Traffic Noise Pollution Research Articles

Traffic noise induces oxidative stress and phytohormone imbalance in two urban plant species

Traffic noise pollution is one of the major environmental concerns in crowded cities worldwide. The objective of the present study was to investigate the effects of traffic noise on growth, hormonal balance, oxidative damage, and activity of antioxidant systems in two urban plant species, Tagetes patula and Salvia splendens. Each of the plant species were equally divided into 2 groups (control and traffic noise treatment) and each group was grown under identical controlled conditions in two separate growth chambers for two months. Traffic noise was recorded during peak traffic hours in a highly congested area of the city. Frequency analysis of traffic noise was conducted on the samples during the recording process. Plants in the traffic noise treatment group were exposed to 16 h of road traffic noise each day for a total of 15 days, while the control group was kept under complete silence. Traffic noise exposure led to significant decrease in growth indices of both plant species. The content of H2O2 and malondialdehyde (MDA) significantly increased upon traffic noise treatment in both species, followed by an increase in DPPH (2,2-diphenyl-1-picrylhydrazyl) anti-oxidant activity and the activity of antioxidant enzymes including catalase, peroxidase, and ascorbate peroxidase. Road traffic noise significantly reduced the content of phytohormones including zeatin, salicylic acid, indole-3-acetic acid, and gibberellic acid. On the other hand, the content of stress-related hormones including abscisic acid and jasmonic acid significantly increased in response to road traffic noise in both species. Based on our results, daily traffic noise can negatively affect growth and physiology of plants by inducing of oxidative damage and interfering with hormonal balance.

Pemetaan Bunyi Bising Trafik Menggunakan Teknik Interpolasi Inverse Distance Weight Kajian Kes Di Bandar Kulim

Traffic noise pollution is one of the problems in the urban environment as a result of rapid economic development which has led to an increase in the volume of diversified motor vehicle traffic flows. This condition may affect the well-being and human health as well as its surroundings if exposed to high noise levels for a long period of time or continuously. Therefore, this study was conducted to measure the level of traffic noise at 15 selected observation stations around Kulim city, Kedah. Next, the noise distribution coverage area is mapped by using Inverse Distance Weight (IDW) interpolation technique. Sound data was measured in the morning, noon, evening and night of working and non-working days by using the Sound Level Meter device. Sound observation data was analyzed to depict the concentration areas of traffic noise at the station involved in form of map visualization. Noise level comparisons were analyzed together with Department of Environment (DOE) and World Health Organization (WHO) standards. The results of the study found that all the sampling stations exceeded the standard limits allowed on working days as well as non -working days for urban and commercial land-use types. The mean values of the highest noise levels (Leq) on working days and non-working days at day times were 78.2 (S7) and 80.3 dBA (S5) respectively. Meanwhile, the highest noise level readings recorded on working days and not working at night were 76.6 and 76.9 dBA respectively, which involves observation stations 2 and 3. Several mitigation strategies were stated to overcome the problem of traffic noise in the city as a measure to curb noise pollution in the affected areas.

Evaluation of traffic noise pollution using geographic information system and descriptive statistical method: a case study in Mashhad, Iran.

Environmental consequences and the epidemiologic results of noise pollution have chronic effects leading to widespread complications in the long run. As far as we know, there are a few studies for pollution monitoring and control systems in comparison with other environmental pollutants. One of the largest metropolitan cities located in Iran is Mashhad city as known as one of the biggest religious cities in the world. Different properties of this city including historical, industrial, and religious draw thousands of visitors to Mashhad, yearly. This fact motivates us to contribute to the concept of noise pollution in streets and sidewalks around the Holy Shrine, namely, Imam Reza. In this regard, different measurements using geographic information system (GIS) and descriptive statistical methods were conducted for our case study in Mashhad, Iran. All measurements and records were done during the peak of morning crowd (10–12 AM) and evening crowd (4–6 PM) on both sidewalks of each street around the Holy Shrine. This study showed that the pollution in the evening time span (4–6 PM) has the maximum level of noise. Among all streets in our case study in Mashhad, Iran, Tabarsi street has the most amount of noise pollution with a mean of 78 dB(A) for the mean intensity for each point, and Imam Reza street has the minimum amount of pollution with a mean of 72.75 dB(A). Our findings from the temporal perspective analysis confirm that the noise pollution peaks in the evening, when weather conditions are favorable. From the spatial perspective analysis, the most intensive noise pollution was observed around residential and accommodation land uses, which have the highest number of arterial routes towards the Holy Shrine.

Traffic noise prediction using machine learning and monte carlo data augmentation: a case study on the Patiala city in India

Traffic noise pollution is a serious problem in the modern urban areas especially to design new architecture of smart cities, highways, hospitals, schools for an efficient and healthy environment. To analyse this aspect, we have proposed a machine learning based prediction of sound pressure level on an original dataset collected in Patiala city in India. Vehicular traffic and sound pressure level data was collected on different sites in the city. A total of 502 data samples on the identified sites were obtained for the study. Further this data is augmented using Monte Carlo simulation to 10 times of its initial size and the Artificial Neural Networks (ANN) have been trained and compared with other Machine learning methods for the vehicular traffic noise prediction. The input parameters in the model are traffic volume Q, percentage of heavy vehicles P and the average speed of vehicles V and the output parameter is the equivalent continuous sound pressure level, Leq dB(A). The experimental results show ANN which is trained on the augmented data using Monte Carlo simulations outperforms other advanced methods making it an effective measure for vehicular traffic noise prediction to develop a healthy environment which is free of noise pollution.

Electric vehicles can have only a minor role in reducing transport's energy and environmental challenges

<abstract> <p>Many governments have supported the introduction of electric vehicles (EVs) through purchase subsidies or waiving fuel taxes. The key findings of this paper are that the benefits of EVs may have been overstated, at least for some countries, as their energy savings and climate mitigation advantages depend on such factors as annual kilometres travelled per vehicle, electricity fuel mix, vehicle size and even local conditions. Because serious climate change has already arrived in the form of increasing frequency and severity of extreme events, we do not have the decades required for electricity production to be predominantly from non-carbon sources. Further, there are a variety of other challenges facing private transport—EVs just as much as conventionally powered vehicles. These include traffic casualties, non-engine air and noise pollution, light pollution, land requirements for roads and parking, and the intrusion of roads into natural habitats. With the promotion of EVs, these other transport problems run the risk of being downplayed. If all the environmental challenges facing road vehicles are to be effectively and quickly tackled, significant reductions in road vehicular travel are needed.</p> </abstract>

Urban road traffic noise monitoring, mapping, modelling, and mitigation: A thematic review

Abstract This article analyses the most recent studies on urban traffic noise. About 67 relevant articles on urban road traffic noise and its mitigation strategies were preferred for a critical review. Only 5.97% of items describe how to monitor and record the noise measurement for urban roads, while 7.46% of articles enumerated urban traffic noise pollution exposure. 29.85% of articles proposed a model to evaluate noise reduction effects and predict the noise level. Also, many articles reported noise map generation and its analysis. 56.71% of articles described the noise mitigation strategies in detail, concerning noise control by green vegetation, land use planning, low noise tire and pavement material, noise reduction through façade shielding. Noise pollution standards are being breached in all areas. There is a need for the proper implementation of rules and regulations. Therefore, noise mitigation strategies such as designing noise barriers and other noise control materials are needed. Finally, it is summarized that economic and low-cost optimized noise pollution mitigation strategies like ingeniously made noise barriers, vegetation and landscaping are need of the hour for urban areas of developing countries.

Appraisal of Urban Road Traffic Noise in tier-II City (Surat City), India

Urban road traffic noise pollution has always been identified as a severe problem that affects urban populants. In developing nation, road traffic noise pollution depends on the composition of heterogeneous traffic composition. These traffic compositions contain vehicles, which have different sizes, speeds variations, a different dimension of vehicles. Environmental noise measurements have been carried out during day-time and night-time in different locations of tier-II city of India. The noise levels have been continuously measured over 24 h periods using kimo DB 300 class-2 noise level meter. The data contained in this research paper represent 768 measurement hours. All the information has been used to investigate the time patterns of the noise levels under a wide range of different conditions and to study the relationships between noise levels and traffic in urban areas. Maximum LAeq was observed 73.3 dB(A) at B14 location and the minimum was recorded 65.7 dB(A) at C3 location, which was greater than the central pollution control board (CPCB) prescribed limits during night time. A major reason for the generation of road traffic noise is due to the equal composition of 2-wheeler and 4-wheeler on the arterial road and heavy vehicles were recorded during morning peak and evening peak even though they are prohibited during peak hours.

Optimized design research of fully enclosed noise barrier

Fully enclosed noise barrier has been used to prevent and control traffic noise pollution because of its effectiveness. It has become the best solution for controlling environmental noise of high-rise buildings from expressways, urban viaducts and railways that cross the crowded downtown area. However, its high cost has become an important retarding factor on its application and popularization, so reducing the cost become an urgent problem. The research theory, structure frame, sound absorbing and insulating materials are investigated for the fully enclosed noise barrier, so as to provide technical solutions for optimizing the design scheme and reducing the construction cost.

Schools Traffic Noise Pollution Levels Along Federal Roads in Muallim District, Perak, Malaysia

This article aims to identify the school traffic noise level along federal roads in Muallim District, Perak, namely SMK Khir Johari (SMKKJ), SMK Agama Slim River (SMKASR) and SMK Dato’ Zulkifli Muhammad (SMKDZ). Data were collected during school hours and on weekends, from 7 September to 13 September 2019 and three times a day, 7.00 -8.00 am, 9.00 -10.00 am and 12.00 -1.00 pm. Data was collected using Digital Sound Level Meter AS804, which was mounted on a tripod approximately one meter high from the road surface to prevent road sound reflection. Sound recordings were performed using decibel scale weights (dBA) and values for Max (Maximum Noise Level), Min (Minimum Noise Level), Leq (Average Overall Noise) with five minutes time delay and descriptive analysis. The findings show all stations values exceeded the standards set by the Malaysia Department of Environment (DOE) (60 dBA). SMKKJ shows the Min value is 57.7 dBA, the Max value is 94.5 dBA and Leq 76.1 dBA during working hours. In conclusion, the study showed that all stations recorded high noise during working hours and non-working hours surpass the standard set by DOE during the day. The need for the Malaysian Ministry of Education to assess and plan to reduce this noise level through short-term and long-term measures.

The built environment as determinant of childhood obesity: A systematic literature review.

We evaluated the epidemiological evidence on the built environment and its link to childhood obesity, focusing on environmental factors such as traffic noise and air pollution, as well as physical factors potentially driving obesity-related behaviors, such as neighborhood walkability and availability and accessibility of parks and playgrounds. Eligible studies were (i) conducted on human children below the age of 18 years, (ii) focused on body size measurements in childhood, (iii) examined at least one built environment characteristic, (iv) reported effect sizes and associated confidence intervals, and (v) were published in English language. A z test, as alternative to the meta-analysis, was used to quantify associations due to heterogeneity in exposure and outcome definition. We found strong evidence for an association of traffic-related air pollution (nitrogen dioxide and nitrogen oxides exposure, p < 0.001) and built environment characteristics supportive of walking (street intersection density, p < 0.01 and access to parks, p < 0.001) with childhood obesity. We identified a lack of studies that account for interactions between different built environment exposures or verify the role and mechanism of important effect modifiers such as age.

Quiet Route Planning for Pedestrians in Traffic Noise Polluted Environments

Noise pollution is one of the main stressors in urban environments, having negative impacts on people’s quality of life and health. For some groups of citizens, such as school children, patients, and elders, there is a need to support them in finding pedestrian routes in noise polluted areas of cities. In this paper, we focus on the estimation of traffic noise, and present an approach to provide quiet routing services, taking into account the estimated noise levels of roads. By combining Volunteered Geographic Information, official socio-economic data, and open-access GPS trajectory data, we develop a set of traffic related variables, and apply machine learning methods to perform traffic volume estimations. Given the estimated traffic information, an existing traffic noise model is then employed to derive the noise polluted areas. For generation of quiet routes, a new routing algorithm is proposed based on the Dijkstra algorithm. It minimizes the exposure of pedestrians to traffic noise pollution while taking into account the route distance constraint. We apply our quiet routing approach to the city of Heidelberg (Germany). The application results demonstrate the efficacy of our algorithms in the generation of quiet routes customized to pedestrian preferences.

Assessing Strength of Plastic Paving Blocks for Construction of Non-Motorised Transport Facilities.

Walking remains the oldest and most common mode of transport making up a portion if not most or all our daily commute. However, infrastructure developments in our urban areas have a bias towards motorised transport leading to pedestrians and cyclists being the most affected in road incidents. Increasing non-motorised transport facilities in our urban centres would increase levels of walkability, reduce the number of crashes involving non-motorised transport users, reduce traffic congestion, noise and air pollution making them sustainable and more liveable. Three hundred million tonnes of plastic waste are produced annually in the world with Kenya contributing 407,506 tonnes. The waste produced shall continue to increase with increasing population, urbanisation and development. The non-biodegradability of plastic makes proper disposal of plastic waste a menace and due to widespread littering, plastic waste has become a common sight in our urban and rural areas. The conventional materials used for the construction of walkways and cycle lanes, asphalt and concrete are most ideal for the motorised transport pavements. Plastic paving blocks can be affordable, readily available and environmentally friendly making a suitable alternate construction material if adopted. This study assessed the strength of plastic paving blocks for use in the construction of walkways and cycle lanes; by undertaking a tensile strength test according to SANS 1058:2021 on samples provided by Corec Kenya. The samples satisfied all of the strength specifications and from this it can be recommended that the plastic paving blocks can be used on low traffic pavements such as walkways and cycle lanes.

Study of general health among older adult in ardakan city and its relation with traffic noise pollution at the residence Summary

Introduction: The body's response to noise pollution is very similar to the way the body responds to stress, which can lead to poor health over time. In this regard, this study was conducted to determine the general health of the elderly and its relationship with traffic noise pollution in Ardakan. Methods: This descriptive cross-sectional study was conducted in 1399. Participants were 200 elderly people in Ardakan and were included in the study by random sampling. Data were collected through sound measurement with SVANTEK device, General Health Questionnaire 28 (GHQ-28). After completing the questionnaires and measurements, the collected data were entered into SPSS-19 software and descriptive statistics as well as analytical statistics including: Chi-square, regression and analysis of variance were used to analyze it. Results: The overall average sound level equivalent in decibels at all measuring stations was within the standard range. There was a significant relationship between noise annoyance and the dimensions of "anxiety symptoms and sleep disorders" and "depressive symptoms" of general health (p &lt;0.001). Conclusion: People with more voice annoyance have more anxiety, sleep disorders and depression. Existence of noise pollution levels in the allowable or standard level can lead to the observation of no significant relationship between traffic noise pollution levels and general health. Reducing anxiety, depression and sleep disorders in the elderly is possible with interventions to reduce voice irritation in them.

Relationship Analysis between Vegetation and Traffic Noise Pollution: A Case Study Lahore, Pakistan

The problem of exposure to traffic noise pollution is rapidly increasing day by day and is closely associated with the rapid industrialization and urbanization process taking place over the glob (Margaritis and Kang, 2016). Nowadays, more than 54% of the world’s population lives in urban areas, a proportion that is expected to increase to 66% by the year 2050 (United Nations, 2018). The special effects of vegetation cover on propagation of sound have been the theme of much debate for a number of years (Bell et al., 2007). There is increasing evidence that this is not always true and a significant reduction of noise may be accomplished through vegetation cover if present in adequate density and depth (Peng et al., 2014). Development in urban transportation is increasing day by day. Transportation of goods from one place to another place has become very easy. Specifically, in urban areas, it is common to move people and goods from one place to another. The use of transportation in terms of vehicles makes noise. The noise of vehicles causes many problems including health issues (Monazzam et al., 2015). Noise pollution is very dangerous for human health (Selander et al., 2009). It is also very harmful to the environment. It adversely affects the quality of the environment. Noise pollution badly affects mental health. It develops aggressiveness in public. It leads to higher blood pressure. It increases the stress level. 65 dB of routine contact can cause hypertension. A level of noise above 75 dB can cause stress. It also causes heart diseases. It can cause hearing loss. The cases of noise pollution complaints are more than other environmental issues (Doygun and Gurun, 2008).

Flexible ceramic nanofibrous sponges with hierarchically entangled graphene networks enable noise absorption

Traffic noise pollution has posed a huge burden to the global economy, ecological environment and human health. However, most present traffic noise reduction materials suffer from a narrow absorbing band, large weight and poor temperature resistance. Here, we demonstrate a facile strategy to create flexible ceramic nanofibrous sponges (FCNSs) with hierarchically entangled graphene networks, which integrate unique hierarchical structures of opened cells, closed-cell walls and entangled networks. Under the precondition of independent of chemical crosslinking, high enhancement in buckling and compression performances of FCNSs is achieved by forming hierarchically entangled structures in all three-dimensional space. Moreover, the FCNSs show enhanced broadband noise absorption performance (noise reduction coefficient of 0.56 in 63–6300 Hz) and lightweight feature (9.3 mg cm–3), together with robust temperature-invariant stability from –100 to 500 °C. This strategy paves the way for the design of advanced fibrous materials for highly efficient noise absorption.

A Cross-Sector Framework to Boost the Sustainable Implementation of Integrated Transport and Spatial Strategies to Improve Safety and Mobility of Moto-Taxi Riders

Globally, motorcycle riders make up the biggest percentage of vulnerable road user fatalities. The percentages are higher in African cities where moto-taxi transport is not only the most readily available mode of transport but also a source of income for many especially the youth. Many African cities are re-strategizing on how to sustainably deal with problems like traffic congestion, unplanned development, flooding, air and noise pollution. The authors carried out a questionnaire survey targeting a variety of road safety stakeholders to identify road safety solutions that they think best fit the current situation. Another questionnaire survey targeting moto-taxi riders was carried out, and the data analyzed by SEM to identify how different solutions interact to affect the behavior of riders. Results show that education and training programs have the biggest effect on improving behavior of riders as they adapt to new city regulations such as zoning. Other aspects such as presence of customers, behavior of other road users, road environment were also explored. Currently, city authorities in some developing nations are looking to introduce spatial regulations such as zoning and traffic management strategies such as exclusive lanes. Therefore, the contribution of this study is a proposal of a combination of solutions that different road safety stakeholders in developing countries can contribute toward the sustainable improvement of the safety of riders.

Study on Noise Reduction with Paving Different Low Noise Pavement Materials

This paper evaluates the amount of noise reduction when using different pavement materials in two adjacent lanes, where the close-proximity (CPX) method is introduced to analyze the tire/road noise before and after pavement maintenance. We consider four types of pavement materials, including ECA-10, PUC-10, PAC-13, and double-layer porous asphalt pavement (PAC-13+PUC-10), where these materials and their combinations are paved on two adjacent lanes. We measure the tire/road noise with the CPX method using a two-wheel trailer that can install two types of tires in different tests. This study provides some guidelines on controlling traffic noise pollution by using the combination of low noise pavement materials. From the experimental and theoretical results, one can see that the highest amount of noise reduction can be obtained when both the inner and outer lanes use the double-layer porous asphalt pavement. To make a balance between the noise reduction performance and the road maintenance cost, one can have the suboptimal choice, where the inner lane uses PUC-10 and the outer lane uses the double-layer porous asphalt pavement.

Automatic Generation of Urban Road Planning Network Under Deep Learning

With the rapid advancement of China’s urbanization process and the rapid increase of the number of motor vehicles, now the vast majority of cities in China are faced with traffic congestion, environmental pollution, noise pollution and other problems. Facing these problems, a road network with reasonable structure, proper layout and sufficient capacity has become an important basic condition for the sustainable development of urban traffic system. This paper mainly studies the automatic generation method of urban road planning network based on deep learning. In this paper, a model based on deep neural network is proposed, which integrates the knowledge of road planning domain and generative adversarial network, and can realize the generation of road network simply and quickly.

Experimental Investigation on Sound Absorption Property of a Panel Board Using Maize Stem

In this project our aim is to produce a soundproofing board which is made out of Maize stem (stalk). Due to increase in number of industries, factories and vehicle traffic cause serious noise pollution. So to overcome this, use of Sound proofing panels in the structural elements could significantly help to reduce the sound as well as gives a good environment to work. People nowadays demand for a better working environment. Many commercialized sound proofing materials are available in the market, the cost of these materials would be much higher and also it could cause health issues when people are exposed with the material. So use of sound proofing panels made with agro-waste could be a better solution, an attempt has been made to produce a soundproofing board which is made out of stalk portion of maize plant. The board’s sound absorption property is studied in this research by using Impedance test and the cost of maize stem board is cheap enough so that everyone can afford it.

Modelling and Prediction of Road Transportation Noise Pollution in Some Capital Cities in Eastern Nigeria by Use of Artificial Neural Network

The study attempts to model and predict road transportation noise pollution in five capital cities in Eastern Nigeria. The capital cities are Calabar, Uyo, Umuahia, Owerri and Port Harcourt. Feed-forward neural network (FNN) with negative back-propagation algorithm was used to do this. The software used was NeuroXL. The ability of this software to handle multiple non-linear relationships makes it ideall y suited for this work. The input data used were total road traffic volume, road traffic mix, road traffic noise pollution response data, and distances from road centre-line to measurement points. The output data used was A-weighted energy mean sound level (L A eq). Models based on this negative back- propagation neural network were trained, validated and tested using data collected. The performance of the model was tested by an error measure, root mean square error (RMSE). RMSE is low as expected, ranges from 1.007 - 1.814, showing that the model is good for the prediction of road traffic noise data. The correlation between observed and predicted noise levels (LAeq) was also obtained, and ranges between +0.757 to +0.974, showing that there is no significant difference between observed and predicted noise levels, thereby, proving the model accurate and reliable. Keywords : Artificial neural network, back-propagation, road traffic noise modeling, road traffic noise prediction, NeuroXL software DOI: 10.7176/JEES/11-10-06 Publication date: October 31 st 2021