When well water becomes contaminated to the extent that is does not meet EPA drinking water quality standards, it is considered unsafe for consumption. Nitrate and total coliform bacteria are both health contaminants and are both regulated in public water systems. A nitrate concentration of 10 mg/L or higher is considered unsafe, as is the presence of total coliform bacteria. Well degradation, inadequate well construction, and aquifer contamination can all result in contamination of well water. Factors such as well type, well age, well depth, treatment devices, population density, household plumbing pipe materials, and nearby pollution sources may affect household water quality. The specific objective of this study was to determine which factors influence nitrate levels and total coliform presence/absence of household well water. If possible, these influencing factors would be used to develop a relationship that would allow household residents to predict the nitrate level and total coliform presence/absence of their well water. As a result, a means of predicting the contamination risk to a specific well water supply under a given set of conditions, in addition to increasing awareness, could provide the homeowner with a rationale for further investigating the possibility of contamination.

Existing data from the Virginia Cooperative Extension Household Water Quality Testing and Information Program were assembled for analyses in this project. The data consisted of 9,697 private household water supplies sampled from 1989-1999 in 65 Virginia counties. Initially, the entire state of Virginia was analyzed, followed by the five physiographic provinces of Virginia: the Blue Ridge, Coastal Plain, Cumberland Plateau, Ridge & Valley, and Piedmont. Ultimately, Louisa County was investigated to evaluate the possibility that better models could be developed using smaller land areas and, consequently, less geological variation. Least squares regression, both parametrically and non-parametrically, was used to determine the influence of various factors on nitrate levels. Similarly, logistic regression was used to determine the influence of the same parameters on nitrate categories, presence/absence of total coliform, and risk categories.

Using stepwise model-building techniques, based primarily on statistical significance (p-values) and partial coefficient of determination (partial-R2), first and second-order linear models were evaluated. The best-fitting model only explained 58.5% of the variation in nitrate and none of the models fit well enough to be used for prediction purposes. However, the models did identify which factors were, in a statistical sense, significantly related to nitrate levels and total coliform presence/absence and quantified the strength of these relationships in terms of the percent of variation explained.