Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data

Abstract

Freshwater cyanobacterial harmful algal blooms (cyanoHABs) are a worldwide problem resulting in substantial economic losses, due to harm to drinking water supplies, commercial fishing, wildlife, property values, recreation, and tourism. Moreover, toxins produced from some cyanoHABs threaten human and animal health. Climate warming can affect the distribution of cyanoHABs, where rising temperatures facilitate more intense blooms and a greater distribution of cyanoHABs in inland freshwater. Nutrient runoff from adjacent watersheds is also a major driver of cyanoHAB formation. While some of the physicochemical factors behind cyanoHAB dynamics are known, there are still major gaps in our understanding of the conditions that trigger and sustain cyanoHABs over time. In this perspective, we suggest that sufficient data sets, as well as machine learning (ML) and artificial intelligence (AI) tools, are available to build a comprehensive model of cyanoHAB dynamics based on integrated environmental/climate, nutrient/water chemistry, and cyanoHAB microbiome and 'omics data to identify key factors contributing to HAB formation, intensity, and toxicity. By taking a holistic approach to the analysis of all available data, including the rapidly growing number of biological data sets, we can provide the foundational knowledge needed to address the increasing threat of cyanoHABs to the security of our water resources.

Temporal Dynamics of Cyanobacterial Bloom Community Composition and Toxin Production from Urban Lakes

“…Certain micropeptins have been shown to cause mortality and morbidity effects in fish models, and more research is needed to understand the full toxin suite in cyanoHAB events and their potential organismal effects. More community and toxin composition data from various environments will provide predictive tools with the biological and chemical data needed to forecast these events to a greater extent . This will necessitate including the heterotrophic bacterial community and determining the impact that these organisms have on bloom biomass and cyanotoxin production.…”

Toward a Brighter Future: Enhanced Sustainable Methods for Preventing Algal Blooms and Improving Water Quality

“…This includes evaluating the advantages and disadvantages of using phytoplankton, zooplankton, benthic macroinvertebrates, microorganisms, aquatic plants, fish, and allelochemicals, integrating findings from relevant studies. The exploration of innovative technologies, such as remote sensing, DNA sequencing, and machine learning, for early detection and prevention of HABs is discussed, highlighting their potential for real-time monitoring and prediction across various types of aquatic infrastructure [170][171][172][173][174][175]. This review of biological methods for controlling HABs underscores the importance of assessing potential benefits and impacts on native communities and potential toxicity within different infrastructure settings.…”

“…Comparative Evaluation of Biological Control Strategies for Harmful Algal Blooms: Assessing Economic Efficiency, Ecological Impact, and Infrastructure Applicability[170][171][172][173][174][175].…”

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…The special issue includes several review articles encompassing a wide spectrum, ranging from a historical perspective of water data to computational modeling in wastewater treatment to ML modeling of environmental chemical reactions, environmental toxicology, heavy metal removal, and cyanobacterial harmful algal blooms (HABs) . One significant application of these innovative tools is ML-assisted environmental monitoring, which can address diverse problems, such as predicting effluent nutrients or influent flow rates and nutrient loads at wastewater treatment plants, , formation of disinfection byproducts, drivers of the accumulation of potentially toxic elements in sediments, greenhouse gas emissions, , occurrence of PFAS, water quality assessment, microplastics, microcystins, and differentiation of landfill leachate and domestic sludge .…”