Modeling groundwater nitrate concentrations in private wells in Iowa

Wheeler, David C.; Nolan, Bernard T.; Flory, Abigail R.; DellaValle, Curt T.; Ward, Mary H.

doi:10.1016/j.scitotenv.2015.07.080

Cited by 128 publications

(101 citation statements)

References 43 publications

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“…Alluvial and shallow aquifers are thus particularly vulnerable to nitrate pollution, while deep confined aquifers are generally better protected. The inverse relation between depth and nitrate is consistent with previous groundwater studies that considered well depth or depth of the screened interval as explanatory variables (Nolan and Hitt, 2006;Nolan et al, 2014;Wheeler et al, 2015;. Nitrate generally moves relatively slowly in soil and groundwater, and therefore there is a significant time lag between the polluting activity and detection of the pollutant in groundwater (typically between 1 and 20 years, depending on the situation) (Boy-Roura, 2013;.…”

Section: Discussionsupporting

confidence: 88%

“…More recently, other investigators showed the process of nitrate accumulation in the unsaturated zone Wang et al, 2016;Worall et al, 2015). The long travel distances towards deep aquifer systems increase the probability that nutrients will react for instance through denitrification (Stevenson and Cole, 1999;Thayalakumaran et al, 2004;Aljazzar, 2010;Wheeler et al, 2015). Denitrification is facilitated by the absence of oxygen.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, many studies showed that non-linear statistical models of groundwater contamination outperform the linear models (e.g. Pineros-Garcet et al, 2006;Oliveira et al, 2012;Wheeler et al, 2015). To uncover non-linear relationships, non-parametric data mining approaches provide obvious advantages (Olden et al, 2008;Wiens, 1989;Dungan et al, 2002).…”

Section: Ouedraogo and M Vanclooster: A Meta-analysis And Statistmentioning

confidence: 99%

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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: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Ouedraogo and M Vanclooster: A Meta-analysis And Statistmentioning

confidence: 99%

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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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“…However, nitrogen fertilization is commonly applied in surplus and leaches below the roots, mainly as the conservative anion nitrate (NO − 3 ), which has strict limits under drinkingwater standards worldwide. As a consequence, nitrate has become the most common groundwater contamination caused by agricultural activity in many countries (Jalali, 2005;Vitousek et al, 2009;Burow et al, 2010;Kourakos et al, 2012;Yue et al, 2014;Wheeler et al, 2015;Wang et al, 2016). In Israel, for example, more than half of all the wells that have been disqualified as sources of drinking water were disqualified due to nitrate contamination (Israel Water Authority; IWA, 2015a).…”

Section: Groundwater Contamination By Nitrate Under Agricultural Landmentioning

confidence: 99%

“…Nitrate contamination of the groundwater below agricultural land is often characterized by significant spatial variability of the nitrate concentrations in wells (Hu et al, 2005;Liu et al, 2005;Wheeler et al, 2015). This variability may evolve from the spatial variability of the soil properties.…”

Section: The Path From Nitrogen Fertilizer To Nitrate In Groundwatermentioning

confidence: 99%

Modeling nitrate from land surface to wells' perforations under agricultural land: success, failure, and future scenarios in a Mediterranean case study

Levy

Shapira

Chefetz

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Contamination of groundwater resources by nitrate leaching under agricultural land is probably the most troublesome agriculture-related water contamination worldwide. Contaminated areas often show large spatial variability of nitrate concentration in wells. In this study, we tried to assess whether this spatial variability can be characterized on the basis of land use and standard agricultural practices. Deep soil sampling (10 m) was used to calibrate vertical flow and nitrogen-transport numerical models of the unsaturated zone under different agricultural land uses. Vegetable fields (potato and strawberry) and deciduous orchards (persimmon) in the Sharon area overlying the coastal aquifer of Israel were examined. Average nitrate-nitrogen fluxes below vegetable fields were 210-290 kg ha −1 yr −1 and under deciduous orchards were 110-140 kg ha −1 yr −1 . The output water and nitrate-nitrogen fluxes of the unsaturated-zone models were used as input data for a three-dimensional flow and nitratetransport model in the aquifer under an area of 13.3 km 2 of agricultural land. The area was subdivided into four agricultural land uses: vegetables, deciduous orchards, citrus orchards, and non-cultivated. Fluxes of water and nitratenitrogen below citrus orchards were taken from a previous study in the area. The groundwater flow model was calibrated to well heads by changing the hydraulic conductivity. The nitrate-transport model, which was fed by the abovementioned models of the unsaturated zone, succeeded in reconstructing the average nitrate concentration in the wells. However, this transport model failed in calculating the high concentrations in the most contaminated wells and the large spatial variability of nitrate concentrations in the aquifer. To reconstruct the spatial variability and enable predictions, nitrate fluxes from the unsaturated zone were multiplied by local multipliers. This action was rationalized by the fact that the high concentrations in some wells cannot be explained by regular agricultural activity and are probably due to malfunctions in the well area. Prediction of the nitrate concentration 40 years in the future with three nitrogen-fertilization scenarios showed that (i) under the "business as usual" fertilization scenario, the nitrate concentration (as NO − 3 ) will increase on average by 19 mg L −1 ; (ii) under a scenario of 25 % reduction of nitrogen fertilization, the nitrate concentration in the aquifer will stabilize; (iii) with a 50 % reduction of nitrogen fertilization, the nitrate concentration will decrease on average by 18 mg L −1 .

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Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification

Tesoriero

Gronberg

Juckem

et al. 2017

Water Resources Research

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Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox‐active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide‐bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

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Modeling groundwater nitrate concentrations in private wells in Iowa

Cited by 128 publications

References 43 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

Modeling nitrate from land surface to wells' perforations under agricultural land: success, failure, and future scenarios in a Mediterranean case study

Predicting redox‐sensitive contaminant concentrations in groundwater using random forest classification

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