AIMC Topic: Eutrophication

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Keystone microbial taxa identified by deep learning reveal mechanisms of phosphorus stoichiometric homeostasis in submerged macrophytes under different hydrodynamic states.

Water research
Phosphorus (P) pollution in aquatic ecosystems triggers eutrophication, disrupting ecological processes. Although phytoremediation using submerged macrophytes is promising, its efficacy depends on plant-microbe interactions and stoichiometric homeost...

Assessment of marine eutrophication: Challenges and solutions ahead.

Marine pollution bulletin
Marine eutrophication remains a pressing global environmental challenge, demanding urgent advances in science-based assessment frameworks to mitigate its ecological and socio-economic impacts. Current methodologies, however, face critical limitations...

Exploring the nexus between coastal tourism growth and eutrophication: Challenges for environmental management.

Marine pollution bulletin
Coastal tourism has witnessed rapid growth over the past two decades, often accompanied by increasing environmental concerns, particularly eutrophication in sensitive marine zones. This study explores the relationship between coastal tourism expansio...

Spatiotemporal dynamics of cyanobacterial blooms: Integrating machine learning and feature selection techniques with uncrewed aircraft systems and autonomous surface vessel data.

Journal of environmental management
Cyanobacterial blooms pose significant threats to aquatic ecosystems and public health due to their ability to release harmful toxins, degrade water quality, disrupt aquatic habitats, and endanger human and animal health through contact or consumptio...

U-shaped deep learning networks for algal bloom detection using Sentinel-2 imagery: Exploring model performance and transferability.

Journal of environmental management
Inland water sources, such as lakes, support diverse ecosystems and provide essential services to human societies. However, these valuable resources are under increasing pressure from rapid climate changes and pollution resulting from human activitie...

Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie.

Journal of environmental management
Algal blooms, which have substantial adverse effects, are increasingly occurring worldwide in the context of global warming and eutrophication. Machine learning models (MLMs) are emerging as efficient and promising tools for predicting algal blooms. ...

Enhancing short-term algal bloom forecasting through an anti-mimicking hybrid deep learning method.

Journal of environmental management
Accurately predicting algal blooms remains a critical challenge due to their dynamic and non-stationary nature, compounded by high-frequency fluctuations and noise in monitoring data. Additionally, a common issue in time-series forecasting is data re...

Machine learning to identify environmental drivers of phytoplankton blooms in the Southern Baltic Sea.

Scientific reports
Phytoplankton blooms exhibit varying patterns in timing and number of peaks within ecosystems. These differences in blooming patterns are partly explained by phytoplankton:nutrient interactions and external factors such as temperature, salinity and l...

Multi-modal learning-based algae phyla identification using image and particle modalities.

Water research
Algal blooms in freshwater, which are exacerbated by urbanization and climate change, pose significant challenges in the water treatment process. These blooms affect water quality and treatment efficiency. Effective identification of algal proliferat...

Cyanobacteria hot spot detection integrating remote sensing data with convolutional and Kolmogorov-Arnold networks.

The Science of the total environment
Prompt and accurate monitoring of cyanobacterial blooms is essential for public health management and understanding aquatic ecosystem dynamics. Remote sensing, in particular satellite observations, presents a good alternative for continuous monitorin...