**Screening-Level Assessments of Public Water Supply Well Vulnerability
to Natural Contaminants**

**By Stephen Hinkle^1^, Mary Ann Thomas^2^, Craig Brown^3^, Kathy
McCarthy^1^, Sandra Eberts^2^, Leon Kauffman^4^, Michael Rosen^5^, and
Brian Katz^6^**

1 USGS, 10615 SE Cherry Blossom Drive, Portland, OR 97216

2 USGS, 6480 Doubletree Avenue, Columbus, OH 43229

3 USGS, 101 Pitkin Street, East Hartford, CT 06108

4 USGS, 810 Bear Tavern Road, West Trenton, NJ 08628

5 USGS, 333 W. Nye Lane, Carson City, NV 89706

6 USGS, 2010 Levy Avenue, Tallahassee, FL 32310

*2006 National Monitoring Conference: Concurrent Session K: K3-Assessing
Ground Water Vulnerability Through Statistical Methods II, May 7-11,
2006, San Jose, CA.*

**Biographical Sketches of Authors**

All eight authors are hydrologists with the USGS. They have worked on
numerous water-quality and water-quantity projects at local, regional
and national scales, focusing on topics such as anthropogenic and
natural contaminant source and fate, flow and transport modeling,
ground-water age-dating, nutrient cycling, and many other topics. The
authors are part of a larger team of USGS National Water-Quality
Assessment Program hydrologists currently studying factors affecting the
transport of natural and anthropogenic contaminants to public water
supply wells at multiple scales.

**Abstract**

Geochemical data and ground-water flow models (particle tracking) were
used to identify process-based variables for use in quantitative
screening-level vulnerability assessments for uranium and arsenic in
public water supply wells. These results, from eight USGS National
Water-Quality Assessment Program study areas in principal aquifers,
demonstrate an approach that can be undertaken with existing tools and
data and applied to these or other natural contaminants. Such
assessments may be useful for prioritizing locations and types of more
refined vulnerability assessments.

Principal components analysis suggests that uranium is associated with
oxidizing water, and arsenic with both oxidizing and reducing water.
These statistical relations are consistent with the geochemistry of
these elements: uranium generally is mobilized under oxidizing
conditions but attenuated under reducing conditions, whereas arsenic can
be mobilized under both oxidizing and reducing conditions. Spearman
correlation analysis of variables from particle-tracking simulations
indicates that uranium is positively correlated with variables that
represent (1) overall ground water time-of-travel (TOT), (2) TOT through
oxidizing regions, and (3) water fluxes to wells from oxidizing regions.
Uranium also is inversely correlated with variables representing TOT
through reducing regions. TOT may be an important variable because many
geochemical reactions are kinetically limited. Arsenic was positively
correlated with variables representing overall TOT, but correlations
with redox TOT variables were ambiguous in the combined (eight aquifer)
dataset, likely resulting from the presence of both oxidizing and
reducing arsenic mobilization processes.

Classification tree analysis (a nonparametric method in which data are
partitioned recursively into increasingly homogeneous subsets, similar
to dichotomous classification keys) on arsenic concentrations yielded a
quantitative categorical model using two TOT variables and a variable
representing solid-phase arsenic concentrations. The classification tree
model accuracy on the learning data subset was 70%, and on the testing
data subset, 79%, demonstrating the usefulness of TOT variables in
vulnerability assessments.