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Noise pollution in urban areas, primarily stemming from road traffic, poses a significant challenge in environmental management. Current road traffic noise prediction models are unable to accurately handle noise mapping and complex traffic flows. Existing models rely on deterministic approaches, which cannot effectively predict complex traffic patterns. This study focuses on developing a model using a probabilistic approach for the accurate prediction and mapping of straight road traffic noise levels, a crucial step for effective noise control and mitigation strategies. This approach allows for a comprehensive evaluation of the model's effectiveness under varying traffic conditions. The methodology involves a series of seven case studies within the Klang Valley, Malaysia, including both one-way and two-way lane scenarios, to validate the accuracy of the probabilistic approach framework. The results of the study are promising, demonstrating a high degree of accuracy in noise level predictions. The predictions show a close alignment with actual field measurements, maintaining an absolute difference of less than 3.0 dBA across different scenarios, with an average difference of 1.7 dBA. This accuracy is further supported by comparative analyses and R-correlation assessments, which confirm the model's reliability even in the face of moderate variations during off-peak times, mainly due to human-made uncertainties in studied areas. The average size of the correlation for peak sessions is 0.8338, and for off-peak sessions, it is 0.8311, with both strength correlations being very high. However, while the model shows high effectiveness in straightforward road scenarios, it requires modifications for complex road layouts. In summary, this prediction model can be used to predict noise levels for straight roads, as it shows strong correlations when predicting peak and off-peak sessions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had profound global repercussions, leading to substantial fatalities and economic losses worldwide. However, amidst these adversities, the pandemic inadvertently offered a unique opportunity to assess the impact of reduced human activity on environmental noise levels. This study focuses on monitoring and mapping noise pollution levels at 101 locations managed by the Bhubaneswar Development Authority (BDA) in Bhubaneswar, Odisha, India, during three distinct periods: pre-COVID, during COVID-lockdown, and post-COVID. Using ArcGIS 10.2.1, noise data are spatially analysed across different zones designated by the Central Pollution Control Board (CPCB), namely Industrial, Commercial, Residential, and Silence Zones. The findings reveal a significant reduction in traffic noise during the COVID-19 lockdown period compared to both pre-COVID and post-COVID periods across all four zones. To assess the potential impact on human health, the study employs equations developed by Miedema and Vos (Acoust Soc Am 104:3432–3445, 1998, ) for calculating percentages of highly annoyed individuals and estimating sleeping disorders. Additionally, Structural Equation Modeling (SEM) is applied to explore associations between noise levels and health outcomes across different time periods. Despite the significant reduction in traffic noise observed during the COVID-19 lockdown, our analysis suggests that this decrease did not have a statistically significant effect on annoyance levels (p-value: 0.0542) or sleeping disorders (p-value: 0.121). This study provides valuable insights into the unintended consequences of urban lockdowns on environmental noise pollution and their potential implications for public health and urban planning. [ABSTRACT FROM AUTHOR]

High-speed craft (HSC) present unique challenges regarding on-board noise levels, affecting crew safety, comfort, and operational efficiency. This study investigates noise exposure and mitigation strategies aboard three Ro-Pax HSC vessels operating in southern Spain, with a focus on noise sources, regulatory compliance, and crew health. Full-scale experimental measurements were conducted in critical on-board locations, and noise maps were developed to identify areas where sound levels exceed International Maritime Organization (IMO) and European Directive 2003/10/EC thresholds. Results highlight that engine rooms and propulsion systems are the primary sources of excessive noise, with significant transmission to passenger and crew accommodation areas. Noise exposure calculations reveal that several crew roles, particularly engineers and deckhands, face exposure to hazardous noise levels during routine operations. Mitigation strategies, including improved insulation, noise mapping, and the implementation of hearing protection, are recommended to enhance on-board acoustic conditions. This research contributes to a deeper understanding of noise pollution on HSC vessels and proposes practical interventions to reduce exposure, improving overall maritime safety and occupational health. [ABSTRACT FROM AUTHOR]

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities. [ABSTRACT FROM AUTHOR]

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings. [ABSTRACT FROM AUTHOR]

Abstract Noise pollution is defined as any disturbing or unwanted sound that disrupts or harms human health or wildlife. Noise pollution can have profound effects on both human health and the environment. For humans, exposure to excessive noise levels has been linked to a range of health issues, including hearing damage, stress, and sleep disturbances. Noise monitoring and mapping play a crucial role in understanding, managing, and mitigating the impacts of noise pollution on communities and the environment. Urban areas near borders are generally less studied due to restrictions. Therefore, a study has been conducted aiming the monitoring of noise level and looking into its propagation through 2D noise maps in the urban areas of Jammu and Kashmir lying near the Indian border. The monitoring results revealed that even boarder areas are exposed to high noise levels. Bhatindi in Jammu city recorded highest equivalent noise level, Leq of 91.2 dB(A) during evening. Nowshera is calmest among other study area with noise level, Leq of 80.1 dB(A). Further, 2D noise mapping showed some critical locations in study areas of Jammu and Kashmir that the noise propagates to the nearby residential and commercial areas which require immediate remedial measures. The findings not only contribute to the scientific understanding of noise patterns in border regions but also hold practical implications for urban planning, public health, and environmental conservation.

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. [ABSTRACT FROM AUTHOR]

Hearing loss and deafness have been globally recognized as one of the major public health concerns that need immediate attention. Noise mapping refers to the systematic process of measuring and visualizing the noise level distribution in a well-defined location, preferably in urban settings or industrial areas. This exercise of noise mapping is generally done with the intention of identifying the noise hotspots and measuring the extent of exposure to noise levels in different areas. The process of noise mapping at the community level is expected to have some challenges and these must be identified to effectively deal with them. In conclusion, we cannot undermine the fact that there is an immense need to implement effective noise management strategies to reduce the development of health-related implications. In dealing with this global concern, noise mapping is a crucial tool to provide comprehensive insights about noise levels in different areas, which in turn can be utilized to take specific actions for building a healthier and sustainable environment.

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-Konyaaltı 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. [ABSTRACT FROM AUTHOR]

The F1 Race is one of the most popular international sports events in the world, attracting a huge number of tourists every year. However, the sound emission may lead to hearing damage or unpleasantness from a psychological or physiological perspective. This study selected two sites near the Singapore Formula 1 Night Race racing tracks in 2023 to record and compare the noise profiles with the respective ambient conditions. Both the regular sound pressure level and the psychoacoustic analysis will be included. The A-weighted Leq value was over 95 dBA near the straight racing tracks during the Formula 1 Singapore Airline Singapore Grand Prix session, and the LAmax value can reach more than 120 dBA. Meanwhile, the LAeq and LAmax values near the bend during the afternoon race sessions were over 91 dBA and 117 dBA. Besides, the average LCeq values can be even higher. Additionally, the psychoacoustic parameters of loudness, sharpness and roughness differentiate the acoustic character at the bends and straight tracks. With similar LAeq and LAmax values, the averaged loudness and sharpness values were significantly higher at the straight tracks, while the roughness values were higher at the bend.

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.

ABSTRACT: Hearing loss and deafness have been globally recognized as one of the major public health concerns that need immediate attention. Noise mapping refers to the systematic process of measuring and visualizing the noise level distribution in a well-defined location, preferably in urban settings or industrial areas. This exercise of noise mapping is generally done with the intention of identifying the noise hotspots and measuring the extent of exposure to noise levels in different areas. The process of noise mapping at the community level is expected to have some challenges and these must be identified to effectively deal with them. In conclusion, we cannot undermine the fact that there is an immense need to implement effective noise management strategies to reduce the development of health-related implications. In dealing with this global concern, noise mapping is a crucial tool to provide comprehensive insights about noise levels in different areas, which in turn can be utilized to take specific actions for building a healthier and sustainable environment. [ABSTRACT FROM AUTHOR]

High-speed craft (HSC) present unique challenges regarding on-board noise levels, affecting crew safety, comfort, and operational efficiency. This study investigates noise exposure and mitigation strategies aboard three Ro-Pax HSC vessels operating in southern Spain, with a focus on noise sources, regulatory compliance, and crew health. Full-scale experimental measurements were conducted in critical on-board locations, and noise maps were developed to identify areas where sound levels exceed International Maritime Organization (IMO) and European Directive 2003/10/EC thresholds. Results highlight that engine rooms and propulsion systems are the primary sources of excessive noise, with significant transmission to passenger and crew accommodation areas. Noise exposure calculations reveal that several crew roles, particularly engineers and deckhands, face exposure to hazardous noise levels during routine operations. Mitigation strategies, including improved insulation, noise mapping, and the implementation of hearing protection, are recommended to enhance on-board acoustic conditions. This research contributes to a deeper understanding of noise pollution on HSC vessels and proposes practical interventions to reduce exposure, improving overall maritime safety and occupational health.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations (n = 282,764) were collected in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n = 70) and 23 noise hot spots between 27 April 2022 and 2 March 2023. We found that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sports and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day but expands during the day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning. [ABSTRACT FROM AUTHOR]

Environmental noise is a major environmental concern in metropolitan cities. The rapid social and economic growth in the 20th century is not always accompanied by adequate land planning and environmental management measures. As a consequence of rapid urbanization processes, cities are facing an increase in noise pollution. Noise is being recognized as a serious environmental problem and one which must be accounted for in a sustained development policy designed to improve the quality of life for citizens. Therefore, the monitoring of noise is a crucial aspect of urban planning to allow urban planners to create harmonious and livable environments for communities worldwide. This research aims at assessing the noise levels and associated sources of noise in downtown areas through the involvement of crowdsourcing techniques. The incorporation of noise mapping and increased public awareness are achieved by a framework that enables a comparative scheme between two cities: Alexandria, Egypt and Zagreb, Croatia. The methodology depends on combining crowdsourcing techniques using mobile applications and geographic information system (GIS) tools to detect and analyze noise. Finally, this research provides a comparison between the two cities using the adopted methodology and introduces recommendations to enhance urban planning decisions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The present study presents an investigation into the utilization of artificial neural networks (ANN) and multiple linear regression model (MLR) for the prediction of traffic noise levels in various locations of Dhanbad city at varying intervals. Traffic noise indices measurements were carried out using sound pressure level meter (SLM). The prediction of equivalent A-weighted sound level (LAeq) and the sound level exceeding 10 percent of the time (L10) is carried out using various influencing factors such as number of cars, number of 2 wheelers, number of 3 wheelers, number of heavy vehicle, number of medium commercial vehicles, and traffic speed. The findings demonstrate the ANN model's proficiency in comparison of MLR model for providing precise prediction accuracy of traffic noise indices with a R2 of 0.94 for LAeq and 0.91 for L10. Furthermore, the frequency spectrum analysis reveals that high peaks were observed at lower frequencies ranging from 31.5 Hz to 50 Hz, middle frequencies from 500 to 800 Hz and higher frequencies from 3.5 kHz to 5 kHz. The noise maps at varying intervals revealed that most of the locations are having higher noise levels due to increase in vehicular movement. The study emphasizes on the potential significance of the proposed neural network-based prediction model in collaboration with noise mapping as a vital tool for the anticipation of traffic noise levels and the formulation of noise mitigation strategies in context of the mid-sized smart city like Dhanbad, India.

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping. [ABSTRACT FROM AUTHOR]

The civil security sirens are used by the authorities in a wide range of countries to signal an imminent or ongoing threat. Even if their sound level is known, it is nevertheless difficult to evaluate their audibility across a given zone, especially in complex urban environments. An experimental protocol was deployed around a siren installed in a town in France, to assess its audibility perceptually and through modeling. Sound level measurements during source activation were made with the NoiseCapture smartphone application at different distances and on several axes by a group of 25 participants. They were also asked to fill in a questionnaire on perceptual information about the siren such as its audibility, the perceived sound level, or the masking of the siren by passing vehicles. A comparison between acoustic measurement levels using NoiseCapture and simulated sound levels using NoiseModelling was performed. The results of this study validate the use of the Common Noise Assessment Methods in Europe model to evaluate the audibility of a warning system located in an urban environment within a radius of 2.8 km around the siren. Finally, a metric linking audibility to modeled sound level is proposed, enabling the development of siren audibility maps in the study area.

The Environmental Noise Directive (END), published in 2002, was transposed into Romanian local law in 2005, and it was the starting point for the first urban noise mapping exercises, initially conducted in nine Romanian cities. This paper presents the main evolutionary aspects of the noise assessment process, the development of strategic noise maps, and action plans, dealing with both the legislative side and the practical approach and results obtained. The study considers the specific regulations established by the European Commission regarding environmental noise assessment and deals with the global context at the country level, in which they have been implemented and applied.

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. [ABSTRACT FROM AUTHOR]

The present study aimed to evaluate the possible impact of noise pollution. This study was conducted in Raipur, the capital of Chhattisgarh state, India, to analyze the relationship between noise pollution and health complaints. A total of 18 locations were selected for monitoring noise pollution levels in the morning (9:00-10:30 AM) and evening (7:00-8:30 PM). Noise maps were prepared for both the time interval, and it was found that the highest equivalent noise level (Leq) of 81.31 dBA was observed at location L3 whereas the lowest Leq of 63.25 dBA was observed at L16 in the morning and in the evening 77.33 dBA at L3 and 60.14 dBA at L16 were observed. A questionnaire survey was performed on the population (n = 400) exposed to noise and analyzed through a variance-based partial least square (PLS) structural equation model (SEM). From the survey, it was found that most of the respondents are exposed to higher noise levels and are facing health issues of "pain in the ear," "rise in blood pressure," "loss of sleep," "whistling and buzzing" in their ear, "headache," "heaviness" and "efficiency problem." A total of 109 hypotheses were proposed and analyzed through bootstrapping with a subsample size of 5000 in SmartPLS software. 18 hypotheses were found to be significant in the proposed model. SEM analysis revealed an interrelation between noise pollution and health effects. It is recommended that strict regulation in nearby sensitive areas must be imposed and an awareness drive on a large scale shall be conducted to enlighten the city's population regarding noise effects as well as various measures for controlling. [ABSTRACT FROM AUTHOR]

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This study presents the results of a traffic simulation analysis and emissions (greenhouse gas and noise) assessment comparing pre-pandemic (2019) and post-pandemic (2022) periods. The estimation of road traffic demand is based on conventional data sources and floating car data; next, the traffic simulation procedure was performed providing road network traffic volumes, which are the input for the emission models. The diffusion of teleworking, e-commerce, as well as the digitization of many processes, services and activities, lead to a significant change in urban mobility. Results show a significant though still not complete resumption of commuters travel activity (−10% compared to pre-pandemic period) in the morning peak-hour. This translates into an 11% reduction of greenhouse gas emissions and a 0.1% increase in noise emissions.

The paper presents an original methodology for the identification of intervention priorities through a tailored priority index IP in areas that are highly-exposed to port noise. The methodology is applied to a case study developed in the framework of the European project ANCHOR, acronym of Advanced Noise Control strategies in HarbOuR, funded as part of the announcement Life 2017.

The present study reports the analysis of hourly equivalent noise levels of 100 sites in the NCT (National Capital Territory) of Delhi and correlates with the community perception that has not been reported so far. The study presents the environmental noise scenario of 100 sites in the NCT of Delhi, India, including 19 commercial, 9 industrial, 16 residential, 43 silence zones, 3 highway sites, and 10 intersection sites. The hourly equivalent noise levels of 100 sites were monitored at various timings between 9 AM and 8 PM to analyze the environmental noise pollution scenario in the city. Along with the hourly equivalent continuous sound levels (LAeq, 1 h); 10-percentile exceeded sound levels (L10), Traffic Noise Index (TNI), Noise Climate (NC), and Noise Pollution Level (NPL) of these sites were also computed. The LAeq, 1 h levels varied from 66.5 to 84.7 dB(A), and majority of the sites (90.0% sites) exhibited LAeq, 1 h between 70 and 80 dB(A). A noise map was developed in addition to a socio-acoustic survey conducted to analyze the public perception toward noise pollution and associated health hazards. The study revealed that road traffic noise along with honking noise is the major dominant source of noise pollution. It shall be very helpful in recommending appropriate noise control strategies for abatement and control of environmental noise pollution in the city. [ABSTRACT FROM AUTHOR]

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.

Environmental noise is a major environmental concern in metropolitan cities. The rapid social and economic growth in the 20th century is not always accompanied by adequate land planning and environmental management measures. As a consequence of rapid urbanization processes, cities are facing an increase in noise pollution. Noise is being recognized as a serious environmental problem and one which must be accounted for in a sustained development policy designed to improve the quality of life for citizens. Therefore, the monitoring of noise is a crucial aspect of urban planning to allow urban planners to create harmonious and livable environments for communities worldwide. This research aims at assessing the noise levels and associated sources of noise in downtown areas through the involvement of crowdsourcing techniques. The incorporation of noise mapping and increased public awareness are achieved by a framework that enables a comparative scheme between two cities: Alexandria, Egypt and Zagreb, Croatia. The methodology depends on combining crowdsourcing techniques using mobile applications and geographic information system (GIS) tools to detect and analyze noise. Finally, this research provides a comparison between the two cities using the adopted methodology and introduces recommendations to enhance urban planning decisions.

Excessive noise exposure during the manufacturing process is a widespread issue. This research aimed to analyze noise exposure in the kernel recovery station in the manufacturing process of palm oil. We conduct some measurements and calculations for noise exposure analysis to get the Equivalent Noise Level (Leq) value during the daytime for each measurement point. The noise measurement was conducted on four machines, including a depericarper and polishing drum machine, ripple mill machine, silo dryer machine, and clay bath machine. In addition, this research showed noise contour mapping to define the noise distribution. The results of this research are that only the clay bath machine had Ls below the threshold. The clay bath machine workstation has good noise levels. The Depericarper, Polishing Drum, Ripple Mill, and Silo Dryer Machine must be quieter. Thus, this research used the Hierarchy of Control Approach to reduce and control hazardous noise exposure. The stage of HOC includes elimination, substitution, engineering, administrative control, and Personal Protective Equipment (PPE).

Although air filter towers are commonly used to improve air quality in urban areas, their operation often produces significant noise levels, and their noise impact is unknown. In this research paper, a case study on the noise impacts of an outdoor air filter tower in city open areas and on building façades is conducted by noise mapping. Noise levels around the filter tower were first measured in situ, and a prediction model for noise mapping was built and well-calibrated by comparing the predicted and measured results. Noise mapping was then carried out to investigate noise exposure in city open areas and on façades in four typical urban blocks with different morphology. Simulated results highlight noise level improvement with the increase of façade height at some high-rise buildings whose heights are higher than the neighboring ones. In addition, urban morphology, including building height and density, as well as the distance between the source and the building, has an impact on noise exposure. The utilization of sound-absorbing building envelopes has been identified as an effective means of reducing noise on building façades, with a maximum attenuation of approximately 4 dB(A) observed, but the effectiveness of this method is limited at certain positions. This study provides valuable insights into the impact of filter towers on noise levels and offers suggestions for urban sound planning.