Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 251

The DOI matches its Crossref record.

PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles

Open access
Beddows, David C.S., Harrison, Roy M.·2021·Atmospheric Environment

Abstract

Abstract Governments around the world are legislating to end the sale of conventionally fuelled (gasoline and diesel) internal combustion engine vehicles (ICEV) and it is assumed that battery-electric vehicles (BEV) will take their place. It has been suggested that due to their increased weight, non-exhaust emissions of particles from BEV may exceed all particle emissions, including exhaust, from an ICEV. This paper examines the vehicle weight-dependence of PM10 and PM2.5 emissions from abrasion (brake, tyre and road surface wear) and road dust resuspension and generates a comparison of the two vehicle types. The outcome is critically dependent upon the extent of regenerative braking relative to use of friction brakes on the BEV, but overall there will be only modest changes to the total local emissions of particles from a passenger car built to current emissions standards.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.atmosenv.2020.117886

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 251

Metadata

Authors
Beddows, D. C. S., & Harrison, R. M.
Venue
Atmospheric Environment
Publisher
Elsevier BV
Published
2021-01
Volume
244
Pages
117886
ISSN
1352-2310 (ISSN-L)
OpenAlex
W3080219596
Cited by
251
References
0
FWCI
9.73
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles. https://app.cochat.ai/papers/10.1016/j.atmosenv.2020.117886
The paper
Beddows, D. C. S., & Harrison, R. M. (2021). PM10 and PM2.5 emission factors for non-exhaust particles from road vehicles: Dependence upon vehicle mass and implications for battery electric vehicles. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2020.117886

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.