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Rivers are a vital component of both urban and rural aquatic ecosystems, and the rapidly increasing pollution in rivers severely threatens the security of these ecosystems. In this study, the surface water quality in the Maozhou River basin, Guangdong Province, China, was assessed from 2018 to 2020 using multivariate statistical techniques and the Integrated Water Quality Index (IWQI). In addition, spatial trends in surface water quality were studied using a geographic information system. The results indicated that the water quality in 82.17% of the studied section met the Class V standard for surface water quality, with IWQI values ranging from 12.157 to 3.650. The surface water quality was clustered into eight groups and further divided into unsuitable (low quality) and suitable (acceptable quality) according to standard surface water quality thresholds (China in Environmental quality standards for surface water, 2002). Four groups (G1, G2, G3, and G4) were classified as unsuitable, because they fell short of the Class V standard for surface water quality. In these groups, industrial sewage, endogenous pollution, domestic sewage, and rainfall runoff, were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were COD, NH3-N, TP, and LAS. The other four groups were classified as suitable. In these groups, endogenous pollution and rainfall runoff were the primary sources of pollution, and the main background pollutants for the water quality target of the functional zones were NH3-N and TP. Among them, NH3-N and LAS were recognized as responsive and sensitive to the surface water quality and spatio-temporal variability. Owing to pollution treatment and management measures undertaken by the Chinese government, the black-odorous water in the Maozhou River basin has disappeared, and the water quality in the Maozhou River basin has been maintained at the "medium and good" level. However, the surface water quality in the estuary region and the southwest tributary in the basin requires further improvement. This calls for further efforts to improve surface water quality and to properly deal with various sources of pollution in the watershed. This combined method has proved to be effective for surface water quality evaluation and management at river or basin scales. The results of this work are expected to provide a scientific foundation for aquatic ecosystem management and planning. [ABSTRACT FROM AUTHOR]

The River Ganges being the most sacred river and lifeline to millions of Indians in serving their water requirements is facing excessive threat of pollution. Under various river management and conservation strategies for its protection, the assessment of water quality of its main tributary Ramganga River is lacking. This study focuses on assessment of physicochemical and heavy metal pollution of the Ramganga River by application of multivariate statistical techniques. Sampling of Ramganga River at sixteen sampling sites was carried out in three seasons (summer, monsoon and winter) of 2014. The collected water samples were analyzed for physicochemical parameters and heavy metals. Results from cluster analysis (CA) of the data divided the whole stretch of the river into three clusters as elevation from 1304 to 259 m as less polluted, from 207 to 154 m as moderately polluted and from elevation 154 to 139 m as high-polluted stretches with anthropogenic as main sources of pollution in high-polluted stretch. Principal component analysis of the seasonal dataset resulted in three significant principal components (PC) in each season explaining 72-8% of total variance with strong loadings (>0.75) of PC1 on fluoride (F), chloride (Cl), sodium (Na), calcium (Ca), magnesium (Mg), bicarbonate (HCO ), total dissolved solids and electrical conductivity. Temporal variation by one-way ANOVA (Analysis of Variance) showed significant seasonal variation was in the pH, chemical oxygen demand, biochemical oxygen demand, turbidity, HCO , F, Zn, cadmium (Cd) and Mn ( p < 0.05). Turbidity showed approximately a twofold increase in monsoon season due to rainfall in the catchment area and subsequent flow of runoff into the river. Concentration of HCO , F and pH also showed similar increase in monsoon. The concentration of Zn, Cd and Mn showed an increasing trend in summers compared to monsoon and winter season due to dilution effect in the monsoon season and its lasting effect in winters. [ABSTRACT FROM AUTHOR]

The evaluation and interpretation of the spatio-temporal pattern of surface water quality is crucial for the assessment, restoration and protection of drinking water sources. This case study reports different multivariate statistical techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis, which had been applied for 6 years (2005–2010) water quality data set generated from 19 parameters at 14 different sites within the Fei-Tsui Reservoir basin. Hierarchical cluster analysis grouped 14 sampling sites into three clusters: high-, moderate- and low-pollution regions. This study revealed that water release from the dam outlet will further increase the concentrations of pollutants in the downstream river. PCA/FA did not result in considerable data reduction, as it points to 13 parameters (68 % of original 19) required to explain the 72.8 % of the total variance in the water quality data set. The varifactors obtained from PCA suggested that parameters responsible for water quality variation were mainly related to mineral-related parameters (natural), nutrient group (non-point sources pollution), physical parameters (natural) and organic pollutants (anthropogenic sources). Discriminant analysis used only five parameters: water temperature, dissolved oxygen (DO), calcium, total dissolved solids (TDS) and turbidity; and seven parameters: BOD, DO, nitrate nitrogen, TDS, total alkalinity, turbidity and WT, to discriminate between temporal and spatial with 88 and 90 % correct assignation, respectively. This study illustrated the usefulness of multivariate statistical techniques for designing the sampling and analytical protocol, analysis and interpretation of complex data sets, identification of pollution sources/factors, and provides a reliable guideline for selecting the priorities of possible controlling measures in the sustainable management of Fei-Tsui Reservoir basin. [ABSTRACT FROM AUTHOR]

The concentrations of heavy metals (Cr, Co, Ni, Cu, Zn, Pb, Cd, As, Hg, and Fe) in sediments of the Yangtze River, China, were investigated to evaluate levels of contamination and their potential sources. The lowest heavy metal concentrations were found in the source regions of the river basin. Relatively high concentrations of metals, except Cr, were found in the Sichuan Basin, and the highest concentrations were in the Xiangjiang and Shun'anhe rivers. All concentrations, except Ni, were higher than global averages. Principal component analysis and hierarchical cluster analysis showed that Zn, Pb, As, Hg, and Cd were derived mainly from the exploitation of various multi-metal minerals, industrial wastewater, and domestic sewage. Cu, Co, and Fe were derived mainly from natural weathering (erosion). Cr and Ni were derived mainly from agricultural activities, municipal and industrial wastewater. Sediment pollution was assessed using the geoaccumulation index ( I) and enrichment factor (EF). Among the ten heavy metals assessed, Cd and Pb had the highest I values, followed by Cu, As, Zn, and Hg. The I values of Fe, Cr, Co, and Ni were <0 in all sediments. EF provided similar information to I: no enrichment was found for Cr, Co, and Ni. Cu, Zn, As, and Hg were relatively enriched at some sites while Cd and Pb showed significant enrichment. [ABSTRACT FROM AUTHOR]

Multivariate statistical techniques, i.e., correlation coefficient analysis, principal components analysis (PCA), and hierarchical cluster analysis (CA), were applied to the total and water-soluble concentrations of potentially hazardous metals in sediments associated with the Sarcheshmeh mine, one of the largest Oligo-Miocene porphyry copper deposits in the world. The samples were analyzed for hazardous metal concentration levels by inductively coupled plasma mass spectrometry method. Results indicate that the contaminant metals As, Cd, Cu, Mo, S, Sb, Sn, Se, Pb, and Zn were positively correlated with the total concentrations. These hazardous metals also have strong association in the PCA and CA results. Different anthropic versus natural sources of contaminant metals were distinguished by using CA method. Water-soluble fraction of hazardous metals showed that the hydro-geochemical behavior of these metals in sediments is different considerably. Elements such as Cd, Co, Cr, Cu, Fe, Mn, Ni, S, and Zn are readily water soluble from contaminated samples, especially from evaporative mineral phases, while the release of As, Mo, Sb, and Pb into the water is limited by adsorption processes. Results obtained from the application of multivariate techniques on the water-soluble fraction data set show that the hazardous metals are categorized into three groups including (1) Ni, S, Co, Cu, Cr, and Fe; (2) Se, Mn, Cd, and Zn; and (3) Sb, As, Mo, and Sn. This classification describes the hydro-geochemical behavior of hazardous metals in water-sediment environments of the Sarcheshmeh porphyry copper mine and can be used as a basis in remedial and treatment strategies. [ABSTRACT FROM AUTHOR]