Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems

• Published in: Microorganisms (Basel) 2023-03, Vol.11 (4), p.851
• Main Authors: Saleem, Faizan, Jiang, Jennifer L, Atrache, Rachelle, Paschos, Athanasios, Edge, Thomas A, Schellhorn, Herb E
• Format: Article
• Language: English
• Subjects: Algae; Algal blooms; Aquatic ecosystems; Aquatic plants; Artificial intelligence; Biosensors; Chemical contaminants; Chemical properties; Cyanobacteria; cyanobacteria lysis; cyanotoxins; DNA sequencing; Ecosystems; Environmental aspects; Environmental monitoring; Environmental protection; Enzyme-linked immunosorbent assay; Eutrophic environments; Eutrophication; Freshwater ecosystems; Great Lakes; harmful algal blooms; Health aspects; Imaging techniques; Immunoassays; Lakes; Learning algorithms; Lysis; Machine learning; Mass spectrometry; Mass spectroscopy; Methods; microcystin; Monitoring; Polymerase chain reaction; Prediction models; Public health; Recreational waters; Remote sensing; Review; Satellite imagery; Satellite imaging; Water analysis; Water bloom; Water quality; Wildlife
• ISSN: 2076-2607; 2076-2607
• DOI: 10.3390/microorganisms11040851

Cyanobacteria (blue-green algae) can accumulate to form harmful algal blooms (HABs) on the surface of freshwater ecosystems under eutrophic conditions. Extensive HAB events can threaten local wildlife, public health, and the utilization of recreational waters. For the detection/quantification of cyanobacteria and cyanotoxins, both the United States Environmental Protection Agency (USEPA) and Health Canada increasingly indicate that molecular methods can be useful. However, each molecular detection method has specific advantages and limitations for monitoring HABs in recreational water ecosystems. Rapidly developing modern technologies, including satellite imaging, biosensors, and machine learning/artificial intelligence, can be integrated with standard/conventional methods to overcome the limitations associated with traditional cyanobacterial detection methodology. We examine advances in cyanobacterial cell lysis methodology and conventional/modern molecular detection methods, including imaging techniques, polymerase chain reaction (PCR)/DNA sequencing, enzyme-linked immunosorbent assays (ELISA), mass spectrometry, remote sensing, and machine learning/AI-based prediction models. This review focuses specifically on methodologies likely to be employed for recreational water ecosystems, especially in the Great Lakes region of North America.