Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria)

Edet, Aniekan; Offiong, O. E.

doi:10.1023/b:gejo.0000007250.92458.de

Cited by 491 publications

(295 citation statements)

References 17 publications

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“…In Al-Gharraf River, it ranged from 0.8 to 10.1 mg/l and it was relatively low. The quality standard of WHO is \3 mg/l (Edet and Offiong 2002); therefore, BOD of the first three stations reached the criteria, but at the last, seven stations did not, especially in summer and early autumn.…”

Section: Heavy Metal Pollution Index (Hpi)mentioning

confidence: 92%

Water quality evaluation of Al-Gharraf river by two water quality indices

Ewaid

2016

Appl Water Sci

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Water quality of Al-Gharraf river, the largest branch of Tigris River south of Iraq, was evaluated by the National Sanitation Foundation Water Quality Index (NFS WQI) and the Heavy Metal Pollution Index (HPI) depending on 13 physical, chemical, and biological parameters of water quality measured monthly at ten stations on the river during 2015. The NSF-WQI range obtained for the sampling sites was 61-70 indicating a medium water quality. The HPI value was 98.6 slightly below the critical value for drinking water of 100, and the water quality in the upstream stations is better than downstream due to decrease in water and the accumulation of contaminants along the river. This study explains the significance of applying the water quality indices that show the aggregate impact of ecological factors in charge of water pollution of surface water and which permits translation of the monitoring data to assist the decision makers.

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Section: Heavy Metal Pollution Index (Hpi)mentioning

confidence: 92%

Water quality evaluation of Al-Gharraf river by two water quality indices

Ewaid

2016

Appl Water Sci

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“…Selected environmental pollution indexes for water samples 'Heavy Metal Evaluation Index (HEI) of the Groundwaters' [6], as for soil samples 'Mobility of Metals' [7], 'Single-Factor and Composite Pollution Index of Soils' [8], Anthropogenic Factor (AF) [9] and 'Enrichment Factor (EF) Indexes of Soil' [10], 'Potential Ecological Risk Factor Indexes' [11], and as for plant samples 'Heavy Metal Transfer (Bioconcentration) Factor' and 'Target Hazard Quotient (THQ) of Food' [12], Hazard Index (HI) [13] were used for comprehensive and integrated evaluation of parameters.…”

Section: Methodsmentioning

confidence: 99%

Assessment of Heavy Metal Pollution of Greenhouse Areas in the Eastern Part of Antalya (Turkey)

2015

International Conference on Chemical, Agricultural and Biological Sciences (CABS-2015) Sept. 4-5, 2015 Istanbul (Turkey)

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“…Figure 1 shows the locations of sampling stations. The groundwater samples were prepared for metal content analysis through the method described by Edet and Offiong (29). Finally, contents of As, Pb, and Zn in groundwater samples were determined using inductively coupled plasma-optical emission spectrometer (Varian, 710-ES, Australia) at the wavelengths of 188.98 nm for As, 220.35 nm for Pb, and 206.20 nm for Zn.…”

Section: Sample Collection and Preparationmentioning

confidence: 99%

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

Alizamir

Sobhanardakani

2018

Jundishapur J Health Sci

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Background: The quality of groundwater as the most important source for domestic, irrigation, and industrial purposes is affected by discharge of the chemicals from the anthropogenic resources. Therefore, the current study aimed at predicting heavy metals (As, Pb, Cu, and Zn) contamination in groundwater resources of Toyserkan Plain as an important agricultural area in Hamedan Province, West of Iran using artificial neural network -particle swarm optimization (ANN-PSO) approach. Methods: In the current study, samples were randomly selected from 20 groundwater wells with depth of 10 -90 m. The samples were filtered and kept cool in polyethylene bottles and then taken for the analysis of metal contents; they were acidified using nitric acid to reach pH < 2. Finally, element contents were determined using inductively coupled plasma -optical emission spectrometry (ICP-OES). Also, the performance of the PSO model was compared with that of ANN using Bayesian regulation (BR) training algorithm in terms of accuracy and model prediction efficiency.

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Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria)

Cited by 491 publications

References 17 publications

Water quality evaluation of Al-Gharraf river by two water quality indices

Water quality evaluation of Al-Gharraf river by two water quality indices

Assessment of Heavy Metal Pollution of Greenhouse Areas in the Eastern Part of Antalya (Turkey)

An Artificial Neural Network - Particle Swarm Optimization (ANN- PSO) Approach to Predict Heavy Metals Contamination in Groundwater Resources

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