Evidence map›Paper›PMID 39493282›Full record

ArticleMicroplastics and nanoplastics2024

All black: a microplastic extraction combined with colour-based analysis allows identification and characterisation of tire wear particles (TWP) in soils.

Alexandra Foetisch, Adrian Grunder, Benjamin Kuster, Tobias Stalder, Moritz Bigalke

Abstract read
In one paragraph

Article in Microplastics and nanoplastics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

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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

3 citing papers in PubMed.

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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

5 authors.

Alexandra FoetischInstitute of Geography, University of Bern, Hallerstraβe 12, Bern, 3012 Switzerland.
Adrian GrunderInstitute of Geography, University of Bern, Hallerstraβe 12, Bern, 3012 Switzerland.
Benjamin KusterInstitute of Geography, University of Bern, Hallerstraβe 12, Bern, 3012 Switzerland.
Tobias StalderInstitute of Geography, University of Bern, Hallerstraβe 12, Bern, 3012 Switzerland.
Moritz BigalkeInstitute of Geography, University of Bern, Hallerstraβe 12, Bern, 3012 Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

While tire wear particles (TWP) have been estimated to represent more than 90% of the total microplastic (MP) emitted in European countries and may have environmental health effects, only few data about TWP concentrations and characteristics are available today. The lack of data stems from the fact that no standardized, cost efficient or accessible extraction and identification method is available yet. We present a method allowing the extraction of TWP from soil, performing analysis with a conventional optical microscope and a machine learning approach to identify TWP in soil based on their colour. The lowest size of TWP which could be measured reliably with an acceptable recovery using our experimental set-up was 35 µm. Further improvements would be possible given more advanced technical infrastructure (higher optical magnification and image quality). Our method showed a mean recovery of 85% in the 35-2000 µm particle size range and no blank contamination. We tested for possible interference from charcoal (as another black soil component with similar properties) in the soils and found a reduction of the interference from charcoal by 92% during extraction. We applied our method to a highway adjacent soil at 1 m, 2 m, 5 m, and 10 m and detected TWP in all samples with a tendency to higher concentrations at 1 m and 2 m from the road compared to 10 m from the road. The observed TWP concentrations were in the same order of magnitude as what was previously reported in literature in highway adjacent soils. These results demonstrate the potential of the method to provide quantitative data on the occurrence and characteristics of TWP in the environment. The method can be easily implemented in many labs, and help to address our knowledge gap regarding TWP concentrations in soils. Supplementary Information: The online version contains supplementary material available at 10.1186/s43591-024-00102-9.

Indexed as

Machine learningMicroplasticOptical microscopySegmentationSoil pollutionTire wear

Identifiers

PMID39493282
PMCPMC11525289

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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.