Evidence for Legacy Contamination of Nitrate in Groundwater of North Carolina Using Monitoring and Private Well Data Models

Abstract:

Nitrate (NO₃^-) is a widespread contaminant of groundwater and surface water across the United States that has deleterious effects to human and ecological health. Legacy contamination, or past releases of NO₃^-, is thought to be impacting current groundwater and surface water of North Carolina. This study develops a model for predicting point-level groundwater NO₃^- at a state scale for monitoring wells and private wells of North Carolina. A land use regression (LUR) model selection procedure known as constrained forward nonlinear regression and hyperparameter optimization (CFN-RHO) is developed for determining nonlinear model explanatory variables when they are known to be correlated. Bayesian Maximum Entropy (BME) is then used to integrate the LUR model to create a LUR-BME model of spatial/temporal varying groundwater NO₃^- concentrations. LUR-BME results in a leave-one-out cross-validation r² of 0.74 and 0.33 for monitoring and private wells, effectively predicting within spatial covariance ranges. The major finding regarding legacy sources NO₃^- in this study is that the LUR-BME models show the geographical extent of low-level contamination of deeper drinking-water aquifers is beyond that of the shallower monitoring well. Groundwater NO₃^- in monitoring wells is highly variable with many areas predicted above the current Environmental Protection Agency standard of 10 mg/L. Contrarily, the private well results depict widespread, low-level NO₃^-concentrations. This evidence supports that in addition to downward transport, there is also a significant outward transport of groundwater NO₃^- in the drinking water aquifer to areas outside the range of sources. Results indicate that the deeper aquifers are potentially acting as a reservoir that is not only deeper, but also covers a larger geographical area, than the reservoir formed by the shallow aquifers. Results are of interest to agencies that regulate surface water and drinking water sources impacted by the effects of legacy NO₃^- sources. Additionally, the results can provide guidance on factors affecting the point-level variability of groundwater NO₃^- and areas where monitoring is needed to reduce uncertainty. Lastly, LUR-BME predictions can be integrated into surface water models for more accurate management of non-point sources of nitrogen.