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Academic literature on the topic 'Noise pollution. Noise. Noise mapping'
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Journal articles on the topic "Noise pollution. Noise. Noise mapping"
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Mikhno, O., V. Dobrovolsky, and O. Mykolaenko. "EVALUATED MAPPING OF NOISE POLLUTION OF URBANIZED TERRITORY." Visnyk Taras Shevchenko National University of Kyiv. Military-Special Sciences, no. 1 (43) (2020): 61–65. http://dx.doi.org/10.17721/1728-2217.2020.43.61-65.
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Due to the acceleration of scientific and technological progress in the word, the level of traffic flows, industrialization, thenumber of automotive and technical equipment of the urban economy has increased, as a result of which people are constantlyexposed to high-intensity noise. However, few people know that, by its effect on the human body, noise is more harmful thanchemical pollution. Constant exposure to noise not only reduces hearing, but also causes other harmful effects – ringing in theears, dizziness, headache, increased fatigue. People working in noisy conditions have an increased level of neuropsychiatricdisorders.The article discusses the algorithm for evaluative mapping of noise pollution in the settlement area, which will ensurebringing the city territory to sanitary standards. The resulting noise map, which should be part of the master plan, captures thecurrent and future state of the noise regime in the city. Accurate noise maps require the analysis of large number of differentnoise sources and, as a result, are very costly, although in most cases only an estimate of the noise loading is required.Therefore, it is advisable to build a hierarchy of noise sources and take into account the most significant. Such the mostsignificant source of noise in settlements, as a rule, is a road network - streets and lanes on which movement of auto transport isallowed. The procedure for creating a geospatial model of noise pollution in the environment of a geographic information systemdescribed in the article includes creating a multibuffer for each noise source, translating the resulting vector image into a rastersurface, superposition of individual object noise rasters, and creating an integrated evaluated map. Formed ways to improve thenoise situation based on the results.
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Alías, Francesc, Rosa Ma Alsina-Pagès, Ferran Orga, and Joan Claudi Socoró. "Detection of Anomalous Noise Events for Real-Time Road-Traffic Noise Mapping: The Dynamap’s project case study." Noise Mapping 5, no. 1 (2018): 71–85. http://dx.doi.org/10.1515/noise-2018-0006.
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Abstract Environmental noise is increasing year after year, especially in urban and suburban areas. Besides annoyance, environmental noise also causes harmful health effects on people. The Environmental Noise Directive 2002/49/EC (END) is the main instrument of the European Union to identify and combat noise pollution, followed by the CNOSSOS-EU methodological framework. In compliance with the END legislation, the European Member States are required to publish noise maps and action plans every five years. The emergence of Wireless Acoustic Sensor Networks (WASNs) have changed the paradigm to address the END regulatory requirements, allowing the dynamic ubiquitous measurement of environmental noise pollution. Following the END, the LIFE DYNAMAP project aims to develop a WASN-based low-cost noise mapping system to monitor the acoustic impact of road infrastructures in real time. Those acoustic events unrelated to regular traffic noise should be removed from the equivalent noise level calculations to avoid biasing the noise map generation. This work describes the different approaches developed within the DYNAMAP project to implement an Anomalous Noise Event Detector on the low-cost sensors of the network, considering both synthetic and real-life acoustic data.Moreover, the paper reflects on several open challenges, discussing how to tackle them for the future deployment of WASN-based noise monitoring systems in real-life operating conditions.
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Alam, Pervez, Kafeel Ahmad, S. S. Afsar, and Nasim Akhtar. "3D noise mapping for preselected locations of urban area with and without noise barriers: A case study of Delhi, India." Noise Mapping 7, no. 1 (2020): 74–83. http://dx.doi.org/10.1515/noise-2020-0006.
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AbstractNoise pollution has been rising as a critical issue in recent days particularly for the people living in urban areas. This study has been conducted to find out the effects of traffic induced noise on nearby residential building through 3D noise mapping with and without noise Barriers. Monitoring has been carried out at various densely populated preselected locations of Delhi, India. Thereafter, 3D noise mapping has been done using hourly average noise levels for the locations exposed with maximum noise. The developed 3D noise map shows the variation of noise level along X, Y and Z direction for all selected locations before and after installation of noise barriers. Moreover, the result also shows that exact assessment of noise impact is possible through 3D noise mapping, when a multistory building close to the source of noise is taken into consideration. This paper also elaborates the adequate height, distance and NRC value of noise barrier to reduce the effect of road traffic noise on nearby high rise building. Reduction pattern of noise level can easily be visualized and evaluated by using these maps. This type of study could support decision makers during adaptation of suitable remedial measures.
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Aziz, Md Abdul, Ahasanul Karim, Md Mehedi Hassan Masum, and Kazi Kader Newaz. "Evaluation of Noise Environment and Noise Mapping with ArcGIS in Chittagong City, Bangladesh." Journal of Korean Society of Environmental Engineers 43, no. 4 (2021): 230–40. http://dx.doi.org/10.4491/ksee.2021.43.4.230.
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Objectives : Noise pollution in Chittagong City of Bangladesh is a big concern because of huge population growth and urbanization. The objective of the study was to evaluate and mapping the noise levels in twelve locations of this city at different periods.Methods : Noise level data were collected at various locations by a precision grade sound level meter and the noise pollution parameters (Lmin, LAeq,2h and Lmax) were calculated. Furthermore, the noise pollution maps of Chittagong City were developed using geographical information system to address the locations which are prone to the environmental hazards.Results and discussion : The study revealed that the average noise levels were 64.6, 76.9, and 75.5 dB(A) for residential, commercial and silence zones, respectively. The interpolated noise maps showed that noise environment of this city was unsatisfactory, especially, in the areas of New Market, Nasirabad, and Bawa School & College were exposed to high noise pollutions. The afternoon and evening periods were experiencing higher noise pollution in the commercial and silence zones than the morning period of the day.Conclusions : The study suggests that the noise levels are above the acceptable limit and hence urgent measures should be taken into consideration to control the level of noise pollution in the city.
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Sonaviya, Dipeshkumar R., and Bhaven N. Tandel. "Integrated road traffic noise mapping in urban Indian context." Noise Mapping 7, no. 1 (2020): 99–113. http://dx.doi.org/10.1515/noise-2020-0009.
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AbstractRoad traffic noise has been recognized as a serious issue that affects the urban regions. Due to urbanization and industrialization, transportation in urban areas has increased. Traffic noise characteristics in cities belonging to a developing country like India are highly varied compared to developed nations because of its heterogeneous conditions. The objective of the research study is to assess noise pollution due to heterogeneous traffic conditions and the impact of horn honking due to un-authorized parked vehicles on the main roadside. Noise mapping has been done using the computer simulation model by taking various noise sources and noise propagation to the receiver point. Traffic volume, vehicular speed, noise levels, road geometry, un-authorized parking, and horn honking were measured on tier-II city roads in Surat, India. The study showed not so significant correlation between traffic volume, road geometry, vehicular speed and equivalent noise due to heterogeneous road traffic conditions. Further, analysis of traffic noise showed that horn honking due to un-authorized parked vehicles contributed an additional up to 11 dB (A), which is quite significant. The prediction models such as U.K’s CoRTN, U.S’s TNM, Germany’s RLS-90 and their modified versions have limited applicability for heterogeneity. Hence, the noise prediction models, which can be used for homogeneous road traffic conditions are not successfully applicable in heterogeneous road traffic conditions. In this research, a new horn honking correction factor is introduced with respect to unauthorized parked vehicles. The horn honking correction values can be integrated into noise model RLS-90, while assessing heterogeneous traffic conditions.
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Dubey, R., S. Bharadwaj, M. I. Zafar, V. Bhushan Sharma, and S. Biswas. "COLLABORATIVE NOISE MAPPING USING SMARTPHONE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 253–60. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-253-2020.
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Abstract. Noise pollution is considered to be one of the most prevalent environmental challenges affecting human health. Noise pollution is increasing in cities needing techniques to monitor and predict the noise. The monitoring of traffic noise levels in different parts of the cities at different times has become very difficult due to logistic constraints. It is thus required to measure the noise levels at certain strategic locations, such as, near the noise sources (e.g., roads), and then to utilize it to predict the noise levels at surrounding locations. The challenge of monitoring the noise near several road crossings in a city can be reduced using a smartphone-based noise monitoring technique. However, the prediction of noise levels and showcase it as maps require terrain data, noise data, and noise prediction models. The requirement of terrain data can be met using open-source terrain data, from which various terrain parameters can be extracted and integrated with a standard prediction model on the web platform to predict the noise map for an area. Smartphone-based noise monitoring and its subsequent mapping can be a very popular and effective option, which uses a crowdsourcing approach. The entire methodology is tried to be applied over Lucknow city in India. Noise levels are monitored at three different slots, daily, over 14 road crossings using the smartphone-based app. Further, collected noise levels were calibrated against standard noise meter to ascertain accurate noise levels for these locations. Thereafter, three categories of noise environments are chosen from it and mapped using open-source satellite images and standard noise models, over the web on the GIS platform. The predicted noise levels on the maps were verified with the recorded noise data from similar locations using standard noise meter. For these three crossings at different times the predictions are found to be accurate within ±4.5 dB.
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Sonaviya, Dipeshkumar R., and Bhaven N. Tandel. "2-D noise maps for tier-2 city urban Indian roads." Noise Mapping 6, no. 1 (2019): 1–7. http://dx.doi.org/10.1515/noise-2019-0001.
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Abstract In today’s era, vehicular noise pollution has been identified as a serious danger that influence the attribute of the urban regions. To identify the influence of noise effects, noise maps are very useful. A noise mapping study has been carried out to study the propagation of urban road traffic noise in the areas along with field measurements. The computer simulation model (Sound- PLAN software) is used to developed noise maps. In developing nations like India, traffic composition is heterogeneous. These traffic compositions contain vehicles, which have different sizes, speeds variations and operating systems. Because of fluctuating speeds, deficiency of lane disciplines, and non-authorized parking on main road lanes, honking events becomes inevitable, which changes and affects the urban soundscape of nations like India. Due to horn events (heterogeneous traffic condition), noise level (LAeq) increase by 0.5–8 dB (A) as compared to homogeneous traffic conditions.
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Park, Tae Hong, Minjoon Yoo, Charles Shamoon, Christopher Dye, Stacey Hodge, and Asheque Rahman. "Mitigating noise and traffic congestion through measuring, mapping, and reducing noise pollution." Journal of the Acoustical Society of America 141, no. 5 (2017): 3801. http://dx.doi.org/10.1121/1.4988389.
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Aletta, Francesco, Stefano Brinchi, Stefano Carrese, et al. "Analysing urban traffic volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease." Noise Mapping 7, no. 1 (2020): 114–22. http://dx.doi.org/10.1515/noise-2020-0010.
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AbstractThis study presents the result of a traffic simulation analysis based on Floating Car Data and a noise emission assessment to show the impact of mobility restriction for COVID-19 containment on urban vehicular traffic and road noise pollution on the road network of Rome, Italy. The adoption of strong and severe measures to contain the spreading of Coronavirus during March-April 2020 generated a significant reduction in private vehicle trips in the city of Rome (-64.6% during the lockdown). Traffic volumes, obtained through a simulation approach, were used as input parameters for a noise emission assessment conducted using the CNOSSOS-EU method, and an overall noise emissions reduction on the entire road network was found, even if its extent varied between road types.
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Poslončec-Petrić, V., V. Vuković, S. Frangeš, and Ž. Bačić. "VOLUNTARY NOISE MAPPING FOR SMART CITY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W1 (September 5, 2016): 131–37. http://dx.doi.org/10.5194/isprs-annals-iv-4-w1-131-2016.
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One of the main concept objectives of smart cities is to create a quality living environment that is long-term sustainable and economically justified. In that context, modern cities are aware of the exposure to various forms of physical and non-physical pollution that needs to be remediated, eliminated or reduced. To achieve that it is necessary to quality determine the sources and reasons of each pollution. The most prominent examples of physical pollution that affects the quality of life of citizens in cities are light and noise pollution. Noise pollution or noise, is mostly the consequence of road and rail traffic in cities and it directly affects the health of citizens. Traffic control, reduction of peak congestion, dispersion and traffic redirection or building protective barriers, are ways that cities use to reduce the amount of noise or its effects. To make these measures efficient it is necessary to obtain the information related to the level of noise in certain areas, streets, cities. To achieve this, smart cities use noise mapping. <br><br> The city of Zagreb since 2012, participates in the i-SCOPE project (interoperable Smart City services trough Open Platform for urban Ecosystems). i-SCOPE delivers an open platform on top of which it develops, three "smart city" services: optimization of energy consumption through a service for accurate assessment of solar energy potential and energy loss at building level, environmental monitoring through a real-time environmental noise mapping service leveraging citizen's involvement will who act as distributed sensors city-wide measuring noise levels through an application on their mobile phones and improved inclusion and personal mobility of aging and diversely able citizens through an accurate personal routing service. The students of Faculty of Geodesy University of Zagreb, who enrolled in the course Thematic Cartography, were actively involved in the voluntary data acquisition in order to monitor the noise in real time. In this paper are presented the voluntary acquisitioned data of noise level measurement in Zagreb through a mobile application named Noise Tube, which were used as the basis for creating the dynamic noise map. <br><br> The paper describes how citizens through voluntarily collected geoinformation can directly influence decision-making in their community, which certainly affects the quality of life.