Evidence map›Paper›PMID 38589565›Full record

ArticleJournal of exposure science & environmental epidemiology2025

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Christopher Zuidema, Jianzhao Bi, Dustin Burnham, Nancy Carmona, Amanda J Gassett, David L Slager, Cooper Schumacher, Elena Austin, Edmund Seto, Adam A Szpiro and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2025. 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

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

11 authors.

Christopher ZuidemaDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-2233-2706
Jianzhao BiDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-3807-6927
Dustin BurnhamDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0009-0006-9028-1150
Nancy CarmonaDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-2235-5043
Amanda J GassettDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-0889-7460
David L SlagerDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-2525-2039
Cooper SchumacherDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-8061-4461
Elena AustinDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0002-4724-1042
Edmund SetoDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-4058-0313
Adam A SzpiroDepartment of Biostatistics, University of Washington, Seattle, WA, USA.ORCID http://orcid.org/0000-0003-4995-238X
Lianne SheppardDepartment of Occupational and Environmental Health Sciences, University of Washington, Seattle, WA, USA. sheppard@uw.edu.ORCID http://orcid.org/0000-0003-1622-508X

Funding

Statistical Methods to Account for Exposure Uncertainty in Environmental EpidemiologyR01ES026246 · NIEHS · YALE UNIVERSITY · PI SPIEGELMAN, DONNA L · 2018 to 2022
$3.1M
NIEHS NIH HHS R01 ES026246
6 · The paper itself

Abstract

backgroundStatistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

objectiveDevelop and evaluate a spatiotemporal model for nitrogen dioxide (NO

methodsWe developed a spatiotemporal NO

resultsThe best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureEnvironmental MonitoringNitrogen DioxideHumansModels, StatisticalWashingtonAir PollutantsNitrogen DioxideAir pollutionEnvironmental epidemiologyGaseous pollutantsLow-cost sensorsNO2Spatiotemporal model

Identifiers

PMID38589565

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.