DOI: 10.5194/hess-20-2353-2016
Scopus记录号: 2-s2.0-84975519814
论文题名: A meta-analysis and statistical modelling of nitrates in groundwater at the African scale
作者: Ouedraogo I ; , Vanclooster M
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2016
卷: 20, 期: 6 起始页码: 2353
结束页码: 2381
语种: 英语
Scopus关键词: Aquifers
; Climate models
; Contamination
; Groundwater
; Groundwater resources
; Health risks
; Hydrogeology
; Information management
; Nitrates
; Normal distribution
; Pollution
; Population statistics
; Potable water
; Statistical methods
; Statistics
; Water management
; Water pollution
; Anthropogenic pollution
; Environmental attributes
; Groundwater monitoring
; Maximum concentrations
; Nitrate concentration
; Nitrate contamination
; Space time distribution
; Statistical modelling
; Groundwater pollution
; anthropogenic effect
; aquifer
; concentration (composition)
; drinking water
; environmental monitoring
; groundwater
; groundwater pollution
; health risk
; meta-analysis
; nitrate
; numerical model
; population density
; soil type
; vulnerability
; water management
; Africa
英文摘要: 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. © 2016 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78818
Appears in Collections: 气候变化事实与影响
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作者单位: Earth and Life Institute, Universite Catholique de Louvain, Croix du Sud 2, Louvain-la-Neuve, Belgium
Recommended Citation:
Ouedraogo I,, Vanclooster M. A meta-analysis and statistical modelling of nitrates in groundwater at the African scale[J]. Hydrology and Earth System Sciences,2016-01-01,20(6)