Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies DOI
Rajendran Thavasimuthu,

P. M. Vidhya,

S. Sekar

et al.

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(7)

Published: July 1, 2024

Abstract The contamination of microplastics (MPs) creates a substantial risk to both the environment and human health, necessitating development efficient methods for detecting categorizing these micro pollutant particles. As solution, Dense‐UNet with Convolutional Vision Transformer (Dense‐UNet‐CvT), novel deep learning (DL)‐based model is proposed detect classify MPs by performing computer vision tasks. main objective this work enhance detection accuracy in classified from input images. Initially, holographic image dataset comprising primary classes such as polyethylene (PE), polystyrene (PS), low‐density (LDPE), polyhydroxyalkanoate (PHA) collected training evaluating research model. images are preprocessed resizing, Recursive Exposure based Sub‐Image Histogram Equalization (RESIHE)‐based enhancement, Gaussian Adaptive Bilateral Filtering (GABF)‐based denoising improve visual quality applied segmentation using semantic segmentation. CvT implemented extract useful features perform classification on known unknown labeled dataset. performances computed terms rate, accuracy, f1‐score, precision. Dense‐UNet‐CvT achieved 98.22% 98.59% 98.35% 98.76% These compared current models proper validation, which outperformed all performance. Overall, demonstrates superior performance across multiple evaluation metrics, suggesting its effectiveness classifying

Language: Английский

Plant Tissues as Biomonitoring Tools for Environmental Contaminants DOI Creative Commons

Mariam Tarish,

Rania T. Ali, Muhammad Shan

et al.

International Journal of Plant Biology, Journal Year: 2024, Volume and Issue: 15(2), P. 375 - 396

Published: April 28, 2024

Environmental toxins pose significant threats to ecosystems and human health. Monitoring assessing these are crucial for effective environmental management public health protection. Recently, plant species have garnered increasing attention as potential bioindicators identifying evaluating ecological toxins. Since plants often come into touch with harmful compounds in soil, water, the atmosphere, they particularly valuable analyzing how activities influence terrestrial ecosystem, aquatic system, atmosphere. This review paper emphasizes using a resource tracking pollution contaminants. We focused on because indicators of air quality changes. Many been used bio-indicators assess predict pollution, toxicity, These include Allium cepa, Vicia faba, Pisum sativum, Zea mays, Nicotiana tabacum, lichens, mosses. The idea is discussed current paper, focus possible candidates toxin assessment related outcomes.

Language: Английский

Citations

9
Avances en métodos de muestreo para la caracterización de microplásticos en ecosistemas fluviales DOI Open Access
Margarita del Rosario Salazar‐Sánchez, Rosmery Carolina Imbachi Hoyos, José Fernando Solanilla‐Duque

et al.

REVISTA AMBIENTAL AGUA AIRE Y SUELO, Journal Year: 2024, Volume and Issue: 15(1), P. 1 - 20

Published: May 5, 2024

Este artículo de investigación presenta una revisión bibliográfica exhaustiva sobre los métodos muestreo aplicados en la evaluación microplásticos ecosistemas fluviales. La creciente preocupación torno a contaminación por entornos acuáticos exige enfoques rigurosos. El objetivo principal este estudio es evaluar críticamente las metodologías existentes, destacando sus fortalezas y limitaciones. Al examinar técnicas convencionales emergentes, busca ofrecer recomendaciones para mejorar futuras investigaciones. A través un análisis meticuloso investigaciones previas, tiene como comprensión presencia sistemas

Citations

1
Microplastics Detection Techniques DOI
Amit Joshi, Nahid Akhtar, Ajay Kumar

et al.

Published: Jan. 1, 2024

Citations

1
Vibrational spectroscopy for microplastic detection in water: a review DOI
Eun Su Jung,

Jin Hyun Choe,

JinUk Yoo

et al.

Applied Spectroscopy Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 22

Published: Nov. 3, 2024

This study focuses on the application of vibrational spectroscopy—Raman and FT-IR—for detecting microplastics (MPs) in various water bodies, including oceans, lakes, drinking water. Given growing concern about environmental health impacts MPs, accurate identification analysis are essential. The review discusses fundamental principles Raman FT-IR spectroscopy, emphasizing their nondestructive nature capability to provide detailed chemical identification. Sample preparation methods explored enhance detection efficiency, particularly complex matrices where organic matter may cause spectral interference. Highlighting recent studies, this aims evaluate effectiveness these techniques identifying MPs diverse aquatic systems, offers insight into challenges future perspectives for advancing microplastic research environments.

Language: Английский

Citations

1
Effect of Land Use Patterns on Soil Microplastics Pollution DOI

M. Kothari,

Priyank Nimje,

Divya Mistry

et al.

Published: Dec. 29, 2024

Language: Английский

Citations

1
Enhanced classification of microplastic polymers (polyethylene, polystyrene, low‐density polyethylene, polyhydroxyalkanoate) in waterbodies DOI
Rajendran Thavasimuthu,

P. M. Vidhya,

S. Sekar

et al.

Polymers for Advanced Technologies, Journal Year: 2024, Volume and Issue: 35(7)

Published: July 1, 2024

Abstract The contamination of microplastics (MPs) creates a substantial risk to both the environment and human health, necessitating development efficient methods for detecting categorizing these micro pollutant particles. As solution, Dense‐UNet with Convolutional Vision Transformer (Dense‐UNet‐CvT), novel deep learning (DL)‐based model is proposed detect classify MPs by performing computer vision tasks. main objective this work enhance detection accuracy in classified from input images. Initially, holographic image dataset comprising primary classes such as polyethylene (PE), polystyrene (PS), low‐density (LDPE), polyhydroxyalkanoate (PHA) collected training evaluating research model. images are preprocessed resizing, Recursive Exposure based Sub‐Image Histogram Equalization (RESIHE)‐based enhancement, Gaussian Adaptive Bilateral Filtering (GABF)‐based denoising improve visual quality applied segmentation using semantic segmentation. CvT implemented extract useful features perform classification on known unknown labeled dataset. performances computed terms rate, accuracy, f1‐score, precision. Dense‐UNet‐CvT achieved 98.22% 98.59% 98.35% 98.76% These compared current models proper validation, which outperformed all performance. Overall, demonstrates superior performance across multiple evaluation metrics, suggesting its effectiveness classifying

Language: Английский

Citations

0