Erratum to: Application of multivariate statistical techniques in the assessment of water quality in Sakarya River, Turkey

Yerel, Suheyla; Ankara, Huseyin

doi:10.1007/s12594-012-0019-x

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…The statistical analysis of WQ results was executed by PCA and HCA using SPSS, version 18.0 software. They used widely in recent years for analyzing WQ data and describe the meaningful information (Kazi et al, 2009;Salah, Turki, & Al-Othman, 2012;Varol, Gokot, Bekleyen, & Sen, 2012;Yerel & Ankara, 2012). PCA was applied for each sampling location to identify the most influential parameter of WQ deterioration and to predict the source of pollution in the river system (Tripathi & Singal, 2019).…”

Section: Multivariate Statistical Methodsmentioning

confidence: 99%

“…The quality of river systems is directly and indirectly changed by climatic factors (surface run-off, land erosion, weathering of rocks) and human interventions (Kumar, Sharma, & Taxak, 2017b) particularly in the urban areas and agricultural activities around the rural areas (Ayeni, 2010;Kankal, Indurkar, Gudadhe, & Wate, 2012;Raj & Azeez, 2009). Wastewaters from these sources contain toxic organic and inorganic substances in addition to heavy metals (Alkarkhi, Ahmad, & Easa, 2009;Kumar, Bisht, Joshi, Singh, & Talwar, 2010;Muduli, Swain, Bhuyan, & Dhal, 2006;Sinha, Saxena, & Saxena, 2006;Yerel & Ankara, 2012), that cause severe damage to the ecosystem like eutrophication and pose serious health issues (Alcamo, Henrichs, & Rosch, 2000;EIWR, 2008;Meitei & Bhargava, 2004). It is a serious and growing problem in most developing countries including India (Miller, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Appraisal of seasonal variations in water quality of river Cauvery using multivariate analysis

Sharma

Ravichandran²

2021

Water Science

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The present study determines the water quality (WQ) of river Cauvery and its distributaries in Tiruchirappalli district, Tamil Nadu state, India, through WQ parameters and also using multivariate analysis. The water samples were collected by grab sampling method during the four seasons, namely, winter, summer, southwest, and northeast monsoons of the year 2014-15. The study reveals that total dissolved solids, turbidity, total alkalinity, total hardness, biological oxygen demand, chemical oxygen demand, and total coliform results of river Cauvery and the distributaries exceeded the drinking water standards, which are mainly in summer. Also, the outcomes of principal component analysis and cluster analysis confirmed the high pollution loads in the water samples. It could be caused by the sewage discharges from residential and commercial areas. Moreover, solid wastes dumping and all sorts of cleaning activities were found at all stations. The present study concluded that the water from river Cauvery and its distributaries are not potable directly. With awareness creation and water treatment methods among the consumers, the river system can be conserved for proper usages.

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Section: Multivariate Statistical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Appraisal of seasonal variations in water quality of river Cauvery using multivariate analysis

Sharma

Ravichandran²

2021

Water Science

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“…Factor analysis (FA) is a multivariate statistical technique that can be utilized to examine the underlying patterns or relationship for a large number of variables and summarize information in a smaller set of factors or components for prediction purposes [15]. The main purpose of factor analysis is to reduce the contribution of less significant variables and to simplify even more the data structure coming from the PCA.…”

Section: Factor Analysismentioning

confidence: 99%

Assessment of Seasonal Variation in Water Quality in River Rwizi Using Multivariate Statistical Techniques, Mbarara Municipality, Uganda

Ojok¹,

Wasswa²,

Ntambi³

2017

JWARP

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Assessment of seasonal variations in surface water quality characteristics is an essential aspect for evaluating water pollution due to both natural and anthropogenic influences on water resources. In this study, temporal variations of water quality in river Rwizi section within Mbarara municipality, Uganda, were assessed using multivariate statistical methods. This river section is a major source of water for the inhabitants of Mbarara municipality. Water samples from five sites were analyzed for physicochemical parameters such as pH, EC, turbidity, temperature, TSS, TDS, alkalinity, salinity, colour, NH 3 -N, 2 4 SO − , total hardness, BOD, COD, DO, Ca, Mg, Fe, and Mn. About 50% of sites recorded colour above 800 Pt Co, 60% of sites recorded turbidity above maximum permissible limit of 100 mg/l, attributable to erosion and mineral matter. pH for dry season ranged between 6.5 and 8.5 whereas for rainy season was below 6.0. All study sites recorded total Fe above 0.3 mg/l and Mn below 0.5 mg/l, attributable to chemical weathering of host rock materials as well as from industrial effluent. About 60% of sites recorded COD above 100 mg/l, 40% and 80% of study sites showed BOD above 50 mg/l in dry and rainy seasons respectively. Hardness ranged between 50 and 100 mg/l indicating that the water is moderately soft. Colour, turbidity, alkalinity, TSS, TDS, salinity, pH, hardness, Fe, Mn, NH 3 -N, BOD, COD, and DO were higher in rainy season, as a result of erosion, discharge of domestic and industrial waste. Mg, Ca, and 2 4 SO − were higher during dry season due to high evaporation of water from the river. PCA/FA determined that 81.2% of the total variance was explained by the first factor for the dry season and 69.2% for rain season. These results revealed that water pollution resulted primarily from domestic waste water, agricultural runoff and industrial effluents.

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“…The application of different multivariate statistical techniques, such as cluster analysis (CA), principle component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system [4,5] and interpretation of the complex databases offers a better understanding of the temporal and spatial variations in the identification of discriminate parameters that are of use in optimizing monitoring network [5,6,7]. Multivariate statistical techniques have been applied in water quality assessment and sources apportionment of water bodies over the last decade [3,[9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Water Quality of Euphrates River Using Cluster Analysis

Salah¹,

Turki²,

Al-Othman³

2012

JEP

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Multivariate statistical method including cluster analysis (CA) was used to assess temporal and spatial variations in the water quality of Euphrates River, Iraq, for a period 2008-2009 using 16 parameters at 11 sampling sites. Hierarchical CA grouped the 8 months into three periods (I, II and III) and classified the 11 sampling sites into two groups (I and II) based on similarities of water quality characteristics. The temporal pattern shows that April has higher pollution level relative to the other months. Spatially, sampling site 7 (S7) has lower pollution level while the other sampling sites have higher pollution level. Thus, this study shows usefulness of cluster analysis method for analyzing and interpreting of surface water dataset to assess the temporal and spatial variations in the water quality parameters and the optimization of regional water quality sampling network.

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Erratum to: Application of multivariate statistical techniques in the assessment of water quality in Sakarya River, Turkey

Cited by 15 publications

References 14 publications

Appraisal of seasonal variations in water quality of river Cauvery using multivariate analysis

Appraisal of seasonal variations in water quality of river Cauvery using multivariate analysis

Assessment of Seasonal Variation in Water Quality in River Rwizi Using Multivariate Statistical Techniques, Mbarara Municipality, Uganda

Assessment of Water Quality of Euphrates River Using Cluster Analysis

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