Characterization of the molecular differential responses in marine benthic macroinvertebrates exposed to polycyclic aromatic hydrocarbons

Onyena, Amarachi P.; Manohar, Cathrine S.; Nkwoji, Joseph A.; Chukwu, Lucian O.

doi:10.1007/s10452-023-10064-4

Cited by 2 publications

(1 citation statement)

References 150 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PAHs as organic chemicals with known adverse effects, including oxidative stress and carcinogenicity, require standardised methods for environmental risk assessment. As robust bioindicators of PAHs, benthic macroinvertebrates play a crucial role, emphasising the need for a consistent approach to contamination exposures and biomarker interpretation (Onyena et al, 2023) Bromacil is a herbicide commonly used in agriculture, and Pisidium species, which belong to the subclass Bivalvia, are known to be relatively tolerant of this chemical. However, it is important to remember that any herbicide, even at tolerated levels, can have an impact on aquatic ecosystems and non-target organisms.…”

Section: Discussionmentioning

confidence: 99%

Use of artificial neural networks (ANNs) to assess xenobiotics in a river catchment using macroinvertebrates as bioindicators

Krtolica,

Kamenko,

Paunović

et al. 2024

Preprint

View full text Add to dashboard Cite

The Danube flows through densely populated areas and is exposed to numerous stress factors such as dams, canalisation, agriculture, and urbanisation, which cause most of the changes in the Danube catchment area. This paper highlights the benefits of using cutting-edge Machine Learning (ML) models on data gathered from the Joint Danube Survey 3 (JDS 3) dataset to detect xenobiotics in rivers using reliable biomarkers. Recognized as key indicators under the Water Framework Directive, macroinvertebrate communities specifically signal chemical pollution through their varied responses to chemical stressors. The use of ML models such as 4-Layer Perceptron, Long Short-Term Memory, and Transformer Neural Networks allows for a precise determination of the ecological conditions of rivers based on biological and chemical parameters. Certain xenobiotics, especially pesticides like 2,4-Dinitrophenol, Chloroxuron, Bromacil, Fluoranthene, and Bentazone, showed a significant correlation with macroinvertebrates in the Danube River basin. The most suitable ML model is an Artificial Neural Network developed by a specific combination of inputs and outputs. The observation of the correlation between 2,4-Dinitrophenol and Bentazone concentrations and the macroinvertebrate communities indicates the high effectiveness of Long Short-Term Memory models in modelling the ecological status of rivers. The 4-Layer Perceptron model excels in predicting 2,4-Dinitrophenol and Fluoranthene output parameters, while Transformer Neural Networks perform optimally in modelling Bromacil and Fluoranthene concentrations with macroinvertebrates throughout the Danube River Basin. These established artificial neural network architectures can also be applied to other lotic systems and biological parameters.

show abstract

Section: Discussionmentioning

confidence: 99%