The use of artificial neural networks in modelling migration pollutants from the degradation of microplastics.

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.

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Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This publication reflects the implementation of Research Project No. 2019/03/X/ST10/01557 funded by the Polish National Science Center. The research leading to these results has received funding from the commissioned task entitled “VIA CARPATIA Universities of Technology Network named after the President of the Republic of Poland Lech Kaczyński” contract no. MEiN/2022/DPI/2578 action entitled “In the neighborhood - inter-university research internships and study visits.”