Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4883
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dc.contributor.authorArmah, Frederick Ato-
dc.contributor.authorPaintsil, Arnold-
dc.contributor.authorYawson, David Oscar-
dc.contributor.authorAdu, Michael Osei-
dc.contributor.authorOdoi, Justice O.-
dc.date.accessioned2021-03-15T09:44:48Z-
dc.date.available2021-03-15T09:44:48Z-
dc.date.issued2017-
dc.identifier.issn23105496-
dc.identifier.urihttp://hdl.handle.net/123456789/4883-
dc.description15p:, ill.en_US
dc.description.abstractChemometric techniques were applied to evaluate the spatial and temporal heterogeneities in groundwater quality data for approximately 740 goldmining and agriculture-intensive locations in Ghana. The strongest linear and monotonic relationships occurred between Mn and Fe. Sixty-nine per cent of total variance in the dataset was explained by four variance factors: physicochemical properties, bacteriological quality, natural geologic attributes and anthropogenic factors (artisanal goldmining). There was evidence of significant differences in means of all trace metals and physicochemical parameters (p < 0.001) between goldmining and non-goldmining locations. Arsenic and turbidity produced very high value F’s demonstrating that ‘physical properties and chalcophilic elements’ was the function that most discriminated between non-goldmining and goldmining locations. Variations in Escherichia coli and total coliforms were observed between the dry and wet seasons. The overall predictive accuracy of the discriminant function showed that non-goldmining locations were classified with slightly better accuracy (89%) than goldmining areas (69.6%). There were significant differences between the underlying distributions of Cd, Mn and Pb in the wet and dry seasons. This study emphasizes the practicality of chemometrics in the assessment and elucidation of complex water quality datasets to promote effective management of groundwater resources for sustaining human health. Frederick Ato Armah (corresponding author) Department of Environmental Science, School of Biological Sciences, College of Agriculture & Natural Sciences, University of Cape Coast, Cape Coast, Ghana E-mail: farmah@ucc.edu.gh Arnold Paintsil Department of Civil and Environmental Engineering, Faculty of Engineering, Spencer Engineering Building, Western University, London, Ontario N6A 5B9, Canada David Oscar Yawson Department of Soil Science, School of Agriculture, College of Agriculture & Natural Sciences, University of Cape Coast, Cape Coast, Ghana Michael Osei Adu Department of Crop Science, School of Agriculture, College of Agriculture & Natural Sciences, University of Cape Coast, Cape Coast, Ghana Justice O. Odoi Nature Today Ghana, P.O. Box OS 1455, Osu, Accra, Ghanaen_US
dc.language.isoenen_US
dc.publisherUniversity of Cape Coasten_US
dc.subjectBacteriologicalen_US
dc.subjectDiscriminant analysisen_US
dc.subjectGoundwateren_US
dc.subjectNegative log-logen_US
dc.subjectPhysicochemicalen_US
dc.subjectRegressionen_US
dc.titleModelling spatio-temporal heterogeneities in groundwater quality in Ghana: a multivariate chemometric approachen_US
dc.typeArticleen_US
Appears in Collections:Department of Crop Science

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