Abstract Cyanobacteria harmful algal blooms (cHABs) are increasingly becoming an emerging threat to aquatic life, ecotourism, and certain real estate investments. Their spontaneous yet sporadic occurrence has made mitigation measures a cumbersome task; moreover, current trends regarding anthropogenic activities, especially in agriculture and industry portend further undesirable events. Apart from the aesthetic degeneration they create in their respective habitats, they are equally capable of secreting toxins, which altogether present grave environmental and medical consequences. In this paper, we gave an update on factors that influence cHABs, cyanotoxin exposure routes, and environmental public health implications, especially impacts on fish, pets, and livestock. We discussed social economic impacts, risk assessment, and management problems for cHABs and, thereafter, assessed the extant management approaches including prevention, control, and mitigation of the proliferation of cyanobacterial blooms. In light of this, we suggest that more intensified research should be directed to the standardization of procedures for cyanotoxin analysis. Also, the provision of standardized reference material for the quantification of cyanotoxins is vital for routine monitoring as well as the development of strong in situ sensors capable of quantifying and detecting HABs cells and toxins in waterbodies to prevent the adverse impacts of cHABs. Also, more investigations into the natural and environmentally friendly approach to cyanobacteria management and the necessary and appropriate deployment of artificial intelligence are required. Finally, we wish to redirect the focus of public health authorities to protecting drinking water supply sources, agriculture products, and food sources from cyanotoxins contamination as well as to implement proper monitoring and treatment procedures to protect citizens from this potential health threat. 

Cyanobacteria Harmful Algae Blooms: Causes, Impacts, and Risk Management

Remote sensing for mapping algal blooms in freshwater lakes: a review

Application and recent progress of inland water monitoring using remote sensing techniques

UAV and satellite remote sensing for inland water quality assessments: a literature review.

ABSTRACT Although remote sensing techniques have been used to monitor toxic cyanobacteria with hyperspectral data in inland water, it is difficult to optimize conventional bio-optical algorithms for individual water bodies because of the complex optical properties of various water components. Therefore, this study adopted a spatial attention convolutional neural network (spatial attention CNN) to estimate the chlorophyll-a (Chl-a) and phycocyanin (PC) concentrations in the Geum, Nakdong, and Yeongsan rivers in South Korea in order to evaluate cyanobacteria using remote sensing reflectance data. The CNN model utilized a spatial attention module to analyze the importance of the bands in the reflectance data. Then, the spatial attention CNN model was compared with different bio-optical algorithms for each study area. The spatial attention CNN model was generalized to estimate the pigment concentrations in the target rivers, and the model performance was evaluated by correlation coefficient (R) and root mean squared error (RMSE) between the observed and estimated concentrations of the algal pigments. The spatial attention CNN model, which was generalized to estimate the pigment concentrations in the target rivers, had R values above 0.87 and 0.88 for Chl-a and PC, respectively. However, the optimized band ratio algorithms for Chl-a and PC had R values above 0.83 and 0.70, respectively. Hence, it showed better performance than the conventional bio-optical algorithms. The spatial attention module provided attention weights for visualizing important features in the reflectance data. Specifically, the 600 nm, 650 nm, and near-infrared regions had high attention weights for estimating the concentrations of Chl-a and PC. Based on these findings, this study demonstrated that the spatial attention CNN model has a high potential for good application performance in various water bodies.

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2037887?needAccess=true

Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery

Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data.

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Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data.