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Relating Nitrogen Sources and Aquifer Susceptibility to Nitrate in Shallow Ground Waters of the United States

By Bernard T. Nolan

[Ground Water, vol. 39, no. 2, March-April 2001, p. 290-299]

Abstract
Characteristics of nitrogen loading and aquifer susceptibility to contamination were evaluated to determine their influence on contamination of shallow ground water by nitrate. A set of 13 explanatory variables was derived from these characteristics, and variables that have a significant influence were identified using logistic regression (LR). Multivariate LR models based on more than 900 sampled wells predicted the probability of exceeding 4 mg/L of nitrate in ground water. The final LR model consists of the following variables: (1) nitrogen fertilizer loading (p-value = 0.012), (2) percent cropland-pasture (p < 0.001), (3) natural log of population density (p < 0.001), (4) percent well-drained soils (p = 0.002), (5) depth to the seasonally high water table (p = 0.001), and (6) presence or absence of a fracture zone within an aquifer (p = 0.002). Variables 1-3 were compiled within circular, 500-m radius areas surrounding sampled wells, and variables 4-6 were compiled within larger areas representing targeted land use and aquifers of interest. Fitting criteria indicate that the full logistic-regression model is highly significant (p < 0.001), compared with an intercept-only model that contains none of the explanatory variables. A goodness-of-fit test indicates that the model fits the data very well, and observed and predicted probabilities of exceeding 4 mg/L nitrate in ground water are strongly correlated (r2 = 0.971). Based on the multivariate LR model, vulnerability of ground water to contamination by nitrate depends not on any single factor but on the combined, simultaneous influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics.


Table of Contents
Introduction
Background
Methods
Results and discussion
  Univariate logistic-regression models
  Multivariate logistic-regression models
    Nested multivariate logistic-regression models
    Final multivariate logistic-regression model and related explanatory variables
Conclusions
Acknowledgments
References


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