Environmental Noise Research Articles

Identification of strategic location for environmental noise control through the application of land use regression (lur) in a neural network platform

The significant causes of rising noise pollution within urban areas lie in extensive transportation networks, industrial operations, and commercial activities. Addressing this complexity, our study employs Land Use Regression (LUR) modelling to assess the intricate relationship between land-use features and environmental noise in urban landscape. The objective of this study is to provide a framework for predicting environmental noise levels in Delhi, India. For which, Lday, Lnight, LAeq, 24h, and Ldn noise descriptors were modelled at daily resolution data from 31 fixed-site monitors. The prediction at first stage for LUR was found to be R2 = 0.53 which was validated using the n-1 method. The model was later optimized using a NN. Output from the NN is included in the GIS environment to identify the hotspot of the noise. Previously identified engineering interventions applicable to urban contexts that were listed from the literature review were allocated as per hotspots because of the modelled output. The study also explored the avenue of using Delhi-specific transport variables developed from Google travel time data for prediction improvement of the noise level. The research contributes to advancement of noise pollution assessment and management methodologies, particularly in situations with no available validated traffic noise models.

Environmental noise protection problems of Hungary's old heavy industrial areas

Hungary's first domestic environmental noise regulation entered into force in 1984. Several heavy industrial areas operate in the country, which were established in the first half of the last century or in the decades following the Second World War. During this period, aspects of environmental noise protection were not yet considered. These heavy industry plants are usually located in huge industrial areas, operate thousands of diverse technological equipment and still remain significant factors of the Hungarian national economy. The reason for the development of the environmental noise problem is that during the intensive socialist industrialization, to satisfy the huge demand for labor, they developed residential areas around the industrial areas. This led to a situation, when environmental noise protection legislation came into force, these heavy industry plants found themselves in violation without any changes in their operations. The plants implemented noise reductions and attempted to comply with the limits, but mostly with little success. This presented an unsolvable problem to the plants, the authorities, and the residents too. The solution to the problem is the introduction of regulations on the designated highly noisy areas and several additional measures, following the international practice of designating noise protection zones.

A deep learning approach to predicting UAV sound affect

The most prevalent method by which the impact of an acoustic environment upon people is assessed is the `environmental noise' approach, in which the sounds are quantified based entirely on their recorded weighted sound pressure levels. This does not account, however, for the character of sounds outside of their loudness. Objective metrics such as psychoacoustic annoyance have been developed to attempt to rectify this, however this is still heavily weighted towards loudness as the most important factor and may not provide a good estimate for subjective perceived annoyance for all sound sources, including unconventional aircraft such as unmanned aerial vehicles (UAVs). Here we present a novel method using a convolutional neural network (CNN) to predict perceived annoyance directly from mel-spectrograms derived from recorded audio featuring both traditional and unconventional aircraft sources. The network is trained and tested using data collated from five separate listening tests, and is shown to outperform conventional regression models based on psychoacoustic annoyance as a predictor of perceived annoyance. The time-frequency resolution of the calculated mel-spectrograms is shown to be a key factor in performance.

Umbrella+ review on interventions to reduce risks and impacts on health of children and adolescents in Europe from environmental noise

The health burden from environmental noise underscores the need for interventions to reduce exposure and protect children and adolescents. This Umbrella+ review aims to present the latest knowledge on interventions minimizing the health impacts of environmental noise in this demographic. Initially, we conducted a scoping review anchored in the WHO's systematic review on transportation noise interventions and their impacts on health. The subsequent Umbrella+ review prioritizes recent high-quality systematic reviews and includes new, high-quality original studies from Europe published after 2014 to complement identified reviews. Study eligibility follows a PICOS (Population, Intervention, Comparator, Outcome, and Study) approach, focusing on interventions targeting children and adolescents up to age 18. We also consider broader population interventions and their potential effects on children. We consider intervention studies addressing noise exposure from road, rail, and aircraft. Potential health outcomes identified from the scoping process include changes in noise exposure and health outcomes, such as annoyance, sleep disturbance, cardiovascular disease, cognitive development, and birth outcomes. This Umbrella+ review is poised to contribute to the 2024 European Topic Centre on Human Health and the Environment report, offering insights to guide action plans, policies, and legislation related to noise intervention for the health of children and adolescents.

Monitoring the short-term biological response of environmental noise in real-life sleep conditions

A protocol is developed to measure the short-term biological response to environmental noise during sleep. It is designed as potential component in the Human Biomonitoring Program of the Flemish Government. The focus of the protocol is the user-friendliness and the use of non-invasive but accurate technology. The sensor boxes are deployed without third party support using a simple set of instructions. The setup includes simultaneous indoor and outdoor spectral noise monitoring to discriminate between indoor and outdoor disturbances. Actimetry and heart rate are monitored on-torso to detect potential arousals and awakenings. The data analysis is performed in a 10 second resolution and is based on the heart rate variability (HRV) of nine teenage subjects. The bedroom insulation is included using the difference between outdoor and indoor noise. The relevant biomarker is the HF/LF ratio, a HRV parameter related to the activation of the autonomous nervous system, shows significant correlation with the environmental noise. Peak emergence and augmented background noise also result in enhanced activation of the autonomous nervous system. This novel approach delivers an objective measure of the short-term biological effect of noise during sleep in real-life conditions. The affordability and low-invasive design allow large-scale deployment in future health studies.

Comparative analysis of CNN and TNN models in environmental noise source identification

This paper presents a focused examination of environmental noise, an issue of relevance due to its implications for human health and well-being, compliant with ISO 1996-2:2017 standards. Moving beyond the traditional methodologies that primarily rely on level exceedance, our study integrates advanced machine learning techniques to address the challenges in noise source identification. With the advent of IoT Noise Monitoring Terminals, the conventional manual auditory analysis methods have become less feasible. Our research explores the application of convolutional neural networks (CNNs), a standard in environmental noise analysis, and assesses the emerging utility of Transformer Neural Network (TNN) models in this domain. The aim is to conduct an objective comparison of these models, applying them to identical datasets to determine their effectiveness in identifying noise sources. Through this analysis, the study seeks to contribute to the field of environmental acoustics by offering insights into the comparative strengths and limitations of CNN and TNN models.

Statistical evaluation of road traffic noise in complex sound environment by using fuzzy inference

In order to evaluate road traffic noise in a sound environment, it is necessary to predict the sound levels at evaluation points based on the observations at a reference point. In actual sound environment, road traffic noise shows various types of probability distribution of non-Gaussian distribution and nonstationary property. Furthermore, there potentially exist various nonlinear correlations in addition to the linear correlation between the sound levels at evaluation and reference points. Consequently, the relationship between both points cannot be represented by a simple linear model based on only the linear correlation and lower order statistics. In this study, a complex sound environment difficult to analyze by using usual structural method is considered. By introducing a nonlinear model based on conditional probability distribution with various correlations between the sound levels, and applying fuzzy inference, an evaluation method for road traffic noise is theoretically proposed in a suitable form for the complex sound environment with nonlinear, non-Gaussian and nonstationary properties. The effectiveness of the proposed method is experimentally confirmed by applying it to the observed road traffic noise data in a complex sound environment.

The LIFE SILENT project's approach to sustainable noise mitigation

The main objective of the LIFE SILENT PROJECT is the development of sustainable and eco-friendly solutions to mitigate noise in complex urban environments, where multiple and diverse noise sources, mainly roads and railways, coexist across densely populated areas. Mitigating noise in such environments generally excludes the use of solutions that might interfere with the urban context, such as noise barriers. This is mainly due to the proximity of receivers to the noise source, which reduces the visibility of the surroundings and air circulation, leading to microclimatic modifications and social opposition. This is why noise mitigation measures acting directly on the source are recommended, such as low-noise pavements for roads, and dampers, rail grinding, and silent brakes for railways. However, these solutions suffer from either durability issues or high implementation costs. Thus, an improvement is necessary. In the LIFE SILENT project, innovative and sustainable low-noise and long-lasting pavements, as well as low-height noise barriers will be developed and demonstrated in a real test environment. Additionally, procedures will be established to facilitate their synergistic implementation, providing transport infrastructure owners and managers with solid information to support their widespread adoption. In this paper, a general overview of the LIFE SILENT project is given.

The role of noise as part of neighborhood quality and its impact on well-being in German preschoolers: Results from the Equal-Life project

Current research confirms the significant role of environmental quality in children´s well-being and mental health. Respective studies targeting young children focused on characteristics of the families´ immediate housing situation, e.g., household chaos. In the framework of the Equal-Life project, we investigated the relationships between neighborhood quality and mental health in preschoolers (N=111). Data were acquired through parent questionnaires. Stepwise regression analysis identified noise and air pollution as the most important predictors of overall neighborhood quality ratings. As expected, different components of neighborhood quality were significantly correlated, confirming a gap between favorable and unfavorable conditions in children´s environments. All components of neighborhood quality were significantly associated with children´s well-being (KIDSCREEN-10), but stepwise regression analyses identified noise and air pollution as the most important predictors. In contrast, children´s mental health problems (Strength and Difficulties Questionnaire) were unrelated to all components of neighborhood quality. Furthermore, lower neighborhood quality was associated with higher parental stress, which in turn was a significant predictor of children´s mental health problems and well-being. These findings underline the interdependencies of psychosocial and physical exposures, and thereby the necessity to adopt an holistic (exposome) approach in order to design environments that foster children´s mental health.

Construction site noise management

In recent years, the number of noise nuisance complaints has increased dramatically. Prominent among these are complaints about noise nuisance from construction sites. Most construction sites are the source of high, repetitive and annoying noise levels. The need for acoustic comfort, on the part of those who live or work in buildings near a construction site, requires that all strategies be implemented within the construction site to ensure that the noise generated is as low as possible. The article describes the approach followed by a recent Italian standard regarding the actions to be taken, by both the client and the contractor, to ensure that the construction site of the work commissioned by the client and realised by the contractor is as quiet as possible. The standard requires that the design of the construction site must include the use of both electrical machines and shielding of proven effectiveness for these applications. The best results can only be seen with a preventive approach. It can be achieved if the Contractor not only understands that the site where he is working requires him to take special care to reduce noise, but also if he demonstrates how he will put everything into good practice.

3D audio recording of complex underwater sound fields

In this study, we developed a method to capture underwater sound fields and reproduce them for human listeners, providing an immersive 3D auditory experience. We recorded the sound field with an array of four hydrophones arranged in a tetrahedral structure. The side length of the tetrahedron (around 70 cm) was a compromise between capturing accurately the low frequency pressure gradients and spatial aliasing effects at higher frequencies. Subsequently, we transformed the signals into the domain of spherical harmonics facilitating a representation as first-order Ambisonics that is independent of the recording. For playback on devices such as headphones or loudspeaker systems, we used established decoding routines that enhance the immersive audio experience with techniques such as parametric decoding and diffuse sound field equalization. During an expedition to northern Norway, we recorded the underwater soundscape in deep fjords with vocalizing killer whales and singing humpback whales. The resulting binaural renderings for headphones of these sound scenes are astonishing and sparked interest among scientist, artists and general public. This research also helps to enhance our understanding and perception of these intelligent marine mammals and their unique acoustic underwater world, ultimately aiding in better protecting them from noise pollution.

Advanced concept for noise monitoring in smart cities through wireless sensor units with AI classification technologies

The rapid urbanization in the cities has indicated the importance of sustainable development for creating smart cities with high quality of life. Environmental noise as one of the main concerns has to be addressed according to International Directives and Legislation. Traditional noise estimation methods are costly and time-consuming, but the emerging technologies like low-cost wireless sensor units (WSUs) offer more granular data collection and analysis. Requirements for more accurate assessment of the noise pollution by detecting the dominant noise sources impose the need to apply novel approaches in the technical systems for noise monitoring. To this aim, this paper investigates the advanced methods for noise monitoring through WSUs with AI classification technologies. The proposed concept of the device not only quantitively describes the noise pollution, but also tends to recognize the class of the disturbing sound events. The accuracy of the WSUs are configured by comparing the results with professional hand-held analyzer, showing powerful and affordable low-cost units able to publish results through cloud and fog computing based on the IoT and smart city technologies. The paper discusses the challenges for merging noise measurement technology and the AI algorithms to classify and quantify the different classes of sound events.

Systematic analysis of acoustic source localization maps for performance assessment

Noise pollution is a raising problematic mainly associated to the development of industry, cities, and transportation. In that context, the characterization of noise sources is key point to help the understanding of noise generation mechanisms and to be able to develop effective noise reduction strategies. Microphones phased array measurements can be used as input to solve the inverse problem of source localization allowing to obtain source maps for each frequency of interest. Conventional beamforming (CBF) is the most famous source localization algorithm which uses the interpretation of the propagation delays measured between each array microphones. However, the analysis of output source maps can be made difficult by the low spatial resolution in the low-frequency range and the presence of side-lobes associated with microphones distribution that can lead to additional wrong sources. This paper presents a systematic analysis of source maps to identify potential noise sources by using different methods (local maxima, Metropolis-Hastings). The set of potential noise sources can then be compared to the set of real sources to assess the CBF performances. This methodology is successfully applied to measurements performed in anechoic room for various source configurations highlighting CBF limitations (spatial resolution, correlated sources, reflective environment).

Study of indoor and outdoor noise in primary schools in the city of São Paulo - Brazil

Noise pollution has been treated by the World Health Organization as the cause of several diseases, in addition to being the second largest environmental polluter, being surpassed only by air pollution. For schools, excessive noise, in addition to causing illness, is responsible for low levels of cognition and low productivity for both students and teachers. In general, Brazilian public schools are built on land provided by the government, in places that are not suitable for their siting. Thus, in Brazilian schools, students are exposed to noise from the surroundings, as well as noise generated indoors, which is not negligible, reaching levels that can be harmful to health and incompatible with educational activities. This study shows the evaluation of the level of indoor and outdoor noise in classrooms of five primary schools in the city of São Paulo, in the pre-pandemic period of COVID-19, comparing the results with those established by legislation, in addition to evaluating other descriptors such as LAeq, L10, L90, and SIL. The results show that classroom users are exposed to noise levels that complicate the communication of the teacher's message to the student, disrupt concentration and can even be affecting their health

A review of the application of continuous real-time monitoring in noise risk management

Background: In the year 2020, the Mineral's Council Chief Executive Officer (CEO) Zero Harm Forum (CEOZHF) reviewed the slow progress towards achieving the 2024 Occupational Health Milestones and agreed that there was a need to improve the current measures in place to reduce occupational diseases. Following discussions with Group Environmental Engineers, it was proposed to adopt key occupational health indicators for tracking progress through Continuous Real-Time Monitoring (CRTM) to reduce workplace exposures. Methods: A systematic review methodology was employed to gather data from reputable academic databases. Results: CRTM in Noise Risk Management enables an encompassing practical and intuitive analysis of building noise pollution with prompt alerts for poor acoustic comfort. Real-time graphical display of noise measurements, utilizing a warning signal framework, facilitated area-specific alerts and responses. Additionally, CRTM enabled the visualization of noise levels and sources through frequency analysis, contributing to noise pollution analysis for zoning and strategic planning in smart cities. Conclusions: While CRTM has been extensively applied in noise monitoring globally, its utilization in the mining industry, especially in South Africa, remains limited. This study underscores the feasibility of implementing CRTM in Noise Risk Management, emphasizing the need for further research within the mining industry.

Construction site environmental pollution management - initiatives, innovation and research

The management of pollution, and in particular of the impact of noise and vibrations, is very important for construction activities, even more so when it comes to urban construction sites. Indeed, some operations have very significant emissive potential and it is appropriate, for the proper conduct of the activities, to adopt a certain strategy in order to control the impacts and thus ensure good acceptance of the construction by the neighborhood. Environmental monitoring is the key to achieving this dual objective: prevent pollution and communicate. This paper presents our innovative approach to localize, identify and report noise pollution on construction sites for efficient noise mitigation. More precisely, usage of acoustic high resolution beamforming is discussed to both identify predominant sources and estimate reliably their contribution to overall noise pollution captured at a given reference point by à Class I metrological device. By combining the easy-to-deploy ATD directional acoustic sensor with its 10cm aperture microphone array, with video recording for event confirmation and labelling on the highly responsive Cadence interface, we show how we can solve complex use cases for a wide range of users, from site managers to acoustic experts.

Research on the influence mechanism of park landscape characteristics on psychological perception around expressway based on soundscape theory: a case study of Chengdu Bailuwan Wetland Park

With the continuous development of China's transportation infrastructure strategies and urbanization, urban road networks have seen ongoing improvement. Yet noise pollution along expressways is gradually becoming a prominent issue within the broader challenge of urban noise. Noise control has recently emerged as a bottleneck for building livable cities, while well-designed landscapes can improve the quality of psychological perception. By precisely quantifying the multi-sensory composite perception characteristics of urban green space landscapes, this research explores the mechanism through which such landscapes influence psychological perception. The study site was selected in Bailuwan Wetland Park, Chengdu. Objective and subjective environmental data and questionnaire feedback were collected through sound walks and big data analysis based on the geographic information system, after which quantitative analysis was conducted on how landscape environmental characteristics influence environmental experiential perception. The research explores the mechanism of the influence of the regulation of landscape environment characteristic elements on psychological perception and constructs a visual prediction model to provide a universal theoretical basis and methodological support for the planning and regulation of urban green space landscape along expressways, which is of positive significance in guiding the regulation of landscape space.

The future of airport noise: a comprehensive analysis of population exposure at Dublin airport 2023 to 2030

There are conflicting trends in the impact of aircraft noise as increased air traffic is offset by advancements in low noise aircraft technology. This paper considers the case of Dublin Airport and projects the noise impact of operations over the timeline to 2030. The airport projects an (Average Annual Growth Rate) of 1.2 %, translating to an estimated rise in annual air traffic movements from over 212736 flights in 2023 to around 244635 flights in 2030. This estimated increase poses not only logistical challenges for airports but also an escalation in noise pollution in the surrounding areas. The most significant challenge in predicting future airport noise exposure lies in the shifts in aircraft fleet distribution and the adoption of technology in newer generations of aircraft. This paper evaluates the most likely combination of fleet distribution and technology uptake. In 2023, the noise footprint is projected to affect a population of approximately 105762 people with Lden ≥ 55 dB. By 2030, this exposure increases to 168267 people. The increase in air traffic numbers also leads to a notable increase in nighttime flights, bringing nighttime noise exposure Ln ≥ 50 dB from 127720 in 2023 to 189376 in 2030.

Noise within hospital wards: a survey method

Noise pollution is one of the most disturbing environmental factors. It directly affects people's lives, especially vulnerable people such as children, old-age, and hospital patients. Hospitals are complex buildings with manifold spaces and facilities where different users live with different sensitivity to noise. Several studies highlight the discomfort that patients, staff, and visitors experience in hospitals due to inadequate noise levels. All this leads to stress, sleep disorders, interference in communication, aggression, and annoyance. Noise exposure in hospitals is due to several sound sources. Each varies throughout the day, and their detection could be challenging. The present study proposes a clustering technique to analyze long-term sound level meter monitoring in an Italian hospital (Pisa, Tuscany). Equivalent short-term levels obtained with an interval time of 0.1 s are used as the database to exploit the occurrences curves of different chunks of the monitoring. Then, through a validated method, the spectra of different kinds of sound sources are reconstructed via the Gaussian Mixture Model. Results are compared with near-field measurements of single sources.

Case-study analysis of traffic noise at Vesterbrogade, Aarhus, Denmark

This paper analyses the livability, concerning traffic noise, in apartment buildings along one of the most heavily trafficked streets in Aarhus, Denmark. The case-study site is analyzed with noise prediction models and different improvements on the sound environment are proposed through qualitative aspects. The analyses are conducted using two different noise prediction models, opeNoise in QGIS and SoundPLAN. OpeNoise uses the noise prediction method CNOSSOS and SoundPLAN uses Nord2000. Noise maps 1,5 m above the terrain and facade noise levels are calculated. The results show noise levels on the facade far above WHO's limit, of . The noise pollution in the open space next to the roads also shows noise levels far above the recommendations, while the inner courtyard shows acceptable noise levels. Several subjective improvements of the sound environment are proposed, such as vegetation between the road and the building. Furthermore, different designs were proposed to reduce the amount of noise-polluted facades. This research work emphasizes the importance of considering qualitative parameters simultaneously with the noise prediction models.