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.

Acoustic performance evaluation of railway boundary walls using a computational fluid dynamics-based simulation approach.

Railway noise has become a significant concern for trackside residents due to increased volume of high-speed passenger and freight train traffic. To address this, active measures, such as reducing noise at the source, and passive measures, such as installing noise barriers along the transmission path, are widely being used. In urban areas, railway boundary walls are constructed to prevent encroachments of railway lands and to avoid pedestrian trespassing of railway tracks. This study aims to evaluate the effectiveness of such a boundary wall for reducing noise and proposes an improved alternative through computational fluid dynamics (CFD) simulations. Various noise barriers with different geometry, shape, and surface materials were simulated and validated with the field conditions based on a rectangular wall of height 2.75 m. Noise attenuation was evaluated by measuring railway noise spectra at different positions, including 0.5 m in front and behind the barrier and at the facade of the residential area. The insertion loss based on field measurements for a rectangular barrier of height 2.75 m was observed to be 5.2 dBA. The simulation results indicated a positive correlation between barrier height and insertion loss, with a maximum attenuation of 17 dBA achieved with a barrier of height 6 m. The most effective noise barrier for reducing railway noise was a T-shaped barrier with a height of 6 m and a projection length of 2 m, with an insertion loss of 22 dBA. This study recommends constructing the barrier with soft materials on its surface to reflect and absorb sound waves effectively. These findings have potential implications for urban planners and policymakers in designing effective noise barriers in residential areas near railway lines.

Long-term exposure to transportation noise in relation to global cognitive decline and cognitive impairment: Results from a Swedish longitudinal cohort.

BACKGROUND AND AIMS: Transportation noise is an environmental exposure with mounting evidence of adverse health effects. Besides the increased risk of cardiovascular and metabolic diseases, recent studies suggest that long-term noise exposure might accelerate cognitive decline in older age. We examined the association between transportation noise and cognitive function in a cohort of older adults. METHODS: The present study is based on 2594 dementia-free participants aged 60 + years from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Global cognition score and CIND (cognitive impairment, no dementia) were assessed with a comprehensive neuropsychological battery at baseline and up to 16 years. Residential transportation noise resulting from road traffic, railway, and aircraft were estimated at the most exposed façade and the time-weighted average exposure was assessed. Linear mixed-effect models were used to assess the effect of long-term traffic noise exposure on the rate of change in global cognition score. Hazard ratios (HRs) and 95 % confidence intervals (CIs) of CIND by transportation noise exposure were obtained with Cox proportional hazard models. RESULTS: Global cognition score decreased at an average rate of -0.041 (95 %CI -0.043, -0.039) per year. Aircraft noise was associated with a 0.007 (per 10 dB Lden; 95 %CI -0.012, -0.001) faster annual rate of decline. Global cognition score seems to be not affected by road traffic and railway noise. During the follow-up, 422 (21 %) participants developed CIND. A 10-dB Lden difference in exposure to aircraft and railway noise was associated with a 16 % (HR 1.16, 95 %CI 0.91, 1.49) and 26 % (HR 1.26, 95 %CI 1.01, 1.56) increased hazard of CIND in the multi-pollutant model, respectively. No association was found for road traffic (HR 1.00, 95 %CI 0.83, 1.21). CONCLUSIONS: Transportation noise was linked to cognitive impairment and faster cognitive decline among older adults. Future studies are warranted to confirm our results.

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.

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.

Random effect generalized linear model-based predictive modelling of traffic noise.

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

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.

Health position paper and redox perspectives - Disease burden by transportation noise.

Transportation noise is a ubiquitous urban exposure. In 2018, the World Health Organization concluded that chronic exposure to road traffic noise is a risk factor for ischemic heart disease. In contrast, they concluded that the quality of evidence for a link to other diseases was very low to moderate. Since then, several studies on the impact of noise on various diseases have been published. Also, studies investigating the mechanistic pathways underlying noise-induced health effects are emerging. We review the current evidence regarding effects of noise on health and the related disease-mechanisms. Several high-quality cohort studies consistently found road traffic noise to be associated with a higher risk of ischemic heart disease, heart failure, diabetes, and all-cause mortality. Furthermore, recent studies have indicated that road traffic and railway noise may increase the risk of diseases not commonly investigated in an environmental noise context, including breast cancer, dementia, and tinnitus. The harmful effects of noise are related to activation of a physiological stress response and nighttime sleep disturbance. Oxidative stress and inflammation downstream of stress hormone signaling and dysregulated circadian rhythms are identified as major disease-relevant pathomechanistic drivers. We discuss the role of reactive oxygen species and present results from antioxidant interventions. Lastly, we provide an overview of oxidative stress markers and adverse redox processes reported for noise-exposed animals and humans. This position paper summarizes all available epidemiological, clinical, and preclinical evidence of transportation noise as an important environmental risk factor for public health and discusses its implications on the population level.

Consistent traffic noise impacts few fitness-related traits in a field cricket.

BACKGROUND: Anthropogenic habitat change is occurring rapidly, and organisms can respond through within-generation responses that improve the match between their phenotype and the novel conditions they encounter. But, plastic responses can be adaptive or maladaptive and are most likely to be adaptive only when contemporary conditions reasonably mimic something experienced historically to which a response has already evolved. Noise pollution is a ubiquitous anthropogenic stressor that accompanies expanding urbanization. We tested whether the amplitude of traffic noise influences a suite of fitness-related traits (e.g. survival, life history, reproductive investment, immunity) and whether that depends on the life stage at which the noise is experienced (juvenile or adult). Our treatments mimic the conditions experienced by animals living in urban roadside environments with variable vehicle types, but continuous movement of traffic. We used the Pacific field cricket, an acoustically communicating insect that was previously shown to experience some negative behavioral and life history responses to very loud, variable traffic noise, as a model system. RESULTS: After exposing crickets to one of four traffic noise levels (silence, 50dBA, 60dBA, and 70dBA which are commonly experienced in their natural environment) during development, at adulthood, or both, we measured a comprehensive suite of fifteen fitness-related traits. We found that survival to adulthood was lower under some noise treatments than under silence, and that the number of live offspring hatched depended on the interaction between a female's juvenile and adult exposure to traffic noise. Both of these suggest that our noise treatments were indeed a stressor. However, we found no evidence of negative or positive fitness effects of noise on the other thirteen measured traits. CONCLUSIONS: Our results suggest that, in contrast to previous work with loud, variable traffic noise, when noise exposure is relatively constant, plasticity may be sufficient to buffer many negative fitness effects and/or animals may be able to habituate to these conditions, regardless of amplitude. Our work highlights the importance of understanding how the particular characteristics of noise experienced by animals influence their biological responses and provides insight into how commensal animals thrive in human-dominated habitats.

Effects of Aircraft Noise on Sleep: Federal Aviation Administration National Sleep Study Protocol.

Aircraft noise can disrupt sleep and impair recuperation. The last U.S. investigation into the effects of aircraft noise on sleep dates back more than 20 years. Since then, traffic patterns and the noise levels produced by single aircraft have changed substantially. It is therefore important to acquire current data on sleep disturbance relative to varying degrees of aircraft noise exposure in the U.S. that can be used to check and potentially update the existing noise policy. This manuscript describes the design, procedures, and analytical approaches of the FAA's National Sleep Study. Seventy-seven U.S. airports with relevant nighttime air traffic from 39 states are included in the sampling frame. Based on simulation-based power calculations, the field study aims to recruit 400 participants from four noise strata and record an electrocardiogram (ECG), body movement, and sound pressure levels in the bedroom for five consecutive nights. The primary outcome of the study is an exposure-response function between the instantaneous, maximum A-weighted sound pressure levels (dBA) of individual aircraft measured in the bedroom and awakening probability inferred from changes in heart rate and body movement. Self-reported sleep disturbance due to aircraft noise is the secondary outcome that will be associated with long-term average noise exposure metrics such as the Day-Night Average Sound Level (DNL) and the Nighttime Equivalent Sound Level (Lnight). The effect of aircraft noise on several other physiological and self-report outcomes will also be investigated. This study will provide key insights into the effects of aircraft noise on objectively and subjectively assessed sleep disturbance.

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.

Contributions of residential traffic noise to depression and mental wellbeing in Hong Kong: A prospective cohort study.

Prior studies on the association between traffic noise and mental health have been mostly conducted in settings with lower population densities. However, evidence is lacking in high population-density settings where traffic noise is more pervasive and varies by topography and the vertical elevation of the residential unit. This study aimed to assess the mental health impact of residential traffic noise in one of the world's most urbanised populations. Data were analysed from 13,401 participants aged ≥15 years in a prospective cohort in Hong Kong from 2009 to 2014. Residential traffic noise level was estimated using 3D-geocoding and validated models that accounted for sound propagation in a highly vertical landscape. The 24-h day-night exposure to traffic noise, denoted as Ldn, was estimated with a 10-dB(A) penalty for night hours. Probable depression and mental wellbeing were assessed using the Patient Health Questionnaire-9 and the Short Form Health Questionnaire SF-12v2, respectively. Mixed effect regressions with random intercepts were used to examine the association between traffic noise and mental health outcomes. Residential road traffic noise (for each increment of 10 A-weighted decibels [dB(A)] 24-h average exposure) was associated with probable depression (odds ratio (OR) = 1.17, 95% CI: 1.05, 1.31), and poorer mental wellbeing (mean difference = -0.19, 95% CI: 0.31, -0.06), adjusting for sociodemographics, smoking, body mass index, self-reported health, proximity to green space, and neighbourhood characteristics (average household income, population density, and Gini coefficient). The results were robust to further adjustment for air pollution. In stratified analyses, residential traffic noise was associated with probable depression and poorer mental wellbeing among students and individuals aged 15-34 years. Residential traffic noise was associated with probable depression and poorer mental wellbeing in a highly urbanised setting. As traffic noise is increasing in urban settings, the public health impact of noise pollution could be substantial.

Decision trees and labeling of low noise pavements as support for noise action plans.

Low noise pavements (LNPs) are a market driven trend to mitigate the high road traffic noise exposure levels. Their improvement towards acoustic efficiency and durability over time is a challenge since these factors can conflict with road primary functions, such as safety. LNPs are not always the most cost-effective solution in health effects prevention. Whilst Green Public Procurement (GPP) highlighted the importance of reducing rolling noise emissions by introducing new regulations for new-layed LNPs, the fixed minimum requirements are not exhaustive. Generally, limits are set following the Close ProXimity method, which is only source oriented. This method does not consider real traffic flows and it is not aimed at evaluating citizens' disturbance. This work presents strategy tools that could assist policymakers in choosing LNPs, when truly effective, over other mitigations. The approach includes a variety of indicators that would allow for comparing different facets of noise assessment. The proposed methodology does not require additional efforts from stakeholders because the measurements required for the estimation of the indicators must already be carried out for both verification of legal limits and GPP. The strategy tools are a decisional tree to support the evaluation of the applicability of a LNP before its approval, and an evaluation flowchart applicable after its laying to evaluate its efficiency. Finally, a first LNP labeling approach, based on the same set of indicators, is proposed. As a case study, these tools are applied to measurements performed before and after the laying of twelve LNPs part of the LIFE NEREiDE project.

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.

Acute exposure to simulated nocturnal traffic noise and cardiovascular complications and sleep disturbance-results from a pooled analysis of human field studies.

OBJECTIVES: A series of human field studies demonstrated that acute exposure to simulated nocturnal traffic noise is associated with cardiovascular complications and sleep disturbance, including endothelial dysfunction, increased blood pressure, and impaired sleep quality. A pooled analysis of these results remains to be established and is of tremendous interest to consolidate scientific knowledge. METHODS: We analyzed data from four randomized crossover studies (published between 2013 to 2021 and conducted at the University Medical Center Mainz, Germany). A total of 275 subjects (40.4% women, mean age 43.03 years) were each exposed to one control scenario (regular background noise) and at least to one traffic noise scenario (60 aircraft or train noise events) in their homes during nighttime. After each night, the subjects visited the study center for comprehensive cardiovascular function assessment, including the measurement of endothelial function and hemodynamic and biochemical parameters, as well as sleep-related variables. RESULTS: The pooled analysis revealed a significantly impaired endothelial function when comparing the two different noise sequences (0-60 vs. 60-0 simulated noise events, mean difference in flow-mediated dilation -2.00%, 95% CI -2.32; -1.68, p < 0.0001). In concordance, mean arterial pressure was significantly increased after traffic noise exposure (mean difference 2.50 mmHg, 95% CI 0.54; 4.45, p = 0.013). Self-reported sleep quality, the restfulness of sleep, and feeling in the morning were significantly impaired after traffic noise exposure (all p < 0.0001). DISCUSSION: Acute exposure to simulated nocturnal traffic noise is associated with endothelial dysfunction, increased mean arterial pressure, and sleep disturbance.

Long-Term Exposure to Traffic Noise and Risk of Incident Cardiovascular Diseases: a Systematic Review and Dose-Response Meta-Analysis.

While noise pollution from transportation has become an important public health problem, the relationships between different sources of traffic noise and cardiovascular diseases (CVDs) remain inconclusive. A comprehensive meta-analysis was therefore conducted to quantitatively assess the effects of long-term exposure to road traffic, railway, and aircraft noise on CVDs and relevant subtypes. We systematically retrieved PubMed, Embase, and Web of Science for articles published before April 4, 2022. Summary relative risks (RRs) and 95% confidence intervals (CIs) were estimated by the fixed- or random-effects models. In total, 23 articles were included in our meta-analysis. The risk of CVDs increased by 2% (RR 1.020, 95% CI 1.006-1.035) and 1.6% (RR 1.016, 95% CI 1.000-1.032) for every 10 dB increment of road traffic and aircraft noise. For CVD subtypes, the risk increased by 3.4% (1.034, 1.026-1.043) for stroke and 5% (1.050, 1.006-1.096) for heart failure with each 10 dB increment of road traffic noise; the risk of atrial fibrillation increased by 1.1% (1.011, 1.002-1.021) with each 10 dB increment of railway noise; and the risk increased by 1% (1.010, 1.003-1.017) for myocardial infarction, 2.7% (1.027, 1.004-1.050) for atrial fibrillation, and 2.3% (1.023, 1.016-1.030) for heart failure with each 10 dB increment in aircraft noise. Further, effects from road traffic, railway, and aircraft noise all followed positive linear trends with CVDs. Long-term exposure to traffic noise is positively related to the incidence risk of cardiovascular events, especially road traffic noise which significantly increases the risk of CVDs, stroke, and heart failure.

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.