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Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms
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Researchers used machine learning to predict how micro- and nanoplastics affect freshwater algae, training models on a decade of published experimental data. The best-performing model identified plastic concentration, exposure time, and particle size as the most important factors determining toxicity. The study offers a data-driven framework that could reduce the need for time-consuming laboratory experiments when assessing microplastic risks to aquatic organisms.
Micro- and nano-plastics (MNPs) pose a growing threat to freshwater microalgae, leading to water quality and biodiversity. Traditional experiments often encounter difficulties in terms of cost, time, and capturing complex interactions when exploring this critical issue. To overcome these limitations, we applied eight machine learning models to predict MNPs' effects on microalgae activity using literature data from the past decade. Of these, Extreme Gradient Boosting (XGB), optimized via Bayesian methods with 5-fold cross-validation, performed best (R² = 0.89, RMSE = 0.09) without overfitting. Key predictors included reactive oxygen species (ROS) production, MNP type and size, photosystem II activity, and microalgae species. Notably, MNP size and algal species had the most direct influence on activity, while ROS levels played a central role in mediating toxicity. Variance partitioning confirmed ROS as the most critical factor, enhancing the explanatory power when combined with other variables. Our findings also identified polyvinylchloride (PVC), particularly at sizes under 160 μm, as the most harmful plastic type. Chlorella pyrenoidosa emerged as the most sensitive species. These insights offer valuable guidance for improving MNP pollution management, developing bioremediation strategies, and refining ecological risk assessments in aquatic ecosystems.
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Predicting microplastic impacts on microalgae: A machine learning approach to understand dynamic interactions in aquatic ecosystems
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Scientists used artificial intelligence to figure out how microplastic pollution harms microalgae, tiny organisms that form the base of the food chain in oceans and lakes and produce much of the oxygen we breathe. They found that temperature and the plastic's chemical properties (like particle size and charge) play the biggest role in how much damage occurs, with water temperature mattering most early on and plastic characteristics mattering more over time. This matters because microalgae support entire aquatic food webs (including the fish we eat), so understanding what makes microplastics most harmful can help guide better pollution control to protect both ecosystems and our food sup
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Machine-Learning-Accelerated Prediction of Water Quality Criteria for Microplastics
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