Comparative evaluation of soundscapes in human activities spatial contexts of pedestrian spaces adjacent to arterial roads

Pedestrian spaces adjacent to arterial roads are characterized by the dominance of traffic noise alongside various human activities. Research on the impact of traffic noise on the soundscape evaluation of pedestrian spaces has not considered human activities spatial contexts. To address this research gap, the present study constructed auditory environments for pedestrian spaces in the contexts of commuting, residential, and commercial activities. A total of seven auditory environments were subjected to laboratory auditory evaluations, including perceived dominance of sound source, acoustic comfort, and perceived affective quality of the soundscape. The results indicated that in pedestrian spaces with constant traffic noise, the presence of significant human activity sounds led to a decreased perceived dominance of traffic noise and an increased acoustic comfort, despite the higher acoustic energy. Thus, pedestrian spaces with a variety of human activity received better soundscape evaluations. The elements that reflected the human activities spatial contexts, including the types and intensity of human activities, played a crucial role in soundscape evaluations. Better acoustic comfort was reported in pedestrian spaces characterized by low-intensity residential activities and high-intensity commercial activities. Additionally, pedestrian spaces with more intense activities offered an actively engaging soundscape. The findings can provide reference for a more accurate evaluation of the soundscape in pedestrian spaces and guide the soundscape design of pedestrian environments.

Forests as natural metamaterial barriers for urban railway-induced vibration attenuation

Noise and vibrations generated by railway traffic can seriously affect the adjacent buildings and their residents. Different mitigation methods have been proposed in the past decades to tackle this challenge. Despite many mitigation measures presented in the literature, some of these measures have shown limitations in their application, while for others their carbon footprint does not justify their implementation in real projects. This study introduces the concept of forests as natural metamaterials to attenuate the vibrations generated at the wheel-rail interaction. In particular, a group of natural metamaterials, in the form of a forest, is introduced into a vehicle/track/soil validated model based on the two-step approach. The ideal tree/soil unit-cell constituting the forest is obtained through a parametric investigation of the geometrical and material properties in order to have the first band-gap within the main range of frequencies generated by railway traffic in urban areas. The vibration attenuation levels obtained by the introduction of the natural metamaterial are then evaluated by considering a range of operational velocities for the T2000 Brussels tram LRV (Light Rail Vehicle). Finally, some insights on the attenuation efficiency of the selected forest towards vibrations generated by HSTs (High-Speed Trains) are given by considering a mono-wheel model with a higher range of vehicle speeds.

Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342 h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.

Investigating important and necessary conditions to analyse traffic noise levels at intersections in mid-sized cities

Traffic noise is a major problem for urban residents, especially near intersections. In order to effectively manage and control traffic noise, there is a need for a better understanding of noise-influencing variables at intersections. In this way, the study aims to identify and distinguish the important and necessary conditions corresponding to the particular traffic noise level. Using 342 h of field data from 19 intersections in Kanpur, the current research has used the Partial Least Square-Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA). The study determines that traffic volume, honking, speed, and median width are important factors. Traffic volume and honking are positively affecting traffic noise level, while speed and median width have a negative effect. Further investigation reveals that only traffic volume and honking are necessary to achieve a particular traffic noise level. Policymakers can use these findings to manage and control traffic noise at intersections.

Traffic noise distribution characteristics of high-rise buildings along ultra-wide cross section highway with multiple noise reduction measures

Nowadays, ultra-wide cross section highway is a hotspot in construction and brings some unique noise distribution characteristics. In this work, we further investigate noise distribution characteristics of diverse building layouts along ultra-wide cross section highway in Guangdong Province with multiple noise mitigation measures. By the aid of vehicle noise emission model and noise mapping, the influence of high-rise building layouts and shielding in the urban planning on noise mitigation is also considered. Some key findings are summarized as follows: (1) Under the same distance, the noise level of non-frontage building facades is higher than frontage building facades. After taking noise reduction measures, the noise reduction effect of non-street-facing building facades, buildings facing the road, and buildings at a long distance to the road is greater than street-facing building facades, buildings sideways to the road, and buildings at a short distance; (2) the distribution trend of insertion loss (IL) of non-frontage buildings is influenced by the height of the frontage buildings. Specifically, the trend of insertion loss first increases and then decreases as the floor rises when the height of non-frontage buildings is higher than frontage buildings. Comparatively, the trend of insertion loss decreases as the floor rises when the height of non-frontage buildings is equal to frontage buildings; (3) when double noise reduction measures are implemented, the noise distribution trend in buildings is similar to that observed with individual noise reduction measure, where the difference between both is only 0.6 dB(A). Thanks to the high representativeness of the case area, this work can provide some design guidance for the urban planning and the selection of noise reduction measures along the ultra-wide cross section highway.

The impact of traffic noise on the capitalization of public walking area: A hedonic analysis of Vienna, Austria

Traffic noise is a burden at home and outdoors. Economic literature confirms mostly negative effects of traffic noise on house prices, often based on distance between high noise and house location. We extend this literature using rich micro data to examine not only the impact of traffic noise at the house but also provide new results on the impact of traffic noise in public areas surrounding a home. Using Hedonic regression in Vienna, Austria, we confirm that very loud traffic noise (≥65 dB) experienced at the house reduces housing prices and further show that the value of public walking areas near a home, while positive overall, are substantially reduced when exposed to noise. Our findings help to establish spatial patterns in noise capitalization reflecting household exposure and the impact on the capitalized values of public areas in a context where active transportation (e.g. walking, biking) is an important mode of transportation. For policymakers, our findings help quantify and raise important questions as how to address and link the public bad nature of noise pollution to nearby residents.

Urban noise exposure assessment based on principal component analysis of points of interest

Accurate qualitative and quantitative information on the characteristics of traffic noise exposure in densely populated urban areas is an important prerequisite for reasonable traffic noise control. The primary objective of this study is the development and application of a traffic noise exposure evaluation method based on points of interest (POIs). First, an automatic query arithmetic is used to acquire geospatial information, POIs data, building and network information from the webmap. Second, the attribute matrix of preprocessed POIs for the population is constructed. And the population distribution is obtained by principal component analysis (PCA) of POIs and Gaussian decomposition of demographic data. Then, the modified traffic noise line-source model is applied to calculate the noise distribution considering attenuation among buildings based on measured traffic flow parameters. Finally, with the help of the proposed noise evaluation indicators, and considering the noise function requirements (NFRs, which can be divided into four classes according to different area land-use types), traffic noise evaluation is realized. The proposed method is applied to a typical region with four NFR classes. It is concluded that the characteristics of traffic noise exposure are affected by traffic conditions, buildings, NFR classes and population distribution. And the crowds exposed to noise present aggregation effects, which are usually centered around specific buildings. In addition, POI types which people actives related suffer more serious noise exposure, and exposure is overestimated at low requirement regions without considering crowd distribution of the setting scenario.

Association between residential exposure to road traffic noise and cognitive and motor function outcomes in children and preadolescents

BACKGROUND

Exposure to environmental noise is increasing in recent years but most of the previous literature in children has evaluated the effect of aircraft noise exposure at schools on cognition.

OBJECTIVE

To assess whether residential exposure to road traffic noise during pregnancy and childhood is associated with cognitive and motor function in children and preadolescents.

METHODS

The study involved 619 participants from the Spanish INMA-Sabadell cohort and 7,115 from the Dutch Generation R Study. We used noise maps to estimate the average day-evening-night road traffic noise levels at each participant's residential address during pregnancy and childhood periods. Validated tests were administered throughout childhood in both cohorts to assess non-verbal and verbal intelligence, memory, processing speed, attentional function, working memory, cognitive flexibility, risky decision-making, and fine and gross motor function. Linear models, linear mixed models, and negative binomial models were run depending on the outcome in cohort-specific analysis and combined with a random-effects meta-analysis. All models were adjusted for several socioeconomic and lifestyle variables and results corrected for multiple testing.

RESULTS

Average road traffic noise exposure levels during pregnancy and childhood were 61.3 (SD 6.0) and 61.5 (SD 5.4) dB for the INMA-Sabadell cohort and 54.6 (SD 7.9) and 53.5 (SD 6.5) dB for the Generation R Study, respectively. Road traffic noise exposure during pregnancy and childhood was not related to any of the cognitive and motor function outcomes examined in this study (e.g. -0.92 (95 % CI -2.08; 0.24) and 0.20 (95 % CI -0.96; 1.35) in overall estimates of memory and fine motor function, respectively, when road traffic noise increases by 10 dB during childhood).

CONCLUSIONS

These findings suggest that child's cognitive or motor functions are not affected by residential exposure to road traffic noise. However, more studies evaluating this association at school and home settings as well as noise events are needed.

Aircraft noise exposure and risk for recurrent cardiovascular events after acute coronary syndrome: A prospective patient cohort study

In several population based cohort studies associations between aircraft noise and various diagnoses of cardiovascular disease were observed. However, no study has yet addressed the risk of recurrences in relation to transportation noise in patients with acute coronary heart disease. We conducted a prospective patient cohort study of 737 individuals recruited from eleven cardiac centers in the Rhine-Main region in the vicinity of Frankfurt Airport. All patients had an angiographically confirmed acute coronary syndrome diagnosed between July 2013 and November 2018. Individual aircraft noise exposure at the place of residence was calculated using Soundplan software, and exposure to road traffic and railway noise was obtained from noise maps provided by the Hessian State Agency. Data was analyzed by means of Cox regression adjusted for relevant confounders. Recurrent event as non-fatal endpoint was defined as myocardial infarction, stroke, bypass surgery or percutaneous coronary intervention with stent implantation. In addition, all-cause mortality was evaluated. Follow-up data including socioeconomic and confounder information was obtained from 663 (90%) patients covering a mean follow-up period of 42 (range: 1-80) months. Mean Lden aircraft noise exposure was 48.1 dB. Adjusted hazard ratio (HR) for recurrence was 1.24 (95%-CI: 0.97-1.58) per 10 dB increase in Lden aircraft noise exposure. A combined analysis of recurrence and all-cause mortality yielded a HR of 1.31 (95%-CI: 1.03-1.66). Similar HRs were found for Lday and Lnight aircraft noise exposure. HRs for road traffic and railway noise were above unity but less pronounced and not significant. Observed exposure-response associations for aircraft noise were more pronounced than previously observed in population-based cohort studies suggesting that acute coronary heart disease patients are particularly vulnerable to effects from transportation noise. Measures to reduce environmental noise exposure may thus be helpful in improving clinical outcome of patients with coronary heart disease.

Heterogeneous road traffic noise modeling at mid-block sections of mid-sized city in India

This study attempted to develop a computer-based software for monitoring the traffic noise under heterogeneous traffic condition at the morning peak (MP), off peak (OP), and evening peak (EP) periods of mid-block sections of mid-sized city in India. Traffic noise dataset of 776 (LAeq, 1hr) were collected from 23 locations of Gorakhpur mid-sized city in the state of Uttar Pradesh in India. K-nearest neighbor (K-NN) algorithm was adopted for traffic noise prediction modeling. Moreover, principal component analysis (PCA) technique was used for the dimensionality reduction and to overcome the problem of multi-collinearity. The developed model exhibits R2 value of 0.81, 0.78, and 0.77 in the MP, OP, and EP, respectively, for Leq, and a value of 0.86, 0.80, and 0.84 for L10. The proposed model can predict more than 94% observations within an accuracy of ±3%. Ultimately, a user-friendly noise level calculator named "Traffic Noise Prediction Calculator for Heterogeneous Traffic (TNPC-H)" was developed for the benefit of field engineers and policy planners.

Associations of particulate matter, gaseous pollutants, and road traffic noise with the prevalence of asthma in children

The purposes of this study were to elucidate the associations between exposure to particulate matter, gaseous pollutants, and road traffic noise and asthma prevalence and to determine the interaction between exposure to multiple pollutants and asthma in children. A total of 3,246 children were recruited from 11 kindergartens in New Taipei City, Taiwan. Land use regression (LUR) was used to establish predictive models for estimating individual exposure levels of particulate matter, gaseous pollutants, and the 24 h A-weighted equivalent sound pressure level (LAeq,24). Multiple logistic regression was performed to test the associations between exposure to these environmental factors and asthma prevalence in children. Multiple-exposure models revealed that an interquartile-range (IQR) increase in PM2.5 (1.17 μg/m3) and PM10 (10.69 μg/m3) caused a 1.34-fold (95% confidence interval [CI] = 1.05-1.70) and 1.17-fold (95% CI = 1.01-1.36) increase in risk of asthma prevalence in children after adjusting for LAeq,24 and NO2. Co-exposure to PM2.5, LAeq,24, and O3, SO2, or CO, as well as co-exposure to PM10, LAeq,24, and CO produced similar findings. Only exposure to one IQR of SO2 (0.15 ppb) was observed a significant association (odds ratio = 1.16, 95% CI = 1.00-1.34) with the asthma prevalence in children after adjusting for PM10 and LAeq,24. Exposure to PM2.5, PM10, and SO2 may be associated with a higher asthma prevalence in children, while other gaseous pollutants and road traffic noise did not demonstrate significant associations. The interaction of exposure to air pollutants and road traffic noise on asthma prevalence in children was not observed in this study.

Development of response surface method prediction model for traffic-related roadside noise levels based on traffic characteristics

Recently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of Leq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that Leq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R2 = 95.87% and R2 adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.

Long-term exposure to traffic noise and risk of incident colon cancer: A pooled study of eleven Nordic cohorts

Background Colon cancer incidence is rising globally, and factors pertaining to urbanization have been proposed involved in this development. Traffic noise may increase colon cancer risk by causing sleep disturbance and stress, thereby inducing known colon cancer risk-factors, e.g. obesity, diabetes, physical inactivity, and alcohol consumption, but few studies have examined this. Objectives The objective of this study was to investigate the association between traffic noise and colon cancer (all, proximal, distal) in a pooled population of 11 Nordic cohorts, totaling 155,203 persons. Methods We identified residential address history and estimated road, railway, and aircraft noise, as well as air pollution, for all addresses, using similar exposure models across cohorts. Colon cancer cases were identified through national registries. We analyzed data using Cox Proportional Hazards Models, adjusting main models for harmonized sociodemographic and lifestyle data. Results During follow-up (median 18.8 years), 2757 colon cancer cases developed. We found a hazard ratio (HR) of 1.05 (95% confidence interval (CI): 0.99-1.10) per 10-dB higher 5-year mean time-weighted road traffic noise. In sub-type analyses, the association seemed confined to distal colon cancer: HR 1.06 (95% CI: 0.98-1.14). Railway and aircraft noise was not associated with colon cancer, albeit there was some indication in sub-type analyses that railway noise may also be associated with distal colon cancer. In interaction-analyses, the association between road traffic noise and colon cancer was strongest among obese persons and those with high NO₂-exposure. Discussion A prominent study strength is the large population with harmonized data across eleven cohorts, and the complete address-history during follow-up. However, each cohort estimated noise independently, and only at the most exposed façade, which may introduce exposure misclassification. Despite this, the results of this pooled study suggest that traffic noise may be a risk factor for colon cancer, especially of distal origin.

Models of Aviation Noise Impact in the Context of Operation Decrease at Tan Son Nhat Airport

Air traffic bans in response to the spread of the coronavirus have changed the sound situation of urban areas around airports. This study aimed to investigate the effect of this unprecedented event on the community response to noise before and after the international flight operation at Tan Son Nhat Airport (TSN) in March 2020. The "before" survey was conducted in August 2019, and the two "after" surveys were conducted in June and September 2020. Structural equation models (SEMs) for noise annoyance and insomnia were developed by linking the questionnaire items of the social surveys. The first effort aimed to achieve a common model of noise annoyance and insomnia, corresponding to the situation before and after the change, respectively. Approximately, 1200 responses were obtained from surveys conducted in 12 residential areas around TSN in 2019 and 2020. The average daily flight numbers observed in August 2019 during the two surveys conducted in 2020 were 728, 413, and 299, respectively. The sound pressure levels of the 12 sites around TSN decreased from 45-81 dB (mean = 64, SD = 9.8) in 2019 to 41-76 dB (mean = 60, SD = 9.8) and 41-73 dB (mean = 59, SD = 9.3) in June and September 2020, respectively. The SEM indicated that the residents' health was related to increased annoyance and insomnia.

Contribution of outdoor noise-induced health risk in schools located in urbanized arid country

Ambient noise pollution is deemed as one of the major growing public health issues, especially in developing countries. Therefore, it is crucial to assess the impact of noise pollution on public health. The aim of this study is to investigate the health risk of noise exposure levels in three schools: Kaab Bin Zeyd of Basic Education (school A), Hail Al-Awamour Girls school (school B), and Al-Fikr School (school C) in Muscat, Oman. The study employed a survey of 300 students, dose-response models, and regression models to quantify health risk and to determine the relationship between noise levels and perceived noise annoyance sources. The study found average noise levels (L_Aeq) of school A (70.03±8.21 dBA), school B (69.54±7.75 dBA), and school C (55.95± 5.67 dBA) to be higher than WHO's outdoor schools environment standard of 55 dBA and European (EN16798-1) classroom's critical limits of 30-34 dBA. Most of the students from schools A (30.9%), B (33.3%), and C (63%) have reported noise produced from traffic as extremely annoyed compared to aircraft of 15.4%, 11.5%, and 27.2%, respectively. Regression analysis shows that perceived traffic noise was strongly correlated with L_Aeq in school A (R² =0.481), B (R² =0.121), and C (R² = 0.132) when compared with other subjective noise types. The health risk assessment results show that the percentage of highly annoyed (%HA) was higher in school A (15.2%) and school B (14.95%) than in school C (8.18%). The estimated highly sleep disturbed (%HSD) based on mean noise levels were almost the same in schools A (15.62%) and B (15.19%) but far higher compared to school C (6.01%). However, there was an association between the mean noise exposure levels and the risk of developing ischemic heart diseases (IHD) in school A (RR= 1.172, 95% CI: 1.020-1.334), school B (RR=1.167, 95% CI: 1.020-1.325), and school C (RR=1.051, 95% CI: 1.006-1.095). Moreover, attributable risk percentage (AR%) for school A (AR% =14.675, 95% CI: 2.028-25.037), school B (AR% =14.310, 95% CI: 1.960-24.528), and school C (AR% = 4.852, 95% CI:0.596-8.742) have shown that a substantial portion of the population could be prevented from developing IHD. It is expected that findings of the study can be applied in other arid regions with sprawl urbanized built environments.

Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland

BACKGROUND

Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise.

METHODS

Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM_2.5, PM_2.5 components (Cu, Fe, S and Zn), NO₂, black carbon (BC) and ozone (O₃) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges.

RESULTS

During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM_2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m³ PM_2.5, 1.050 (1.041, 1.059) per 10 µg/m³ NO₂, 1.057 (1.048, 1.067) per 0.5 × 10^-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO₂, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM_2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m³ for PM_2.5 and NO₂, < 1.5 10^-5/m BC and < 53 dB Lden total transportation noise. The O₃ association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC).

CONCLUSION

Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO₂, BC and transportation noise.

Air pollution and traffic noise interact to affect cognitive health in older Mexican Americans

BACKGROUND

Both air pollution and noise exposures have separately been shown to affect cognitive impairment. Here, we examine how air pollution and noise exposures interact to influence the development of incident dementia or cognitive impairment without dementia (CIND).

METHODS

We used 1,612 Mexican American participants from the Sacramento Area Latino Study on Aging conducted from 1998 to 2007. Air pollution (nitrogen dioxides, particulate matter, ozone) and noise exposure levels were modeled with a land-use regression and via the SoundPLAN software package implemented with the Traffic Noise Model applied to the greater Sacramento area, respectively. Using Cox proportional hazard models, we estimated the hazard of incident dementia or CIND from air pollution exposure at the residence up to 5-years prior to diagnosis for the members of each risk set at event time. Further, we investigated whether noise exposure modified the association between air pollution exposure and dementia or CIND.

RESULTS

In total, 104 incident dementia and 159 incident dementia/CIND cases were identified during the 10 years of follow-up. For each ∼2 µg/m3 increase in time-varying 1- and 5-year average PM2.5 exposure, the hazard of dementia increased 33% (HR = 1.33, 95%CI: 1.00, 1.76). The hazard ratios for NO2-related dementia/CIND and PM2.5-related dementia were stronger in high-noise (≥65 dB) exposed than low-noise (<65 dB) exposed participants.

CONCLUSION

Our study indicates that PM2.5 and NO2 air pollution adversely affect cognition in elderly Mexican Americans. Our findings also suggest that air pollutants may interact with traffic-related noise exposure to affect cognitive function in vulnerable populations.

The association between road traffic noise and type 2 diabetes: a systematic review and meta-analysis of cohort studies

The association between road traffic noise and type 2 diabetes (T2DM) was inconsistent. To address this, we have synthesized available cohort studies about their association by meta-analysis. PubMed, Web of Science, EBSCO, Cochrane Library, EMBASE, and Scopus databases were searched up to July 2022. The Quality-effect model (QE) was used to incorporate the results of included studies. The possibility of publication bias was assessed by the Doi plots and Luis Furuya-Kanamori index. Sensitivity analyses included leave-one-out meta-analysis, subgroup meta-analysis, and meta-regressions. The Recommendations for Assessment, Development, and Evaluation (GRADE) guidelines were conducted to evaluate the overall quality of evidence. Eight cohort studies with 4,989,846 participants and 416,799 diabetes cases were included. Based on the fully adjusted models from 8 cohort studies (10 estimates; Lden range ≈ 15-98.5 dB(A)), we found "high" evidence of RR per 10 dB(A) = 1.07 (1.05, 1.10), high heterogeneity (I2 = 0.91%, p < 0.001), and high publication bias (LKF index = 4.55). Sensitivity analyses showed stable model results, and the GRADE assessment suggested the current overall quality of evidence is high. Comprehensive evidence from cohort studies supports that increasing exposure to road traffic noise may be associated with higher risk of T2DM.

Use of artificial neural networks to assess train horn noise at a railway level crossing in India

Urban environment noise is a complex mixture of transportation, industrial, household, and recreational noise, which is identified as an emerging environmental threat. Present study monitors and evaluates a noise pollution hotspot: a railway level crossing, where several activities related to transportation noise were involved. Train honking, train movement, road vehicles, and pedestrians contribute to the noise level at a railway level crossing. Train horns are generally performed as train approach railway level crossings and they are mandatorily used to alert road users. However, the train horns are regarded as nuisance to the nearby residents. A detailed evaluation of train horn effectiveness is very much essential in the current contemporary environment. Thus, the main objective of this study is to measure noise levels emanating from train horns at a level crossing with due consideration to train types and climatic conditions. A comprehensive noise monitoring survey was conducted at an access-controlled level crossing. Furthermore, an artificial neural network (ANN)-based railway noise prediction model was developed to forecast maximum ([Formula: see text]) and equivalent (L_eq) noise levels. Results revealed that train horn produced impulsive sound signals which fall under high frequency one-third octave bands causing severe irritation to trackside inhabitants. The proposed ANN models produced accurate results for [Formula: see text] and L_eq noise levels and this model is identified as a vital tool for railway noise abatement. The results from this study are helpful to the urban planning and development authorities to implement strategic laws and policies to eradicate the urban environment noise.

Traffic Noise at Moderate Levels Affects Cognitive Performance: Do Distance-Induced Temporal Changes Matter?

Urbanization leads to an increased demand for urban housing, which can be met by building dwellings closer to streets. Regulations often limit equivalent sound pressure levels which do not account for changes in time structure that occur when decreasing the road distance. This study investigates the effect of such temporal changes on subjective workload and cognitive performance. A group of 42 participants performed a continuous performance test as well as a NASA-TLX workload test under three different sound conditions, i.e., close traffic, far traffic, both with the same equivalent sound pressure level of LAeq≈40 dB, and silence. Additionally, participants answered a questionnaire regarding their preferred acoustic environment for concentrated working. Significant effects of the sound condition on the multivariate workload results as well as on the number of commission errors in the continuous performance test were found. Post hoc tests showed no significant differences between the two noise conditions, but there were significant differences between noise and silence. This indicates that moderate traffic noise levels can influence cognitive performance and perceived workload. If there is a difference in the human response to road traffic noise with constant LAeq but different time structures, the used methods are not suitable to detect them.

A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information. Ambient noise levels were obtained at different locations of Dhanbad city using sound level meter at varying intervals, viz. 09AM-12PM, 03PM-06PM, and 08PM-11PM. The proposed method was compared with some well-known neural network techniques such as K-nearest neighbors (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long-short-term memory (LSTM). The experimental results indicate that the proposed method outperforms other techniques and can be a reliable approach for prediction of noise annoyance with an accuracy of 93.8%. It can be concluded from noise maps that the noise levels in all locations of the Dhanbad city were higher than 70 dB(A) and noise sensitivity is the most important input variable of traffic-induced noise annoyance, followed by honking noise, education, exposure hours, L_Aeq, sleeping disorder, and chronic disease. The study shall facilitate in developing a decision support tool for prediction of noise annoyance and promoting implementation of suitable public policy in urban cities.

How Do Road Traffic Noise and Residential Greenness Correlate with Noise Annoyance and Long-Term Stress? Protocol and Pilot Study for a Large Field Survey with a Cross-Sectional Design

Urban areas are continuously growing, and densification is a frequent strategy to limit urban expansion. This generally entails a loss of green spaces (GSs) and an increase in noise pollution, which has negative effects on health. Within the research project RESTORE (Restorative potential of green spaces in noise-polluted environments), an extended cross-sectional field study in the city of Zurich, Switzerland, is conducted. The aim is to assess the relationship between noise annoyance and stress (self-perceived and physiological) as well as their association with road traffic noise and GSs. A representative stratified sample of participants from more than 5000 inhabitants will be contacted to complete an online survey. In addition to the self-reported stress identified by the questionnaire, hair cortisol and cortisone probes from a subsample of participants will be obtained to determine physiological stress. Participants are selected according to their dwelling location using a spatial analysis to determine exposure to different road traffic noise levels and access to GSs. Further, characteristics of individuals as well as acoustical and non-acoustical attributes of GSs are accounted for. This paper presents the study protocol and reports the first results of a pilot study to test the feasibility of the protocol.

Analogical Assessment of Train-Induced Vibration and Radiated Noise in a Proposed Theater

This study presents the analogical assessment of the train-induced vibration and radiated noise in a proposed theater. The theater is to be constructed in a region with crowded metro lines, and the assessment is implemented in an analogical building with comparable structural type and metro condition. Prior to the assessment, the comparability of the analogical building with the theater is validated using the train-induced ground vibration. With the same horizontal distance from the metro line, the train-induced vibration level in the analogical building is 9 dB higher than that in the construction site of the theater. Such results indicate that the lack of soil layers may lead to a dramatic increase in train-induced vibration in the building. In the staircase of the analogical building, the train-induced radiated noise reached 55 dB (A), which is 10 dB (A) higher than the daytime allowable level. As the most important indicator, the noise rating number in the cinema of the analogical building is NR-43, which put forward an enormous challenge on the construction of the theater with a denoise demand of 23 dB. The analogical method applied in this study provides an effective and practical way for the assessment of train-induced vibration and radiated noise in proposed vibration-sensitive buildings. The assessment results that provide necessary reference and support for the anti-vibration design will help guarantee the stage effect of the theater.

Acoustic information masking effects of natural sounds on traffic noise based on psychological health in open urban spaces

The use of existing resources, such as natural sounds, to promote the mental health of citizens is an area of research that is receiving increasing attention. This research contributes to existing knowledge by combining a field psychological walk method and an experimental acoustic control method to compare the acoustic information masking effects of water and birdsong sounds on traffic noise based on the psychological health responses of 30 participants to such effects. The influence of traffic noise and contextual sounds on the psychological health of participants identified the potential of natural sounds in the acoustic information masking of traffic noise. Furthermore, it was found that 65.0 dBA water sounds did not mask 60.0 dBA traffic noises. However, 45.0 dBA birdsong sounds did mask it, but this effect was not significant. Additionally, contextual factors with and without crowd activity sounds were not significant in influencing psychological health through birdsong. This study contributes to public health cost savings. It may also guide the development of new ideas and methods for configuring open urban spaces according to public health needs.

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

The Effect of Land-Use Categories on Traffic Noise Annoyance

Land-use categories are often used to define the exposure limits of national environmental noise policies. Often different guideline values for noise are applied for purely residential areas versus residential areas with mixed-use. Mixed-use includes living plus limited activities through crafts, commerce, trade, agriculture, and forestry activities. This differentiation especially when rating noise from road, railway, and air traffic might be argued by different expectations and therefore noise annoyance in those two categories while scientific evidence is missing. It should be tested on empirically derived data. Surveys from two studies in the state of Tyrol in urban and rural areas were retrospectively matched with spatial data to analyze the potential different influences on noise effects. Using non-parametric tests, the correlation between land-use category on self-reported noise sensitivity and noise annoyance was investigated. Exposure-response for the two analyzed land-use categories showed no significant impact on noise sensitivity and exposure-response relationships for the three traffic noise sources. Including only noise annoyance, there is not sufficient evidence to define different noise policies for those two land-use categories.

Pathways to Environmental Inequality: How Urban Traffic Noise Annoyance Varies across Socioeconomic Subgroups

The article investigates how socioeconomic background affects noise annoyance caused by residential road traffic in urban areas. It is argued that the effects of socioeconomic variables (migration background, education, and income) on noise annoyance tend to be underestimated because these effects are mainly indirect. We specify three indirect pathways. (1) A "noise exposure path" assumes that less privileged households are exposed to a higher level of noise and therefore experience stronger annoyance. (2) A "housing attributes path" argues that less privileged households can shield themselves less effectively from noise due to unfavorable housing conditions and that this contributes to annoyance. (3) Conversely, an "environmental susceptibility path" proposes that less privileged people are less concerned about the environment and have a lower noise sensitivity, and that this reduces their noise annoyance. Our analyses rest on a study carried out in four European cities (Mainz and Hanover in Germany, Bern and Zurich in Switzerland), and the results support the empirical validity of the three indirect pathways.

Statistical Pass-By for Unattended Road Traffic Noise Measurement in an Urban Environment

Low-noise surfaces have become a common mitigation action in the last decade, so much so that different methods for feature extraction have been established to evaluate their efficacy. Among these, the Close Proximity Index (CPX) evaluates the noise emissions by means of multiple runs at different speeds performed with a vehicle equipped with a reference tire and with acoustic sensors close to the wheel. However, signals acquired with CPX make it source oriented, and the analysis does not consider the real traffic flow of the studied site for a receiver-oriented approach. These aspects are remedied by Statistical Pass-By (SPB), a method based on sensor feature extraction with live detection of events; noise and speed acquisitions are performed at the roadside in real case scenarios. Unfortunately, the specific SPB requirements for its measurement setup do not allow an evaluation in urban context unless a special setup is used, but this may alter the acoustical context in which the measurement was performed. The present paper illustrates the testing and validation of a method named Urban Pass-By (U-SPB), developed during the LIFE NEREiDE project. U-SPB originates from standard SPB, exploits unattended measurements and develops an in-lab feature detection and extraction procedure. The U-SPB extends the evaluation in terms of before/after data comparison of the efficiency of low-noise laying in an urban context while combining the estimation of long-term noise levels and traffic parameters for other environmental noise purposes, such as noise mapping and action planning.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

A comprehensive experimental study of the influence of temperature on urban road traffic noise under real-world conditions

The effect of road traffic noise in urban environments is an issue of social and scientific interest, due to its public health and economic impacts. Scientific literature showed a decrease in the level of tyre/road noise generated as temperature increases, but usually under standardised traffic conditions in non-urban environments. Based on a wide network for the hourly monitoring of road traffic flow, air temperature and noise levels across the city of Madrid (Spain), this work proposes and applies a new experimental methodology for studying the dependence of urban road traffic noise on temperature. This study was conducted under real-world traffic conditions involving a wide variability in urban configurations and in the type and state of preservation of vehicles, tires and pavements. From the analysis of data for a whole year, a time interval was identified (from Tuesday to Thursday and between 8 a.m. and 8 p.m.) in which the variability in road traffic flow for the whole city of Madrid was stable enough to allow for a linear regression study between temperature and noise levels from urban road traffic. The relationships found were highly significant (p ≤ 0.001) for data from all the noise monitoring stations, with values of higher than 20% and up to 42% for the explanation of the variability in the measured noise levels by temperature at most of the measurement points. The values of the slope coefficients at the noise monitoring stations ranged from -0.036 to -0.125 dB/°C, with an average value of -0.090 ± 0.011 dB/°C. These results are within the range of values reported in the scientific literature for experimental tests conducted under conditions of controlled or free-flowing traffic.

Traffic noise monitoring and modelling - an overview

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.

Recent advancements in the challenges and strategies of globally used traffic noise prediction models

It is the need of an era to develop efficient traffic noise prediction models with optimum accuracy. In this context, the present work tries to comprehend the performance-related potential parameters based on earlier published articles worldwide that are responsible for deviation in noise values for different traffic noise prediction models and find out critical gaps. This study reviewed the process involved in source modeling and sound propagation algorithms, applicability, limitations, and recent modification in 9 principal traffic noise prediction models adapted by different countries all around the globe. The result of this review shows that many researchers had carried out comparative analysis among various traffic noise prediction models, but no emphasis was made on the recent modifications, limitations associated with those models, and strategies involved without ignoring the propagation and attenuation mechanism in the developing phase of these models. The findings of this study revealed that the major challenge for any traffic noise prediction model to be efficient enough is the inclusion of all the factors responsible for the generation and deviation of traffic noise before reaching the receiver. These responsible factors include a factor for source emission, sound propagation and attenuation, road characteristics, and other miscellaneous factors such as absorption characteristics of building facades, honking, and dynamic behavior of traffic. This study adds to the broader domain of research and will be used as reference material for future traffic noise modeling strategies.

Vehicular traffic noise modelling of urban area-a contouring and artificial neural network based approach

Road traffic vehicular noise is one of the main sources of environmental pollution in urban areas of India. Also, steadily increasing urbanization, industrialization, infrastructures around city condition causes health risks among the urban populations. In this study, we have explored noise descriptors (L₁₀, L₉₀, L_dn, LNI, TNI, NC), contour plotting and find the suitability of artificial neural networks (ANN) for the prediction of traffic noise all around the Dhanbad township in 15 monitoring stations. In order to develop the prediction model, measuring noise levels of five different hours, speed of vehicles, and traffic volume in every monitoring point have been studied and analyzed. Traffic volume, percent of heavy vehicles, speed, traffic flow, road gradient, pavement, road side carriageway distance factors were taken as input parameter, whereas L_Aeq as output parameter for formation of neural network architecture. As traffic flow is heterogenous which mainly contains 59%, two wheelers and different vehicle specifications with varying speeds also affect driving and honking behavior which constantly changing noise characteristics. From radial noise diagrams shown that average noise levels of all the stations beyond permissible limit and the highest noise levels were found at the speed of 50-55 km/h in both peak and non-peak hours. Noise descriptors clearly indicate high annoyance level in the study area. Artificial neural network with 7-7-5 formation has been developed and found as optimum due to its sum of square and overall relative error 0.858 and .029 in training and 0.458 and 0.862 in testing phase respectively. Comparative analysis between observed and predicted noise level shows very less deviation up to ± 0.6 dB(A) and the R² linear values are more than 0.9 in all five noise hours indicating the accuracy of model. Also, it can be concluded that ANN approach is much superior in prediction of traffic noise level to any other statistical method.

Ground vibration from freight railway: environmental impact and potential mitigation measure at propagation path

The freight train-induced vibrations and noise generate increasing environmental problems owing to its heavier axle loads and longer pass-by duration. To develop useful mitigation measures, the vibration characteristic induced by this type of rail transportation needs to be better learned. In the present work, firstly, the in situ measurements were carried out on two railway lines which were used for mixed freight and passenger trains. Both the track vibrations and ground vibrations resulted from different train types were measured and compared. Then, based on the dominant frequencies of ground vibrations from experimental results, the mitigation measure of periodic piles was proposed as a mitigation measure by impeding propagation. The periodic theory of solid-state physics was introduced and three-dimensional (3D) finite element (FE) simulation was employed to analyse the vibration reduction performance of periodic piles, while the attenuation zone (AZ) of the piles was also calculated. The measurement results indicate that the freight train can generate a larger level of vibrations on both the track structure and ground at the near field, especially below 10 Hz. Even though the speed of freight trains is as low as 40-55 km/h, the vibration exposure level (VEL) is higher than normal passenger trains (80-90 km/h) and EMU trains (120 km/h). The simulation results show that the proposed solution of installing periodic piles at the propagation path raises the positive influence on vibration reduction.

Exposure to traffic-related air pollution and noise during pregnancy and childhood, and functional brain connectivity in preadolescents

BACKGROUND

The amount of people affected by traffic-related air pollution and noise is continuously increasing, but limited research has been conducted on the association between these environmental exposures and functional brain connectivity in children.

OBJECTIVE

This exploratory study aimed to analyze the associations between the exposure to traffic-related air pollution and noise during pregnancy and childhood, and functional brain connectivity amongst a wide-swath of brain areas in preadolescents from 9 to 12 years of age.

METHODS

We used data of 2,197 children from the Generation R Study. Land use regression models were applied to estimate nitrogen oxides and particulate matter levels at participant's homes for several time periods: pregnancy, birth to 3 years, 3 to 6 years, and 6 years of age to the age at magnetic resonance imaging (MRI) assessment. Existing noise maps were used to estimate road traffic noise exposure at participant's homes for the same time periods. Resting-state functional MRI was obtained at 9-12 years of age. Pair-wise correlation coefficients of the blood-oxygen-level-dependent signals between 380 brain areas were calculated. Linear regressions were run and corrected for multiple testing.

RESULTS

Preadolescents exposed to higher levels of NO₂, NO_x, and PM_2.5 absorbance, from birth to 3 years, and from 3 to 6 years of age showed higher correlation coefficients among several brain regions (e.g. from 0.16 to 0.19 higher correlation coefficient related to PM_2.5 absorbance exposure, depending on the brain connection). Overall, most identified associations were between brain regions of the task positive and task negative networks, and were mainly inter-network (20 of 26). Slightly more than half of the connections were intra-hemispheric (14 of 26), predominantly in the right hemisphere. Road traffic noise was not associated with functional brain connectivity.

CONCLUSIONS

This exploratory study found that exposure to traffic-related air pollution during the first years of life was related to higher functional brain connectivity predominantly in brain areas located in the task positive and task negative networks, in preadolescents from 9 to 12 years of age. These results could be an indicator of differential functional connectivity in children exposed to higher levels of air pollution.

Predicting traffic noise using land-use regression-a scalable approach

BACKGROUND

In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.

OBJECTIVE

Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.

METHODS

Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator L_den. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.

RESULTS

The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (L_den) variability (R² = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).

SIGNIFICANCE

This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Evaluation of noise barriers based on geometries and materials: a review

The acoustical properties of a barrier are highly dependent on the physical properties of the material and the internal structure of the material. The acoustical material can curtail the quality of sound or enhance the dispersion, depending on the application being considered. The efficient acoustic performance of noise barriers possessing different shapes and materials including waste materials is reviewed for field implementation to achieve the low-cost sustainable noise barrier application in the Indian context. The review analysis of research papers demonstrates that the acoustic performance of barriers is dependent on different shapes, materials, and textures as well as onsite geometry. Based on the review study, T-shaped barriers with a soft top surface are found to be efficient at noise attenuation. For transparent barriers, perceived loudness and noise annoyance are assessed lower than that for opaque barriers and utilization of waste materials viz. plastic, rubber, bottom coal ash, etc. gives high noise attenuation along with low-cost efficiency. Noise pollution levels are high from prescribed noise limits in most of the Indian metropolitan cities. The Indian government is working on mitigation strategies of noise pollution as well as abiding laws regarding noise standards for the zone (residential, industrial, commercial, and silences) wise. In contrast, some communities which are adjacent to roads are suffering from high noise levels in the ambience. Therefore, it requires a coherent strategy for long-term measures intended at minimizing exposure of noise hence providing much more comfort to live, work, and shop near high-traffic roads. Noise barriers are highly beneficial in mitigating the emitted noise from the traffic.

Modelling traffic noise in a wide gradient interval using artificial neural networks

As classical traffic noise prediction models lack a deeper consideration of the impact of the gradient, the characteristics of longitudinal gradients from multiple roads were collected as data in the mountain city of Chongqing county, which was chosen as the entry point, to study the noise characteristics for a wide range of road gradients and to build a traffic noise prediction model based on artificial neural networks (ANNs). The field data consisted of traffic volumes, heavy-vehicle ratios, average vehicle speeds, road gradients, and corresponding equivalent sound pressure levels. An optimal ANN model was determined and compared with two classical models. The results demonstrated that a one-hidden-layer ANN model was suitable for traffic noise prediction in mountain cities and presented better predictive performance than the conventional models. The best-performing ANN model yielded a determination coefficient of 0.9447 and a mean-squared error of 0.2708 dBA. Moreover, this study confirmed that road gradients were significant for constructing traffic noise prediction models.

Experimental noise and vibration characteristics of elevated urban rail transit considering the effect of track structures and noise barriers

Vibration of the elevated urban rail transit (URT) severely affects the health of nearby residents, threatens the integrity of adjacent historic buildings, and aggravates the performance of vibration-sensitive instruments in buildings, and the accompanied annoying structure-borne noise always arouses public complaint. Vibration and noise mitigation measures through track structures and/or noise barriers are increasingly favored to deal with these challenging issues. This paper presents systematic field measurements on noise and vibrations of elevated URT. The vibration experiment covers vibration of track structures, bridge girders and piers, and ground soil under three different track structures, i.e., embedded sleeper track, ladder sleeper track, and floating slab track (FST) with rubber mats. Noise measurements were also conducted considered the effect of track structures and with or without fully enclosed noise barriers. It is shown that ladder sleeper track and FST were more effective in control bridge vibration than ground vibration. The overall vibration level of the bridge is 8~10 dB greater than the ground vibrations. The noise reduction effect through track structure was limited for far-field ground. Furthermore, it is found that the noise barrier was more effective to reduce near-field wheel/rail rolling noise rather than far-field noise. Good correlation between structure-borne noise and vibration was observed for both the embedded sleeper track and FST at the bottom slab of the box girder bridge.

Effects of exposure to surrounding green, air pollution and traffic noise with non-accidental and cause-specific mortality in the Dutch national cohort

BACKGROUND

Everyday people are exposed to multiple environmental factors, such as surrounding green, air pollution and traffic noise. These exposures are generally spatially correlated. Hence, when estimating associations of surrounding green, air pollution or traffic noise with health outcomes, the other exposures should be taken into account. The aim of this study was to evaluate associations of long-term residential exposure to surrounding green, air pollution and traffic noise with mortality.

METHODS

We followed approximately 10.5 million adults (aged ≥ 30 years) living in the Netherlands from 1 January 2013 until 31 December 2018. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 and 1000 m), annual average ambient air pollutant concentrations [including particulate matter (PM2.5), nitrogen dioxide (NO2)] and traffic noise with non-accidental and cause-specific mortality, adjusting for potential confounders.

RESULTS

In single-exposure models, surrounding green was negatively associated with all mortality outcomes, while air pollution was positively associated with all outcomes. In two-exposure models, associations of surrounding green and air pollution attenuated but remained. For respiratory mortality, in a two-exposure model with NO2 and NDVI 300 m, the HR of NO2 was 1.040 (95%CI: 1.022, 1.059) per IQR increase (8.3 µg/m3) and the HR of NDVI 300 m was 0.964 (95%CI: 0.952, 0.976) per IQR increase (0.14). Road-traffic noise was positively associated with lung cancer mortality only, also after adjustment for air pollution or surrounding green.

CONCLUSIONS

Lower surrounding green and higher air pollution were associated with a higher risk of non-accidental and cause-specific mortality. Studies including only one of these correlated exposures may overestimate the associations with mortality of that exposure.

Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach

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 (Leq,1h), maximum noise level (L10,1h), and background noise level (L90,1h). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for Leq,1h , L90,1h , and L10,1h 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.

A community noise survey in Southwest Detroit and the value of supplemental metrics for truck noise

Noise exposure can affect sleep, health and cognitive performance, and it disproportionately affects communities of color. This study has the objective of evaluating both conventional and supplemental noise metrics in a community noise survey examining Southwest Detroit, Michigan, a densely populated and industrialized area with extensive truck traffic on residential streets. Sound pressure level (SPL) monitors were deployed at 21 residential sites within 900 m of a major interstate highway. With assistance from youth volunteers, continuous SPL measurements were obtained for 1.5-7 days at each site, and short-term vehicle counts on local roads were recorded. We calculated conventional noise metrics, including the day-evening-night average sound level L_DEN and the 90th percentile 1-hr maximum L₁₀(h), and evaluated the effect of distance from highways, traffic volume, time-of-day, and other factors. Supplemental metrics potentially appropriate for intermittent traffic noise were calculated, including fraction of time over specific SPL thresholds using a new metric called F_DEN, which is the fraction of time over 60, 65 and 70 dB during night, evening and daytime periods, respectively, and a peak noise metric called L_2P(h), which utilizes the 98th percentile SPL within time blocks to increase robustness. The conventional metrics indicated five sites that exceeded 70 dB, and the highest noise levels were found within ~50 m of truck routes, arterials and freeway ramps. The estimated impact of truck traffic ranged up to 17 dB for hourly averages and to 33 dB for 1-s peaks. The conventional metrics did not always capture short-term noise exposures, which may be especially important to annoyance and sleep issues. In addition to showing widespread exposure to traffic noise in the study community that warrants consideration of noise abatement strategies, the study demonstrates the benefits of supplemental noise metrics and community engagement in noise assessment.

Environmental noise and vibration characteristics of rubber-spring floating slab track

The floating slab track is considered one of the most effective track structures to reduce the adverse effects of underground railway noise and vibration. This paper reports a new type of rubber-spring float track (RSFS), which adopts a well-designed rubber-spring vibration isolator and is conceptually designed as a float track structure. The dynamic performance of different track structures, namely RSFS track, fixed slab track, and transition section, were studied. Vibration response of the car body and the track structure was obtained. Internal noise from the train and external noise near the tracks were also recorded. The results show that the measured track structures can ensure the safety of train operation. Compared with the fixed plate track, the RSFS track has a good vibration isolation effect, and the RMS vibration reduction at the tunnel wall was 15.1 dB. However, it amplifies the vibration above the isolation layer and slightly increases the internal noise of the train. RSFS track structure should be evaluated comprehensively before implementation. In addition, the track stiffness has a significant impact on the vibration level of the track, thus affecting the vibration isolation effect. The noise distribution inside the train is not uniform and is not sensitive to the stiffness of the track structure. Due to the uncertainty of train-induced vibration, a probabilistic framework is needed to evaluate or predict the train-induced environmental vibrations.

Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (L_Aeq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N = 111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models' performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced L_Aeq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in L_Aeq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 L_Aeq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

ENVINT-D-20-01309: Long-term exposure to air pollution, road traffic noise, residential greenness, and prevalent and incident metabolic syndrome: Results from the population-based KORA F4/FF4 cohort in Augsburg, Germany

BACKGROUND

A growing number of epidemiological studies show associations between environmental factors and impaired cardiometabolic health. However, evidence is scarce concerning these risk factors and their impact on metabolic syndrome (MetS). This analysis aims to investigate associations between long-term exposure to air pollution, road traffic noise, residential greenness, and MetS.

METHODS

We used data of the first (F4, 2006-2008) and second (FF4, 2013-2014) follow-up of the population-based KORA S4 survey in the region of Augsburg, Germany, to investigate associations between exposures and MetS prevalence at F4 (N = 2883) and MetS incidence at FF4 (N = 1192; average follow-up: 6.5 years). Residential long-term exposures to air pollution - including particulate matter (PM) with a diameter < 10 µm (PM₁₀), PM < 2.5 µm (PM_2.5), PM between 2.5 and 10 µm (PM_coarse), absorbance of PM_2.5 (PM2.5_abs), particle number concentration (PNC), nitrogen dioxide (NO₂), ozone (O₃) - and road traffic noise were modeled by land-use regression models and noise maps. For greenness, the Normalized Difference Vegetation Index (NDVI) was obtained. We estimated Odds Ratios (OR) for single and multi-exposure models using logistic regression and generalized estimating equations adjusted for confounders. Joint Odds Ratios were calculated based on the Cumulative Risk Index. Effect modifiers were examined with interaction terms.

RESULTS

We found positive associations between prevalent MetS and interquartile range (IQR) increases in PM₁₀ (OR: 1.15; 95% confidence interval [95% CI]: 1.02, 1.29), PM_2.5 (OR: 1.14; 95% CI: 1.02, 1.28), PM_coarse (OR: 1.14; 95% CI: 1.02, 1.27), and PM_2.5abs (OR: 1.17; 95% CI: 1.03, 1.32). Results further showed negative, but non-significant associations between exposure to greenness and prevalent and incident MetS. No effects were seen for exposure to road traffic noise. Joint Odds Ratios from multi-exposure models were higher than ORs from models with only one exposure.

Exposure assessment to road traffic noise levels and health effects in an arid urban area

Road traffic noise exposures have been recognized as serious environmental health concerns, especially in most developing countries with arid climate conditions, rapid increase in vehicle population, and limited traffic management systems. The excessive noise exposure level is associated with increase in the incidence of cardiovascular diseases and anxiety, including annoyance. This study aimed at determining traffic noise levels in residential areas, including the assessment of its annoyance and health effects based on the people's perception and reportage. To do so, field measurement and traffic noise modeling were carried out in six road points to estimate the current noise levels along various roads close to human inhabitants in Muscat Governorate, Sultanate of Oman. The detailed measured noise levels in urban residential areas across the selected roads showed that noise levels have exceeded the local and international threshold limits at all locations during the entire day. The high sound levels (48.0-56.3 dBA) were observed using the US Federal Highway Administration's Traffic Noise Model (TNM, version 2.5) results, which were in agreement with the observed (56.3-60.4 dBA) data. To assess health implication to residents through interviews (n = 208), annoyance at home was found to be little (32%), moderate (28%), and high (9%) in comparison with workplace settings of 42%, 43%, and 15%, respectively. Nineteen percent of the interviewees had difficulties in sleeping, while 19.8% experienced stress due to road traffic noise exposures. Moreover, a strong association (p < 0.05) was established between the use and objection of noise barriers. The study revealed high noise levels and the prevalence of annoyance and health effects among the exposed population. Therefore, immediate action is required to tackle the current noise levels.

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking

Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2 years, viz., 2012 and 2019. The study concludes an increment of 5-6 dB(A) in noise level, 4-6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of - 1.7 to + 1.4 dB (Leq) with 84% of observations in the range of - 1 to + 1 dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

The association between road traffic noise and myocardial infarction: A systematic review and meta-analysis

This systematic review and meta-analysis study aimed to investigate the association between exposure to road traffic noise (RTN) and myocardial infarction (MI). Of 681 studies found by searching in databases, including Scopus, Web of Science, Embase, and PubMed on November 29, 2019, the number of 13 studies, including seven cohort, five case-control, and one cross-sectional studies with 1,626,910 participants and 45,713 cases of MI was included. The pooled relative risk (RR) and 95% confidence interval (CI) of MI were calculated using a random-effect model across studies. Heterogeneity measures by reporting the I-square index. Subgroup analysis according to the designs and sensitivity analysis based on the Jackknife approach was performed. We observed in the eight studies the association was investigated in different noise exposure groups and in the 10 studies (including two conference papers) the risk of MI was provided per specific unit increment of RTN. We ran two independent types of meta-analyses involving a categorical analysis (comparing the highest and the lowest category of noise exposure groups) and an exposure-response analysis (the risk of MI per 10-dB increment of RTN). The pooled RR (95% CI) of MI for the categorical and exposure-response meta-analyses was calculated 1.03 (0.93, 1.13) and 1.02 (1.00, 1.05), respectively. For both types of meta-analyses, subgroup analysis indicates a significant association in the studies with case-control and cross-sectional designs but not cohort studies. For the exposure-response meta-analysis, a significantly greater risk of MI was observed after excluding the two conference papers (RR = 1.03 and 95% CI = 1.00, 1.05) and by further excluding the studies provided originally the risk of MI only for the categorical analysis (RR = 1.02 and 95% CI = 1.01, 1.03). We did not show a significant publication bias across studies. In conclusion, our study suggests a significant odds of association between exposure to RTN and the risk of MI.

Attitudes to Noise Inside Dwellings in Three Megacities: Seoul, London, and São Paulo

This study investigated people's attitudes towards noise inside their homes. Online questionnaire surveys were conducted in Seoul, London, and São Paulo. The questionnaire was designed to assess annoyance caused by noise from neighbours and environmental noise (transportation). Information was also collected on situational, personal, and socio-demographic variables. Respondents that were more annoyed by outdoor noise inside their dwelling reported higher neighbour noise annoyance. In Seoul, neighbour noise was found to be more annoying than outdoor noise, and those with higher noise sensitivity reported higher annoyance towards neighbour noise. However, neighbour noise and outdoor noise was found to be equally annoying in London and São Paulo. For neighbour noise, the average percentage of respondents hearing structure-borne sources compared to airborne sources differed in each city. Most neighbour noise sources in São Paulo gave rise to higher annoyance ratings than Seoul and London. Education and income levels had a limited effect on annoyance and coping strategy. Annoyance with indoor noise from neighbours was found to have stronger relationships with cognitive and behavioural coping strategies than outdoor noise annoyance.