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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]

The present communication system is not enough efficient for the data rate requirement for the real-time applications like video conferencing and file sharing. To meet the requirement of the data rate, the concept of F-OFDM (Filter- Orthogonal Frequency Division Multiplexing) has been proposed. To further improve the efficiency of the system, Cognitive Radio is used. Cognitive Radio is capable of recognizing the spectrum utilization of the licensed users and accordingly switches the frequency bands. The aim of the paper is to detect the primary users in an efficient and novel way. Therefore, a hybrid filter detection technique is proposed for the Multi-User Multiple Input Multiple Output (MU-MIMO) F-OFDM system where multiple users are served with the same time and frequency resource and improve signal strength. The proposed technique uses the combination of cosine filtering, Bartlett segmenting, and Hamming windowing. The cosine filtering differentiated between the 5G traffic and the noise. Then, the filtered signals are segmented with the help of the Bartlett Segmenting for decreasing the noise variance then every segment is windowed using the Hamming windowing for maintaining the signal resolution. This paper proposes a Validation Hybrid Filter Detection for Multi-User Multiple Input Multiple Output (MIMO) Frequency-Division Multiplexing Orthogonal Frequency Division Multiplexing (F-OFDM) by Universal Software Radio Peripheral (USRP) based on Cognitive Radio (CR). Detection performance dropped to its least value for M−ary = 256 and 10 user SUs. Further, false alarm probability at −24 dB SNR was less than 0.01, which is excellent considering system characteristics of less than 0 dB SNR, 0.092% and > 98% global system error and detection likelihoods, respectively. The study also evaluated spectral efficiency to offer optimum detection at acceptable complexity levels for low SNR signals. The performance of the proposed technique is compared with the existing techniques and it is found that the proposed technique better and can effectively improve the detection accuracy of the signal with low SNR. [ABSTRACT FROM AUTHOR]

Sound vibrations caused by chewing herbivores are used by plants as a general cue to activate their defense machinery, prompting the suggestion that other ecological sound cues exist (e.g., in mutualistic plant–animal interactions). Similar to animals, plant species may operate within a set frequency and sound intensity range. Sound frequencies and intensities outside this range may potentially have negative effects on plants. Sound produced by humans (i.e., highway or industrial noise) can prevent plants from perceiving important environmental information. Noise pollution could therefore be a factor that should be taken into account both in ecological and agronomic settings. Some data suggest involuntary sound-based plant–plant communication, but a deliberate sound production and sound-based communication mechanism may also exist. In addition to positive effects on plant growth and resilience, sound alerts plants of potential danger and aids in defense. Sound guides plants towards essential resources, like water, through phonotropic root growth. Sound also facilitates mutualistic interactions such as buzz pollination. Molecularly, sound induces Ca2+ signatures, K+ fluxes, and an increase in reactive oxygen species (ROS) levels in a mechanosensitive ion channel-dependent fashion. We review the two major open questions in the field of plant acoustics: (i) what is the ecological relevance of sound in plant life, and (ii) how is sound sensed and transduced to evoke a morphophysiological response? We highlight the clear need to combine the ecological and molecular perspectives for a more holistic approach to better understand plant behavior. [ABSTRACT FROM AUTHOR]

• We develop a more robust long short-term memory network for short-term traffic flow forecasting. • We relax the distribution of prediction error every iteration to arbitrary distribution for the long short-term memory. • We demonstrate the superior performance of our model by extensive experiments. Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term memory (LSTM) network is considered qualified to capture the long-term temporal dependencies in traffic flow. However, the training of LSTM networks is often guided by the mean square error (MSE) criterion. Such criterion depends on a strong assumption that the errors between the traffic flow and its predictions are Gaussian independent identically distributed. In this regard, the forecasting performance is seriously deteriorated by non-Gaussian noises inside the traffic flow sequences. To address this issue, we relax the assumption of the prediction errors to arbitrary distribution by a negative guided mixed correntropy criterion. Then, we formulate a robust loss function by the negative guided mixed correntropy criterion. We subsequently equip the loss function in an LSTM network, termed Δ free -LSTM, for short-term traffic flow forecasting. Extensive experiments on four benchmark datasets demonstrate that the Δ free -LSTM network outperforms the traditional parametric and nonparametric models, as well as state-of-the-art LSTM family models. The source code is available at https://github.com/541764418/Delta-free-LSTM. [ABSTRACT FROM AUTHOR]

• Rail traffic is increasing following policy recommendations for sustainability. • People living close to rail tracks are exposed to both vibration and noise. • We show strong dose–response relationships for modelled vibration and annoyance. • The associations are strongest for freight trains. • Important to consider railway noise in the investigation of vibration annoyance. Rail traffic is increasing following policy recommendations for a sustainable transportation model. However, the health effects of rail traffic vibration on residents living close to railways remain underexplored. We aimed to investigate the relationships between rail traffic vibration and vibration annoyance from different types of trains and to explore the influence of railway noise on these relationships. The study population (N = 7280) was randomly selected from adults living within 1 km of a trafficked railway in Sweden. Survey data were combined with modelled rail traffic vibration and noise. A cross-sectional design and logistic regression analysis were used. Results support a dose–response relationship between vibration and annoyance (stronger for freight trains and weaker for passenger trains). In the adjusted analysis, we observe a robust association between vibration and annoyance from all types of trains, accounting for socioeconomic factors. Furthermore, railway noise appears to modulate the vibration annoyance response. [ABSTRACT FROM AUTHOR]

With rapid urbanisation, traffic noise pollution has become a growing community concern. However, traffic noise can be valuable due to its ubiquity and cleanliness, which can be exploited for acoustic energy harvesting systems. This paper aims to develop an efficient acoustic energy harvester to improve energy utilisation efficiency and reduce noise levels by using deep-learning methods. We propose a temporal graph attention convolutional network (TGACN) to predict noise frequency, providing guidance for the acoustic energy harvester design. Power efficiency can be maximised by adjusting the harvester's neck size to the frequency predicted by the TGACN model. The relationship between neck size and the optimum frequency range is obtained through simulations and experiments. We analysed the performance differences among the various cavities in multiple-cavity acoustic energy harvesters and discussed the circuit connection methods between each cavity to identify the optimal configurations. Additionally, we evaluated the superiority of TGACN over the state-of-the-art methods in terms of prediction accuracy by conducting extensive experiments with two real-world datasets. • An efficient acoustic energy harvester is proposed, and the parameters of different sizes of acoustic energy harvesters are analysed. • The TGACN is proposed to predict vehicle speed. • The velocity prediction results are used to predict the noise emission frequency, and the accuracy of the prediction of traffic frequency can reach 92.19 %. • The acoustic energy harvester can change the size according to the predicted frequency. [ABSTRACT FROM AUTHOR]

The North Sea is one of the most industrialised marine regions globally. We integrated cetacean-dedicated aerial surveys (2015–2022) with environmental covariates and ship positions from the Automatic Identification System (AIS) to investigate the disturbance radius and duration on harbour porpoise distribution. This study is based on 81,511 km of line-transect survey effort, during which 6511 harbour porpoise groups (8597 individuals) were sighted. Several proxies for ship disturbance were compared, identifying those best explaining the observed distribution. Better model performance was achieved by integrating maritime traffic, with frequent traffic representing the most significant disturbance to harbour porpoise distribution. Porpoises avoided areas frequented by numerous vessels up to distances of 9 km. The number of ships and average approach distance over time improved model performance, while reasons for the lower performance of predicted ship sound levels remain unclear. This study demonstrates the short-term effects of maritime traffic on harbour porpoise distribution. [Display omitted] • Cetacean survey data (2015–2022) in the North Sea were combined with vessel positions. • Effects of vessel presence on harbour porpoise distribution were investigated. • Spatial avoidance was detected, particularly in areas with heavy or frequent traffic. • Best predictors were the number of vessels and average approach distance over time. • This broad-scale study shows short-term effects over 9 km on porpoise distribution. [ABSTRACT FROM AUTHOR]

Traffic noise poses significant mental health risks primarily through stress responses. Although labyrinths have been proven to reduce stress and promote relaxation, their efficacy in mitigating stress induced by traffic noise remains unclear. To address this issue, this study conducted repeated-measures experiments to investigate the effects of different labyrinth-like path designs on stress responses to traffic noise. Heart rate variability and electrodermal activity were measured using the E4 wristband to evaluate participants' physiological stress levels. The results suggest that: (1) Walking on a continuous curved path layout (CS) effectively mitigated stress responses to traffic noise, as indicated by multiple physiological stress measures; (2) Regarding stress-mitigating path continuity attributes, 180-degree turns were positive attributes (p < 0.05), while 90-degree turns were negative attributes (p < 0.01); (3) Regarding stress-mitigating path layout features, curved segments (p < 0.01) and switchbacks (p < 0.05) were positive features, while U shapes were negative features (p < 0.01). This study indicates that labyrinth-like path designs can effectively mitigate stress responses to traffic noise; however, complex path layouts with frequent directional changes should be avoided in noisy environments, particularly in areas lacking resources for sound barriers and noise-absorbing vegetation. Our findings contribute to the development of evidence-based guidelines for designing stress-mitigating streets in high-traffic environments. When mainstreaming these guidelines through public codes, further comprehensive analyses including feasibility assessments, economic evaluations, and community engagement are warranted to ensure practical and sustainable implementation. • Investigate the stress-mitigating effects of different path layouts using E4 sensors. • Walking a continuous curved path effectively mitigates the stress response to traffic noise. • This study identifies stress-mitigating path continuity attributes and path layout features. • Avoid complex path layouts with frequent directional changes in noisy areas to mitigate stress. • Mainstreaming these findings through public codes warrants further comprehensive analyses. [ABSTRACT FROM AUTHOR]

The rapid development of highway transportation has led to a gradual deterioration of the sound environment along highways. The theory of periodic structures provides a new approach for the design of traffic noise barriers. However, the lack of practical numerical models and on-site experimental validation has hindered the widespread application of periodic structure sound barriers. This study investigates the noise reduction performance of nested structure periodic sound barriers. Results from semi-anechoic chamber tests demonstrate excellent wave attenuation at the air gap of the barrier, achieving a peak noise reduction of 16 dB without the addition of any sound-absorbing materials or treatment measures, particularly at local peaks in the highway noise spectrum. To enhance the noise reduction performance of nested structure periodic sound barriers, three optimization measures were explored: filling sound-absorbing materials inside the scattering elements, adding micro-perforated panels, and their combined effects. The results indicate that both porous sound-absorbing materials and micro-perforated panels can improve noise reduction effectiveness, especially outside the air gap. In the optimization design of nested structure periodic sound barriers, adding porous sound-absorbing materials inside the micro-perforated panels effectively enhances noise reduction within the air gap. Considering the abundance of abandoned w-beam guardrails in highway projects, which also meet the requirements of scattering elements, this study proposes the use of abandoned w-beam guardrails as scattering elements. This approach aligns with principles of cost-effectiveness and environmental sustainability, and it provides design guidance to promote the application of nested structure periodic sound barriers in traffic noise control. [ABSTRACT FROM AUTHOR]

Migratory fish populations have experienced great declines, and considerable effort have been put into reducing stressors, such as chemical pollution and physical barriers. However, the importance of natural sounds as an information source and potential problems caused by noise pollution remain largely unexplored. The spatial distribution of sound sources and variation in propagation characteristics could provide migratory fish with acoustic cues about habitat suitability, predator presence, food availability and conspecific presence. We here investigated the relationship between natural soundscapes and local river conditions and we explored the presence of human-related sounds in these natural soundscapes. We found that 1a) natural river sound profiles vary with river scale and cross-sectional position, and that 1b) depth, width, water velocity, and distance from shore were all significant factors in explaining local soundscape variation. We also found 2a) audible human activities in almost all our underwater recordings and urban and suburban river parts had elevated sound levels relative to rural river parts. Furthermore, 2b) daytime levels were louder than night time sound levels, and bridges and nearby road traffic were much more prominent with diurnal and weekly patterns of anthropogenic noise in the river systems. We believe our data show high potential for natural soundscapes of low-land river habitat to serve as important environmental cues to migratory fish. However, anthropogenic noise may be particularly problematic due to the omnipresence, and relatively loud levels relative to the modest dynamic range of the natural sound sources, in these slow-flowing freshwater systems. [Display omitted] • Underwater sounds carry information on river characteristics and soniferous animals. • Acoustic information may be used in behavioural decisions of fish, especially when migrating. • Traffic noise leads to diurnal cycles of increased underwater sound levels. • We raise serious concern about the potential impacts of traffic noise on aquatic life. [ABSTRACT FROM AUTHOR]

Automatic identification system (AIS) is a maritime communication system that uses a transceiver to automatically exchange navigational data. These data help navigation and allow to monitor the maritime traffic. However, this system can be hacked and malicious users can easily transmit false data to mislead the coastguards or vessels navigating around. While previous research has proposed methods to detect these falsifications, none of them suggest strategies that detect AIS identity spoofing combining the tracking of the ship position and AIS transceiver's carrier frequency offset (CFO). The CFO, caused by the carrier frequencies mismatch between emitter and receiver and Doppler effect, is used as a radiometric signature to identify materially every transceiver independently of its transmitted identity. It can drift over time and this is why it is tracked thanks to a Kalman filter (KF). In addition, position is also considered to reduce the miss probability of spoofing detection. The KF is noise adaptive to be robust against various CFO drifts and noise levels of the environment. The strategy is tested on real AIS data and the results demonstrate its efficacy: false alarm and miss probabilities were respectively 1% and 1.7%. These results show the ability of the test to correctly detect identity spoofing and the interest of CFO as a radiometric signature. This signature, used for the first time in an AIS application, could be used with other signatures in a future work to improve identity spoofing detection. This is why we made open source in GitHub our algorithm and the real AIS data used. [ABSTRACT FROM AUTHOR]

• First use of binary images for building noise attenuation calculation. • Binary image & Bresenham algorithm for efficient building noise attenuation calculation. • Strategic noise sub-map tasking of road segments enhances parallel computation. • Multi-source data approach for acquiring strategic noise maps. Generally, when generating noise maps, the Weiler-Atherton algorithm is employed for polygon intersection calculations to estimate building obstruction attenuation. However, for larger areas, the computational efficiency of this method heavily depends on computational power and expensive hardware. To address these challenges, our method employs binary images to streamline the calculation of intersection area between the building polygon and the noise propagation path, thereby improving computational efficiency. The proposed method consists of two features: converting polygon shapefiles into binary images and subsequently applying the Bresenham Algorithm to calculate building occlusion attenuation. Our study demonstrated that this approach reduced the calculation time to 39.1 % of the original duration, with a mean absolute discrepancy between the two methods of 0.77 dB(A). By utilizing the proposed method to generate noise maps with comparable accuracy, our approach offers an attractive alternative that reduces hardware requirements. [ABSTRACT FROM AUTHOR]

Feed restriction could induce physiological stress in broiler chickens, leading to welfare issues. Prenatal stimulation could improve stress-coping mechanisms in poultry. The present study aimed to elucidate the effects of subjecting developing embryos to auditory stimulation on physiological stress response to feed restriction in broiler chickens at market age. A total of 423 hatching eggs of Cobb 500 (Gallus domesticus) were subjected to the following auditory treatments: 1) no additional sound treatment other than the background sound of the incubator's compressors at 40 dB (CONTROL), 2) exposure to pre-recorded traffic noise at 90 dB (NOISE), and 3) exposure to Mozart's Sonata for Two Pianos in D Major, K 488 at 90 dB) (MUSIC). The NOISE and MUSIC treatments were for 20 min/h for 24 h (a total of 8 h/d), starting from embryonic days (ED) 12 to hatching. On d 42, an equal number of birds from each prenatal auditory stimulation (PAS) group were subjected to either ad libitum feeding (AL) or 30-h of feed restriction (FR) in a completely randomised design. The FR chickens exhibited significantly higher serum levels of corticosterone (CORT), and heat shock protein (HSP) 70 compared to those of AL. Prenatal auditory stimulation, particularly NOISE, led to lower serum levels of CORT and alpha-1-acid glycoprotein (AGP) levels compared to the CONTROL group. Additionally, NOISE significantly increased brain mRNA glucocorticoid receptor and HSP70 gene expression. The cecal population of E. coli and Lactobacillus spp. was not significantly affected by prenatal auditory stimulation. In conclusion, our findings suggest that prenatal auditory stimulation, particularly NOISE, positively impacts broiler chickens' ability to cope with feed restriction. [ABSTRACT FROM AUTHOR]

This study seeks to reveal urban noise exposure patterns and inequalities using noise complaint data and vision-language hybrid method. By applying a natural language processing model to 17,243 noise complaint records, we uncovered distinct patterns of traffic, industrial, and living noise exposures across residential communities. Our analysis of street view images near complaint locations, utilizing a Residual Network (ResNet) model and Class Activation Mapping (CAM), identified the key environmental elements of different noise sources. Notably, our assessment of noise exposure inequality across 9791 communities yielded a counterintuitive finding: contrary to previous studies in Western contexts, rich communities in China experience higher and more unequal noise exposure compared to average communities, with Gini coefficients exceeding 0.8. This unexpected result likely stems from China's unique rapid urbanization process. Our use of crowdsourced complaint data aligns more closely with human subjective perceptions of noise, offering a novel perspective on noise exposure inequality. These findings challenge existing assumptions about the relationship between socioeconomic status and environmental quality in urban China, and have significant implications for urban planning and noise management strategies in rapidly developing cities. • The study used natural language processing to categorize and map noise exposure. • It identified different types of noise exposures in residential communities. • It applied ResNet and CAM on street view images to identify noise sources. • High-income communities suffer higher and more variable noise exposure. [ABSTRACT FROM AUTHOR]

• A hybrid broadband and narrowband active sound profiling algorithm is proposed for active hybrid noise control. • Simulations of stationary and non-stationary hybrid noise sources prove the stability and robustness of the algorithm. • The proposed algorithm can selectively control the desired narrowband noise components with specific magnitudes in hybrid noise. • Real vehicle experiments, conducted using headrests, validate the theoretical analysis. The vehicle interior noise consists of both broadband road noise and narrowband engine order noise. Currently, the road noise control (RNC) system and engine noise control (EOC) system operate independently in engineering applications. This paper introduces a novel multi-channel hybrid broadband and narrowband active noise control (HBNANC) algorithm, designed to simultaneously attenuate road noise and engine order noise inside vehicles. On this basis, by integrating an error signal design (ESD) subsystem and a sinusoidal error separation (SES) subsystem, a multi-channel hybrid broadband and narrowband active sound profiling (HBNASP) algorithm is further proposed, enabling specific attenuation or amplification of individual narrowband components to meet subjective acoustic comfort requirements. The paper validates the algorithms' effectiveness and robustness through simulations, and real vehicle experimental measurements under various conditions. The research offers a comprehensive approach to enhancing the auditory experience inside vehicles, contributing to the advancement of active noise control technology. [ABSTRACT FROM AUTHOR]

The double-layer porous asphalt (DLPA) pavement is a practical way to mitigate traffic noise from the source. However, there hasn't been much research done on the spectral noise characteristics of the car/truck on this type of pavement with different surface conditions yet. The objective of this paper was to reveal these noise characteristics and the behaviors of the DLPA pavement by laboratory tests and field measurements. First, measurements were made of the road performance of different porous asphalt (PA) mixtures and DLPA structures. Then, a vector impedance measurement system was utilized to evaluate the effects of porosity, aggregate size, and thickness on the sound absorption coefficient (SAC) of the DLPA slabs. Third, a sound meter was used to record the noise levels outside/inside the car/truck on the roads with three surface conditions for the DLPA (PA-10+PA-20) and stone matrix asphalt (SMA) test section, which were paved on an elevated urban road. Finally, an analysis was done on the overall and spectral noise characteristics. The results demonstrated good road performance of these PA mixtures and DLPA structures. Relatively fine and coarse aggregates in the upper and lower layers, respectively, enhanced the sound absorption. But an increase in porosity beyond 20 % and a slight variation in thickness did not considerably improve the sound absorption. Compared to SMA pavement, the overall noises levels of the DLPA pavement were 4–8 dBA lower on the "dry" surface and 6 dBA lower on the "wet" surface. The DLPA pavement exhibited a noise reduction at the full frequency band, which attributed to the pore structure and the relatively fine aggregate used. The interior sound energy always concentrated on the low-frequency band. However, the exterior sound energy on the "dry" and "wet" road surfaces tended to the low-frequency and high-frequency bands, respectively. These findings would shed light on the spectral noise characteristics of the DLPA pavement and provide a new knowledge about the behaviors of the lower layer and upper layer in relation to noise reduction and road performance. [Display omitted] • The PA-10+PA-20 structure was measured in the laboratory and test section. • The effects of porosity, aggregate size and thickness were evaluated. • Both the overall and spectral noises were measured and analyzed. • Both the exterior and interior noises were measured and analyzed. • The noises on three road surface conditions were measured and analyzed. [ABSTRACT FROM AUTHOR]

This paper presents a novel deep learning model called Self-Attention Layer within a Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in extensive datasets containing emergency vehicle sirens and road noise. SACNN leverages a combination of EfficientNet and One Dimensional Convolutional Neural Networks (1D_CNN) to improve the precision and efficiency of detection. The dataset comprises 3-second WAV-format audio files categorized into Emergency Vehicles (Ambulance and Firetruck sirens) and Traffic (plain traffic sounds), with each category having 200 sound files and their corresponding 200 spectrogram images per audio file. Experimental results demonstrate SACNN's superior performance across various metrics, including accuracy, precision, recall, and F1 score, while also achieving notable advancements in computational efficiency. The research addresses the need for more effective solutions in the domain of emergency vehicle siren and road noise detection by introducing SACNN as a cutting-edge solution. By integrating EfficientNet and 1D_CNN, SACNN surpasses existing models in accuracy and computational efficiency. Comparative analysis with EfficientNet and 1D_CNN using a large-scale dataset validates SACNN's superior performance, with an average accuracy, precision, recall, and F1 score of 1.00. Additionally, SACNN exhibits the highest computational efficiency, averaging 0.20883 s per sample. These findings highlight SACNN's efficacy in detecting and classifying emergency vehicle sirens and road noise in large-scale datasets, with implications for traffic management, public safety, and noise pollution monitoring. SACNN offers the potential for constructing real-time acoustic data detection systems. [ABSTRACT FROM AUTHOR]

• Cellular automata traffic flow model for signal-controlled intersections. • A new dynamic simulation method for intersection traffic noise. • The emission characteristics of traffic noise at intersections are explored. Predicting and assessing road traffic noise is a crucial task in urban environmental management. To accurately predict traffic noise at signal-controlled intersections, this study introduces a dynamic traffic noise simulation model based on cellular automata. Firstly, a cellular automata traffic flow model for a multi-lane signal-controlled intersection is established. The vehicle noise emission and noise propagation models are incorporated into the dynamic simulation of traffic flow to accurately calculate traffic noise. Subsequently, the simulated data is compared with the measured data. The results demonstrate that the proposed method can effectively predict the statistical distribution of traffic noise at the intersection, with prediction errors of less than 2 dB(A) for L eq , L 50 , L 90 , and less than 3 dB(A) for L 10. Finally, the proposed method is employed to simulate the operational status and noise emissions of vehicles at intersections under various traffic conditions. Based on the simulation results, the intrinsic connection between traffic operation and noise emission is explored, and the characteristics of traffic noise emission and distribution at intersections are analyzed. The method and its findings are anticipated to serve as a valuable reference for the management of road traffic noise. [ABSTRACT FROM AUTHOR]

[Display omitted] • Study of air pollution/ noise exposure and psychological outcomes. • 7555 London-based adolescents from the SCAMP cohort took part. • First adolescent study to link ozone pollution and executive functioning. • Other pollutants associated with executive functioning and externalising problems. • Traffic noise associated with anxiety, speech processing and intelligence. There is increasing evidence that air pollution and noise may have detrimental psychological impacts, but there are few studies evaluating adolescents, ground-level ozone exposure, multi-exposure models, or metrics beyond outdoor residential exposure. This study aimed to address these gaps. Annual air pollution and traffic noise exposure at home and school were modelled for adolescents in the Greater London SCAMP cohort (N=7555). Indoor, outdoor and hybrid environments were modelled for air pollution. Cognitive and mental health measures were self-completed at two timepoints (baseline aged 11–12 and follow-up aged 13–15). Associations were modelled using multi-level multivariate linear or ordinal logistic regression. This is the first study to investigate ground-level ozone exposure in relation to adolescent executive functioning, finding that a 1 interquartile range increase in outdoor ozone corresponded to −0.06 (p < 0.001) z-score between baseline and follow-up, 38 % less improvement than average (median development + 0.16). Exposure to nitrogen dioxide (NO 2), 24-hour traffic noise, and particulate matter < 10 µg/m3 (PM 10) were also significantly associated with slower executive functioning development when adjusting for ozone. In two-pollutant models, particulate matter and ozone were associated with increased externalising problems. Daytime and evening noise were associated with higher anxiety symptoms, and 24-hour noise with worse speech-in-noise perception (auditory processing). Adjusting for air pollutants, 24-hour noise was also associated with higher anxiety symptoms and slower fluid intelligence development. Ozone's potentially detrimental effects on adolescent cognition have been overlooked in the literature. Our findings also suggest harmful impacts of other air pollutants and noise on mental health. Further research should attempt to replicate these findings and use mechanistic enquiry to enhance causal inference. Policy makers should carefully consider how to manage the public health impacts of ozone, as efforts to reduce other air pollutants such as NO 2 can increase ozone levels, as will the progression of climate change. [ABSTRACT FROM AUTHOR]

Metro operations inevitably generate noise inside trains. For better understanding the characteristics of interior noise, we conducted interior noise measurements on 12 subway lines in a Chinese city, objectively explored the reasons for psychological annoyance. By conducting a survey on passengers, we investigated the noise-components that cause an increase in subjective annoyance and used ordinal logistic regression to explore the key factors. Finally, the noise time–frequency characteristics under different operating conditions were discussed. The results showed that loudness is the primary factor causing psychological annoyance. Rail corrugation, especially in the 63–125 Hz range, and broadcasts are the main noise sources that cause annoyance. There was a strong correlation between subjective annoyance and noise sensitivity, and track spatial position and alignment, while vehicle type and seating position had no significant effect. A new index has been proposed for quantifying subjective annoyance. During maintenance, attention should be paid to rail corrugation. [ABSTRACT FROM AUTHOR]

Anurans commonly use sound signals as the main form of communication. This system is diverse in both structure and social functions, and can be used from mate recognition until evaluation of conspecifics. Consequently, when in noisy environments, the main constraint is to correctly identify acoustic signals. Thus, this study aims to evaluate the influence of traffic noise in call parameters of Bokermannohyla hylax. We recorded individuals in collecting sites closer and further from the boulevard, inside the area of Parque Estadual das Fontes do Ipiranga, an urban Atlantic Forest fragment at municipality of São Paulo, Brazil. We evaluated if noisy or silent collecting sites influence temporal and spectral variables and signal-to-noise ratio. As expected, we observed influence of collecting site on temporal variables and on signal-to-noise ratio, but not on spectral variables. Spectral traits, unlike temporal ones, are more related to intrinsic aspects of individuals, and consequently they tend to present limited or no modification in face of anthropogenic noise. We also observed an influence of traffic noise on signal-to-noise ratio, which means that if the traffic noise is more intense, individuals increased calling power amplitude. This increase has several implications in different aspects of the life of individuals, even influencing the spent of energy in other anuran life story traits. Consequently, traffic noise can impact the long-term permanence of the species in highly noisy environments. [ABSTRACT FROM AUTHOR]

Background: Evidence about associations of pollutants with sleep is limited, and most of studies focused on individual sleep behaviors, neglecting their interrelation. We aimed to assess the relationship between ambient air pollution and road traffic noise with overall sleep health.Methods: The study included 378,223 participants from the UK Biobank. Including five sleep behaviors (chronotype, sleep duration, insomnia, snoring, and daytime sleepiness) to construct overall sleep pattern. Ambient air pollution exposure was estimated using Land Use Regression model. Road traffic noise exposure was estimated using a simplified version of the Common Noise Assessment Methods model. Using multinomial and binary logistic regression models to identify the associations between pollutants with overall and individual sleep behaviors, respectively.Results: Participants were derived in three sleep patterns: healthy (n = 140,490), intermediate (n = 220,627), and poor (n = 17,106). After adjustment for potential confounders, compared with the lowest quartile of PM2.5, the highest quartile had higher odds of intermediate and poor compared to healthy sleep pattern [OR (95% CI) for poor: 1.28 (1.21-1.36); for intermediate: 1.11 (1.09-1.14)]. We observed similar relationships for PM10, PM2.5 absorbance, PMcoarse, NOx, and NO2. In unadjusted model, compared with low exposure of Lnight, high Lnight exposure had higher odds of intermediate and poor compared to healthy sleep pattern [OR (95% CI) for poor: 1.13 (1.06-1.20); for intermediate: 1.03 (1.00-1.06)]. However, such associations disappeared after further adjustment for potential confounders.Conclusions: Long-term ambient air pollution is associated with overall sleep health. Road traffic noise itself is weakly associated with overall sleep health. [ABSTRACT FROM AUTHOR]

Introduction: Exposure to environmental noise from within homes has been associated with poor mental health. Existing evidence rests on cross-sectional studies prone to residual confounding, reverse causation, and small sample sizes, failing to adequately consider the causal nature of this relationship. Furthermore, few studies have examined the sociodemographic distribution of noise exposure at a country level.Methods: The study, conducted in 2021, examined the impact of environmental noise from road traffic, airplanes, trains, and industry on mental health and psychological distress as reported by 31,387 respondents using a 19-year longitudinal data set in Australia (2001‒2019). To improve the capacity to make causal inference and reduce bias from measurement error, reverse causation, and unobserved confounders, analyses used instrumental variables, fixed-effects models, and an aggregated area-level measure of noise exposure. Utilizing the large-scale national data set, sociospatial distributions of noise exposure were described.Results: Private and public rental tenants, lone parents, residents of socioeconomically disadvantaged areas, and those with long-term health conditions were more likely to report residential noise exposure. This exposure to noise was consistently associated with poorer mental health (self-reported noise: β= -0.58; 95% CI= -0.76, -0.39; area-level noise: β= -0.43; 95% CI= -0.61, -0.26), with the relationship strongest for traffic noise (β= -0.79; 95% CI= -1.07, -0.51). Notably, when noise exposure decreased over time, there was an increase in mental health (β= 0.43; 95% CI= 0.14, 0.72).Conclusions: The study provides strong evidence of a negative mental health effect of perceived residential noise, and the results have implications for healthy home design and urban planning. These findings should be validated with further studies that measure noise intensity and housing quality. [ABSTRACT FROM AUTHOR]

• A sound absorbing material of sound barriers is prepared by waste tire rubber to reduce the black pollution. • The size of tire rubber particles and the ratio of different masses can impact the sound absorption of the material. • The sound barrier composed 30% waste tire rubber blended with nitrile foam rubber achieve optimal noise reduction. To reduce traffic noise and black pollution caused by moving vehicles, this study takes into account of the key parameters such as voids in the porous media acoustic model. This study proposes a cost-effective method. Waste tires are crushed into a certain size containing metal wires, and the porous sound-absorbing material is prepared by blow-filling method using polyurethane adhesive and nitrile foam rubber. Considering the compatibility between traffic noise spectrum and effective sound absorption frequency band of the material, this study examines and analyzes the noise reduction performance of the porous material made from waste rubber tires under various material parameters. This includes thickness, particle size distribution, particle size, and mixed quality combination. The results of this study indicate that the thickness, particle size, and the combination of mixed qualities are the three main factors influencing the sound absorption performance of porous material derived from waste rubber tires. Optimal comprehensive sound absorption and durability properties were achieved when 30 % of waste tire rubber was incorporated into the nitrile foam rubber. Compared to commonly used metal sound barriers, the noise reduction of the porous material prepared from waste rubber tires in this study increased the insertion loss of the sound barrier by up to 2.0 dB. This approach reduces material costs and reduces the black pollution caused by waste rubber tires. Concurrently, this contributes to the sustainable development of the traffic environment. [ABSTRACT FROM AUTHOR]

An accurate understanding of noise perception is important for urban planning, noise management and public health. However, the visual and acoustic urban landscapes are intrinsically linked: the intricate interplay between what we see and hear shapes noise perception in the urban environment. To measure this complex and mixed effect, we conducted a mobility-based survey in Hong Kong with 800 participants, recording their noise exposure, noise perception and GPS trajectories. In addition, we acquired Google Street View images associated with each GPS trajectory point and extracted the urban visual environment from them. This study used a multi-sensory framework combined with XGBoost and Shapley additive interpretation (SHAP) models to construct an interpretable classification model for noise perception. Compared to relying solely on sound pressure levels, our model exhibited significant improvements in predicting noise perception, achieving a six-classification accuracy of approximately 0.75. Our findings revealed that the most influential factors affecting noise perception are the sound pressure levels and the proportion of buildings, plants, sky, and light intensity. Further, we discovered non-linear relationships between visual factors and noise perception: an excessive number of buildings exacerbated noise annoyance and stress levels and diminished objective noise perception at the same time. On the other hand, the presence of green plants mitigated the effect of noise on stress levels, but beyond a certain threshold, it led to worsened objective noise perception and noise annoyance instead. Our study provides insight into the objective and subjective perception of noise pressure, which contributes to advancing our understanding of complex and dynamic urban environments. [Display omitted] • Data on dynamic noise exposure and subjective noise perception were collected. • An interpretable machine learning model was used to analyze the audio-visual environment. • The influences of visual factors showed non-linear relationships. • The model can effectively predict noise perception in different areas. [ABSTRACT FROM AUTHOR]

This paper aims to make recommendations about the convenience of using low-noise pavements to mitigate traffic noise under different traffic and population scenarios. To do so, a life cycle approach has been applied by performing a Life-cycle Assessment (LCA) and Life-cycle Cost Analysis (LCCA). To account for the noise mitigation achieved by the road surfaces, which is the main benefit compared to conventional road surfaces, the damage effect of traffic noise has been incorporated in both analyses. Thus, open source noise emission and propagation modelling and Geographical Information System (GIS) tools have been used to determine the noise level at which residents are exposed. The impact of noise emissions on human health is estimated and expressed in disability-adjusted life years (DALYs) and the related external cost have been calculated by monetarizing the environmental impacts and by integrating them within the cost assessment. The methodology followed allowed the comparison of different scenarios and the results show a huge potential for reducing environmental impacts and costs when using low-noise road surfaces although accurate information about the acoustic aging of the materials is key in order to ensure that benefits are obtained. [Display omitted] • The feasibility of low-noise road surfaces has been evaluated combining LCA and LCCA. • Noise exposure is assessed using open-source GIS and noise modelling tools. • Population and traffic density influence low-noise surfaces viability. • Traffic noise impacts significantly outweigh other LCA/LCCA categories. • Acoustic ageing estimates of low-noise surfaces affect low-noise surfaces viability. [ABSTRACT FROM AUTHOR]

[Display omitted] • Air pollution, traffic noise and greenness, and myocardial infarction incidence. • Time-varying information on exposures, socioeconomic and lifestyle factors. • Inverse fully adjusted association between greenness and myocardial infarction. • Associations with air pollution (PM 2.5) in subgroups only, e.g. less educated. • No associations between road traffic noise and myocardial infarction. Emerging evidence shows that long-term exposure to air pollution, road traffic noise, and greenness can each be associated with cardiovascular disease, but only few studies combined these exposures. In this study, we assessed associations of multiple environmental exposures and incidence of myocardial infarction using annual time-varying predictors. In a population-based cohort of 20,407 women in Sweden, we estimated a five-year moving average of residential exposure to air pollution (PM 2.5 , PM 10 and NO 2), road traffic noise (L den), and greenness (normalized difference vegetation index, NDVI in 500 m buffers), from 1998 to 2017 based on annually varying exposures and address history. We used adjusted time-varying Cox proportional hazards regressions to estimate hazard ratios (HR) and 95 % confidence intervals (95 % CI) of myocardial infarction per interquartile range (IQR). Furthermore, we investigated interactions between the exposures and explored potential vulnerable subgroups. In multi-exposure models, long-term exposure to greenness was inversely associated with incidence of myocardial infarction (HR 0.89; 95 % CI 0.80, 0.99 per IQR NDVI increase). Stronger associations were observed in some subgroups, e.g. among women with low attained education and in overweight (BMI ≥ 25 kg/m2) compared to their counterparts. For air pollution, we observed a tendency of an increased risk of myocardial infarction in relation to PM 2.5 (HR 1.07; 95 % CI 0.93, 1.23) and the association appeared stronger in women with low attained education (HR 1.30; 95 % CI 1.06, 1.58). No associations were observed for PM 10 , NO 2 or road traffic noise. Furthermore, there were no clear interaction patterns between the exposures. Over a 20-year follow-up period, in multi-exposure models, we found an inverse association between residential greenness and risk of myocardial infarction among women. Furthermore, we observed an increased risk of myocardial infarction in relation to PM 2.5 among women with low attained education. Road traffic noise was not associated with myocardial infarction. [ABSTRACT FROM AUTHOR]

Long-term exposure to road traffic noise is associated with cardiovascular disease, but the evidence on respiratory diseases is just emerging. We aimed to examine the association between long-term exposure to road traffic noise and the incidence of acute lower respiratory infections (ALRIs) in adults. We followed 23,141 female nurses (age ≥ 44 years) from the Danish Nurse Cohort from baseline (1993 or 1999) to their first hospital contact (inpatient, outpatient, or emergency room) for ALRI, death, emigration or the end of 2015. The residential annual mean levels of road traffic noise (L den) during the follow-up were estimated using the Nord2000 model. We applied time-varying Cox models to estimate the association of 3-year mean exposure to L den with ALRIs incidence and piecewise analysis to estimate the threshold of L den. We examined the robustness of the results by adjusting for residential exposure to air pollution, and the effect modification by attained age, socioeconomic status (SES), comorbidity, and lifestyle. During 18.5 years of follow-up, 2,004 nurses developed ALRIs. In a linear model, we detected a statistically significant positive association between L den and ALRI, with a hazard ratio (HR) of 1.11 (95 % confidence interval (CI): 1.04, 1.17) per 9.2 dB (interquartile range, IQR). We observed non-linear association with a threshold at 57 dB, above which the HR was 1.25 (95 % CI: 1.09, 1.43) per IQR. Further adjustment for PM 2.5 reduced the HRs slightly to 1.21 (95 % CI: 1.04, 1.40). The associations were stronger for nurses with asthma, and in those with lowest SES. We present novel findings in support of the association between long-term exposure to road traffic noise and ALRIs, independent of air pollution, suggesting noise as a risk factor for infectious respiratory diseases. [ABSTRACT FROM AUTHOR]

The freemium business strategy has been widely embraced by content platforms. We develop an analytical model to explore the optimal freemium strategy for a monopolistic content platform. We identify three common strategies for releasing freemium: differentiation only by content (OC model), differentiation only by advertising (OA model), and differentiation by both content and advertising (CA model). Under OC, consumers can access limited content in the free tier and full content in the paid tier while being required to view ads in both tiers. Under OA, consumers can access full content in both tiers but do not see ads in the paid tier. Under CA, consumers can access full content and remove ads in the paid tier while only being able to access limited content and being required to view ads in the free tier. Consumers are heterogeneous in the amount of content they want to consume and in the nuisance cost they perceive from viewing ads. Several factors, such as the ad revenue rate and the proportion of consumers with a high nuisance cost, have crucial effects on the optimal design of the freemium strategies. When the ad revenue rate is moderate, the equilibrium advertising intensity, subscription fee, and the amount of content in the free tier show nonmonotonic changes. • The optimal freemium strategy for a monopolistic content platform is studied. • We develop an analytical model to analyze three common freemium strategies. • We identify the factors that have crucial effects on the optimal freemium strategy. [ABSTRACT FROM AUTHOR]

While urban pedestrianisation schemes typically attract vociferous opposition, it is often unclear whether these negative views are representative of the general population. For this reason, understanding how residents perceive the impacts of such schemes is critical to their evaluation. To this aim, this article assesses residents' perceptions and preferences regarding the pedestrianisation of multiple streets in the Eixample district of Barcelona in recent years, through a representative survey of the district's residents (n = 1211). Our research examines how residents' views on pedestrianisation vary depending on their mobility habits, sociodemographic background, and the level of traffic calming of the street they live on. Our results show that most respondents (66%) consider that the impacts of existing pedestrianisations have been positive regardless of whether they live on a pedestrianised street or not, even though residents of recently pedestrianised streets are more positive than those living on other streets. At the same time, many residents of pedestrianised streets consider that the benefits of reduced traffic externalities may be partially counterbalanced by new problems caused by an intensive use of public space (e.g. noise from people and cafes, touristification, nightlife). Our results also show that there is a strong relationship between support for pedestrianisation, age and mobility habits: daily car and motorbike users are much less supportive of pedestrianisation than public transport users and cyclists, while support for pedestrianisation decreases markedly with advancing age. [ABSTRACT FROM AUTHOR]

Previous research has shown that biophysical and social characteristics of urban green spaces (UGS) and individual factors may play a role in preferences for restorative environments. However, little is known about the trade-offs between (bio)physical and social characteristics of UGS on different health-related psychological benefits between different population groups. This exploratory study used an image-based discrete choice experiment, in which 298 adolescent pupils, 159 university students, and 142 adults were asked which urban green spaces they would prefer for general recovery, stress reduction, and concentration improvement. Each of the 128 green space scenarios depicted different attributes of varying levels of recreational infrastructure, numbers of trail users, distances between forest vegetation and trail, traffic noise, background scenery, both in winter and in summer. The results indicate that visitor numbers and traffic noise strongly influenced green space preferences for all study groups, but groups differed in terms of their preferences and trade-offs for the psychological benefits. The biophysical environment was less important for adolescent pupils, while recreational infrastructure was less important for the adults. There were group differences in stated preferences for general recovery, stress reduction, and concentration enhancement. Implications for urban green space planning are presented. • Green space preferences differed between adolescent pupils, students and adults. • Green space preferences were similar for different health-related psychological benefits. • Most important for preferences were the number of visitors and traffic noise. • Adolescent pupils placed more importance on recreational infrastructure. • Traffic noise was important for attention restoration. [ABSTRACT FROM AUTHOR]

A ventilated acoustic resonator (VAR), a type of acoustic metamaterial (AM) has emerged as a promising solution for mitigating urban noise pollution and traffic noise which simultaneously require ventilation. However, due to the high nonlinearity, the inverse design of complex VAR is intractable with analytical methods. Deep learning-based inverse design methods are gaining prominence as an alternative to analytical methods but still exhibit significant challenges: limited design flexibility in parameter-based approaches and the deterioration of essential shapes for sound attenuation performance in pixel image-based approaches. To address these challenges, we propose an inverse design framework of ultra-broadband non-parametric VAR through a genetic algorithm (GA) optimization-based latent space exploration strategy. The GA-based exploration on the dimension-reduced latent space of a conditional variational autoencoder (CVAE) enables the generation of the ultra-broadband non-parametric VAR preserving essential shape for sound attenuation with reduced computational costs. The GA-optimized non-parametric VARs show an average 28.76% bandwidth increase compared with the training dataset and, also demonstrate a considerably wider bandwidth compared to the parameter-based optimization methods, which expands the limit of the sound attenuation performance. Our novel approach paves the way for the optimization of complex mechanical structures. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of noise interference in road defect detection, caused by various environmental factors or acquisition equipment. In this article, we add three different levels of salt & pepper noise to the road defect dataset and propose a novel anti-noise dual-branch network (ADNet). The proposed ADNet leverages two backbone networks equipped with the dual-branch interaction (DI) modules to learn the defect information from noise and clear images for improving noise immunity. Then, the weighted feature representation (WFR) module is designed to extract more context-aware cues from the multi-level feature. Additionally, the region perception unit is proposed, where channel-spatial attention optimization (CSAO) module extracts more defect region information by utilizing the attention mechanism and multi-scale refinement (MR) optimizes the boundary information with the U-Net structure. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art methods, making it a promising solution for detecting road defects in noisy environments. [ABSTRACT FROM AUTHOR]

• Two urban plant species, Tagetes patula and Salvia splendens, were exposed to daily traffic noise. • Traffic noise exposure induced accumulation of H 2 O 2 and MDA in both plant species. • Activity of enzymatic and non-enzymatic antioxidant system was enhanced in response to traffic noise exposure. • Traffic noise exposure altered the phytohormone profile of plants. • Traffic noise can be considered environmental stressor for plants. Traffic noise pollution is one of the major environmental concerns in crowded cities worldwide. The objective of the present study was to investigate the effects of traffic noise on growth, hormonal balance, oxidative damage, and activity of antioxidant systems in two urban plant species, Tagetes patula and Salvia splendens. Each of the plant species were equally divided into 2 groups (control and traffic noise treatment) and each group was grown under identical controlled conditions in two separate growth chambers for two months. Traffic noise was recorded during peak traffic hours in a highly congested area of the city. Frequency analysis of traffic noise was conducted on the samples during the recording process. Plants in the traffic noise treatment group were exposed to 16 h of road traffic noise each day for a total of 15 days, while the control group was kept under complete silence. Traffic noise exposure led to significant decrease in growth indices of both plant species. The content of H 2 O 2 and malondialdehyde (MDA) significantly increased upon traffic noise treatment in both species, followed by an increase in DPPH (2,2-diphenyl-1-picrylhydrazyl) anti-oxidant activity and the activity of antioxidant enzymes including catalase, peroxidase, and ascorbate peroxidase. Road traffic noise significantly reduced the content of phytohormones including zeatin, salicylic acid, indole-3-acetic acid, and gibberellic acid. On the other hand, the content of stress-related hormones including abscisic acid and jasmonic acid significantly increased in response to road traffic noise in both species. Based on our results, daily traffic noise can negatively affect growth and physiology of plants by inducing of oxidative damage and interfering with hormonal balance. [ABSTRACT FROM AUTHOR]

Urban noise is becoming more serious and increasingly concerning environmental problems. This has led to numerous study on traffic noise. However, not much studies has been done on the noise from a human perspective as they go about their daily life. In this paper we report on the study of noise level in a campus environment, in open common areas which are frequented by students. Using crowd sourcing noise data by volunteers, we are able to map out the noise across the campus from the perspective of the students. The noise level increase through the day as the student gather around popular open spaces. Our study shows that the sound level on campus are due mainly to human and mechanical noise. By mashing the noise data with Wi-Fi log data, we were able to show the good correlation between sound level and human density in an area. [ABSTRACT FROM AUTHOR]

Traffic flow dynamics are complex and nonlinear, and depend on the interactions of many vehicles moving together. Especially, the stochastic driving behavior is a significant reason for many complex traffic phenomena such as the capacity drop or traffic instabilities without lane changing. To date, there has not been a satisfactory general theory that can be consistently applied to represent such traffic flow conditions. While there have been efforts to introduce stochastic behavior in deterministic traffic flow models, these attempts have mainly focused on the small stochastic fluctuations around the theoretical equilibrium fundamental diagram or model parameters. Little attention has been given to the physical relevance of the stochastic component, and the existing models do not consider large deviations from the equilibrium fundamental diagram that occur in the transient phases of traffic flow (i.e acceleration and/or deceleration). The goal of this paper is thus to improve current continuum traffic models, with the primary focus on the stochastic behavior arising in the acceleration and/or deceleration process. Our analytical results show noise-induced (long-wave length) stop-and-go waves in the low speed regime, which conform to the recent empirical findings. In addition, it is found that the stochasticity can lead to situations in which a bottleneck sometimes is activated and at other times is not activated under identical traffic demand. • Propose a class of stochastic higher continuum traffic flow models. • Derive general stochastic linear stability conditions using the proposed model. • Demonstrate that the introduction of Brownian noise in a stable traffic model allows to describe realistic noise-induced stop-and-go waves observed in reality. • Show the performance of the proposed model using real data. [ABSTRACT FROM AUTHOR]

Aircraft noise exposure is linked to cardiovascular disease risk. One understudied candidate pathway is obesity. This study investigates the association between aircraft noise and obesity among female participants in two prospective Nurses' Health Study (NHS and NHSII) cohorts. Aircraft day-night average sound levels (DNL) were estimated at participant residential addresses from modeled 1 dB (dB) noise contours above 44 dB for 90 United States (U.S.) airports in 5-year intervals 1995–2010. Biennial surveys (1994–2017) provided information on body mass index (BMI; dichotomized, categorical) and other individual characteristics. Change in BMI from age 18 (BMI18; tertiles) was also calculated. Aircraft noise exposures were dichotomized (45, 55 dB), categorized (<45, 45–54, ≥55 dB) or continuous for exposure ≥45 dB. Multivariable multinomial logistic regression using generalized estimating equations were adjusted for individual characteristics and neighborhood socioeconomic status, greenness, population density, and environmental noise. Effect modification was assessed by U.S. Census region, climate boundary, airline hub type, hearing loss, and smoking status. At baseline, the 74,848 female participants averaged 50.1 years old, with 83.0%, 14.8%, and 2.2% exposed to <45, 45–54, and ≥55 dB of aircraft noise, respectively. In fully adjusted models, exposure ≥55 dB was associated with 11% higher odds (95% confidence interval [95%CI]: −1%, 24%) of BMIs ≥30.0, and 15% higher odds (95%CI: 3%, 29%) of membership in the highest tertile of BMI18 (ΔBMI 6.7 to 71.6). Less-pronounced associations were observed for the 2nd tertile of BMI18 (ΔBMI 2.9 to 6.6) and BMI 25.0–29.9 as well as exposures ≥45 versus <45 dB. There was evidence of DNL-BMI trends (p trends ≤ 0.02). Stronger associations were observed among participants living in the West, arid climate areas, and among former smokers. In two nationwide cohorts of female nurses, higher aircraft noise exposure was associated with higher BMI, adding evidence to an aircraft noise-obesity-disease pathway. [ABSTRACT FROM AUTHOR]

[Display omitted] Low-noise pavements (LNPs) are among the most frequently used measures to mitigate road traffic noise. CNOSSOS-EU model enables improved predictions of noise exposure by customizing surface coefficients. However, accurately estimating their benefits to citizens remains a challenge. Specific LNPs can be defined by the user, but their calibration remains difficult, with different approaches in literature. Using standard roadside measurements and the recent Urban Statistical Pass-By methodology, this study presents a procedure for tuning user-defined coefficients of pavements in a real-world scenario. Test cases results are assessed against reference scenarios to evaluate the population exposure differences and reduction of potential harmful effects. The procedure can be a valuable instrument in aiding action plans in compliance with the 2002/49/EC Directive. The work also highlights the need to assess the acoustical characteristics of LNPs to effectively evaluate health benefits, as well as the need for comprehensive studies defining the exact characteristics of LNPs. [ABSTRACT FROM AUTHOR]

• An improved YOLOv5 model for tunnel crack detection is proposed. • Model overcomes low light, low contrast and high noise challenges in tunnels. • Improved model achieves 90% detection accuracy while realizing real-time detection. • Improved model can be applied to practical engineering through field validation. An improved real-time tunnel lining crack detection model based on YOLOv5 is proposed. This model maintains high precision and accuracy crack detection in low-light, low-contrast and high-noise environments by introducing several effective data augmentation techniques as well as semantic context encoding (SCE) and detail preserving encoding (DPE) at the head of the network structure. It achieves 90 % precision, 91 % recall, and 92 % mAP@50. The model demonstrates better detection performance than YOLOv4-tiny, YOLOv5s, YOLOv8s, and traditional threshold segmentation method, especially in complex environments to reduce misdetection and omission. The average detection time is only 12 ms per image, demonstrating the feasibility of its real-time application. The robust and generalization performance of the model is validated in specific engineering applications, showing great potential for improving detection efficiency, cost-effectiveness, and reliability in tunnel safety assessment and disasters management. [ABSTRACT FROM AUTHOR]

To investigate the impact of environmental noise on the cognitive abilities of drivers, this study, using in-vehicle voice interaction as an example, conducted laboratory experiments to assess the effects of road traffic noise, entertainment noise, and white noise stimuli on drivers' attention and short-term memory. The noise levels simulated to mimic acoustic conditions during car driving ranged from 35 dB(A) to 65 dB(A). The conclusions drawn were as follows: (1) Noise levels directly influenced subjective annoyance levels, with annoyance linearly increasing as noise levels escalated; (2) Both attention and short-term memory task reaction times of drivers were significantly influenced by noise types. Compared to traffic noise and white noise, drivers' cognitive efficiency was lower under entertainment noise. (3) Performance in complex cognitive tasks was more susceptible to noise levels compared to simple cognitive tasks; (4) Experimentally, it was found that drivers exhibited the highest cognitive efficiency in cognitive tasks when the environmental noise level was 55 dB(A), as opposed to noise levels of 35 dB(A), 45 dB(A), and 65 dB(A). [ABSTRACT FROM AUTHOR]

Shipping is one of the largest industries globally, with well-known negative impacts on the marine environment. Despite the known negative short-term (minutes to hours) impact of shipping on individual animal behavioural responses, very little is understood about the long-term (months to years) impact on marine species presence and area use. This study took advantage of a planned rerouting of a major shipping lane leading into the Baltic Sea, to investigate the impact on the presence and foraging behaviour of a marine species known to be sensitive to underwater noise, the harbour porpoise (Phocoena phocoena). Passive acoustic monitoring data were collected from 15 stations over two years. Against predictions, no clear change occurred in monthly presence or foraging behaviour of the porpoises, despite the observed changes in noise and vessel traffic. However, long-term heightened noise levels may still impact communication, echolocation, or stress levels of individuals, and needs further investigation. • Elevated shipping noise and vessel traffic did not influence porpoise occurrence. • No change was observed in either detection rate or foraging behaviour of porpoises. • Porpoises continued to use preferred habitat despite changes in underwater noise. • Long-term impacts on communication, echolocation, or stress remain unknown. [ABSTRACT FROM AUTHOR]

Few studies have investigated the association between noise complaints and construction noise. Reviewing complaint data could help better understand community annoyance to noise and help plan future major construction work. The aim of this study was to determine the relationship between noise complaints and noise levels, and to develop a tool to analyze the complaints lodged by citizens concerning the rehabilitation of a large road interchange construction project, namely the Turcot Project. A total of 1294 complaints were collected and transcribed by the Ministry of Transport of Quebec between January 6, 2017, and January 11, 2021. A descriptive analysis of the complaints was conducted following an inductive approach. Citizens complained mostly about noise, but also about several other aspects related to the rehabilitation work such as the desire to obtain a specific mitigation measure and dust and particle levels. The number of noise complaints was correlated with noise levels, especially for noise indicators that consider sound levels emerging from the ambient noise. When individuals complain about construction noise, their annoyance may be influenced by an accumulation or interaction with other nuisances associated with construction work. It is therefore necessary to consider all aspects of the source of the nuisance, and the characteristics of the environment and of the complainant to better understand the genesis of community noise complaints and annoyance. Our results can be used by government agencies and authorities to improve noise management during construction activities and reduce community noise complaints. • Noise is the main reason of complaint and annoyance towards construction work. • Combination of construction nuisances can lead a person to complain about noise. • Complaint numbers are significantly correlated with noise levels. • Better implementing mitigation measures can reduce community noise complaints. [ABSTRACT FROM AUTHOR]

Low-altitude flights of unmanned aircraft systems (UASs), such as drones, can cause substantial noise pollution in urban areas. This study aims to tackle the crucial issue of noise in UAS flight planning and analyze the feasibility of implementing low-noise urban flights at a micro level. We employ a virtual flight simulator to accurately model the UAS noise impact in realistic flights and optimize the flight path via a heuristic algorithm accordingly. The flight noise assessment incorporates a sound source modeling technique for practical UAS models and an efficient Gaussian beam tracing method for outdoor noise propagation in realistic environments. Case studies for flight simulations are conducted in a representative resident community and metropolitan area to evaluate the noise impact resulting from different flight paths. By comparing our approach with benchmark shortest-distance paths, we validate its effectiveness in significantly reducing en-route noise exposure, encompassing both instantaneous and continuous noise levels. This method holds great potential for contributing to the planning and management of urban air transport systems. It effectively mitigates noise impact, promoting quieter and more environmentally friendly UAS operations and sustainable urban transportation. [ABSTRACT FROM AUTHOR]

Built environment characteristics and related environmental exposures and behaviors have been, separately, implicated in the development of poor mental health. However, it is unclear how these factors act together in relation to mental health. We studied these factors simultaneously to evaluate the impact of the built environment, and the mediating role of environmental exposures and physical activity, on mental health, while also studying moderation by sex, age, and length of residence. We used a cross-sectional population-based sample of 3145 individuals aged 15–97 years from Barcelona, Spain. Time spent walking and mental health status were assessed with validated questionnaires, administered through a face-to-face interview. We characterized the built environment (e.g., building, population and intersection density and green space), road traffic noise, and ambient air pollution at the residential level using land cover maps, remote sensing, noise maps and land use regression models. Adjusted regression models accounting for spatial clustering were analyzed to study associations between built environment attributes and mental health, and mediation and moderation effects. Density attributes were directly or indirectly, through air pollution and less consistently through walking, associated with poor mental health. Green space indicators were associated with lower prevalence of poor mental health, partly through lower air pollution exposure and more walking. In some cases, these associations differed by sex, age or length of residence. Non-linear associations of density indicators with environmental exposures, and of particulate matter with poor mental health indicated threshold effects. We conclude that living in dense areas with high air pollution concentrations was associated with poor mental health. On the other hand, green areas with lower air pollution concentrations were protective against poor mental health. Greater urban density might benefit health, but might only do so when air pollution concentrations are low. [Display omitted] • Urban density was, through air pollution, associated with poor mental health. • Green space was, through lower air pollution, associated with good mental health. • Non-linear associations indicated threshold effects of population density and air pollution. • Greater urban density might benefit health, but only when air pollution is low. [ABSTRACT FROM AUTHOR]

This study introduces an agent-specific assessment method of traffic noise exposure in agent mobility simulations. The assessment is achieved through a combination of an energy-based noise exposure impact assessment using noise exposure cost, and the state-of-the-art traffic noise prediction tool NoiseModelling coupled with the activity-based agent mobility simulation software MATSim. The agent-specific noise exposure cost is a measure to evaluate how the noise emissions from the transport of agents relate to the noise-related impact on other agents performing stationary activities. By introducing an agent-specific level, each agent's individual responsibility for the noise exposure may be estimated. The potential of the agent-specific noise exposure cost concept, combined with the MATSim-NoiseModelling framework, is illustrated through a case study, applying activity-based agent mobility simulations across Nantes, France. The results of the case study highlight, among other considerations, the insights that an agent-specific, activity-based noise exposure cost approach provides by visualizing the noise exposure "footprint" resulting from an agent's transportation activities. • Novel contribution to agent-specific, activity-based noise impact assessment. • Insightful representation of the traffic noise impact arising with mobility needs. • Influence of timing and location of journeys on activity-based noise exposure cost. • Application of the methodology to an urban test bed in Nantes, France. [ABSTRACT FROM AUTHOR]

[Display omitted] • Higher level of aircraft noise was associated with faster cognitive decline. • Aircraft and railway noise increased the hazard of cognitive impairment. • Exposure to more noise sources increased the risk of cognitive impairment. Transportation noise is an environmental exposure with mounting evidence of adverse health effects. Besides the increased risk of cardiovascular and metabolic diseases, recent studies suggest that long-term noise exposure might accelerate cognitive decline in older age. We examined the association between transportation noise and cognitive function in a cohort of older adults. The present study is based on 2594 dementia-free participants aged 60 + years from the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). Global cognition score and CIND (cognitive impairment, no dementia) were assessed with a comprehensive neuropsychological battery at baseline and up to 16 years. Residential transportation noise resulting from road traffic, railway, and aircraft were estimated at the most exposed façade and the time-weighted average exposure was assessed. Linear mixed-effect models were used to assess the effect of long-term traffic noise exposure on the rate of change in global cognition score. Hazard ratios (HRs) and 95 % confidence intervals (CIs) of CIND by transportation noise exposure were obtained with Cox proportional hazard models. Global cognition score decreased at an average rate of −0.041 (95 %CI −0.043, −0.039) per year. Aircraft noise was associated with a 0.007 (per 10 dB L den ; 95 %CI −0.012, −0.001) faster annual rate of decline. Global cognition score seems to be not affected by road traffic and railway noise. During the follow-up, 422 (21 %) participants developed CIND. A 10-dB L den difference in exposure to aircraft and railway noise was associated with a 16 % (HR 1.16, 95 %CI 0.91, 1.49) and 26 % (HR 1.26, 95 %CI 1.01, 1.56) increased hazard of CIND in the multi-pollutant model, respectively. No association was found for road traffic (HR 1.00, 95 %CI 0.83, 1.21). Transportation noise was linked to cognitive impairment and faster cognitive decline among older adults. Future studies are warranted to confirm our results. [ABSTRACT FROM AUTHOR]

Urban environmental exposures associate with adult depression, but it is unclear whether they are associated to postpartum depression (PPD). We investigated associations between urban environment exposures during pregnancy and PPD. We included women with singleton deliveries to liveborn children from 12 European birth cohorts (N with minimum one exposure = 30,772, analysis N range 17,686–30,716 depending on exposure; representing 26–46 % of the 66,825 eligible women). We estimated maternal exposure during pregnancy to ambient air pollution with nitrogen dioxide (NO 2) and particulate matter (PM 2.5 and PM 10), road traffic noise (L den), natural spaces (Normalised Difference Vegetation Index; NDVI, proximity to major green or blue spaces) and built environment (population density, facility richness and walkability). Maternal PPD was assessed 3–18 months after birth using self-completed questionnaires. We used adjusted logistic regression models to estimate cohort-specific associations between each exposure and PPD and combined results via meta -analysis using DataSHIELD. Of the 30,772 women included, 3,078 (10 %) reported having PPD. Exposure to PM 10 was associated with slightly increased odds of PPD (adjusted odd ratios (OR) of 1.08 [95 % Confidence Intervals (CI): 0.99, 1.17] per inter quartile range increment of PM 10) whilst associations for exposure to NO 2 and PM 2.5 were close to null. Exposure to high levels of road traffic noise (≥65 dB vs. < 65 dB) was associated with an OR of 1.12 [CI: 0.95, 1.32]. Associations between green spaces and PPD were close to null; whilst proximity to major blue spaces was associated with increased risk of PPD (OR 1.12, 95 %CI: 1.00, 1.26). All associations between built environment and PPD were close to null. Multiple exposure models showed similar results. The study findings suggest that exposure to PM 10, road traffic noise and blue spaces in pregnancy may increase PPD risk, however future studies should explore this causally. [ABSTRACT FROM AUTHOR]

As urbanization grows at an unexpectedly rapid pace around the world, the underground space is gradually regarded as a largely undeveloped natural resource. Passive surface wave methods based on short-time traffic-induced noise measurements have been shown to be powerful tools for underground imaging and monitoring in urban areas. The common surface-wave investigations mainly focus on the use of Rayleigh waves in vertical components. We have developed an application of using three-component traffic-induced surface waves to investigate near-surface structures in Hangzhou City, Eastern China. With the deployment of two dense linear arrays along the orthogonal roads, we analyze the temporal and spectral characteristics of the three-component traffic noise. The retrieval of Rayleigh and Love waves using the horizontal components could be biased due to the uneven noise source distribution and errors in the orientation of the horizontal receivers. We use the match-field processing technique to obtain the direction of noise source distribution, and apply polarization analysis to evaluate the misorientation of the horizontal components. Subsequently, high-quality Rayleigh and Love waves are retrieved based on seismic interferometry within 1 h noise recordings. We invert the multicomponent surface waves to determine near-surface shear (S)-wave velocity structures. The joint analysis of Rayleigh and Love waves improves the reliability and stability of the result. Comparisons among the results from the individual inversion of Rayleigh waves, the joint inversion of Rayleigh and Love waves, and the logging data demonstrate that the jointly inverted S-wave velocity profiles provide better correspondence with the logging results, which confirms that the combined use of Rayleigh and Love waves from multicomponent traffic noise can be a more powerful technique for near-surface characterization in urban areas. • Developed an application using three-component traffic-induced surface waves to investigate urban near-surface structures. • We analyze the noise sources direction by MFP method and the misorientation of horizontal component by polarization analysis. • Combined use of Rayleigh and Love waves from traffic noise offers an efficient method for urban subsurface characterization. [ABSTRACT FROM AUTHOR]