Evidence map›Paper›PMID 41144350›Full record

ArticlePloS one2025

A HAZOP-based hazard identification model for urban gas accidents: Development and empirical validation.

Bin Tian, Haibing Li, Xiaojun Cui, Zhihong Hu, Zibo Zhou, Wei Shi

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Bin TianCollege of Modern Science and Technology, China Jiliang University, Yiwu, China.ORCID https://orcid.org/0009-0001-3391-9158
Haibing LiCollege of Modern Science and Technology, China Jiliang University, Yiwu, China.
Xiaojun CuiCollege of Modern Science and Technology, China Jiliang University, Yiwu, China.
Zhihong HuCollege of Modern Science and Technology, China Jiliang University, Yiwu, China.
Zibo ZhouYiwu Natural Gas Co., Ltd, Yiwu, China.
Wei ShiYiwu Natural Gas Co., Ltd, Yiwu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban gas accidents pose significant threats to public safety and urban infrastructure, with traditional hazard identification methods often relying on manual inspections and experience-based judgments, leading to incomplete or inconsistent results. To address these issues, this study proposes a structured hazard identification model based on Hazard and Operability Analysis (HAZOP) deviation theory for urban gas systems. By integrating four key dimensions-human, machine, environment, and management-a comprehensive framework was developed to define system nodes, select relevant parameters, and apply guide words to identify potential hazards in a standardized manner. This approach allows for dynamic adjustment of influencing factors according to different application scenarios. The model was validated through a case study involving a restaurant, where it identified 65 potential gas-related hazards, over eight times more than traditional inspection approaches. Results demonstrate significant improvements in comprehensiveness, accuracy, and efficiency of hazard identification. This model provides a practical tool for urban gas safety management, supporting standardized hazard screening across various sectors including restaurants, residential buildings, schools, and commercial complexes. Furthermore, it lays the foundation for future integration of quantitative risk assessment and artificial intelligence-driven risk analysis, contributing to the digitalization and standardization of urban gas safety governance.

Indexed as

AccidentsGasesModels, TheoreticalSafety ManagementCitiesHumansRisk AssessmentGases

Identifiers

PMID41144350
PMCPMC12558524

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.