Evidence map›Paper›PMID 34280416›Full record

ArticleEnvironmental research2021

Assessment and mitigation of toddlers' personal exposure to black carbon before and during the COVID-19 pandemic: A case study in Singapore.

Phuong T M Tran, Max G Adam, Rajasekhar Balasubramanian

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 37% 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

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Review
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 at 1 institution in 2 countries.

Phuong T M TranDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore; Faculty of Environment, University of Science and Technology, The University of Danang, 54 Nguyen Luong Bang Street, Lien Chieu District, Danang City, Viet Nam.
Max G AdamDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore.
Rajasekhar BalasubramanianDepartment of Civil and Environmental Engineering, National University of Singapore, Singapore, 117576, Singapore. Electronic address: ceerbala@nus.edu.sg.
National University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Black carbon (BC), an important indicator of traffic-related air pollution (TRAP) in urban environments, is receiving increased attention because of its adverse health effects. Personal exposure (PE) of adults to BC has been widely studied, but little is known about the exposure of young children (toddlers) to BC in cities. We carried out a pilot study to investigate the integrated daily PE of toddlers to BC in a city-state with a high population density (Singapore). We studied the impact of urban traffic on the PE of toddlers to BC by comparing and contrasting on-road traffic flow (i.e., volume and composition) in Singapore in 2019 (before the COVID-19 pandemic) and in 2020 (during the COVID-19 pandemic). Our observations indicate that the daily BC exposure levels and inhaled doses increased by about 25% in 2020 (2.9 ± 0.3 μg m

Indexed as

Air PollutantsAir PollutionCOVID-19AdultCarbonChild, PreschoolCommunicable Disease ControlEnvironmental ExposureEnvironmental MonitoringHumansPandemicsParticulate MatterPilot ProjectsSARS-CoV-2SingaporeVehicle EmissionsAir PollutantsCarbonParticulate MatterVehicle EmissionsBlack carbonCOVID-19MitigationPersonal exposureTraffic

Identifiers

PMID34280416
PMCPMC9749899
OpenAlexW3185269537

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.