Water quality index, hydrogeochemical facies and pollution index of groundwater around Middle Benue Trough, Nigeria

Agidi, B. M.; Akakuru, Obinna Chigoziem; Aigbadon, Godwin Okumagbe; Schoeneich, Krzysztof; Isreal, H.; Ofoh, Ifeyinwa Juliana; Joy, N.; Esomonu, I.

doi:10.1007/s42108-022-00187-z

Cited by 20 publications

(23 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The generation of reactive oxygen species, which causes oxidative damage and detrimental consequences on health, is acknowledged as the main mechanism underlying heavy metal-induced toxicity (Akakuru et al 2022). Thus, the use of heavy metal-contaminated water contributes to high rates of sickness and mortality around the world (Agidi et al 2022). The ndings of this HRA are comparable to those of Narsimha's (2019) semiarid South Indian research, which discovered that youngsters are more susceptible to HI than adults.…”

Section: Human Health Risk Assessmentsupporting

confidence: 65%

“…items in Table4are not connected, and the poor correlation between them suggests that there is no relationship between the two variables. In other words, while one variable changes, another changes in a completely unrelated way(Eyankware et al 2021;Agidi et al 2022;Akakuru et al 2022). This nding implies that interactions with the atmosphere, the surface environment, soil, and bedrock cause a wide variety of different components to dissolve in groundwater.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Potential health risk and level of heavy metal in groundwater in the highly-dense area of Akure, Southwestern Nigeria

OlajuwonOsisanya

Akakuru

Eyankware

et al. 2022

Preprint

View full text Add to dashboard Cite

The drinking water quality of Akure, in the Ondo State area of southwestern Nigeria, was assessed. 21 groundwater samples were collected and tested for physicochemical characteristics such as pH, main ions, and heavy metals from motorized boreholes and hand-dug wells. From the hydrogeochemical modeling, it shows 77.3% of the samples are of the geochemical zone of 4 (strong acids exceed weak acids) while 22.7% of the samples are of the geochemical zone of 3 (weak acids exceed strong acids) with a hydrogeochemical trend of Mg > Na + K > HCO3 > Cl > SO4 > Ca. WQI results revealed that groundwater needs proper treatment before it can be fit for different purposes. Contamination Factor results showed minimal contamination, Metal Pollution Index revealed that 80.6 percent of the samples had values more than 6, suggesting that they were severely contaminated. Pollution Load Index values in the area were less than 1 showing there isn't any pollution. Geoaccumulation Index results showed that 47.6% of the samples are respectable contamination while 47.6% of the samples are modestly dirty. Principal Component Analysis results indicated loadings of variables within the PCI, PC2, and PC3 while the Correlation Matrix results indicated that there exists weak correlation amongst elements and the majority of the elements. From statistical approach of health risk assessment, it was observed that traceable absorption of poisonous heavy metals have resulted to diarrhea, typhoid fever, cholera, hepatits A and dysentery commonly found in children and youths in the study area. The results of the Hazard index showed that children were more prone to heavy metal hazards. Regular monitoring of the groundwater sources is highly recommended.

show abstract

Section: Human Health Risk Assessmentsupporting

confidence: 65%

mentioning

confidence: 99%

Potential health risk and level of heavy metal in groundwater in the highly-dense area of Akure, Southwestern Nigeria

OlajuwonOsisanya

Akakuru

Eyankware

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Unless there are specific causes, such as dust from lead paint or home recycling of lead-containing materials, blood lead levels are also reduced in most countries (Sundus et al 2021;Yahaya et al, 2021;Agidi et al 2022). Because lead dissolves from natural resources, it is rarely found in tap water.…”

Section: Leadmentioning

confidence: 99%

Non-carcinogenic Health Risk Assessment and Predicting of Organic and Heavy Metal Pollution of Groundwater around Osisioma, Nigeria, using Artificial Neural Networks and Multi-Linear Modeling Principles

Chigoziem

Uzoma²,

Uchechukwu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Non-carcinogenic health risk assessment and predicting of organic and heavy metal pollution of groundwater around Osisioma, Nigeria, using Artificial Neural Networks and Multi-Linear Modeling Principles has been done. 30 groundwater samples were collected systematically and analyzed for organic and heavy metal pollutants. The results of the analysis showed that the heavy metals and organic pollutants within the study area contributed to the pollution of groundwater resources in the locality. However, copper, ethylbenzene, xylene and toluene were within the recommended standard, whereas arsenic, iron, chromium, lead, and benzene were above the recommended standard for drinking water. Correlation matrix and principal component analysis assessment indicated weak correlation and that organic pollutants were major contributors to the loadings. The Contamination factor, Pollution load index, Metal pollution index, Geoaccumulation index, Potential ecological risk index, Elemental Contamination Index, and overall Metal Contamination Index showed no significant pollution, whereas the Heavy Metal Evaluation Index, Pollution Index of Groundwater results showed worrisome impact of the anthropogenic activities on the groundwater quality. Health risk assessment showed that children are more at risk than adults as it related to taking polluted water. MLR models performed better than the ANN. Seven (7) mathematical models were generated for the prediction of pollution indices. Based on the results, this study recommends regular monitoring of groundwater resources and the integration of ANN and MLR modeling approaches for the prediction of pollution indices.

show abstract

“…Groundwater is a significant asset extremely pivotal for the supportability of human existence and the climate. It is trusted that around 65-75% of the earth's assets are water, access to quality water for drinking, homegrown and modern objects is restricted particularly in non-industrial nations (Eyankware et al 2022a;Akakuru et al 2022Groundwater has become a fundamental requirement of life as more than 1.5 billion people around the world rely on groundwater as their primary source of drinking water (He et al, 2019;Agidi et al,). 2022).…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network and multi-linear regression techniques in groundwater quality and health risk assessment around Egbema, Southeastern Nigeria

et al. 2023

View full text Add to dashboard Cite

This paper examined the efficiency of artificial neural network (ANN) and multivariate linear regression (MLR) models in the prediction of groundwater quality parameters such as ecological risk index (ERI), pollution load index (PLI), metal pollution index (MPI), Nemerow pollution index (NPI), and geoaccumulation index (Igeo). 40 groundwater samples were collected systematically and analyzed for mainly heavy metals. Results revealed that adopting measured parameters is effective in modeling the parameters with high level of accuracy. Contamination factor results reveal that Ni, Zn, Pb, Cd, and Cu have relatively low values <1 within the region while the Iron values ranged from low contamination to very high contamination (>6). PLI, MPI, and ERI results indicated low pollution. NPI results indicated that the majority of the samples were heavily polluted. Quantification of Contamination results revealed that most of the sample's quality was geogenically influenced. Igeo results revealed that most of the samples had extreme pollution. The health risk assessment results revealed that children are substantially prone to more health risk more than adults. The ANN and MLR models showed a high effective tendency in the prediction of ERI, PLI, MPI, NPI and Igeo. Principal Component Analysis results showed appreciable variable loadings while the Correlation matrix results reveal that there exists weak and positive correlation amongst elements. Based on the outcome of this study, this research recommends the use of ANN and MLR models in the prediction of groundwater quality parameters as they yielded positive, reliable, acceptable, and appropriate accuracy performances.

show abstract

Water quality index, hydrogeochemical facies and pollution index of groundwater around Middle Benue Trough, Nigeria

Cited by 20 publications

References 34 publications

Potential health risk and level of heavy metal in groundwater in the highly-dense area of Akure, Southwestern Nigeria

Potential health risk and level of heavy metal in groundwater in the highly-dense area of Akure, Southwestern Nigeria

Non-carcinogenic Health Risk Assessment and Predicting of Organic and Heavy Metal Pollution of Groundwater around Osisioma, Nigeria, using Artificial Neural Networks and Multi-Linear Modeling Principles

Application of artificial neural network and multi-linear regression techniques in groundwater quality and health risk assessment around Egbema, Southeastern Nigeria

Contact Info

Product

Resources

About