| Director: |
Greg Olyphant |
| Other Researchers: |
Greg A. Olyphant
|
| Funding: |
U.S. Environmental Protection Agency (EPA)
|
| Issue: |
The swimming beaches along the Lake Michigan shoreline from Milwaukee, Wisc., to Michigan City, Ind., have long been plagued by high Escherichia coli (E. coli) concentrations. Most health officials and beach managers have a policy of closing the beaches to swimming and wading activities whenever E. coli concentrations are above the U.S. Environmental Protection Agency recommended limit. E. coli assays generally take 24 hours to complete, so swimming bans are based on day-old information. Inspections have shown that there have been many instances when beaches were closed when they could have remained open, or left open when they should have been closed. Since no rapid, accurate method for measuring E. coli concentrations exists, indirect methods may improve the accuracy of beach closure decisions. |
| Objective: |
Evaluate E. coli concentrations in stream flow discharging from Dunes Creek into the main swimming beaches at Indiana Dunes State Park and develop predictive models that relate the outfalls of creek water, as well as other hydrometeorological factors, to the concentrations of E. coli bacteria in the swimming zones of the beaches. |
| Approach: |
The study was conducted at the main beach of the Indiana Dunes State Park. The Dunes Creek area was selected for study because Indiana Dunes State Park is a very popular tourist destination and because the beach there has a long history of water-quality problems. Dunes Creek, commonly laden with high concentrations of bacteria, discharges directly into the lake at the beach. Monitoring stations were installed in the lake, on the roof of the pavilion at the beach, and at the inlet to the culvert that discharges Dunes Creek into the lake. The data were analyzed and a statistical regression equation was developed to make predictions concerning the quality of the water. |
| Products: |
We developed a statistical regression equation to make predictions to aid in making closure decisions in a more timely manner. |
| Benefits: |
The results of this study, though representing only one swimming season, demonstrate the utility of predictive models as the basis of an early warning system for protecting the public from contact with unsafe levels of harmful bacteria. |
| Publications: |
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