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有毒蓝藻水华的早期检测方法

Early Detection Methods for Toxic Cyanobacteria Blooms.

作者信息

Grant Lauren, Botelho Diane, Rehman Attiq

机构信息

Department of Chemistry, Saint Mary's University, 923 Robie Street, Halifax, NS B3H 3C3, Canada.

New Brunswick Research and Productivity Council (RPC), 921 College Hill Rd, Fredericton, NB E3B 6Z9, Canada.

出版信息

Pathogens. 2024 Nov 28;13(12):1047. doi: 10.3390/pathogens13121047.

Abstract

Harmful cyanobacterial blooms produce cyanotoxins which can adversely affect humans and animals. Without proper monitoring and detection programs, tragedies such as the loss of pets or worse are possible. Multiple factors including rising temperatures and human influence contribute to the increased likelihood of harmful cyanobacteria blooms. Current approaches to monitoring cyanobacteria and their toxins include microscopic methods, immunoassays, liquid chromatography coupled with mass spectrometry (LCMS), molecular methods such as qPCR, satellite monitoring, and, more recently, machine learning models. This review highlights current research into early detection methods for harmful cyanobacterial blooms and the pros and cons of these methods.

摘要

有害蓝藻水华会产生蓝藻毒素,会对人类和动物产生不利影响。如果没有适当的监测和检测程序,就有可能发生宠物死亡等悲剧,甚至更糟的情况。包括气温上升和人类影响在内的多种因素导致有害蓝藻水华发生的可能性增加。目前监测蓝藻及其毒素的方法包括显微镜检查法、免疫测定法、液相色谱-质谱联用(LCMS)、分子方法如定量聚合酶链反应(qPCR)、卫星监测,以及最近的机器学习模型。这篇综述重点介绍了目前对有害蓝藻水华早期检测方法的研究以及这些方法的优缺点。

相似文献

1
Early Detection Methods for Toxic Cyanobacteria Blooms.有毒蓝藻水华的早期检测方法
Pathogens. 2024 Nov 28;13(12):1047. doi: 10.3390/pathogens13121047.

本文引用的文献

7
One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.一周内预测爱荷华州湖泊中的蓝藻有害藻华。
Environ Sci Technol. 2023 Dec 12;57(49):20636-20646. doi: 10.1021/acs.est.3c07764. Epub 2023 Nov 27.

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