Evidence map›Paper›PMID 40967497›Full record

ArticleEnvironmental research2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke.

Honghyok Kim, Chris Chaeha Lim

Abstract read
In one paragraph

Article in Environmental research, 2025. 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

2 authors.

Honghyok KimDivision of Environmental and Occupational Health Sciences, School of Public Health, University of Illinois Chicago, Chicago, IL, 60661, USA. Electronic address: honghyok@uic.edu.
Chris Chaeha LimDepartment of Community, Environment and Policy, Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85724, USA.

Funding

SOUTHWEST ENVIRONMENTAL HEALTH SCIENCES CENTERP30ES006694 · NIEHS · UNIVERSITY OF ARIZONA · PI George S Watts · 1994 to 2026
$36.7M
Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu, Gail S Prins · 2017 to 2026
$13.6M
Research Project: Evaluating Green Infrastructure Impacts on Climate Change Related Heat, Air Quality, Flooding, and Cardiopulmonary Outcomes in ChicagoP20MD019989 · NIMHD · UNIVERSITY OF ILLINOIS AT CHICAGO · PI MALECKI, KRISTEN MARY CHOSSEK · 2024 to 2024
$3.9M
Southwest Center on Resilience for Climate Change and Health (SCORCH)P20ES036112 · NIEHS · UNIVERSITY OF ARIZONA · PI ARORA, MONA, ERNST, KACEY C · 2023 to 2023
$3.8M
Short-term environmental exposures and emergency medical service activation in the U.SR01ES034740 · NIEHS · UNIVERSITY OF ARIZONA · PI Chris Chaeha Lim · 2024 to 2026
$1.2M
NIEHS NIH HHS P20 ES036112NIEHS NIH HHS P30 ES006694NIEHS NIH HHS P30 ES027792NIEHS NIH HHS R01 ES034740NIMHD NIH HHS P20 MD019989
6 · The paper itself

Abstract

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM

Indexed as

Air PollutantsAir PollutionEnvironmental ExposureEnvironmental MonitoringModels, TheoreticalSmokeWildfiresCitizen ScienceHumansIllinoisMachine LearningOzoneParticulate MatterAir PollutantsOzoneParticulate MatterSmokeExposure assessmentExposure modelingMachine learningPreferential samplingUncertaintyValidation

Identifiers

PMID40967497
PMCPMC12599859

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.