Swedish National Road and Transport Research Institute (VTI), Linkoping, -, SE-58195, Sweden
Popular version of 1pNSb9 – Acoustic labelling of tires, road vehicles and road pavements: A vision for substantially improved procedures
Presented at the 185th ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0022814
Please keep in mind that the research described in this Lay Language Paper may not have yet been peer reviewed.
Not many vehicle owners know that they can contribute to reducing traffic noise by making an informed choice of their tires, while not sacrificing safety or economy. At least you can do so in Europe, where there is a regulation requiring tires be labelled with noise level (among others). But it has substantial flaws for which we propose solutions by applying state-of-the-art and innovative solutions.
It is here where consumer labels come in. In most parts of the world, we have consumer labels including noise levels on household appliances, lawn mowers, printers, etc. But when it comes to vehicles, tires, and road pavements, a noise label on the product is rare. So far, it is mandatory only on tires sold in the European Union, and it took a lot of efforts of noise researchers to get it accepted along with the more “popular” labels for energy (rolling resistance), and wet grip (skid resistance). Figure 1 shows and explains the European label.
Figure 1: The present European tire label, which must be attached to all tires sold in the European Union, here supplemented by explanations.
Why so much focus on tires? Figure 2 illustrates how much of the noise energy that comes from European car tires compared to the “propulsion noise”; i.e. noise from engine, exhaust, transmission, and fans. For speeds above 50 km/h (31 mph) over 80 % of the noise comes from tires. For trucks and busses, the picture is similar although above 50 km/h it may be 50-80 % from the tires. For electric powered vehicles, of course, the tires are almost entirely dominating as a noise source at all speeds. Thus, already now but even more in the future, consumer choices favouring lower noise tires will have an impact on traffic noise exposure. To achieve this progress, tire labels including noise are needed, and they must be fair and discriminate between the quiet and the noisy.
Figure 2: Distribution of tire/road vs propulsion noise. Calculated for typical traffic with 8 % heavy vehicles in Switzerland [Heutschi et al., 2018].
The EU label is a good start, but there are some problems. When we have purchased tires and made noise measurements on them (in A-weighted dB), there is almost no correlation between the noise labels and our measured dB levels. To identify the cause of the problem and suggest improvements, the European Road Administrations (CEDR) funded a project named STEER (Strengthening the Effect of quieter tyres on European Roads), also supplemented by a supporting project by the Swedish Road Administration. STEER found that there were two severe problems in the noise measuring procedure: (1) the test track pavement defined in an ISO standard showed rather large variations from test site to test site, and (2) in many cases only the noisiest tires were measured, and all other tires of the same type (“family”) were labelled with the same value although they could be up to 6 dB quieter. Such “families” may include over 100 different dimensions, as well as load and speed ratings. Consequently, the full potential of the labelling system is far from being used.
The author’s presentation at Acoustics 2023 will deal with the noise labelling problem and suggest in more detail how the measurement procedures may be made much more reproducible and representative. This includes using special reference tires for calibrating test track surfaces, production of such test track surfaces by additive manufacturing (3D printing) from digitally described originals, and calculating the noise levels by digital simulations, modelling, and using AI. Most if not all the noise measurements can go indoors, see an existing facility in Figure 3, to be conducted in laboratories that have large steel drums. Also in such a case a drum surface made by 3D printing is needed.
Figure 3: Laboratory drum facility for measurement of both rolling resistance and noise emission of tires (both for cars and trucks). Note the microphones. The tire is loaded and rolled against one of the three surfaces on the drum. Photo from the Gdansk University of Technology, courtesy of Dr P Mioduszewski.
Brigham Young University, Provo, Utah, 84602, United States
Kent. L. Gee, Mark K. Transtrum, Shane V. Lympany
Popular version of 4aCA5 – Big data to streamlined app: Nationwide traffic noise prediction
Presented at the 184 ASA Meeting
Read the abstract at https://doi.org/10.1121/10.0018816
VROOM! Vehicles are loud, and we hear them all the time. But how loud is it near your home, or at the park across town? The National Transportation Noise Map can’t give you more than an average daily sound level, even though it’s probably a lot quieter at night and louder during rush hour. So, we created an app that can predict the noise where, when, and how you want. How loud is it by that interstate at 3 AM, or at 5 PM? Using physics-based modeling, we can predict that for you. Why does the noise sound lower in pitch near the freeway than near other roads? Probably because of all the large trucks. How does the noise on your street during the winter compare to that across town, or on the other side of the country? Our app can predict that for you in a snap.
This (aptly named) app is called VROOM, for the Vehicular Reduced-Order Observation-based Model. It was made by using observed hourly traffic counts at stations across the country. It also uses information such as the average percentage of heavy trucks on freeways at night and the average number of delivery trucks on smaller roads on weekdays to predict sound characteristics across the nation. The app includes a user-friendly interface, and with only 700 MB of stored data can predict traffic noise for roads throughout the country, including near where you live. You don’t need a supercomputer to get a good estimate. The app will show you the sound levels by creating an interactive map so you can zoom in to see what the noise looks like downtown or near your home.
Olivia C Coiado – email@example.com
Department of Biomedical and Translational Sciences, Carle Illinois College of Medicine, Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois, 61801, United States
Erasmo F. Vergara
Laboratory of Vibration and Acoustics, Department of Mechanical Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil.
Lizandra G. Lupi Vergara
Laboratory of Ergonomics, Department of Production and Systems Engineering, Federal University of Santa Catarina, Florianópolis, SC, Brazil.
Popular version of 3pNS4-Noise Pollution in Hospitals and its Impacts on the Health Care Community and Patients, presented at the 183rd ASA Meeting.
If you ever had to be hospitalized in your life, you probably know that spending a night in a hospital room and getting some sleep is almost an impossible mission! Why? Noise in hospitals is a common problem for patients, families and teams of professionals and employees. Most of a hospital’s environment is affected by the sounds of equipment and machines with high sound pressure levels (SPL) or “noise”.
What can we do?
Fig 1: Sound pressure meter positioned in front of the reception desk in Brazil.
We used a sound pressure meter (Fig. 1) to record noise of medical equipment such as machines, medical devices, tools, alarms used in the medical activities in hospitals in Brazil and in the United States. SPLs inside hospitals may have high average values, higher than 60 decibels (dB), with peak SPL values of 100 dB and may not meet the international requirements. The World Health Organization (WHO) suggests that the average SPL in hospitals should be around 35 dB during the day and 30 dB at night. SPLs above 65 dB can cause behavioral disorders and affect the quality of sleep and cause changes in the physiological responses to stress in hospitalized patients. High noise levels exceeding 55 dB can affect both patients and staff. The noise effects can cause memory lapses and mental exhaustion in performing tasks, exposing technical and support teams to risks, accidents and errors in the performance of their work. For instance, a plane taking off (Fig. 2) can reach up to 100 dB and a noisy hospital environment can reach up to 70 dB, more than double of the noise recommended by the WHO!
Figure 2: Image adapted from Bayo, Garcia and Garcia 1989.
Our research considered both quantitative aspects, through numerical and qualitative descriptors (subjective and psychological assessment of patients, medical staff, employees, etc.), to assess noise pollution in hospitals. Our model analyzed the relationship between the acoustic characteristics of the environment and people’s sound perception.
We interviewed 47 people in a Brazilian Hospital, the responses were collected from nurses, nursing assistants, doctors, and other staff members. 60% of the participants responded that they needed to speak louder and felt discomfort with the noise in the work environment, 57% said they felt discomfort with the noise coming from the medical equipment, 72% of the participants said the work environment is moderately or very noisy. The next phase of our research is to repeat the same measurements in a United Stated Hospital and compare the results. Then we can make a reflection, what can we do to reduce the effects of noise pollution in hospitals? How to reduce the noise coming from medical equipment? Our “dream” is to provide a more comfortable environment for patients and the health community. Hoping they can finally get a good night of sleep in Brazil in the U.S or any other hospital in the world.
Daniel Fink – firstname.lastname@example.org
Board Chair, The Quiet Coalition, 60 Thoreau Street Suite 261, Concord, MA, 01742, United States
The Quiet Coalition is a program of Quiet Communities, Inc.
Popular version of 3pNS1-What is the safe noise level to prevent noise-induced hearing loss?, presented at the 183rd ASA Meeting.
Ear structures including outer, middle, and inner ear. Image courtesy of CDC
If something sounds loud, it’s too loud, and your auditory health is at risk. Why? The safe noise exposure level to protect your hearing- to prevent noise-induced hearing loss (NIHL) and other auditory disorders like tinnitus, also known as ringing in the ears, might be lower than you think. Noise damages delicate structures in the inner ear (cochlea). These include minuscule hair cells that actually perceive sound waves, transmitted from the air to the ear drum, then from bones to the fluid in the cochlea.
Figure 1. Normal hair cells (left) and hair cells damaged by noise (right). Image courtesy of CDC
[A little detail about sound and its measurement. Sound is defined as vibrations that travel through the air and can be heard when they reach the ear. The terms sound and noise are used interchangeably, although noise usually has a connation of being unpleasant or unwanted. Sound is measured in decibels. The decibel scale is logarithmic, meaning that an increase in sound or noise levels from 50 to 60 decibels (dB) indicates a 10-times increase in sound energy, not just a 20% increase as might be thought. A-weighting (dBA) is often used to adjust unweighted sound measurement to reflect the frequencies heard in human speech. This is used in occupational safety because the inability to understand speech after workplace noise exposure is the compensable industrial injury.]
Many audiologists still use the industrial-strength 85 dB noise level as the level at which auditory damage begins. This is incorrect. The 85 dBA noise level is the National Institute for Occupational Safety and Health (NIOSH) recommended occupational noise exposure level (REL). This does not protect all exposed workers from hearing loss. It is certainly not a safe noise level for the public. Because of the logarithmic decibel scale, 85 decibel sound has approximately 30 times more sound energy than the Environmental Protection Agency’s 70 decibel safe sound level, not about 20% as might be thought.
The EPA adjusted the NIOSH REL for additional exposure time- 24 hours a day instead of only 8 hours at work, 365 days a year instead of 240 days- to calculate that 70 dB average noise exposure for a day would prevent noise-induced hearing loss. This is the only evidence-based safe noise level I have been able to find.
But the real safe noise level to prevent NIHL must be lower than 70 dB. Why? EPA used the 40-year occupational exposure in its calculations. It didn’t adjust for lifetime exposure (approaching 80 years in the United States before the COVID pandemic). NIHL comes from cumulative noise exposure. This probably explains why so many older people have trouble hearing, the same way additional years of sun exposure explains the pigmentation changes and wrinkles in older people.
My paper explains that the NIOSH REL, from which EPA calculated the safe noise level, was based on studies of workers using limited frequency audiometry (hearing tests), only up to 4000 or 6000 Hertz (cycles per second). More sensitive tests of hearing, such as extended-range audiometry up to 20,000 Hertz, shows auditory damage in people with normal hearing on standard audiometry. Tests of speech in noise- how well someone can hear when background noise is added to the hearing test- also show problems understanding speech, even if standard audiometry is normal.
The actual noise level to prevent hearing loss may be as low as 55 dBA. This is the noise level needed for the human ear to recover from noise-induced temporary threshold shift, the muffling of sound one has after exposure to loud noise. If you’ve ever attended a rock concert or NASCAR race and found your hearing muffled the next morning, that’s what I’m talking about. (By the way, there is no such thing as temporary hearing loss. The muffling of sound, or temporary ringing in the ears after loud noise exposure, indicates that permanent auditory damage has occurred.)
55 dB is pretty quiet and would be difficult to achieve in everyday life in a modern industrialized society, where average daily noise exposures are near 75 dB. But I hope that if people know the real safe noise level to prevent hearing loss, they will avoid loud noise or use hearing protection if they can’t.
Takahiro Tamesue – email@example.com Yamaguchi University 1677-1 Yoshida, Yamaguchi Yamaguchi Prefecture 753-8511 Japan
Popular version of poster 4aPPa24, “Effects of meaningful or meaningless noise on psychological impression for annoyance and selective attention to stimuli during intellectual task” Presented Thursday morning, December 1, 2016 172nd ASA Meeting, Honolulu
Open offices that make effective use of limited space and encourage dialogue, interaction, and collaboration among employees are becoming increasingly common. However, productive work-related conversation might actually decrease the performance of other employees within earshot — more so than other random, meaningless noises. When carrying out intellectual activities involving memory or arithmetic tasks, it is a common experience for noise to cause an increased psychological impression of “annoyance,” leading to a decline in performance. This is more apparent for meaningful noise, such as conversation, than it is for other random, meaningless noise. In this study, the impact of meaningless and meaningful noises on selective attention and cognitive performance in volunteers, as well as the degree of subjective annoyance of those noises, were investigated through physiological and psychological experiments.
The experiments were based on the so-called “odd-ball” paradigm — a test used to examine selective attention and information processing ability. In the odd-ball paradigm, subjects detect and count rare target events embedded in a series of repetitive events. To complete the odd-ball task it is necessary to regulate attention to a stimulus. In one trial, subjects had to count the number of times the infrequent target sounds occurred under meaningless or meaningful noises over a 10 minute period. The infrequent sound — appearing 20% of the time—was a 2 kHz tone burst; the frequent sound was a 1 kHz tone burst. In a visual odd-ball test, subjects observed pictures flashing on a PC monitor as meaningless or meaningful sounds were played to both ears through headphones. The most infrequent image was 10 x 10 centimeter-squared red image; the most frequent was a green square. At the end of the trial, the subjects also rated their level of annoyance at each sound on a seven-point scale.
During the experiments, the subjects brain waves were measured through electrodes placed on their scalp. In particular, we look at what is called, “event-related potentials,” very small voltages generated in the brain structures in response to specific events or stimuli that generate electroencephalograph waveforms. Example results, after appropriate averaging, of wave forms of event-related potentials under no external noise are shown in Figure 1. The so-called N100 component peaks negatively about 100 milliseconds after the stimulus and the P300 component positive peaks positively around 300 milliseconds after a stimulus, related to selective attention and working memory. Figure 2 and 3 show the results of event-related potentials for infrequent sound under the meaningless and meaningful noise. N100 and P300 components are smaller in amplitude and longer in latency because of the meaningful noise compared to the meaningless noise.
Figure 1. Averaged wave forms of evoked Event-related potentials for infrequent sound under no external noise.
Figure 2. Averaged wave forms of evoked Event-related potentials for infrequent sound under meaningless noise.
Figure 3. Averaged wave forms of auditory evoked Event-related potentials under meaningful noise.
We employed a statistical method called, “principal component analysis” to identify the latent components. Results of statistical analysis, where four principal components were extracted as shown in Figure 4. Considering the results, where component scores of meaningful noise was smaller than other noise conditions, meaningful noise reduces the component of event-related potentials. Thus, selective attention to cognitive tasks was influenced by the degree of meaningfulness of the noise.
Figure 4. Loadings of principal component analysis
Figure 5. Subjective experience of annoyance (Auditory odd-ball paradigms)
Figure 5 shows the results for annoyance in the auditory odd-ball paradigms. These results demonstrated that the subjective experience of annoyance in response to noise increased due to the meaningfulness of the noise. The results revealed that whether the noise is meaningless or meaningful had a strong influence not only on the selective attention to auditory stimuli in cognitive tasks, but also the subjective experience of annoyance.
That means that when designing sound environments in spaces used for cognitive tasks, such as the workplace or schools, it is appropriate to consider not only the sound level, but also meaningfulness of the noise that is likely to be present. Surrounding conversations often disturb the business operations conducted in such open offices. Because it is difficult to soundproof an open office, a way to mask meaningful speech with some other sound would be of great benefit for achieving a comfortable sound environment.