Evidence map›Paper›PMID 40295616›Full record

ArticleScientific reports2025

Machine learning-based quantification and separation of emissions and meteorological effects on PM

Nishit Aman, Sirima Panyametheekul, Ittipol Pawarmart, Di Xian, Ling Gao, Lin Tian, Kasemsan Manomaiphiboon, Yangjun Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

8 authors.

Nishit AmanDepartment of Environmental and Sustainable Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand.
Sirima PanyametheekulDepartment of Environmental and Sustainable Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand. sirima.p@chula.ac.th.
Ittipol PawarmartPollution Control Department, Ministry of Natural Resources and Environment, Bangkok, Thailand.
Di XianNational Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, China.
Ling GaoNational Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, China.
Lin TianNational Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing, China.
Kasemsan ManomaiphiboonThe Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Yangjun WangSchool of Environmental and Chemical Engineering, Shanghai University, Shanghai, China.

Funding

Thai Health Promotion Foundation 68-E1-0083
6 · The paper itself

Abstract

This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM

Indexed as

Air PollutantsAir PollutionEnvironmental MonitoringMachine LearningParticulate MatterHumansMeteorological ConceptsMeteorologySeasonsThailandWeatherAir PollutantsParticulate MatterExplainable machine learningHimawari-8Hurst exponentMeteorological normalizationPM2.5 mappingSHAP

Identifiers

PMID40295616
PMCPMC12038008

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.