A Multiscale Assessment of Shallow Groundwater Salinization in Michigan.

Managing nonpoint-source (NPS) pollution of groundwater systems is a significant challenge because of the heterogeneous nature of the subsurface, high costs of data collection, and the multitude of scales involved. In this study, we assessed a particularly complex NPS groundwater pollution problem in Michigan, namely, the salinization of shallow aquifer systems due to natural upwelling of deep brines. We applied a system-based approach to characterize, across multiple scales, the integrated groundwater quantity-quality dynamics associated with the brine upwelling process, assimilating a variety of modeling tools and data-including statewide water well datasets scarcely used for larger scientific analysis. Specifically, we combined (1) data-driven modeling of massive amounts of groundwater/geologic information across multiple spatial scales with (2) detailed analysis of groundwater salinity dynamics and process-based flow modeling at local scales. Statewide "hotspots" were delineated and county-level severity rankings were developed based on dissolved chloride (Cl- ) concentration percentiles. Within local hotspots, the relative impact of upwelling was determined to be controlled by: (1) streams-which act as "natural pumps" that bring deeper (more mineralized) groundwater to the surface; (2) the occurrence of nearly impervious geologic material at the surface-which restricts fresh water dilution of deeper, saline groundwater; and (3) the space-time evolution of water well withdrawals-which induces slow migration of saline groundwater from its natural course. This multiscale, data-intensive approach significantly improved our understanding of the brine upwelling processes in Michigan, and has applicability elsewhere given the growing availability of statewide water well databases.

Spatial analysis of Mycobacterium bovis infection in white-tailed deer (Odocoileus virginianus) in Michigan, USA.

The wild white-tailed deer (Odocoileus virginianus) population in Michigan, USA, has endemic Mycobacterium bovis. We determined whether there were spatial clusters of retrospective TB cases in white-tailed deer in northeastern Michigan and identified specific factors associated with the spatial clusters. Data from hunter-harvested deer (age, gender, TB status, and geographic section) were collected by the Michigan Department of Natural Resources (MDNR) during TB surveillance from 1995 to 2002. Land cover (vegetation, land-use) and land type (soil types and drainage characteristics, landforms) described potential deer habitats. Specific locations of large-scale supplemental feeding sites were collected from the MDNR aerial surveillance program from 1997 to 2002. Analyses were conducted using principal components derived from environmental data (and other risk factors) on spatial clusters of disease (identified by the spatial scan statistic). Spatial effects were incorporated into the multivariable analyses by using a neighborhood approach. A total of 420 deer with M. bovis infection were identified from 1995 to 2002, out of 39,451 harvested deer from 3216 TRS units, and spatial clusters of cases were identified. A total of seven principal components of environmental data were generated. Clusters were associated with the presence of large expanses of deciduous forests on moraine ridges separated by low areas of forested wetlands, and the presence of many small lakes. Factors that promoted congregation of deer for extended periods of time (natural cover, access to water, and less human contact) appeared to be associated with increased odds of TB positivity. This suggests that there are specific areas where interventions can be implemented to reduce congregation of animals and disrupt the cycle of infection transmission.