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Integration of bio-inspired algorithms with machine learning for modeling microplastic contamination transport processes in natural aquatic habitats using morphological features
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Scientists built a computer model that can accurately predict how microplastic particles move through rivers, lakes, and oceans based on their shape and size. This matters because understanding where these tiny plastic bits travel and accumulate, including in water sources we may eventually drink or in seafood we eat, can help authorities track pollution hotspots and create smarter policies to keep our water supplies cleaner.
The increasing presence of microplastics (MPs) in aquatic environments poses significant environmental challenges, necessitating accurate prediction models for their transport behavior. This study develops a machine learning (ML)-based predictive framework to estimate MPs' speeds using artificial neural network (ANN) integrated with optimization techniques. Key hydrodynamic and particle properties influencing MP transport were identified using the Minimum Redundancy Maximum Relevance (MRMR) algorithm. Also, local interpretable model-agnostic explanation (LIME) was used in the current study to show the individual contributions of each feature. The comparative analysis of many optimization techniques, such as Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Bayesian Optimization (BO), and Random Search (RS), was implemented to optimize the ANN. The obtained results indicated that ANN-ABC-C1 (r = 0.720, RMSE = 0.037, MSE = 0.0006) and ANN-ABC-C2 (r = 0.764, RMSE = 0.035, MSE = 0.00048) display poor predictive ability, and thus it can be concluded that ABC is not particularly well-adapted to optimizing ANN models in this context. Conversely, Bayesian BO and RS perform better. ANN-BO-C2 (r = 0.971, RMSE = 0.019, MSE = 4.87E-05) and ANN-RS-C2 (r = 0.981, RMSE = 0.017, MSE = 2.46E-05) are the best models, which proves that they are highly generalized. ANN-RS-C2 model has the highest predictive accuracy, with r (0.981) and the RMSE = 0.017, and MSE = 2.46E-05. The research equally highlighted the role of modeling in environmental pollution analysis, whereby MP dispersion and accumulation are predicted. The findings are useful in terms of pollution prevention, the development of regulatory policies, and sustainable planning. The proposed framework offers a solution to the problem of real-time MP transport forecast that will contribute to the sound management of water resources and the protection of the ecosystem.
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Researchers developed a machine-learning model to trace where microplastics in aquatic environments come from and where they end up, potentially giving scientists and regulators a more powerful tool to identify pollution sources and prioritize cleanup efforts.
Microplastics in aquatic environments: Bridging occurrence and mitigation through machine learning detection and bioremediation strategies
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Tiny plastic particles called microplastics are showing up in shockingly high amounts in our water, sometimes millions of particles per liter, and they can end up affecting both aquatic life and, potentially, the food and water we consume. This review paper (summarizing existing research) highlights two promising solutions: certain fungi and algae that can remove over 90% of microplastics from water in lab settings, and AI tools that can quickly detect and track plastic pollution. The catch is that these cleanup methods work great in controlled settings but haven't yet been proven to work reliably in messy, real-world water systems.
Multi-Type Microplastic Migration Model Driven by River Hydrodynamic Conditions
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Scientists built a model that predicts how different types of microplastics move through rivers, some float and travel quickly downstream, others sink to the riverbed, and some stay suspended in the water depending on their size, shape, and material. This matters because most of the microplastics found (over 70%) were smaller than half a millimeter, tiny enough to be easily ingested by fish and potentially end up in our drinking water or food supply, so understanding where they travel and accumulate can help target cleanup efforts and pollution control where they're needed most.
Transport Process of Microplastics from Terrestrial to Aquatic Environment: Evaluation of the Current Knowledge
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Researchers reviewed how microplastics travel from terrestrial sources — including agriculture, urban runoff, and atmospheric deposition — into rivers, lakes, and ultimately the ocean, synthesizing current knowledge on transport mechanisms and knowledge gaps. Understanding land-to-water transport pathways is critical for identifying where interventions can most effectively reduce the flow of plastics into aquatic ecosystems and human food sources.
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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