Environmental Noise Levels Research Articles

Indoor environmental quality in schools: NOTECH solution vs. standard solution

Background: In many Danish schools, the indoor environmental quality (IEQ) is challenging and studies document a poor IEQ in a majority of existing schools. Municipalities cannot afford comprehensive renovations and expensive mechanical ventilation solutions, hence public schools often suffer from poor indoor environment conditions. This study tests a new façade based, demand-controlled ventilation solution called NOTECH in the renovation of school. The study tests NOTECH vs. existing mechanical ventilation solution, comparing performance of both solutions at Skovbrynet Skole in Denmark. Methods: The project implements and investigates the effect of the NOTECH solution in a primary school classroom, comparing it to a similar classroom with conventional, mechanical ventilation. Methodically, indoor environmental quality and energy performance is monitored in the two identical classrooms during one school year 2018 - 2019. Results: The results show that both systems keep the conditions within acceptable limits and CO2 levels below 1000 ppm, which is the requirement according to the Danish Building Regulations. In terms of costs, the NOTECH system has a lower overall cost than the mechanical ventilation system, with total estimated costs for installation, heating, electricity and maintenance amounting to approximately 35% of the mechanical system’s costs. Finally, the results show that the NOTECH solution has a smaller embedded CO2 footprint for building materials, reducing the estimated carbon load by 95% compared to the mechanical ventilation solution. Conclusions: The performance of the two systems roughly is the same in relation to the indoor environmental quality, temperature, CO2 levels, humidity and noise level. Costs for implementation, energy consumption for heating and CO2 footprint for building materials however, are significantly lower for the NOTECH solution, compared to the mechanical solution.

Evaluating the Effectiveness of Speed Humps Related to Speed Profile and Noise Profile

Speed humps are an effective traffic calming measure to improve the safety of road users. On the other hand, speed humps have certain drawbacks, such as increasing emergency response time, causing damage to cars, and high noise levels due to excessive traffic. These impacts further vary with different hump profiles. Thus, the primary objective of this research is to investigate how the geometric profile of speed humps affects vehicle speed and noise level. The secondary objective is to find the Level of Service in the presence and absence of a speed hump by using VISSIM microsimulation. In this study, Lake Drive Road, Nawala, was selected with four different speed hump profiles. The Sound Meter smartphone application was used for noise monitoring. A drone camera footage was utilized to capture vehicle flows while speed trajectories of each vehicle were developed using tracking software. The developed speed profiles were used for the simulation purpose. Then, a Multiple Linear Regression (MLR) model was developed and validated to predict the hump height for the desired speed reduction and desired noise level for each selected four-vehicle category. Further, the average noise levels were found to be higher than the Central Environmental Authority's permissible noise level, and it increases with the height of the hump. It was also observed that as the height of the hump increases, vehicle speed decreases. The largest speed reduction, 42.13 %, was observed in passenger cars, while the lowest speed reduction, 23.5 %, was observed in motorcycles. Therefore, speed analysis findings reveal that passenger cars have a significant speed reduction when compared to other categories. However, the average speed reduction for all vehicles was identified as 33.85 %, and VISSIM simulations revealed that the average Level of Service (LOS) drops to LOS C from LOS A due to the presence of the speed hump. KEYWORDS: Level of Service, Speed hump profile, Speed hump, VISSIM

Noise mapping and acoustic evaluation of different pavements in the city of Fortaleza, northeast Brazil.

The Climate Action Plan developed by the Brazilian city of Fortaleza has encouraged active mobility aiming pedestrians' prioritization, thermal and drainage benefits, which has led to replacement of Asphalt Concrete (AC) surface layers by Interlocking Concrete Pavers (ICP) and Porous Friction Course (PFC). This work aims to evaluate the impact of these infrastructure changes on environmental noise levels. As a case study, 25 measurements on an urban avenue were made in 6 road sections with AC, ICP and PFC pavement surfaces. It was modeled in CADNA-A software and some scenarios with different traffic conditions were compared. The results showed that the use of PFC led to a noise attenuation of 3 dB(A) in LAeq when compared to AC. A reduction in the maximum speed limit from 60 kmph to 50 kmph led to a noise attenuation of 1.2 dB(A). In the section with ICP, measured data showed reduction in speed and traffic flow when compared to other pavements. Thus, despite the higher tire/road noise caused by the ICP, its application on urban roads led to a similar LAeq. The authors conclude that the application of these alternative pavements, when accompanied by traffic calming strategies, can reduce road traffic noise.

Objective and subjective analysis of the acoustic performance of a ZEB test-building

The paper describes an experimentation campaign, including both objective and subjective analysis, carried out in a Zero-Energy full-scale test building and aiming at analysing the acoustic performance of the construction and the acoustic comfort of the rooms. The objective analysis consists of sound insulation measurements of facades and partitions, noise level measurements of service equipment and environmental noise level monitoring. The subjective analysis is based on a survey carried out on 100 participants to collect the answers about their evaluation of the acoustic quality in two different test rooms. The outcomes of the questionnaire were compared to the outcomes of the measurements in order to find a relationship between the subjective and objective data.

Monitoring Occupational Noise Exposure in Firefighters Using the Apple Watch.

Occupational noise exposure and hearing loss are prominent in the fire service. Firefighters are routinely exposed to hazardous levels of noise arising from the tools and equipment they use, from sirens and alarm tones to the emergency response vehicles they drive. The present study utilized the Apple Watch to continuously measure environmental noise levels for on-duty firefighters. Participants included 15 firefighters from the metropolitan South Florida area, and 25 adult non-firefighter control subjects. Firefighters were recruited from a variety of roles across two stations to ensure noise exposure profiles were appropriately representative of exposures in the fire service. All participants wore an Apple Watch for up to three separate 24 h shifts and completed a post-shift survey self-reporting on perceived exposures over the 24 h study period. Cumulative exposures were calculated for each shift and noise dose was calculated relative to the NIOSH recommended exposure limit of 85 dBA as an 8 h time-weighted average. The maximum dBA recorded on the Apple Watches was statistically significant between groups, with firefighters experiencing a median of 87.79 dBA and controls a median of 77.27 dBA. Estimated Exposure Time at 85 dBA (EET-85) values were significantly higher for firefighters when compared to controls: 3.97 h (range: 1.20-14.7 h) versus 0.42 h (range: 0.05-8.21 h). Only 2 of 16 firefighters reported the use of hearing protection devices during their shifts. Overall, our results highlight the utility of a commonly used personal device to quantify noise exposure in an occupationally at-risk group.

Analysis of Noise Measurement at Engine Room Station: A Case Study on PT Socfindo Seunagan

Noise is one of the factors that can interfere with the welfare and comfort of employees' work. Too high noise can cause hearing loss, stress, and decreased concentration. Therefore, it is important to measure the noise level in the work environment and take the necessary measures to reduce the unsafe noise level. To measure the noise level at the machine room station PT. Socfindo Seunagan, the researcher used a noise measuring instrument that corresponds to the dar booth (sound Level Meter). This research was conducted at several points at the PT machine room station. Socfindo Seunagan, including near the main machines, around the production area, and in the employee's work area. The results showed that the noise level in the machine room station PT. Socfindo Seunagan exceeds the safe limits set by the Indonesian National standard. The highest noise level detected was 174 dB(A). Based on the results of this study, measures are needed to reduce the noise level at the PT. Socfindo Seunagan in order not to harm the welfare and comfort of work of employees. Possible actions include the use of ear protection, reduction of noise sources, and rearrangement of the layout of the engine room station.

Evaluation and Analysis of Environmental Noise Levels in NCT of Delhi, India

The present study reports the analysis of hourly equivalent noise levels of 100 sites in the NCT (National Capital Territory) of Delhi and correlates with the community perception that has not been reported so far. The study presents the environmental noise scenario of 100 sites in the NCT of Delhi, India, including 19 commercial, 9 industrial, 16 residential, 43 silence zones, 3 highway sites, and 10 intersection sites. The hourly equivalent noise levels of 100 sites were monitored at various timings between 9 AM and 8 PM to analyze the environmental noise pollution scenario in the city. Along with the hourly equivalent continuous sound levels (LAeq, 1 h); 10-percentile exceeded sound levels (L10), Traffic Noise Index (TNI), Noise Climate (NC), and Noise Pollution Level (NPL) of these sites were also computed. The LAeq, 1 h levels varied from 66.5 to 84.7 dB(A), and majority of the sites (90.0% sites) exhibited LAeq, 1 h between 70 and 80 dB(A). A noise map was developed in addition to a socio-acoustic survey conducted to analyze the public perception toward noise pollution and associated health hazards. The study revealed that road traffic noise along with honking noise is the major dominant source of noise pollution. It shall be very helpful in recommending appropriate noise control strategies for abatement and control of environmental noise pollution in the city.

A Study of the Ambient Noise in the Public Space on Campus and the Correlation Between the Campus Crowds’ Ambient Noise and the WiFi Log

Urban noise is becoming more serious and increasingly concerning environmental problems. This has led to numerous studies on traffic noise. However, not many studies have been done on noise from a human perspective as they go about their daily life. In another aspect, using of the crowd-sourcing platform is on the rise as the usage of personal devices (smartphones) and the deployment of Internet-of-thing increases. Thus, a large pool of data collected via mobile applications enables users to measure the environmental factor directly and provide immediate feedback for and community’s greater good. In this paper, we utilize the crowd-sourcing platform to collect noise data by volunteers to study the noise level in a campus environment, in open common areas which are frequented by students. We are able to map out the noise across the campus from the perspective of the students. The noise level increase through the day as the student gather around popular open spaces. Our study shows that the sound level on campus is due mainly to human and mechanical noise. By combining the noise data with WiFi log data, we were able to show a good correlation between sound level and human density in an area

Investigation of Asphalt Pavement to Improve Environmental Noise and Water Sustainability

Controlling environmental noise and reducing excessive noise is necessary to protect human health and provide auditory comfort. People are frequently exposed to traffic noise in their daily life and traffic noise is a considerable problem on the city scale. This study investigates the sound absorption coefficient in asphalt pavements so as to reduce traffic and environmental noise. The important parameters affecting the sound absorption coefficient in asphalt pavement, such as the use of porous asphalt pavements, clogging pores, the mixture content change, the thickness change, and getting wet, were identified. It is stated that the sound absorption coefficient in the asphalt layer can be increased with the use of a porous asphalt layer, and by changing the thickness and mixture content. In addition, with the effects of global warming, water supply problems are observable in cities. In this context, the use of rainwater is of vital importance. Permeability of the asphalt layer and storage of rainwater in the lower layer are recommended. The accumulation of rainwater with the aid of drainage in porous asphalt pavements has been investigated and the use of rainwater within the scope of recycling is explored. Within the scope of the research, the construction of porous asphalt pavements is suggested in a system that can store water, reuse rainwater with a new system proposal, and reduce environmental noise by increasing sound absorption performance. Accordingly, it will be possible to reduce the environmental noise level and to use rainwater within the scope of recycling. According to varying thicknesses, porous asphalt pavements were modeled using the finite element method, and the Von Mises stress and vertical deformation results of different thicknesses were compared.

Analisis Persebaran Tingkat Kebisingan Kereta Api di Rel Double Track Winongo Kota Madiun

Noise is an annoying sound when listened to for a long time. The standard for noise level in housing is based on KepMenLH No. 48 of 1996 worth 55 dB(A). Double track rails lead to increased rail operations and noise in the surrounding environment. This research was conducted to analyze the environmental noise level due to the train crossing the double track rail at Graha Kirana Winongo Housing, Madiun City by using a Sound Level Meter (SLM) device. Measurements were made at two points, namely at a distance of 7.5 meters and 21 meters. The results of the study the average value of environmental noise due to trains at a distance of 7.5 meters is 80 dB(A) and a distance of 21 meters is 75 dB(A). This value exceeds the standard noise level of 55 dB(A). Researchers provide mitigation in the form of suggestions for housing residents to build noise barriers or plant trees with dense leaves, and if residents are going to build new housing, it is recommended to be at a distance of 75 to 80 meters from the train tracks because the noise value at that distance is approximately 55 dB (A).

The National Institute for Occupational Safety and Health recommended exposure level for noise needs to be revised downwards

Noise-induced hearing loss (NIHL) is caused by cumulative occupational and non-occupational noise exposure. The National Institute for Occupational Safety and Health (NIOSH) developed recommendations for occupational noise exposure in 1972, revised in 1998. Fifty years after the NIOSH 85 dBA recommended exposure level (REL) was first published, it needs to be revised downwards. In its calculations, NIOSH assumed that workers have quiet (<60 dBA) when not at work, something no longer true. Everyday public noise exposure now exceeds the Environmental Protection Agency's safe noise level, a time-weighted average of 70 dB for 24 hours, with multiple studies reporting average exposures near 75 dBA. Also, NIOSH calculations were based on occupational studies of material hearing loss measured by limited frequency pure tone audiometry, an insensitive measure of noise-induced auditory damage. Modern audiology techniques include extended range audiometry, speech in noise testing, diffusion product otoacoustic emissions testing, and questions about tinnitus and hyperacusis. Doing the needed research to support downward REL revision, detecting auditory damage in noise-exposed workers with these newer techniques, is a daunting task, but this is necessary both to protect workers and because the NIOSH REL forms the basis for the world's recommendations for non-occupational noise exposure.

Real-time acoustic detection and identification of drones in operational conditions

Unmanned Aerial Vehicles (UAV), commonly known as drones, can fly below the radar horizon, can have very small radar cross-sections, and can drop munitions, which makes them a serious potential threat to public facilities, such as airports and shopping centers. However, UAVs emanate unique acoustic signatures that can be used to identify them and track their locations using microphone arrays. This paper presents a deep-learning approach to performing noise-robust drone detection and identification in real-time. In particular, a Convolutional Neural Network (CNN) with densely connected layers at the output was employed. Detection was accomplished by including an additional output for when only background environmental noise is present. The inputs were log-magnitude FFTs extracted from 10 ms Hanning windowed frames with 50% overlap. As long as the training data include a higher or equal level of noise than the target data, accuracy at detecting and identifying seven drones was above 90% for environmental noise levels as low as 20 dB SNR. Current research is focused on using a Bayesian version of the network to enable the classifier to report confidence levels in its decisions as well as detect novel drones, i.e., when a detected craft is not represented in the training data.

Unintended impacts of the Open Streets program on noise complaints in New York City

Background and Aim: Exposure to high levels of environmental noise and noise annoyance have been associated with detrimental health outcomes. In 2012, a campaign in New York City (NYC) measured noise levels frequently exceeding recommended standards. Noise complaints have been used as a proxy of noise annoyance and exposure. NYC has been opening parts of its street network to recreation and physical activity through the Open Streets program. We aim to describe the citywide distribution of noise complaints by census tract percentage of Open Streets. Methods: We conducted analyses at the census tract level from 2019 to 2021 across NYC. We used noise complaints from 311 calls for specific locations and times, which we aggregated to daily census tract counts of noise complaints. We focused on two categories of noise complaints that may be impacted by Open Streets: vehicle and street/sidewalk noise complaints. We used a mixed-effects model with random intercepts for census tracts to estimate the association between changes in noise complaints and area covered by Open Streets (%) given tract-level socioeconomic status and street geometry. We allowed for nonlinear exposure–outcome relationships using natural splines. We also carried out sensitivity analyses using data from a citizen’s survey on Open Streets implementation levels gathered by Transportation Alternatives (TA) to assess its impact on model results. Results: Census tracts with increasing use of street space as Open Streets (i.e. higher than 5%) reported more daily street/sidewalk noise complaints. For vehicle noise complaints, 5 to 10% of area covered by Open Streets showed increasing noise complaints but estimates plateaued at higher percentages. The sensitivity analyses using TA implementation data showed that our estimates were fairly robust. Conclusions: Our preliminary results suggest that Open Streets were associated with increases in street/sidewalk noise complaints. Keywords (2-6): Policy, Noise annoyance, Open Streets, Urban planning, Cities

Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information.

Effect of lockdown amid second wave of COVID-19 on environmental noise scenario of the megacity Delhi, India.

This paper analyzes the impact of second wave of COVID-19 lockdown on environmental noise levels of 25 sites in Delhi city and compares the noise scenario during pre-lockdown, lockdown, and post-lockdown periods. The study utilized the noise monitoring data acquired from 25 real-time ambient noise monitoring stations, installed by the Delhi Pollution Control Committee, Delhi, at various sites throughout Delhi city. A significant reduction of up to 10 and 3 dB(A) in day and night equivalent noise levels, respectively, had been observed during the lockdown period as compared to the pre-lockdown and post-lockdown periods. The study also revealed that only nine sites, including four industrial and five commercial zone sites, complied with the ambient noise standards during lockdown period, and no silence or residential zone sites complied with the ambient noise standards even during the lockdown period. A roadmap for environmental noise management and control is suggested. The study also reports the community's perception toward the change in acoustic environment of Delhi city during the lockdown period by conducting an environmental noise perception survey. The present study should be helpful in devising noise control action plans and policy interventions for environmental noise management and control in the metropolitan city Delhi, India.

A Low Cost IoT Cyber-Physical System for Vehicle and Pedestrian Tracking in a Smart Campus.

Human tracking and traffic monitoring systems are required to build advanced intelligent, innovative mobility services. In this study, we introduce an IoT system based on low-cost hardware that has been installed on the campus of the University of Malaga, in Spain. The sensors gather smart wireless devices (Bluetooth and Wi-Fi) anonymous information and environmental noise level around them. This research studies the spatio-temporal behavior of people and noise pollution in the campus as a short-scale Smart City, i.e., a Smart Campus. Applying specific machine learning algorithms, we have analyzed two months of captured data (61 days). The main findings from the analysis show that most university community members move through the campus at similar hours, generating congestion problems. In addition, the campus suffers from acoustic pollution according to regulations; therefore, we conclude that the proposed system is useful for gathering helpful information for the university community members and managers. Thanks to its low cost, it can be easily extended and even used in other similar environments, allowing democratic access to Smart City services as an excellent added value.

The Ability of Bumblebees Bombus terrestris (Hymenoptera: Apidae) to Detect Floral Humidity is Dependent Upon Environmental Humidity.

Flowers produce local humidity that is often greater than that of the surrounding environment, and studies have shown that insect pollinators may be able to use this humidity difference to locate and identify suitable flowers. However, environmental humidity is highly heterogeneous, and is likely to affect the detectability of floral humidity, potentially constraining the contexts in which it can be used as a salient communication pathway between plants and their pollinators. In this study, we use differential conditioning techniques on bumblebees Bombus terrestris audax (Harris) to explore the detectability of an elevated floral humidity signal when presented against different levels of environmental noise. Artificial flowers were constructed that could be either dry or humid, and individual bumblebees were presented with consistent rewards in either the humid or dry flowers presented in an environment with four levels of constant humidity, ranging from low (~20% RH) to highly saturated (~95% RH). Ability to learn was dependent upon both the rewarding flower type and the environment: the bumblebees were able to learn rewarding dry flowers in all environments, but their ability to learn humid rewarding flowers was dependent on the environmental humidity, and they were unable to learn humid rewarding flowers when the environment was highly saturated. This suggests that floral humidity might be masked from bumblebees in humid environments, suggesting that it may be a more useful signal to insect pollinators in arid environments.

Establishing the appropriate maximum sound pressure levels in absence of a noise ordinance for a residential community near an indoor firing range.

Environmental noise surveys assess the ambient noise levels of a specific area, as well as any noise sources deemed inappropriate for the noise environment. Recently, an environmental noise survey was completed to understand the impact of an indoor firing range on adjacent residential properties. Because of the transient nature of the gunfire, day-night equivalent sound pressure level (Ldn), a standard found often in community noise ordinances, was deemed an inappropriate metric to truly determine the intrusiveness of the noise on the surrounding environment. Rather, fast-response maximum sound pressure levels (Lmax) were captured and analyzed in their relation to ambient background noise levels for their effectiveness in translating the impact of the gunfire on adjacent residential properties. Furthermore, multiple frequency-weighting filters were applied to these measurements to fully understand the characteristics of this broadband, high-impact noise. This paper discusses the appropriateness of Lmax when focusing on transient environmental noise levels caused by an indoor firing range as well as how these metrics should be characterized in community noise ordinances.

Detecting the orbital angular momentum from speckle via convolutional neural network

The inhomogeneity of turbid medium disrupts the coherent vortex structure of the vortex beam and causes the formation of a speckle pattern. Here, we propose a new, flexible approach to measure the orbital angular momentum (OAM) of vortex beams through a turbid medium by a convolutional neural network. The proposed technique directly recognizes the speckle image and obtains the corresponding OAM mode information. The accuracies exceed 99% and 97% in simulations and experiments respectively. In addition, the relationship between the recognition accuracy and environmental noise level, is present. The results show great potential in fiber communication, biomedical imaging and astronomical application, etc.

Urban noise does not affect cognitive performance in wild Australian magpies

City-living animals are exposed to pervasive human-generated noise from traffic and industry. There is growing evidence that this exposure may have deleterious physiological and behavioural effects. Cognitive processes could be particularly sensitive to the disruptive effects of noise, but little is known about the potential effects of noise on cognition and cognitive development. We compared the cognitive performance of wild, urban-dwelling Australian magpies, Cracticus tibicen tyrannica , in resident groups exposed to differing levels of environmental noise. We found no evidence that cognitive performance (measured across four behavioural tests estimating different aspects of cognitive ability) covaried with the amplitude of ambient anthropogenic noise across an urban gradient of noise levels. With the exception of one task (spatial memory), the cognitive development of adolescent magpies measured at two different developmental stages (on average 160 and 290 days postfledging) was also similar, irrespective of whether they had been exposed to relatively high or low levels of anthropogenic noise. Our results suggest that levels of anthropogenic noise typical of city parklands do not appear to negatively impact general cognitive performance. • Anthropogenic noise had little effect on Australian magpies' cognitive performance. • Presence of increased urban noise during development may impact spatial memory. • Age was the best predictor of performance on the test battery presented.