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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 6
Hydrol. Earth Syst. Sci., 20, 2353–2381, 2016
https://doi.org/10.5194/hess-20-2353-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 2353–2381, 2016
https://doi.org/10.5194/hess-20-2353-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 17 Jun 2016

Research article | 17 Jun 2016

A meta-analysis and statistical modelling of nitrates in groundwater at the African scale

Issoufou Ouedraogo and Marnik Vanclooster Issoufou Ouedraogo and Marnik Vanclooster
  • Earth and Life Institute, Université catholique de Louvain, Croix du Sud 2, Box 2, B-1348 Louvain-la-Neuve, Belgium

Abstract. Contamination of groundwater with nitrate poses a major health risk to millions of people around Africa. Assessing the space–time distribution of this contamination, as well as understanding the factors that explain this contamination, is important for managing sustainable drinking water at the regional scale. This study aims to assess the variables that contribute to nitrate pollution in groundwater at the African scale by statistical modelling. We compiled a literature database of nitrate concentration in groundwater (around 250 studies) and combined it with digital maps of physical attributes such as soil, geology, climate, hydrogeology, and anthropogenic data for statistical model development. The maximum, medium, and minimum observed nitrate concentrations were analysed. In total, 13 explanatory variables were screened to explain observed nitrate pollution in groundwater. For the mean nitrate concentration, four variables are retained in the statistical explanatory model: (1) depth to groundwater (shallow groundwater, typically < 50 m); (2) recharge rate; (3) aquifer type; and (4) population density. The first three variables represent intrinsic vulnerability of groundwater systems to pollution, while the latter variable is a proxy for anthropogenic pollution pressure. The model explains 65 % of the variation of mean nitrate contamination in groundwater at the African scale. Using the same proxy information, we could develop a statistical model for the maximum nitrate concentrations that explains 42 % of the nitrate variation. For the maximum concentrations, other environmental attributes such as soil type, slope, rainfall, climate class, and region type improve the prediction of maximum nitrate concentrations at the African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the data set, we do not develop a statistical model for these data. The data-based statistical model presented here represents an important step towards developing tools that will allow us to accurately predict nitrate distribution at the African scale and thus may support groundwater monitoring and water management that aims to protect groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data become available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.

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In this paper, we present a meta-analysis of nitrate contamination in groundwater at the pan-African scale. A nitrate data set is constructed based on publications in the web of sciences, and combined with high-resolution generic spatial environmental attributes. A statistical model explains 65 % of the variation of nitrate contamination in groundwater in terms of generic spatial attributes. Nitrate contamination of groundwater at the pan-African scale is mainly affected by population density.
In this paper, we present a meta-analysis of nitrate contamination in groundwater at the...
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