Top PDF ROAD TRAFFIC AS A SOURCE OF NOISE POLLUTION (CASE STUDY: TRSTENIK MUNICIPALITY)

ROAD TRAFFIC AS A SOURCE OF NOISE POLLUTION  (CASE STUDY: TRSTENIK MUNICIPALITY)

ROAD TRAFFIC AS A SOURCE OF NOISE POLLUTION (CASE STUDY: TRSTENIK MUNICIPALITY)

1. AMSS Crossroads – settled at the entrance to Trstenik. It is categorized as: city center, craft, commercial, administrative zone with apartments, zones along highways, main and urban roads (belt exposed to direct noise from the main road, 25m width, on both sides of the road (allowed noise level - day and evening 65 dB, night 55 dB) Measurement was carried out from the yard of the Auto-Moto Association of Serbia, in the vicinity of the intersection of two very busy streets, surrounded by residential buildings and noise from traffic.
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Evaluation of Road Traffic Noise Pollution in Quetta (Pakistan)

Evaluation of Road Traffic Noise Pollution in Quetta (Pakistan)

Noise pollution is the consequence of urbanization and industrialization and is considered as major problem of urban areas. The most important factors raising noise pollution in urban areas include vehicular traffic, neighborhood electrical appliances, TV and music systems, public address systems, and railway and air traffic. Increase in noise level, across the world, has motivated the researchers of the world to this study the problem and its impact on the environment. Also, researchers have reported that the road traffic noise is the leading source of noise in urban areas [1]. Noise pollution is not only the growing problem of developing countries but also of the developed countries. According to researchers, over 130 million people in Europe suffer from exposure to noise levels above 65 dB(A) [2].
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Exposure to aircraft and road traffic noise and associations with heart disease and stroke in six European countries: a cross-sectional study

Exposure to aircraft and road traffic noise and associations with heart disease and stroke in six European countries: a cross-sectional study

Data on air pollution co-exposures at a comparable spatial resolution to that for road traffic noise were avail- able for three countries. The results from this subsample analysis suggested that associations between road traffic noise and ‘heart disease and stroke’ were confounded by air pollution, although the smaller number of cases in- creased the uncertainty of the estimates. However, the associations between aircraft noise and ‘heart disease and stroke’ did not appear to be affected by adjustment for air pollution. In relation to aircraft noise, these re- sults are consistent with previous studies in that associa- tions between aircraft noise and MI or CHD mortality have not been found to be confounded by exposure to air pollution [8,9]. The results regarding road traffic noise are consistent with a cohort study in the Netherlands which found the association between road traffic noise and cardiovascular mortality reduced after adjustment for black smoke and traffic intensity on the nearest road [15]. However, our results differ from four studies which found an independent effect of road traffic noise after adjustment for air pollution: cohort studies in Canada [9] and Denmark [10,14] and a case–control study in Sweden [13] found increased risks of CHD, MI and stroke in relation to traffic noise. Differences between studies on whether air pollution is confounding associations between road traffic noise and cardiovascular disease [18] may result from differences in the local characteristics of study areas, given that the spatial correlation between noise and air pollution is influenced by urban design features and local meteorological conditions [16,41,42].
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A study on ambient and traffic noise pollution in Vijayawada, Andhra Pradesh, India

A study on ambient and traffic noise pollution in Vijayawada, Andhra Pradesh, India

Noise is unwanted sound that is harmful, annoying, causes disturbance and may adversely impact the work efficiency and hearing. Increased activities and needs in modern urban life are causing severe noise pollution. Noise pollution due to automobiles is a major source of pollution in urban areas. growing vehicle population in urban regions in the recent years, has resulted in tremendous increase in traffic on roads causing alarming noise pollution, besides air pollution. Traffic noise is affected by factors like traffic volume, vehicle mix, pavement type and vehicle condition (Marathe, 2012). Noise level increases with traffic volume in an exponential manner and depends on several parameters such as source, medium, vehicle speed, road conditions, distance from source etc. (Vilas and Nagarale, 2013, Suhas and Adavi, 2015). Hence, the overall noise is dependent on the characteristics of the vehicle and the relative proportions of the vehicle types included in the flow. People generally hear sounds between the “threshold of hearing” and the “threshold of pain”. In terms of pressure, this is 20 μPa – 20 kPa (Garg, 2014). Noise level is measured in terms of decibels (dB). The Noise levels are measured using a sound level meter and calculated values such as L10
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“Noise pollution”: an overview

“Noise pollution”: an overview

Road traffic is the most widespread source of noise in all countries and the most prevalent cause of annoyance and interference. It is directly proportional to the volume of vehicles. Increasing of population is increasing of vehicles and hence increasing of Noise pollution. The major sources of noise in automobiles are exhaust, intake, engine and fan, and tires at high speed. The noise output of all components increases with speed. The Road traffic noise not only depends on volume of vehicles and also depends on several factors; some of them are Road conditions, Traffic clearance, Condition of vehicles, Speed of the vehicle and the people living near roadside (highway) are mainly exposed. For example the study conducted around the main roads inside the urban perimeter of Curitiba, simultaneous measurements were done regarding noise levels, vehicle flow and traffic composition and thus some mathematical models have been developed in order to estimate those sound pressure levels. It was confirmed that people living or working in these areas are exposed to noise levels beyond the legislated norms (Calixto et al., 2003) and the two models for predicting in-city road-traffic noise pollution of Mashhad were obtained by Rahmani et al., (2010). Rail Traffic Noise
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Scenario of Traffic Noise Pollution and its impact on human health: An Empirical Case Study of Kolkata City

Scenario of Traffic Noise Pollution and its impact on human health: An Empirical Case Study of Kolkata City

Noise pollution is the most significant worldwide environmental issues. Worldwide every civilized society suffers a lot from environmental pollution such as air, soil, water and noise pollution is not an exception in this regard. Out of the several sources of noise pollution vehicular traffic is one of the more prevalent and damaging source of noise. Vehicular traffic noise is one of those everyday pollutions one comes across and it is very much evident in the city of Kolkata. These noises are not only annoying in ambience nature but also have adverse impact on human health among the people in the city. Though this is ignored by the human being in everyday life but in reality this is killing many persons slowly. Therefore due to noise pollution different effect could me immanence on human being such as Sleep *Corresponding author: Dr. Indrajit Roy Chowdhury
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A CASE STUDY ON NOISE POLLUTION FOR VEAVY TRAFFIC ZONE AT AKOLA CITY

A CASE STUDY ON NOISE POLLUTION FOR VEAVY TRAFFIC ZONE AT AKOLA CITY

Noise is generally described as unwanted sound and wholly subject to personal tastes and tolerance levels. In addition, the sensitivity of the human ear to noise depends on a number of contextual factors which typically include wind factor, humidity, traffic density, etc. Nevertheless, it is generally accepted that a 55 dB(A) sound will be disturbing whereas a 65 dB(A) noise level will be deemed intolerable, causing severe sleep disturbance. Much of this is caused by traffic-related sound originating from all modes of transport. Operational noise from transportation system alone contributes about 70 % of total noise, whereas road traffic noise is responsible for 55 % of total noise. Ambiant Air Quality Noise Standards (Aaqns)
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Environmental noise exposure, early biological risk and mental health in nine to ten year old children: a cross-sectional field study

Environmental noise exposure, early biological risk and mental health in nine to ten year old children: a cross-sectional field study

Conservative bias is expected from the latter, where those with missing data generally had lower socioeco- nomic status and thus higher rates of mental health pro- blems. In terms of the measures used, the parent-rated version of the SDQ may underreport internalising disor- ders [23], however, a self-report version is not available for this age group and may be less reliable [24], thus the parent-rated version represents the most appropriate measure under such circumstances. An additional tea- cher-rated SDQ would have improved the identification of externalising disorders but was not possible because of teacher burden [23,25]. The early biological risk vari- able combined information on gestation and birth weight preventing assessment of the individual contribu- tions of each variable, however such variables are likely to be highly related. Further, the early biological risk variable relied upon parental reports. Parents of children with early biological risk may be sensitised to perceive vulnerability in their children which could lead to increased reports of mental health problems for such children. Road traffic is a source of both noise pollution and air pollution which have both been linked to mental health outcomes [12,26]. This study did not adjust for the effects of air pollution which may act as a confoun- der or have a synergistic effect when coupled with noise [27]. Despite these limitations this study benefits from the fact that a range of noise levels were investigated and that the findings adjusted for multiple sociodemo- graphic status factors.
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Effect of Road Traffic Noise Pollution in Cuddalore Town: A Case Study

Effect of Road Traffic Noise Pollution in Cuddalore Town: A Case Study

Abstract: The paper suggests that vulnerable institutions like schools and hospitals should be located about 50m away from the roadside unless any special arrangement to alleviate sound is used. Noise measurements were taken at seventeen chosen areas with high population density, heavy traffic, commercial and residential buildings. At each sampling site six readings were taken after an interval of every 45 minutes in June 2012. Average, maximum and minimum values were calculated and compared with standards prescribed by the Central Pollution Control Board. Noise pollution was measured and analyzed and it was noticed that the maximum average of noise level was measured. The major contribution of the traffic noise, towards overall noise pollution scenario, is a well-known established fact. Traffic noise from highways creates problems for surrounding areas, especially when there are high traffic volumes and high speeds. Vehicular traffic noise problem is contributed by various kinds of vehicles like heavy, medium trucks/buses, automobiles and two wheelers. Amongst, noise pollution is an important type, which causes more annoyance and health problems to the human beings. The present work discusses the fundamentals of acoustics and analysis of vehicular traffic noise. The vehicles represent the most important noise source. It was estimated to about 80% from road vehicles. Road traffic noise is one of the most widespread and growing environmental problems in urban areas. The impact of road traffic noise on the community depends on various factors such as road location and design, land use planning measures, building design, vehicle standards and driver behavior. In the study area 17 locations are identified to measure noise level. By using sound level meter noise levels are measured at different peak sessions i.e. morning, afternoon and evening.
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Study of RStudy of Road Condition & Traffic in accordance with Road Safety Case Study of NH 44oad Condition & Traffic in accordance with Road Safety Case Study of NH 44

Study of RStudy of Road Condition & Traffic in accordance with Road Safety Case Study of NH 44oad Condition & Traffic in accordance with Road Safety Case Study of NH 44

Accidental rate of all four stretches was studied and frequency of accidents on each stretch was also calculated. From the Table 5 and 6 it is observed that frequency and rate of accident is more for stretch-4 followed by stretch-1,2,3 respectively. Fig. 4.1 shows the annual variation in accidents of total stretches during year 2002-2017. It is observed that percentage accidents are increasing relatively in most of the year. In the year 2013 accident rate was high and low in the year 2002.It may be due to increase in no of vehicles, bad traffic environment, and increase in population. Accident rate was increased from 2014 to 2016 from 89 accidents in 2014 to 136 accidents in 2016.Kashmir Division came under devastating floods in year 2014 and roads got blocked for almost 3 months which subsequently lead to decrease in traffic flow and accident rate thereby got decreased. Due to introduction of Bye Pass (NH 44) on existing NH 1A bottle necks of Khanabal crossing in Anantnag District , Bijbehara T- Intersection and Padgampora T intersection in Pulwama District of Kashmir division got nullified for National Highway Traffic right from Qazigund to Srinagar. These bottle necks were the major problems for the smooth flow of traffic on National Highway 1A which were corrected and good results were achieved in terms of reduction in accident rates from 136 accidents in 2016 to only 99 accidents in 2017.
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Genesis of Road Traffic Noise on Human Health

Genesis of Road Traffic Noise on Human Health

The degree is 67% to 100% with general %age of 96%. Regardless, % of such people in age social affair of >60 years is intangibly lower. Larger piece of respondents across finished different ages clusters feel that car noise impacts their activities. A reasonably little degree of respondents (half across finished diverse age clusters) perceives adversarial effect of noise created by vicinities. The general %age of respondents assert that Noise beginning from religious limits impacts them. At the point when all is stated, beside the boisterous speakers and vehicles, religious limits, likewise neighborhoods go about as gigantic wellsprings of noise. Thusly, urban zones are transforming into a loss of new class of noise. By and large, aside from the loudhailer and vehicles, sacred event, too nearness goes about as critical wellsprings of noise pollution. In this manner, urban areas are turning into a casualty of new class of pollution i.e. noise.
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Comparisons on the perceptions of reproduced urban soundfields and urban soundscapes : a mixed model approach

Comparisons on the perceptions of reproduced urban soundfields and urban soundscapes : a mixed model approach

These two dimensions, ‘motivation-affordance fit’ and ‘mediation’ are of particular significance not only because they explain the greatest variance but because they align sonic descriptors (such as ‘artificial’) with appraisals (such as ‘like’). The third and fourth dimensions do not do this and may be deemed the ‘left-overs’ – the sound entity/spatiality semantics that did not play a role in the subjects’ judgements of motivation-affordance fit in the first dimension. Their grouping merely reflects their lexical meaning. The second dimensions generally contained all the sound content/communication semantics for the subjects’ mediations. It is hypothesised that if more content/communication differentials had been included, there would have been another dimension of sound content/communication ‘left-overs’ too. The fewer number of content differentials than entity differentials meant this was not the case with the 18 semantic differentials used.
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Urban Noise in a Metropolitan Towns

Urban Noise in a Metropolitan Towns

Within the last few years, concern about the protection of the environment has grown rapidly as it has become generally recognized that steady rise in pollution of all kinds cannot be allowed to continue indefinitely. The acoustic environment has likewise suffered from the increase in use and power of the machines in the workplace, increasing road traffic, larger aircrafts etc. To combat this, many countries have introduced legislation making it a legal requirement to measure noise levels to reduce noise from vehicles at the source and maintain acceptable noise levels in factories to prevent hearing loss. India has emerged as fast developing country resulting in an in- crease in activity of the workforce. In 1989, Central Pollution Control Board (CPCB) [13], promulgated the Ambient Air Quality Standards for Noise, hereby establishing the noise limits for residential, commercial and silence zone areas. For assessing the urban noise problem and suggesting the mitigation measure, it is imperative that accurate data be obtained/measured of the noise levels at different locations at different times of the day.
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Road traffic noise and children’s inattention

Road traffic noise and children’s inattention

mothers categorized as unexposed to rail traffic noise had residential address outside a radius of 700 m from a rail- way line and 300 m for trams and metros, since outside these radii, the rail traffic noise is either nonexistent or is so low that it is masked by other noise sources. The noise exposure assessment was based on input data for the years 2011 and 2006 and included data on topography, building polygons, traffic counts (but estimations for smaller roads without counts), estimated values for 24 h traffic distribu- tion (75% day, 15% evening and 10% night for highways, and 65%, 20% and 15% for municipal roads), signed speed, information on noise barriers, and ground surface (hard or soft). The search radius of 1000 m was used for high- ways, and 500 m for municipal roads. Residential exposure to rail traffic noise was modeled separately and in a similar way as road traffic noise. For rail traffic, rail time tables were used to obtain information on traffic volume and diurnal distribution of traffic.
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Developing a Composite Index of Child Road Safety in a Municipality

Developing a Composite Index of Child Road Safety in a Municipality

Prior to the analysis, the data collected were arranged in a form suitable for a quantitative analysis—see Ap- pendix A. In addition, in each group, the basic indicators were examined aiming to recognize those having a more “summary” character compared to others, i.e. providing more focused message as to the municipality’s perform- ance in the domain considered. For example, considering the Injury domain (Table A1), among 12 basic indicators, 5 were selected as summary ones: the rates of children killed and injured, per population, in the municipality’s territory; percentage of children out of total traffic inju- ries and the shares of children injured as pedestrians or vehicle passengers, in the town. This selection was based on the assumption that authorities’ responsibility is more relevant for cases observed in the municipality’s territory as opposed to those that happened to the town’s residents throughout the country. Similarly, in the group of behav- ior indicators concerning bicycle riders (Table A3(b)), the final share of children wearing bicycle helmets in the municipality was recognized as a summary indicator on the subject (compared to other indicators which mostly describe the riding conditions).
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Soundscapes of Urban Parks in and around Bhubaneswar and Puri, Odisha, India: A Comparative Study

Soundscapes of Urban Parks in and around Bhubaneswar and Puri, Odisha, India: A Comparative Study

at 20°15' North Latitude and 85°52' East Longitude, while Puri is located at 19°48' North Latitude and 85°51' East Longitude. The former is the capital of Odisha State and the later, one of the imperative holy tourist places of India, situated on the coast of the Bay of Bengal, 60 km off Bhubaneswar. Noise descriptors in and around different selected parks of Bhubaneswar and Puri were measured and analyzed. The criterion for parks selection was mainly based on the fact that the studied parks were located in the areas of great urban density, surrounded by streets with heavy vehicle traffic (Zannin et al., 2006). Table 1 gives the other basic information about the studied parks. The measurements were taken on the foot paths of these parks (Zannin et al., 2006). Thus, the locations and number of measurement points cover the whole area of the parks, corresponding to the sites frequently visited by the parks' visitors.
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Application of GIS in Urban Traffic Noise Pollution

Application of GIS in Urban Traffic Noise Pollution

points data collected in the field using calibrated sound level meters CELL-450 and Quest-2900. Preliminary traffic information was obtained through city authorities. Measurements were done at the traffic peak time and also when the traffic was at its minimum, during three successive months. The numbers of crossing vehicles as well as their speeds were documented. All data processing was carried out in ArcGIS, and SPSS-W software environment. The study area of this research was the first district of Tehran, the capital city of Iran that is located in latitude 35° 45', North and Longitude: 51° 30', East (Fig. 1). The district faces a heavy traffic jam, and as such all major roads are the subjects of this research. In the 20th century, Tehran faced a large migration of people from all around Iran such that the city population reached to 11 million people in 2006. More than 3 million cars are running in the city now. A survey of urban noise was done to determine the positions of measurements using the available city maps and Ikonos imagery. Four types of survey practices were identified: receptor-oriented, source-oriented, randomly chosen, and density-oriented sampling methods [ 10]. In order to reduce any systematic tendencies, random sampling was exercised in this research.
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Modelling the impacts of transport policies on the urban environment

Modelling the impacts of transport policies on the urban environment

Traffic calming has been implemented in a variety of forms, scales and creative designs, noticeably in continental Europe (Tolley, 1990). There is a growing appreciation by the local authorities in the U.K. of the attractiveness provided by traffic calming schemes. The most common traffic calming schemes are road humps and ‘20-mile-per-hour’ speed zones. They can consist of speed bumps, cushions, ramps or tables, rumble strips, pinch points, chicanes, width restrictions, mini-roundabouts, speed control islands, pedestrian refuges, footway widening, entry treatment, traffic throttle, loop road, road depression, raised plateau, road closure, signs and road markings, culs-de-sac, pavement peninsula, etc. Traffic calming may be applied in conjunction with other street furniture, design paving, trees, seats and other pedestrian or cycling facilities. There are no rules for the adoption of particular types of traffic calming schemes. However, central governments can provide advice and regulations about where and how traffic calming measures can be used. Special regulations have been made in Britain by the Department of Transport in relation to road humps, which have recently relaxed previous stringent controls, giving more freedom in the siting of their features (Hass-Klau et al., 1992).
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Traffic Demand Management In Three Historic Cities: Results Of A Multivariate Analysis of Business Attitudes.

Traffic Demand Management In Three Historic Cities: Results Of A Multivariate Analysis of Business Attitudes.

Of all of the possible business reactions to the introduction of traffic demand management policies in urban areas, the potentially most important in economic terms is the relocation of businesses out of the urban core. Any significant degree of business evacuation of the urban core would have a profound impact on the ability of the urban economy to support the local population. In addition, any spatial restructuring of the local economy would have implications for traffic flows, shifting the locations of major traffic attractors from the urban core to the periphery. Although this may alleviate congestion in the urban core, it may serve only to create congestion elsewhere rendering traffic demand management policies somewhat counter- productive in the long run.
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PEDESTRIAN RESPONSE TO ROAD TRAFFIC NOISE FOR MEDIUM SIZE CITY IN INDIA

PEDESTRIAN RESPONSE TO ROAD TRAFFIC NOISE FOR MEDIUM SIZE CITY IN INDIA

noisy. It can be seen from the Table 4 that the proportion of individuals exposed to environmental noise levels exceeding the standard values in most of the selected study locations during day time. From this study, it was observed that at residential area, noise level varied from 67.7 dBA to 77.1 dBA which should not exceed 55dB (A) as per Indian standard. As previously stated that the guideline value above have recommended by ARAI in India, noise emissions should not exceed 65 dBA during daytime in commercial area but in this survey this value reaching maximum up to 87.4 dBA and Leq is 82.3 dBA. There is need to control noise pollution in Roorkee.
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