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Cited by 356

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Microplastic pollution in water and sediment in a textile industrial area

Deng, Hua, Wei, Ren, Luo, Wenya, Hu, Lingling, Li, Bowen, Di, Ya’nan, Shi, Huahong·2020·Environmental Pollution

Abstract

Microplastics pollution in the environment is closely determined by the surrounding industrial and human activities. In present study, we investigated microplastics in water and sediment samples collected from a textile industrial area in Shaoxing city, China. The abundance of microplastics varied from 2.1 to 71.0 items/L in surface water samples, and from 16.7 to 1323.3 items/kg (dw) in sediment samples. The polymer type was dominated by polyester both in water (95%) and sediment (79%) samples. The majority of the detected microplastics was predominantly colored fibers smaller than 1 mm in diameter. The high level of microplastic pollution detected in local freshwater and sediment environments was attributed to the production and trading activities of textile industries, for which severe regulations should be envisaged in the future to effectively reduce the local microplastic pollution.

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Metadata

Authors
Deng, H., Wei, R., Luo, W., Hu, L., Li, B., Di, Y., & Shi, H.
Venue
Environmental Pollution
Publisher
Elsevier BV
Published
2020-03
Volume
258
Pages
113658
ISSN
0269-7491 (ISSN-L)
OpenAlex
W2991215652
Cited by
356
References
5
FWCI
8.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Microplastic pollution in water and sediment in a textile industrial area. https://app.cochat.ai/papers/10.1016/j.envpol.2019.113658
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Deng, H., Wei, R., Luo, W., Hu, L., Li, B., Di, Y., & Shi, H. (2020). Microplastic pollution in water and sediment in a textile industrial area. Environmental Pollution. https://doi.org/10.1016/j.envpol.2019.113658

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.