A meta-analysis of groundwater contamination by nitrates at the African scale
A meta-analysis of groundwater contamination by nitrates at the African scale
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非洲范围内硝酸盐地下水污染的荟萃分析
DOI：
10.5194/hess-2016-120
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Vanclooster
中科院分区：
文献类型：
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作者：
I. Ouédraogo;M. Vanclooster
硝酸盐污染地下水对非洲各地数百万人构成重大健康风险。评估这种污染的时空分布，以及理解解释这种污染的因素，对于在区域范围内管理可持续饮用水非常重要。本研究的目的是评估变量，有助于在泛非规模的地下水硝酸盐污染的统计建模。我们编制了地下水中硝酸盐浓度的文献数据库（约250项研究），并将其与土壤，地质，气候，水文地质和人为数据等物理属性的数字地图相结合，用于统计模型开发。分析了观测到的最大、中等和最小硝酸盐浓度。总共筛选了13个解释变量来解释观测到的地下水硝酸盐污染。对于平均硝酸盐浓度，统计解释模型中保留了4个变量：（1）地下水深度（浅层地下水，通常<50 m）;（2）补给率;（3）含水层类型;（4）人口密度。前三个变量代表地下水系统对污染的内在脆弱性，而后一个变量是人为污染压力的代理。该模型解释了65%的平均硝酸盐污染在泛非洲尺度的地下水的变化。使用相同的替代信息，我们可以开发一个最大硝酸盐浓度的统计模型，解释42%的硝酸盐变化。对于最大浓度，其他环境属性，如土壤类型，坡度，降雨量，气候类别和区域类型，提高了预测的最大硝酸盐浓度在泛非洲的规模。至于最小硝酸盐浓度，在数据集没有正态分布假设的情况下，我们没有为这些数据建立统计模型。这里提出的基于数据的统计模型是朝着开发工具迈出的重要一步，这些工具将使我们能够准确地预测硝酸盐在非洲范围内的分布，从而可以支持旨在保护地下水系统的地下水监测和水管理。然而，当更详细和统一的数据变得可用和/或与水文系统中营养物命运的更多概念性描述相结合时，它们应进一步完善和验证。
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 to manage sustainable drinking water at the regional scale. This study aims assessing the variables that contribute to nitrate pollution in groundwater at the pan African scale by statistical modeling. 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, 4 variables are retained in the statistical explanatory model: (1) Depth to groundwater (shallow groundwater, typically <50m); (2) Recharge rate; (3) Aquifer type; and (4) Population density. The former three variables represent intrinsic vulnerability of groundwater systems towards 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 pan Africa 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 improves the prediction of maximum nitrate concentrations at the pan African scale. As to minimal nitrate concentrations, in the absence of normal distribution assumptions of the dataset, we do not develop a statistical model for these data. The data based statistical model presented here represents an important step toward 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 protecting groundwater systems. Yet they should be further refined and validated when more detailed and harmonized data becomes available and/or combined with more conceptual descriptions of the fate of nutrients in the hydrosystem.