Noise Levels In Urban Areas Research Articles

Urban planning, noise pollution and mental health outcomes

IntroductionIn large cities around the world, many sources of noise including traffic, domestic, construction, and industrial activities, contribute to urban noise pollution, which is now, a major concern in public health as declared by the WHO, for more than a decade (in 2011).ObjectivesThe aim of this study was to try to find potential recommendations and references in terms of urban planning, particularly with the emergence of smart cities, to combat the problem of noise pollution and related mental health hazards.MethodsWe conducted a comprehensive review of the scientific literature using the following keywords: cities, smart cities, noise, pollution and mental health.ResultsOur research found that the continuous exposure to high noise levels could lead to psychological and physiological problems, such as hearing disorders, high blood pressure, heart disease, inconvenience and sleep disorders. While recent evidence indicates that road traffic noise has a negative impact on mental health and that aircraft noise significantly increases the risk of depression, there are not enough studies to date to properly assess the relationship between urban noise pollution and mental health hazards such as anxiety, mood disorders, sexual disturbance, cognitive impairment, learning disabilities, dementia, etc. In the field of urban planning, there is also a lack of reliable data on individual exposure to environmental noise in space and time, and on its effects on mental health.ConclusionsVarious noise mitigation strategies in urban renewal plans are proposed, such as the implementation of noise mapping to provide the detailed spatial distribution of noise levels in urban areas, their sources and time intervals, noise barriers along traffic arteries, vegetation and landscaping. New infrastructure projects involving new expressways and high-speed trains as well as the widening of major roads in their central areas are also suggested.Disclosure of InterestNone Declared

On Approaches to Organizing Noise Observations in Urban Areas

Introduction: A rapid growth of cities accompanied by the increase in population density and expansion of the road network has been observed recently. When conducting road traffic noise studies, preparation for observations, analysis of the situation, and selection of observation points are of great importance. Speed, volume, and density of the traffic flow, periods of observation, and the measurement interval are taken into account when measuring. At present, there is a need to justify approaches to organizing observations of noise levels and obtaining objective data on the urban territory. Objective: To justify approaches to organizing observations of noise levels in urban residential areas. Materials and methods: Roadway noise was measured on the territory of residential areas to establish equivalent and maximum sound levels. Organization of observations consisted of making a list of control addresses and distributing them by groups of observations with the same and comparable values of indicators. Results: The work was carried out in accordance with the proposed approach to organizing observations with the list of control addresses generated, the addresses grouped, parameter values determined, and the control points selected. The essential parameters included the type of urban development, the type of green spaces, characteristics of traffic flow and road surface. Each group contained at least three control addresses. At each address, average sound levels were determined and control was carried out for their differences in each observation group. Conclusion: Approaches to organizing observations of noise levels in urban areas have been developed, which help establish real-life acoustic conditions with a reduced number of measurements.

FL-NoiseMap: A Federated Learning-based privacy-preserving Urban Noise-Pollution Measurement System

AbstractIncreasing levels of noise pollution in urban environments are a primary cause of various physical and psychological health issues. There is an urgent requirement to manage environmental noise by assessing the current levels of noise pollution by gathering real-world data and building a fine-granularity real-time noise map. Traditionally, simulation-based, small-scale sensor-network-based, and participatory sensing-based approaches have been used to estimate noise levels in urban areas. These techniques are inadequate to gauge the prevalence of noise pollution in urban areas and have been shown to leak private user data. This paper proposes a novel federated learning-based urban noise mapping system, FL-NoiseMap, that significantly enhances the privacy of participating users without adversely affecting the application performance. We list several state-of-the-art urban noise monitoring systems that can be seamlessly ported to the federated learning-based paradigm and show that the existing privacy-preserving approaches can be used as an add-on to enhance participants’ privacy. Moreover, we design an “m-hop” application model modification approach for privacy preservation, unique to FL-NoiseMap. We also describe techniques to maintain data reliability for the proposed application. Numerical experiments on simulated datasets showcase the superiority of the proposed scheme in terms of users’ privacy preservation and noise map reliability. The proposed scheme achieves the lowest average normalized root mean square error in the range of 4% to 7% as the number of participants varies between 500 and 5000 while providing maximum coverage of over 95% among various competing algorithms. The proposed malicious contribution removal framework can decrease the average normalizedroot mean square error by more than 50% for simulations having up to 20% malicious users.

Impact of the COVID-19 Lockdown Measures on Noise Levels in Urban Areas-A Pre/during Comparison of Long-Term Sound Pressure Measurements in the Ruhr Area, Germany.

Background: A major source of noise pollution is traffic. In Germany, the SARS-CoV-2 lockdown caused a substantial decrease in mobility, possibly affecting noise levels. The aim is to analyze the effects of the lockdown measures on noise levels in the densely populated Ruhr Area. We focus on the analysis of noise levels before and during lockdown considering different land use types, weekdays, and time of day. Methods: We used data from 22 automatic sound devices of the SALVE (Acoustic Quality and Health in Urban Environments) project, running since 2019 in Bochum, Germany. We performed a pre/during lockdown comparison of A-weighted equivalent continuous sound pressure levels. The study period includes five weeks before and five weeks during the SARS-CoV-2 induced administrative lockdown measures starting on 16 March 2020. We stratified our data by land use category (LUC), days of the week, and daytime. Results: We observed highest noise levels pre-lockdown in the ‘main street’ and ‘commercial areas’ (68.4 ± 6.7 dB resp. 61.0 ± 8.0 dB), while in ‘urban forests’ they were lowest (50.9 ± 6.6 dB). A distinct mean overall noise reduction of 5.1 dB took place, with noise reductions occurring in each LUC. However, the magnitude of noise levels differed considerably between the categories. Weakest noise reductions were found in the ‘main street’ (3.9 dB), and strongest in the ‘urban forest’, ‘green space’, and ‘residential area’ (5.9 dB each). Conclusions: Our results are in line with studies from European cities. Strikingly, all studies report noise reductions of about 5 dB. Aiming at a transformation to a health-promoting urban mobility can be a promising approach to mitigating health risks of noise in cities. Overall, the experiences currently generated by the pandemic offer data for best practices and policies for the development of healthy urban transportation—the effects of a lower traffic and more tranquil world were experienced firsthand by people during this time.

Dominant frequency of songs in tropical bird species is higher in sites with high noise pollution.

The structure and organization of acoustic signals arise through evolutionary processes and adaptive pressures on each species. During learning, natural or anthropogenic factors, such as high noise levels in urban areas, pose challenges to acoustic communication in birds. Many species adjust their acoustic signals to higher noise levels by increasing the frequency of vocalizations. The objectives of this study were to compare the dominant frequency of songs among birds dwelling in forest fragments distant from and near to urban areas, establish correlations between the dominant frequency of song and noise levels in these environments and verified the difference of response between oscines, suboscines and non-passerines. We recorded vocalizations of birds between July/2013 and November/2014 in four forest fragments, two of them near and two distant from urban areas. We used Audacity software to measure the dominant frequency. We measured the ambient noise by a calibrated sound pressure level meter in decibels (dBA) in each of the forest fragments. We analyzed 3740 vocalizations of nine tropical bird species. Forest fragments near to urban areas have higher noise levels than more distant forest fragments. Eight of nine studied species presented higher dominant frequencies of songs in forest fragments near to urban areas. Only one species, Myiothlypis flaveola, did not change the dominant frequency of song between the four analyzed forest fragments. The difference in dominant frequency between the forest fragments distant and closer to the urban areas did not vary between oscines, suboscines and non-passerines. Eight tropical birds exhibited higher dominant frequencies of song in forest fragments near urban areas with high level of ambient noise. Oscine, suboscine and non-passerine showed song variations. Bird species that have differences in the vocalization dominant frequency can be used in environmental monitoring and in ethological studies, as they are sensitive to high noise levels. Noise pollution caused by the vehicular traffic and urbanization are correlates with changes in the vocalization of tropical birds in forest fragments.

Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring

One of the main priorities of smart cities is improving the quality of life of their inhabitants. Traffic noise is one of the pollutant sources that causes a negative impact on the quality of life of citizens, which is gaining attention among authorities. The European Commission has promoted the Environmental Noise Directive 2002/49/EC (END) to inform citizens and to prevent the harmful effects of noise exposure. The measure of acoustic levels using noise maps is a strategic issue in the END action plan. Noise maps are typically calculated by computing the average noise during one year and updated every five years. Hence, the implementation of dynamic noise mapping systems could lead to short-term plan actions, besides helping to better understand the evolution of noise levels along time. Recently, some projects have started the monitoring of noise levels in urban areas by means of acoustic sensor networks settled in strategic locations across the city, while others have taken advantage of collaborative citizen sensing mobile applications. In this paper, we describe the design of an acoustic low-cost sensor network installed on public buses to measure the traffic noise in the city in real time. Moreover, the challenges that a ubiquitous bus acoustic measurement system entails are enumerated and discussed. Specifically, the analysis takes into account the feature extraction of the audio signal, the identification and separation of the road traffic noise from urban traffic noise, the hardware platform to measure and process the acoustic signal, the connectivity between the several nodes of the acoustic sensor network to store the data and, finally, the noise maps’ generation process. The implementation and evaluation of the proposal in a real-life scenario is left for future work.

Assessment of traffic noise levels in urban areas using different soft computing techniques.

Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

A traffic noise model for road intersections in the city of Cartagena de Indias, Colombia

Road traffic noise models are fundamental tools for designing and implementing appropriate prevention plans to minimize and control noise levels in urban areas. The objective of this study is to develop a traffic noise model to simulate the average equivalent sound pressure level at road intersections based on traffic flow and site characteristics, in the city of Cartagena de Indias (Cartagena), Colombia. Motorcycles are included as an additional vehicle category since they represent more than 30% of the total traffic flow and a distinctive source of noise that needs to be characterized. Noise measurements are collected using a sound level meter Type II. The data analysis leads to the development of noise maps and a general mathematical model for the city of Cartagena, Colombia, which correlates the sound levels as a function of vehicle flow within road intersections. The highest noise levels were 79.7dB(A) for the road intersection María Auxiliadora during the week (business days) and 77.7dB(A) for the road intersection India Catalina during weekends (non-business days). Although traffic and noise are naturally related, the intersections with higher vehicle flow did not have the highest noise levels. The roadway noise for these intersections in the city of Cartagena exceeds current limit standards. The roadway noise model is able to satisfactorily predict noise emissions for road intersections in the city of Cartagena, Colombia.

An Experimental Study of the Noise Due to Traffic in a Congested Urban Area

Noise pollution in an urban environment is an issue of serious concern in the major cities of India. There are various factors that contribute to the increase of noise levels in urban areas. The intensity of traffic is one of the factors which contributes to a drastic increase in environmental noise. The management of noise pollution has to be considered in the decision making process. In this paper, an attempt is made to study the existing noise level due to the traffic in Velachery which is declared as a sensitive area by the Ministry of Environment and Forestry (MoEF). The noise level data is collected using the MS6710 digital sound meter. The Custic simulation software version 3.2 is used for finding the propagation of noise. The spatial patterns of measurement were also calculated, in the sub-urban area of Velachery, Chennai, Tamilnadu, India. A means of transmitting this data to vehicles moving in the area, through a wireless medium is simulated using NCTUns 6.0 (network simulator), to enable drivers to understand the environmental conditions. A hardware was also designed which can be used to transmit and receive the noise data using the Zigbee module. A noise transmitting station is placed at a junction, so that it can transmit this noise data to the receivers which are fitted inside the vehicles.

SISTEMA DE INFORMAÇÃO GEOGRÁFICA COMO MEIO DE IDENTIFICAÇÃO DOS NÍVEIS DE RUÍDO LOCAIS NO CENTRO URBANO DE MARINGÁ/PR

<p>Neste trabalho, foram analisados 12 pontos referentes aos níveis de ruído de tráfego na área urbana, na porção central do município de Maringá-PR. Os dados foram coletados no período matutino e tratatos para obtenção dos níveis de pressão sonora L10, L90, bem como o Traffic Noise Index (TNI). A partir destes dados, obteve-se através da análise estatística o comportamento das variáveis mais explicativas para carro, moto, caminhão e ônibus. Em seguida foi analisada distribuição espacial pelo método da Krigagem, utilizando os softwares SPRING e SURFER 10, dos níveis de ruído durante o intervalo de tempo avaliado conforme a Norma Técnica NBR 10.151 (2000). As informações obtidas através da análise dos dois níveis de ruído, bem como do índice, permitiu comparar as regiões de maior concentração de comércio e serviços com áreas de parques na região de estudo. Desta forma, foi possível verificar que, durante os cinco minutos de medição, a população está exposta 100% do tempo a ruídos acima do definido como permitido.</p>

A nonlinear mixed model for the assessment of traffic noise levels in urban areas

Quantification of traffic noise levels in urban areas can be especially useful for environmental and public health policies. A statistical model is proposed here for this quantification in small urban areas. The model is nonlinear and mixed, with two components: a fixed component for traffic noise emission (source effect) and a random component for assessing traffic noise propagation. The proposed model requires only sample data and has been used to estimate basic noise indicators established in European standards. A composite indicator, the Lden indicator, has been computed and used to draw up a noise ranking of urban areas. The proposed approach is applied to the city of Leganes (Spain).

Spatial Mapping of Traffic Noise Levels in Urban Areas

This paper combines field data with an analytical approach to spatially map noise levels due to traffic movements at relatively high traffic volume signalized intersections utilizing the potential of Geographic Information Systems (GIS). Noise data were collected using a discrete mapping technique at 29 signalized intersections, as well as between intersections, and at their respective neighborhood areas in Amman, capital of Jordan. Data were collected in three different highly congested traffic peak periods: 7:30 a.m.-9:00 a.m., 1:30 p.m.-3:00 p.m., and 9:00 p.m.-11:00 p.m. A portable precision sound level meter capable of measuring noise levels from 34 to 134 decibels (dB) was used during the data collection process. The highest recorded noise level at some signals was 80 dB, while the lowest was 34 dB. In fact, some signalized intersections showed higher noise levels than the acceptable or the standard ones, i.e., 65 dB for daytime and 55 dB for nighttime in residential areas at city center. Two-dimensional (2D) vector and raster maps of noise levels, at different time periods for signals' areas and neighborhoods, were spatially displayed. Results showed that the developed GIS maps could be useful for city planning and other environmental management applications for the purpose of: 1) temporal monitoring and queries of noise level changes as a function of time, 2) spatial queries to find the highest noise disturbance location and its time of the day, 3) development of an online noise information system, 4) using noise level based spatial maps as indicators of variation in land prices, and 5) forecasting and current assessment of the acoustic climate of urban areas.

포장노면과 타이어간의 마찰음 분석을 통한 교통소음예측 소프트웨어 개발

고속도로 이용차량의 증가와 함께 차량의 대형화와 고속화로 인해 고속도로 교통 소음레벨이 높아지고 있으며 저소음 포장노면 및 방음시설 설치 요청도 급격하게 증가하고 있다. 따라서 고속도로 교통소음으로 인한 민원예방과 함께 효율적이고 경제적인 소음저감 대책을 수립하기 위해서는 정확한 소음 예측 기술 마련이 필요하다. 본 연구에서는 시험도로에 포설된 다양한 포장노면에 대해서 CPX(Close Proximity Test) 및 Pass-by 소음 계측 방법을 혼용한 소음 계측 데이터를 이용하였고 차종별 단독 주행 시험을 실시하여 차량 및 노면별 음향파워레벨 산정식이 마련된 데이터를 이용하였다. 아울러, 상기 산정식의 정확성을 검증하기 위하여 고속도로 12개 지점에 대한 총 38회의 소음 계측한 데이터를 이용하여 해당 지점에 대한 소음 예측 모델을 구성하여 측정값과 예측값을 비교 평가하였다. 최종적으로 3차원 GUI 기능을 지원하는 도로교통 소음 예측 프로그램 KRON(Korea Road Noise)을 개발하였다. 이와 더불어 각 포장형태별 및 차종별에 따른 소음특성을 분석하였다. Domestic economic development, industrialization, and urbanization have brought along not only increased highway traffic but also elevated traffic noise levels. Thus, it is necessary to accurately predict the traffic noise levels in order to address the public demand of alleviating the noise levels in urban areas. In this study, the method of evaluating the sound power level of road traffic was investigated in terms of considering the types of road surface and vehicle, based on previous researches. Regarding CPX (Close Proximity Test) and Pass-by test, the measured noise data of Test Road of Korea Highway Corporation were utilized in order to construct the database of sound power levels of various vehicles. Specifically, the 38 noise measurement and analysis in 1/1-octave band frequencies at 12 pre-selected sites were carried out, considering topography and road surface. Finally, the comparison study was conducted between predicted and measured data in terms of traffic noise. The traffic noise prediction was based on the KRON (Korea Road Noise) program, which was developed being equipped wit 3-dimensional GUI. In addition, the traffic noise characteristics were evaluated in terms of vehicle types and pavement surface conditions.

Valuing noise level reductions in a residential location context

Although noise levels in urban areas frequently surpass the designated norms the consequences on health are only beginning to be examined. A stated preference experiment is used to estimate the willingness-to-pay for reducing noise levels in a group-based residential location context. The experiment considers variations in travel time to work, monthly house rent, sun orientation of the dwelling and subjective noise level inside it; objective noise levels are also measured after the experiment. Multinomial and mixed logit models are estimated based on a consistent microeconomic framework, including non-linear utility functions and allowing for various stratifications of the data. The more flexible models allow for the treatment of repeated observations problem common to stated preference data and provide a better fit to the data, although willingness-to-pay results remain almost invariant. Subjective values of time derived from these models are consistent with previous values obtained in the country, giving support to the experimental design and quality of the sample.

Multivariate analysis of road traffic noise in Gandia (Spain) during 24 hours and its evolution in the last decades

Road traffic noise is one of the most widespread and growing problems in urban areas. While it has long been known that hearing can be damaged by exposure to noise, it is also believed that continual noise, even at low or moderate intensity, can cause psychological discomfort and sleep disorders. Traffic noise levels in urban areas are increasing, and the areas affected by noise are spreading. As a result of these problems, legislation has been introduced by the city council to control the noise produced by individual vehicles with the aim of eventually producing traffic noise levels which are acceptable to the public. In this paper, therefore, aspects which connect the evolution of the road traffic noise in the last decades and the development of the infrastructure of the city are discussed. The study was carried out in Gandia (Spain), which is situated on the East Coast of the country, 65 kms from Valencia and 100 kms from Benidorn. The population has increased from 50 000 to 60 000 in the last 15 years, and the traffic distribution of the city has changed during this time, with a new bridge and some highways around the urban area. The main objective of this paper is to characterize the effects of the road traffic noise during 24 h (between 1983 and 1997), and including such aspects as traffic density and psychological aspects. The analysis part was performed by using multivariate analysis methods (MVA). Multivariate analysis methods can be used to investigate relationships between all the variables by extracting information from data with many variables and treating them simultaneously.

Statistical analysis of noise levels in urban areas

Environmental noise measurements have been carried out during recent years in different cities and locations of Spain. The noise levels have been continuously sampled over 24 h periods using a noise level analyzer. The data contained in this paper represent a total of 4200 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 several noise descriptors in urban areas.

Levels of ambient noise in Hong Kong

Road traffic noise is nearly always the main contributor to long-term ambient noise levels in urban areas. Noise measurements conducted outside dwellings in Hong Kong show that the background levels ( L 90 ) from road traffic are high, with half of the study population exposed to levels some 20 dB above those measured in a UK study of the (whole) population of England. L 10 levels are also generally higher than for urban sub-populations in the USA and the UK, but not much different from those in London. The high levels, and the compressed range of the noise distribution, result from the absence, in a high-rise city, of space and of the shielding and ground effects which play a major role in attenuating road traffic noise levels in most urban areas. Despite the complex urban form, the noise level distribution, or L 90 by itself, can be predicted from basic information on geometry and traffic flow, but with an accuracy that can only be regarded as marginal for practical use. The receptor-oriented sampling approach in this study has produced information about the exposure of the population rather than the site-specific data commonly produced in urban noise surveys. Editing non-road-traffic sources from the measurements has enabled an efficient collection of data and ensured that the results could be replicated.