The present cross-sectional study aimed to evaluate the risk factors for traffic noise–induced annoyance and also assess the awareness levels among the exposed population concerning the health impacts caused by traffic noise. Field measurements were made to validate the application of the standard noise models, which were later used to present the acoustical environment and assess the exposure level around a super-speciality hospital surrounded by a residential zone. Results from the noise maps and façade maps revealed that the area was exposed to noise levels exceeding the upper safe limits by more than 10 dB(A). The effect of exposure in the form of annoyance and the awareness level were evaluated using a questionnaire survey in a sample of 565 residents. Attention questions were incorporated in the questionnaire, and the awareness level was evaluated using the mean awareness index score. Respondents living in noisy areas were having a higher risk for annoyance as compared to those living in quiet areas (OR = 4.06; 95% CI = 2.79–5.88). Reporting poor sleep quality, being sensitive to noise, and noise perception at home were the significant risk factors for annoyance. Most of the respondents were classified as having no/little awareness about serious health ailments caused by traffic noise. Lower awareness levels, despite a higher literacy rate and a higher percentage of the young population, imply that there is a need for undertaking mass awareness programmes so that the impacts can be reduced to a minimum, if not eliminated.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11356-021-15208-3.
A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study’s main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (L
eq,1h
), maximum noise level (L
10,1h
), and background noise level (L
90,1h
). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for L
eq,1h
, L
90,1h
, and L
10,1h
respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.
Epidemiological studies have established that noise from transportation sources exceeding the safe limits elevates the risk for cardiovascular diseases. The results however have remained heterogeneous. The present study was conducted to investigate the association between road traffic noise exposure and prevalence of coronary artery disease besides sub-group analysis was performed for identifying the most susceptible population. Traffic noise exposure was measured using the L den metric in both continuous and categorical forms. A cross-sectional study was performed and information about sociodemographic, lifestyle, and health-related factors was collected. Noise level < 60 dB(A) representing the quiet areas was used as the reference group. Univariate and multivariate logistic regressions were performed to estimate the odds for self-reported coronary artery disease concerning road traffic noise after adjusting for confounding variables. The residents living in noisy areas were found to have a 2.25 times higher risk per 5 dB(A) increment in the noise levels (95% CI = 1.38 to 3.67). Males were at a higher risk of CAD (OR = 2.61; 95% CI = 1.84 to 3.72) as compared to females (OR = 2.07; 95% CI = 1.37-3.13). The subgroup analysis revealed that being sensitive to noise, belonging to a higher age group, reporting higher stress levels, and poor sleep quality were associated with higher risk. The study also provides evidence that exposure to noise levels greater than 60 dB(A) is associated with the prevalence of coronary artery disease in adults.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.