Evidence map›Paper›PMID 41482899›Full record

ArticleNoise & health

A Cross-sectional Survey Using Structural Equation Model for Prediction of Annoyance and Psychological Health Issues Due to Noise Pollution: A Case Study of Chandigarh City.

Bhopinder Singh, Parveen Kumar Saini, Naveen Garg

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

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5 · Who and what money

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3 authors.

Bhopinder SinghDepartment of Mechanical Engineering, National Institute of Technology, Kurukshetra, Haryana, India.
Parveen Kumar SainiDepartment of Mechanical Engineering, National Institute of Technology, Kurukshetra, Haryana, India.
Naveen GargAcoustics and Vibration Standards, CSIR-National Physical Laboratory, New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNoise pollution, primarily caused by intense traffic activity, is a significant environmental stressor in cities such as Chandigarh, India. Prolonged exposure to environmental noise can trigger various psychological and physiological health disturbances. This study aims to identify the direct and indirect factors contributing to noise annoyance (NA) and associated health problems using Structural Equation Modeling.

methodsA socioacoustic survey was conducted at 25 locations across Chandigarh from January 2, 2024 to June 1, 2024. A total of 562 participants provided responses on demographic details, perceived noise levels, noise sensitivity, NA, and health complaints, including sleeping disorders, blood pressure (BP) issues, anxiety, and headache. SmartPLS software (Version 3.0, SmartPLS GmbH, Boenningstedt, Germany) was used to develop and validate the Partial Least Squares Structural Equation Modeling (PLS-SEM). Reliability, convergent validity, and discriminant validity were confirmed through measurement model assessment.

resultsThe PLS-SEM demonstrated that noise sources, noise sensitivity, and demographic characteristics significantly predicted NA. Noise sources emerged as the strongest predictor (β = 0.41, t = 7.05, P < 0.001), followed by noise sensitivity (β = 0.35, t = 6.15, P < 0.001) and demographics (β = 0.21, t = 6.75, P < 0.001). The structural model showed substantial predictive accuracy for NA with R2 = 0.78. NA further exhibited significant effects on health-related outcomes, including sleeping disorder (β = 0.50, t = 11.79, P < 0.001), BP (β = 0.77, t = 13.01, P < 0.001), anxiety (β = 0.62, t = 9.21, P < 0.001), and headache (β = 0.52, t = 8.29, P < 0.001). Overall, the model confirmed that NA acts as the dominant pathway linking acoustic and non-acoustic factors with perceived health disturbances.

conclusionThis study provides a comprehensive model explaining how environmental noise contributes to annoyance and psychological health problems. The findings can inform evidence-based urban planning and public health policies to mitigate the harmful effects of environmental noise.

Indexed as

Environmental ExposureNoise, TransportationAdolescentAdultAnxietyCitiesCross-Sectional StudiesFemaleHeadacheHumansIndiaLatent Class AnalysisMaleMiddle AgedNoiseSleep Wake Disordersannoyanceanxietyhypertensionnoisesurveys and questionnaires

Identifiers

PMID41482899
PMCPMC12818492

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LicenceCC BY-NC-ND
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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.