We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Interpretable machine learning models reveal the partnership of microplastics and perfluoroalkyl substances in sediments at a century scale
AI summary Read the abstract
Interpretable machine learning models applied to sediment core data from Taihu Lake revealed a strong spatial and temporal partnership between microplastics and perfluoroalkyl substances (PFASs), with MPs acting as co-transport vectors for PFASs. The study provided mechanistic insights into how these two classes of emerging contaminants interact in lacustrine sediments.
It is challenging to explore the complex interactions between perfluoroalkyl substances (PFASs) and microplastics in lake sediments. The partnership of perfluoroalkyl substances (PFASs) and microplastics in lake sediments are difficult to determine experimentally. This study utilized sediment cores from Taihu Lake to reconstruct the coexistence history and innovatively reveal the collaboration between PFASs and microplastics by using post-hoc interpretable machine learning methods. Microplastics and PFASs emerged in the 1960s and have significantly increased since the 1990s. PFASs and microplastics had the highest growth rate in the 0-10 cm range, with average growth rates of 35.96 pg/g/year and 4.40 items/year per 100 g, respectively. Extreme gradient boosting demonstrated the best simulation of PFASs and microplastics in machine learning models. Feature importance and Shapley additive explanations semi-quantitatively clarified the importance of transparent and pellet microplastics on PFASs concentrations, as well as the importance of perfluorooctane sulfonate (PFOS) and ΣPFASs on microplastics. Moisture content, redox potential, χfd, and χ were the key influencing factors on contaminants. Partial dependence plots showed the influencing thresholds were 0.30 ng/g for ΣPFASs and 0.15 ng/g for PFOS on microplastics, and 10 items per 100 g for pellets and 12 items per 100 g for transparent plastics on PFASs. This study elucidated the interactions between two typical emerging contaminants on a century-scale through the intersection of environmental geochemistry and interpretable machine learning.
More Papers Like This
Decoding the PlasticPatch: Exploring the Global MicroplasticDistribution in the Surface Layers of Marine Regions with InterpretableMachine Learning
AI summary Read the abstract
Researchers applied four interpretable machine learning algorithms to a calibrated global marine microplastic dataset to construct a predictive model of surface-layer microplastic distribution, finding that biogeochemical and anthropogenic factors are the dominant drivers of global marine microplastic pollution patterns.
Exploring the response of bacterial community functions to microplastic features in lake ecosystems through interpretable machine learning
AI summary Read the abstract
Researchers used machine learning models to investigate how different characteristics of microplastics affect bacterial communities in lake ecosystems. They found that the color, shape, and polymer type of microplastics all influenced bacterial functions related to carbon and nitrogen cycling and human health. The study suggests that specific microplastic features, such as yellow coloring and PET polymer type, have distinct impacts on microbial communities in freshwater environments.
Influence of microplastics on the bioavailability of organic pollutants in freshwater sediments: a data-driven modeling study
AI summary Read the abstract
Scientists used machine learning to study how tiny plastic particles in river and lake sediment affect the way other pollutants (like industrial chemicals) behave in the environment. They found that very small microplastics (30-100 micrometers) and certain common plastic types, like those found in packaging and clothing, make it easier for these pollutants to become "available," meaning they can more easily enter the food chain through fish and other wildlife. This matters because it suggests microplastic pollution isn't just a standalone problem, it may actually make other environmental toxins more likely to build up in the ecosystem we ultimately eat from.
Predicting aqueous sorption of organic pollutants on microplastics with machine learning
AI summary Read the abstract
Researchers developed machine learning models to predict how organic pollutants bind to microplastics in water, using data from 475 published experiments. The models outperformed traditional approaches by accounting for properties of both the microplastics and the pollutants simultaneously. The study provides a more universal tool for understanding how microplastics can transport and concentrate harmful chemicals in freshwater systems.
Prediction of Microplastic Concentrations in Freshwater Sediments of Türkiye Using Machine Learning and Explainable AI
AI summary Read the abstract
Researchers combined data from 79 freshwater sediment samples across Türkiye into one dataset and used computer models to predict microplastic pollution levels. One model performed best, finding that region and season, especially the Marmara Region and summer months, were the strongest factors linked to higher microplastic concentrations.
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.