NON-DESTRUCTIVE IDENTIFICATION OF MICROPLASTICS IN SOIL USING SPECTROSCOPY AND HYPERSPECTRAL IMAGING DOI
Muhammad Fahri Reza Pahlawan, Ye-Na Kim,

Umuhoza Aline

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118216 - 118216

Published: Feb. 1, 2025

Language: Английский

Polystyrene microplastics induce size-dependent multi-organ damage in mice: Insights into gut microbiota and fecal metabolites DOI
Zhu Zhang, Wenqing Chen, Hiutung Chan

et al.

Journal of Hazardous Materials, Journal Year: 2023, Volume and Issue: 461, P. 132503 - 132503

Published: Sept. 6, 2023

Language: Английский

Citations

61
Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning DOI
Lifang Xie,

Siheng Luo,

Yangyang Liu

et al.

Environmental Science & Technology, Journal Year: 2023, Volume and Issue: 57(46), P. 18203 - 18214

Published: July 3, 2023

The increasing prevalence of nanoplastics in the environment underscores need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification is challenging due to their small size complex composition. In this work, we combined highly reflective substrates machine learning accurately identify using Raman spectroscopy. Our approach established spectroscopy data sets nanoplastics, incorporated peak extraction retention processing, constructed a random forest model that achieved an average accuracy 98.8% identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% accuracy, demonstrated applicability algorithm real-world environmental samples through experiments rainwater, detecting nanoscale polystyrene (PS) polyvinyl chloride (PVC). Despite challenges processing low-quality nanoplastic spectra study potential forests distinguish from other particles. results suggest combination holds promise developing particle strategies.

Language: Английский

Citations

54
A comprehensive review of micro- and nano-plastics in the atmosphere: Occurrence, fate, toxicity, and strategies for risk reduction DOI
Van‐Giang Le, Minh‐Ky Nguyen, Hoang‐Lam Nguyen

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 904, P. 166649 - 166649

Published: Sept. 1, 2023

Language: Английский

Citations

51
Microplastics monitoring in freshwater systems: A review of global efforts, knowledge gaps, and research priorities DOI
Bu Zhao,

Ruth E Richardson,

Fengqi You

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 477, P. 135329 - 135329

Published: July 27, 2024

Language: Английский

Citations

24
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors DOI
Binbin Hu,

Yaodan Dai,

Haidong Zhou

et al.

Journal of Hazardous Materials, Journal Year: 2024, Volume and Issue: 474, P. 134865 - 134865

Published: June 12, 2024

Language: Английский

Citations

18
Recent advances in the application of machine learning methods to improve identification of the microplastics in environment DOI

Jia-yu Lin,

Hongtao Liu,

Jun Zhang

et al.

Chemosphere, Journal Year: 2022, Volume and Issue: 307, P. 136092 - 136092

Published: Aug. 19, 2022

Language: Английский

Citations

60
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives DOI
Xiaotong Wu,

Qixing Zhou,

Mu Li

et al.

Journal of Hazardous Materials, Journal Year: 2022, Volume and Issue: 438, P. 129487 - 129487

Published: June 28, 2022

Language: Английский

Citations

43
Machine learning-assisted photoluminescent sensor array based on gold nanoclusters for the discrimination of antibiotics with test paper DOI

Jinming Xu,

Xihang Chen, Huangmei Zhou

et al.

Talanta, Journal Year: 2023, Volume and Issue: 266, P. 125122 - 125122

Published: Aug. 25, 2023

Language: Английский

Citations

26
Current applications and future impact of machine learning in emerging contaminants: A review DOI
Lang Lei,

Ruirui Pang,

Zhibang Han

et al.

Critical Reviews in Environmental Science and Technology, Journal Year: 2023, Volume and Issue: 53(20), P. 1817 - 1835

Published: March 23, 2023

With the continuous release into environments, emerging contaminants (ECs) have attracted widespread attention for potential risks, and numerous studies been conducted on their identification, environmental behavior bioeffects, removal. Owing to superiority of dealing with high-dimensional unstructured data, a new data-driven approach, machine learning (ML), has gradually applied in research ECs. This review described fundamental principle, algorithms, workflow ML, summarized advances ML applications typical ECs (per- polyfluoroalkyl substances, nanoparticles, antibiotic resistance genes, endocrine-disrupting chemicals, microplastics, antibiotics, pharmaceutical personal care products). methods showed practicability, reliability, effectiveness predicting or analyzing occurrence, distribution, removal ECs, various algorithms derived models were developed optimized obtain better performance. Moreover, size homogeneity data set strongly influence application choosing appropriate different characteristics is crucial addressing specific problems related sets. Future efforts should focus improving quality adopting more advanced developing quantitative structure-activity relationship, promoting applicability domains interpretability models. In addition, development codeless tools will benefit accessibility

Language: Английский

Citations

25
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning DOI
Zhen Yu, Lei Wang, Hongwen Sun

et al.

Environmental Pollution, Journal Year: 2023, Volume and Issue: 331, P. 121834 - 121834

Published: May 18, 2023

Language: Английский

Citations

24