Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 445

The DOI matches its Crossref record.

Emissions from Nine Heavy Trucks Fueled by Diesel and Biodiesel Blend without Engine Modification

Wang, W. G., Lyons, D. W., Clark, N. N., Gautam, M., Norton, P. M.·2000·Environmental Science & Technology

Abstract

Biodiesel, a fuel that can be made from renewable biological sources such as vegetable oils or animal fats, has been recognized recently as an environment friendly alternative fuel for diesel engines. In this paper, we describe a study that compared exhaust emissions from in-use heavy trucks fueled with a biodiesel blend with those from trucks fueled with petroleum diesel. The biodiesel blend tested is a mixture of 35% biodiesel and 65% petroleum diesel, a blend designated as B35. The study is based on the field test results from West Virginia University's Transportable Heavy Duty Chassis Dynamometer Emissions Testing Laboratory and sponsored by the U.S. Department of Energy. The heavy trucks we tested performed well when the originally equipped compression-ignition engine (diesel engine) was fueled with B35 without any engine modifications. Fuel economy (in terms of gallon per mile) of the two fuels was about the same. The emissions test results have shown that the heavy trucks fueled by B35 emitted significantly lower particulate matter (PM) and moderately lower carbon monoxide (CO) and hydrocarbon (HC) than the same trucks fueled by no. 2 diesel (D2). Oxides of nitrogen (NO x ) emissions from B35 and D2, however, were generally in the same level. Emissions variations from two different engine models and two driving cycles were also observed. Although we recommend more tests for biodiesel vehicles, the data obtained in this study indicate that biodiesel has promise as an emissions-reducing alternative fuel for diesel engines.

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 · 10.1021/es981329b

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

Metadata

Authors
Wang, W. G., Lyons, D. W., Clark, N. N., Gautam, M., & Norton, P. M.
Venue
Environmental Science & Technology
Published
2000-02-09
Volume
34
Issue
6
Pages
933-939
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W1965375621
Cited by
445
References
0
FWCI
9.09
Type
journal article
Language
en
Open access
closed

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

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.