Evaluation of physico-chemical parameters in water and total heavy metals in sediments at Nakdong River Basin, Korea

Chung, Sang Yong; Senapathi, Venkatramanan; Park, N.; Ramkumar, T.; Sujitha, S.B.; Jonathan, M.P.

doi:10.1007/s12665-015-4836-2

Cited by 42 publications

(18 citation statements)

References 43 publications

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“…Statistical analysis of field-collected soil samples uses STATISTICA software (ver.8). Each soil property was assessed in terms of descriptive statistics like mean, median, mode, standard deviation, correlation matrix, and cluster analysis which strengthen the findings of geostatistical parameters analysis (Chung et al, 2016;Venkatramanan et al, 2015).…”

Section: Discussionmentioning

confidence: 92%

“…Correlation coefficients less than 0.5 are not considered as they are not at significant levels. The interrelationship of different parameters is useful in studying the association of soil parameters (Andrew et al, 2018;Tellen & Yerima, 2018;Chung et al, 2016).…”

Section: Correlation Matrixmentioning

confidence: 99%

“…Two-way-joining cluster analysis is used for grouping objects of Q-mode and R-mode of the same data to reorganize the rows and columns of the data matrix. It's built with the combination of cases (sample sites) and variables (chemical parameters) to make the discrete patterns of clusters (Andrew et al, 2018;Chung et al, 2016;Oumenskou et al, 2018;Tellen & Yerima, 2018;Venkatramanan, Chung, Ramkumar, Gnanachandrasamy, & Kim, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Soil fertility analysis in and around magnesite mines, Salem, India

Paramasivam

Anbazhagan

2019

Geology, Ecology, and Landscapes

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The opencast mining operations have periodical effects in surrounding landscape and it is important to monitor the quality of the soil. The pre-and post-mining activities influence the soil quality due to removal of vegetation and topsoil cover. The low-grade magnesite ores and dumping of waste materials are the root causes of contamination of soil. The samples collected from the depth of 15 cm on the surface of soil with different physical (soil texture and soil moisture) and chemical (pH, organic carbon, and nitrogen) parameters are analyzed to study the effect of opencast mining on the surrounding soil. Nine soil samples collected from three magnesite mine sites include agriculture land in close proximity. The result of this analysis predicts the sample soils deficiency of nutrients contents like nitrogen, phosphorus, potassium, and calcium. The soil analysis comprises chemical parameters such as pH, lime status, texture, electrical conductivity, available macronutrients nitrogen (N), phosphorous (P), potassium (K), and micronutrients such as Fe, Mn, Zn, and Cu percentages which are used to determine the condition and quality of the soil. The STATISTICA software (ver.8) was used for analysis of Pearson's correlation with linear relationship of soil samples and rows and columns of data matrix on cluster analysis.

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Section: Discussionmentioning

confidence: 92%

Section: Correlation Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Soil fertility analysis in and around magnesite mines, Salem, India

Paramasivam

Anbazhagan

2019

Geology, Ecology, and Landscapes

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“…Based on a study, the membership of the fuzzy comprehensive evaluation method is calculated by the reduced half trapezoid function, and the sectional limit value is the standard value of corresponding environmental quality [10]. The membership function of a heavy metal for grade I heavy metal pollution is:…”

Section: Membership Functionmentioning

confidence: 99%

Evaluation model of major heavy metals pollution factors in coastal waters and sediments

Feng¹,

Niu²

2019

Desalination and Water Treatment

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a b s t r a c tIt has great a subjective influence on the evaluation of major heavy metal pollution factors in coastal waters and sediments by using the traditional Nemerow exponential method, without considering the harmful differences of different heavy metals, and resulting in a low accuracy of the evaluation results. In this paper, a new model to evaluate the major heavy metal pollution factors in coastal waters and sediments is established, to calculate the single pollution index and potential ecological risk index of heavy metals. On this basis, the fuzzy comprehensive evaluation method is used to construct the main heavy metal pollution factor model of coastal waters and sediments. The heavy metal pollution factor is reasonably evaluated and the risk grade of heavy metal pollution factor in coastal waters and sediments is obtained. The results of sampling analysis show that the evaluation results obtained by the model are as follows: the pollution index of heavy metals in the coastal water with the order of Pb > Cu > Hg > As > Cd > Zn; the potential ecological risk index of heavy metals in sediments is Pb > Cu > As > Zn > Hg > Cd. The pollution degree of area C is the highest, area A is the second, and area B is the smallest. The results show that the model can accurately evaluate the main heavy metal pollution factors in coastal waters and sediments.

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“…Heavy metal contamination in surface water and sediments due to industrial discharge is an issue of major environmental concern in a fast-growing country like India and has attracted the considerable attention of scientific and regulatory communities due to the persistence and toxicity of heavy metals in the aquatic system. Indiscriminate discharge of untreated sewage, industrial waste and other anthropogenic activities caused a significant deterioration in water and sediment quality, especially near large industrial complexes and in the lower basin (Kumar and Maiti 2015;Chung et al 2016). In recent times, considerable research has been conducted to investigate heavy metal contamination in water and sediments of rivers/natural streams in industrially polluted various regions in India.…”

Section: Introductionmentioning

confidence: 99%

Sources evaluation and ecological risk assessment of heavy metals accumulated within a natural stream of Durgapur industrial zone, India, by using multivariate analysis and pollution indices

et al. 2019

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In this study, variations in physicochemical parameters and heavy metal contamination in water-sediments of a natural stream in the Durgapur industrial zone have been investigated. pH, COD, Cl − , CN − and heavy metals, viz. Pb, Hg and Fe concentrations in channel water, are higher than Indian standards. Metal concentrations in sediments are many folds higher than background value, where Pb, Cd, Hg and Cr contents exceed the sediment quality guidelines. Contamination factor (C f) value of channel water follows the order of Hg > Pb > Fe > Cr > Cd > Cu > Ni, whereas enrichment factor and geoaccumulation index (I geo) values in channel sediments are in the order of Hg > Cr > Ni > Pb > Cd > Fe > Cu. The assessment of contamination index (C d), modified contamination index (mC d) and pollution load index indicates that channel water and sediment samples in the study area are strongly contaminated by heavy metals. Sediment samples based on PELQ and ERMQ are highly toxic, with high degree of potential ecological risk at all the monitored stations. Multivariate analysis infers that heavy metals in channel water and sediments are majorly sourced from industrial discharge.

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Evaluation of physico-chemical parameters in water and total heavy metals in sediments at Nakdong River Basin, Korea

Cited by 42 publications

References 43 publications

Soil fertility analysis in and around magnesite mines, Salem, India

Soil fertility analysis in and around magnesite mines, Salem, India

Evaluation model of major heavy metals pollution factors in coastal waters and sediments

Sources evaluation and ecological risk assessment of heavy metals accumulated within a natural stream of Durgapur industrial zone, India, by using multivariate analysis and pollution indices

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