Computing a psychological health risk assessment model for road traffic noise

Abstract

IntroductionPhysical air pollutants comprising noise, temperature, and humidity are among health risk factors that influence human well-being. No study has been carried out on how the three physical pollutants affect the psychological health. Therefore, the objectives of this research are to assess road traffic noise, temperature, and relative humidity in association with psychological health. Final objective is to propose a formula capable of computing psychological health index using the three above-mentioned risk factors. MethodsAt the chosen sample points throughout Baharan Town situated in the city of Sanandaj in Iran, data of four independent variables encompassing age of the residents, traffic noise, ambient temperature, and ambient relative humidity were collected from January to November 2021. Simultaneously, the 12-item General Health Questionnaires (GHQ-12) were distributed between residents of Baharan Town to figure out their psychological health index. Age and gender was already added to GHQ-12 as two questions. Finally, a model was formulated for general health by means of multiple linear regression analysis in the SPSS software. ResultsThe results reveal that age of the residents does not have a significant relationship with psychological health index. Hence, the final multiple regression model for psychological health index depends upon three predictor factors including traffic noise, ambient temperature, and ambient relative humidity with 0.521 as its coefficient of determination (R Square). After verifying the final model, it was witnessed that computed psychological health index data were in agreement with measured data. In the end, a conceptual model is proposed that implies if no observed noise levels are available, then a road traffic noise model and its output i.e. estimated LAeq values are applied as traffic noise (input) in the aforementioned health risk assessment model. ConclusionsThe three above-mentioned predictors explain 52.1% of variance of the dependent variable i.e. psychological health index. Namely, the model's error equals 0.479. Given the amount of error that this formula generates, it is recommended new predictors such as financial conditions of the residents, ambient particulate matter (PM2.5 and PM10), ambient chemical air pollutants (SO2, CO, NO2, etc.), and other sources of environmental noise pollution (industrial noise, community noise, aircraft noise, railway noise, and harbor noise) to be used to explain more amount of variance of the dependent variable i.e. psychological health index, and therefore less error to be produced.