Forecasting Global Microplastic Exposure from Processed Foods: Data-Driven Forecasts and Detection

Microplastics are one of the major contaminants of processed foods at a global scale and they contain high risks for human health. Even though the public understanding of the issue has become wider, the knowledge of individual levels of exposure is still very much limited together with the practical tools which can estimate microplastic ingestion. This study proposes a complete data pipeline and a machine learning framework for predicting microplastic contamination and estimating personalised exposure to microplastics depending on country, specific consumption patterns and contamination trends of a long, term nature. The dataset consisted of approximately 18 food groups across 109 countries. So far the data has been through a very thorough preprocessing stage, exploratory analysis, and feature engineering was undertaken, which among other things, included microplastic load aggregation, the addition of lagged variables, and mixing serving sizes information. Random Forest and XGBoost regressors models were trained to predict the levels of contamination from 2019 to 2030. Polynomial Regression delivered the highest accuracy on the training data of R2= 0.9897. While XGBoost gave the best generalization result of R2 = 0.9469 and was therefore chosen as a final forecasting model. The consumption of microplastics through the global food chains is predicted to keep increasing. The originality of this study is in the combination of the long, term contamination data with the selective food, category modelling that allows to generate a reliable framework for the forecasting of the individual intake and to provide to the policy makers EBP (Evidence, Based Policy) advice.