Cumulative Uncertainty in Measured Groundwater Iron Content of Rigasa Watershed, Kaduna
O. S. Owolabi, C. N. Okafo, M. O. Nwude
Current Journal of Applied Science and Technology · pp. 1–11 · Published 6 Jan 2016
10.9734/BJAST/2016/21541Abstract
Fresh water quality has become the principal limitation for sustainable development in many countries and the major human and environmental health dimensions of the global fresh water quality problem is constituted in numerous effects of water borne diseases. Therefore, the assessment of water quality, its management and regulation rely on the water quality data. The aim of this paper is to establish the measurement uncertainty in groundwater quality data in Rigasa Watershed Area in the north-west of Kaduna Metropolis. Measurement uncertainty results from the water quality data monitoring methods. When uncertainty is not considered in measurements it results in un-optimized monitoring projects, unsustainable water resources development and management, and lack of ecosystem security considering their cost-effectiveness and data quality when measurement uncertainty and alternatives to reduce uncertainty are not included in the projects design and implementation. Water quality assessment of the boreholes in Rigasa Watershed was carried out for a period of 12 months to portray the regimes of the phenomena and establish the uncertainty in the measured data. Iron content among the various parameters measured in the boreholes water was found to be in excess of the required limits by the World Health Organization (WHO) and thus formed the focus of this paper. The measurement result showed that the Fe content in groundwater sample was 3.189 mg/l, with the expanded uncertainty measurement ±1.482 mg/l (coverage factor, k = 2, at confidence level 95%). The significant uncertainties showed that the measured values were largely spread around the mean values of the measurand.
Cited by 2
Saadu Umar Wali, Noraliani Alias, Sobri Bin Harun · SN Applied Sciences · 2020
Saadu Umar Wali, Noraliani Alias, Sobri Bin Harun · Environmental Earth Sciences · 2021
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