Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models

Microplastics (MPs) are polymeric particles, mainly fossil-based, widely found in marine ecosystems, linked to environmental and public health impacts due to their persistence and ability to carry pollutants. In São Paulo’s northern coast, geomorphological factors and anthropogenic activities intensify the deposition of these pollutants. Through multivariate techniques, this study aims to investigate the role of the morphometrical parameters as independent variables in quantifying the distribution of MPs on the region’s sandy beaches. Using beach face slope (tanβ) and orientation (Aspect) derived from remote sensing images, calibrated by in situ topographic profiles collected through GNSS positioning, and laboratory analyses, six machine learning models Random Forest, Gradient Boosting, Lasso and Ridge regression, Support Vector Regression, and Partial Least Squares regression were tested and evaluated for performance. The Gradient Boosting model demonstrated the best performance, indicating its superior capacity to capture complex relationships between predictor variables and MPs deposition, followed by Random Forest model. Morphometric analysis revealed, once again, that in this coastal section of São Paulo, beaches with Sloping profiles oriented toward the SSW are more susceptible to MPs accumulation, especially near urban centers. Ultimately, incorporating geomorphological variables into predictive models enhances understanding of MPs deposition, providing a foundation for environmental policies focused on marine pollution mitigation and coastal ecosystem conservation while also contributing to achieve SDG 14.