Search

As the first step to obtain a city urban area noise map of road traffic noise, sound power levels of vehicles on the roads should be accurately estimated over a wide area. In Japan, ASJ RTN-Model 2018 was proposed as the representative road traffic noise prediction model, and by using the model sound power level of a vehicle can be determined if the vehicle type, traveling speed, and driving mode are known. As such data on urban road network, the Ministry of Land, Infrastructure and Transport of Japan publishes the road traffic census including road traffic volume and travel speed of major roads in Japan. The data, however, is limited to major roads and there is no data on minor roads. In this study, to estimate noise condition and situation on arbitrary road, a method for estimating the traveling speed and the traffic volume of vehicles on the road from aerial photographs was examined. Road traffic noise levels along several roads in Tokyo were analyzed by the proposed method and the validity of the calculation results were verified by comparing with short-time measurement results obtained along the target roads.

The strict enforcement of lockdown in India and worldwide to contain the spread of the COVID-19 pandemics has brought a signiicant change in the environmental quality, particularly in cities. The study deliberates an overview of the status of the ambient noise quality of the tier-2 city Bhandara, India, for lockdown and posts lockdown circumstances. In the course of lock-down, it is unfeasible to walk out with standard instruments for onsite noise monitoring with the conventional method in such adversity. Make use of the crowdsourcing approach proves to be the best alternative for collecting noise data with the help of an android-based noise application, łNoise Tracker.ž On assessing gathered data, it ascertained that the noise levels deplete by 8.0 dB during the lockdown. Results indicated that the average noise levels in more than 80% of the locations are lowered during lock-down compared to post lockdown in Bhandara City. A signiicant reduction in average noise levels at all sites during the lockdown period attributing to limiting in the road, rail, and other traic movements. The study is an exclusive example of ordinary people from tier-2 city indulging in environmental scientiic research. The study also visualizes the noise pollution scenario of lockdown and post lockdown periods using noise maps.

This paper describes a new method developed within the BEEP project (Big data for Environmental and occupational EPidemiology) to estimate road traffic flows and to improve the truthfulness of noise maps for agglomerations through Big Data treatment. This new approach, based on data provided by Google API, acquires information regarding travel time to estimate traffic volumes using link delay functions. To achieve this goal, an appropriate experimental plan was designed to simultaneously collect travel times by Google Application Programming Interface (API) and traffic volumes on site. The experimental survey, carried out in the cities of Rome and Pisa, involved different types of road links with traffic lights or roundabouts and different number of lanes. The influence of link characteristics on the correlation between travel time and traffic flow was analysed. The method developed was used in a small area of the city of Rome, and noise maps derived by Big Data were compared to noise maps produced via conventional means.

Environmental noise affects life and health within urban environments through interfering with sleep, rest, study and personal communication. Noise mapping is an important issue of local authorities but due to its requirements (staff, costs and frequency), the available data are limited or outdated. Our aim was to involve people with smartphones in the mapping process and to determine the accuracy of the measurements performed with these devices in a natural environment. The main questions were whether the measured data were dependent on the type of applied software and smartphones. We tested three software (Noise Watch, Noise Meter and Sound Level Meter) and 12 different smartphones. We evaluated the measurements with hypothesis testing and correlation analysis. Although the accuracy of smartphones was below the professional equipment, measurements can be conducted easily due to their availability; thus, a reliability analysis is important. We found that comparison between professional devices and smartphones in a laboratory was misleading as it lacks the environmental factors biasing the measurements. The best method to compare the measurements carried out with smartphones and professional Noise Meters was to use large number of measuring points in a heterogenic outdoor environment where the noise ranged from the low to large values. We revealed that both the applied software and smartphones have relevant effect on the measurements, and, although it is possible to use these devices for noise mapping, one should consider not to apply different software and smartphones. Accordingly, crowdsourcing is not a reliable data collection method because: (1) measurements should be supervised, (2) smartphones’ accuracy should be tested and (3) measurement circumstances should be the same. If any of these requirements are violated, the quality of the resulting maps can be questioned.

The paper discusses noise mapping from the prospective of general evaluation of the state of the city environment. Suggested is a noise evaluation procedure based on a two-step spatial discretization - coarse and fine grids. The coarse grid is used for evaluation of average noise levels (background noise). For this, rather simple method is proposed, where average noise levels are estimated directly for the whole coarse grid cells instead of averaging the noise levels computed point-wise. The fine grid is used for finding the obstacle density to apply in calculations over the coarse grid. It may be used also for additional noise levels detailing in the close vicinity of noise sources where noise propagation is strongly affected by surrounding structures. The detailed results allow correction of the averages over the coarse grid. In comparison with other approaches, the suggested procedure takes little computing time to execute for the entire city. Test example shows reasonable agreement with results computed using the ‘Ecolog-Noise’ software package that has gained popularity in Russian Federation since its introduction in 2008. Another example describes the application of the proposed method for a moderate size densely built city.

Existing studies in Singapore on environmental noise are scarce and limited in scale due to the need for expensive equipment and sophisticated modelling expertise. This study presents the approach of using participatory sensing and mobile phones to monitor environmental sound levels around Singapore. iPhones running the AmbiCiti application was adopted to sample equivalent continuous 30-second average outdoor sound levels (LAeq ,30 sec). The aggregated mean of each region was evaluated and the spatial distribution of environmental noise was analysed using noise maps generated from the measurement data. A total of 18,768 LAeq ,30 sec measurements were collected over ten weeks. About 93.6% of the daytime measurements (07:00 – 19:00) exceeded the WHO recommended level of 55 dBA to minimise negative non-auditory health effects due to noise. The results of this study suggest that the population of Singapore is potentially at risk of adverse non-auditory health effects and, to a lesser extent, hearing loss due to community noise levels. However, the measurements exceeding 70 dBA were frequent enough to warrant concern about contributions to the cumulative lifetime sound exposure contributing to hearing loss. The work also demonstrates that sound maps of an area can be efficiently generated using calibrated applications running on smart phones.

Monitoring workers’ exposure to occupational noise is essential, especially in industrial areas, to protect their health. Therefore, it is necessary to collect information on noise emitted by machines in industries. This research aims to map the noise from mechanized mineral ore industry using the kriging interpolation method, and ArcGIS 10.5.1 to spatially process and analyze data. The experimental calculation result of the semivariogram showed a 0.83 range value, with an essential parameter of 1.75 sill and a spherical total theoretical model. The result shows that the main machines with the highest power consumption and the Leq value are located in the southwest position of the sampled areas with a noise map-projected to assess the workers’ noise exposure level. In conclusion, the study found that the highest noise level was generated ranged from 88 to 97 dBA and contributed to the whole sound pressure level at certain positions.

Athens International Airport (A.I.A) is the first major transportation infrastructure in Greece with the participation of the private sector, a pioneer international Public-Private Partnership. Environmental protection is a priority, and AIA, is committed to protect the environment and preventing or lessening negative impacts, through a comprehensive Environmental Policy and Procedures. Within this framework, AIA has already carried out the study for Strategic Noise Map (SNM) and the Noise Action Plan (NAP) for the Aircraft Noise. According to the European Directive 49/2002 the study should be repeated every 5 years. This research article focuses on the comparative study for the latest SNMs 2017 & 2019 (ECAC Doc.29) and for 2019 (executed by the new methodology CNOSSOS-GR), for the respective traffic data 2016 & 2018, and presents the results of the acoustic model in order to create the Strategic Noise Maps for L den & L night indicators. With a view to implementing the legislation, an analysis of aircraft mix for every year (except helicopters, military and other specific flights) was carried out in accordance with the categorisation provided for in the relevant recommendation of the Committee of 6 August 2003 and the European Commission adopted Directive 2015/996. The potential health effects were further analyzed using the World Health Organization (WHO’s) Disability Adjusted Life Year’s (DALY’s) metrics for aircraft noise in relation to the exposure of the population based on the results of alternative comparative Strategic Noise Maps. The aim of the study is to show how the combination of both the implementation of the European Directive 2002/49 and 2015/996 and the DALY approach is an analysis tool for the evaluation of the acoustic environment. As we can observe in the results, the overall findings are significantly lower in the case of SNM 2019 (executed by the new methodology CNOSSOS-GR) than in the others.

Road 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.

This research presents a modeling framework that allows checking the statistical requirements for producing noise maps based on mobile measurements. First, a sound field of reference is created based on a micro-simulation traffic modeling coupled with acoustic modeling, which outputs sound levels each second on a grid of receivers. The aggregated indicators ( L Aeq ) calculated from this sound field serve then as reference. Mobile targets performing measurements evolve within the simulation, aiming to estimate these indicators. The difference between the reference noise map and the one generated by the moving receivers, characterized by the Root Mean Square Error (RMSE), is computed for different aggregation radius of mobile receivers, and as a function of the number of passes-by and to the distance to its nearest cross street. It is observed that the mobile sampling is actually possible and the RMSE can be reduced by setting an optimal aggregation radius and a minimum number of passes-by. With the optimal parameters, 95% of the mobile samples fall within an estimation error interval of [−3.0, 2.2] dBA from the reference. It is also shown that the distance to the nearest cross street affects the estimation error depending on the traffic flow, producing a RMSE greater than 2 dB for distances lower than 30 m.

We review a Dynamap European Life project whose main scope was the design, commissioning, and actual implementation of “real-time” acoustic maps in a district of the city of Milan (District 9, or Z9, composed of about 2000 road stretches), by employing a small number of noise monitoring stations within the urban zone. Dynamap is based on the idea of finding suitable sets of roads displaying similar daily traffic noise behavior, so that one can group them together into single dynamical noise maps. The Dynamap sensor network has been built upon twenty-four monitoring stations, which have been permanently installed in appropriate locations within the pilot zone Z9, by associating four sensors to each one of the six group of roads considered. In order to decide which road stretches belong to a group, a non-acoustic parameter is used, which is obtained from a traffic flow model of the city, developed and tested over the years by the “Enviroment, Mobility and Territory Agency” of Milan (EMTA). The fundamental predictive equation of Dynamap, for the local equivalent noise level at a given site, can be built by using real-time data provided by the monitoring sensors. In addition, the corresponding contributions of six static traffic noise maps, associated with the six group of roads, are required. The static noise maps can be calculated from the Cadna noise model, based on EMTA road traffic data referred to the ‘rush-hour’ (8:00–9:00 a.m.), when the road traffic flow is maximum and the model most accurate. A further analysis of road traffic noise measurements, performed over the whole city of Milan, has provided a more accurate description of road traffic noise behavior by using a clustering approach. It is found that essentially just two mean cluster hourly noise profiles are sufficient to represent the noise profile at any site location within the zone. In order words, one can use the 24 monitoring stations data to estimate the local noise variations at a single site in real time. The different steps in the construction of the network are described in detail, and several validation tests are presented in support of the Dynamap performance, leading to an overall error of about 3 dB. The present work ends with a discussion of how to improve the design of the network further, based on the calculation of the cross-correlations between monitoring stations’ noise data.

Background: The present study aimed to replace the integrated sound level meter by the noise map of a work environment in order to estimate the equivalent continuous sound level (Leq) as an important quantity in the noise monitoring of continuous noise sources. Methods: In this theoretical-experimental study, the grid method was initially used. Sound pressure level (SPL) was measured at the selected stations in three noisy industrial halls. Data analysis was performed in ArcGIS 10.2 software, and the noise map was plotted for each hall separately. Afterwards, the different zones with various SPL intervals were calculated on each noise map, and Leq was determined. For the comparisons, Leq was also calculated using logarithmic equations, based on which the integrated sound level meters were programmed. Results: The proposed method was highly accurate with the relative error of less than 2%. Furthermore, it decreased the number of mathematical operations 7-15 times compared to the conventional logarithmic method. Conclusion: According to the results, the available GIS-based software could be accurately replaced by the routine Leq measurement hardware to estimate the Leq spatial noise in noisy industrial environments.

Deliberately changing terrain undulation and ground characteristics (“acoustical landscaping”) is an potential noise abatement solution near roads. However, there is hardly any research regarding the validity of sound propagation models to predict its effectiveness. Long-term continuous sound pressure level measurements near a complex road traffic and sound propagation case were performed. Three types of modeling approaches were validated, covering the full spectrum of available techniques. A two-dimensional full-wave technique (the finite-difference time-domain method, FDTD), but also an advanced engineering model (the Harmonoise point-to-point model), provide accurate transmission loss predictions, both in 1/3 octave bands and for total A-weighted sound pressure levels. Two common and widely used semi-empirical engineering methods (ISO9613-2 and CNOSSOS) yield rather inaccurate results, notwithstanding the short propagation distance. The sensitivity to input data was assessed by modeling various scenarios with the FDTD method. Detailed ground effect modeling was shown to be of main importance.

International audience; Regulatory road traffic noise maps are based on input data that are sometimes incomplete, erroneous or non-existent. When designing them, it is therefore necessary to label and qualify these data by giving priority to certain sources of information and certain parameters over others. Beforehand, a sensitivity analysis of the sound prediction model to these input parameters should be carried out to concentrate efforts on the most influential inputs (either physical or configurational parameters). In this paper, an overall sensitivity analysis of the CNOSSOS-EU model is proposed using the Morris screening method. It is conducted using the open source software Noise Modelling, on a case study of a French city. The analysis is performed on 15 of its input parameters at 14,343 receivers. The selection of the parameters and their ranges of variation were chosen to mimic those faced by an operator when producing monthly noise maps. Whether or not to consider diffraction at the horizontal edges appears to be the most influential parameter when estimating the number of people exposed to sound levels above 65 dB(A). However, a finer analysis by receiver shows how the influence of each parameter strongly depends on the source-receiver configuration.

The paper presents some results concerning the development of a multi-sensor hybrid approach for measuring rail roughness using acoustic and vibration signals measured on-board. The main objective of the system is to provide better input data for noise mapping and maintenance strategies. Several improvements to existing systems are proposed, based on the analysis of transfer functions. First numerical and experimental results are discussed.

Maritime transportation is recognized to have advantages in terms of environmental impact compared to other forms of transportation. However, an increment in traffic volumes will also produce an increase in noise emissions in the surroundings for a greener source, as ports are frequently surrounded by urban areas. When more sources or higher noise emissions are introduced, the noise exposure of citizens increases, and the likelihood of official complaints rises. As a consequence, among the most demanding aspects of port management is effective noise management aimed at a reduction in the exposure of citizens while ensuring the growth of maritime traffic. At the same time, the topic has not been thoroughly studied by the scientific community, mostly because port areas are challenging from a noise management point of view, they are often characterized by a high degree of complexity, both in terms of the number of different noise sources and their interaction with the other main transportation infrastructure. Therefore, an effective methodology of noise modeling of the port area is currently missing. With regard to the INTERREG Maritime Program, the present paper reports a first attempt to define noise mapping guidelines. On the basis of the current state-of-the-art and the authors’ experiences, noise sources inside port areas can be divided into several different categories: road sources, railway sources, ship sources, port sources, and industrial sources. A further subdivision can be achieved according to the working operation mode and position of the sources. This classification simplifies actions of identification of the responsible source from control bodies, in the case that noise limits are exceeded or citizen complaints arise. It also represents a necessary tool to identify the best placing of medium/long-term noise monitoring stations. The results also act as a base for a future definition of specific and targeted procedures for the acoustic characterization of port noise sources.

Continuous noise monitoring based on mobile systems could provide a quick feedback to assess the effect of policies implemented by authorities to mitigate noise pollution. The present research verifies that mobile measurements taken along a main street and aggregated in time and space can accurately estimate noise levels at static points. As a consequence, mobile sensors would be suitable to build, and continuously update, noise maps. Furthermore, the experiment computes the optimum aggregation distance of the mobile measurements. To perform the mobile noise measurements, a low-cost noise sensor with an integrated GPS was mounted on a bicycle. One hour worth of measurements was taken along a main avenue with the mobile receiver simultaneously to 6 static measurement points. For the mobile receiver, the LAeq was computed aggregating samples within a radius from 1 m to 100 m around the static measurement points. Then, the error between the aggregated LAeq of the mobile and the static receivers for the same time period was computed. It is observed that the RMSE and the measurement uncertainties decrease as the aggregation distance increases, having a minimum at an aggregation radius of 33 m and reaching a stabilization due to the constant traffic of the studied street. Special thanks to CONACyT for the support with the grant (260828/409838).

Cities are getting bigger and bigger, this means an increase in the concentration of population and therefore noise. This can pose a danger to our health, because without realizing it we could be exposing our body to noise levels much higher than permitted, but due to routine and being used to them, we do not give importance. To try to tackle this problem, the first step is to know the noise levels. To this end, cities carry out noise mapping measurement campaigns. The problem is that these campaigns are carried out every few years and the measures are implemented at specific points in the city. Nowadays, thanks to the development of small embedded systems which can integrate high capacity data processors, as well as long and short-range communications systems, it is possible to design intelligent devices, under the IoT paradigm, which provide detailed and ubiquitous information in real time about noise levels in cities.

This Special Issue is focused on all the technologies necessary for the development of an efficient wireless acoustic sensor network, from the first stages of its design to the tests conducted during deployment; its final performance; and possible subsequent implications for authorities in terms of the definition of policies. This Special Issue collects the contributions of several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks, with a focus on the publication of good practices for the design and deployment of intelligent networks in any locations.

International audience; Noise pollution has been an issue since ancient times. Recently, this problem has been exacerbated due to rapid population growth and urbanization. Noise mapping is a strategic action plan that visualizes the long-term and real-time noise pollution of our cities, industrial sites, and other regions of interest. This article first discusses the working principle of general model-based noise mapping and the lessons learned. Then, in-depth descriptions of the technical challenges and design issues of noise mapping using mobile crowdsensing and acoustic sensor networks are presented. Finally, we provide our insights for future research directions regarding artificial intelligence assisted noise prediction, constructive interference for multimedia transmission, and simultaneous noise sensing and sound energy harvesting as well as inaudible sound attacks and defense.

In this paper the results of noise maps counted for cars and trams, in 10 areas in the city of Poznan, are analyzed in the context of possible action plans. These results are compared with the information from surveys, involving 794 participants, performed at the same 10 areas. The general results showed that basing solely on the values of noise indicators, i.e. information from the noise maps, is not sufficient for reliable planning of noise action plans, particularly those created in response to multiple noise sources. The exceedance of a noise limit for a given sound source does not necessarily mean that this source is the most annoying for inhabitants. To find a solution to this problem, we propose conducting dedicated noise surveys in the specific areas in each city (in which the noise maps are calculated) where we know a priori that more than one noise source will occur at the same time and could be potentially annoying. In the proposed survey we suggest using two specified questions: whether a given sound source is annoying or not, and which sound source should be the first one to be eliminated, if this is possible. This new survey proposal is the result of an analysis of the survey performed within this study. Some methodological changes are proposed with regard to the survey that was actually used. This new approach could improve decisions about action plans and provide more complex information about peoples’ assessment of noise annoyance evoked by different sound sources.

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.

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs.

International audience; The urbanisation phenomenon and related cities expansion and transport networks entail preventing the increase of population exposed to environmental pollution. Regarding noise exposure, the Environmental Noise Directive demands on main metropolis to produce noise maps. While based on standard methods, these latter are usually generated by proprietary software and require numerous input data concerning, for example, the buildings, land use, transportation network and traffic. The present work describes an open source implementation of a noise mapping tool fully implemented in a Geographic Information System compliant with the Open Geospatial Consortium standards. This integration makes easier at once the formatting and harvesting of noise model input data, cartographic rendering and output data linkage with population data. An application is given for a French city, which consists in estimating the impact of road traffic-related scenarios in terms of population exposure to noise levels in relation to both a threshold value and level classes.

This paper addresses the use of noise measurements when noise mapping simulations are intended to be validated. For this, a discussion about the measurement uncertainty and how to include it in the process of validation is carried out. Then, the contribution provided by the geo-referencing error of a GPS and its impact in the uncertainty is assessed. This is achieved by means of environmental noise measurements, which are conducted based on the accuracy radius given by a standard GPS. Furthermore, an assessment of the approach is conducted by means of numerical simulations using the commercial package SoundPLAN. The results indicate that the uncertainty of noise measurements should be included as it restricts the validation threshold (usually assumed as ±3 dB). Furthermore, the error generated by the inaccuracy of the geo-referencing process increases the uncertainty of the measurements and, therefore, of the noise maps. Finally, a method to calculate a total uncertainty, which partially includes the contributions of the geo-referencing error is also proposed.

After the European Environmental Noise Directive prescribed noise maps and action plans, wide scientific literature and a consistent number of mitigation strategies emerged for road, railway, airport, and industrial noise. Unfortunately, very little attention has been paid to the noise produced by ports in their surroundings, even though there could be many areas affected by it. At present, more attention seems to be paid to noise produced underwater, mostly for military and security reasons and for its interference with wildlife, rather than airborne noise and its influence on human health. In the framework of a project aiming to shed more light on a topic so far under-investigated, this paper presents an acoustical characterization of different small vessels at various speeds that move around on a daily basis in every type of port, produced by means of short- and long-term measurements. The new information acquired was used to produce a map of noise generated by vessels moving in Livorno&rsquo, s canals, which branch off in a densely inhabited area. The simulations were validated using long-term measurement. The number of citizens exposed was also estimated and used to calculate the number of highly annoyed people according to the recent curve for road traffic noise proposed by Guski et al. In order to prevent citizen exposure to noise and possible complaints about small boats, different scenarios and possible future situations such as various vessel speeds, limited flow, restricted areas for some categories, or new residential areas were studied.

Smartphones have been used for crowdsourcing of environmental noise data and generation of noise maps. The main issue is the accuracy of smartphones for sound measurement due to huge diversity of smartphone models available in the market. This paper reports an experimental study with a group of twenty-nine graduate students to demonstrate the viability of crowdsourcing of environmental noise data using smartphones. Central to the viability of this study is an in-house developed app for both Android and iOS smartphones and the associated calibration method which makes the smartphones accurate enough for noise mapping. This study has demonstrated the viability of using calibrated smartphones for the monitoring of environmental noise. There is also a potential to use calibrated smartphones for generation of noise maps on a larger scale for cities around the world.

DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a &ldquo, real-time&rdquo, noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network.

This paper deals with the problem of traffic noise in urban areas in terms of noise mapping. It explains in detail the Mobile Crowdsensing (MCS) method and, furthermore, compares the results obtained with this method with the results gained from the standard method that uses a sound level metre. The research done in this paper shows that the MCS method can make noise mapping easier, cheaper and less time-consuming in terms of creating representative noise maps developed on measurements but also noise maps developed on calculations and simulations. The main idea is to show that accuracy and precision of measurements obtained by using calibrated smartphones are acceptable. The paper suggests that when using the smartphone measurement application, the calibration of the measurement chain can be done in free field with class 1 sound level metre, and noise map can be checked in a much larger number of points (in comparison with the standard measurement method) and therefore, smartphones can be used as instruments for creating or even checking final noise maps in urban environment. Another advantage of this method is that citizens can engage in noise monitoring in urban areas and become aware of the noise pollution in their cities.

Rubber product manufacturing industry was found with severe occupational noise exposure problems due to improper noise management and lack of reliable noise information at the workplace. Strategic noise mapping provides important information in monitoring the occupational noise. Therefore, a case study was conducted to investigate the current noise exposure circumstances based on the information from the noise map and noise risk zones. The stochastic noise mapping simulation method was applied to predict these maps. Based on the results, most of the regions in the operation area were bounded with a noise contour level of 80 dBA and some small regions were exceeded the noise level of 100 dBA. More than 45 % of mapping area was categorised as extremely high risk and high risk zones. Workers are exposed to the high noise level in this workplace. The management should take immediate action for controlling noise and always supervise their workers in using the hearing protection equipment.

This paper presents results of an ongoing project which aims to develop a purpose-built platform for using smart phones as alternative to sound level meters for citizen-science based environment noise assessment. In order to manage and control environmental noise effectively, the extent of the problem must first be quantified. Across the world, strategic noise maps are used to assess the impact of environmental noise in cities. Traditionally, these maps are developed using predictive techniques, but some authors have advocated the use of noise measurements to develop more reliable and robust noise maps. If adopted correctly, smartphones have the capability to revolutionize the manner in which environmental noise assessments are performed. The development of smartphone technology, and its impact on environmental noise studies, has recently begun to receive attention in the academic literature. Recent research has assessed the capability of existing smartphone applications (apps) to be utilized as an alternative low-cost solution to traditional noise monitoring. Results show that the accuracy of current noise measurement apps varies widely relative to pre-specified reference levels. The high degree of measurement variability associated with such apps renders their robustness questionable in their current state. Further work is required to assess how smartphones with mobile apps may be used in the field and what limitations may be associated with their use. To over come the above issues, this project is developing a platform specifically for citizen science noise assessment. The platform consists of a smartphone app that acquires a sound signal and transfers the data to a server via a web based API for post processing purposes. This then returns key information to the user, as well as logging the data for use in a massive noise mapping study. The structure of the proposed platform maintains a clear separation between client (phone) and server. This approach will allow implementation of future open source client side apps for both Android and iOS operating systems.

© 2017. This version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This paper proposes a temporal sampling strategy that increases the accuracy of long-term noise level estimation and allows to establish the estimation error according to the number of sampled days. Days of the week are stratified into working days and weekend days. This research shows how to use measurements of Leq on working days to estimate the corresponding values for weekend days. This is possible because working days have higher noise levels and less variability than weekend days. The improvement in accuracy allows for a reduction in the number of required sampled days compared to taking samples randomly, which would help to reduce the uncertainty in environmental noise assessment. As a reference, to obtain a 90% confidence interval of ±1 dB for Lday, the proposed sampling strategy reduces the required measurement days by more than 38%. For LDEN, the reduction is close to 18% of the total number of days. The proposed strategy could be adapted to different environments by simply changing a few parameters.

This paper presents a set of software for prediction, calculation and noise mapping. These are useful both in assessing noise and adopting practical measures in order to reduce noise in areas with intense traffic. The software, through their working tools, allows making comparisons concerning noise before and after implementing noise reduction measures. The analyzed software's of noise prediction, calculation and mapping are specialized for noise generated by traffic.

This paper presents a privacy-preserving system for participatory sensing, which relies on cryptographic techniques and distributed computations in the cloud. Each individual user is represented by a personal software agent, deployed in the cloud, where it collaborates on distributed computations without loss of privacy, including with respect to the cloud service providers. We present a generic system architecture involving a cryptographic protocol based on a homomorphic encryption scheme for aggregating sensing data into maps, and demonstrate security in the Honest-But-Curious model both for the users and the cloud service providers. We validate our system in the context of NoiseTube, a participatory sensing framework for noise pollution, presenting experiments with real and artificially generated data sets, and a demo on a heterogeneous set of commercial cloud providers. To the best of our knowledge our system is the first operational privacy-preserving system for participatory sensing. While our validation pertains to the noise domain, the approach used is applicable in any crowd-sourcing application relying on location-based contributions of citizens where maps are produced by aggregating data – also beyond the domain of environmental monitoring.

Nowadays, transportation plays a more and more significant role in our daily life but produces noise. Noise not only causes annoyance and health problems, but also shows effects on economics. In 2002, the European Union published the Common Noise Assessment methods (Kephalopoulos et al. 2012). The objective of this paper is to present a method for simulating the noise propagation in 3D and calculating traffic noise on building facade level with different height by using 3D city model and integrating all noise coming from individual traffic such as cars or motorcycles as well as planes and railroad based vehicles. Since noise sources are located in our 3D urban environment—the analysis and the mapping has to cover the 3D aspect as well. A neighborhood of Berlin was chosen as research area. Currently, we propose a semi-automatic solution for 3D noise mapping: generating 3D observer points from CityGML building data; modeling 3D propagation path and calculating different kinds of traffic noise level. The total noise levels are then calculated by estimating the total annoyance based on effect equivalent sound pressure levels for different types of traffic source. The results are presented as a 3D map. In the future this approach can be further developed to an on-the-fly tool, that makes use publicly available data and processes to determine the noise for one building to a certain point in time. Besides that we found out that more investigation and evaluation on noise calculation methods are needed. Thus the development of near real time calculation methods together with noise measurements is required.

A &ldquo, real-time&rdquo, noise mapping project, named DYNAMAP, has been developed in the framework of a Life+ 2013 program and cofunded by the European Commission. The project aims at giving a real picture of the noise generated by vehicular traffic. To this purpose, a dedicated platform has been developed to elaborate the information from distributed noise monitoring stations. The methodology has been implemented along the ring road encircling the city of Rome (Italy). A detailed description of the system is given together with a report on the testing campaign that allowed evaluation of the accuracy and reliability of the system. From the monitoring campaign satisfactory results have been achieved, showing an average overall prediction error of ~1.5 dB.

The grid method is the most widely used technique for measurement-based noise assessment, and indeed is part of the ISO 1996-2 standard. Nevertheless it has certain disadvantages. The present work is an analysis of the grid method for evaluating noise, firstly in the city of Cáceres and, secondly in two other smaller towns. Using as reference a 200 metre grid study, a study was made of the effect of varying the size and form of the grid on the city’s overall noise value, the percentage of data found to lie above some reference thresholds, and the noise value assigned to a certain zone of the city. The ISO 1996 recommendations of the necessity of new sampling points and the method’s predictive capacity for these new measurements were also analyzed.

In this study, we attempted to assess the feasibility of collecting population health data via mobile devices. Specifically, we constructed noise maps based on sound information monitored by individuals' smartphones. We designed a sustainable way of creating noise maps that can overcome the shortcomings of existing station-based noise-monitoring systems. Three hundred and nine Seoul residents aged 20-49 years who used Android-based smartphones were recruited, and the subjects installed a special application that we developed for this study. This application collected information on sound and geographical location every 10 min for 7 days. Using GIS, we were able to construct various types of noise maps of Seoul (e.g., daytime/nighttime and weekdays/weekends) using the information on sound and geographical location obtained via the users' smartphones. Despite the public health importance of noise management, a number of countries and cities lack a sustainable system to monitor noise. This pilot study showed the possibility of using the smartphones of citizen scientists as an economical and sustainable way of monitoring noise, particularly in an urban context in developing countries.

The high temporal and spatial granularities recommended by the European regulation for the purpose of environmental noise mapping leads to consider new alternatives to simulations for reaching such information. While more and more European cities deploy urban environmental observatories, the ceaseless rising number of citizens equipped with both a geographical positioning system and environmental sensors through their smartphones legitimates the design of outsourced systems that promote citizen participatory sensing. In this context, the OnoM@p system aims at offering a framework for capitalizing on crowd noise data recorded by inexperienced individuals by means of an especially designed mobile phone application. The system fully rests upon open source tools and interoperability standards defined by the Open Geospatial Consortium. Moreover, the implementation of the Spatial Data Infrastructure principle enables to break up as services the various business modules for acquiring, analysing and mapping sound levels. The proposed architecture rests on outsourced processes able to filter outlier sensors and untrustworthy data, to cross- reference geolocalised noise measurements with both geographical and statistical data in order to provide higherlevel indicators, and to map the collected and processed data based on web services.

The Environmental Noise Directive (END) requires that regular updating of noise maps is implemented every five years to check and report about the changes occurred during the reference period. The updating process is usually achieved using a standardized approach, consisting in collating and processing information through acoustic models to produce the updated maps. This procedure is time consuming and costly, and has a significant impact on the budget of the authorities responsible for providing the maps. Furthermore, END requires that simplified and easy-to-read noise maps are made available to inform the public about noise levels and actions to be undertaken by local and central authorities to reduce noise impacts. To make the updating of noisemaps easier and more cost effective, there is a need for integrated systems that incorporate real-time measurement and processing to assess the acoustic impact of noise sources. To that end, a dedicated project, named DYNAMAP (DYNamic Acoustic MAPping), has been proposed and co-financed in the framework of the LIFE 2013 program, with the aim to develop a dynamic noise mapping system able to detect and represent in real time the acoustic impact of road infrastructures. In this paper, after a comprehensive description of the project idea, objectives and expected results, the most important steps to achieve the ultimate goal are described.

CITYHUSH project supports city administrations in the implementation of noise action plans according to the EU directive 2002/49/EC. The action “Noise and vibration control at source – Acoustically green vehicles” validates noise score rating models in a case in the Athens area, including Q-Zones (Quiet zones) and embedded parks, identifying perceived benefits on noise. Annoyance for PTW (powered two-wheeler) is masked in general traffic annoyance. However, a PTW pass may become recognizable and clearly distinct within the traffic noise environment formed by the road traffic flow and synthesis. Several strategic noise mapping scenarios with high noise levels from PTWs, as well as low frequency noise annoyance events, were evaluated proving that the noise levels in a Q-Zone and embedded parks may be reduced with an increased park capacity. Measures with a special focus on the reduction of conventionally powered motorbikes and scooters (PTWs) were evaluated. PTW’s emission values were derived, from known values and past measurements, in the form of a correction function as a function of speed to perform simulation analysis for various analogies of conventionally versus electrically driven PTWs. Relevant noise difference maps indicate noise reductions, especially when additional measures are included, such as speed limits.

The Digital Terrain Model is the most basic and cumbersome element of any large-scale mapping projects. Accurate assessment of Digital Terrain Model data is an intricate but vital process. The impact of its accuracy on noise mapping has not been fully researched. The aim of this research on a specific case study is to analyse the differences in noise mapping results obtained from acoustic simulations carried out with different accuracies in Digital Terrain Model data. It seems that mapping with a 0.5 m degree of accuracy in elevation is sufficient for acoustic simulation, apart from the fact that it is easily achievable with current available techniques. In contrast, it can be concluded that mapping with 5 m accuracy in elevation is insufficient and may drastically change the evaluation of the percentage of people affected by noise.

This article aims to discuss the influence of input data on the simulation model when designing Strategic Noise Maps. The studied noise map was made in the Macrocenter of the Independent City of Buenos Aires (Argentina), which has an approximated extension of 20 km2 and about 500,000 inhabitants. The several input data for the model are analyzed, for their quality and the lack of some of them could affect the final result. Also, the evolution and validity of experimental measurements are analyzed when validating a simulated map. Finally, a study of the uncertainty of the map based on the input data is made, comparing it with the recommendations internationally adopted.