Evidence map›Paper›PMID 39565242›Full record

ArticleEnvironmental science & technology2024

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO

Jia Xing, Bok H Baek, Siwei Li, Chi-Tsan Wang, Ge Song, Siqi Ma, Shuxin Zheng, Chang Liu, Daniel Tong, Jung-Hun Woo and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental science & technology, 2024. 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. Advances in Emissions, Modeling, and Source Apportionment.Environmental science & technology · 2026
    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

12 authors.

Jia XingCenter for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.ORCID 0000-0002-3716-8646
Bok H BaekCenter for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.ORCID 0000-0003-1054-6325
Siwei LiHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China.
Chi-Tsan WangCenter for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.
Ge SongHubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430000, China.
Siqi MaCenter for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.
Shuxin ZhengMicrosoft Research AI for Science, Beijing 100080, China.
Chang LiuMicrosoft Research AI for Science, Beijing 100080, China.
Daniel TongCenter for Spatial Information Science and Systems, George Mason University, Fairfax, Virginia 22030, United States.
Jung-Hun WooGraduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea.
Tie-Yan LiuMicrosoft Research AI for Science, Beijing 100080, China.
Joshua S FuDepartment of Civil and Environmental Engineering, The University of Tennessee, Knoxville, Tennessee 37996, United States.ORCID 0000-0001-5464-9225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO

Indexed as

Air PollutantsAir PollutionDeep LearningEnvironmental MonitoringNitrogen DioxideAir PollutantsNitrogen Dioxidedeep learningmodel-measurement fusionNO2physically constrainedTROPOMI satellite

Identifiers

PMID39565242
PMCPMC11618989

What OpenQuestion holds

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LicenceCC BY
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.