OBJECTIVES: Studies have shown that in addition to energy, kurtosis plays an important role in the assessment of hearing loss caused by complex noise. The objective of this study was to investigate how to use noise recordings and audiometry collected from workers in industrial environments to find an optimal kurtosis-adjusted algorithm to better evaluate hearing loss caused by both continuous noise and complex noise. DESIGN: In this study, the combined effects of energy and kurtosis on noise-induced hearing loss (NIHL) were investigated using data collected from 2601 Chinese workers exposed to various industrial noises. The cohort was divided into three subgroups based on three kurtosis (β) levels (K 1 : 3 ≤ β ≤ 10, K 2 : 10 <β ≤ 50, and K 3 : β > 50). Noise-induced permanent threshold shift at test frequencies 3, 4, and 6 kHz (NIPTS 346 ) was used as the indicator of NIHL. Predicted NIPTS 346 was calculated using the ISO 1999 model for each participant, and the actual NIPTS was obtained by correcting for age and sex using non-noise-exposed Chinese workers (n = 1297). A kurtosis-adjusted A-weighted sound pressure level normalized to a nominal 8-hour working day (L Aeq,8h ) was developed based on the kurtosis categorized group data sets using multiple linear regression. Using the NIPTS 346 and the L Aeq.8h metric, a dose-response relationship for three kurtosis groups was constructed, and the combined effect of noise level and kurtosis on NIHL was investigated. RESULTS: An optimal kurtosis-adjusted L Aeq,8h formula with a kurtosis adjustment coefficient of 6.5 was established by using the worker data. The kurtosis-adjusted L Aeq,8h better estimated hearing loss caused by various complex noises. The analysis of the dose-response relationships among the three kurtosis groups showed that the NIPTS of K 2 and K 3 groups was significantly higher than that of K 1 group in the range of 70 dBA ≤ L Aeq,8h < 85 dBA. For 85 dBA ≤ L Aeq,8h ≤ 95 dBA, the NIPTS 346 of the three groups showed an obvious K 3 > K 2 > K 1 . For L Aeq,8h >95 dBA, the NIPTS 346 of the K 2 group tended to be consistent with that of the K 1 group, while the NIPTS 346 of the K 3 group was significantly larger than that of the K 1 and K 2 groups. When L Aeq,8h is below 70 dBA, neither continuous noise nor complex noise produced significant NIPTS 346 . CONCLUSIONS: Because non-Gaussian complex noise is ubiquitous in many industries, the temporal characteristics of noise (i.e., kurtosis) must be taken into account in evaluating occupational NIHL. A kurtosis-adjusted L Aeq,8h with an adjustment coefficient of 6.5 allows a more accurate prediction of high-frequency NIHL. Relying on a single value (i.e., 85 dBA) as a recommended exposure limit does not appear to be sufficient to protect the hearing of workers exposed to complex noise.

Smartphones have evolved into powerful devices with computing capabilities that rival the power of personal computers. Any smartphone can now be turned into a sound-measuring device because of its built-in microphone. Many sound measuring apps exist on the market for various mobile platforms. In our earlier research, we showed that a smart device with an adequate app can achieve compliance with most of the Class 2 requirements for periodic testing. In this paper, we present the methods and results of measuring directional response of a sound level meter consisting of a smartphone and one commercially available sound level meter app in the horizontal plane. We used both the built-in smartphone microphone and an external microphone according to relevant IEC [International Electrotechnical Commission] and ANSI [American National Standards Institute] sound level meter standards. The results show that the sound level meter app and an external microphone can achieve compliance with the requirements for Class 2 of IEC 61672/ANSI S1.4–2014 standard in the horizontal plane. © 2020 Elsevier Ltd

OBJECTIVES: To evaluate (1) the accuracy of the International Organization for Standardization (ISO) standard ISO 1999 [(2013), International Organization for Standardization, Geneva, Switzerland] predictions of noise-induced permanent threshold shift (NIPTS) in workers exposed to various types of high-intensity noise levels, and (2) the role of the kurtosis metric in assessing noise-induced hearing loss (NIHL). DESIGN: Audiometric and shift-long noise exposure data were acquired from a population (N = 2,333) of screened workers from 34 industries in China. The entire cohort was exclusively divided into subgroups based on four noise exposure levels (85 ≤ LAeq.8h < 88, 88 ≤ LAeq.8h < 91, 91 ≤ LAeq.8h < 94, and 94 ≤ LAeq.8h ≤ 100 dBA), two exposure durations (D ≤ 10 years and D > 10 years), and four kurtosis categories (Gaussian, low-, medium-, and high-kurtosis). Predicted NIPTS was calculated using the ISO 1999 model for each participant and the actual measured NIPTS was corrected for age and sex also using ISO 1999. The prediction accuracy of the ISO 1999 model was evaluated by comparing the NIPTS predicted by ISO 1999 with the actual NIPTS. The relation between kurtosis and NIPTS was also investigated. RESULTS: Overall, using the average NIPTS value across the four audiometric test frequencies (2, 3, 4, and 6 kHz), the ISO 1999 predictions significantly (p < 0.001) underestimated the NIPTS by 7.5 dB on average in participants exposed to Gaussian noise and by 13.6 dB on average in participants exposed to non-Gaussian noise with high kurtosis. The extent of the underestimation of NIPTS by ISO 1999 increased with an increase in noise kurtosis value. For a fixed range of noise exposure level and duration, the actual measured NIPTS increased as the kurtosis of the noise increased. The noise with kurtosis greater than 75 produced the highest NIPTS. CONCLUSIONS: The applicability of the ISO 1999 prediction model to different types of noise exposures needs to be carefully reexamined. A better understanding of the role of the kurtosis metric in NIHL may lead to its incorporation into a new and more accurate model of hearing loss due to noise exposure.

Wildland firefighters are exposed to numerous noise sources that may be hazardous to their hearing. This study examined the noise exposure profiles for 264 wildland firefighters across 15 job categories. All 264 firefighters completed questionnaires to assess their use of hearing protection devices, enrollment in hearing conservation programs, and their overall perception of their noise exposure. Roughly 54% of firefighters' noise exposures exceeded the National Institute for Occupational Safety and Health recommended exposure limit of 85 decibels, A-weighted, over 8 hr, and 32% exceeded the Occupational Safety and Health Administration permissible exposure limit of 90 decibels, A-weighted, over 8 hr. Questionnaire results indicated good agreement between noise exposures and firefighters' perceptions of the noise hazard. Approximately 65% reported that they used some form of hearing protection; however, only 19% reported receiving any proper training regarding the use of hearing protection devices, with the majority of those firefighters relying on earplugs, including electronic and level-dependent earplugs, over earmuffs or other forms of hearing protectors. The results also suggest that improved communication and situational awareness play a greater role in the consistent use of hearing protection devices than other factors such as risk of developing noise-induced hearing loss. The study highlighted the challenges facing wildland firefighters and their management and the need for a comprehensive wildland fire agencies' hearing conservation program especially for firefighters who were exempt based on their occupational designations.

Many mobile sound measurement applications (apps) have been developed to take advantage of the built-in or fit-in sensors of the smartphone. One of the concerns is the accuracy of these apps when compared to professional sound measurement instruments. Previously, a research team from the National Institute for Occupational Safety and Health (NIOSH) developed the NIOSH Sound Level Meter (SLM) app for iOS smart devices. The team found the average accuracy of this app to be within 1 dBA when using calibrated external microphones with a type 1 reference device and measuring pink noise at levels from 65 to 95 dBA in 5-dBA increments. The studies were conducted in a reverberant noise chamber at the NIOSH Acoustics Laboratory in Cincinnati. However, it is still unknown how this app performs in measuring industrial/mining sound levels outside of a controlled laboratory environment. The current NIOSH study evaluates the NIOSH SLM app to measure sound levels from a jumbo drill (a large mining machine). The study was conducted in a hemi-anechoic chamber at the NIOSH Pittsburgh Mining Research Division and followed by a field evaluation in an underground metal mine. Six different iOS smart devices were used with two types of external microphones chosen from previous studies to measure sound levels during jumbo drill operations, and the results were compared with a reference device. Results show that the average sound levels measured by the NIOSH SLM app are within 1 dBA of the reference device both in the laboratory and field. However, the type of operation being performed, the selection and use of external microphones, distance from a noise source, and environmental factors (e.g., air movement) may all influence the accuracy of the app's performance. Although additional validation is still needed, the results from this study suggest a potential for using the NIOSH SLM app, with calibrated external microphones, to measure sound levels in mining operations.

New technologies are reshaping health interventions across disciplines. This technological surge offers a clear opportunity to expand and improve hearing health, particularly in hearing loss prevention. A person's hearing health trajectory is defined by his or her overall hazardous exposures, environmental factors, and genetic determinates.1 Among the many factors that can contribute to hearing health (such as overall health, smoking, diet, and ototoxicant exposure), reducing noise exposure—particularly at work—has the greatest potential to significantly decrease the burden of hearing loss and tinnitus.2 About 24 percent of hearing impairment cases among U.S. workers is attributable to workplace noise exposures. Because noise-induced hearing loss is preventable, approximately one-fourth of hearing impairment cases in this population may be avoided by adopting preventive measures.3 While progress has been made toward the prevention of work-related hearing loss, it remains among the most common occupational illnesses. Overall, nearly one in four U.S. adults has audiometric evidence of noise-induced hearing loss—and most do not realize it.4 People continue to focus on the use of hearing protection to reduce noise exposure, even though only limited evidence is available on the effectiveness of this approach.5 However, new technologies to measure and control noise and test hearing hold the promise of expedited progress.

Smartphones have evolved into powerful devices with computing capabilities that rival the power of personal computers. Any smartphone can now be turned into a sound-measuring device because of its built-in microphone. The ubiquity of these devices allows the noise measuring apps to expand the base of people being able to measure noise. Many sound measuring apps exist on the market for various mobile platforms, but only a fraction of these apps achieve sufficient accuracy for assessing noise levels, let alone be used as a replacement for professional sound level measuring instruments. In this paper, we present methods and results of calibrating our in-house developed NoiSee sound level meter app according to relevant ANSI (American National Standards Institute) and IEC (International Electrotechnical Commission) sound level meter standards. The results show that the sound level meter app and an external microphone can achieve compliance with most of the requirements for Class 2 of IEC 61672/ANSI S1.4-2014 standard.

The aim of this exploratory study was to examine whether the kurtosis metric can contribute to investigations of the effects of combined exposure to noise and solvents on human hearing thresholds. Twenty factory workers exposed to noise and solvents along with 20 workers of similar age exposed only to noise in eastern China were investigated using pure-tone audiometry (1000-8000 Hz). Exposure histories and shift-long noise recording files were obtained for each participant. The data were used in the calculation of the cumulative noise exposure (CNE) and CNE adjusted by the kurtosis metric for each participant. Passive samplers were used to measure solvent concentrations for each worker exposed to solvents over the full work shift. Results showed an interaction between noise exposure and solvents for the hearing threshold at 6000 Hz. This effect was observed only when the CNE level was adjusted by the kurtosis metric.

Wildland fire fighters use many tools and equipment that produce noise levels that may be considered hazardous to hearing. This study evaluated 174 personal dosimetry measurements on 156 wildland fire fighters conducting various training and fire suppression tasks. Noise exposures often exceeded occupational exposure limits and suggest that wildland fire fighters may be at risk of developing noise-induced hearing loss, particularly those operating chainsaws, chippers, and masticators. The authors recommend a comprehensive approach to protecting these fire fighters that includes purchasing quieter equipment, noise and administrative controls, and enrolling these fire fighters into a hearing conservation program.

This follow-up study examines the accuracy of selected smartphone sound measurement applications (apps) using external calibrated microphones. The initial study examined 192 apps on the iOS and Android platforms and found four iOS apps with mean differences of +/-2 dB of a reference sound level measurement system. This study evaluated the same four apps using external microphones. The results showed measurements within +/-1 dB of the reference. This study suggests that using external calibrated microphones greatly improves the overall accuracy and precision of smartphone sound measurements, and removes much of the variability and limitations associated with the built-in smartphone microphones.

This study reports on the accuracy of smartphone sound measurement applications (apps) and whether they can be appropriately employed for occupational noise measurements. A representative sample of smartphones and tablets on various platforms were acquired, more than 130 iOS apps were evaluated but only 10 apps met our selection criteria. Only 4 out of 62 Android apps were tested. The results showed two apps with mean differences of 0.07 dB (unweighted) and -0.52 dB (A-weighted) from the reference values. Two other apps had mean differences within +/-2 dB. The study suggests that certain apps may be appropriate for use in occupational noise measurements.

Peak sound pressure level measurements conducted at indoor firing ranges ranged from 157-168 decibels (dB). Exposure to high-intensity impulsive noise during target shooting at indoor firing ranges has been identified as a significant contributor to noise-induced hearing loss among shooters. In addition, firing ranges that are constructed with adjacent areas or housed within a larger building structure require minimal sound transmission to occur outside the firing range. Several principles of noise control engineering can be applied to improve the absorption of impulse noise inside the firing ranges and limit the transmission of such impulses to adjacent areas and spaces. Although little can be done to control the direct exposure of shooters to the firing of their own firearms, several noise control solutions are presented to reduce the secondary exposure off reflected surfaces and from other shooters. This paper will provide a general overview of noise control solutions aimed to improve sound absorption inside the firing range and reduce the transmission of airborne and structuralborne sounds to adjacent areas and facilities. 2010 Institute of Noise Control Engineering.

Noise in stock car racing is accepted as a normal occurrence but the exposure levels associated with the sport have not been adequately characterized. Researchers from the National Institute for Occupational Safety and Health (NIOSH) conducted an exploratory assessment of noise exposures to drivers, racing team members, and spectators at three stock car racing events. Sound level measurements were conducted using sound level meters, personal noise dosimeters, and a digital audio tape recorder that made sound recordings for later laboratory analysis. Area sound level measurements were made during race preparation, practice, qualification, and competition. Personal dosimetry measurements were conducted on drivers, team members, and spectators. Findings showed time-weighted averages (TWA) that ranged from A-weighted 96 decibels (dBA) for a spectator in the stands during a race to 114 dBA for a driver inside a car during practice. Peak sound pressure levels exceeded the maximum allowable limit of 140 dB during race competitions. Personal exposure measurements exceeded the NIOSH recommended exposure limit of 85 dBA as an 8-hr TWA in less than a minute for one driver during practice, within several minutes for team members, and less than one hour for spectators during the race. Hearing protection use was variable and intermittent among team members and spectators. Among drivers and team members, there was greater concern for communication performance than for hearing protection.