Version 1
: Received: 12 December 2020 / Approved: 14 December 2020 / Online: 14 December 2020 (10:13:09 CET)
How to cite:
Armah, F.; Paintsil, A.; Adu, M.; Yawson, D.O.; Odoi, J. Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana. Preprints2020, 2020120321. https://doi.org/10.20944/preprints202012.0321.v1
Armah, F.; Paintsil, A.; Adu, M.; Yawson, D.O.; Odoi, J. Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana. Preprints 2020, 2020120321. https://doi.org/10.20944/preprints202012.0321.v1
Armah, F.; Paintsil, A.; Adu, M.; Yawson, D.O.; Odoi, J. Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana. Preprints2020, 2020120321. https://doi.org/10.20944/preprints202012.0321.v1
APA Style
Armah, F., Paintsil, A., Adu, M., Yawson, D.O., & Odoi, J. (2020). <strong>Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana</strong>. Preprints. https://doi.org/10.20944/preprints202012.0321.v1
Chicago/Turabian Style
Armah, F., David Oscar Yawson and Justice Odoi. 2020 "<strong>Relationship of Trace Metal Covariates and pH Distribution in Groundwater within Gold mining and Non-Gold mining Areas in Ghana</strong>" Preprints. https://doi.org/10.20944/preprints202012.0321.v1
Abstract
One of the most important defining characteristics of groundwater quality is pH as it fundamentally controls the amount and chemical form of many organic and inorganic solutes in groundwater. Groundwater data are frequently characterized by a wide degree of variability of the factors which possibly influence pH distribution. For this reason, it is challenging to link the spatio-temporal dynamics of pH to a single environmental factor by the ordinary least squares regression technique of the conditional mean. In this study, quantile regression was used to estimate the response of pH to nine environmental factors (As, Cd, Fe, Mn, Pb, turbidity, electrical conductivity, total dissolved solids and nitrates). Results of 25%, 50%, 75% quantile regression and ordinary least squares (OLS) regression were compared. The standard regression of the conditional means (OLS) underestimated the rates of change of pH due to the selected factors in comparison with the regression quantiles. The effect of arsenic increased for sampling locations with higher pH values (higher quantiles) likewise the influence of Pb and Mn. However, the effects of Cd and Fe decreased for sampling locations in higher quantiles. It can be concluded that these detected heterogeneities would be missed if this study had focused exclusively on the conditional means of the pH values. Consequently, quantile regression provides a more comprehensive account of possible spatio-temporal relationships between environmental covariates in groundwater. This study is one of the first to apply this technique on groundwater systems in sub-Saharan Africa. The approach is useful and interesting and has broad application for other mining environments especially tropical low-income countries where climatic conditions can drive rapid cycling or transformations of pollutants. It is also pertinent to geopolitical contexts where regulatory; monitoring and management capacities are weak and where mining pollution of groundwater largely occur.
Keywords
quantile regression; groundwater; environmental; multivariate; metals; health
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
