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

Water Air & Soil Pollution, Journal Year: 2024, Volume and Issue: 235(1)

Published: Jan. 1, 2024

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 industry portend further undesirable events. Apart from the aesthetic degeneration they create their respective habitats, equally capable of secreting toxins, which altogether present grave environmental medical consequences. In this paper, we gave update on factors that influence cHABs, cyanotoxin exposure routes, public health implications, impacts fish, pets, livestock. We discussed social economic impacts, risk assessment, management problems for cHABs and, thereafter, assessed extant approaches including prevention, control, proliferation cyanobacterial blooms. light this, suggest more intensified research should be directed standardization procedures analysis. Also, provision standardized reference material quantification cyanotoxins is vital routine monitoring as well development strong situ sensors quantifying detecting HABs cells toxins waterbodies prevent adverse cHABs. investigations into natural environmentally friendly approach cyanobacteria necessary appropriate deployment artificial intelligence required. Finally, wish redirect focus authorities protecting drinking water supply sources, products, food sources contamination implement proper treatment protect citizens potential threat.

Language: Английский

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Applications of deep learning in water quality management: A state-of-the-art review

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 613, P. 128332 - 128332

Published: Aug. 23, 2022

Language: Английский

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Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Water Research, Journal Year: 2022, Volume and Issue: 228, P. 119367 - 119367

Published: Nov. 16, 2022

Language: Английский

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

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(8), P. 19602 - 19616

Published: Jan. 16, 2023

Language: Английский

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Current status and prospects of algal bloom early warning technologies: A Review

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 349, P. 119510 - 119510

Published: Nov. 9, 2023

Language: Английский

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Classification of inland lake water quality levels based on Sentinel-2 images using convolutional neural networks and spatiotemporal variation and driving factors of algal bloom

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102549 - 102549

Published: Feb. 29, 2024

Water quality monitoring in inland lakes is crucial to ensuring the health and stability of aquatic ecosystems. For regional water environment agencies researchers, remote sensing offers a cost-effective alternative traditional in-situ sampling methods. In this study, we designed convolutional neural network (CNN) based on AlexNet represent relationship between Sentinel-2 images situ levels Lake Dianchi from November 2020 April 2023. The model incorporated an algal bloom extraction algorithm utilized correlation analysis, redundancy analysis (RDA), random forest (RF) method establish connections two environmental factors: meteorology, area (AAB). findings revealed improvement Dianchi's quality, with Levels A (good quality) B (mildly polluted averaging 1.24% 84.28%, respectively. Starting October 2022, stabilized at Level B, 98.17%. Seasonal variations demonstrated best spring worst summer (Level C, severely accounting for 5.19% 21.68%, respectively). Algal presence was minimally observed, average AAB value 1.75%, peaking autumn (4.05%) hitting low winter (0.38%). significant identified AAB, notable spatial trend decreasing C north south, featuring lower Southern Waihai compared Central Waihai. Statistical pinpointed total phosphorus (TP) as dominant factor influencing while meteorological factors such wind speed (WS), relative humidity (RH), precipitation (PP) playing secondary roles. Despite fluctuations TP concentration, recent stabilization 0.05 mg/L suggests positive trajectory future management Dianchi.

Language: Английский

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An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Environmental Science & Technology, Journal Year: 2024, Volume and Issue: 58(35), P. 15607 - 15618

Published: March 4, 2024

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order develop an intelligent early warning system for HABs, big data deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring (VAMS). Subsequently, analysis stratification of layer conducted employing "DeepDPM-Spectral Clustering" method. This approach drastically reduced number predictive enhanced adaptability system. The Bloomformer-2 model developed conduct both single-step multistep predictions Chl-a, integrating " Alert Level Framework" issued by World Health Organization accomplish HABs. case study Taihu Lake revealed that during winter 2018, water column could be partitioned into four clusters (Groups W1-W4), while summer 2019, five S1-S5). Moreover, subsequent task, exhibited superiority performance across all 2018 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, MAPE: 0.228-2.279 prediction; MAE: 0.184-0.505, 0.101-0.378, 0.243-4.011 prediction). prediction 3 days indicated Group W1 I alert state at times. Conversely, S1 mainly under alert, with seven specific time points escalating II alert. Furthermore, end-to-end architecture system, coupled automation its various processes, minimized human intervention, endowing it characteristics. research highlights transformative potential artificial intelligence environmental management emphasizes importance interpretability machine applications.

Language: Английский

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A state-of-the-art review of long short-term memory models with applications in hydrology and water resources

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112352 - 112352

Published: Oct. 1, 2024

Language: Английский

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Machine Learning-Based Early Warning of Algal Blooms: A Case Study of Key Environmental Factors in the Anzhaoxin River Basin

Water, Journal Year: 2025, Volume and Issue: 17(5), P. 725 - 725

Published: March 1, 2025

Algal blooms are a major risk to aquatic ecosystem health and potable water safety. Traditional statistical models often fail accurately predict algal bloom dynamics due their complexity. Machine learning, adept at managing high-dimensional non-linear data, provides superior predictive approach this challenge. In study, we employed support vector machine (SVM), random forest (RF), backpropagation neural network (BPNN) the severity of in Anzhaoxin River Basin based on an density-based grading standard. The SVM model demonstrated highest accuracy with training test set accuracies 0.96 0.92, highlighting its superiority small-sample learning. Shapley Additive Explanations (SHAP) technique was utilized evaluate contribution environmental variables various models. results show that TP is most significant factor affecting outbreak River, phosphorus management strategy more suitable for artificial body northeast China. This study contributes exploring potential application learning diagnosing predicting riverine ecological issues, providing valuable insights protection ecosystems Basin.

Language: Английский

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Semantic segmentation based on Deep learning for the detection of Cyanobacterial Harmful Algal Blooms (CyanoHABs) using synthetic images

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 141, P. 110315 - 110315

Published: April 19, 2023

Cyanobacterial Harmful Algal Blooms (CyanoHABs) in lakes and reservoirs have increased substantially recent decades due to different environmental factors. Its early detection is a crucial issue minimize health effects, particularly potential drinking recreational water bodies. The use of Autonomous Surface Vehicles (ASVs) equipped with machine vision systems (cameras) onboard, represents useful alternative at this time. In regard, we propose an image Semantic Segmentation approach based on Deep Learning Convolutional Neural Networks (CNNs) for the CyanoHABs considering ASV perspective. these models justified by fact that their convolutional architecture, it possible capture both, spectral textural information context pixel its neighbors. To train necessary data, but acquisition real images difficult task, capricious appearance algae surfaces sporadically intermittently over time after long periods time, requiring even years permanent installation system. This justifies generation synthetic data so sufficiently trained are required detect patches when they emerge surface. training semantic segmentation contextual determine need proposal, as well novelty contribution. Three datasets containing generated: (a) first contains foreground background, limited number examples; (b) second, generated state-of-the-art Style-based Generative Adversarial Network Adaptive Discriminator Augmentation (StyleGAN2-ADA) Style Transfer (c) third set, combination previous two. Four model architectures (UNet++, FPN, PSPNet, DeepLabV3+), two encoders backbone (ResNet50 EfficientNet-b6), evaluated from each dataset test distributions. results show feasibility UNet++ EfficientNet-b6, dataset, achieves good generalization performance images.

Language: Английский

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