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Kumar K, Bhartia A, Mishra RK, Jadon RPS, Kumar J. Diurnal rail noise measurement, analysis, and evaluation of associated health impacts on residents living in the proximity of rail track area. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:543. [PMID: 38740673 DOI: 10.1007/s10661-024-12681-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 04/27/2024] [Indexed: 05/16/2024]
Abstract
In India, railway is the major transportation mode for carrying goods and people. The tracks for the movement of the rail were initially constructed in the city for the pre-eminence and expediency of the vantage of the people. Rapid modernization and increasing population in the city crammed the area around the railway tracks. Moving rail on the tracks passing through the city is not compatible, which is creating problems for the nearby residents. In the urban and suburban regions, the railway noise has become a major problem. This study was conducted to examine the perception of the physiological and psychological effects of railway noise in the nearby areas of railway stations in Delhi, India. For this purpose, 10 sites near the railway station were selected for the study. To assess the impact of railway noise pollution on the health of humans, a questionnaire survey was conducted. The data of 344 individuals were collected through the questionnaire survey and analyzed to get the perception towards railway noise. Noise level was monitored by a Sound Level Meter (SLM) and the equivalent noise level (Leq) in dB(A) was used to compute the noise pollution in three shifts, i.e., morning, noon, and evening time. Results showed that 57.65% of female and 86.11% of male respondents in the survey reported the disturbance due to railway noise. The level of noise pollution was found higher in the evening time as compared to the noon and morning period, which exceeds the limit set by the Central Pollution Control Board (CPCB) at all the monitored locations. Findings of the study show that the primary cause of the health problems is railroad noise, which is negatively impacting the health of the residents, who are living in the proximity of the rail track region. The perception survey reported that headache, sleep disturbance, irritation, and stress are common health issues among the locals residing around the railway track proximity in Delhi.
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Affiliation(s)
- Kranti Kumar
- School of Liberal Studies, Dr. B. R. Ambedkar University Delhi, Delhi, 110006, India.
- Department of Mathematics, Central University of Himachal Pradesh, Dharamshala, 176206, India.
| | - Arun Bhartia
- School of Liberal Studies, Dr. B. R. Ambedkar University Delhi, Delhi, 110006, India
| | - Rajeev Kumar Mishra
- Department of Environmental Engineering, Delhi Technological University, Delhi, 110042, India
| | - Ravi Pratap Singh Jadon
- Department of Environmental Engineering, Delhi Technological University, Delhi, 110042, India
| | - Jitendra Kumar
- Department of Mathematics, Central University of Haryana, Mahendragarh, 123031, India
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Wang H, Yan X, Chen J, Cai M. Urban noise exposure assessment based on principal component analysis of points of interest. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:123134. [PMID: 38092340 DOI: 10.1016/j.envpol.2023.123134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/05/2023] [Accepted: 12/08/2023] [Indexed: 01/26/2024]
Abstract
Accurate qualitative and quantitative information on the characteristics of traffic noise exposure in densely populated urban areas is an important prerequisite for reasonable traffic noise control. The primary objective of this study is the development and application of a traffic noise exposure evaluation method based on points of interest (POIs). First, an automatic query arithmetic is used to acquire geospatial information, POIs data, building and network information from the webmap. Second, the attribute matrix of preprocessed POIs for the population is constructed. And the population distribution is obtained by principal component analysis (PCA) of POIs and Gaussian decomposition of demographic data. Then, the modified traffic noise line-source model is applied to calculate the noise distribution considering attenuation among buildings based on measured traffic flow parameters. Finally, with the help of the proposed noise evaluation indicators, and considering the noise function requirements (NFRs, which can be divided into four classes according to different area land-use types), traffic noise evaluation is realized. The proposed method is applied to a typical region with four NFR classes. It is concluded that the characteristics of traffic noise exposure are affected by traffic conditions, buildings, NFR classes and population distribution. And the crowds exposed to noise present aggregation effects, which are usually centered around specific buildings. In addition, POI types which people actives related suffer more serious noise exposure, and exposure is overestimated at low requirement regions without considering crowd distribution of the setting scenario.
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Affiliation(s)
- Haibo Wang
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China.
| | - Xiaolin Yan
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China
| | - Jincai Chen
- School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, 510006, China
| | - Ming Cai
- School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, 518107, China
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Huang CH, Seto E. Estimates of Population Highly Annoyed from Transportation Noise in the United States: An Unfair Share of the Burden by Race and Ethnicity. ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 2024; 104:107338. [PMID: 37994374 PMCID: PMC10662932 DOI: 10.1016/j.eiar.2023.107338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Transportation is one of the most pervasive sources of community noise. In this study, we used a spatially-resolved model of transportation-related noise with established transportation noise exposure-response functions to estimate the population highly annoyed (HA) due to aviation, road, and railway traffic sources in the United States. Additionally, we employed the use of the Fair Share Ratio to assess race/ethnicity disparities in traffic noise exposures. Our results estimate that in 2020, 7.8 million (2.4%) individuals were highly annoyed by aviation noise, while 5.2 million (1.6%) and 7.9 million (2.4%) people were highly annoyed by rail and roadway noise, respectively, across the US. The Fair Share Ratio revealed that Non-Hispanic Asian, Black, NHPI, and Other, and Hispanic populations were disproportionally highly annoyed by transportation noise nationwide. Notably, Hispanic populations experienced the greatest share of high annoyance from aviation noise (1.69 times their population share). Non-Hispanic Black populations experienced the greatest share of high annoyance from railway noise (1.48 times their population share). Non-Hispanic Asian populations experienced the greatest share of high annoyance from roadway noise (1.51 times their population share). Analyses at the state and Urban Area levels further highlighted varying disparities in transportation noise exposure and annoyance across different race ethnicity groups, but still suggested that Non-Hispanic White populations were less annoyed by all sources of transportation noise compared to non-White populations. Our findings indicate widespread presence of transportation noise annoyance across the US and emphasize the need for targeted source-specific noise mitigation strategies and policies to minimize the disproportionate impact of transportation noise in the US.
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Affiliation(s)
- Ching-Hsuan Huang
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington
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Lamrini M, Chkouri MY, Touhafi A. Evaluating the Performance of Pre-Trained Convolutional Neural Network for Audio Classification on Embedded Systems for Anomaly Detection in Smart Cities. SENSORS (BASEL, SWITZERLAND) 2023; 23:6227. [PMID: 37448075 DOI: 10.3390/s23136227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/26/2023] [Accepted: 07/05/2023] [Indexed: 07/15/2023]
Abstract
Environmental Sound Recognition (ESR) plays a crucial role in smart cities by accurately categorizing audio using well-trained Machine Learning (ML) classifiers. This application is particularly valuable for cities that analyzed environmental sounds to gain insight and data. However, deploying deep learning (DL) models on resource-constrained embedded devices, such as Raspberry Pi (RPi) or Tensor Processing Units (TPUs), poses challenges. In this work, an evaluation of an existing pre-trained model for deployment on Raspberry Pi (RPi) and TPU platforms other than a laptop is proposed. We explored the impact of the retraining parameters and compared the sound classification performance across three datasets: ESC-10, BDLib, and Urban Sound. Our results demonstrate the effectiveness of the pre-trained model for transfer learning in embedded systems. On laptops, the accuracy rates reached 96.6% for ESC-10, 100% for BDLib, and 99% for Urban Sound. On RPi, the accuracy rates were 96.4% for ESC-10, 100% for BDLib, and 95.3% for Urban Sound, while on RPi with Coral TPU, the rates were 95.7% for ESC-10, 100% for BDLib and 95.4% for the Urban Sound. Utilizing pre-trained models reduces the computational requirements, enabling faster inference. Leveraging pre-trained models in embedded systems accelerates the development, deployment, and performance of various real-time applications.
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Affiliation(s)
- Mimoun Lamrini
- Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
- SIGL Laboratory, National School of Applied Sciences of Tetuan, Abdelmalek Essaadi University, Tetuan 93000, Morocco
| | - Mohamed Yassin Chkouri
- SIGL Laboratory, National School of Applied Sciences of Tetuan, Abdelmalek Essaadi University, Tetuan 93000, Morocco
| | - Abdellah Touhafi
- Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium
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Arranz-Paraiso D, Baeza-Moyano D, González-Lezcano RA. Sound and Light Waves in Healthy Environments. ADVANCES IN RELIGIOUS AND CULTURAL STUDIES 2023:145-162. [DOI: 10.4018/978-1-6684-6924-8.ch007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Architects need the freedom to design their projects with the assurance that they will be inspiring aesthetic as well as healthy places, i.e., buildings, streets, parks, avenues, and squares that offer a complete living experience in an environment that takes into account light, sound, vibration, climate, and all those aspects that can disturb people's well-being. We know that prolonged exposure to noise can cause discomfort and sleep disorders, which affect the quality of life. This noise is not the only pollutant as there are other sound waves such as infrasound and ultrasound that are not perceptible but potentially harmful to health. Not forgetting electromagnetic waves, the light that reaches our bodies and which has regulated our lives throughout the existence of the species. The invention of electric lighting had the consequence that people spend practically all day indoors. Days are poorly illuminated, and the nights have too much light. On the other hand, the intensity of artificial light is a fraction of that of daylight and the spectral composition is also different.
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MosAIc: A Classical Machine Learning Multi-Classifier Based Approach against Deep Learning Classifiers for Embedded Sound Classification. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time.
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Siswanto A, Chang CY, Kuo SM. Multirate Audio-Integrated Feedback Active Noise Control Systems Using Decimated-Band Adaptive Filters for Reducing Narrowband Noises. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20226693. [PMID: 33238463 PMCID: PMC7700328 DOI: 10.3390/s20226693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 11/20/2020] [Accepted: 11/21/2020] [Indexed: 06/11/2023]
Abstract
Audio-integrated feedback active noise control (AFANC) systems deliver wideband audio signals and cancel low frequency narrowband noises simultaneously. The conventional AFANC system uses single-rate processing with fullband adaptive active noise control (ANC) filter for generating anti-noise signal and fullband audio cancelation filter for audio-interference cancelation. The conventional system requires a high sampling rate for audio processing. Thus, the fullband adaptive filters require long filter lengths, resulting in high computational complexity and impracticality in real-time system. This paper proposes a multirate AFANC system using decimated-band adaptive filters (DAFs) to decrease the required filter lengths. The decimated-band adaptive ANC filter is updated by the proposed decimated filtered-X least mean square (FXLMS) algorithm, and the decimated-band audio cancelation filter can be obtained by the proposed on-line and off-line decimated secondary-path modeling algorithms. The computational complexity can be decreased significantly in the proposed AFANC system with good enough noise reduction and fast convergence speed, which were verified in the analysis and computer simulations. The proposed AFANC system was implemented for an active headrest system, and the real-time performances were tested in real-time experiments.
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Alsina-Pagès RM, Bellucci P, Zambon G. Smart Wireless Acoustic Sensor Network Design for Noise Monitoring in Smart Cities. SENSORS (BASEL, SWITZERLAND) 2020; 20:E4765. [PMID: 32842531 PMCID: PMC7506735 DOI: 10.3390/s20174765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 08/21/2020] [Indexed: 11/16/2022]
Abstract
This Special Issue is focused on all the technologies necessary for the development of an efficient wireless acoustic sensor network, from the first stages of its design to the tests conducted during deployment; its final performance; and possible subsequent implications for authorities in terms of the definition of policies. This Special Issue collects the contributions of several LIFE and H2020 projects aimed at the design and implementation of intelligent acoustic sensor networks, with a focus on the publication of good practices for the design and deployment of intelligent networks in any locations.
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Affiliation(s)
- Rosa Ma Alsina-Pagès
- GTM–Grup de recerca en Tecnologies Mèdia, La Salle–Universitat Ramon Llull. c/Quatre Camins, 30, 08022 Barcelona, Spain
| | - Patrizia Bellucci
- ANAS S.p.A., DIV Research and Development. Via Monzambano, 10-00185 Rome, Italy;
| | - Giovanni Zambon
- Department of Earth and Environmental Sciences (DISAT), Universitá degli Studi di Milano Bicocca, Piazza della Scienza 1, 20126 Milano, Italy;
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Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review. SENSORS 2020; 20:s20082256. [PMID: 32316202 PMCID: PMC7218845 DOI: 10.3390/s20082256] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 04/10/2020] [Accepted: 04/12/2020] [Indexed: 01/28/2023]
Abstract
Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.
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