Delineation of the role of nutrient variability and dreissenids (Mollusca, Bivalvia) on phytoplankton dynamics in the Bay of Quinte, Ontario, Canada

Large batch bench-scale dissolved air flotation system for simulating full-scale turbidity removal

One of the expected outcomes of global warming is increased algal and cyanobacterial blooms. Based on its ability to separate algal particles, dissolved air flotation (DAF) is considered as a climate change adaptation technology for water treatment. The feasibility of DAF treatment is often assessed using DAF jar tests; however, they are not particularly good at predicting a full-scale DAF system's turbidity removals. Therefore, our group has developed a more reliable larger-diameter/larger-volume batch apparatus (LB-DAF), which was optimized by comparison with a full-scale DAF plant treating a low turbidity, highly coloured river water (SUVA ∼ 4.3). The objective of this study was to verify that the LB-DAF was capable of simulating full-scale DAF systems treating two significantly different waters. One was water from a large eutrophic bay in Lake Ontario (SUVA ∼2.6) and the second was a river water (SUVA ∼3.5). The turbidity removals achieved by the full-scale DAF systems treating these waters were compared with those for the LB-DAF tests conducted using different flocculation velocity gradients, saturated water pressures, recycle ratios and water depth to diameter ratios. The LB-DAF tests are good predictors of the full-scale DAF turbidity removals, the average difference for the two waters tested were 2% and 6%. The LB-DAF natural organic matter (NOM) removals for both waters differed by less than 1% from that measured at the corresponding treatment plants. In addition, as in our previous LB-DAF study, varying the different LB-DAF operational variables did not have a significant impact on turbidity and NOM removals.

Growth limitation status and its role in interpreting chlorophyll a response in large and shallow lakes: A case study in Lake Okeechobee

Understanding the response of Chlorophyll a concentration (Chl-a, an indicator of phytoplankton biomass) to environmental factors is critical for eutrophication management. Light and nutrients often act as two main limiting environmental factors in large and shallow lakes. However, the limitation status is usually not considered explicitly when building empirical relationships even though the growth limitation is the possible mechanism controlling the behaviors of these relationships. Here we chose a typical large and shallow eutrophic lake (Lake Okeechobee) to study the response of Chl-a concentration under different growth limitation conditions. Using an existed decision tree model followed by Carlson's trophic state index, monitoring data from 1994 to 2020 were classified into light-limitation, nitrogen-limitation, or phosphorus-limitation. The spatio-temporal patterns of limitation status were revealed. By subdivision of observations according to these growth limitation classes, our results demonstrated three main findings. First, algae responded differently between light limitation and nutrient limitation. Chl-a concentrations were lower with smaller variability when light was limiting than those when nutrient was limiting. In addition, the evolution of Chl-a in enduring nutrient limitation events were more dynamic. Second, limitation-specific regressions provided a more straightforward interpretation compared with those without consideration of limitation status. Chl-a ∼ nutrient relationship based on limitation classification displayed a higher R² with a positive slope. This positive slope indicates the sensitivity of Chl-a to that specific nutrient. Moreover, response of Chl-a to phosphorus was successfully detected by identifying P-limited samples. Otherwise, the Chl-a ∼ TP response would be muted since nitrogen is the main limiting nutrient in Lake Okeechobee. Third, a spatial heterogeneity of Chl-a ∼ TN relationship was revealed by Bayesian hierarchical modelling. This indicates the necessity of focusing more on hot spots where Chl-a displays a higher sensitivity to increase of nutrient. Our findings demonstrate the advantage of developing the limitation-specific and zone-specific relationships between algal biomass and environmental factors.

Using Bayesian hierarchical modelling to capture cyanobacteria dynamics in Northern European lakes

Cyanobacteria blooms in lakes and reservoirs currently threaten water security and affect the ecosystem services provided by these freshwater ecosystems, such as drinking water and recreational use. Climate change is expected to further exacerbate the situation in the future because of higher temperatures, extended droughts and nutrient enrichment, due to urbanisation and intensified agriculture. Nutrients are considered critical for the deterioration of water quality in lakes and reservoirs and responsible for the widespread increase in cyanobacterial blooms. We model the response of cyanobacteria abundance to variations in lake Total Phosphorus (TP) and Total Nitrogen (TN) concentrations, using a data set from 822 Northern European lakes. We divide lakes in ten groups based on their physico-chemical characteristics, following a modified lake typology defined for the Water Framework Directive (WFD). This classification is used in a Bayesian hierarchical linear model which employs a probabilistic approach, transforming uncertainty into probability thresholds. The hierarchical model is used to calculate probabilities of cyanobacterial concentrations exceeding risk levels for human health associated with the use of lakes for recreational activities, as defined by the World Health Organization (WHO). Different TN and TP concentration combinations result in variable probabilities to exceed pre-set thresholds. Our objective is to support lake managers in estimating acceptable nutrient concentrations and allow them to identify actions that would achieve compliance of cyanobacterial abundance risk levels with a given confidence level.

A Bayesian risk assessment framework for microcystin violations of drinking water and recreational standards in the Bay of Quinte, Lake Ontario, Canada

Freshwater ecosystems can experience harmful algal blooms, which negatively impact recreational uses, aesthetics, taste, and odor in drinking water. Cyanobacterial toxins can have dire repercussions on aquatic wildlife and human health, and the most ubiquitous worldwide are the hepatotoxic compounds known as microcystins. The factors that influence the occurrence and magnitude of cyanobacteria blooms and toxin production vary in space and time and remain poorly understood. It is within this context that we present a suite of statistical models, parameterized with Bayesian inference techniques, to link the retrospective analysis of important environmental factors with the probability of exceedance of threshold microcystin levels. Our modelling framework is applied to the Bay of Quinte, Lake Ontario, Canada; a system with a long history of eutrophication problems. Collectively, 16.1% of the samples of the system collected during the study period (2003-2016) exceeded the drinking water guideline of 1.5 μgL^-1, while approximately 3% of recorded values exceeded the recommended recreational threshold of 20 μgL^-1. Using a segmented regression model with a stochastic breakpoint of microcystin concentrations estimated at 0.54 μg L^-1, we demonstrate that the environmental conditions associated with increased probability of exceedance of the drinking water standard are chlorophyll a concentration ≥7 μg L^-1, water temperature ≥20 °C, ammonium concentration ≤40 μgL^-1, total phosphorus concentration ≥25 μg L^-1, and wind speed ≤37 km h^-1. Considering the multitude of factors that can influence the ambient levels of toxins, our study argues that the adoption of probabilistic water quality criteria offers a pragmatic approach to accommodate the associated uncertainty by permitting a realistic frequency of violations. In this context, we present a framework to evaluate the confidence of compliance with probabilistic standards that stipulate less than 10% violations of microcystin threshold ambient levels.

When can we declare a success? A Bayesian framework to assess the recovery rate of impaired freshwater ecosystems

Evaluating the degree of improvement of an impaired freshwater ecosystem resembles the statistical null-hypothesis testing through which the prevailing conditions are compared against a reference state. The pillars of this process involve the robust delineation of what constitutes an achievable reference state; the establishment of threshold values for key environmental variables that act as proxies of the degree of system impairment; and the development of an iterative decision-making process that takes advantage of monitoring data to assess the system-restoration progress and revisit management actions accordingly. Drawing the dichotomy between impaired and non-impaired conditions is a challenging exercise that is surrounded by considerable uncertainty stemming from the variability that natural systems display over time and space, the presence of ecosystem feedback loops (e.g., internal loading) that actively influence the degree of recovery, and our knowledge gaps about biogeochemical processes directly connected to the environmental problem at hand. In this context, we reappraise the idea of probabilistic water quality criteria, whereby the compliance rule stipulates that no more than a stated number of pre-specified water quality extremes should occur within a given number of samples collected over a compliance assessment domain. Our case study is the Bay of Quinte, Ontario, Canada; an embayment lying on the northeastern end of Lake Ontario with a long history of eutrophication problems. Our study explicitly accounts for the covariance among multiple water quality variables and illustrates how we can assess the degree of improvement for a given number of violations of environmental goals and samples collected from the system. The present framework offers a robust way to impartially characterize the degree of restoration success and minimize the influence of the conflicting perspectives among decision makers/stakeholders and conscious (or unconscious) biases pertaining to water quality management.

Phosphorus retention and internal loading in the Bay of Quinte, Lake Ontario, using diagenetic modelling

Internal phosphorus (P) loading significantly contributes to hysteresis in ecosystem response to nutrient remediation, but the dynamics of sediment P transformations are often poorly characterized. Here, we applied a reaction-transport diagenetic model to investigate sediment P dynamics in the Bay of Quinte, a polymictic, spatially complex embayment of Lake Ontario, (Canada). We quantified spatial and temporal variability of sediment P binding forms and estimated P diffusive fluxes and sediment P retention in different parts of the bay. Our model supports the notion that diagenetic recycling of redox sensitive and organic bound P forms drive sediment P release. In the recent years, summer sediment P diffusive fluxes varied in the range of 3.2-3.6 mg P m^-2 d^-1 in the upper bay compared to 1.5 mg P m^-2 d^-1 in the middle-lower bay. Meanwhile sediment P retention ranged between 71% and 75% in the upper and middle-lower bay, respectively. The reconstruction of temporal trends of internal P loading in the past century, suggests that against the backdrop of reduced external P inputs, sediment P exerts growing control over the lake nutrient budget. Higher sediment P diffusive fluxes since mid-20th century with particular increase in the past 20 years in the shallower upper basins, emphasize limited sediment P retention potential and suggest prolonged ecosystem recovery, highlighting the importance of ongoing P control measures.