National Water-Quality Assessment (NAWQA) Program
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By Bernard T. Nolan, Kerie J. Hitt, and Barbara C. Ruddy
[Environmental Science and Technology, v. 36, no. 10, p. 2138-2145.]This article supersedes ES&T vol. 31, no. 8; WCP vol. 39, no. 12; and FS-092-96.
Abstract
A new logistic regression (LR) model was used to predict the
probability of nitrate contamination exceeding 4 mg/L in predominantly
shallow, recently recharged ground waters of the United States. The
new model contains variables representing (1) N fertilizer loading (p
< 0.001) , (2) percent cropland-pasture (p < 0.001), (3) natural log
of human population density (p < 0.001), (4) percent well-drained
soils (p < 0.001), (5) depth to the seasonally high water table
(p <0.001), and (6) presence or absence of unconsolidated sand and gravel
aquifers (p = 0.002). Observed and average predicted probabilities
associated with deciles of risk are well correlated (r2 = 0.875),
indicating that the LR model fits the data well. The likelihood of
nitrate contamination is greater in areas with high N loading and
well-drained surficial soils over unconsolidated sand and gravels.
The LR model correctly predicted the status of nitrate contamination
in 75% of wells in a validation data set. Considering all wells used
in both calibration and validation, observed median nitrate
concentration increased from 0.24 to 8.30 mg/L as the mapped
probability of nitrate exceeding 4 mg/L increased from
less than or equal to 0.17 to > 0.83.
Table of Contents
Introduction
Methods
Results and discussion
Recalibration of multivariate logistic regression model
Groundwater nitrate in specific areas follows predicted probabilities
Logistic regression model validation
General verification of logistic regression model
Uses and limitations
Acknowledgments
Literature cited