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Humans have always lived with and around algae. At times, algae enable life, and at others, render life difficult. This article examines two sites suffering from atypical—and potentially harmful—algae blooms: Lake Erie in Ohio (USA) and the Riviera Maya (Mexico). Referring to ethnographic fieldwork, as well as to newspaper articles, policy papers, and online fora, we demonstrate how the narratives around algae have changed over time and shed light on how changes in these narratives opened the discussion of wetland repair and coastal integrity. We argue that conceptualizing algae as the "unwanted" unifies people, brings them together, and makes the treatment of lake eutrophication and coastal protection actionable. [ABSTRACT FROM AUTHOR]

This paper estimates beachgoers' preferences for beach quality, including avoidance of harmful algal blooms and bacterial warnings. Following a stratified random sampling schedule, data was collected via interviews conducted at 28 public beaches from Eastern Ohio to Northern Lake St Clair. Randomly selected visitors were interviewed and sent a follow-up choice experiment survey, which measured preferences for beach attributes. We find the average respondent is willing to drive 260 and 266 miles to avoid sites with either current HAB or bacterial warnings, and find a negative stigma effect that remains at least 6 days post-warning. While respondents' aversion to active HAB and bacterial warnings are not statistically different, this aversion decreases more slowly after a bacterial warning; respondents are willing to drive 77 miles to avoid a site with a bacterial warning lifted 6 days earlier, but only 31 miles to avoid a site with a HAB warning lifted 6 days earlier. To test our findings' validity, we used the choice model estimates to simulate responses to contingent behavior questions from the follow-up. Although framed differently, the elicitation formats yield concordant findings. Results indicate that cost-benefit analysis which doesn't evaluate the stigma effect of recently-lifted warnings may understate their costs. • Beachgoers are significantly averse to current and recent HAB & bacterial warnings. • The mean beachgoer is willing to drive up to 260 miles to avoid sites with either warning. • This aversion remains for up to six days after warnings are lifted. • Aversion to bacterial warnings persists longer than aversion to HAB warnings. • This "lag effect" has implications for the accurate measurement of warning costs. [ABSTRACT FROM AUTHOR]

Lake Erie has experienced unprecedented harmful algal blooms since the early 2000s, prompting the 2012 Great Lakes Water Quality Agreement between the United States and Canada, which aims to reduce lake‐wide phosphorous loadings by 40%. Little is known about the economic benefits from this agreement, especially to near lake homeowners. We provide key information on the benefits of harmful algal bloom cleanup by linking housing transactions in 2003 to 2015 from seven Ohio counties bordering Lake Erie with measures of water quality using remote‐sensing images. We further control for endogenous algae production using instrumental variables derived from hydrological processes that link Maumee River runoff to nutrient concentrations in Lake Erie. Using a semiparametric approach, we find the impact of harmful algal blooms on housing prices is spatially limited to properties within 1.2 km of Lake Erie. For the average near lake homeowner, a 1 μg/L increase in algae concentrations is expected to decrease property values by 1.7% ($2205). In aggregate, fulfilling the Great Lakes Water Quality Agreement will provide a yearly benefit of up to $42.9 million, fully covering the current annual expenditure on water quality improvement. [ABSTRACT FROM AUTHOR]

We estimate a cost function for a water treatment plant in Ohio to assess the avoided‐treatment costs resulting from improved source water quality. Regulations and source water concerns motivated the treatment plant to upgrade its treatment process by adding a granular activated carbon building in 2012. The cost function uses daily observations from 2013 to 2016; this allows us to compare the results to a cost function estimated for 2007–2011 for the same plant. Both models focus on understanding the relationship between treatment costs per 1,000 gallons (per 3.79 m3) of produced drinking water and predictor variables such as turbidity, pH, total organic carbon, deviations from target pool elevation, final production, and seasonal variables. Different from the 2007–2011 model, the 2013–2016 model includes a harmful algal bloom toxin variable. We find that the new treatment process leads to a different cost model than the one that covers 2007–2011. Both total organic carbon and algal toxin are important drivers for the 2013–2016 treatment costs. This reflects a significant increase in cyanobacteria cell densities capable of producing toxins in the source water between time periods. The 2013–2016 model also reveals that positive and negative shocks to treatment costs affect volatility, the changes in the variance of costs through time, differently. Positive shocks, or increased costs, lead to higher volatility compared to negative shocks, or decreased costs, of similar magnitude. After quantifying the changes in treatment costs due to changes in source water quality, we discuss how the study results inform policy‐relevant decisions. Key Points: Estimate how drinking water treatment costs change due to changes in source water quality following a plant upgradeUse time series analysis to understand short‐ and long‐run effects on treatment costsRaw water total organic carbon and harmful algal bloom toxin detections are important drivers in drinking water treatment costs [ABSTRACT FROM AUTHOR]