Anthropogenic Pollution of Inland Waters: the Case of the Aboabo River in Kumasi, Ghana

Danquah, Leslie; Abass, Kabila; Nikoi, Aristotle Afutu

doi:10.5539/jsd.v4n6p103

Cited by 25 publications

(18 citation statements)

References 7 publications

(4 reference statements)

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“…This is greater than average values recorded in 1999: 1.3×10 6 CFU/100mL and 2010: 3.5×10 6 CFU/100mL as cited by [1]. Total coliforms also showed a similar pattern to E. coli as shown in Figures 22 and 23.…”

Section: Microbial Parametersmentioning

confidence: 62%

“…It has a length of 16.6km from source to the downstream end -Atonsu flowing at an average velocity of 0.87m/s. It is estimated that the Aboabo River basin is home to about a quarter of the city's over 2million residents [1]. The basin is also a hub of several small-and large-scale industries including palm kernel oil extraction companies, charcoal producing business, metal scrap dealers, leather tanning industries, car washing bays, among others.…”

Section: Study Areasmentioning

confidence: 99%

“…As far as human existence and development is concerned, water plays a crucial role in agricultural, domestic, industrial, recreational, religious activities among others. Due to the enormous importance of water for human development, early civilisations thrived along waterbodies such as the Nile, Tigris and Euphrates in ancient Mesopotamia, Indus in India, and Huang He in China [1]. Studies have shown that, without access to potable water, all efforts aimed at reducing poverty and protecting public health will be futile [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Urbanization and Pollution of Surface Water Resources in the Two Largest Cities in Ghana

Monney¹

2013

IJEMA

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Pollution of freshwater resources draining urban centres in the developing world poses a threat to human health and environmental sustainability. This, apart from inadequate infrastructure, is primarily propelled by explosive urban populations. The study seeks to determine the level of pollution and sources of pollution in selected surface water resources in Accra and Kumasi. Water samples from specific locations of the Aboabo river in Kumasi and the upper reaches of the Korle Lagoon in Accra were collected and analysed for their physico-chemical and microbial quality during the study period. The results from the study pointed out that the upper reaches of the Lagoon is rendered anaerobic during the dry season and with appreciably low DO even during the wet season. Drastic depreciation in DO levels in the Aboabo river as it flows through highly populated areas (Aboabo, Moshie Zongo and Anloga) were depicted through the study. Elevated BOD (upper reaches of the Korle Lagoon: 27.7 -200mg/L; Aboabo river: 38.25mg/L -260mg/L) and E. coli (upper reaches of the Korle Lagoon: 5.0x10 6 -2.8x10 9 CFU/100mL; Aboabo river: 4.0x10 6 CFU/100mL -1.3x10 8 CFU/100mL) levels were also recorded in both waterbodies generally attributed to disposal of organic wastes and faecal matter into them. An integrated approach that takes cognizance of both technical and socio-economic factors behind this phenomenon is proposed by the study.

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Section: Microbial Parametersmentioning

confidence: 62%

Section: Study Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urbanization and Pollution of Surface Water Resources in the Two Largest Cities in Ghana

Monney¹

2013

IJEMA

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“…The CWQI compares observations to a guideline value, which can be a water quality criterion or a locality specific chemical composition of a hydro geological parameter. The CWQI was formulated with the purpose of rendering a mechanism for reducing the documentation of water quality data [11] [12] [13]. As a compendious tool, it furnishes a wide sketch of water quality data and it is functional for various purposes including drinking water quality, data communications, ambient water quality data processing, combined watershed designing, management and policy decisions in the water supply sector.…”

Section: Canadian Water Quality Index (Cwqi)mentioning

confidence: 99%

Application of ANN and MLR Models on Groundwater Quality Using CWQI at Lawspet, Puducherry in India

Nathan¹,

Saravanane²,

Sundararajan³

2017

GEP

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With respect to groundwater deterioration from human activities a unique situation of co-disposal of non-engineered Municipal Solid Waste (MSW) dumping and Secondary Wastewater (SWW) disposal on land prevails simultaneously within the same campus at Puducherry in India. Broadly the objective of the study is to apply and compare Artificial Neural Network (ANN) and Multi Linear Regression (MLR) models on groundwater quality applying Canadian Water Quality Index (CWQI). Totally, 1065 water samples from 68 bore wells were collected for two years on monthly basis and tested for 17 physio-chemical and bacteriological parameters. However the study was restricted to the pollution aspects of 10 physio-chemical parameters such as EC, TDS, TH,, Mg 2+ and K + . As there is wide spatial variation (2 to 3 km radius) with ground elevation (more than 45 m) among the bore wells it is appropriate to study the groundwater quality using Multivariate Statistical Analysis and ANN. The selected ten parameters were subjected to Hierarchical Cluster Analysis (HCA) and the clustering procedure generated three well defined clusters. Cluster wise important physiochemical attributes which were altered by MSW and SWW operations, are statistically assessed. The CWQI was evolved with the objective to deliver a mechanism for interpreting the water quality data for all three clusters. The ANOVA test results viz., F-statistic (F = 134.55) and p-value (p = 0.000 < 0.05) showed that there are significant changes in the average values of CWQI among the three clusters, thereby confirming the formation of clusters due to anthropogenic activities. The CWQI simulation was performed using MLR and ANN models for all three clusters. Totally, 1 MLR and 9 ANN models were considered for simulation. Further the performances of ten models were compared using R 2 , RMSE and MAE (quantitative indicators). The analyses of the results revealed that both MLR and ANN models were fairly good in pre- 100dicting the CWQI in Clusters 1 and 2 with high R 2 , low RMSE and MAE values but in Cluster 3 only ANN model fared well. Thus this study will be very useful to decision makers in solving water quality problems.

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“…To sustain and maximize the benefit of this resource, knowledge about the natural hydro-geological and geo-chemical processes, as well as associated human effects on the ground water resource is a must for a comprehensive and complete scientific understanding of the vulnerability of the aquifers to pollution. In this context, rapid increase in human population coupled with expanding urbanization and industrialization has led to a greater imbalance between water availability and demand [1]. As worldwide extraction of ground water is accelerated to meet increasing demand, the significance of the chemical quality of ground water also increases relative to its economic value and usefulness.…”

mentioning

confidence: 99%

Spatial Variability of Ground Water Quality Using HCA, PCA and MANOVA at Lawspet, Puducherry in India

Nathan¹,

Saravanane²,

Sundararajan³

2017

CWEEE

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In ground water quality studies multivariate statistical techniques like Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Multivariate Analysis of Variance (MANOVA) were employed to evaluate the principal factors and mechanisms governing the spatial variations and to assess source apportionment at Lawspet area in Puducherry, India. PCA/FA has made the first known factor which showed the anthropogenic impact on ground water quality and this dominant factor explained 82.79% of the total variance. The other four factors identified geogenic and hardness components. The distribution of first factor scores portray high loading for EC, TDS, Na + and Cl − (anthropogenic) in south east and south west parts of the study area, whereas other factor scores depict high loading for direction. Further MANOVA showed that there are significant differences between ground water quality parameters. The spatial distribution maps of water quality parameters have rendered a powerful and practical visual tool for defining, interpreting, and distinguishing the anthropogenic, hardness and geogenic factors in the study area. Further the study indicated that multivariate statistical methods have successfully assessed the ground water qualitatively and spatially with a more effective step towards ground water quality management.

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Anthropogenic Pollution of Inland Waters: the Case of the Aboabo River in Kumasi, Ghana

Cited by 25 publications

References 7 publications

Urbanization and Pollution of Surface Water Resources in the Two Largest Cities in Ghana

Urbanization and Pollution of Surface Water Resources in the Two Largest Cities in Ghana

Application of ANN and MLR Models on Groundwater Quality Using CWQI at Lawspet, Puducherry in India

Spatial Variability of Ground Water Quality Using HCA, PCA and MANOVA at Lawspet, Puducherry in India

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