index (PERI). Based on I_geo, the Ierissos Gulf sampling sites IER 2, 3, 7, and 9 exhibit moderate Pb pollution, whereas the sampling sites IER 6 and 8 show moderate to strong and strong Pb pollution, respectively. Based on the PRFi and PERI, the studied heavy metals did not pose any significant environmental risks for most of the investigated sites except IER 6 and 8 sampling sites, which may pose considerable environmental risk for Pb. To evaluate potential sources for each metal, multivariate techniques including hierarchical cluster analysis and ANOVA were used.

Adequacy analysis of drinking water treatment technologies in regard to the parameter turbidity, considering the quality of natural waters treated by large-scale WTPs in Brazil

This paper seeks to present a performance evaluation of large-scale water treatment plants and verify the adjustment of the treatment to the parameter turbidity of natural waters. Nonparametric and multivariate statistical tools were used to analyze raw water and treated water turbidity of a large on-line monitoring databank for the period from 2013 to 2015, from six large-scale treatment plants utilizing different technologies. Cluster analysis was able to differentiate adequately groups of treatment plants with similar raw and treated water quality. Considering the effluent turbidity as a marker parameter, the results indicated that selection of the technology to be applied must be well studied to always seek the best solution, and that other factors than only the raw water characteristics should be evaluated. It was also demonstrated that utilization of the same treatment technology does not always result in the same effluent quality, since there are many factors related to operation, maintenance, raw water variability, climatic interferences, and others.

Effects of Multi-Dike Protection Systems on Surface Water Quality in the Vietnamese Mekong Delta

The Vietnamese Mekong Delta (VMD) is one of the largest rice-growing areas in Vietnam, and exports a huge amount of rice products to destinations around the world. Multi-dike protection systems have been built to prevent flooding, and have supported agricultural intensification since the early 1990s. Semi-dike and full-dike systems have been used to grow double and triple rice, respectively. Only a small number of studies have been conducted to evaluate the water quality in the VMD. This study aimed to analyze the spatiotemporal variation of water quality inside the dike-protected area. Surface water samples were collected in the dry and wet seasons at 35 locations. We used multivariate statistical analyses to examine various water quality parameters. The mean concentrations of COD, NH4+, NO3−, PO43−, EC, and turbidity were significantly higher in water samples inside the full-dike system than in water samples from outside the full-dike systems and inside the semi-dike systems in both seasons. High concentrations of PO43− were detected in most of the primary canals along which residential, tourist areas and local markets were settled. However, NO3− was mainly found to be higher in secondary canals, where chemical fertilizers were used for rice intensification inside the dike system. Water control infrastructures are useful for preventing flood hazards. However, this has an adverse effect on maintaining water quality in the study area.

Assessment of the Impact of Forestry and Settlement-Forest Use of the Catchments on the Parameters of Surface Water Quality: Case Studies for Chechło Reservoir Catchment, Southern Poland

The aim of the study was to determine the impact of natural and anthropogenic factors on the values of 22 quality indicators of surface waters flowing out of two small catchments differing in physiographic parameters and land use, in particular forest cover and urbanization of the area. The research was carried out in the years 2012–2014 at four measurement-control points located on the Chechło river and the Młoszówka stream (Poland), which are the main tributaries of the retention reservoir. Basic descriptive statistics, statistical tests, as well as cluster analysis and factor analysis were used to interpret the research results. The water that outflowed from the forestry-settlement catchment of the Młoszówka stream contained higher concentrations of total phosphorus, phosphates, nitrite, and nitrate nitrogen and salinity indicators than outflow from the Chechło river. Water from the Młoszówka stream was characterized by more favourable oxygen conditions. Higher oxygen concentration in the catchment influenced a large slope of the watercourse and thus higher water velocity, which is promoted by the mixed process. In the case of the forest catchment of the Chechło river, the water quality was generally better than in the Młoszówka stream, mainly in cases of total suspended solids TSS, total phosphorus TP, phosphates PO43−, total nitrogen TN, nitrite N–NO2−, nitrate N–NO3−, and salinity parameters. Despite it being a short section of the river taken into the study, favourable self-purification processes like mixed, nitrification, and denitrification were observed in its water. The research shows that forest areas have a positive effect on the balance of most substances dissolved in water, and natural factors in many cases shape the quality and utility values of surface waters on an equal footing with anthropogenic factors. In the case of a large number of examined parameters and complex processes occurring in water, the interpretation of the results makes it much easier by applying multivariate statistical methods.

Assessment of Groundwater Quality at Yuncheng Basin: Denotation for the Water Management in China

Dramatic decreases in groundwater quality have raised widespread concerns about water supplies and ecological crises in China. In this study, hydrochemistry, stable isotopes, and graphical and multivariate statistical methods are integrated to identify hydrogeochemical processes controlling groundwater quality in the Yuncheng Basin, China. Our results show that groundwater with 21 variables (pH, temperature-T, total dissolved solid, major-trace elements, and stable isotopes) is chemically classified into three distinct clusters: fresh water [C1], brackish-saline water [C2], and saline water [C3]. Groundwater salinization is identified as the prime process in controlling groundwater quality for shallow groundwater and deep groundwater in the lowland areas. Large-scale As, F, or B contaminations found in groundwater are closely related to groundwater salinization, agricultural activity, and the exploration of geothermal water in the area. With respect to the risk of contamination, groundwater in the basin is spatially divided into the following: shallow groundwater with a high risk located in the north side of the Salt Lake, shallow groundwater with a moderate risk, and deep groundwater with a low to moderate risk. Nationally, the increasing demand on groundwater is threatened by a range of environmental and health pressures, including salinization and contaminations of nitrate, As, F, or B. Our study indicates that natural water-rock interactions and hydrogeological conditions are significant factors controlling these contaminations. Systematic management and regulation of existing groundwater resources are required to prevent further deterioration of groundwater resources. Policies should be made and implemented to ensure "green" exploitation of geothermal water.

Differentiation of weathered chemically dispersed oil from weathered crude oil

Oil fingerprinting is a crucial technology to trace the sources and behaviors of spilled oil. The use of dispersants enhances the stay of dispersed oil in a water column and changes the important properties of spilled oil. In case of fingerprinting of dispersed oil driven by dispersants, the fate and behaviors of biomarkers may be affected by the application of dispersants. Limited studies have investigated the statistical difference between fingerprinting of dispersed oil and non-dispersed oil using biomarkers, and the possible influence of the differences, if present. This study applied several principal component analyses (PCA) to differentiate weathered chemically dispersed oil from weathered crude (non-dispersed) oil using 103 diagnostic ratios of the same type of biomarkers and those of two types of biomarkers as input data. It showed that weathered chemically dispersed oil (CDO) can be differentiated from weathered crude oil (WCO) using specific diagnostic ratios that are affected by weathering. PCA analyses indicated the effects of the application of dispersants and weathering duration on weathering of biomarkers in CDO and WCO.

Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO₄, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R² value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.

Analyses on the Temporal and Spatial Characteristics of Water Quality in a Seagoing River Using Multivariate Statistical Techniques: A Case Study in the Duliujian River, China

In the Duliujian River, 12 water environmental parameters corresponding to 45 sampling sites were analyzed over four seasons. With a statistics test (Spearman correlation coefficient) and multivariate statistical methods, including cluster analysis (CA) and principal components analysis (PCA), the river water quality temporal and spatial patterns were analyzed to evaluate the pollution status and identify the potential pollution sources along the river. CA and PCA results on spatial scale revealed that the upstream was slightly polluted by domestic sewage, while the upper-middle reach was highly polluted due to the sewage from feed mills, furniture and pharmaceutical factories. The middle-lower reach, moderately polluted by sewage from textile, pharmaceutical, petroleum and oil refinery factories as well as fisheries and livestock activities, demonstrated the water purification role of wetland reserves. Seawater intrusion caused serious water pollution in the estuary. Through temporal CA, the four seasons were grouped into three clusters consistent with the hydrological mean, high and low flow periods. The temporal PCA results suggested that nutrient control was the primary task in mean flow period and the monitoring of effluents from feed mills, petrochemical and pharmaceutical factories is more important in the high flow period, while the wastewater from domestic and livestock should be monitored carefully in low flow periods. The results may provide some guidance or inspiration for environmental management.

Modeling Water Quality Parameters Using Data-Driven Models, a Case Study Abu-Ziriq Marsh in South of Iraq

Total dissolved solids (TDS) and electrical conductivity (EC) are important parameters in determining water quality for drinking and agricultural water, since they are directly associated to the concentration of salt in water and, hence, high values of these parameters cause low water quality indices. In addition, they play a significant role in hydrous life, effective water resources management and health studies. Thus, it is of critical importance to identify the optimum modeling method that would be capable to capture the behavior of these parameters. The aim of this study was to assess the ability of using three different models of artificial intelligence techniques: Adaptive neural based fuzzy inference system (ANFIS), artificial neural networks (ANNs) and Multiple Regression Model (MLR) to predict and estimate TDS and EC in Abu-Ziriq marsh south of Iraq. As so, eighty four monthly TDS and EC values collected from 2009 to 2018 were used in the evaluation. The collected data was randomly split into 75% for training and 25% for testing. The most effective input parameters to model TDS and EC were determined based on cross-correlation test. The three performance criteria: correlation coefficient (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency coefficient (NSE) were used to evaluate the performance of the developed models. It was found that nitrate (NO3), calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most influential inputs on TDS. While calcium (Ca+2), magnesium (Mg+2), total hardness (T.H), sulfate (SO4) and chloride (Cl−1) are the most effective on EC. The comparison of the results showed that the three models can satisfactorily estimate the total dissolved solids and electrical conductivity, but ANFIS model outperformed the ANN and MLR models in the three performance criteria: RMSE, CC and NSE during the calibration and validation periods in modeling the two water quality parameters. ANFIS is recommended to be used as a predictive model for TDS and EC in the Iraqi marshes.

Using the multidimensional synthesis methods with non-parameter test, multiple time scales analysis to assess water quality trend and its characteristics over the past 25 years in the Fuxian Lake, China

Fuxian Lake is one of the most important conservation areas in the southwest of China. Understanding the water-quality trend and characteristics is the basis of area protection for Fuxian Lake. This study combines of the Mann-Kendall, Mann-Kendall-Sneyers, water pollution index (WPI), the trophic state index and time series analysis to detect and characterize the water quality with a set of indicators: transparency (SD), permanganate index (IMn), total phosphorus (TP), total nitrogen (TN), biochemical oxygen demand (BOD₅), ammonia nitrogen (NH₃-N) and chlorophyll-a (Chl.a). Then, the characteristics are discussed on the scales of long-period, sections divided by the change point, and monthly data. Results show that the water quality of Fuxian Lake is at a good level but decreases consistently. Especially, the increasing trends of IMn, BOD₅, and Chl.a pass the confidence limit of the Z value of 2.56. The year of 2007 was designated as the change point. The characteristics of pollution and typical components vary in the long-period series, between the two sections divided by the change point, and in the monthly data. In the long period, the TN and TP are typical pollution components. However, the IMn, BOD₅, and Chl.a are typical components as the large changes of a 99.1% and 89.5% increase after the change point with respect to the former values, respectively. On the scale of monthly data of a typical year, the pollution grade and components varies within the years. The multidimensional synthesis methodology established by employing evaluation index systems from the aspects of pollution level, multi-attributes and the trophic state status could also be applied to detect and characterize the water quality of other water bodies. The results of the Fuxian Lake study can provide the base information and decision-making support for water quality management and area protection.

Comprehensive evaluation of water quality status for entire stretch of Yamuna River, India

This study represented the first comprehensive assessment of the physicochemical water quality status of the entire Yamuna River stretch in India. The upper zone had "excellent-to-good" water quality index (WQI) with mean 5-day biochemical oxygen demand (BOD₅) values of 2.1 and 2.4 mg/L during monsoon and non-monsoon, respectively. The middle region was described by "poor-to-marginal" WQI with average BOD₅ values of 13.1 mg/L (monsoon) and 32.3 mg/L (non-monsoon). The low WQI observations at the midstream region were due to the negative impact of two major drains, namely Najafgarh and Shahdara, that carry partially treated effluents from industrial units. Further, BOD₅ decreased to 1.9 mg/L (monsoon) and 1.8 mg/L (non-monsoon) in the lower zone, and the WQI values improved to "good" and "excellent". The dilution and depuration effects of the Chambal, Sindh, Betwa, and Ken Rivers recovered the environmental conditions in downstream stations. The oxygen sag curve complied with the water quality status along the river stretch. Based on the principal component analysis, the Yamuna River was strongly influenced by dissolved mineral salts originating from atmospheric deposition, weathering of soils and rocks, and application of deicing chemicals and landfills. Moreover, organic and nutrient substances and biological activities resulting from the discharge of sewage, and the utilization of fertilizers in agriculture, were the second contributors to pollution. The statistical techniques employed in this work could be beneficial for decision-makers (government and stakeholders) to identify the pollution sources/factors and to determine the viability of water bodies for domestic applications.

Spatial Analysis of Temperate Forest Structure: A Geostatistical Approach to Natural Forest Potential

Forest ecosystems represent an important means of ecosystem services; they are key as carbon sinks, water collectors, soil stabilizers, suppliers of great biological diversity, among other benefits. In addition, regionalization based on forest conditions provides a valuable approach to understanding and analyzing spatial patterns, which is useful as a tool for the implementation of forest ecosystem protection and conservation programs. In this research, the structure of a temperate forest in the western Sierra Madre region of Mexico was analyzed and characterized. The study unit was the watershed and the analysis used a geospatial approach combined with multivariate techniques such as: Principal Component Analysis, Cluster Analysis (CA), Discriminant Analysis (DA) and Multivariate Analysis of Variance. We evaluated the relationships among spectral satellite data, thematic maps and structural forest variables. A total of 345 watersheds were grouped based on these variables. The grouping of watersheds under low, medium and high production conditions was carried out with CA, defining 3 groups. The validation of the grouping was performed through DA, estimating errors with the restitution method, as well as with the cross-validation method. Significant differences were found among the groups. The grouping of watersheds provides observable evidence of the variability of the forest condition throughout the area. This study allows identifying forest areas with different levels of productivity and can help to detect levels of vulnerability and ecological fragility in natural forests in temperate zones.

Assessment of spatial variations in the surface water quality of the Velhas River Basin, Brazil, using multivariate statistical analysis and nonparametric statistics

The Velhas River sub-basin, which is located in the third-largest river basin in Brazil (São Francisco), is in an advanced state of degradation. In this work, the surface water quality of the Velhas River Basin was studied at 65 monitoring sites; 16 water quality parameters were sampled quarterly for 11 years (2008 to 2013). Cluster analysis (CA) and a nonparametric Kruskal-Wallis test were associated with the analysis of violations to water quality standards to interpret the water quality data set from the Velhas River Basin and assess its spatial variations. The CA grouped the 65 monitoring sites into four groups. The Kruskal-Wallis test identified significant differences (p < 0.05) between the groups formed by CA. The results show that watercourses located in the upper region of the Velhas River Basin are more affected by the release of industrial effluent and domestic sewage, and the lower region is more affected by diffuse pollution and erosion. This association between multivariate statistical techniques and nonparametric tests was effective for the classification and processing of large water quality datasets and the identification of major differences between water pollution sources in the basin. Therefore, these results provide an understanding of the factors affecting water quality in the Velhas River Basin. The results can aid in decision-making by water managers and these methods can be applied to other river basins.

Hydrochemical characteristics and quality assessment of groundwater from fractured Albian carbonaceous shale aquifers around Enyigba-Ameri, southeastern Nigeria

Enyigba-Ameri area is known for its Pb-Zn mining activities and the mine water is usually discharged directly into nearby streams and surface runoff. In order to determine the impacts of mining activities on the quality of water in the area and the general hydrochemical characteristics, field measurements and laboratory tests were carried out on water samples collected from the area. Field measurements and laboratory analyses of physicochemical parameters were determined using standard methods. In addition to the multivariate analyses (principal component analysis and cluster analysis) and ANOVA analysis, ionic cross-plots were used to determine the groundwater physicochemical characteristics and geochemical evolution. From the results, it was observed that Pb⁴⁺, Zn²⁺, Fe^{2 +  & 3+}, Ca²⁺, Mg²⁺, and K⁺ had a concentration higher than the stipulated guideline values. Three principal components which explained 87.42% of the total dataset were extracted through the data reduction process. Cluster analysis of the hydrochemical data grouped the water samples into three distinct classes. It was observed that the water chemistry is mainly affected by silicate minerals weathering, carbonate weathering, and base ion exchange processes in descending order. ANOVA analysis showed that Zn²⁺, Fe^{2 +  & 3+}, and Mg²⁺ had mean values that significantly differed from each other based on the sources of the samples. The Wilcox diagram revealed 4 classes of irrigation water types and the irrigation water quality indices showed that the groundwater in the area is not generally suitable for irrigation purposes.

Assessment of pulp and paper mill effluent quality and its toxicity to fingerlings of Cyprinus carpio

Effluents from a pulp and paper mill in India were analyzed for various physicochemical parameters and heavy metals. Multivariate statistical analysis was used to interpret the data obtained and predict the toxicity of each component to Cyprinus carpio L. fingerlings. The results showed that most of these parameters exceeded recommended levels set by various agencies, and the effluent was not suitable for discharge into agricultural fields or water bodies without further treatment. The results of static renewal toxicity tests revealed median lethal concentrations at 24, 48, 72, and 96 h to be 42, 25, 18.6 and 14.5%, respectively, and the fingerlings exhibited severe behavioral anomalies. The sublethal dose of 2.9% (v/v) caused alterations in the gill and liver at durations of 7, 14, 21, and 28 d. Anomalies such as mucous secretion, leucocyte infiltration, curling of the secondary lamellae tip, clubbing, and the fusion of the secondary lamellae, aneurism, etc. were prominent. The liver showed inflammatory infiltration, cytoplasmic inclusion bodies, vacuolar degeneration, fatty degeneration, pyknosis, karyolysis, distorted pancreatic area, and piecemeal necrosis, among other pathological symptoms. The study concluded that the effluent, even in greatly diluted form, was highly toxic and the severity of responses depended on the length of exposure.

Investigation and Source Apportionment of Air Pollutants in a Large Oceangoing Ship during Voyage

The aims of this study were to determine compartmental air pollution during navigation of a large oceangoing ship and to identify preliminarily the major pollution sources. During the voyage of a bulk carrier ship, air samples were collected at 18 selected sites using a stratified sampling method. The concentrations of 15 pollutants were determined using gas chromatography. Results showed the concentrations of these pollutants varied significantly among the sampling sites, indicating major pollution sources at or nearby those locations. Five common factors extracted using factor analysis explained 89.092% of the total variance. Multivariate linear regression analysis showed the contributions to air pollution of these five common factors, i.e., the volatilization of ship paint, volatilization of ship-based oil, cooking activities, high-temperature release of rubber components on the ship and daily use of chemical products, and the application of deodorant and insecticide, were 41.07%, 25.14%, 14.37%, 11.78%, and 7.63%, respectively. Three significant groups were determined using cluster analysis based on their similarity, i.e., high, medium, and low pollution of sampling sites. This study established that the air of the bulk carrier ship was heavily polluted, and that effective identification of pollution sources could provide a scientific basis for its control.

Combining Spatial Analysis and a Drinking Water Quality Index to Evaluate Monitoring Data

Drinking water monitoring is essential for identifying health-related risks, as well as for building foundations for management of safe drinking water supplies. However, statistical analyses of drinking water quality monitoring data are challenging because of non-normal (skewed distributions) and missing values. Therefore, a new method combining a water quality index (WQI) with spatial analysis is introduced in this paper to fill the gap between data collection and data analysis. Water constituent concentrations in different seasons and from different water sources were compared based on WQIs. To generate a WQI map covering all of the study areas, predicted WQI values were created for locations in the study area based on spatial interpolation from nearby observed values. The accuracy value of predicted and measured values of our method was 0.99, indicating good predication performance. Overall, the results of this study indicate that this method will help fill the gap between the collection of large amounts of drinking water data and data analysis for drinking water monitoring and process control.

Multivariate statistical approach and water quality assessment of natural springs and other drinking water sources in Southeastern Nigeria

This baseline study investigated the physico-chemical and trace elements (including potentially toxic elements, PTEs) contents of ground and surface water sources used for domestic purposes in some districts within the shale bedrock terrain of Southeastern Nigeria. A total of 124 water samples from 13 natural springs, 24 streams, 80 boreholes and 7 hand-dug wells were collected from rural and urban areas and analysed by ICP-OES, GF-AAS and CV-AFS for 21 elements. The distribution pattern, sources of contamination, health risk of potentially toxic elements (PTEs), together with water quality index were investigated. The results were compared with national and international guidelines for drinking water. Al and Fe were implicated as the dominant pollutants in the water bodies. Water quality index (WQI) indicated that all the water sources had either excellent or good water quality (WQ) with the exception of a borehole, which had poor WQ. Different multivariate statistical approaches applied to evaluate the origins of the elements in the water bodies identified six source types that accounted for 70.88% of the total variance. Anthropogenic activities were considered to contribute much of Cu, Pb, Cd, Cr, Li and P, while Al, As, Co, Fe, Se, Ni, Y and V were likely from crustal materials, minerals and ores, and natural environments. Both anthropogenic and natural sources accounted for the Hg, Mn and Zn. Cluster analysis (CA) was adopted to classify 124 sample points into two groups of water pollution, reflecting influences from crustal materials and anthropogenic sources. From the result of hazard quotient and index (HQ/HI), there is little or no health risks arising from PTEs in using water from the region.

Elemental geochemistry in acid sulphate soils - A case study from reclaimed islands of Indian Sundarban

Sundarban along with its networks of rivers, creeks and magnificent mangroves form a unique ecosystem. Acid sulphate soils have developed in this ecosystem under anoxic reducing conditions. In the present study, we have investigated the distribution of acid sulphate soils along with its elemental characterization and possible sources in four reclaimed islands of Indian Sundarban like Maushuni (I1), Canning (I2), Bally (I3) and Kumirmari (I4). Elements show moderate to strong correlation with each other (P < 0.01; P < 0.05). Except Si, Ca and Pb, a higher enrichment factor was observed for K, Cr, Mn, Fe, Ni, Cu and Zn. Geo-accumulation index values of all sampling locations reveal that Cr, Fe, Cu and Zn are in I_geo class 1. The pollution load index value of the reclaimed islands of Indian Sundarban varies between 1.31 and 1.48. The observation of this study could help to strategize policies to mitigate and manage acid sulphate soils in Indian Sundarban.

Greater health risk in wet season than in dry season in the Yellow River of the Lanzhou region

The Yellow River flows through Lanzhou city and is the only drinking water source for 3.6 million residents. Yet, little is known regarding the safety and quality of the Yellow River for resident consumption. To address this knowledge-gap, water samples were collected from different sites within this section during the dry and wet seasons. Physico-chemical parameters and microbial community metrics were analyzed to assess the health risk associated with this Chinese mother river. Water quality of the river was better during the dry season (March-April) than in the wet season (September-October). Fifteen conventional physico-chemical and biological indices, such as NH₃-N, NO₂^--N, total nitrate (TN), five day biochemical oxygen demand (BOD₅), chemical oxygen demand (COD), volatile phenol (VP), and coliform abundances, generally exceeded acceptable standards. The average abundance of coliforms was 2.8 times that of acceptable standards in the dry season and 4.6 times the standards in the wet season. The concentration of the toxic metal As was more than two times than that of the national standard in waters from the wet season. Microbial community analysis also indicated that community diversity and species richness were positively correlated with the concentrations of several physico-chemical parameters. The results indicate that As and Cr⁶⁺ pose potential risk for human health through consumption by residents. Further, the results indicated that human activities are the main causes of water pollution, and that long-term strict monitoring should be conducted to ensure the safety of drinking water consumption and the health of the environment.

Characterisation of spatial variability in water quality in the Great Barrier Reef catchments using multivariate statistical analysis

Water quality monitoring is important to assess changes in inland and coastal water quality. The focus of this study was to improve understanding of the spatial component of spatial-temporal water quality dynamics, particularly the spatial variability in water quality and the association between this spatial variability and catchment characteristics. A dataset of nine water quality constituents collected from 32 monitoring sites over a 11-year period (2006-2016), across the Great Barrier Reef catchments (Queensland, Australia), were evaluated by multivariate techniques. Two clusters were identified, which were strongly associated with catchment characteristics. A two-step Principal Component Analysis/Factor Analysis revealed four groupings of constituents with similar spatial pattern and allowed the key catchment characteristics affecting water quality to be determined. These findings provide a more nuanced view of spatial variations in water quality compared with previous understanding and an improved basis for water quality management to protect nearshore marine ecosystem.

Heavy metal speciation, leaching and toxicity status of a tropical rain-fed river Damodar, India

Speciations of metals were assessed in a tropical rain-fed river, flowing through the highly economically important part of the India. The pattern of distribution of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) were evaluated in water and sediment along with mineralogical characterization, changes with different water quality parameters and their respective health hazard to the local population along the Damodar River basin during pre-monsoon and post-monsoon seasons. The outcome of the speciation analysis using MINTEQ indicated that free metal ions, carbonate, chloride and sulfate ions were predominantly in anionic inorganic fractions, while in cationic inorganic fractions metal loads were negligible. Metals loads were higher in sediment phase than in the aqueous phase. The estimated values of I_geo in river sediment during both the seasons showed that most of the metals were found in the I_geo class 0-1 which represents unpolluted to moderately polluted sediment status. The result of partition coefficient indicated the strong retention capability of Cr, Pb, Co and Mn, while Cd, Zn, Cu and Ni have resilient mobility capacity. The mineralogical analysis of sediment samples indicated that in Damodar River, quartz, kaolinite and calcite minerals were dominantly present. The hazard index values of Cd, Co and Cr were > 1 in river water, which suggested potential health risk for the children. A combination of pragmatic, computational and statistical relationship between ionic species and fractions of metals represented a strong persuasion for identifying the alikeness among the different sites of the river.

Assessment of the environmental health of an ecologically sensitive, semi-enclosed, basin - A water quality modelling approach

Semi-enclosed basins are environmentally dynamic and some of the most anthropogenically affected components of the coastal realm. They can reflect various environmental impacts, thus qualifying as natural laboratories to study these impacts. The Gulf of Khambhat (GoK) is such a system where analysis of in situ parameters indicated polluted conditions. The sources of various contaminants were deciphered. Though there are considerable inputs of pollutants, the assimilative capacity of the GoK holds good with high Dissolved Oxygen (DO) (6-9.3 mg/L) content as revealed in situ and in silico. High Biochemical Oxygen Demand (BOD) and marginal ammonia contamination prevail in the region. Simulations revealed that the rivers bring in a considerable amount of nitrate, organic material and phosphate into the Gulf. Considering the prevailing environmental condition, the current study posits to have regular water quality monitoring; and the carrying capacity of the Gulf should be assessed before the authorization of anthropogenic activities.

Health Risk Assessment of Household Drinking Water in a District in the UAE

The quality of household drinking water in a community of 30 houses in a district in Abu Dhabi, United Arab Emirates (UAE) was assessed over a period of one year (January to November 2015). Standard analytical techniques were used to screen for water quality parameters and contaminants of concern. Water quality was evaluated in the 30 households at four sampling points: kitchen faucet, bathroom faucet, household water tank, and main water pipe. The sampling points were chosen to help identify the source when an elevated level of a particular contaminant is observed. Water quality data was interpreted by utilizing two main techniques: spatial variation analysis and multivariate statistical techniques. Initial analysis showed that many households had As, Cd, and Pb concentrations that were higher than the maximum allowable level set by UAE drinking water standards. In addition, the water main samples had the highest concentration of the heavy metals compared to other sampling points. Health risk assessment results indicated that approximately 30%, 55%, and 15% of the houses studied had a high, moderate, and low risk from the prolonged exposure to heavy metals, respectively. The analysis can help with planning a spatially focused sampling plan to confirm the study findings and set an appropriate course of action.

Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin, Brazil

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.

Apportionment and evolution of pollution sources in a typical riverside groundwater resource area using PCA-APCS-MLR model

Comparative analysis was performed of changing groundwater quality over ten years (2006-2016) by source apportionment and spatial distribution characteristics. This shallow groundwater in a typical riverside groundwater resource area was studied using principal component analysis (PCA) and factor analysis (FA), coupled with the absolute principal-component-score multiple-linear-regression (APCS-MLR) receptor model. The relationship among land-use types, hydro-chemical composition, and evolution of the quality of groundwater from natural and anthropogenic sources was demonstrated. The results showed that water-rock interaction, agricultural fertilizer, and domestic and industrial wastewater were responsible for the evolution of contamination in the groundwater. The major potential pollution sources that had significant effect on groundwater quality variables were categorized into three groups: heavy metals (iron, manganese), nutrients (ammonia nitrogen, nitrite and nitrates), and organic pollution (chemical oxygen demand). The APCS-MLR model considered the average contribution of each different potential pollution source to these categories separately. The potential pollution sources in the groundwater presented an obvious spatial distribution with an area of high concentration distributed mainly in the western and northwestern areas downstream from the Songhua River. The variation of land use type and evolution of the spatial distribution of the pollution sources in the groundwater showed good consistency. Eventually, PCA /FA coupled with APCS-MLR became a versatile tool for comprehensive source apportionment of groundwater.

Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin

To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index.

New Patterns of Temporal and Spatial Variation in Water Quality of a Highly Artificialized Urban River-Course—a Case Study in the Tongzhou Section of the Beiyun River

This paper aims to gain a better understanding of urban river pollution through evaluation of water quality. Data for 10 parameters at eight sites of the Tongzhou Section of the Beiyun River (TSBR) are analyzed. Hierarchical cluster analysis, fuzzy comprehensive assessment, discriminant analysis and Spearman’s correlation analysis were used to estimate the water situation of each cluster and analyze its spatial-temporal variations. Principal component analysis/factor analysis were applied to extract and recognize the sources responsible for water-quality variations. The results showed that temporal variation is greater than spatial and sewage discharge is the dominant factor of the seasonal distribution. Moreover, during the rapid-flow period, water quality is polluted by a combination of organic matter, phosphorus, bio-chemical pollutants and nitrogen; during the gentle-flow period, water quality is influenced by domestic and industrial waste, the activities of algae, aquatic plants and phosphorus pollution. In regard to future improvement of water quality in TSBR, the control of reclaimed wastewater from adjacent factories should first be put in place, as well as other techniques, for example, an increase of the impervious area, low-impact development, and integrated management practices should also be proposed in managing storm water runoff.

Long-Term River Water Quality Trends and Pollution Source Apportionment in Taiwan

The application of multivariate statistical techniques including cluster analysis and principal component analysis-multiple linear regression (PCA-MLR) was successfully used to classify the river pollution level in Taiwan and identify possible pollution sources. Water quality and heavy metal monitoring data from the Taiwan Environmental Protection Administration (EPA) was evaluated for 14 major rivers in four regions of Taiwan with the Erren River classified as the most polluted river in the country. Biochemical oxygen demand (6.1 ± 2.38), ammonia (3.48 ± 3.23), and total phosphate (0.65 ± 0.38) mg/L concentration in this river was the highest of the 14 rivers evaluated. In addition, heavy metal levels in the following rivers exceeded the Taiwan EPA standard limit (lead: 0.01, copper: 0.03, and manganese: 0.03) mg/L concentration: lead-in the Dongshan (0.02 ± 0.09), Jhuoshuei (0.03 ± 0.03), and Xinhuwei Rivers (0.02 ± 0.02) mg/L; copper: in the Dahan (0.036 ± 0.097), Laojie (0.06 ± 1.77), and Erren Rivers are (0.05 ± 0.158) mg/L; manganese: in all rivers. A total 72% of the water pollution in the Erren River was estimated to originate from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research demonstrated that applying PCA-MLR and cluster analysis on long-term monitoring water quality would provide integrated information for river water pollution management and future policy making.