Evidence map›Paper›PMID 27005742›Full record

ArticleJournal of exposure science & environmental epidemiology2017

Use of mobile and passive badge air monitoring data for NO

Wei Xu, Erin A Riley, Elena Austin, Miyoko Sasakura, Lanae Schaal, Timothy R Gould, Kris Hartin, Christopher D Simpson, Paul D Sampson, Michael G Yost and 3 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2017. 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

13 authors.

Wei XuDepartment of Environmental Engineering, East China University of Science and Technology, Shanghai, China.
Erin A RileyDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Elena AustinDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Miyoko SasakuraDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Lanae SchaalDepartment of Statistics, University of Washington, Seattle, Washington, USA.
Timothy R GouldDepartment of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA.
Kris HartinDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Christopher D SimpsonDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Paul D SampsonDepartment of Statistics, University of Washington, Seattle, Washington, USA.
Michael G YostDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Timothy V LarsonDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.
Guangli XiuDepartment of Environmental Engineering, East China University of Science and Technology, Shanghai, China.
Sverre VedalDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA.

Funding

NIOSH CDC HHS U54 OH007544
6 · The paper itself

Abstract

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO

Indexed as

Air PollutantsAir PollutionAutomobilesBaltimoreEnvironmental MonitoringGeographyHumansMaps as TopicNitrogen OxidesOzoneRegression AnalysisSeasonsVehicle EmissionsAir PollutantsNitrogen OxidesOzoneVehicle Emissions

Identifiers

PMID27005742
PMCPMC9810542

What OpenQuestion holds

Textmetadata
LicenceTDM
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.