Environmental Noise Research Articles

Assessing Community Vulnerability to Neurological Diseases: A National Study of Source-Specific Noise and Air Pollution Exposures near the US Airports

Assessing Community Vulnerability to Neurological Diseases: A National Study of Source-Specific Noise and Air Pollution Exposures near the US Airports

Modeling of the effect of transportation system accessibility on residential real estate prices: The case of Washington metropolitan area, USA

Deep learning accurate predictions of house prices are essential for prospective homeowners, investors, appraisers, and insurers. However, some studies lack accuracy as they overlook critical factors like accessibility and economic attributes that influence house prices. This paper aims to predict house prices by considering structural, locational, accessibility, and economic attributes, while also exploring the effect of accessibility on housing prices. The dataset contains 2,019,663 real estate transaction records from 1975 to 2018 in the Washington metropolitan area, obtained from the Zillow website. In this study, the accessibility index is calculated using Distance, Cumulative Opportunities, and Gravity measures, with the gravity measure surpassing others due to its consideration of both land use and transportation aspects. Economic attributes are then utilized to predict the average monthly house price using deep learning algorithms such as LSTM, GRU, and Simple RNN, with the Simple RNN demonstrating superior performance. Following the amalgamation of structural and locational attributes with the accessibility index and average house prices, various machine learning algorithms—including Linear Regression, Lasso, Ridge, Random Forest, GBM, LightGBM, XGBoost, Decision Tree, AdaBoost, Artificial Neural Network, and Stacked Generalization—are employed for prediction. Subsequent evaluation reveals that Stacked Generalization (ANN + LightGBM) provides the best performance, with an R-squared value of 0.96 and RMSE of $23,290. Moreover, this paper identifies accessibility index thresholds (80,003 for large buildings and 160,103 for small buildings) and demonstrates that a higher accessibility index leads to lower housing prices, attributed to noise pollution, decreased privacy, and increased supply responses.

Environmental noise exposure effect on cortisol levels in children from seven European birth cohorts: preliminary results

Environmental noise exposure effect on cortisol levels in children from seven European birth cohorts: preliminary results

Association between differential exposure to urban environmental noise and sleep quality of adults residing in Medellín-Colombia, 2022

Association between differential exposure to urban environmental noise and sleep quality of adults residing in Medellín-Colombia, 2022

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Measurement and analysis of intra-urban environmental noise in Melbourne, Australia

Urban environmental noise and depression causal pathway: Potential roll of chronic conditions as mediator

Urban environmental noise and depression causal pathway: Potential roll of chronic conditions as mediator

A Novel Intelligent Fault Diagnosis Method for Bearings with Multi-Source Data and Improved GASA.

In recent years, single-source-data-based deep learning methods have made considerable strides in the field of fault diagnosis. Nevertheless, the extraction of useful information from multi-source data remains a challenge. In this paper, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with a multi-source data convolutional neural network (MSCNN) for the fault diagnosis of rolling bearing. This method aims to identify bearing faults more accurately and make full use of multi-source data. Initially, the bearing vibration signal is transformed into a time-frequency graph using the continuous wavelet transform (CWT) and the signal is integrated with the motor current signal and fed into the network model. Then, a GASA-MSCNN fault diagnosis method is established to better capture the crucial information within the signal and identify various bearing health conditions. Finally, a rolling bearing dataset under different noisy environments is employed to validate the robustness of the proposed model. The experimental results demonstrate that the proposed method is capable of accurately identifying various types of rolling bearing faults, with an accuracy rate reaching up to 98% or higher even in variable noise environments. The experiments reveal that the new method significantly improves fault detection accuracy.

Dynamic data reconciliation based on elman neural network and particle filter

In the process of modern industries, complex nonlinear dynamic systems present high requirements for measured data. In the actual industrial process system, the measurement data obtained by sensors will inevitably be subject to noise disturbances from the equipment itself or from the outside environment. These noise disturbances will deteriorate the dynamic performance of the system to a certain extent and affect the industrial production. Particle filter (PF) can be used to infer the accurate outputs of nonlinear dynamic system from the contaminated measurement data, but PF is limited to the pre-known state space model. In the actual industrial process, it is difficult to summarize the internal behavior of the system and obtain the pre-known state space model. Therefore, it is impossible to directly use PF in the nonlinear dynamic system with unknown model. In order to solve the above problems, this paper proposes a dynamic data reconciliation method called ENN-PF, which combines Elman neural network (ENN) data-driven modeling with PF. In this method, ENN is used for data-driven modeling, that is, the system model is dynamically identified by using the input and output data of the system, and then the dynamic data reconciliation is carried out by using PF according to the model identified by ENN. Finally, the proposed ENN-PF method is validated by simulations and practical experiments to effectively reduce the interference of measurement noise and improve the dynamic performance of the system.

Studying the factors that affect urban road noise-case study: El-Tahrir Street, Al-Dokki Cairo, Egypt.

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

The Challenges of Naval Electromobility in Amazônia

Introduction: This study aims to assess the feasibility of replacing a diesel-powered tourist boat with a Solar Electric Hybrid Catamaran with a capacity for fifty people to promote sustainable tourism in the Amazon, specifically in the Belém-Pará region. The initiative seeks to take advantage of the opportunity presented by COP 2030 to innovate in the waterway transportation matrix through renewable energy sources. The research is based on sustainability concepts, the environmental impacts of using fossil fuels and the advantages of using solar energy for waterway transportation. The methodology includes a comparative analysis of diesel-powered vessels' operational and environmental costs and a Hybrid Electric system based on data compiled from a master's thesis. The expected results include identifying significant benefits in terms of reduced carbon emissions. The discussion will address the challenges faced in implementation, such as the initial investment costs, the maintenance of the new technologies, and the adaptation of operators and tourists to the latest transportation conditions. The implications of this research are vast; it can serve as an incentive for energy transition in the river transportation sector. Furthermore, this research pioneers the proposal of replacing diesel-powered tourist boats with solar electric hybrid models in the Amazon region. The study adds value by presenting a practical and sustainable solution to the environmental challenges the tourism sector faces, in line with the global sustainability goals set by COP 2030. Objective: This research outlines the importance and potential of naval electric mobility in the Eastern Amazon region, highlighting its relevance within COP 2030 and the SDGs for promoting ecotourism for the Amazon region. Theoretical Framework: The research is based on sustainability concepts, the environmental impacts of using fossil fuels, and the advantages of using solar energy for water transportation. In addition, previous experiences of implementing similar technologies in other regions and the potential for replicability in the Amazon are considered, focusing on environmental conservation and economic development. Method: This research is a compilation of the master's thesis entitled "Waterway electric propulsion technologies: conceptual design of a hybrid vessel with a focus on meeting environmental, social and Governance (ESG) practices," where the study was carried out in the municipality of Belém, Pará in the year 2023. Results and Discussion: The expected results include identifying significant benefits in terms of reduced carbon emissions, lower noise pollution and improved air quality. The discussion will address the challenges faced in implementation, such as initial investment costs, maintaining the new technologies and adapting operators and tourists to the new transportation conditions. Social acceptance and the potential for a positive impact on local tourism will also be assessed. Research Implications: This research has vast implications, ranging from contributing to climate change mitigation to promoting more sustainable tourism practices in the Amazon. Adopting the Solar Electric Hybrid Catamaran can serve as a model for other areas with similar geographical characteristics, encouraging public policies aimed at using renewable energies in the transportation sector. Originality/Value: This research pioneers the proposal of the replacement of diesel-powered tourist boats with a solar electric hybrid model in the Amazon region. The study adds value by presenting a practical and sustainable solution to the environmental challenges faced by the tourism sector, which is in line with the global sustainability goals set by COP 2030.

The impact of a low traffic neighbourhood intervention on urban noise measured with low-cost sensors in Oxford, UK

Noise pollution, particularly in urban environments, is often caused by road traffic and is linked to increased risk of a range of adverse health outcomes. In recent years, Low Traffic Neighbourhoods (LTN) have been implemented in a number of UK towns and cities as an intervention to reduce road traffic and improve health and wellbeing among those living in residential areas. In this study, eight low-cost acoustic sensors (AudioMoth) were used to evaluate the effect of a LTN intervention on urban noise in a residential area of Oxford, UK. The acoustic energy and LAeq of the noise is evaluated before and after the introduction of the LTN at locations where traffic filters have been erected, other locations in the LTN, possible displacement locations, and a control location. In addition, source apportionment (anthropogenic or biotic) is undertaken using the Normalised Difference Soundscape Index (NDSI) using the frequency of the measured noise. The results show that in terms of acoustic energy almost all locations experienced a reduction in noise after the introduction of the LTN. In terms of the source apportionment, all locations except for the control location experienced more biotic noise after the introduction of the LTN. The results are also compared to other more traffic focussed locations in Oxford and the soundscape is much more biotic inside the LTN. Overall, the study demonstrates the impact of the LTN on urban noise, and the efficacy of this new sensing and analytical approach for evaluating noise pollution interventions.

Hexagonal Boron Nitride Quantum Simulator: Prelude to Spin and Photonic Qubits.

The quest for qubit operation at room temperature is accelerating the field of quantum information science and technology. Solid state quantum defects with spin-optical properties are promising spin- and photonic qubit candidates for room temperature operations. In this regard, a single boron vacancy within hexagonal boron nitride (h-BN) lattice such as VB- defect has coherent quantum interfaces for spin and photonic qubits owing to the large band gap of h-BN (6 eV) that can shield a computational subspace from environmental noise. However, for a VB- defect in h-BN to be a potential quantum simulator, the design and characterization of the Hamiltonian involving mutual interactions of the defect and other degrees of freedom are needed to fully understand the effect of defects on the computational subspace. Here, we studied the key coupling tensors such as zero-field splitting, Zeeman effect, and hyperfine splitting in order to build the Hamiltonian of the VB- defect. These eigenstates are spin triplet states that form a computational subspace. To study the phonon-assisted single photon emission in the VB- defect, the Hamiltonian is characterized by electron-phonon interaction with Jahn-Teller distortions. A theoretical demonstration of how the VB- Hamiltonian is utilized to relate these quantum properties to spin- and photonic-quantum information processing. For selecting promising host 2D materials for spin and photonic qubits, we present a data-mining perspective based on the proposed Hamiltonian engineering of the VB- defect in which h-BN is one of four materials chosen to be room temperature qubit candidates.

Real-time monitoring of fast gas dynamics with a single-molecule resolution by frequency-comb-referenced plasmonic phase spectroscopy

Surface plasmon resonance (SPR) sensors are based on photon-excited surface charge density oscillations confined at metal-dielectric interfaces, which makes them highly sensitive to biological or chemical molecular bindings to functional metallic surfaces. Metal nanostructures further concentrate surface plasmons into a smaller area than the diffraction limit, thus strengthening photon-sample interactions. However, plasmonic sensors based on intensity detection provide limited resolution with long acquisition time owing to their high vulnerability to environmental and instrumental noises. Here, we demonstrate fast and precise detection of noble gas dynamics at single molecular resolution via frequency-comb-referenced plasmonic phase spectroscopy. The photon-sample interaction was enhanced by a factor of 3,852 than the physical sample thickness owing to plasmon resonance and thermophoresis-assisted optical confinement effects. By utilizing a sharp plasmonic phase slope and a high heterodyne information carrier, a small atomic-density modulation was clearly resolved at 5 Hz with a resolution of 0.06 Ar atoms per nano-hole (in 10–11 RIU) in Allan deviation at 0.2 s; a faster motion up to 200 Hz was clearly resolved. This fast and precise sensing technique can enable the in-depth analysis of fast fluid dynamics with the utmost resolution for a better understanding of biomedical, chemical, and physical events and interactions.

Perbandingan Tingkat Kebisingan Berbagai Bahan Peredam pada Knalpot Sepeda Motor Racing

The use of racing exhausts on motorised vehicles can cause uncomfortable and dangerous noise pollution if it exceeds the threshold set by the Minister of Environment. Based on the Decree of the Minister of Environment No 07/2009, the noise limit for motorbikes with engine capacity below 80 cc is 77 dB, 80-175 cc is 80 dB, and above 175 cc is 83 dB. This study aims to observe the comparison of noise levels of various silencer materials on racing motorbike exhausts in order to find the most effective material in reducing noise in accordance with these regulations. The noise measurement method follows the ISO 5130 standard, carried out when the engine is running idle and at engine speed range from 0 to 8000 RPM with a variety of different damping materials. The results show that at idle conditions, yellow Glasswool is the best damping material with the lowest noise level of 90.31 dBA, while Stainless wool shows the worst performance with an average noise level of 100.81 dBA. In contrast, in the 0-8000 RPM rotation condition, Stainless wool became the most superior silencer material with a noise level of 105.4 dBA, while grandma's hair Glasswool (fibreglass) became the worst with the highest noise level of 106.4 dBA. The conclusion of this study is that the optimal choice of silencer material can differ depending on the operating conditions of the racing exhaust, with yellow Glasswool being more effective at idle conditions and Stainless wool being more effective at maximum revs.

Study On The Suitability Of Industrial Estates In The Northern Region Of Medan City To The Carrying Capacity Of The Environment

Industrial estates in the northern region of Medan City are currently developing close to community settlements, which have the potential to have a negative impact on the environment and public health. These impacts include air and noise pollution, water and soil pollution, declining property values, increased crime problems, and environmental damage. Therefore, it is necessary to conduct a study on the suitability of industrial estates to the carrying capacity of the environment. This study aims to assess the suitability of industrial estates with the Carrying Capacity and Carrying Capacity of Ecosystem Services-Based Environment. The method used in this study is quantitative descriptive, which describes environmental characteristics and the phenomenon of industrial estate development. The results of the study show variations in land suitability in the northern region of Medan City. In Medan Belawan District, most of the land (1,566.22 hectares) is not suitable for industrial estates, while in Medan Deli, most of the land (1,358.26 hectares) is very suitable. Medan Labuhan District is dominated by suitable land (2,157.42 hectares), while Medan Marelan has an even distribution between very suitable and suitable land. In coastal reclamation areas, most of the land (262.46 hectares) is not suitable.

Non-Markovianity in High-Dimensional Open Quantum Systems using Next-generation Multicore Optical Fibers

With the advent of quantum technology, the interest in communication tasks assisted by quantum systems has increased both in academia and industry. Nonetheless, the transmission of a quantum state in real-world scenarios is bounded by environmental noise, so that the quantum channel is an open quantum system. In this work, we study a high-dimensional open quantum system in a multicore optical fiber by characterizing the environmental interaction as quantum operations corresponding to probabilistic phase-flips. The experimental platform is currently state-of-the-art for quantum information processing with multicore fibers. At a given evolution stage we observe a non-Markovian behaviour of the system, which is demonstrated through a proof-of-principle implementation of the Quantum Vault protocol. A better understanding of phase-noise in multicore fibers will improve several real-world communication protocols, since they are a prime candidate to be adopted in future telecom networks.

Teachers’ Perception of Urban Noise in The Classroom: Case Study in Mukah, Sarawak

Environmental noise is an unwanted sound created by urbanisation and industrialisation processes, which has been linked to problems of teachers in the school such as lack of concentration, annoyance, speech interference and low performances. The impact of noise in the classroom during the teaching session can cause increased stress and fatigue to teachers. This study aims to identify the noise factor in the school and the impacts of urban noise on teachers during the teaching session through subjective evaluation. In the present study, a questionnaire survey was distributed to the teachers at three secondary schools in Mukah, Sarawak. The developed questionnaire was evaluated by the experts and pilot study was carried out prior to the actual survey. A total of 204 respondents from secondary schools named as School A, School B and School C were participated in this study. The questionnaires were sent to all teachers in three schools in the form of Google Forms and sent by using the WhatsApp application. The main factor of environmental noise that affects teachers from all schools studied came from transportation. Most of the teachers agreed that they need to raise their voice and walk around inside the classroom during the class in order to make sure students can hear their message clearly

Dispersion network-transition entropy: A metric for characterizing the complexity of nonlinear signals.

Extracting meaningful information from signals has always been a challenge. Due to the influence of environmental noise, collected signals often exhibit nonlinear characteristics, rendering traditional metrics inadequate in capturing the dynamic properties and complex structures of signals. To address this challenge, this study proposes an innovative metric for quantifying signal complexity-dispersion network-transition entropy (DNTE), which integrates the concepts of complex networks and information entropy. Specifically, we assign single cumulative distribution function values to network nodes and utilize Markov chains to represent links, transforming nonlinear signals into weighted directed complex networks. Subsequently, we assess the importance of network nodes and links, and employ the mathematical expression of information entropy to calculate the DNTE value, quantifying the complexity of the original signal. Next, through extensive experiments on simulated chaotic models and real underwater acoustic signals, we confirm the outstanding performance of DNTE. The results indicate that, compared to Lempel-Ziv complexity, permutation entropy, and dispersion entropy, DNTE not only more accurately reflects changes in signal complexity but also exhibits higher computational efficiency. Importantly, DNTE demonstrates optimal performance in distinguishing different categories of chaotic models, ships, and modulation signals, showcasing its significant potential in extracting effective information from signals.

Robust phase extraction from complex signals/fringe patterns for optical communications in 5G and beyond through continuous wavelet ridge technique

AbstractThe rapid progress in high‐precision optical instruments necessitates sophisticated image processing techniques for extracting vital information from generated images. Most of these instruments output images comprising RGB pixels. Deciphering these pixel values into phase information poses a significant challenge, especially in the presence of background noise. This study focuses on utilizing Two‐Dimensional Continuous Wavelet Transforms (2‐D CWT) for analyzing fringe patterns with varying noise levels and fringe alignments, crucial for high‐precision optical systems pivotal in enhancing the performance and reliability of optical communication systems in advanced 5G networks. The simulation results demonstrate that 2‐D CWT efficiently extracts phase information from complex and highly noisy fringes while requiring less computational time. Furthermore, the algorithm effectively handles noise disturbances with a commendable degree of accuracy, ensuring robust performance in 5G‐enabled optical systems critical for supporting ultra‐high‐speed data transmission and low‐latency communication requirements. This research contributes to optimizing image analysis techniques for 5G optical systems, facilitating their integration into next‐generation communication networks.

Civil Liability Resulting from The Phenomenon of Random Construction and Land Encroachment : Solution and Proposals

The phenomenon of random construction and land abuse is not a new phenomenon. It has been around for a long time, but it has become widespread after 2003 as it was limited before the mentioned date, Many cities, especially Baghdad, have witnessed the spread of the phenomenon of random construction, encroachment on land belonging to the state or owned by others, and the construction of random housing units that lack the minimum construction conditions Without adopting and not taking into account the urban and urban planning regulations issued by the Ministry of Planning, and due to the worsening housing crisis, the outrageous high prices and the absence of solutions, land abuse, abuse of state real estate and random construction have become the illegal alternative to housing It has become a problem with economic, social and environmental dimensions that clearly reflects the deficit in economic structures and a major social imbalance, making it a serious phenomenon due to its large size. It is no longer possible to ignore it. The continuation of this serious phenomenon will lead to environmental congestion and noise due to irregular construction, which affects the human rights enshrined in the Iraqi Constitution of 2005 as stipulated in Article/