Evidence map›Paper›PMID 30699896›Full record

ArticleInternational journal of environmental research and public health2019

Understanding Potential Exposure of Bicyclists on Roadways to Traffic-Related Air Pollution: Findings from El Paso, Texas, Using Strava Metro Data.

Kyuhyun Lee, Ipek N Sener

Open access · goldAbstract read
In one paragraph

Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.9field-weighted citation impact, top 10% of its field
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

8 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Short-Term Impact of Traffic-Related Particulate Matter and Noise Exposure on Cardiac Function.International journal of environmental research and public health · 2020
    Article
  8. 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

2 authors at 1 institution in 1 country.

Kyuhyun LeeTexas A&M Transportation Institute, College Station, TX 77843, USA. k-lee@tti.tamu.edu.ORCID 0000-0003-2314-8326
Ipek N SenerTexas A&M Transportation Institute, Austin, TX 78752, USA. i-sener@tti.tamu.edu.ORCID 0000-0001-5493-8756
Texas Department of Transportation · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As bicycling on roadways can cause adverse health effects, there is an urgent need to understand how bicycle routes expose bicyclists to traffic emissions. Limited resources for monitoring reveal that bicycle travel patterns may constrain such understanding at the network level. This study examined the potential exposure of bicyclists to traffic-related air pollution in El Paso, Texas, using Strava Metro data that revealed bicycle patterns across the city networks. An initial spatial mapping analysis was conducted to explore the spatial patterns of bicycling and traffic pollutant emission, followed by exploratory descriptive statistics. A spatial bicycle model was then developed to explore factors influencing bicycling activity in El Paso. Analysis results indicated significant associations between greater bicycle volume and both higher levels of particulate matter (PM

Indexed as

Fitness TrackersAdultAgedBicyclingEnvironmental ExposureFemaleHumansMaleMiddle AgedParticulate MatterTexasTraffic-Related PollutionVehicle EmissionsYoung AdultParticulate MatterVehicle Emissionsbicyclecrowdsourced datafitness tracking apphealth effectsPM2.5public healthspatial effectsStravatraffic emission

Identifiers

PMID30699896
PMCPMC6388168
OpenAlexW2914627459

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.