Evidence map›Paper›PMID 36785709›Full record

ArticlePeerJ2023

Particulate matter resuspension from simulated urban green floors using a wind tunnel-mounted closed chamber.

Inhye Seo, Chan Ryul Park, Gayoung Yoo

Abstract read
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Inhye SeoDepartment of Applied Environmental Science, Kyung Hee University, Yongin, Republic of Korea.
Chan Ryul ParkUrban Forests Division, National Institute of Forest Science, Seoul, Republic of Korea.
Gayoung YooDepartment of Applied Environmental Science, Kyung Hee University, Yongin, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Green areas are thought to reduce particulate matter (PM) concentrations in urban environments. Plants are the key to PM reduction via various mechanisms, although most mechanisms do not lead to the complete removal of PM. Ultimately, PM falls into the soil via wind and rainfall. However, the fallen PM can re-entrain the atmosphere, which can affect plants capacity to reduce PM. In this study, we simulated an urban green floor and measured the resuspension of PM from the surface using a new experimental system, a wind tunnel-mounted closed chamber. Methods: The developed system is capable of quantifying the resuspension rate at the millimeter scale, which is measured by using the 1 mm node chain. This is adequate for simulating in situ green floors, including fallen branches and leaves. This addressed limitations from previous studies which focused on micrometer-scale surfaces. In this study, the surfaces consisted of three types: bare sand soil, broadleaves, and coniferous leaves. The resuspended PM was measured using a light-scattering dust detector. Results: The resuspension rate was highest of 14.45×10 Conclusion: Our findings suggest that the management of fallen leaves on the urban green floor is important in controlling PM concentrations and that the coniferous floor is more effective than the broadleaved floor in reducing PM resuspension. Future studies using the new system can be expanded to derive PM management strategies by diversifying the PM types, surfaces, and atmospheric conditions.

Indexed as

Air PollutantsTracheophytaClimateDustParticulate MatterPlantsAir PollutantsDustParticulate MatterFloor structureManagementParticulate matterResuspensionUrban ecosystem serviceUrban green floorUrban litter layer

Identifiers

PMID36785709
PMCPMC9921991

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Registered trials

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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.