Assessment of heavy metal contamination in water of Kali River using principle component and cluster analysis, India

Mishra, S. K.; Kumar, Amit; Yadav, Silpa; Singhal, Mukesh Kumar

doi:10.1007/s40899-017-0141-4

Cited by 66 publications

(22 citation statements)

References 38 publications

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“…No location in the study area has exceeded this limit. Nevertheless, as stated by Herojeet et al [67] HPI results were classified as low (<15), medium (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), or high (>30) pollution. In this case, only two locations (S1 and S16) are not highly contaminated by heavy metals.…”

Section: Contamination Evaluation Indicesmentioning

confidence: 98%

“…These indices refer to the overall water quality in terms of heavy metals contamination. Many indices are used for the purpose concerned, such as heavy metals pollution index (HPI), heavy metals evaluation index (HEI), and the degree of C d [16] [18]. C d is distinguished by the fact that it implicates heavy metals and other coexisting contaminants in water quality evaluation [19].…”

Section: Assessment Of Heavy Metals Contamination In Drinking Water Omentioning

confidence: 99%

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Assessment of Heavy Metals Contamination in Drinking Water of Garmian Region, Iraq

Issa¹,

Alshatteri²

2018

UHD J SCI TECH

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Drinking water of safe quality is a critical issue for human survival and health. Water pollution by heavy metals is very crucial because of their toxicity. This study assesses potential of heavy metals pollution in drinking water in Garmian Region, East Iraq. Water samples were investigated for 23 heavy metals and 6 chemical contaminants collected from 16 locations. The analysis was performed by using coupled plasma optical emission spectroscopy (ICPOES, Spectro Arcos). High levels of Al, Se, Sr, and Fe have been detected at certain locations in study area. Statistical analysis techniques of the correlation matrix (CM), and cluster hierarchical analysis (CA) were conducted. The heavy metals pollution index (HPI), heavy metals evaluation index (HEI), and contamination Index (Cd) were used applied. These indices linked with the statistical analysis to interpret relationships among the tested parameters in water samples and to investigate the pollution sources over the study region. Even with the significant correlations between the HPI, Cd, and HEI, they showed dissimilar impact of examined heavy metals on the water quality. The most reliable pollution evaluation index of HEI for drinking water showed that 44% of the water samples is critically polluted. Sources of the contamination are most likely coming from natural geological sources. Anthropogenic impact was only noticed at several sites in the study area.

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Section: Contamination Evaluation Indicesmentioning

confidence: 98%

Section: Assessment Of Heavy Metals Contamination In Drinking Water Omentioning

confidence: 99%

Assessment of Heavy Metals Contamination in Drinking Water of Garmian Region, Iraq

Issa¹,

Alshatteri²

2018

UHD J SCI TECH

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“…PCA was used to modify the original datasets into a new and uncorrelated set -i.e., a linear combination of the original sets (Vasco, 2012). It helps to extract the information from major parameters by eliminating those of lower importance with minimal loss of original information (Kannel, Lee, Lee, Kanel, & Khan, 2007;Mishra, Kumar, Yadav, & Singhal, 2018). After extracting the principal components, factor analysis (through "varimax rotation") was used to simplify the datasets, revealing the same information as contained in the original sets (Shrestha & Kazama, 2007).…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Water quality assessment of Narmada River along the different topographical regions of the central India

et al. 2020

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“…Temuan ini sejalan dengan banyak penelitian (Ye and Liu, 2009;Venkatesharaju et al, 2010;Pratt and Chang, 2012). Selain itu, BOD dan COD yang tinggi juga diakibatkan oleh industri (Widayati, Suprayogi and M. Widyastuti, 2011;Mishra, Kumar and Yadav, 2018). Permukiman yang terdapat di sekitar titik F belum memiliki IPAL Terpadu dan masih banyak masyarakat yang tidak sadar lingkungan seperti membuang sampah ke saluran air, sehingga dapat menyumbang limbah padat domestik.…”

Section: A Parameter Fisika-kimia Air Sungaiunclassified

Evaluation of Water Quality Using Pollution Index in Cimanuk Watershed, Indonesia

Nurrohman

Widyastuti

Suprayogi

2019

EJES

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Land use is one of the macro parameters that affects water quality in a watershed. The Cimanuk watershed which is dominated by agricultural land use and settlements has encountered a decline in water quality. There are eight parameters including pH, TDS, TSS, Nitrate, Phosphate, Sulphate, BOD, and COD analyzed to evaluate water quality utilizing the Pollution Index (IP) method. This research has provided a clear understanding of the status of water quality in the Cimanuk watershed based on six monitoring points carried out at the start of the 2018 rainy season. Overall, the IP values obtained ranged from 2.05 to 5.96 with the lightly polluted category at points A, B, C, D, and E, while for point F it was in the moderately polluted category. The key parameters that have the most influence on water quality pollution are Nitrate, Phosphate, Sulphate from fertilization activities in agricultural land, while runoff from settlements contribute to increasing parameters of BOD and COD. These results can be beneficial for sustainable management of water and land resources in the Cimanuk watershed.

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Assessment of heavy metal contamination in water of Kali River using principle component and cluster analysis, India

Cited by 66 publications

References 38 publications

Assessment of Heavy Metals Contamination in Drinking Water of Garmian Region, Iraq

Assessment of Heavy Metals Contamination in Drinking Water of Garmian Region, Iraq

Water quality assessment of Narmada River along the different topographical regions of the central India

Evaluation of Water Quality Using Pollution Index in Cimanuk Watershed, Indonesia

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