Associations between extreme precipitation, drinking water, and protozoan acute gastrointestinal illnesses in four North American Great Lakes cities (2009-2014).

Climate change is already impacting the North American Great Lakes ecosystem and understanding the relationship between climate events and public health, such as waterborne acute gastrointestinal illnesses (AGIs), can help inform needed adaptive capacity for drinking water systems (DWSs). In this study, we assessed a harmonized binational dataset for the effects of extreme precipitation events (≥90th percentile) and preceding dry periods, source water turbidity, total coliforms, and protozoan AGIs - cryptosporidiosis and giardiasis - in the populations served by four DWSs that source surface water from Lake Ontario (Hamilton and Toronto, Ontario, Canada) and Lake Michigan (Green Bay and Milwaukee, Wisconsin, USA) from January 2009 through August 2014. We used distributed lag non-linear Poisson regression models adjusted for seasonality and found extreme precipitation weeks preceded by dry periods increased the relative risk of protozoan AGI after 1 and 3-5 weeks in three of the four cities, although only statistically significant in two. Our results suggest that the risk of protozoan AGI increases with extreme precipitation preceded by a dry period. As extreme precipitation patterns become more frequent with climate change, the ability to detect changes in water quality and effectively treat source water of varying quality is increasingly important for adaptive capacity and protection of public health.

Informing Public Engagement Strategies to Motivate the Public to Protect the Great Lakes: Lessons learned from the 2018 Great Lakes Basin Binational Poll.

Engaging the public in protecting water resources is a critical yet challenging task. A wealth of social science studies has identified psychological predictors for individual pro-environmental behaviors. These predictors can guide communication in public engagement and inform the allocation of engagement efforts. However, a thorny challenge is to select influential factors among many candidates. This paper addresses this challenge by using social science research to guide the development of strategies to motivate the public to protect the North American Great Lakes. We considered a variable selection technique, the LASSO regression, in the post-hoc analysis of the International Joint Commission's 2018 Binational Great Lakes Binational Poll data. The poll surveyed 4250 Canadian and U.S. residents of the Great Lakes basin. We fit LASSO logistic models to predict respondents' intentions to take three public actions to protect the Great Lakes, including contacting public officials, attending public meetings, and engaging in online forums and groups. The models included 41 predictors encompassing demographic characteristics as well as respondents' awareness, beliefs, and values that are pertinent to Great Lakes policy development and management. Results revealed eight variables that consistently predicted the three public actions, including indigenous status, political ideology, impacts of the specific policy issues of nuclear wastes, policy awareness and interests, and the Great Lakes values for personal benefits and wildlife. Based on these findings, we recommend strategies to motivate the public to take public actions to protect the Great Lakes.