Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 189

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring

Open access
Rager, Julia E., Strynar, Mark J., Liang, Shuang, McMahen, Rebecca L., Richard, Ann M., Grulke, Christopher M., Wambaugh, John F., Isaacs, Kristin K., Judson, Richard, Williams, Antony J., Sobus, Jon R.·2016·Environment International

Abstract

There is a growing need in the field of exposure science for monitoring methods that rapidly screen environmental media for suspect contaminants. Measurement and analysis platforms, based on high resolution mass spectrometry (HRMS), now exist to meet this need. Here we describe results of a study that links HRMS data with exposure predictions from the U.S. EPA's ExpoCast™ program and in vitro bioassay data from the U.S. interagency Tox21 consortium. Vacuum dust samples were collected from 56 households across the U.S. as part of the American Healthy Homes Survey (AHHS). Sample extracts were analyzed using liquid chromatography time-of-flight mass spectrometry (LC-TOF/MS) with electrospray ionization. On average, approximately 2000 molecular features were identified per sample (based on accurate mass) in negative ion mode, and 3000 in positive ion mode. Exact mass, isotope distribution, and isotope spacing were used to match molecular features with a unique listing of chemical formulas extracted from EPA's Distributed Structure-Searchable Toxicity (DSSTox) database. A total of 978 DSSTox formulas were consistent with the dust LC-TOF/molecular feature data (match score≥90); these formulas mapped to 3228 possible chemicals in the database. Correct assignment of a unique chemical to a given formula required additional validation steps. Each suspect chemical was prioritized for follow-up confirmation using abundance and detection frequency results, along with exposure and bioactivity estimates from ExpoCast and Tox21, respectively. Chemicals with elevated exposure and/or toxicity potential were further examined using a mixture of 100 chemical standards. A total of 33 chemicals were confirmed present in the dust samples by formula and retention time match; nearly half of these do not appear to have been associated with house dust in the published literature. Chemical matches found in at least 10 of the 56 dust samples include Piperine, N,N-Diethyl-m-toluamide (DEET), Triclocarban, Diethyl phthalate (DEP), Propylparaben, Methylparaben, Tris(1,3-dichloro-2-propyl)phosphate (TDCPP), and Nicotine. This study demonstrates a novel suspect screening methodology to prioritize chemicals of interest for subsequent targeted analysis. The methods described here rely on strategic integration of available public resources and should be considered in future non-targeted and suspect screening assessments of environmental and biological media.

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.envint.2015.12.008

Cites

4 of 4 references verified0 citation errors · 0 not checkable

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

Cites

Show all 4 references

Cited by

See all 189

Metadata

Authors
Rager, J. E., Strynar, M. J., Liang, S., McMahen, R. L., Richard, A. M., Grulke, C. M., Wambaugh, J. F., Isaacs, K. K., Judson, R., Williams, A. J., & Sobus, J. R.
Venue
Environment International
Publisher
Elsevier BV
Published
2016-03
Volume
88
Pages
269-280
ISSN
0160-4120 (ISSN-L)
OpenAlex
W2286180017
Cited by
189
References
4
FWCI
11.89
Type
journal article
Language
en
Open access
hybrid

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: Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. https://app.cochat.ai/papers/10.1016/j.envint.2015.12.008
The paper
Rager, J. E., Strynar, M. J., Liang, S., McMahen, R. L., Richard, A. M., Grulke, C. M., Wambaugh, J. F., Isaacs, K. K., Judson, R., Williams, A. J., & Sobus, J. R. (2016). Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environment International. https://doi.org/10.1016/j.envint.2015.12.008

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.