Cyanobacteria, also known as blue-green algae for a long time, are the most ancient and problematic bloom-forming phylum on earth. An alert levels framework has been established by World Health Organization(WHO) to prevent the potential harmful cyanobacterial blooms. Normally, low cyanobacteria levels are found in surface water. 2000 cyanobacterial cells/mL and 100,000 cyanobacterial cells/mL are established for WHO Alert Level 1 and 2, respectively. However, eutrophication, climate change and other factors may promote the spread of cyanobacteria and increase the occurrence of harmful cyanobacterial blooms in water on a global scale. Hence, a rapid real time cyanobacteiral monitoring system is required to protect public health from the cyanotoxins produced by toxic cyanobacterial species.
Current methods to control or prevent the development of harmful cyanobacterial blooms are either expensive, time consuming or not effective in the long term. The best method to control the blooms is to prevent the formation of the blooms at the very beginning. Although emerging advanced autofluorescence-based sensors, imaging flow cytometry applications, and remote sensing have been utilized for rapid real-time enumeration and classification of cyanobacteria, the need to accurately monitor low-level cyanobacterial species in water remains unsolved.
Microflow cytometry has been employed as a functional cell analysis technique in past decades, and it can provide real-time, accurate results. The autofluorescence of cyanobacterial pigments can be used for determination and counting of cyanobacterial density in water. A pre-concentration system of an automated cyanobacterial concentration and recovery system (ACCRS) based on tangential flow filtration and back-flushing technique was applied to reduce the sample assay volume and increase the concentration of target cells for further cell capture and detection. In this project, a microflow cytometry platform with a microfluidic device and an automated pre-concentration system was established to monitor cyanobacteria and provide early warning alerts for potential harmful blooms.
In this work, quantification of low-level cyanobacterial samples (∼ 5 cyanobacterial cells/mL) in water has been achieved by using a microflow cytometer together with a
pre-concentration system (ACCRS). Meanwhile, this platform can also provide early warning alerts for potential harmful cyanobacterial blooms at least 15 days earlier before reaching WHO Alert Level 1. Results have shown that this platform can be applied for rapid determination of cyanobacteria and early warning alerts can be triggered for authorities to protect the public and the environment. / Thesis / Doctor of Engineering (DEng) / Harmful cyanobacterial blooms have been a rising risk to the public heath across the world in recent decades. Alert levels of cyanobacteria in water has also been established. In this case, a rapid on-side monitoring system for cyanobacteria is required. In this thesis, a microflow cytometer platform combined with a bacterial concentration and recovery system was built to quickly monitor the relatively low level of cyanobacteria for early warning alerts. A pre-enrichment system based on tangential flow filtration and back-flushing technique was applied to increase the concentration levels of microbial samples and a microfluidic device capable of collecting phycocyanin fluorescence was designed to count cyanobacterial cells. The limit of quantification for cyanobacterial concentration based on the microflow cytometry platform was as low as ∼ 5 cells/mL. We can claim that the microflow cytometry platform can provide useful early warning alerts for the decision-makers to control the potential harmful
cyanobacterial blooms at the very early stage and protect the aquatic animals and public health.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27037 |
| Date | January 2021 |
| Creators | Zhang, Yushan |
| Contributors | Xu, Changqing, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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