Evidence map›Paper›PMID 36765331›Full record

ArticleInternational journal of health geographics2023

Cyclists' exposure to air pollution, noise, and greenery: a population-level spatial analysis approach.

Elias Willberg, Age Poom, Joose Helle, Tuuli Toivonen

Abstract read
In one paragraph

Article in International journal of health geographics, 2023. 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. Review
  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

4 authors.

Elias WillbergDigital Geography Lab, Faculty of Science, University of Helsinki, Helsinki, Finland. elias.willberg@helsinki.fi.
Age PoomDigital Geography Lab, Faculty of Science, University of Helsinki, Helsinki, Finland.
Joose HelleDigital Geography Lab, Faculty of Science, University of Helsinki, Helsinki, Finland.
Tuuli ToivonenDigital Geography Lab, Faculty of Science, University of Helsinki, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes' potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach's feasibility by analysing cyclists' air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.

Indexed as

Air PollutantsAir PollutionBicyclingCitiesEnvironmental ExposureHumansNoiseTravelTravel-Related IllnessAir PollutantsActive travelAir pollutionEnvironmental exposureGreeneryNoiseRoute choice

Identifiers

PMID36765331
PMCPMC9921333

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.