Evidence map›Paper›PMID 40594555›Full record

ArticleScientific reports2025

Deep learning framework for hourly air pollutants forecasting using encoding cyclical features across multiple monitoring sites in Beijing.

Abdel Salam Alsabagh, Omer A Alawi, Haslinda Mohamed Kamar, Ahmed Adil Nafea, Mohammed M Al-Ani, Hussein A Mohammed, S N Kazi, Atheer Y Oudah, Zaher Mundher Yaseen

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Trimester-specific exposure to PMBMC pregnancy and childbirth · 2026
    Article
  2. 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

9 authors.

Abdel Salam AlsabaghDepartment of Mechanical Engineering, Faculty of Engineering Technology, Al-Balqa Applied University, Amman, Jordan.
Omer A AlawiDepartment of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia.
Haslinda Mohamed KamarDepartment of Thermofluids, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor Bahru, Malaysia.
Ahmed Adil NafeaDepartment of Artificial Intelligence, College of Computer Science and IT, University of Anbar, Ramadi, Iraq.
Mohammed M Al-AniCenter for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
Hussein A MohammedMechanical Engineering Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, 31261, Saudi Arabia.
S N KaziDepartment of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
Atheer Y OudahDepartment of Computer Sciences, College of Education for Pure Science, University of Thi-Qar, Nasiriyah, 64001, Iraq.
Zaher Mundher YaseenCivil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia. z.yaseen@kfupm.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Environmental managers and citizens alike are concerned with air quality. Early warning systems for air pollution are essential to prevent health issues and implement effective prevention strategies. This paper proposes a comprehensive, reliable system with air quality prediction and assessment modules for China's air pollution. In this study, six air pollutants were observed, including Carbon Monoxide (CO), Nitrogen Dioxide (NO

Indexed as

Air pollution monitoringAir quality forecastingDeep learningEarly warning systemsEnvironmental assessment

Identifiers

PMID40594555
PMCPMC12216515

What OpenQuestion holds

Textmetadata
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.