Evidence map›Paper›PMID 35007580›Full record

ReviewThe Science of the total environment2022

Environmental risks of polymer materials from disposable face masks linked to the COVID-19 pandemic.

Hao Du, Shushi Huang, Jun Wang

Abstract readReview
In one paragraph

Review in The Science of the total environment, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hao DuCollege of Marine Sciences, South China Agricultural University, Guangzhou 510641, China.
Shushi HuangInstitute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning 530007, China.
Jun WangCollege of Marine Sciences, South China Agricultural University, Guangzhou 510641, China; Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 528478, China; Institute of Eco-Environmental Research, Guangxi Academy of Sciences, Nanning 530007, China. Electronic address: wangjun2016@scau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The indispensable role of plastic products in our daily life is highlighted by the COVID-19 pandemic again. Disposable face masks, made of polymer materials, as effective and cheap personal protective equipment (PPE), have been extensively used by the public to slow down the viral transmission. The repercussions of this have generated million tons of plastic waste being littered into the environment because of the improper disposal and mismanagement amid. And plastic waste can release microplastics (MPs) with the help of physical, chemical and biological processes, which is placing a huge MPs contamination burden on the ecosystem. In this work, the knowledge regarding to the combined effects of MPs and pollutants from the release of face masks and the impacts of wasted face masks and MPs on the environment (terrestrial and aquatic ecosystem) was systematically discussed. In view of these, some green technologies were put forward to reduce the amounts of discarded face masks in the environment, therefore minimizing MPs pollution at its source. Moreover, some recommendations for future research directions were proposed based on the remaining knowledge gaps. In a word, MPs pollution linked to face masks should be a focus worldwide.

Indexed as

COVID-19PlasticsEcosystemHumansMasksPandemicsPolymersSARS-CoV-2PlasticsPolymersCOVID-19Face mask wasteHealth risksMicroplasticsPlastic pollution

Identifiers

PMID35007580
PMCPMC8741336

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.