Toxicity Effects of Micro- and Nanoplastics in Terrestrial Environment

Reddy, A. Vamshi Krishna; Shankaraiah, Golla; Kumar, Palakeerti Srinivas

doi:10.1007/978-3-031-21195-9_9

Cited by 2 publications

(2 citation statements)

References 83 publications

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“…La liberación de MP y NP aumenta con el incremento del volumen de llenado de CO 2 , impulsado por el efecto sinérgico entre las burbujas producidas por el CO 2 y la presión ejercida. Reddy et al (2023) argumentan que los plásticos son materiales sintéticos compuestos por polímeros orgánicos y aditivos químicos como bisfenoles, ftalatos y retardantes de flama, que les otorgan sus propiedades únicas. Los plásticos se utilizan en una amplia gama de aplicaciones comerciales debido a su bajo costo, facilidad de fabricación, flexibilidad e hidrofobicidad.…”

Section: Macro Y Microplásticos Biodegradablesunclassified

Daños Causados al Ambiente, Plantas y Humanos, por Micro y Nano Plásticos

Lira-Saldivar,

Ramírez-Luna,

Cárdenas-Flores

et al. 2023

JER

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Section: Macro Y Microplásticos Biodegradablesunclassified

Daños Causados al Ambiente, Plantas y Humanos, por Micro y Nano Plásticos

Lira-Saldivar,

Ramírez-Luna,

Cárdenas-Flores

et al. 2023

JER

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“…However, improper disposal and mismanagement of plastic waste have raised significant environmental concerns. The accumulation of plastic waste poses a severe threat to ecosystems, including marine life [1] and terrestrial habitats [2], making effective waste management and recycling strategies urgent. For plastic waste sorting, conventional techniques such as NIRS [3][4][5], electrostatic separators [6], and magnetic density separation [7] have been employed.…”

Section: Introductionmentioning

confidence: 99%

Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms

Choi,

Lim,

Yoo

2023

Applied Sciences

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Plastics, with their versatility and cost-effectiveness, have become indispensable materials across various industries. However, the improper disposal and mismanagement of plastic waste have led to significant environmental issues, including pollution, habitat destruction, and threats to wildlife. To address these challenges, numerous methods for plastic waste sorting and recycling have been developed. While conventional techniques like near-infrared spectroscopy (NIRS) have been effective to some extent, they face difficulties in accurately classifying chemically similar samples, such as polyethylene terephthalate (PET) and PET-glycol (PET-G), which have similar chemical compositions but distinct physical characteristics. This paper introduces an approach that adapts image sensors and deep learning object detection algorithms; specifically, the You Only Look Once (YOLO) model, to enhance plastic waste classification based on the shape of the waste. Unlike conventional methods that rely solely on spectral analysis, our methodology aims to significantly improve the accuracy and efficiency of classifying plastics, especially when dealing with materials having similar chemical compositions but differing physical attributes. The system developed using image sensors and the YOLO model proves to be not only effective but also scalable and adaptable for various industrial and environmental applications. In our experiments, the results are strikingly effective. We achieved a classification accuracy rate exceeding 91.7% mean Average Precision (mAP) in distinguishing between PET and PET-G, surpassing conventional techniques by a considerable margin. The implications of this research extend far and wide. By enhancing the accuracy of plastic waste sorting and reducing misclassification rates, we can significantly boost recycling efficiency. The proposed approach contributes to a more sustainable and efficient plastic waste management system, alleviating the strain on landfills and mitigating the environmental impact of plastic waste, contributing to a cleaner and more sustainable environment.

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Toxicity Effects of Micro- and Nanoplastics in Terrestrial Environment

Cited by 2 publications

References 83 publications

Daños Causados al Ambiente, Plantas y Humanos, por Micro y Nano Plásticos

Daños Causados al Ambiente, Plantas y Humanos, por Micro y Nano Plásticos

Advancing Plastic Waste Classification and Recycling Efficiency: Integrating Image Sensors and Deep Learning Algorithms

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