Traffic Noise Pollution Research Articles

Comparison of current and proposed travel modes for students considering travelling and sustainable indicators

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions.

Impact of car electrification on urban noise pollution: a microscopic traffic simulation study

Noise pollution is a major health and well-being concern, being associated with, e.g., sleep disturbance, annoyance, and cardiovascular diseases. Road traffic is, by far, the largest contributor to this noise pollution in urban areas. With the growing electrification of cars in developed countries, it is commonly believed that road traffic noise pollution and the associated problems will disappear shortly in urban areas. However, at high speeds, the difference in noise emissions between internal-combustion-engine and electric vehicles is not significant, and at low speeds, the propulsion noise of electric cars is not negligible. Few works have quantitatively estimated the actual impact of passenger cars electrification on population exposure to noise, especially considering such aspects as the gradual transition towards a fully electric fleet. The present contribution thus aims to assess the evolution of population exposure to noise pollution, using a microscopic traffic simulation on the scale of an entire city, noise mapping, and various indicators, at multiple stages of this transition.

Analysis of the underwater acoustic environment in the Catalan Shore: Badalona and Port of Barcelona cases in the framework of the Deuteronoise JPI-Oceans Project

Maritime traffic noise pollution is a significant concern for ocean's health and living organisms' welfare, particularly in European sea basins. Research has predominantly focused on vertebrates, linking noise pollution to hearing impairment, affecting their predator and prey detection, communication, and navigation. However, recent findings suggest that tunicates, marine invertebrates related to vertebrates, may also be susceptible to noise due to their similar acoustic and particle motion sensory organs. The JPI Oceans project, Deuteronoise, addresses this research gap by characterizing noise from maritime traffic in selected European sites, investigating the effects of noise on marine deuterostomes. In this paper, we introduce the pipeline and retrieval methods, followed by an analysis of the data collected from two contrasting locations: Port of Barcelona, noise polluted area, and Badalona, non-polluted one, focusing on the noise impact on Oikopleura dioica, a representative appendicularian tunicate. Initial laboratory analyses of recorded sounds have identified different vessel and sound features, providing distinction between the two sites. With this data analysis, we aim to bring valuable insights that will enhance our understanding of maritime noise pollution. This research not only advances our knowledge of the ecological consequences of noise pollution but also informs conservation strategies for vulnerable marine ecosystems.

Health impact assessment of exposure to road traffic noise and air pollution according to pre- and post-densification scenarios in Helsingborg, Sweden

A prevalent solution to accommodate population growth due to urbanization is densification. However, this often pushes new residential housing closer to roads, increasing exposure to both noise and air pollution. The present study’s aim was to estimate the health impacts of road traffic-related noise and air pollution for a low-income area (Drottninghög) in Helsingborg, Sweden, according to pre-densification (2012) and post-densification (2030) scenarios.Road traffic noise was simulated at the façade of residential buildings using the Nordic prediction method, and exposure was assessed using SoundPLAN. Exposure-response functions (ERF) from the WHO were utilized for the following health outcomes associated with noise: annoyance, adverse sleep disturbance, ischemic heart disease (IHD) incidence and IHD mortality. Air pollution (nitrogen dioxide, NO2) was assessed using a Gaussian dispersion model (AERMODE). Health outcomes associated with NO2 included natural cause mortality, pediatric asthma, respiratory hospitalizations and low birth weight (LBW). ERFs were derived from meta-analyses. Health impact assessments were then performed for both scenarios.Densifying Drottninghög according to the municipality’s planned strategy would lead to a 15 % unit increase in the proportion of residents exposed to road traffic noise above the WHO’s health-based guideline value (53 dB(A) Lden). This was estimated to markedly increase the proportion of residents highly annoyed by traffic noise (7.4–13.9 %) as well as those highly sleep disturbed (3.0–4.9 %). IHD incidence and IHD mortality attributed to noise would increase by an estimated 49 % and 44 %, respectively, post-densification. NO2 exposure was estimated to increase slightly (0.7 µg/m3) post-densification, which would contribute to an estimated 4–6 % increase in natural cause mortality, pediatric asthma, respiratory hospitalization and LBW.Urban planning initiatives need to consider these prevalent urban environmental exposures and integrate a public health perspective into densification strategies. Doing so can create synergies in the built environment that promote healthy, sustainable cities.

Long term exposure to road traffic noise and air pollution and risk of infertility in men and women: nationwide Danish cohort study

ObjectiveTo investigate associations between long term residential exposure to road traffic noise and particulate matter with a diameter <2.5 µm (PM2.5) and infertility in men and women.DesignNationwide prospective cohort study.SettingDenmark.Participants526...

Knowledge and perception regarding the prevention of traffic noise pollution among students of North Karnataka

Background: Noise pollution is an insidious and underestimated environmental threat, that poses diverse health risks such as sleep disturbances, cardiovascular ailments, impaired work and academic performance, hearing impairments, and mental health issues. Understanding student’s awareness, perspectives, and attitudes toward traffic noise pollution and its health impacts is crucial. Objective was to evaluate the knowledge and perceptions of students towards traffic noise pollution. Methods: The present study was conducted in Vijayapura during August and September 2023, this cross-sectional study involved 304 students. A self-administered questionnaire was used to assess the existing knowledge and perceptions regarding traffic noise pollution prevention among students of north Karnataka. Results: Among 304 studied participants, 56.3% were females and 43.8% males. Knowledge analysis that revealed 63.5% with good understanding, while 23% had good perception towards traffic noise pollution. Chi-square analysis showed that knowledge correlated significantly with age (p=0.010), favoring those over 20 years. Gender did not show a significant impact on knowledge (p=0.453). Perception showed age-related significance (p=0.037) but gender showed no significant influence on perception (p=0.721). Conclusions: The study highlighted positive knowledge levels about traffic noise pollution due to its often-overlooked nature. However, only a small portion exhibited favorable perceptions. This highlights the necessity for focused interventions, especially among the younger generation, to address traffic noise pollution effectively. Consistent education and reinforcement of traffic rules could induce positive change in the health and safety of society.

An efficient acoustic energy harvester by using deep learning-based traffic prediction

With rapid urbanisation, traffic noise pollution has become a growing community concern. However, traffic noise can be valuable due to its ubiquity and cleanliness, which can be exploited for acoustic energy harvesting systems. This paper aims to develop an efficient acoustic energy harvester to improve energy utilisation efficiency and reduce noise levels by using deep-learning methods. We propose a temporal graph attention convolutional network (TGACN) to predict noise frequency, providing guidance for the acoustic energy harvester design. Power efficiency can be maximised by adjusting the harvester's neck size to the frequency predicted by the TGACN model. The relationship between neck size and the optimum frequency range is obtained through simulations and experiments. We analysed the performance differences among the various cavities in multiple-cavity acoustic energy harvesters and discussed the circuit connection methods between each cavity to identify the optimal configurations. Additionally, we evaluated the superiority of TGACN over the state-of-the-art methods in terms of prediction accuracy by conducting extensive experiments with two real-world datasets.

Multidimensional analysis of road traffic noise and probable public health hazards in Barisal city corporation, Bangladesh

Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

Road traffic noise pollution and prevalence of ischemic heart disease: modelling potential association and abatement strategies in noise-exposed areas.

In many developing countries with surging vehicular traffic and inadequate traffic management, excessive road traffic noise exposure poses substantial health concerns, linked to increased stress, insomnia and other metabolic disorders. This study aims to assess the linkage between sociodemographic factors, traffic noise levels in residential areas and health effects using a cross-sectional study analyzing respondents' perceptions and reports. Noise levels were measured at 57 locations in Srinagar, India, using noise level meter. Sound PLAN software was employed to generate noise contour maps, enabling the visualization of noise monitoring locations and facilitating the assessment of noise levels along routes in proximity to residential areas. Correlation analysis showed a strong linear relationship between field-measured and modelled noise (r2 = 0.88). Further, a questionnaire-based survey was carried out near the sampling points to evaluate the association of ischemic heart disease with traffic noise. Residents exposed to noise levels (Lden > 60dB(A)) were found to have a 2.24 times higher odds ratio. Compared to females, males reported a 16% higher prevalence of the disease. Multi-faceted policy strategies involving noise mapping initiatives, source noise standards, traffic flow urban mobility optimization, smart city initiatives and stringent litigatory measures could significantly reduce its detrimental impact on public health. Finally, this study envisions a region-specific strong regulatory framework for integrating noise pollution mitigation strategies into the public health action plans of developing nations.

Exploring Progress and Future Trends in Reducing Traffic Noise: A Scoping Review on the Influence of Pavement Characteristics

This scoping review seeks to summarize current research on reducing traffic noise, focusing on how pavement characteristics affect road-tyre mechanical vibrations, which contribute significantly to urban noise pollution. The present study analyzes numerous research investigations on novel asphalt mixtures and rubber binders to identify current trends in pavement design that help reduce noise. Issues such as the longevity of noise-reducing pavements and the trade-off between noise reduction and other practical needs such as safety and cost-efficiency are examined. This assessment emphasizes the impact of new technologies, such as the Internet of Things (IoT), on improving pavement performance and reducing noise levels. Future directions stress the importance of a multidisciplinary approach involving material science, urban design, and acoustic engineering to create sustainable and effective solutions for traffic noise pollution. This review provides valuable input to the scholarly discussion on reducing traffic noise and also presents useful guidance for politicians and engineers aiming to improve urban livability by decreasing noise levels.

Urban network noise control based on road grade optimization considering comprehensive traffic environment benefit

A double-decision optimization model based on the road grade optimization strategy and considered comprehensive traffic environment benefit is proposed to control the traffic noise. The upper-level model maximizes the comprehensive traffic environment benefit, including network noise emission and traffic efficiency. Adjusting the emphasis on noise optimization benefits and traffic efficiency in road network planning through setting weights. The lower-level resolves the question of network traffic flow assignment using a stochastic user-equilibrium model. The increase of traffic environment demand, network noise emissions decrease and travel time rises. In the case, with a low environmental requirement (weighting with 1.1), the sound pressure emission of the network decreases by 9.23% with only a 4.01% increase in travel time. Under the high environmental requirement (weighting with 0.2), the sound pressure decreases by 26.8%, but the travel time rises by as high as 30.9%. The network is optimized towards road grade degradation and is the first to optimize the arterial roads. In addition, it is found that the influence of speed on traffic noise is greater than that of traffic volume through case validation. This method proposing traffic noise optimization strategies at the road network planning level provides technical support for the proactive governance of traffic noise pollution and the improvement of traffic sound environment quality.

Field Study to Design Public Transportation Network for One of the Libyan Cities (The City of Houn is an Example)

The cultural and urban development that the cities of the world in general have experienced over the years, including the Arab countries, has led to the existence of many urban problems, including urban transportation problems, mainly represented by traffic congestion, noise, and environmental pollution in all its visual and audio forms. Through this, the need for many systems to manage life aspects within these cities emerged from a social, economic and political perspective, including public transportation systems, which are considered one of the most important causes of urban stability in many cities of the world, whether developed or developing countries. The urban transportation system in Libya (as one of the developing countries in the world) suffers from the lack of planning based on scientific foundations for the transportation system - especially the public transportation network - that takes into account data collected in the field on how to design public transportation time tables according to the number of passengers, the length of the line, the trip time, and the number of buses in public transportation network, hence the need to conduct such a field study to plan public transportation networks within Libyan cities is very important. In this study, the city of Houn was chosen as a model for designing a public transportation network. The methodology was divided in two stages. The first stage was for data collection. A questionnaire (1080 surveys) was conducted for the main and active places in the city, including (educational, service, and employment places), as well as traffic counts were conducted by monitoring and recording traffics in the field during the morning and evening peak periods. The second stage was for analyzing and studying the data using Excel program, converting them into charts, comparing and studying the collected data. Based on analyzing and studying the data, the study concluded with the design of public transportation network in which the bus route for the network was determined, the locations and number of bus stops were determined, taking into account the required distances between stations. In addition, the number of buses was determined as well as the size of buses and the frequency of buses for morning and evening peak periods. Keywords: public transportation, questionnaire, traffic counts, peak hours

Improving Traffic-noise-mitigation Strategies with LiDAR-based 3D Tree-canopy Analysis

The leaves on trees absorb road noise and serve as noise barriers. Tree structures such as tree belts and isolated trees have various methods for absorbing sounds. The depth, surface area, and noise-absorption coefficient of trees contribute to noise absorption. Therefore, this study aims to address this issue of traffic-noise pollution through the use of trees; in particular, by analyzing the noise-absorption coefficient of leaves, the surface area of the leaves, and the depths of the trees. However, the study stresses the need for 3D tree-canopy visualization to identify these factors. To achieve this, the study used LiDAR point clouds to provide accurate data for the convex hull visualizations of canopies. Additionally, a formulated equation for calculating traffic noise after absorption has been suggested by combining the traffic-noise absorption and Henk de Kluijver traffic-noise models. The study also compares the effectiveness of tree belts and isolated trees in reducing noise pollution, concluding that, below a canopy of trees, there is no noise reduction. Finally, the study has demonstrated that the number and sizes of leaves affect noise absorption, showing that noise pollution can be reduced by 1 to 3 dB(A) in the research area by using trees.

Assessment of traffic noise pollution in Arau, Perlis

Abstract Traffic noise pollution has become an important factor in the growth of society. The noise from a motor vehicle can cause severe damage to human health and reduce labour productivity. The study was conducted in December 2020 in Arau, Perlis. The data collected of traffic composition, volume, traffic flow, traffic noise level (Leq ) and traffic speed were analyzed using Microsoft Excel to elucidate the pattern of roadside noise generated by the vehicle’s movement during peak hours. From the observation, the noise level for both studied areas exceeds DOE guidelines due to increased traffic volume and speed. Traffic volume for both studied areas was increased during morning peak hour, especially in school session. Another factor may contribute to noise level are road condition, wind speed and vehicle speed. Driving a car at a higher speed pass through a yellow line or poor road condition contribute to produce high level noise, especially for heavy vehicles. Furthermore, the correlation in SPSS found that traffic noise level is associated with traffic speed but not traffic volume. Field measurement was taken simultaneously to interpret the relationships among the traffic characteristics using various models, the Burgess model, Fagotti model, and NAISS model. The NAISS model was better than the other models used in term of field measurements.

Concurrent exposure to light and traffic noise pollution augments behavioral deficits in rats: Protective effect of curcumin

The current study was aimed to explore the effect of light and traffic noise pollution exposure on behavioral and neurochemical deficits in adult male Wistar rats. We hypothesized that exposure to these pollutants alone or in combination may cause behavioral deficits in rats. Animals were allocated into different groups and were exposed to either varying light cycles (150 lx-5 lx, 14:10 h) or traffic noise with sound level 100 dBA for 6 h/day or both for 28 days. Concurrently, Curcumin (100 mg/kg; per oral; 28 days) was administered in varying light cycles and noise exposed animal groups to study the preventive effect. After the exposure time, the Morris water maze test, elevated plus maze test, novel object recognition test, locomotor activity test, and forced swimming test were conducted. The neurobehavioral results suggested that only combined exposure of light and noise pollution markedly impaired the learning as well as spatial and reference memory in rats. Furthermore, we found significant anxiety (less no. of open arm entries in EPM) and depressive-like behavior (increased immobility time) in light or/and traffic noise pollution exposed group animals in comparison to normal control groups. Curcumin treatment significantly prevented the behavioral anomalies in combined light and noise exposed rats. Furthermore, our results revealed augmentation of acetylcholinesterase, oxidative stress markers and interleukin (IL)-1β in the hippocampal region of noise and light-exposed groups which were markedly ameliorated by curcumin. In conclusion, our results suggest that curcumin significantly modulates light and noise pollution exposed behavioral and neurochemical anomalies in rats.

Exposure of satellite tagged bowhead whales (Balaena mysticetus) to transiting vessels in the Eastern Canadian Arctic

AbstractClimate change poses new challenges to Arctic marine mammals, with increasing vessel traffic and associated underwater noise pollution emerging as significant threats. The bowhead whale (Balaena mysticetus), an endemic Arctic cetacean, faces these new threats. The Eastern Canada‐West Greenland (ECWG) bowhead whale population migrates through areas with the highest levels of vessel traffic in the Canadian Arctic. Here, we document the spatial and temporal overlap between 36 satellite‐tagged ECWG bowhead whales and vessels equipped with Automatic Identification System (AIS) transponders during 2012–2017. We report 1,145 instances where vessels were within 125 km of a tagged whale, with 306 occurrences within distances ≤50 km. Overlap between vessels and tagged bowhead whales was quantified monthly within years to investigate individual whale encounter rates. Results indicate that ECWG bowhead whales encounter the majority (79%) of vessels annually during August–October, with the highest number of encounters (42%) observed in September. Encounter rates ranged from 0.25 to 0.51 vessels encountered per day per whale during August–October compared to &lt;0.07 vessels per day in all other months in this study. To better inform conservation strategies, further research is required to assess bowhead whale behavioral responses relative to distance from vessels.

Design and validation of a new asphalt mixture to reduce road traffic noise pollution in urban areas

The aim of this paper is to design a novel asphalt mixture capable of reducing tyre/pavement interaction noise for urban roads at speeds around 50 km/h. To this end, various mechanical and functional tests were carried out both in the laboratory and on a real road in the city of Florence. The asphalt mixture developed improves the noise reduction properties without compromising its mechanical performance or neglecting its rolling and skid resistance. To this end, various mechanical tests were carried out in the laboratory, including water sensitivity or wheel tracking tests, as well as functional tests relating to texture, flow resistance and sound absorption. Full-scale measurements were carried out using the CPX method. The result of the study was a novel asphalt mixture that reduced noise by 3 dB compared to a conventional road.

Acoustic data detection in large-scale emergency vehicle sirens and road noise dataset

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. SACNN leverages a combination of EfficientNet and One Dimensional Convolutional Neural Networks (1D_CNN) to improve the precision and efficiency of detection. The dataset comprises 3-second WAV-format audio files categorized into Emergency Vehicles (Ambulance and Firetruck sirens) and Traffic (plain traffic sounds), with each category having 200 sound files and their corresponding 200 spectrogram images per audio file. Experimental results demonstrate SACNN's superior performance across various metrics, including accuracy, precision, recall, and F1 score, while also achieving notable advancements in computational efficiency. The research addresses the need for more effective solutions in the domain of emergency vehicle siren and road noise detection by introducing SACNN as a cutting-edge solution. By integrating EfficientNet and 1D_CNN, SACNN surpasses existing models in accuracy and computational efficiency. Comparative analysis with EfficientNet and 1D_CNN using a large-scale dataset validates SACNN's superior performance, with an average accuracy, precision, recall, and F1 score of 1.00. Additionally, SACNN exhibits the highest computational efficiency, averaging 0.20883 s per sample. These findings highlight SACNN's efficacy in detecting and classifying emergency vehicle sirens and road noise in large-scale datasets, with implications for traffic management, public safety, and noise pollution monitoring. SACNN offers the potential for constructing real-time acoustic data detection systems.

Crosstalk between Oxidative Stress and Inflammation Caused by Noise and Air Pollution-Implications for Neurodegenerative Diseases.

Neurodegenerative diseases are often referred to as diseases of old age, and with the aging population, they are gaining scientific and medical interest. Environmental stressors, most notably traffic noise and air pollution, have recently come to the forefront, and have emerged as disease risk factors. The evidence for a connection between environmental risk factors and neurodegenerative disease is growing. In this review, the most common neurodegenerative diseases and their epidemiological association with traffic noise and air pollution are presented. Also, the most important mechanisms involved in neurodegenerative disease development, oxidative stress, and neuroinflammation are highlighted. An overview of the in vivo findings will provide a mechanistic link between noise, air pollution, and neurodegenerative pathology. Finally, the importance of the direct and indirect pathways, by which noise and air pollution cause cerebral damage, is discussed. More high-quality data are still needed from both epidemiological and basic science studies in order to better understand the causal connection between neurodegenerative diseases and environmental risk factors.

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.