We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Prediction of Microplastic Concentrations in Freshwater Sediments of Türkiye Using Machine Learning and Explainable AI
No summary available — this paper's abstract is not included in the open metadata provided by the publisher. Learn why →
More Papers Like This
Microplastics in aquatic environments: Bridging occurrence and mitigation through machine learning detection and bioremediation strategies
AI summary Read the abstract
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.
Machine Learning Approaches for Predicting Microplastic Removal
AI summary Read the abstract
Researchers applied Bayesian optimization combined with machine learning models — including boosted regression trees, neural networks, and support vector regression — to predict microplastic removal during coagulation, finding the BOA-BRT hybrid outperformed conventional methods by up to 71%, with microplastic particle size identified as the most influential variable.
A Machine-Learning Model for Investigating Microplastics Source–Receptor Relationships in Aquatic Environments
AI summary Read the abstract
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.
Explainable Detection of Microplastics Using Transformer Neural Networks
AI summary Read the abstract
Transformer neural network models can detect and classify microplastics in images with high accuracy while providing explainable outputs that identify which visual features drive classification decisions. Automated detection tools like this are key to scaling up microplastic monitoring programs and generating the standardized data needed to quantify pollution trends globally.
A Novel Approach for Fast Microplastic Quantification in Sediments Using Machine Learning—Spectrometer Combinations
AI summary Read the abstract
Researchers combined visible near-infrared and FTIR spectroscopy with machine learning models (SVR, PLSR, BPNN) to rapidly estimate concentrations of PE, PP, PS, and PVC microplastics in river and loess sediments without extraction or manual counting. Faster, non-destructive quantification methods are crucial for scaling up microplastic monitoring across diverse environmental matrices and enabling consistent cross-study comparisons of pollution levels.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.