State of knowledge on early warning tools for cyanobacteria detection

Ecological Indicators

Suraj Ajjampur¹  Eric C. Wert²  Faith A. Kibuye²  Husein Almuhtaram³  Christine Owen³  Virginie Gaget⁴  Caitlin M. Glover⁵  Arash Zamyadi⁶  Ron Hofmann⁷ 

[1] Corresponding author.;Mineral Engineering, University of Toronto, 35 St George St, Toronto ON M5S 1A4, Canada;;Department of Civil &Department of Civil Engineering, McGill University, Montreal, QC H3A 0G4, Canada;Department of Research and Development, Southern Nevada Water Authority, Henderson, NV 89015, USA;University of Adelaide, Water Research Centre, Department of Ecology and Evolutionary Biology, School of Biological Sciences, South Australia 5005, Australia;Water Research Australia (WaterRA) Melbourne Based Position Hosted by Melbourne Water, 990 La Trobe St, Docklands, VIC 3008, Australia;

关键词: Monitoring;    Remote sensing;    Drones;    Automated cell imaging;    Artificial intelligence;    Phycocyanin;   

DOI  :  

来源: DOAJ

【 摘 要 】

The potential for cyanobacterial blooms to impact recreational and drinking water source quality is a growing concern. Numerous monitoring tools have been developed that can alert stakeholders to the onset of cyanobacterial blooms to initiate mitigation efforts for waters used for recreation or drinking water supply. Early warning monitoring systems need to consider multiple aspects of a cyanobacterial bloom: whether a bloom is occurring in the source water, whether it might be transported to drinking water intakes, whether toxin or taste and odor compound producers are present and what proportion of the cells in a bloom they comprise, and whether cells are entering a utility at concentrations above threshold levels. No single monitoring tool can provide all this information, so multi-barrier approaches are needed. Reviews of monitoring tools and their variations are available, but they are generally limited to one type of tool. Instead, a review and comparison of all the available tools is needed to inform stakeholders of them and their relative advantages and limitations. Therefore, this review covers conventional tools including microscopic enumeration, pigment extraction, qPCR, probes, and remote sensing as well as emerging techniques including next-generation sequencing, photonic systems, biosensors, drones, and applications of machine learning and discusses them primarily from a practical and operational standpoint. Moreover, a three-tier framework is proposed for designing comprehensive early warning systems that groups monitoring tools by their analytical targets: biological activity or algal biomass, cyanobacteria or cyanobacteria-related genes, and cyanobacterial metabolites. First tier tools are generally simple and inexpensive to use, including turbidity, optical density, visual inspection, drones, chlorophyll a, and adenosine triphosphate. Changes in water quality conditions detected using a first tier tool triggers the use of a second tier tools for identification and quantification of cyanobacteria by microscopy, phycocyanin, biosensors, hyperspectral remote sensing, or next-generation sequencing. If potentially harmful concentrations of cyanobacteria are confirmed, third tier tools are deployed for quantifying concentrations of cyanotoxins and taste and odor compounds or the genes that encode for them using enzyme-linked immunosorbent assays, mass spectrometry, qPCR, or other analytical methods. This framework is designed to minimize the time and cost associated with cyanobacteria monitoring without compromising the ability of stakeholders to detect the onset of a bloom.

【 授权许可】

Unknown