Evidence map›Paper›PMID 41260014›Full record

ArticleEnvironment international2025

Knowledge-informed deep learning to mitigate bias in joint air pollutant prediction.

Lianfa Li, Roxana Khalili, Frederick Lurmann, Nathan Pavlovic, Jun Wu, Yan Xu, Yisi Liu, Karl O'Sharkey, Beate Ritz, Luke Oman and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environment international, 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. 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

15 authors.

Lianfa LiState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources, Chinese Academy of Sciences, No. A11, Rd. Datun, Beijing, 100101, Beijing, China; Department of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA. Electronic address: lilf@lreis.ac.cn.
Roxana KhaliliDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Frederick LurmannSonoma Technology, Inc., 1450 N McDowell Blvd #200, Petaluma, 94954, CA, USA.
Nathan PavlovicSonoma Technology, Inc., 1450 N McDowell Blvd #200, Petaluma, 94954, CA, USA.
Jun WuProgram in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, 92697, CA, USA.
Yan XuDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Yisi LiuDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Karl O'SharkeyDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Beate RitzDepartments of Epidemiology and Environmental Health, Fielding School of Public Health, University of California, Los Angeles, 90095, CA, USA.
Luke OmanGoddard Space Flight Center, National Aeronautics and Space Administration, Greenbelt, 20771, MD, USA.
Meredith FranklinDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA; Department of Statistical Sciences, University of Toronto, Toronto, ON M5S 3G3, Canada.
Theresa BastainDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Shohreh F FarzanDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Carrie BretonDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA.
Rima HabreDepartment of Population and Public Health Sciences, University of Southern California, 1845 N Soto St, Los Angeles, 90032, CA, USA; Spatial Sciences Institute, University of Southern California, Los Angeles, 90089, CA, USA.

Funding

Translational Research Support CoreP30ES007048 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI ROB S MCCONNELL · 1996 to 2026
$46.4M
Lifecourse Approach to Developmental Repercussions of Environmental Agents on Metabolic and Respiratory Health (LA DREAMERs)UH3OD023287 · OD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BASTAIN, THERESA M, BRETON, CARRIE VAN DOREN · 2018 to 2022
$28.4M
NIEHS NIH HHS P30 ES007048NIH HHS UH3 OD023287
6 · The paper itself

Abstract

Accurate prediction of atmospheric air pollutants is critical for public health protection and environmental management. Traditional machine learning (ML) methods achieve high spatial resolution but lack physicochemical constraints, leading to systematic biases that compromise exposure estimates for epidemiological studies. Chemical transport models incorporate atmospheric physics but require expensive parameterization and often fail to capture local-scale variability crucial for health impact assessment. This gap between data-driven accuracy and physical realism presents a major obstacle to advancing air quality science. We address this challenge through a novel physics-informed deep learning framework that integrates advection-diffusion equations and fluid dynamics constraints directly into neural network architectures for multi-pollutant prediction. Our approach models air pollutant pairs across geographically distinct domains (NO

Indexed as

Air PollutantsAir PollutionDeep LearningEnvironmental MonitoringCaliforniaChinaNitrogen OxidesParticulate MatterAir PollutantsNitrogen OxidesParticulate MatterAir pollutionBias mitigationDeep learningJoint predictionKnowledge fusionPhysics-informed modeling

Identifiers

PMID41260014
PMCPMC12810719

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.