Aquifer susceptibility and GIS: Illustrating the need for scrutiny and localized reconnaissance work.

The city of Cedar Falls, Iowa and the University of Northern Iowa rely heavily on a highly productive carbonate bedrock aquifer underlying this portion of the state as a municipal water supply and a source of once through cooling-water, respectively. This high quality groundwater resource is also tapped by local residents, businesses, and other entities for private drinking water and an ever-growing number of geothermal installations.

Detailed hydrogeological and historical work recently completed in this area used geographic information system (GIS) software to reveal, analyze, and illustrate some significant findings regarding aquifer susceptibility, source water protection, groundwater contaminant sources, and past hydrogeological misconceptions about the area. This presentation will highlight how GIS was combined with a simple mix of primary and secondary data to uncover and illustrate a much more representative picture of this aquifer’s characteristics and vulnerability to surface contamination. Findings show that more focused, detailed efforts are needed to accurately characterize local hydrogeological conditions and provide more reliable data needed for source water protection, planning and zoning decision making, municipal well siting, and watershed restoration efforts.

Developing a Bayesian approach to dose response assessment: an application to regulating trihalomethanes in drinking water

Pervasive uncertainty is a dominant analytical difficulty that continues to hinder the EPA's risk assessment process for setting standards for environmental contaminants, particularly within the dose-response step.

Currently, the EPA handles this by applying deterministic factors referred to as safety or uncertainty factors. This approach has long been criticized as arbitrary, obscuring the true uncertainty, and limiting the ability of policy-makers to make adequately informed risk management decisions. We propose a hierarchical Bayesian model approach to synthesize evidence from toxicological and epidemiological studies, allowing for explicit statement of uncertainty assumptions in the prior distributions, and pre-processing data using Bayesian Model Averaging (BMA) to account for model uncertainty. We apply this model to a case study of chloroform, a disinfection byproduct, in drinking water. We use the same data set considered by the EPA when setting their regulatory standards for chloroform, exploring four different health outcomes that were either cancer or considered pre-cursors to cancer. Final model estimates demonstrated that incorporating more scientific information into the priors had minimal impacts on mean estimates, but reduced the uncertainty surrounding the final estimates.

Benchmark dose (BMD) and lower-bound benchmark (BMDL) dose estimates from the model were mostly lower than those estimated by the EPA, indicating that not considering the full body of scientific evidence fails to capture the true uncertainty surrounding the final estimate. As a result, Maximum Contaminant Level Goal (MCLG) estimates using the Bayesian model were consistently lower than EPA estimates, and in particular were lower than the MCLG standard for chloroform currently in place. This Bayesian model provide an alternative approach to incorporating and quantifying various sources of uncertainty in the dose-response step, and may be applicable in other aspects of risk assessment.

Minnesota’s Assessment Source Water Monitoring Study

The Minnesota Department of Health (MDH) conducted a pilot study to evaluate the approach it might use for conducting assessment source water monitoring under the federal Ground Water Rule. A scoring routine was developed to identify public water supply wells that may be at risk to pathogen contamination using information about 1) well construction, 2) hydrogeological setting, 3) previous monitoring history, and 4) nearby potential pathogen sources.  Then, a subgrouping of potential high risk candidate wells was sent to MDH field staff for their review and comment prior to contacting public water supply systems to participate.  The sampling set consisted of 52 community and 43 noncommunity wells. Wells were sampled on a monthly basis for one year and each raw water sample was tested for bacteria (total coliform with e. coli follow-up) and general chemistry indicators (ammonia, bromide, chloride, nitrate, and sulfate).  All water samples were submitted to the MDH laboratory.

A groundwater classification scheme that reflects the pathogen risk potential to a well was developed and is based on the analytical results obtained from the bacteriological and chemical parameters used for this study.  This initial scheme has been enhanced by adding analytes such as dissolved oxygen, tritium, and stable isotopes of oxygen and hydrogen that are used to refine groundwater classification. Other factors such as the impacts of surrounding land uses within the likely capture area for a well and the variability in analyte concentrations on a seasonal basis are also considered. This additional information along with each monthly sample set was used to build an overall assessment of the likelihood of whether a well is at risk to pathogen sources.  

The study shows that MDH should include assessment monitoring as part of Ground Water Rule implementation but it will likely be used on a case-by-case basis instead of for all wells.  The scoring routine appears to be a good first step to identify wells that may be at greatest risk and follow-up assessment work would be triggered by a total coliform bacteria positive from a source water sample.  Also, the approach used for assessment monitoring may prove valuable for determining whether a well should be designated as being under the direct influence of surface water.