We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
The use of artificial neural networks in modelling migration pollutants from the degradation of microplastics
AI summary Read the abstract
Researchers used artificial neural networks to model the emission of additives from degrading microplastics, finding that machine learning could predict migration patterns from the vast range of polymer types, chemical structures, and environmental conditions involved. This approach could reduce the need for extensive laboratory testing by identifying high-risk scenarios for further investigation.
The objective of this article was to assess the effectiveness of simulation models in predicting the emission of additives from microplastics. The variety of plastics, their chemical structure, physicochemical properties, as well as the influence of environmental factors on their decomposition generate countless cases for analysis in the laboratory. The search for methods to reduce unnecessary laboratory analyses is a necessary action to protect the environment and ensure economic efficiency. In this study, machine learning techniques, specifically the methodology of artificial neural networks (ANNs), were employed to predict the leaching of contaminants from microplastics. The network's development was based on laboratory test results obtained using gas chromatography coupled to a mass spectrometer (GC-MS). The conducted research revealed the significant utility of the multilayer perceptron (MLP) - type networks, which exhibited correlation levels exceeding 95 % for various predicted values. One comprehensive ANN was developed, encompassing all the parameters analyzed, alongside individual networks for each parameter. A common network for all factors enabled for satisfactory results. Temperature and holding time had the greatest influence on the values of parameters such as the electrolytic conductivity of water (EC), dissolved organic carbon (DOC), and di(2-ethylhexyl) phthalate (DEHP). Correlation results ranged from 0.94 to 0.99 for EC, DEHP and DOC between the model data and laboratory data in each set of training, test, and validation data. The conducted research demonstrated that ANNs are a valuable machine learning method for analyzing and predicting pollutant emissions during the decomposition of microplastics.
More Papers Like This
Assessment of machine learning-based methods predictive suitability for migration pollutants from microplastics degradation
AI summary Read the abstract
Researchers assessed the usefulness of machine learning methods for predicting the migration of chemical pollutants from microplastics. The study found that artificial neural networks and support vector methods showed strong potential for modeling and predicting the leaching of plasticizers and other contaminants, which could reduce the need for extensive laboratory analyses.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
AI summary Read the abstract
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Machine Learning to Predict the Adsorption Capacity of Microplastics
AI summary Read the abstract
Researchers developed machine learning models to predict the adsorption capacity of microplastics for chemical pollutants, providing a computational tool to better understand how microplastics act as vectors for contaminant dispersal in aquatic environments.
Current applications and future impact of machine learning in emerging contaminants: A review
AI summary Read the abstract
This review examines how machine learning is being applied to emerging contaminant research including microplastics, covering identification, environmental behavior prediction, bioeffect assessment, and removal optimization of these pollutants.
[Overview of the Application of Machine Learning for Identification and Environmental Risk Assessment of Microplastics].
AI summary Read the abstract
This review examines the application of machine learning (ML) methods for identifying microplastics and assessing their environmental risks, covering techniques for improving the accuracy and reliability of microplastic detection across different environmental media. Researchers highlight how ML can systematically analyse pollution characteristics and support ecological risk evaluation of microplastic contamination.
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.