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An infill programme with 30 plans targeting 7,000 new suburban dwellings led to 647 individuals complaining, despite informal early dialogues supported by developers, of whom 24 were interviewed. Contents of complaints were classified according to 27 reasons. Many complaints concerned parking and greenspaces. The elderly more often emphasized parking, while younger complainants mentioned noise and traffic. Gender differences in opinion were smaller. Two plans attracted many complaints: one where owners of neighbouring semi-detached houses complained primarily of the loss of greenspace and another where housing cooperatives faced costly reshaping of parking. The detailed analysis of complaints could guide infill planners. [ABSTRACT FROM AUTHOR]

Steve Paxton presented a series of three solos at Danspace in St. Mark's Church in New York City in May 1999, spanning over 30 years of choreography. The first piece, "Flat" from 1964, showcased Paxton's stillness and methodical undressing and redressing movements. The second piece, "Some English Suites" from 1991, featured Paxton's improvisational dance to J.S. Bach's music, displaying his fluidity and unique movement vocabulary. The final piece, "Ash" from 1997, told a story of Paxton scattering his father's ashes, blending movement and spoken word to create a poignant and theatrical performance. [Extracted from the article]

Train noise is a kind of green, non‐destructive and strong‐energy artificial seismic sources, which is widely used in railway safety monitoring, near‐surface imaging and urban underground space exploration. Distributed acoustic sensing is a new seismic acquisition technology, which has the advantages of dense sampling, simple deployment and strong anti‐electromagnetic interference ability. In recent years, distributed acoustic sensing has been gradually applied in the fields of urban traffic microseism monitoring, crack detection and underground space imaging. However, previous studies mainly focused on microseism interferometry using train event coda noise, and there is limited research on the workflow of interferometry imaging using distributed acoustic sensing–based heavy train events noise (with short coda windows), which produces an abundant of near‐source interference. Aiming at proving the effectiveness of this idea, we investigated a process workflow to get underground shear‐velocity structure based on distributed acoustic sensing recorded heavy traffic noise near Qinhuangdao train station. A weighted sliding absolute average method is used to weaken the strong amplitude to the coda wave level and reduce the near‐source influence. We demonstrated that the cross‐coherence interferometry method, after spectral whitening, has the best effect on sidelobe suppression in the virtual source surface wave shot gathers, through a comparative analysis of cross‐correlation and cross‐coherence results. For obtaining concentrated energy and strong continuity in phase velocity spectra, we selected the time windows with high spatial coherence and signal‐to‐noise ratio not less than 1.2 for stacking from 720 time windows in F–K domain. When dividing subarrays to extract pseudo‐two‐dimensional profile, we set the overlap rate between adjacent time windows to 80% to increase stacking times, enhancing the precision of phase velocity spectra and reducing the errors of picking dispersion curve. Our results show that heavy traffic train events noise (non‐pure coda) can be used to detect underground velocity structure with clear dispersion and high inversion reliability. This research provides a new processing flow for distributed acoustic sensing train noise imaging and can be applied in future urban underground space exploration. [ABSTRACT FROM AUTHOR]

The tire/road noise is one of the major problems facing the tire industry due to the nuisance felt by the vehicle's driver and passengers. Moreover, it is expected in the coming years that this sound nuisance will be one of the main sources of vehicle noise due to the transition from combustion engine driven vehicles to electric vehicles. Tire manufacturers have therefore refined the design of their tire structures to find technological solutions to reduce traffic noise. Tire/road noise is also generated by various mechanisms which depend on different parameters such as the properties of the tire, road texture and driving conditions. This noise is partly caused by the acoustic radiation induced by the tire vibrations due to contacts with the road. The simulation and analysis of the tire's vibrations remains a challenge for the tire industry. Indeed, the simulation of the full dynamic response of a rolling patterned tire requires not only taking onto account various nonlinearities but also the multi-scale nature of the generated dynamic response. Contrary to a straightforward strategy that consists in using a time integrator to predict the multi-scale dynamic response, the strategy proposed in this study is based on a two-step approach to separate the dynamics occurring at different scales. The mathematical formulation of the proposed method is detailed as are the different modeling choices to simulate tire rolling under imposed load. The sensitivity of the tire's vibrations response is analyzed under different rolling conditions and with respect to certain key tire tread pattern parameters. • An adapted two-step approach to simulate nonlinear vibrations by separating the dynamics occurring at different scales. • Mathematical formulation of the proposed method with different modeling choices to simulate tire rolling under imposed loads. • Application to patterned tires rolling on a smooth surface. • Examples of the tire's vibrations response under different rolling conditions and key tire tread pattern parameters. [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]

The article discusses the traditional approach in life cycle assessment (LCA) where characterization factors (CFs) are applied to life cycle inventory (LCI) results to convert them into common units for impact assessment. While this method has been widely accepted and used, there are emerging approaches that deviate from this standard by connecting to intermediate flows or activity variables instead of elementary flows. The article suggests revising LCA standards and software to accommodate these new methods, highlighting the need for a more flexible and inclusive approach in LCA. [Extracted from the article]

In the twenty-first century, global warming has become one of the severe challenges. Around one-fourth of the total carbon footprint is contributed by transport vehicles. It is crucial to provide alternative solutions to unsustainable transportation systems. This study compares the present travel modes of students of the Capital University of Science and Technology, Islamabad, with the proposed sustainable travel mode. A campus survey (N = 565) was conducted to understand travel choices, travel times, origin and destinations, and student preferences, considering university buses and private vehicles. A comparison was made between the travel time analysis of university bus users and non-bus users. A sustainability analysis has been conducted to evaluate the effects of the present travel modes on students and the proposed university bus transport system. Five main factors were compared: fuel consumption and associated fuel costs, carbon and vehicular emissions, traffic noise pollution, traffic congestion, and accident ratio. The results show that 38% of students commute by private cars and 18% by motorbikes. Around 25% of the students avoid university buses due to long travel times, and 30% prefer because their parents ask them. On average, university buses take 56.8 min, and non-bus transports take 38.8 min for one-way travel. If all students travel by university busses, they could be 86% more sustainable in fuel costs and carbon emissions than non-bus travel modes. These results give useful policy implications for providing sustainable travel alternatives to educational institutions. [ABSTRACT FROM AUTHOR]

This paper examines the impact of rain on traffic sign recognition system, addressing one of the challenges posed by harsh environmental conditions like low lighting, extreme weather (rain,fog, snow) and reduced sign visibility. A novel system is proposed in this work, which is capable of handling three different rain types (drizzle, torrential, and heavy). This work explores how different rain types affect training and testing of three customized convolutional neural networks for traffic sign recognition. Results show that the system's performance is dependent on the rain type during training and testing. To address this variability, a multistage classifier is proposed: level 1 classifies rain type, and level 2 selects an appropriate traffic sign classifier based on output of level1. This work also analyzes the effect of augmenting three different types of rain for developing noise robust traffic sign recognition system. Experiments were conducted using publicly available German Traffic Sign Recognition Benchmark dataset. The proposed system attains overall classification accuracy of 95.10% measured through the accuracy score metric, which has not been claimed till now in any previous work in addressing the challenges of recognizing traffic signs in presence of different types of rain. [ABSTRACT FROM AUTHOR]

This study was conducted to assess noise annoyance following the installation of various types of sound barriers on elevated roads and to forecast the noise reduction effect. The level of annoyance was evaluated using a five-level linguistic rating scale, and noise values were anticipated for each floor of the building using Cadna/A software based on different road service levels. The results showed that for each service level road, the sound barrier’s noise reduction impact was most pronounced at heights of 3.5–5m, and the ratio of the acoustic barrier’s length extension to its distance from the building should not be greater than 4:1. After comparison, it was found that the fully enclosed sound barrier has the best noise reduction effect. Additionally, the noise annoyance level varied significantly at different service levels, with 60.5dB chosen as the noise threshold value. This study could offer an optional path for noise reduction facility design. [ABSTRACT FROM AUTHOR]

Transformations of cities into sustainable, health-promoting environments require high-quality acoustic settings. The soundwalk approach according to ISO 12913 (International Organization for Standardization, Geneva, Switzerland) is a participatory tool to assess acoustic environments. Investigation of the relationship between perceived sound type dominance and overall assessments of the surrounding sound environment, comparing two urban areas. Using data of n = 35 soundwalks according to ISO 12913 conducted in an urban neighbourhood and an inner city area in Essen, Germany (2022–2023), 91 participants evaluated, among others, sound type dominance and overall assessment along two routes with six listening stations each. Statistical analyses included descriptive and linear mixed model analyses adjusting for gender, age, wind speed, and temperature. Perceived traffic noise negatively impacts the overall assessments (ß = −0.44, 95% CI: −0.50; −0.38), which is more pronounced in the residential neighbourhood than in the inner city area (−0.59 [−0.69; −0.51], respectively, −0.30 [−0.41; −0.20]). Natural sounds have a positive effect (0.13 [0.08; 0.19]) whereas human sounds showed no effect (−0.07 [−0.12; −0.01]) on the overall assessment. The adjusted effect estimates remain virtually unchanged. The results highlight traffic noise's profoundly negative impact on overall acoustic assessments. The high number of repeated soundwalks provide a robust data set for future evaluations. [ABSTRACT FROM AUTHOR]

Noise from unmanned aerial vehicles, commonly referred to as "drones," will likely shape our acoustic environment in the near future. Yet, reactions of the population to this new noise source are still little explored. The objective of this study was to investigate short-term noise annoyance reactions to drones in a controlled laboratory experiment. Annoyance to (i) two quadcopters of different sizes in relation to common contemporary transportation noise sources (jet aircraft, propeller aircraft, helicopters, single car passbys), and (ii) different drone maneuvers (takeoff; landing; high, medium, and low flybys) flown at different speeds and elevations was systematically assessed. The results revealed that, at the same sound exposure level, drones are perceived as substantially more annoying than other airborne vehicles and passenger cars. Furthermore, for drone maneuvers, landings, and takeoffs are more annoying than flybys, as are maneuvers flown at low speed. Different loudness metrics (LAE, LDE, effective perceived noise level, psychoacoustic loudness level) accounted for drone noise annoyance ratings to an equal degree. An analysis of psychoacoustic parameters highlighted the significant link between drone noise annoyance and tonality, sharpness, and loudness level. The results suggest a different perception and an increased annoyance potential of drones, which will likely require specifically tailored legislation. [ABSTRACT FROM AUTHOR]

Air pollution, greenspace and noise are interrelated environmental factors with the potential to influence human health outcomes. Research has measured these exposures in diverse ways across the globe, but no study has yet performed a country-wide analysis of air pollution, greenspace, and noise in England. This study examined cross-sectional PM2.5, NO2, greenness, accessible greenspace, road noise, and rail noise exposure data at all residential postcodes in England (n = 1,227,681). Restricted cubic spline models were fitted between each environmental exposure and a measure of socioeconomic status, the Index of Multiple Deprivation (IMD) rank. Population-weighted exposures by IMD deciles, urbanicity, and region were subsequently estimated. Restricted cubic spline models were also fitted between greenness and each other environmental exposure in the study. The results show some evidence of inequalities in exposure to air pollutants, greenspace, and noise across England. Notably, there is a socioeconomic gradient in greenness, NO2, PM2.5, and road noise in London. In addition, NO2, PM2.5, and road noise exposure decrease as greenness increases in urban areas. Concerningly, almost all air pollution estimates in our study exceed international health guidelines. Further research is needed to elucidate the socioeconomic patterns and health impacts of air pollution, greenspace, and noise over time. [ABSTRACT FROM AUTHOR]

Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows' variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples. [ABSTRACT FROM AUTHOR]

In this study, A-weighted noise levels were recorded for the entire day (24 h) at each site along the major urban roads of Kurukshetra city in India. The traffic flow (volume and composition) was also recorded simultaneously. Thereafter, the relevant noise and traffic descriptors were computed to assess the traffic noise pollution. The equivalent 2-hourly traffic noise levels, L e q , 2 h , exhibited a somewhat cyclic pattern having noise crests and troughs over the 24 h in the case of all the four studied urban roads, thus identifying the peak and lean noise hours. The equivalent day time noise levels ( L D ) and night time noise levels ( L N ) were in the range of 68.06 – 80.73 dB(A) and 44.12 – 70.91 dB(A) respectively, exceeding the permissible levels as per Indian standards and WHO guidelines at all sites, except L N at two sites. The day-night average noise levels with 24-h' base time, L D N , was above WHO limits of 70 dB(A) barring five sites. The proposed relationship between L D N and passenger car unit (PCU) per hour has the potential application in estimating noise levels from traffic flow data with a high coefficient of determination of 0.8774. The noise climate (N C) and traffic noise index (T N I) varied from 21.62 to 46.01 dB(A) and 116.16 – 187.53 respectively. [ABSTRACT FROM AUTHOR]

Urban noise can potentially disturb the acoustic signals of animals inhabiting urban areas. Although a wide variety of noises from different sources is common in cities, the impact of non-engine noise on animal calls is less studied. In a natural experiment, we evaluated the effect of different urban noises on the advertisement calls of urban frogs (Eleutherodactylus nitidus) in the metropolitan area of Puebla, Mexico. We recorded and analyzed 672 advertisement calls from 28 male frogs and the corresponding environmental noises from three distinct localities. Our analysis focused on four acoustic properties of the calls: inter-call interval, call duration, dominant frequency, and call amplitude. To standardize the amplitude measurements of sound pressure levels produced by noise and frog calls, we employed a practical approach using a reference signal for calibration. We treated the longitudinal data of different noises occurring before, during, and after advertisement calls as repeated measures within the urban locations. During our samplings, frogs called amidst spontaneous urban noises, including dog barking, fireworks, and vehicle engine sounds. Our results indicate that vehicle engine sounds and dog barking do not cause significant distortions in the calls of these urban frogs. However, we observed slight variations in the dominant frequency of calls, decreasing by 11 Hz, during and after fireworks. Given the observed plasticity of E. nitidus in response to noise, urban noises may not pose a severe problem for this urban frog. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound's frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model's ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification. [ABSTRACT FROM AUTHOR]

Urban natural parks represent a remarkable concept that evokes the coexistence of human habitation with a wild environment, and the associated interactions between human and natural territories. In this context, urban noise infringes upon the natural soundscape, leading to various consequences for both realms. This study seeks to characterize the impact of anthropic noise levels on biodiversity in the urban natural Văcărești Park (Bucharest, Romania), utilizing on-site measurements and software simulation techniques. The study seeks to develop a method for evaluating integrative strategies to mitigate the impact of traffic noise on wildlife in an urban wild park, without addressing the specific effects of noise on the perception and communication of individual species. By calibrating field measurements with laboratory results, a more reliable data set will be used to identify areas where the biophonic environment is impacted by anthropogenic noise. Since human-generated noise in an urban natural park predominantly originates from road traffic and industrial sites, managing traffic noise and its propagation pathways could substantially improve the park's soundscape. Additionally, this study will apply software simulations for noise reduction strategies, such as vegetation planting and earthen embankments, to obtain suitable solutions and propose plausible and effective actions to authorities for improving the biophonic environment. This research could also serve as the basis for long-term monitoring, allowing for the assessment of the evolution and impact of implemented measures over time. [ABSTRACT FROM AUTHOR]

Research to date suggests that air pollution may affect children's cognitive development. This study followed 12,159 children in the Millennium Cohort Study in England for 17 years to assess the impacts of lifetime PM2.5 exposure at home and school on cognitive performance while accounting for the inter-related environmental factors of greenness and road noise. Lifetime environmental exposures were measured at home from age 9 months and at school from age 5 years. The relationships between PM2.5 and cognitive test performance at ages 3, 5, 7, 11, 14 and 17 years were investigated using multivariable linear regression models accounting for survey design and controlling for greenness, road noise, and other individual, family, school, and areal characteristics. The results suggest little evidence of observable associations between PM2.5 and cognitive performance in England, with or without adjustment for greenness and road noise, at any age in the study population. These findings also apply to greenness and road noise. This is the first study to quantify the relationship between air pollution, greenspace, noise, and children's cognitive performance in a longitudinal cohort study in England. [ABSTRACT FROM AUTHOR]

The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result. [ABSTRACT FROM AUTHOR]

The effect of urban green spaces on traffic noise reduction has been extensively studied at the level of single vegetation, hedges, etc., but there is a lack of corresponding studies at the scale of spatial patterns of urban green spaces. Therefore, this study aims to analyze the relationship between the spatial pattern of urban green space and the change in green space's noise reduction capacity. Through the morphology spatial pattern analysis method, this analysis divides the urban green space in the Fuzhou high-tech zone into seven types of elements with different ecological definitions and simulates the noise condition of the urban environment with the presence of green space as well as without the presence of green space by computer simulation, calculates the distribution map of the noise reduction produced by the urban green space, and analyzes the correlation between the seven types of green space elements and the noise reduction with the geographically weighted regression modeling analysis. The study finds that (1) Urban green space patterns can significantly affect the net noise reduction of green space. Areas with high green coverage can produce a stronger green space noise reduction effect. (2) More complex green space shapes and more fragmented urban green space can produce higher noise reduction. (3) The green space close to the source of noise can exert a stronger noise reduction effect. Therefore, in the process of planning and design, from the perspective of improving the urban acoustic environment, the configuration of high-quality green spaces in areas with higher levels of noise pollution should be given priority, which may have better noise reduction effects. [ABSTRACT FROM AUTHOR]

Establishing road shelter forests is a key method to reduce traffic noise pollution. However, the characteristics of various types of road shelter forests and their effectiveness in reducing traffic noise remain extensively unexplored. This study focused on five types of pure road shelter forests (PFs) and one type of mixed coniferous broad-leaved forest (MCBLF). By conducting field noise monitoring and spectrum simulations, we analyzed average mass density, additional noise reduction and economic benefits. With a forest belt width of 60 m, the MCBLF reduced additional noise by 6.6 dB(A). Additionally, Forest height, crown shape, average mass density and noise frequency were all positively linked to noise reduction. The width of shelter forests was the main factor affecting noise reduction. Linear regression analysis results showed that cumulative mass surface density was a significant factor in noise reduction (p < 0.01, R2 = 0.93). Furthermore, the type and composition of the shelter forest had indirect effects on noise reduction. The MCBLF had better noise-reducing effects compared to both broad-leaved PFs and needle-leaved PFs due to its more complex structure. Interestingly, as the forest belt became wider, the noise reduction benefits per unit area decreased, implying that a 10 m wide forest belt offered higher economic returns. Considering that a 10 m wide shelter forest belt did not meet noise reduction requirements. This study suggested that the 20 m wide MCBLF was an optimal choice as an urban road shelter forest, providing both effective noise reduction and maximized economic benefits. Our findings provide a basis for the construction and sustainable development of road shelter forests with noise reduction functions. [ABSTRACT FROM AUTHOR]

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability. [ABSTRACT FROM AUTHOR]

Recently, noise pollution has become a considerable environmental concern, with extended exposure linked to numerous health problems, including sleep disturbances, elevated blood pressure, psychological health, and hormone levels. This research delves into the effects of road traffic noise on the cognitive growth of school-aged children, a group particularly sensitive to environmental stressors. The study measured ambient noise levels using two sound level meters and assessed students' performance in cognitive tasks in both prevailing and controlled quiet environments. Cognitive assessments were employed to determine the influence of noise on learning outcomes, and a survey was conducted to investigate both the direct and indirect effects of noise pollution on their health and mental well-being. Results indicate that the observed noise levels significantly surpassed the limit of 35 dB(A) (inside the classroom) set by the World Health Organization (WHO), highlighting a critical level of noise pollution exposure among students. A comparison of student performance showed marked improvements in a controlled, quiet environment compared to the prevailing conditions. Statistical analyses, including paired t-tests, confirmed these findings in most classes. Exposure condition changes significantly decreased by 8.5 dB(A) and 13.6 dB(A) in noise levels at schools A and B, respectively. Additionally, evaluation of cognitive scores pointed to a more pronounced effect of noise pollution on female students than male students. Perception surveys showed severe exposure to noise-induced annoyance and speech intelligibility in the classroom. The study emphasizes the urgent need for effective noise reduction measures in schools to protect children's health and enhance their cognitive development. [ABSTRACT FROM AUTHOR]

Escalating transportation demands have mandated extensive construction of highways in proximity to residential zones, culminating in heightened vehicular noise levels. This investigation centered on the assessment of noise pollution across diverse sites in Lahore, Pakistan, with a specific focus on the Lahore Ring Road (LRR) and surrounding regions. Variables including land utilization, road categorization, arboreal presence, physical impediments, and distances between noise sources and receivers were meticulously analyzed. Utilizing a UNI-T-MS6700 sound level meter, measurements spanning LA99, LA90, LA50, LA10, and LA1 exposed concerning findings, notably recording 80.5 dB from motorcycles in Township (Ali Road) and peaking at 86.5 dB from trucks in Model Town (Ferozepur Road) and Township (Ali Road). While Sui Gas Society demonstrated an 8 dB(A) noise reduction via purposeful tree planting (15 feet), DHA Phase V exhibited a marginal 3 dB(A) decrease due to the absence of such interventions (20 feet). Strikingly, Ashiana-E-Quaid displayed a substantial 19 dB(A) reduction attributed to a considerable distance between noise sources and receivers (300 feet), notwithstanding the absence of trees and barriers. All surveyed locations surpassed the Punjab EPD-prescribed limit of 45-55 dBA, resulting in stress (62.5%) and sleep disruptions (43.3%) among residents. This study underscores the critical roles of tree plantation and source-receiver distance in noise abatement, underscoring the necessity for urban architecture designed with noise barriers to efficaciously manage traffic-related noise. [ABSTRACT FROM AUTHOR]

Aiming to reduce noise pollution and optimize the acoustic quality in highway tunnels, the noise characteristics on different types of pavements were analyzed and compared in this research, based on the on-site noise measurement in two tunnels with the free fields as a control group. Specifically, the noise characteristics include two aspects: various noise and noise time attenuation performance. Various noise includes on-board sound intensity (OBSI) noise and cabin noise. The noise time attenuation performance uses the indicator of reverberation time. Three types of pavements were measured, including dense-graded asphalt concrete (DAC) and single-layered and double-layered porous asphalt (PA) pavement. The results showed that, for the same type of pavement, compared with the free fields, the difference in OBSI noise in tunnels was within a range of less than 1 dBA; the cabin noise increased by 3.4 dBA~6.6 dBA. The noise level in tunnels was greater than that outside tunnels, and the longer tunnel exhibited higher traffic noise and worse noise time attenuation performances. For the same tunnel, PA pavement could reduce the cabin noise by 3.8 dBA~6.7 dBA. PA pavement also exhibited shorter reverberation time. The application of PA pavement could effectively improve the acoustic quality in the tunnel. This research contributes to noise pollution abatement and the improvement of the comfort and safety of drivers in tunnels. [ABSTRACT FROM AUTHOR]

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter. Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent. [ABSTRACT FROM AUTHOR]

Recent, rapid arthropod declines have been tied to human activity. Yet, the propensity for human‐produced noise to disturb the substrate‐borne (vibratory) channel remains virtually untested despite arthropods' ubiquitous use of vibratory information. Characterizing the vibratory landscape at biologically relevant scales is an essential initial step. We aimed to measure vibratory noise across space and time to assess its potential overlap with the urban–rural range and season of a funnel‐weaving spider, Agelenopsis pennsylvanica (C.L. Koch) (Araneae: Agelenidae), a common urban arthropod. We assessed 24‐h ambient vibrations (hereafter vibratory "noise") at 23 sites across urban–rural Lincoln, Nebraska (NE, USA), during the adult season of A. pennsylvanica (August–October). Vibratory noise (amplitude, Leq) varied by ~15 dB across sites overlapping with frequencies within A. pennsylvanica's sensitivity (20–1000 Hz). Urban vibratory noise was positively correlated with the principal component containing estimates of traffic‐induced vibrations (e.g., traffic volume, road proximity, and impervious cover), whereas rural noise levels correlated with probable harvest times. Our findings indicate spatial variation in noise in urban areas and seasonal noise variability in rural areas. We also tested how A. pennsylvanica—collected from four distinct noise survey sites—use their vibratory microhabitat. We assessed daily spider position and dry silk mass within a microcosm that played loud and quiet vibrations (white noise differing by ~15 dB) in separate but connected chambers. Age affected the chamber choice (hereafter "microhabitat use") of spiders collected from the loudest site, as younger adults associated with and used more silk in the loud chamber, but this effect decreased with age. As our data demonstrate that vibratory noise varies with A. pennsylvanica's microhabitat and season with a high potential for impacting behavior, we hypothesize that this environmental noise likely impacts other arthropods as well. [ABSTRACT FROM AUTHOR]

The fast development of technology and transportation systems has resulted in a disturbing level of noise pollution over time that requires research and regulatory. Most existing research is deemed ineffective as it primarily focuses on conducting assessments of traffic noise without providing a structured framework for noise mitigation. This study focused on three main stages to monitor road traffic noise in Sungai Long area: (1) examination of traffic features, (2) derivation of noise indices, and (3) evaluation of the influence of traffic noise. Total of four roads were selected with four stations on each road in Bandar Sungai Long to assess the traffic noise impact. The sound level metre (SLM) was used to calculate the noise indices for each road. Besides, peak-hour sessions and off-peak sessions of traffic flow were measured in field measurement to compare the differences in noise conditions. Additionally, based on the measured noise indices, traffic noise index (TNI), noise pollution level (LNP), and expected community response were calculated to reveal the impact of traffic noise. Overall, this study's findings demonstrated that residential areas experiencing traffic noise disturbance, particularly on roads, namely Jalan Bp Satu and Jalan Persiaran SL 7, had exceeded the Malaysian environmental standard. In addition, two optimized methods are proposed which are prohibiting heavy vehicles and a simple noise barrier was designed on Jalan Bp Satu and Jalan Persiaran SL 7. The expected noise level between these two methods had been compared in the discussion part. In summary, prohibiting heavy vehicles on the road is more suitable if the aim is to save cost. However, if reducing traffic noise is a priority and a long-term solution is needed, constructing noise barrier may be a more effective solution.

Background and purpose: Noise pollution, as an unwanted and annoying sound that can negatively impact human health and well-being, has become one of the major environmental problems in the world. This type of pollution not only disrupts peace and comfort but can also have serious consequences for human physical and mental health. Despite the importance of the issue of exposure to harmful physical factors and their impact on health, few studies have been conducted on traffic noise based on the conditions in Iranian cities. Considering the importance of urban traffic noise, the lack of domestic studies on its characteristics and how it affects the cognitive and physiological performance of exposed individuals, and its importance and decisive role in the occurrence of errors and mental functions, this research aimed to investigate the physiological and mental performance in exposure to traffic noise pollution in Sari city. Materials and methods: This quasi-experimental intervention study was conducted in the summer of 1401 (2023) on 40 students of Mazandaran University of Medical Sciences. To minimize confounding factors, the inclusion criteria were as follows: age range of 20-30 years, normal hearing (hearing loss less than 25 dB), and no history of taking specific medications. All experiments were conducted in an acoustic chamber measuring 3 by 2 meters with a temperature of 23°C, humidity of 50%, and illuminance of 400 lux. Physiological parameters (heart rate, blood pressure, and core body temperature) were measured in acoustic comfort and traffic noise exposure conditions. Cognitive performance was assessed using the Continuous Performance Test (CPT) in exposure and non-exposure conditions. The structural correlation of this test was estimated by the researchers to be 0.89. Data analysis was performed using descriptive and analytical tests in SPSS v24. Results: According to the findings, 52% of the students studied were female. The mean age of the participants was 24.23 years with a standard deviation of 4.99 years, a minimum of 19 years, and a maximum of 38 years. 76.3% of the participants were single and 23.7% were married. Based on the exposure results, the average sound pressure level of traffic noise in the exposure group was reported as 75.45±0.11 dBA. It was observed that the heart rate and core temperature of the exposure group increased significantly (P

Traditional short‐term traffic volume forecasting approaches make it difficult to predict the highly spatiotemporally coupled short‐time traffic. To tackle the problem, this paper first proposes a variational modal algorithm (GWO‐VMD) based on the optimization of the gray wolf search algorithm. It aims to decompose and reduce the noise of short‐time traffic flows. Meanwhile, it reduces the intricacy of data sequences and enhances the regularity pattern. To address the insufficient utilization of spatiotemporal features, this paper presents an innovative deep‐learning traffic prediction framework based on the stacking of multiple temporal trend‐aware graph attention (TGA) layers and gated temporal convolution (GTC) layers, which are called trend‐aware temporal graph neural network (TTGAN). TGA dynamically models the space‐time relationships of traffic data, and GTC models the temporal characteristics of traffic data. The experimental findings demonstrate that the MAPE model, as presented, achieves a reduction of 9% and 2% compared to the AGCRN and GWNET models, respectively, in the domain of deep spatiotemporal graph modeling. Data decomposition and noise reduction are necessary to achieve accurate results. This model has superior performance in terms of mean absolute error (MAE), coefficient of determination (R2), and explained variance score (EVAR). [ABSTRACT FROM AUTHOR]

Background: Previous studies reported an association between transportation noise and self-reported health status (SRHS). They also suggested a mediating role of noise annoyance using conventional statistical methods. These methods are subject to bias in longitudinal studies with time-dependent exposure, mediator and confounding factors. This study aims to investigate the mediating role of aircraft noise annoyance in the effect of aircraft noise on SRHS using a causal inference approach to address time-dependent variables issues. Methods: We used data from 881 participants in all three visits in the DEBATS longitudinal study conducted around three French airports. Participants over 18 years of age reported their self-perceived health status, aircraft noise annoyance, and noise sensitivity by completing a questionnaire at three visits in 2013, 2015 and 2017. Noise maps were used to estimate aircraft noise levels outside their homes. Marginal structural models with inverse probability weighting were used to estimate the total effect of aircraft noise levels on SRHS and its decomposition into direct and indirect effect through aircraft noise annoyance. Results: This study showed a deleterious effect of aircraft noise on SRHS. The odds ratio (OR) corresponding to the total effect and comparing the highest aircraft noise category (≥60 dBA) to the reference category (<50 dBA) was significant (ORpoor/fair_SHRS = 1.25 (95%CI: 1.06 to 2.08)). It also showed no direct effect of aircraft noise levels on SRHS, but an indirect effect through annoyance. This indirect effect increased as aircraft noise levels increased, with a statistically significant OR when comparing the highest noise category (≥60 dBA) to the lowest (<50 dBA) (ORpoor/fair_SHRS = 1.16 (95%CI: 1.03 to 1.52)). Nearly 66% of aircraft noise's effect on SRHS was mediated by aircraft noise annoyance. Conclusion: This study supports the deleterious causal effect of aircraft noise on SRHS. The results highlight the important mediating role of aircraft noise annoyance in the causal pathway from exposure to aircraft noise to poor/fair SRHS. [ABSTRACT FROM AUTHOR]

Predicting and assessing urban traffic noise is crucial for environmental management. This paper establishes a traffic noise simulation method based on microscopic traffic simulation, utilizing a traffic simulation under a mixed distribution probability combining normal and exponential distributions. This method integrates a single-vehicle noise prediction model to compute the spatial distribution of noise. Comparison with empirical data demonstrates that the proposed model effectively predicts the level of traffic noise. The accuracy of the model is validated through comparison with measured data, showing minimum and maximum errors of 3.60 dB(A) and 4.37 dB(A), respectively. Additionally, the noise spatial results under microscopic traffic models are compared with those under line source models, revealing that the proposed model provides a more detailed and realistic noise spatial distribution. Furthermore, the noise variation patterns between stable and time-varying traffic flows are investigated. Results indicate that noise levels fluctuate under stable traffic flow, whereas under time-varying traffic flow, noise values exhibit a stepped change. [ABSTRACT FROM AUTHOR]