Assessment of E. coli Concentrations and Meteorology at Illinois Lake Michigan Beaches, 2010–2013

Concentrations of the fecal indicator bacteria Escherichia coli (E. coli) in surface waters are influenced by many factors, including the ambient meteorological conditions. The main goals of this study were to determine the primary meteorological variables associated with E. coli at study beaches and their patterns of influence, and to then identify the beaches where E. coli did not follow the overall patterns of the primary meteorological variables, with a focus on those beaches that recorded high E. coli over the study years. Spearman’s rank correlation tests analyzed the associations between daily E. coli sample results at 44 Lake Michigan public beaches in Illinois and 10 hourly meteorological variable measures for the 2010–2013 recreational swimming seasons. With 13 distinct meteorological measures actually analyzed with E. coli (due to the four main wind direction categories), seven variables were determined to be primary meteorological variables influencing E. coli at the recreational beaches from 2010–2013: north wind direction, precipitation, relative humidity, solar radiation, south wind direction, wave height, and wind speed. These determinations were made based off the overall strength of the correlation coefficient values returned in the analyses. Solar radiation and south wind direction showed a negative association with E. coli, while the other five primary variables showed a positive association. The 10 most problematic E. coli beaches were examined regarding their associations with these seven variables and three beaches were found to have the lowest percent agreement over the four years and deemed to not follow the overall patterns: North Point Marina Beach, South Shore Beach, and Rainbow Beach. These beaches may warrant further investigation as to other potential influences on their E. coli levels, but would probably not be good fits for predictive models of E. coli based on the seven primary variables alone. While the seven primary meteorological variables found in this study highlight some associations with E. coli at the study beaches, the most effective predictive models and forecasting techniques of E. coli would need to account for many other non-meteorological factors, as well as year-to-year differences in ambient conditions and the important role of site-specificity.