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Updated: Sep 28, 2026
Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
Predicting cyanotoxin concentrations in freshwater ecosystems: Bridging molecular indicators and explainable
Xuan Hou1, Yuxin Liu1, Bohan Li2
1Key Laboratory of Protection and Restoration of Yangtze River-connected Lake (Poyang Lake), Ministry of Ecology and Environment, Jiangxi Academy of Eco-Environmental Sciences and Planning, Nanchang, 330000, China; State Key Laboratory of Water Pollution Control and Green Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China; Institute for the Environment and Health, Nanjing University Suzhou Campus, Suzhou, 215163, China.
Abstract:
Cyanobacterial harmful algal blooms threaten freshwater security because biomass proxies do not reliably indicate cyanotoxin concentrations. This critical review examines how genetic, physiological, ecological and hydrodynamic processes generate biomass-toxicity decoupling and evaluates approaches for predicting direct cyanotoxin endpoints. The review distinguishes bloom/biomass forecasting, toxin occurrence or threshold-risk prediction, and quantitative prediction of total, phase-specific and congener-specific toxin concentrations. It synthesizes evidence from mechanistic, statistical, molecular, remote-sensing and machine-learning studies according to endpoint definition, predictor availability, forecast horizon and validation design. Reported short-horizon forecasts have achieved threshold accuracies of approximately 85-94%. Leave-one-location-out testing of models based on passive-sampling data from 10 lakes and 12 monitoring locations yielded AUCs of 0.54-0.96, indicating substantial variation in cross-location transferability. Molecular measurements improve toxin specificity but provide upstream signals rather than direct toxin concentrations, and their useful lead time is not universal. Biomass and remote-sensing models are therefore treated as enabling evidence unless linked to independently validated toxin relationships. Explainable artificial intelligence (XAI) supports model auditing but does not establish causality, while direct cyanotoxin-specific physics-informed machine learning (PIML) evidence remains limited. The review proposes a site-specific, uncertainty-aware workflow linking ecological observations, toxin forecasting, direct chemical verification, plant-specific phase-aware treatment evaluation and operator-supervised feedback. Future progress depends on harmonized toxin-specific data, latency-aware multimodal integration, validated transfer under domain shift and toxicity-resolved prediction.
