Multidimensional analysis of road traffic noise and probable public health hazards in Barisal city corporation, Bangladesh.

Noise pollution is a major challenge in urban contexts all around the world. The study was designed to assess road traffic noise pollution with possible health effects on those living in the study region. The IDW spatial interpolation approach and an ArcGIS-based evaluation were used to map the recorded noise levels in the research region. The noise descriptors including Noise Climate (NC), Traffic Noise Index (TNI), Equivalent Noise Level (Leq), and Noise Pollution Level (NPL) were computed. The required information has been collected through a questionnaire survey and previously published documents. The study reveals that the current noise level is higher than the recommended national threshold at every location. According to the study, the Nathullabad region had the highest level of noise pollution (86.5 dBA), while the Kaunia Abasik area had the lowest level (67.8 dBA). Study findings also show that in the area context, the highest levels of noise pollution are found in commercial areas (82 dBA), followed by industrial areas (80.4 dBA),mixed areas (81.3 dBA), and residential areas (72.7 dBA). The lowest level is found in sensitive areas (72.5 dBA). Statistical analyses, including one-way ANOVA, Tukey HSD post-hoc and LSD post-hoc test results, showed that there was no statistically significant difference (p > 0.05) between the noise pollution levels (NPL) in the morning, noon, and evening shifts. The results showed that 32 % of respondents stated they felt disturbed while working, and 27% of respondents said it was somewhat sensitive for them. As the last step in minimizing noise pollution in the research area, 37 % of respondents reported enforcing the regulations, 31% suggested making hydraulic horns illegally, and 21 % suggested raising public awareness. This study may contribute to academic knowledge and assist decision-makers of government officials in formulating appropriate local strategies to deal with this grave environmental problem.

Road traffic noise pollution and prevalence of ischemic heart disease: modelling potential association and abatement strategies in noise-exposed areas.

In many developing countries with surging vehicular traffic and inadequate traffic management, excessive road traffic noise exposure poses substantial health concerns, linked to increased stress, insomnia and other metabolic disorders. This study aims to assess the linkage between sociodemographic factors, traffic noise levels in residential areas and health effects using a cross-sectional study analyzing respondents' perceptions and reports. Noise levels were measured at 57 locations in Srinagar, India, using noise level meter. Sound PLAN software was employed to generate noise contour maps, enabling the visualization of noise monitoring locations and facilitating the assessment of noise levels along routes in proximity to residential areas. Correlation analysis showed a strong linear relationship between field-measured and modelled noise (r2 = 0.88). Further, a questionnaire-based survey was carried out near the sampling points to evaluate the association of ischemic heart disease with traffic noise. Residents exposed to noise levels (Lden > 60 dB(A)) were found to have a 2.24 times higher odds ratio. Compared to females, males reported a 16% higher prevalence of the disease. Multi-faceted policy strategies involving noise mapping initiatives, source noise standards, traffic flow urban mobility optimization, smart city initiatives and stringent litigatory measures could significantly reduce its detrimental impact on public health. Finally, this study envisions a region-specific strong regulatory framework for integrating noise pollution mitigation strategies into the public health action plans of developing nations.

Development of web-based software for environmental impact assessment focusing on road traffic noise.

In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.

Assessment and mapping of noise pollution in recreation spaces using geostatistic method after COVID-19 lockdown in Turkey.

Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaalti Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100 m intervals within the study area. Noise measurements were taken during the daytime (07:00-19:00), evening (19:00-23:00), and nighttime (23:00-07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2 months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15 min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions.

Blind Calibration of Environmental Acoustics Measurements Using Smartphones.

Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are produced using numerical simulations, which may not be sufficiently representative, for example, concerning the temporal dynamics of noise levels. Acoustic sensor measurements are also insufficient in terms of spatial coverage. More recently, an alternative approach has been proposed, consisting of using citizens as data producers by using smartphones as tools of geo-localized acoustic measurement. However, a lack of calibration of smartphones can generate a significant bias in the results obtained. Against the classical metrological principle that would aim to calibrate any sensor beforehand for physical measurement, some have proposed mass calibration procedures called "blind calibration". The method is based on the crossing of sensors in the same area at the same time, which are therefore supposed to observe the same phenomenon (i.e., measure the same value). The multiple crossings of a large number of sensors at the scale of a territory and the analysis of the relationships between sensors allow for the calibration of the set of sensors. In this article, we propose to adapt a blind calibration method to data from the NoiseCapture smartphone application. The method's behavior is then tested on NoiseCapture datasets for which information on the calibration values of some smartphones is already available.

Soundscape of an eastern coastal city of India.

The soundscape study of an eastern Indian coastal city (Puri) has been investigated. Acoustic data were collected at 36 sampling locations during two time intervals in and around Puri. A number of noise indices, namely, Lmin, Lmax, and Leq, were calculated to demonstrate the noise level of this city. Noise maps are generated using ARC-GIS to investigate the impact of road traffic noise on the soundscape of the city. The response of the public was appraised by a questionnaire. Due to variable traffic features, the equivalent noise level (Leq) as well as peak (L10) and background noise (L90) levels varied with location and time of the day. It was found that socio-demographic characteristics have no bearing on the amount of annoyance. However, a link was observed between age, hearing condition, and noise perception, as well as between gender and impacts of noise.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique-an artificial neural network in this work-is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2-3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.

Use of noise prediction models for road noise mapping in locations that do not have a standardized model: a short systematic review.

Faced with the accelerated growth of cities and the consequent increase in the number of motor vehicles, urban noise levels caused by vehicular traffic have increased considerably. To assess noise levels in cities and implement noise control measures or identify the problem's location in different urban areas, it is necessary to obtain the noise levels to which people are exposed. Noise maps are tools that have applications as they are cartographic representations of the noise level distribution in an area and over a period of time. This article aims to identify, select, evaluate, and synthesize information, through a systematic literature review, on using different road noise prediction models, in sound mapping computer programs in countries that do not have a standard noise prediction model. The analysis period was from 2018 to 2022. From a previous analysis of articles, the choice of topic was based on identifying various models for predicting road noise in countries without a standardized sound mapping model. The papers compiled by a systematic literature review showed that studies concentrated in China, Brazil, and Ecuador, the most used traffic noise prediction models, were the RLS-90 and the NMPB, and the most used mapping programs were SoundPLAN and ArcGIS with a grid size of 10 × 10 m. Most measurements were carried out during a 15-min period at a height from the ground level of 1.5 m. In addition, it was observed that research on noise maps in countries that do not have a local model has been increasing over time.

Evaluation of small-scale touristic coastal town by noise mapping.

Tourism and the city are in a mutual relationship. Tourism affects the city and is affected by the city. In this context, smart environments that focus on the environment and quality of life come to the forefront in ensuring sustainability in tourism. In sustainable smart environments, noise pollution and control are important in terms of producing plan decisions and determining design criteria. In the study, it was aimed to obtain basic data for the measures to reduce and distribute the noise effect by mapping the noise caused by tourism density in Amasra, which is a small-scale touristic coastal town. In this context, noise maps were created by measuring the noise levels caused by tourism density for the city of Amasra, which is a touristic coastal settlement. In order to determine the noise levels, a total of 15 points were randomly selected, taking into account the proximity to urban uses such as hotels and restaurants as well as urban units such as avenues, streets, open spaces, or squares, provided that they are from various places in the city. Measurements were made regularly on weekdays and weekends in July. As a result of the data obtained, noise pollution levels for the city of Amasra were mapped for different situations (morning, noon, evening, weekdays, weekend, July general) using the Inverse Distance Weighting method. As a result of the measurements and maps prepared, it has been determined that the noise levels of the areas where the vehicle road, parking lot use, shopping and food and beverage places are located are mainly high. Finally, measures for noise control within the scope of sustainable smart environment were created in the study.

Using a Clustering Method to Detect Spatial Events in a Smartphone-Based Crowd-Sourced Database for Environmental Noise Assessment.

Noise has become a very notable source of pollution with major impacts on health, especially in urban areas. To reduce these impacts, proper evaluation of noise is very important, for example by using noise mapping tools. The Noise-Planet project seeks to develop such tools in an open science platform, with a key open-source smartphone tool "NoiseCapture" that allows users to measure and share the noise environment as an alternative to classical methods, such as simulation tools and noise observatories, which have limitations. As an alternative solution, smartphones can be used to create a low-cost network of sensors to collect the necessary data to generate a noise map. Nevertheless, this data may suffer from problems, such as a lack of calibration or a bad location, which lowers its quality. Therefore, quality control is very crucial to enhance the data analysis and the relevance of the noise maps. Most quality control methods require a reference database to train the models. In the context of NC, this reference data can be produced during specifically organized events (NC party), during which contributors are specifically trained to collect measurements. Nevertheless, these data are not sufficient in number to create a big enough reference database, and it is still necessary to complete them. Other communities around the world use NC, and one may want to integrate the data they collected into the learning database. In order to achieve this, one must detect these data within the mass of available data. As these events are generally characterized by a higher density of measurements in space and time, in this paper we propose to apply a classical clustering method, called DBSCAN, to identify them in the NC database. We first tested this method on the existing NC party, then applied it on a global scale. Depending on the DBSCAN parameters, many clusters are thus detected, with different typologies.

Airborne Sound Power Levels and Spectra of Noise Sources in Port Areas.

Airborne port noise has historically suffered from a lack of regulatory assessment compared to other transport infrastructures. This has led to several complaints from citizens living in the urban areas surrounding ports, which is a very common situation, especially in countries facing the Mediterranean sea. Only in relatively recent years has an effort been made to improve this situation, which has resulted in a call for and financing of numerous international cooperation research projects, within the framework of programs such as EU FP7, H2020, ENPI-CBC MED, LIFE, and INTERREG. These projects dealt with issues and aspects of port noise, which is an intrinsically tangled problem, since several authorities and companies operate within the borders of ports, and several different noise sources are present at the same time. In addition, ship classification societies have recently recognized the problem and nowadays are developing procedures and voluntary notations to assess the airborne noise emission from marine vessels. The present work summarizes the recent results of research regarding port noise sources in order to provide a comprehensive database of sources that can be easily used, for example, as an input to the noise mapping phase, and can subsequently prevent citizens' exposure to noise.

Remote work: Aircraft noise implications, prediction, and management in the built environment.

The COVID-19 pandemic has greatly changed workplace management. Most workplaces have adopted the work-from-home policy to minimize the risk of community spread. Consequently, housing estates remain largely occupied during office hours. Since some housing estates are situated in the vicinity of an airport, noise pollution resulted from the takeoff and landing of aircraft is now more noticed by residents, causing annoyance. This problem would be most acute for those located directly under the flight path. Before the pandemic, such aircraft operations had lower effect on the residents because most of them were not at home but at workplaces. Evidently, it is timely that more emphasis should now be placed during urban planning to predict and minimize aircraft noise in the built environment. This article first defines the aircraft noise metrics commonly used to assess environmental impact. Preceded by an overview of how aircraft noise affects the built environment, this article reviews how various aircraft noise prediction models have been used in urban planning. Lastly, this article reviews how aircraft noise can be managed for better acoustic comfort of the residents. Anticipating the adoption of hybrid work arrangement moving forward, this article aims to provide urban planning professionals with an avenue to understand how aircraft noise can negatively affect the built environment, which, in turn, justify why prediction and management of aircraft noise should be emphasized from the outset of urban planning.

A comprehensive experimental study of the influence of temperature on urban road traffic noise under real-world conditions.

The effect of road traffic noise in urban environments is an issue of social and scientific interest, due to its public health and economic impacts. Scientific literature showed a decrease in the level of tyre/road noise generated as temperature increases, but usually under standardised traffic conditions in non-urban environments. Based on a wide network for the hourly monitoring of road traffic flow, air temperature and noise levels across the city of Madrid (Spain), this work proposes and applies a new experimental methodology for studying the dependence of urban road traffic noise on temperature. This study was conducted under real-world traffic conditions involving a wide variability in urban configurations and in the type and state of preservation of vehicles, tires and pavements. From the analysis of data for a whole year, a time interval was identified (from Tuesday to Thursday and between 8 a.m. and 8 p.m.) in which the variability in road traffic flow for the whole city of Madrid was stable enough to allow for a linear regression study between temperature and noise levels from urban road traffic. The relationships found were highly significant (p ≤ 0.001) for data from all the noise monitoring stations, with values of higher than 20% and up to 42% for the explanation of the variability in the measured noise levels by temperature at most of the measurement points. The values of the slope coefficients at the noise monitoring stations ranged from -0.036 to -0.125 dB/°C, with an average value of -0.090 ± 0.011 dB/°C. These results are within the range of values reported in the scientific literature for experimental tests conducted under conditions of controlled or free-flowing traffic.

Tools for evaluation and prediction of industrial noise sources. Application to a wastewater treatment plant.

In recent years, acoustic pollution caused by noise has considerably increased in many countries. Particularly in Spain, the noisiest country in Europe. It is sometimes difficult to predict the noise levels that a new installation or an expansion of industrial equipment will cause in the surroundings. This work introduces a new methodology for the prediction, evaluation, and analysis of industrial noise sources, as well as a novel tool for predicting and categorizing outdoor noise from its measurement at their sources. A Wastewater Treatment Plant (WWTP) has been used to demonstrate the applicability and validity of this methodology. The continuous level of acoustic pressure equivalent has been measured in different points of the plant using an integrating sound level meter. From these values, noise maps have been built to obtain detailed information of the industrial noise generated in the installation. Also, the typical frequency patterns of each type of source have been used for the calculation of source noise apportionments. To achieve this objective, several noise sources have been selected to provide information about their contribution to the industrial noise in the WWTP surrounding area. Finally, predictions have been validated using actual measurements. This methodology is a useful tool to predict personal exposure to noise and the impact on the environment. This information can be used, in particular, to propose mitigation actions.

Improving strategic noise mapping of railway noise in Europe: Refining CNOSSOS-EU calculations using TWINS.

The Environmental Noise Directive (2002/49/EC) requires all European Union Member States to produce strategic noise maps using a common assessment methodology: CNOSSOS-EU. The reliability of CNOSSOS-EU railway noise evaluation is dependent on the input vehicle and track transfer functions. The CNOSSOS-EU default database contains the currently available choices for these transfer functions. However, these available transfer functions are limited and of insufficient quality, resulting in large errors in noise level calculations. An approach is presented, introducing an established analytical railway rolling noise calculation technique (TWINS), to extract more reliable and specific transfer functions. A case study consisting of railway rolling noise mitigation measures is defined and used as the basis for extracting and testing these transfer functions. The extracted transfer functions reduce the average deviation between CNOSSOS-EU and reference calculations using TWINS from 6.1 dB(A) to 0.8 dB(A) in absolute sound power levels, and from 1.2 db(A) to 0.3 dB(A) in estimates of noise reduction potential for the defined mitigation measures. Application of this approach shows potential to improve the quality and depth of the existing CNOSSOS-EU default database. This may lead to more reliable estimations of railway noise in the strategic noise maps and the subsequent assessment of its harmful effects.

Anthropogenic sound field and noise mapping in an Arctic fjord during summer.

Sound Pressure Levels were recorded using an Automated Noise Measurement System, during July 2015 - April 2016 at the Kongsfjorden, Arctic. The fjord houses the NyAlesund port and has many vessels plying during summer, which contributes to anthropogenic noise. Spectral distribution and average sound level at 1/3-octave band from 63 Hz to 5000 Hz has been analyzed and correlated with Automatic Identification System marine traffic data. The radiated acoustic field from vessel transits has been predicted through source level modeling for different category vessels. Further, an acoustic propagation model MMPE based on Parabolic Equation method has been used to evaluate range dependent propagation along the fjord and Transmission Loss estimates have been calculated for upslope and down slope cases. Noise due to shipping has been estimated using Source-Path-Receiver Model using Propagation Loss model estimates, Sound Pressure Level, and Source Level predictions. Noise maps with level contours are generated for shipping, depicting the maximum sound levels for the Kongsfjorden.

Preparation of Industrial Noise Mapping and Improvement of Environmental Quality.

Environmental noise control is necessary for human health and auditory comfort conditions. In this respect, it is required that industrial noise should be kept under control and healthy living spaces should be obtained in residential areas. This paper aims to explain industrial noise control at urban and building scales. In this study, the strategic noise mapping process related to industrial activities in a line with the Environmental Noise Directive (END) was clarified. Besides this, what type of factors are effective in the industrial noise mapping process were defined, and important basic parameters, which are essential in the industrial noise mapping, were detailed. The preparation of the industrial noise mapping process was explained in detail. It was mentioned that the regions exposed to excessive noise should be defined according to the strategic noise map and improvement plans should be performed. Improvement methods that reduce excessive noise in living spaces are clarified and it was explained how the improvement of environmental quality can be acquired. In this regard, this paper gives information on industrial noise mapping and industrial noise control at the urban scale.

Spatio-temporal changes of road traffic noise pollution at ecoregional scale.

Noise pollution is a pervasive factor that increasingly threatens natural resources and human health worldwide. In particular, large-scale changes in road networks have driven shifts in the acoustic environment of rural landscapes during the past few decades. Using sampling plots from the Spanish Landscape Monitoring System (SISPARES), 16 km2 each, we modelled the spatio-temporal changes in road traffic noise pollution in Ecoregion 1 of Spain (approximately 66,000 km2). We selected a study period that was characterised by significant changes in the size of the road network and the vehicle fleet (i.e. between 1995 and 2014) and used standard and validated acoustic computation methods for environmental noise modelling (i.e. European Directive, 2002/49/EC) within sampling plots. We then applied a multiple linear regression to expand noise modelling throughout the whole of Ecoregion 1. Our results showed that the noise level increased by 1.7 dB(A) in average per decade in approximately 65% of the territory, decreased by 1.3 dB(A) per decade in about 33%, and remained unchanged in 2%. This suggests that road traffic noise pollution levels may not grow homogeneously in large geographical areas, maybe due to the concentration of large fast traffic flows on modern motorways connecting towns. Our research exemplifies how landscape monitoring systems such as cost-effective approaches may play an important role when assessing spatio-temporal patterns and the impact of anthropogenic noise pollution at large geographical scales, and even more so in a global context of constricted resources and limited availability of historical data on traffic and environmental noise monitoring.

ZARATAMAP: Noise Characterization in the Scope of a Smart City through a Low Cost and Mobile Electronic Embedded System.

Like other sources of pollution, noise is considered to be one of the main concerns of citizens, due to its invisibility and the potential harm it can cause. Noise pollution could be considered as one of the biggest quality-of-life concerns for urban residents in big cities, mainly due to the high levels of noise to which they may be exposed. Such levels have proven effects on health, such as: sleep disruption, hypertension, heart disease, and hearing loss. In a scenario where the number of people concentrated in cities is increasing, tools are needed to quantify, monitor, characterize, and quantify noise levels. This paper presents the ZARATAMAP project, which combines machine learning techniques with a geo-sensing application so that the authorities can have as much information as possible, using a low-cost embedded and mobile node, that is easy to deploy, develop, and use.

Impact of COVID-19 on environmental noise emitted from the port.

Identification of noise sources and their ranking is a crucial part of any noise abatement program. This is a particularly difficult task when a complex source, such as a seaport, is considered. COVID-19 epidemic has had a significant impact on environmental noise related to road, rail, air and ship traffic and provided a unique opportunity to observe immediate noise reduction. In order to identify the noise sources, whose reduction was most effective in reducing noise from the port area, this study compared and quantified noise emissions between the historical and epidemic periods. Environmental noise measurements from three noise monitoring stations at the port boundary were analysed. In addition, noise emissions from ship, road, rail and industry as well as meteorological data in the historical pre - COVID-19 (January 2018-February 2020) and COVID-19 (April 2020) period were analysed in detail. The characteristics of the noise sources mentioned, geographical data and noise measurements were used to develop and validate a noise model of the port area, which was used to calculate noise contour maps. Our results show that the reduction in noise levels observed at all monitoring stations coincides with the reduced shipping traffic. The A weighted equivalent sound pressure levels in the day, evening and night periods were reduced by 2.2 dB to 5.7 dB compared to the long-term averages, and the area of the 55 dB day-evening-night noise contour was reduced by 23%. Compared to the historical period, the number of people exposed to noise levels above 55 dB(A) in the day-evening-night period due to shipping and industrial activities was reduced by 20% in the COVID-19 period. Such results show that environmental noise generated by moored ships is a problem for port cities that should be regulated internationally. In addition, this paper provides precise guidance on noise emission characteristics, ship categorisation and the post-processing of long-term measurement data, taking into account wind conditions and undesired sound events, which can be applied to future research at other locations near shipping ports and used to prepare strategies for noise reduction in ports.