Evidence map›Paper›PMID 38903571›Full record

ArticleFrontiers in public health2024

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

Weijia Wang, Xin Guan, Xiaoyan Peng, Zeyu Wang, Xinyi Liang, Junfan Zhu

Abstract read
In one paragraph

Article in Frontiers in public health, 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. 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

6 authors.

Weijia Wang *School of Information Technology, Deakin University, Geelong, VIC, Australia.
Xin Guan *Guangzhou Xinhua University, Dongguan, China.
Xiaoyan PengSchool of Government, Sun Yat-sen University, Guangzhou, China.
Zeyu Wang *School of Public Administration, Guangzhou University, Guangzhou, China.
Xinyi LiangSchool of Public Administration, Guangzhou University, Guangzhou, China.
Junfan Zhu *Guangdong Finance and Trade Vocational College, Qingyuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Methods: Three cities were selected for the study: Beijing ( Results: (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities Discussion: These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Indexed as

CitiesEnvironmental MonitoringFuzzy LogicAir PollutionChinaHumansModels, TheoreticalParticulate MatterRisk AssessmentParticulate Mattercorrelation analysiscredibility analysisfuzzy intelligent computing modelhealth riskmulti-media environmental monitoring

Identifiers

PMID38903571
PMCPMC11188348

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.