Nitrate Variability in Groundwater of North Carolina using Monitoring and Private Well Data Models

[Image: see text] Nitrate (NO(3)(–)) is a widespread contaminant of groundwater and surface water across the United States that has deleterious effects to human and ecological health. This study develops a model for predicting point-level groundwater NO(3)(–) at a state scale for monitoring wells and private wells of North Carolina. A land use regression (LUR) model selection procedure is developed for determining nonlinear model explanatory variables when they are known to be correlated. Bayesian Maximum Entropy (BME) is used to integrate the LUR model to create a LUR-BME model of spatial/temporal varying groundwater NO(3)(–) concentrations. LUR-BME results in a leave-one-out cross-validation r(2) of 0.74 and 0.33 for monitoring and private wells, effectively predicting within spatial covariance ranges. Results show significant differences in the spatial distribution of groundwater NO(3)(–) contamination in monitoring versus private wells; high NO(3)(–) concentrations in the southeastern plains of North Carolina; and wastewater treatment residuals and swine confined animal feeding operations as local sources of NO(3)(–) in monitoring wells. Results are of interest to agencies that regulate drinking water sources or monitor health outcomes from ingestion of drinking water. Lastly, LUR-BME model estimates can be integrated into surface water models for more accurate management of nonpoint sources of nitrogen.