Progress and opportunities in advancing near-term forecasting of freshwater quality

An evaluation of statistical models of microcystin detection in lakes applied forward under varying climate conditions

Algal blooms can threaten human health if cyanotoxins such as microcystin are produced by cyanobacteria. Regularly monitoring microcystin concentrations in recreational waters to inform management action is a tool for protecting public health; however, monitoring cyanotoxins is resource- and time-intensive. Statistical models that identify waterbodies likely to produce microcystin can help guide monitoring efforts, but variability in bloom severity and cyanotoxin production among lakes and years makes prediction challenging. We evaluated the skill of a statistical classification model developed from water quality surveys in one season with low temporal replication but broad spatial coverage to predict if microcystin is likely to be detected in a lake in subsequent years. We used summertime monitoring data from 128 lakes in Iowa (USA) sampled between 2017 and 2021 to build and evaluate a predictive model of microcystin detection as a function of lake physical and chemical attributes, watershed characteristics, zooplankton abundance, and weather. The model built from 2017 data identified pH, total nutrient concentrations, and ecogeographic variables as the best predictors of microcystin detection in this population of lakes. We then applied the 2017 classification model to data collected in subsequent years and found that model skill declined but remained effective at predicting microcystin detection (area under the curve, AUC ≥ 0.7). We assessed if classification skill could be improved by assimilating the previous years' monitoring data into the model, but model skill was only minimally enhanced. Overall, the classification model remained reliable under varying climatic conditions. Finally, we tested if early season observations could be combined with a trained model to provide early warning for late summer microcystin detection, but model skill was low in all years and below the AUC threshold for two years. The results of these modeling exercises support the application of correlative analyses built on single-season sampling data to monitoring decision-making, but similar investigations are needed in other regions to build further evidence for this approach in management application.

Disinfection by-product formation potential in response to variability in dissolved organic matter and nutrient inputs: Insights from a mesocosm study

Changes in rainfall patterns driven by climate change affect the transport of dissolved organic matter (DOM) and nutrients through runoff to freshwater systems. This presents challenges for drinking water providers. DOM, which is a heterogeneous mix of organic molecules, serves as a critical precursor for disinfection by-products (DBPs) which are associated with adverse health effects. Predicting DBP formation is complex due to changes in DOM concentration and composition in source waters, intensified by altered rainfall frequency and intensity. We employed a novel mesocosm approach to investigate the response of DBP precursors to variability in DOM composition and inorganic nutrients, such as nitrogen and phosphorus, export to lakes. Three distinct pulse event scenarios, mimicking extreme, intermittent, and continuous runoff were studied. Simultaneous experiments were conducted at two boreal lakes with distinct DOM composition, as reflected in their color (brown and clear lakes), and bromide content, using standardized methods. Results showed primarily site-specific changes in DBP precursors, some heavily influenced by runoff variability. Intermittent and daily pulse events in the clear-water mesocosms exhibited higher haloacetonitriles (HANs) formation potential linked to freshly produced protein-like DOM enhanced by light availability. In contrast, trihalomethanes (THMs), associated with humic-like DOM, showed no significant differences between pulse events in the brown-water mesocosms. Elevated bromide concentration in the clear mesocosms critically influenced THMs speciation and concentrations. These findings contribute to understanding how changing precipitation patterns impact the dynamics of DBP formation, thereby offering insights for monitoring the mobilization and alterations of DBP precursors within catchment areas and lake ecosystems.

Spatio-Temporal Variation in the Exceedance of Enterococci in Lake Burley Griffin: An Analysis of 16 Years' Recreational Water Quality Monitoring Data

Recreational waterbodies with high levels of faecal indicator bacteria (FIB) pose health risks and are an ongoing challenge for urban-lake managers. Lake Burley Griffin (LBG) in the Australian Capital city of Canberra is a popular site for water-based recreation, but analyses of seasonal and long-term patterns in enterococci that exceed alert levels (>200 CFU per 100 mL, leading to site closures) are lacking. This study analysed enterococci concentrations from seven recreational sites from 2001-2021 to examine spatial and temporal patterns in exceedances during the swimming season (October-April), when exposure is highest. The enterococci concentrations varied significantly across sites and in the summer months. The frequency of the exceedances was higher in the 2009-2015 period than in the 2001-2005 and 2015-2021 periods. The odds of alert-level concentrations were greater in November, December, and February compared to October. The odds of exceedance were higher at the Weston Park East site (swimming beach) and lower at the Ferry Terminal and Weston Park West site compared to the East Basin site. This preliminary examination highlights the need for site-specific assessments of environmental and management-related factors that may impact the public health risks of using the lake, such as inflows, turbidity, and climatic conditions. The insights from this study confirm the need for targeted monitoring efforts during high-risk months and at specific sites. The study also advocates for implementing measures to minimise faecal pollution at its sources.

Internet of Things and citizen science as alternative water quality monitoring approaches and the importance of effective water quality communication

The increasing demand for water and worsening climate change place significant pressure on this vital resource, making its preservation a global priority. Water quality monitoring programs are essential for effectively managing this resource. Current programs rely on traditional monitoring approaches, leading to limitations such as low spatiotemporal resolution and high operational costs. Despite the adoption of novel monitoring approaches that enable better data resolution, the public's comprehension of water quality matters remains low, primarily due to communication process deficiencies. This study explores the advantages and challenges of using Internet of Things (IoT) and citizen science as alternative monitoring approaches, emphasizing the need for enhancing public communication of water quality data. Through a systematic review of studies implemented on-field, we identify and propose strategies to address five key challenges that IoT and citizen science monitoring approaches must overcome to mature into robust sources of water quality information. Additionally, we highlight three fundamental problems affecting the water quality communication process and outline strategies to convey this topic effectively to the public.