National Water-Quality Assessment (NAWQA) Program
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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