Machine Learning to Predict the Adsorption Capacity of Microplastics

Nowadays, there is an extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through their own degradation processes, can contaminate ecosystems with micro- and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict different microplastic/water partition coefficients (log K-d) using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients above 0.92 in the query phase, which indicates that these types of models could be used for the rapid estimation of the absorption of organic contaminants on microplastics.

Automated summary

Nowadays, plastic materials are widely produced and used in various industrial activities. These plastics can contaminate ecosystems with micro- and nanoplastics, either from their own degradation or from primary production sources. Once in the aquatic environment, microplastics can adsorb chemical pollutants, facilitating their dispersion and potential harm to living beings. To address the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict microplastic/water partition coefficients (log K-d) using different approximations based on input variables. The selected machine learning models showed correlation coefficients above 0.92 in the query phase, indicating their potential for rapid estimation of organic contaminant adsorption on microplastics.