Evidence map›Paper›PMID 32747729›Full record

ArticleJournal of exposure science & environmental epidemiology2021

Predicting ambient PM

Temuulen Enebish, Khang Chau, Batbayar Jadamba, Meredith Franklin

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 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
–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

1 citing paper in PubMed.

  1. 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.

Temuulen EnebishDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA, 90032, United States. enebish@usc.edu.ORCID 0000-0002-5038-9144
Khang ChauDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA, 90032, United States.
Batbayar JadambaDepartment of Environmental Monitoring, National Agency for Meteorology and Environmental Monitoring, Ulaanbaatar, Mongolia.
Meredith FranklinDepartment of Preventive Medicine, University of Southern California, Los Angeles, CA, 90032, United States.

Funding

Environmental and respiratory health across the lifespan in MongoliaD43ES022862 · NIEHS · CHILDREN'S HOSPITAL OF LOS ANGELES · PI WARBURTON, DAVID · 2013 to 2017
$1.2M
NIEHS NIH HHS D43 ES022862
6 · The paper itself

Abstract

backgroundAccurately assessing individual ambient air pollution exposure is a crucial part of epidemiological studies looking at the adverse health effect of poor air quality. This is particularly challenging in developing countries with high levels of air pollution, mostly due to sparse monitoring networks with a lack of consistent data.

methodsWe evaluated the performance of six different machine learning algorithms in predicting fine particulate matter (PM

resultsRandom forest (RF) and gradient boosting models performed the best with leave-one-location-out cross-validated R

conclusionOur results provide evidence of the advantage and feasibility of machine learning approaches in predicting ambient PM

Indexed as

Air PollutantsAir PollutionCitiesEnvironmental MonitoringHumansMachine LearningMongoliaParticulate MatterAir PollutantsParticulate MatterAir PollutionEnvironmental MonitoringExposure ModelingParticulate Matter

Identifiers

PMID32747729
PMCPMC9871862

What OpenQuestion holds

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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.