Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models
This study focuses on the deposition of microplastics (MPs) on urban beaches along the central São Paulo coastline, utilizing advanced methodologies such as remote sensing, GNSS altimetric surveys, µ-Raman spectroscopy, and machine learning (ML) models. MP concentrations ranged from 6 to 35 MPs/m2, with the highest densities observed near the Port of Santos, attributed to industrial and port activities. The predominant MP types identified were foams (48.7%), fragments (27.7%), and pellets (23.2%), while fibers were rare (0.4%). Beach slope and orientation were found to facilitate the concentration of MP deposition, particularly for foams and pellets. The study’s ML models showed high predictive accuracy, with Random Forest and Gradient Boosting performing exceptionally well for specific MP categories (pellet, fragment, fiber, foam, and film). Polymer characterization revealed the prevalence of polyethylene, polypropylene, and polystyrene, reflecting sources such as disposable packaging and industrial raw materials. The findings emphasize the need for improved waste management and targeted urban beach cleanups, which currently fail to address smaller MPs effectively. This research highlights the critical role of combining in situ data with predictive models to understand MP dynamics in coastal environments. It provides actionable insights for mitigation strategies and contributes to global efforts aligned with the Sustainable Development Goals, particularly SDG 14, aimed at conserving marine ecosystems and reducing pollution.
Citação
@online{anderson_targino_da_silva2025,
author = {Anderson Targino Da Silva , Ferreira and Regina Célia De ,
Oliveira and Eduardo , Siegle and Maria Carolina Hernandez , Ribeiro
and Luciana Slomp , Esteves and Maria , Kuznetsova and Jessica ,
Dipold and Anderson Zanardi De , Freitas and Niklaus Ursus , Wetter},
title = {Microplastic Deposits Prediction on Urban Sandy Beaches:
Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy,
and Machine Learning Models},
volume = {4},
number = {1},
date = {2025-03-05},
doi = {10.3390/microplastics4010012},
langid = {pt-BR},
abstract = {This study focuses on the deposition of microplastics
(MPs) on urban beaches along the central São Paulo coastline,
utilizing advanced methodologies such as remote sensing, GNSS
altimetric surveys, µ-Raman spectroscopy, and machine learning (ML)
models. MP concentrations ranged from 6 to 35 MPs/m2, with the
highest densities observed near the Port of Santos, attributed to
industrial and port activities. The predominant MP types identified
were foams (48.7\%), fragments (27.7\%), and pellets (23.2\%), while
fibers were rare (0.4\%). Beach slope and orientation were found to
facilitate the concentration of MP deposition, particularly for
foams and pellets. The study’s ML models showed high predictive
accuracy, with Random Forest and Gradient Boosting performing
exceptionally well for specific MP categories (pellet, fragment,
fiber, foam, and film). Polymer characterization revealed the
prevalence of polyethylene, polypropylene, and polystyrene,
reflecting sources such as disposable packaging and industrial raw
materials. The findings emphasize the need for improved waste
management and targeted urban beach cleanups, which currently fail
to address smaller MPs effectively. This research highlights the
critical role of combining in situ data with predictive models to
understand MP dynamics in coastal environments. It provides
actionable insights for mitigation strategies and contributes to
global efforts aligned with the Sustainable Development Goals,
particularly SDG 14, aimed at conserving marine ecosystems and
reducing pollution.}
}