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Abstract: Traffic noise is a pervasive pollutant that affects wildlife at individual and group levels through mechanisms such as disrupting communication, affecting antipredator strategy, and/or changing how they use space within a habitat. Urbanization is expanding rapidly—few places remain untouched by anthropogenic noise disturbance—so understanding the implications of noise on wildlife behavior is paramount to conservation efforts. We asked whether traffic noise could change space use and social network metrics in flocks of captive birds. Specifically, we quantified the effects of playbacks of traffic noise on individual sociality (weighted degree, number of social partners weighted by the frequency of interactions with those social partners) and flock clustering (global clustering coefficient, connectivity of neighbors). In this study, we recorded social interactions and space use of flocks of captive zebra finches (Taeniopygia guttata) before, during, and after an experimental traffic noise introduction in two treatments: high- and lower-amplitude noise. Our results demonstrated that individual sociality and flock clustering increased in response to the noise introduction in both high-amplitude and low-amplitude treatments. Additionally, birds in the high-amplitude treatment spent more time in the room with active playback during noise playback whereas birds in the lower-amplitude treatment decreased time spent in the room closest to the high-amplitude treatment. Increased social behavior in response to traffic noise could influence disease transmission, social learning, and mating dynamics. We suggest future studies explore the mechanisms driving increased social behavior in traffic noise, such as perceived predation risk, vigilance, and cross-sensory interference.

Abstract Long-term exposures to environmental factors including airborne as well as noise pollutants, are associated with cardiovascular risk. However, the influence of environmental pollution on the young population is controversial. Accordingly, we aimed to investigate the relationships between long-term exposures to different environmental factors and major cardiovascular and inflammatory parameters and biomarkers in young, healthy subjects. Representative sample of permanent residents of two cities differing in air and noise pollution levels, aged 15–21 years, were recruited. Krakow and Lublin, both located in southern Poland, were chosen in relation to their similarities in demographic and geopolitical characteristics, but differences in air pollution (higher in Krakow) and noise parameters (higher in Lublin). A total of 576 subjects were studied: 292 in Krakow and 284 in Lublin. All subjects underwent health questionnaire, blood pressure measurements and biomarker determinations. Inflammatory biomarkers, such as CRP, hs-CRP, fibrinogen as well as homocysteine were all significantly higher in subjects living in Krakow as opposed to subjects living in Lublin (for hsCRP: 0.52 (0.32–0.98) mg/l vs. 0.35 (0.22–0.67) mg/l; p

Abstract In the present study, an attempt has been made to assess the impact of vehicular noise upon the 3-wheeler tempo drivers and to know whether there is any relationship between hearing loss and cumulative noise exposure. For this purpose, 3-wheeler tempo drivers (Exposed group) and non-commercial light motor vehicle car drivers (Unexposed group) were chosen as study subjects. Three traffic routes were selected to assess the noise level during waiting and running time in the exposed and unexposed groups. Among all three routes, the highest mean noise level (Leq) was observed on the Chowk to Dubagga route for waiting and en-route noise measurement. It was measured as 84.13 dB(A) and 86.36 dB(A) for waiting and en-route periods of 7.68 ± 3.46 and 31.05 ± 6.6 min, respectively. Cumulative noise exposure was found to be significantly different (p

Objectives: The immediate living environment might, like other lifestyle factors, be significantly related to mental well‐being. The current study addresses the question whether five relevant subjective home environment variables (i.e., protection from disturbing nightlight, daylight entering the home, safety at home, quality of window views, and noise disturbance) are associated with levels of self‐reported depression over and above well‐known sociodemographic and common lifestyle variables. Methods: Data from the Hamburg City Health Study (HCHS) were analyzed. In N = 8757 with available PHQ‐9 depression data, multiple linear regression models were computed, with demographic data, lifestyle variables, and variables describing the subjective evaluation of the home environment. Results: The model explained 15% of variance in depression levels, with ratings for the subjective evaluation of home environment accounting for 6%. Better protection from disturbing light at night, more daylight entering the home, feeling safer, and perceived quality of the window views, were all significantly associated with lower, while more annoyance by noise was associated with higher levels of self‐reported depression. Results did not differ if examining a sample of the youngest (middle‐aged participants: 46−50 years) versus oldest (70−78 years) participants within HCHS. Conclusion: Beyond studying the role of lifestyle factors related to self‐reported depression, people's homes may be important for subclinical levels of depression in middle and older age, albeit the direction of effects or causality cannot be inferred from the present study. The development of a consensus and tools for a standardized home environment assessment is needed. [ABSTRACT FROM AUTHOR]

Long-term exposures to environmental factors including airborne as well as noise pollutants, are associated with cardiovascular risk. However, the influence of environmental pollution on the young population is controversial. Accordingly, we aimed to investigate the relationships between long-term exposures to different environmental factors and major cardiovascular and inflammatory parameters and biomarkers in young, healthy subjects. Representative sample of permanent residents of two cities differing in air and noise pollution levels, aged 15–21 years, were recruited. Krakow and Lublin, both located in southern Poland, were chosen in relation to their similarities in demographic and geopolitical characteristics, but differences in air pollution (higher in Krakow) and noise parameters (higher in Lublin). A total of 576 subjects were studied: 292 in Krakow and 284 in Lublin. All subjects underwent health questionnaire, blood pressure measurements and biomarker determinations. Inflammatory biomarkers, such as CRP, hs-CRP, fibrinogen as well as homocysteine were all significantly higher in subjects living in Krakow as opposed to subjects living in Lublin (for hsCRP: 0.52 (0.32–0.98) mg/l vs. 0.35 (0.22–0.67) mg/l; p < 0.001). Increased inflammatory biomarker levels were observed in Krakow in both male and female young adults. Interestingly, significant differences were observed in blood pressure between male and female subjects. Males from Krakow had significantly higher mean systolic blood pressure (127.7 ± 10.4 mm/Hg vs. 122.4 ± 13.0 mm/Hg; p = 0.001), pulse pressure (58.7 ± 8.9 mm/Hg vs. 51.4 ± 12.3 mm/Hg; p < 0.001) and lower heart rate (p < 0.001) as compared to males living in Lublin. This was not observed in young adult females. Long-term exposure to environmental factors related to the place of residence can significantly influence inflammatory and cardiovascular parameters, even in young individuals. Interestingly, among otherwise healthy young adults, blood pressure differences exhibited significant variations based on biological sex. [ABSTRACT FROM AUTHOR]

Noise pollution is expanding at an unprecedented rate and is increasingly associated with impaired reproduction and development across taxa. However, whether noise sound waves are intrinsically harmful for developing young—or merely disturb parents—and the fitness consequences of early exposure remain unknown. Here, by only manipulating the offspring, we show that sole exposure to noise in early life in zebra finches has fitness consequences and causes embryonic death during exposure. Exposure to pre- and postnatal traffic noise cumulatively impaired nestling growth and physiology and aggravated telomere shortening across life stages until adulthood. Consistent with a long-term somatic impact, early life noise exposure, especially prenatally, decreased individual offspring production throughout adulthood. Our findings suggest that the effects of noise pollution are more pervasive than previously realized. [ABSTRACT FROM AUTHOR]

Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution. [ABSTRACT FROM AUTHOR]

Summary: This paper introduces a novel fully actuated multirotor unmanned aerial vehicle (UAV) and develops an adaptive control scheme for it without relying on velocity signals. Conventional multirotors are limited by their strong coupling between translation and rotation motion, which makes it difficult to operate one motion without affecting the other. However, this novel multirotor's fully actuated design allows for independent translation and rotation motion, leading to better operational performance and expanded application scope. Additionally, velocity measurement signals can be prone to noise pollution and signal drift, making them unreliable for control purposes. Therefore, this proposed control scheme is developed without using velocity signals. The study utilizes a control performance improving method and guarantees stability of the closed‐loop system using the direct Lyapunov method. Finally, numerical simulations are employed to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Anthropogenic noise is one of the fastest growing, globally widespread pollutants, affecting countless species worldwide. Despite accumulating evidence of the negative impacts of wind turbines on wildlife, little is known about how the noise they generate affects ecological systems. Songbirds may be susceptible to noise pollution due to their reliance on vocal communication and thus, in this field study, we examined how songbirds are affected by wind turbine noise. We broadcasted noise produced by one wind turbine in a migratory stopover site during the nonbreeding season. Throughout the study, we repeatedly monitored the acoustic environment and songbird community before, during, and after the noise treatments with passive acoustic monitoring and mist netting. We employed generalized linear mixed effects models to assess the impact of experimental noise treatment on birds behavior and likelihood ratio tests to compare models with variables of interest with null models. The daily number of birds in the presence of wind turbine noise decreased by approximately 30% compared with the before and after phases. This reduction had a significant spatial pattern; the largest decrease was closer to the speaker and on its downwind side, fitting measured sound propagation. Although we found no impact on species diversity, two out of three most common species showed clear avoidance behavior: 45% and 36% decrease in abundance for the lesser whitethroat (Sylvia curruca) and Sardinian warbler (Sylvia melanocephala momus), respectively. In the after phase, there were lingering effects on the lesser whitethroat. The age structure of the lesser whitethroat population was affected because only juvenile birds showed avoidance behavior. No difference in avoidance extent was found between migratory and nonmigratory species, but the impacts of displacement on migrants during stopover are especially troubling from a conservation perspective. Our results stress the need to address the impacts of noise pollution on wildlife when planning noise‐generating infrastructures, such as wind turbines, to allow for sustainable development without threatening already declining songbird populations. [ABSTRACT FROM AUTHOR]

With increasing concerns about noise pollution, the pursuit of highly dependable piezoelectric acoustic sensors for real‐time noise monitoring has come to the forefront of scientific research. Lead‐based perovskite piezoelectric films, exemplified by lead zirconate titanate Pb(Zr,Ti)O3 (PZT), surpass traditional piezoelectric materials such as ZnO and AlN in their piezoelectric properties, promising substantial advancements in next‐generation acoustic sensor technologies. However, the toxic nature of lead in PZT materials poses formidable environmental and human health risks. In an unprecedented breakthrough, it presents the pioneering development of an environmentally benign lead‐free piezoelectric Micro‐Electro‐Mechanical System (MEMS) acoustic sensor based on potassium sodium niobate (K,Na)NbO3 (KNN) film. High‐quality <001> textured 3 µm‐thick KNN film is successfully integrated into commercially used Si substrate, rendering exceptional piezoelectricity (transverse piezoelectric coefficients e31* of ≈8.5 C m−2) with satisfactory thermal stability. The atomic‐scale Z‐contrast imaging and piezoresponse force microscopy characterizations reveal that the outstanding piezoresponse originates from the local coexistence of multiple phases and the enhancement of extrinsic piezoelectric contributions from in‐plane polarization anisotropy. Finite element simulation is employed to design the triangular cantilever structure and annular diaphragm structure, each corresponding to different operating bandwidths. The resultant MEMS acoustic sensors stand out with outstanding acoustic performance (the high sensitivity and expansive receiving field of view), which are attributed to the microstructural engineering at multi‐length scales for the excellent piezoelectric properties of KNN film. These features enable sensitive acoustic monitoring in various environments, including large‐scale power grids and urban traffic. [ABSTRACT FROM AUTHOR]

Several cities worldwide are studying the replacement of their trolleybus systems with diesel buses or battery electric buses, due to their flexibility and lower operational costs. Diesel buses are considered a major cause of gas emissions in cities, while battery electric buses employ cutting-edge technology, but there is still discussion around the topic due to their technology costs, autonomy, and the sustainability of battery packs. In this study, we evaluated the trolleybus system's potential for reducing emissions, noise pollution, and greenhouse gases (GHGs) when compared to diesel buses. Furthermore, we compared the trolleybus system with battery electric buses in terms of cost and environmental benefits. To do so, a case study was conducted in São Paulo, Brazil, the largest city in Latin America, which operates the second-highest trolleybus system on the American continent. Our results show that the trolleybus system is a feasible alternative to diesel buses when considering environmental aspects. It can be seen as a complementary service for urban transport systems in the city's transition to clean energy. Finally, the study implications indicate the need for further investigation of the benefits of in-motion-charge technology to generate flexibility in trolleybus systems, and the involvement of stakeholders in the transition matrix energy process in urban bus systems beyond the direct costs. [ABSTRACT FROM AUTHOR]

Underwater noise pollution is a significant environmental issue that can have detrimental effects on marine ecosystems. One of the main sources of underwater noise pollution is ship traffic, which has been shown to negatively impact marine animals by masking communication signals and altering their behaviors. This study represents the first comprehensive analysis of underwater ship noise in the Red Sea, wherein noise maps of ships sailing through the main shipping lane in the Red Sea were simulated by integrating both anthropogenic and environmental variables. These maps offer valuable insights for policymakers, enabling them to make informed decisions and implement targeted mitigation efforts. [ABSTRACT FROM AUTHOR]

The relationship between noise annoyance and risk of cardiovascular disease (CVD) still needs to be fully elucidated. Thus, we examined the relationship between noise annoyance and CVD risk in a large population-based cohort study. Cross-sectional (N = 15,010, aged 35–74 years, baseline investigation period 2007–2012) and prospective data (5- and 10-year follow-up from 2012 to 2022) from the Gutenberg Health Study were used to examine the relationship between noise annoyance due to different sources and risk of prevalent and incident CVD comprising atrial fibrillation, coronary artery disease, myocardial infarction, stroke, chronic heart failure, peripheral artery disease, and venous thromboembolism. In cross-sectional analyses, noise annoyance was an independent risk factor for prevalent CVD, with the strongest associations seen for noise annoyance during sleep (e.g., neighborhood noise annoyance: odds ratio 1.20, 95% confidence interval 1.13–1.27, p < 0.0001). While in the 10-year follow-up, mostly positive associations (although not significant) between noise annoyance and incident CVD were observed, no indication of increased CVD risk was observed after 5 years of follow-up. Noise annoyance due to different sources was associated with prevalent CVD, whereas only weak associations with incident CVD were found. Further large-scale studies are needed to establish the relationship between noise annoyance and risk of CVD. [ABSTRACT FROM AUTHOR]

The acquisition and transmission of magnetic resonance (MR) images are frequently affected by random noise pollution, which hampers the diagnosis of diseases by doctors or automated systems. Hence, the search for advanced denoising methods is of great research interest, particularly in magnetic resonance imaging (MRI) denoising models, which are based on deep learning networks and achieve satisfactory results. However, the mining of noisy contextual information and effective information-guided transfer are often neglected in the denoising process, which leads to poor extraction of information at different scales, and poor retention of details. This greatly hinders the further development of MR image denoising methods. Here, we propose a denoising method, MSDRA-Net, for the mining and exploitation of different hierarchical noise features and construct a multi-scale dilated residual (MSDR) structure to transfer and retain noise information at different levels across the layer. Next, a contextual guidance attention (CGA) module guides and transfers contextual information, utilizing the features learnt from different layers of the model as weights. A reconstruction refinement block (RRB) is utilized to construct clean images from the obtained noise bias and the given noisy images. Experiments on simulated and clinical MRI data validated the effectiveness of our method, which demonstrated a superior performance compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Presently, noise pollution in ducts is considered seriously, and numerous studies concerning sound absorption in pipes are ongoing globally. Aiming at the difficulty to eliminate the low-frequency noise in pipeline, a multi-resonant cavity duct metamaterial is proposed. The acoustic properties of the metamaterial are calculated using the energy method based on the Hamilton principle and the transfer matrix methodology. The result demonstrates that the duct metamaterial, due to its band gaps and negative bulk modulus, obtains several sound absorption peaks and is advantageous to sound elimination in low frequency. The good acoustic characteristics of the duct metamaterial for sound insulation can be attributed to the plate-cavity interaction. Parametric analysis of the proposed structure is also carried out. This study demonstrates the application prospects of duct metamaterials for multi-frequency sound absorption in engineering practice. [ABSTRACT FROM AUTHOR]

Architects prioritize designing different buildings to maximize the utilization of natural resources such as natural lighting, natural ventilation, and renewable energy. However, some individuals overlook the focus on architectural acoustics, which deals with controlling sound transmission from the outside to the inside or improving sound distribution within spaces, and the negative impact that improper design can have on human health. According to The World Health Organization (WHO) last updated its guidelines on environmental noise in 2018. These guidelines provide recommendations and standards for addressing the impact of noise on human health and well-being. Affirming that noise has negative effects and serious health consequences, including hearing loss, cardiovascular diseases, and a decline in physical and mental performance. Therefore, the research addresses a scientific methodology that assists architects in designing spaces that achieve sound comfort, with a particular focus on educational spaces, including a case study on lecture halls in universities... [ABSTRACT FROM AUTHOR]

The acids utilized in the production of graphene oxide aerogel underwent testing, revealing variations from the ones documented in the literature. The powder samples were produced by drying under ambient conditions. They were characterized, and the effect of the graphene oxide aerogels produced using these different acids on sound insulation was investigated. All reaction steps were the same in the study, except that different acids were added in the same molar amounts. Measurements were performed at 200 Hz–6000 Hz frequencies for sound absorption. Samples for sound absorption were prepared in 0.1 mm thickness. According to the results, the aerogel produced with acetic acid provided the best sound insulation at approximately 2000 Hz. We also attempted to explain how these acids affect sound insulation. [ABSTRACT FROM AUTHOR]

Noise pollution is a growing problem in hospitals. Noise is a common complaint among hospital staff, patients, and their families in hospitals. Research shows the negative impact of noise on patients. The common effects of noise on patients and staffs in hospitals are annoyance, irritation, high blood pressure, stress, poor mental health, and fatigue. But peoples are unaware about these effects. There are different noise sources in the hospitals e.g., medical equipment, talking of patient's family members, staff conversation, opening-closing doors etc. There is need to control the noise level in the hospitals from the above noise sources. In this review paper, the noise effects on patients, noise sources in hospitals and noise controlling strategies are summarised and discussed. The aim of this review work is to increase the awareness about the noise effects on human health and show the actual condition of the hospital noise. Further, to increase the researcher's curiosity to do in-depth research on noise pollution and control in hospitals. [ABSTRACT FROM AUTHOR]

The mobility and activity restrictions imposed in Spain due to the COVID-19 pandemic caused a significant improvement in the urban noise pollution that could be objectively measured in those cities with acoustic sensor networks deployed. This significant change in the urban soundscapes was also perceived by citizens who positively appraised this new acoustic scenario. In this work, authors present a comparative analysis between different noise indices provided by 70 sound sensors deployed in Barcelona, both during and before the lockdown, and the results of a perceptual test conducted in the framework of the project Sons al Balcó during the lockdown, which received more than one hundred contributions in Barcelona alone. The analysis has been performed by clustering the objective and subjective data according to the predominant noise sources in the location of the sensors and differentiating road traffic in heavy, moderate and low-traffic areas. The study brings out strong alignments between a decline in noise indices, acoustic satisfaction improvement and changes in the predominant noise sources, supporting the idea that objective calibrated data can be useful to make a qualitative approximation to the subjective perception of urban soundscapes when further information is not available. [ABSTRACT FROM AUTHOR]

Noise pollution, one of the most prominent features of urbanization, is an important factor influencing the vocal behavior of urban wildlife. Studies have reported that many songbirds raise their song minimum frequencies in response to urban noise. It has been proposed that this increased minimum frequency (IMF) of songs is an adaptation that allows urban populations to cope with the masking effect of noise pollution. However, urban populations of some songbirds do not exhibit significant IMF compared with non‐urban populations; thus, the notion that IMF is an adaptation to urban noise has been questioned. Furthermore, the effects of IMF might be influenced by both noise levels and the acoustic structures of songs. Here, we employed dichotomous and gradient effect size approaches to investigate IMF regarding two distinct acoustic structures (whistled and harmonic) in songs of six grey‐cheeked fulvetta Alcippe morrisonia morrisonia populations in Taiwan, three with high noise pollution and three with low noise pollution. We found that when using the dichotomous approach, paired populations with significant divergence in noise levels exhibited weak or insignificant divergence in the minimum frequencies for both whistled and harmonic phrases. In contrast, we found that when using the gradient approach, the effect size of noise‐level divergence was strongly correlated with the effect size of divergence in the minimum frequency of the harmonic phrase and only moderately correlated with the effect size of divergence in the minimum frequency of the whistled phrase. These findings suggest that noise pollution has a more pronounced effect on the divergence in the minimum frequency of harmonic phrases used in short‐range communication, compared to the whistled phrases used in long‐range communication. For population comparisons on the IMF, adopting a gradient approach could provide insights into the impact of noise pollution on the acoustic structures of songs across various communication ranges. [ABSTRACT FROM AUTHOR]

This study analyses the regulatory framework for protecting cetaceans and migratory marine species from the impacts of anthropogenic underwater noise. It examines the applicability of the Convention on the Conservation of Migratory Species of Wild Animals (CMS) and its associated agreements (CMS Family) and other related instruments, including the CMS Family Guidelines on Environmental Impact Assessment for Marine Noise-generating Activities (EIA Guidelines). Although the EIA Guidelines are not binding, the general concept of EIA are widely recognised as a critical instrument to prevent, reduce, and control environmental harm, including anthropogenic underwater noise. In the absence of specific regulations to address this issue, the study suggests applying the EIA Guidelines as a best environmental practice and best available techniques to address the issue. Collaboration among the CMS Family and other relevant legal instruments will enhance the implementation of the EIA Guidelines and improve conservation measures. [ABSTRACT FROM AUTHOR]

The pressure sensors have been widely used to capture the steady-state pressure data for the management of water distribution systems (WDSs). The background noise pollution in WDSs can lead to signal degradation, reducing the reliability of sensor data. Although numerous algorithms have been developed for noise removal, their optimal parameter selection is problem-dependent. The steady-state pressure sensor data are often linearly correlated, and this correlation is related to hydraulic distance. This is a key feature of WDS pressure sensor data that should be considered in noise-removal algorithms. Based on this domain knowledge, two noise-removal metrics are proposed to evaluate the performance of the noise-removal algorithm and aid in selecting the optimal parameters. In addition, four widely used noise-removal algorithms have been reviewed and used for removing noise from the pressure sensor data. The results demonstrate that the noise-removal algorithm can efficiently remove potential random noise and enhance the reliability of the sensor data. [ABSTRACT FROM AUTHOR]

Noise pollution is a significant concern in urban settings, caused by traffic increases, urban expansion, and industrial activity. The transportation sector is a crucial contributor to overall noise pollution, particularly in India, where different vehicles ply the roads, resulting in highly fluctuating noise levels. Consequently, traffic noise modeling is essential for addressing this severe issue. The present study employs the MLR (Multiple Linear Regression) and Artificial Neural Network (ANN) approach to model and predict traffic-induced noise levels. The ANN approach outperforms the MLR technique. The architecture of the ANN model integrates different vehicle categories and average speeds as input, resulting in precise predictions. Evaluation of the model’s performance reveals an average Root Mean Squared Error (RMSE) of 0.204 and a high Coefficient of determination (R2) value of 0.93, emphasizing its accuracy. Similarly, in the case of MLR model the RMSE for the training and testing dataset are 1.55 and 1.69 dBA, respectively with R2 value of 0.84. Subsequently, sensitivity analysis highlights the substantial impact of 2-wheelers, tractors/trailers, and 3-wheelers on noise predictions. This study contributes valuable insights into noise management, urban planning, and sustainable development. It demonstrates the efficiency of the ANN approach in addressing complex noise pollution challenges, offering a path toward quieter and healthier urban environments.

Abstract Many baleen whales are renowned for their acoustic communication. Under pristine conditions, this communication can plausibly occur across hundreds of kilometres. Frequent vocalisations may allow a dispersed migrating group to maintain contact, and therefore benefit from improved navigation via the “wisdom of the crowd”. Human activities have considerably inflated ocean noise levels. Here we develop a data-driven mathematical model to investigate how ambient noise levels may inhibit whale migration. Mathematical models allow us to simultaneously simulate collective whale migration behaviour, auditory cue detection, and noise propagation. Rising ambient noise levels are hypothesised to influence navigation through three mechanisms: (i) diminished communication space; (ii) reduced ability to hear external sound cues and; (iii) triggering noise avoidance behaviour. Comparing pristine and current soundscapes, we observe navigation impairment that ranges from mild (increased journey time) to extreme (failed navigation). Notably, the three mechanisms induce qualitatively different impacts on migration behaviour. We demonstrate the model’s potential predictive power, exploring the extent to which migration may be altered under future shipping and construction scenarios.

Abstract Background Low-frequency noise may cause changes in cognitive function. However, there is no established consensus on the effect of low-frequency noise on cognitive function. Therefore, this systematic review and meta-analysis aimed to explore the relationship between low-frequency noise exposure and cognitive function. Methods We conducted a systematic review and identified original studies written in English on low-frequency noise and cognition published before December 2022 using the PsycINFO, PubMed, Medline, and Web of Science databases. The risk of bias was evaluated according to established guidelines. A random-effects meta-analysis was performed where appropriate. To explore the association between low-frequency noise exposure and cognitive function, we reviewed eight relevant studies. These studies covered cognitive functions grouped into four domains: attention, executive function, memory, and higher-order cognitive functions. The data extraction process was followed by a random-effects meta-analysis for each domain, which allowed us to quantify the overall effect. Results Our analysis of the selected studies suggested that interventions involving low-frequency noise only had a negative impact on higher-order cognitive functions (Z = 2.42, p = 0.02), with a standardized mean difference of -0.37 (95% confidence interval: -0.67, -0.07). A moderate level of heterogeneity was observed among studies (p = 0.24, I2 = 29%, Tau2 = 0.03). Conclusions Our study findings suggest that low-frequency noise can negatively impact higher-order cognitive functions, such as logical reasoning, mathematical calculation, and data processing. Therefore, it becomes important to consider the potential negative consequences of low-frequency noise in everyday situations, and proactive measures should be taken to address this issue and mitigate the associated potential adverse outcomes.

This study investigates the effect of increasing urbanization on the demand for petroleum products, which are limited natural resources. The study evaluated the use of biodiesel and clove oil as additives in diesel fuel to reduce carbon deposition on engine parts and noise emissions. Three fuel samples were tested: B30 (30 % biodiesel and 70 % diesel), clove oil at 3000 parts per million (PPM), and D100 diesel fuel. Endurance tests were conducted at a constant load and revolution per minute (RPM) to analyze carbon deposition on engine parts as well as noise emission levels at constant and variable RPM. It was found that biodiesel blended fuel had higher carbon deposition formation compared to diesel fuel, with a deposition rate of 77.48 % for B30 and 71.84 % for D100. In contrast, when clove oil was added to biodiesel, engine part deposition was decreased compared to diesel fuel, with a deposition rate of 47.71 % for clove oil. Furthermore, the study analyzed noise emissions at four positions (left, right, back, and top) using the three fuel samples. Clove oil showed lower noise emissions compared to diesel, indicating that it can help reduce noise pollution.

Noise pollution is growing nowadays and there is always a conflict between ventilation and noise insulation. The advances in acoustic metamaterials provide an effective solution for this conflict. In this paper, we propose a metastructure with protruded necks based on Helmholtz resonance. Sound transmission loss (STL) of 13 dB at low-frequency of 488 Hz is obtained in experiment with the ventilation of 20%, which is in good agreement with the simulation. It is revealed that introduction of protruded neck can reduce the resonant frequency. Sound absorption is the main mechanism for noise insulation at low frequency. Next, the effects of geometric parameters on sound insulation are discussed. A reduction in cross-sectional area of neck, an increase in neck length, and an increase in ventilation all lead to a decrease in both resonant frequencies and STL amplitudes. Finally, a two-layer structure is constructed by the proposed metastructure with different neck length in each unit. Both the amplitude and frequency range of STL are enlarged. These results exhibit a practical application for ventilated metastructure in sound insulation at low frequency. [ABSTRACT FROM AUTHOR]

The following work evaluates the acoustic performance of different sound-absorbing materials and balcony configurations used in the facades of urban street canyons in the Valle de Aburrá (AV) Colombia. The main insertion loss in the urban canyons was achieved by placing materials such as pumice stone and refractory brick on 16.7% and 8.3% of the facade surfaces. This obtained an environmental noise mitigation of up to 5.1 dB at a 5 kHz frequency and up to 3.6 dB at a frequency of 1.25 kHz. With other materials such as cork wood and metal wool, mitigations of up to 3.9 and 3.7 dB were achieved at the frequencies of 5 and 1.25 kHz, respectively. However, balconies caused an increase in ambient noise levels on pedestrian walkways with increases of up to 4.7 and 3.6 dB at a frequency of 1.6 kHz, regardless of whether materials such as pumice stone or cork wood were adhered to their surface. In recent decades, noise has become one of the most harmful pollutants for urban residents. Among the different sources of noise, car traffic has been identified as the main contributor. This can be aggravated by conditions commonly found on city streets, where tall, hard, close-spaced facades increase noise due to the different reflections that these conditions cause. This work shows the benefits that certain materials such as pumice stone, refractory brick, cork wood, and metal wool can provide in mitigating urban noise when placed in strips along the facade of a building. These materials stood out among others under conditions that might have resistance to weather. However, balconies projecting onto walkways could be counterproductive for pedestrians, since these features tend to increase the noise generated by cars circulating on the road. The potential application of this work is to mitigate noise pollution on urban street canyons. [ABSTRACT FROM AUTHOR]

The increasing prevalence of mental disorders among youth worldwide is one of society's most pressing issues. The proposed methodology introduces an artificial intelligence-based approach for comprehending and analyzing the prevalence of neurological disorders. This work draws upon the analysis of the Cities Health Initiative dataset. It employs advanced machine learning and deep learning techniques, integrated with data science, statistics, optimization, and mathematical modeling, to correlate various lifestyle and environmental factors with the incidence of these mental disorders. In this work, a variety of machine learning and deep learning models with hyper-parameter tuning are utilized to forecast trends in the occurrence of mental disorders about lifestyle choices such as smoking and alcohol consumption, as well as environmental factors like air and noise pollution. Among these models, the convolutional neural network (CNN) architecture, termed as DNN1 in this paper, accurately predicts mental health occurrences relative to the population mean with a maximum accuracy of 99.79%. Among the machine learning models, the XGBoost technique yields an accuracy of 95.30%, with an area under the ROC curve of 0.9985, indicating robust training. The research also involves extracting feature importance scores for the XGBoost classifier, with Stroop test performance results attaining the highest importance score of 0.135. Attributes related to addiction, namely smoking and alcohol consumption, hold importance scores of 0.0273 and 0.0212, respectively. Statistical tests on the training models reveal that XGBoost performs best on the mean squared error and R-squared tests, achieving scores of 0.013356 and 0.946481, respectively. These statistical evaluations bolster the models' credibility and affirm the best-fit models' accuracy. The proposed research in the domains of mental health, addiction, and pollution stands to aid healthcare professionals in diagnosing and treating neurological disorders in both youth and adults promptly through the use of predictive models. Furthermore, it aims to provide valuable insights for policymakers in formulating new regulations on pollution and addiction.

The application of Scirpus grossus (SG) fiber as a sound absorber is important to reduce the level of noise affected the physical and mental wellbeing of people. The sound absorption coefficient (SAC) and noise reduction coefficient (NRC) of the SG specimen were evaluated based on a typical model-based design using the data analysis with MATLAB. The results showed that SG specimen with a thickness of 20 mm coated with the perforated aluminum sheet (PAS) compared to that without coating can improve the capability of sound absorption by 14% at the frequency of 4000 Hz. SG specimen coated with PAS that has a NRC value of 0.39 can absorb 39% of sound and thus reflects 61% of sound wave while SG specimen without coating that has a NRC value of 0.23 absorbs 23% of sound and can reflect 77% of sound wave. The sound absorption class of D for SG specimen coated with PAS should be better that of E for SG specimen without coating, which permits us to get better understanding on the applications of SG fiber as a sound adsorber in the future.

Construction noise is an occupational noise that is potentially harmful, and it usually originates from machinery in construction sites. The impact of construction noise on the health and safety of construction workers is one of the main concerns in the industry. Prolonged noise exposure duration will cause physiological, physical, psychological damage, and negatively affect the working performance of the workers as well. Furthermore, the adverse impacts arising from construction noise may jeopardize public welfare, particularly for those who live nearby the construction site. Therefore, this research aims to develop a Stochastic Deep Learning (SDL) noise prediction model by integrating stochastic modelling and deep learning techniques. Stochastic modelling is applied in this study to generate a set of simulated randomized data based on several parameters as the input for the deep learning model. The deep learning model is trained with the input from stochastic modelling to predict the noise levels emitted from the construction site. The predictive performance of the deep learning model will be assessed with several statistical measures such as absolute difference, mean absolute difference and root mean square deviation. Ten case studies are conducted to validate the reliability and accuracy of the SDL noise prediction model. The SDL model showed high accuracy of prediction results with an average absolute difference of less than 1.2 A-weighted decibels (dBA) among the case studies as compared to the measurement. The reliability of the results from the prediction model is high. In conclusion, the SDL model is established and provided a promising outcome with satisfactory predictive performance. Finally, further development of the model would be worthwhile to fully exploit the potential of the SDL noise prediction model in construction industries as a planning, managerial and monitoring tool.