Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models DOI Creative Commons
Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Eduardo Siegle

et al.

Microplastics, Journal Year: 2025, Volume and Issue: 4(1), P. 12 - 12

Published: March 5, 2025

This study focuses on the deposition of microplastics (MPs) urban beaches along 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 highest densities observed near Port Santos, attributed industrial port activities. The predominant types identified were foams (48.7%), fragments (27.7%), pellets (23.2%), while fibers rare (0.4%). Beach slope orientation found facilitate concentration deposition, particularly for pellets. study’s ML models showed high predictive accuracy, Random Forest Gradient Boosting performing exceptionally well specific categories (pellet, fragment, fiber, foam, film). Polymer characterization revealed prevalence polyethylene, polypropylene, polystyrene, reflecting sources disposable packaging raw materials. findings emphasize need improved waste management targeted beach cleanups, which currently fail address smaller MPs effectively. research highlights critical role combining in situ data understand dynamics coastal environments. It provides actionable insights mitigation strategies contributes global efforts aligned Sustainable Development Goals, SDG 14, aimed at conserving marine ecosystems reducing pollution.

Language: Английский

Microplastic Deposits Prediction on Urban Sandy Beaches: Integrating Remote Sensing, GNSS Positioning, µ-Raman Spectroscopy, and Machine Learning Models DOI Creative Commons
Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Eduardo Siegle

et al.

Microplastics, Journal Year: 2025, Volume and Issue: 4(1), P. 12 - 12

Published: March 5, 2025

This study focuses on the deposition of microplastics (MPs) urban beaches along 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 highest densities observed near Port Santos, attributed industrial port activities. The predominant types identified were foams (48.7%), fragments (27.7%), pellets (23.2%), while fibers rare (0.4%). Beach slope orientation found facilitate concentration deposition, particularly for pellets. study’s ML models showed high predictive accuracy, Random Forest Gradient Boosting performing exceptionally well specific categories (pellet, fragment, fiber, foam, film). Polymer characterization revealed prevalence polyethylene, polypropylene, polystyrene, reflecting sources disposable packaging raw materials. findings emphasize need improved waste management targeted beach cleanups, which currently fail address smaller MPs effectively. research highlights critical role combining in situ data understand dynamics coastal environments. It provides actionable insights mitigation strategies contributes global efforts aligned Sustainable Development Goals, SDG 14, aimed at conserving marine ecosystems reducing pollution.

Language: Английский

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