Association between exposure to road traffic noise and hearing impairment: a case-control study

Impact of occupational noise exposure on the hearing level in hospital staffs: a longitudinal study

The study aimed to evaluate the impact of occupational noise on hearing loss among healthcare workers using audiometry. A longitudinal study was conducted with a six-month follow-up period in a hospital with 21 participants, divided into high-noise-exposure (HNE) and low-noise-exposure (LNE) groups. Mean noise levels were higher in the HNE group (70.4 ± 4.5 dBA), and hearing loss was measured using pure-tone audiometry at baseline and follow-up. The HNE group had significantly higher mean threshold levels at frequencies of 0.25 kHz, 0.5 kHz, 4.0 kHz, and an average of 0.5, 1, 2, and 4 kHz (all p-values < 0.05) after the follow-up period. After adjusting for confounding factors, the HNE group had significantly higher hearing loss levels at 0.25 kHz, 0.5 kHz, and average frequencies of 0.5, 1, 2, and 4 kHz compared to the LNE group at the second measurement. Occupational noise levels above 65 dBA over six months were found to cause significant threshold changes at frequencies of 0.25 kHz, 0.5 kHz, and an average of 0.5-4.0 kHz. This study highlights the risk of noise-induced hearing loss among healthcare workers and emphasizes the importance of implementing effective hearing conservation programs in the workplace. Regular monitoring and assessment of noise levels and hearing ability, along with proper use of personal protective equipment, are crucial steps in mitigating the impact of occupational noise exposure on the hearing health of healthcare workers.

Noise and mental health: evidence, mechanisms, and consequences

The recognition of noise exposure as a prominent environmental determinant of public health has grown substantially. While recent years have yielded a wealth of evidence linking environmental noise exposure primarily to cardiovascular ailments, our understanding of the detrimental effects of noise on the brain and mental health outcomes remains limited. Despite being a nascent research area, an increasing body of compelling research and conclusive findings confirms that exposure to noise, particularly from sources such as traffic, can potentially impact the central nervous system. These harms of noise increase the susceptibility to mental health conditions such as depression, anxiety, suicide, and behavioral problems in children and adolescents. From a mechanistic perspective, several investigations propose direct adverse phenotypic changes in brain tissue by noise (e.g. neuroinflammation, cerebral oxidative stress), in addition to feedback signaling by remote organ damage, dysregulated immune cells, and impaired circadian rhythms, which may collectively contribute to noise-dependent impairment of mental health. This concise review linking noise exposure to mental health outcomes seeks to fill research gaps by assessing current findings from studies involving both humans and animals.

Residential proximity to major roadways and hearing impairment in Chinese older adults: a population-based study

BACKGROUND

With rapid urban sprawl, growing people are living in the vicinity of major roadways. However, little is known about the relationship between residential proximity to major roadways and hearing impairment (HI).

METHODS

We derived data from the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey, and included 13,775 participants aged 65 years or older. Multivariate logistic regressions were employed to examine the association between residential proximity to major roadways and HI. The effects of corresponding potentially modifiable factors were studied by three-way interaction analyses. Sensitivity analyses were performed to verify the robustness of the results.

RESULTS

The prevalence of HI was 38.3%. Participants living near major roadways were more likely to have a higher socioeconomic status. An exposure-response relation between residential proximity to major roadways and HI was observed (Ptrend < 0.05). Compared with individuals living > 300 m away from major roadways, the adjusted odds ratios (OR) were 1.07 (95% CI: 0.96-1.24), 1.15 (95% CI: 1.07-1.34), and 1.12 (95% CI: 1.01-1.31) for those living 101-200 m, 50-100 m, and < 50 m away from the roadways, respectively. Particularly, the association was more pronounced among individuals exposed to carbon monoxide (CO) pollution or opening windows frequently (Pinteraction < 0.05). Three-way interaction analyses confirmed that participants exposed to CO pollution and frequently leaving windows open had the highest OR of 1.73 (95% CI: 1.58-1.89).

CONCLUSIONS

This nation-wide cohort study suggested that residential proximity to major roadways was significantly associated with an increased exposure-response risk of HI in Chinese older adults. Exposure to CO pollution and opening windows frequently might strengthen the relations.

Noise Exposure and Cardiovascular Health

Noise is considered an environmental stressor adversely affecting well-being and quality of life, inter-individual communications, and attention and cognitive function and inducing emotional responses, corresponding to noise annoyance. In addition, noise exposure is associated with nonauditory effects including worsening mental health, cognitive impairments, and adverse birth outcomes, sleep disorders, and increased annoyance. An accumulating body of evidence has indicated that traffic noise is also associated with CVD, through multiple pathways. It has been shown that psychological stress and mental health disorders such as depression and anxiety have a negative impact on the development of cardiovascular diseases and outcomes. Likewise, reduced sleep quality and/or duration has been reported to increase sympathetic nervous system activity, which can predispose to conditions like hypertension and diabetes mellitus, known risk factors for CVD. Finally, there seems to be a disruption in the hypothalamic-pituitary-axis secondary to noise pollution that also results in an increased risk of CVD. The World Health Organization has estimated that the number of DALYs (disability-adjusted life-years) lost resulting from environmental noise in Western Europe ranges from 1 to 1.6 million, making noise the second major contributor to the burden of disease in Europe, only after air pollution. Thus, we sought to explore the relationship between noise pollution and risk of CVD.

The co-benefits of electric mobility in reducing traffic noise and chemical air pollution: Insights from a transit-oriented city

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined. This study aims to examine the impacts of both electric light-duty vehicles and electric buses on traffic noise levels in a highly urbanized city. Traffic noise along the source line and pedestrian network was first estimated and mapped to illustrate its spatiotemporal variations. Then, scenario analysis was used to compare the impacts. Population potentially benefiting from reduced traffic noise in the neighbourhoods and the associated health impacts were also estimated. Results indicate that electric buses have a greater potential to reduce traffic noise, with a maximum reduction of 4.4 dBA during daytime in the urban cores. With all bus fleet electrified, around 60% of the population can benefit from a reduction of 1 dBA at the street environment, 15.3% for 1-2 dBA, and 4.3% for more than 2 dBA. The estimated reduction of preventable deaths and preventable cases of diseases per 100,000 population are 4.15 and 112.99 respectively. The findings shed important insights into prioritizing bus routes to be electrified in urban areas for maximizing health co-benefits.

Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km², and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R² = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R² = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.