Groundwater intrinsic vulnerability and risk mapping

Farjad, Babak; Shafri, Helmi Zulhaidi Mohd; Mohamed, Thamer Ahmed; Pirasteh, Saied; Wijesekara, Nishad

doi:10.1680/wama.10.00018

Cited by 24 publications

(19 citation statements)

References 13 publications

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“…The groundwater pollution risk in Fig. 3 corresponds to the potential of a groundwater body for undergoing groundwater contamination (Farjad et al, 2012). The risk of pollution is determined both by the intrinsic vulnerability of the aquifer, which is relatively static, and the existence of potentially polluting activities at the soil surface.…”

Section: Data Quality Evaluationmentioning

confidence: 99%

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

Ouédraogo

Vanclooster

2016

Hydrol. Earth Syst. Sci.

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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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Section: Data Quality Evaluationmentioning

confidence: 99%

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

Ouédraogo

Vanclooster

2016

Hydrol. Earth Syst. Sci.

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“…They can be grouped into processbased, statistical, and index-overlay methods [8][9][10][11]. Vulnerability in the index-overlay method is determined by weights, indexes, ranges, and classifi cation.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the DRASTIC Method for Nitrate Pollution in Groundwater Vulnerability Assessments: a Case Study in China

He¹,

Li²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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Groundwater vulnerability assessments, using DRASTIC, are important and useful tools for groundwater pollution prevention and control. The DRASTIC method, however, is not appropriate for accurate specifi c vulnerability assessments where nitrate concentrations are high. A new method has been developed that retains the basic structure of DRASTIC while adding or subtracting parameters, and modifying the parameter ratings and weightings. The resulting DRACILM model was used to assess vulnerability to nitrate pollution in the West Liaohe Plain and as a basis for vulnerability mapping. The accuracy, appropriateness, and reliability of the vulnerability mapping were analyzed using a group of integrated indicators, such as correlation, ANOVA F-statistics, and single-parameter sensitivity analysis. The correlation between vulnerability class and the concentration of NO 3 -N in the DRACILM model improved to 0.649, which was 40.6% higher than that obtained by DRASTIC. The ANOVA F-statistic was 27.71, which indicated a lower overlap between the mean values of nitrates in the different vulnerability classes. The single-parameter sensitivity analysis revealed that land use type exhibited the highest and hydraulic conductivity the lowest effective weighting values. The vulnerability maps by DRACILM model could assist planners and government decision-makers with preliminary investigations into planning water protection projects or establishing management scenarios for water resource quality.

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“…The nitrate pollution of groundwater caused by agricultural activity and a substantial increase in fertilizer utilization are also becoming an increasing problem. Groundwater vulnerability mapping is an important key to improving planning and decision-making processes in order to prevent groundwater contamination (Farjad et al 2012;Mahvi et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models

Ghazavi

Ebrahimi

2015

Int. J. Environ. Sci. Technol.

128

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Groundwater is vulnerable to contamination by anthropological activities. Vulnerability mapping is considered as a fundamental aspect of groundwater management. The aim of this study was to estimate aquifer vulnerability by applying the DRASTIC and GOD models in Abarkooh plain, Yazd province, center of Iran. The DRASTIC model uses seven environmental parameters (depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone, and hydraulic conductivity) to characterize the hydrogeological setting and evaluate aquifer vulnerability. GOD is an overlay and index method designed to map groundwater vulnerability over large regions based on three parameters (groundwater confinement, overlying strata, and depth to groundwater). The information layers for models were provided via geographic information system. The overlap techniques were used to provide and produce the vulnerability map of the study areas considering weight coefficients of each layer. Accuracy of the models was evaluated using linear regression between observations values of nitrate and estimated vulnerability to pollution in the measured wells. A significant correlation was observed between measured nitrate and pollution potential evaluated by DRASTIC model (P \ 0.01), but no significant correlation was observed for GOD model (P \ 0.05). The results showed that the DRASTIC model is better than GOD model to estimate groundwater vulnerability to pollution in the measured wells. For DRASTIC model, the correlation coefficient between vulnerability index and nitrate concentration was 68 % that was substantially higher than 28 % obtained for the GOD model. We can conclude that nitrate concentration should be a suitable parameter to investigate the accuracy of the DRASTIC and GOD models.

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Groundwater intrinsic vulnerability and risk mapping

Cited by 24 publications

References 13 publications

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

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

Optimizing the DRASTIC Method for Nitrate Pollution in Groundwater Vulnerability Assessments: a Case Study in China

Assessing groundwater vulnerability to contamination in an arid environment using DRASTIC and GOD models

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