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.

In this study, the combined effects of energy and kurtosis on noise-induced hearing loss (NIHL) were investigated using data collected from 2,601 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=1,297). 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.

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}.

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.

Researchers have long found that impulsive noise or complex noise with impulse/impact components is more hazardous to hearing than continuous steady-state (Gaussian) noise at similar noise exposure levels (e.g.,

Complex noise consists of regular or irregular impulsive/impact components embedded in continuous Gaussian background noise and is very common in certain industrial (such as manufacturing and construction) and military settings. How to properly measure or characterize the great diversity of non-Gaussian noise found in industry is a challenging task.

Kurtosis is a statistical measure that defines how heavily the tails of distribution differ from the tails of a Gaussian distribution. For noise, kurtosis can be used to describe whether there is the presence of a high-amplitude sound (impact/impulse) that is different from the underlying continuous steady-state (Gaussian) noise and the degree of the impulsiveness of the noise (

_{Aeq,8h}) with the equation as follows:
_{N} is the kurtosis of noise and β_{G} is the kurtosis of Gaussian noise which is equal to 3. One of the most attractive features of the Goley et al. model is that it directly corrects the measured noise energy using the kurtosis of noise. It can be seen that using the kurtosis adjustment method is equivalent to adding a penalty, determined by the second term in the formula, to the overall sound pressure level (L_{Aeq,8h}). Because the kurtosis of complex noise (β_{N}) is higher than that of β_{G}, it has a positive correction term indicating that the risk of complex noise is higher. In the formula, λ is a key adjustment coefficient. Although the coefficient is not scaled in dB, the correction can be expressed that way. In the case of fixed noise kurtosis, it determines the degree of kurtosis adjustment for L_{Aeq.8h}.

In this study, we collected a large database of 3,898 participants, including shift-long noise recordings and hearing levels of 2,601 workers from various Chinese industries, and a control group of 1,297 participants with no history of occupational noise exposure. The noise environments in these industries had a wide range of noise levels and kurtosis values that allowed for a comprehensive evaluation of the role of kurtosis in assessing NIHL. The objective of this study was to investigate how workers’ and control data could be used to find an optimal adjustment coefficient (λ) for humans by studying the combined effects of noise level and kurtosis on high-frequency hearing loss, and to determine whether a kurtosis-adjusted L_{Aeq,8h} using the Goley model improves the accuracy of prediction of hearing loss due to complex noise.

This cross-sectional study was conducted in Zhejiang and Jiangsu provinces, eastern China. The study protocol was approved by the Ethics Committee of Zhejiang and Jiangsu Provincial Centers for Disease Control and Prevention (approval reference number: ZJCDC-T-043-R and JSCDCLL-2017–025).

A total of 4,916 subjects were initially introduced to the purpose of the study and invited to participate between 2008 and 2018. This cohort included 3,244 noise-exposed and 1,672 non-noise-exposed workers. All participants signed an informed consent form. For inclusion in the study, all participants had to satisfy the following three criteria: (1) No history of genetic or drug-related hearing loss, head wounds, or ear diseases; (2) No history of military service or shooting activities; and (3) Good conditions of the external auditory canal, tympanic membrane, and the middle ear on otoscopic examination. Noise-exposed participants needed to satisfy additional criteria: (1) Consistently worked in the same job category and at the same worksite (noise exposure area) for the period from the beginning of a worker’s career to the date of the investigation; (2) A minimum of at least one year of employment in their current position; (3) Having an A-weighted noise exposure level (L_{Aeq}) at their jobs between 70 to 95 dBA. As a result, a total of 2,601 noise-exposed participants and 1,297 non-noise-exposed participants (control) were included from the original pool of 3,244 and 1,672, respectively.

The reason for choosing workers exposed to L_{Aeq.8h} between 70 and 95 dBA is that a previous study (_{Aeq,8h} was less than 95 dBA, workers rarely used hearing protection devices; when the noise L_{Aeq,8h} was equal to or greater than 95 dBA, the proportion of hearing protection devices used increased significantly. Because an accurate unprotected dose-response relationship is the basis of this study, we needed to exclude data of workers exposed to higher than 95 dBA.

Most participants still did not use a hearing protection device (HPD) despite the implementation of hearing conservation programs on a wide scale in China starting in 2012. The use of HPDs, usually earplugs, both on and off the job, was assessed through field observations by the researchers and in the questionnaire and reported to be low and infrequent. At high noise exposure levels, that is, ~95 dBA and above, the use of HPDs was observed to be sporadic. The inclusion of these participants would, to some extent, have an effect on the relationship between noise level and NIPTS. We expected this effect to occur primarily in the participants exposed to noise above 95 dBA. For those participants who have never used HPDs, the members of the research team recommended the use of appropriate HPDs after data collection. During this study, workers in the investigated factories received training on how to properly use HPDs.

Our data collection project in China began in 2008, before the large-scale implementation of hearing loss prevention programs in China, so we collected data on many workers who were not using hearing protection devices (HPD) between 2008 and 2012. Although China introduced hearing protection programs in 2012, most participants still did not use HPDs. Using field observations and questionnaires, the researchers assessed the use of HPD (usually earplugs), which was reported to be infrequent and rare for the workers exposed to noise levels below 95 dBA. At high noise exposure levels, i.e., ~95 dBA and above, the number of workers using HPD increased significantly. The inclusion of these participants affected the relationship between noise levels and NIPTS to some extent. Because an accurate (unprotected) dose-response relationship is the basis of this study, we excluded data of workers exposed to higher than 95 dBA. For participants who had never used HPD, team members recommended the use of appropriate HPD after collecting data. In this study, workers at the surveyed factories received training on the proper use of HPD.

All participants were required to complete a noise exposure and health questionnaire, which was followed by a face-to-face interview by an occupational hygienist for quality control. The questionnaire included the following information: general demographic information (age, sex, etc.); occupational history (factory, worksite, job description, length of employment, duration of daily noise exposure, and history of using hearing protection); and overall health status (including any history of ear disease and/or ototoxic drug exposure).

Each participant underwent an otologic and audiometric examination. Otoscopy was carried out initially to ensure participants had no external ear abnormalities. Air-conduction pure-tone hearing threshold levels (HTLs) were tested at 0.5, 1, 2, 3, 4, 6, and 8 kHz in each ear by an experienced audiologist. Testing was conducted in a double-walled audiometric booth using an audiometer (Madsen OB40, Denmark) with an air conduction headphone (Sennheiser HDA 300). The tests were conducted manually, and the measurement was based on the threshold determination methods of the American Speech-hearing-Language Association (

A digital noise recorder (ASV5910-R, Hangzhou Aihua Instruments Co., Ltd., China) was used to record a shift-long personal noise exposure for each participant. The instrument uses a ¼-inch pre-polarized condenser microphone with a broad response frequency (20 Hz to 20 kHz) and high sensitivity level (2.24 mV/Pa). The measurement ranges from 40 dB(A) to 141 dB(A). The recorder can work continuously for 23 hrs. under full charge. One full-shift recording of each participant’s noise exposure was captured by the ASV5910-R at 32-bit resolution with a 48-kHz sampling rate and saved in a raw audio format (WAV file). The noise record was saved on a 32 GB micro SD card and transferred to a portable hard disk for subsequent analysis. It was performed one time for each participant. Before recording, a hygienist confirmed with each participant that this was the noise they were typically exposed to on an average working day. The members of the research team monitored the noise collection of individual participants in the workplace. The recording was saved on a 32-GB micro SD card and then transferred to a computer for subsequent analysis. The microphone was placed on the shoulder of each participant at the start of the work shift and collected at the end of the shift. The participants were trained to wear the recorder properly.

Two noise metrics were used in this study: (1) The A-weighted equivalent sound pressure level normalized to a nominal 8-hour working day (L_{Aeq,8h}); (2) The kurtosis of noise exposure (β_{N}). A program using MATLAB (The MathWorks, R2017) software was developed for analyzing the full-shift noise waveforms that were collected on each participant. The program was designed to extract the L_{Aeq,8h,} and kurtosis, i.e.,

L_{Aeq,8h} level, in A-weighted decibels, is given by the formula (_{Aeq,Te} is the A-weighted equivalent continuous sound pressure level for _{e}; _{e} is the effective duration of the working day in hours, and _{0} is the reference duration (_{0} = 8-hour).

Calculation of the kurtosis of noise exposure in a typical work day (β_{N})

The kurtosis of the recorded noise signal was computed over consecutive 60-second time windows without overlap over the shift-long noise record using a sampling rate of 48 kHz for noise recording (_{i} is the ^{th} value of noise amplitude and _{j}) at every 60 seconds is used as the kurtosis of noise exposure (_{N}), which is calculated

The analysis focused on the noise-sensitive frequency range of 3–6 kHz because the noise-induced hearing loss from continuous noise occurs predominantly in this range initially (

The actual value of NIPTS of each individual participant was compared with the median NIPTS predicted by _{Aeq,8h} is the noise exposure level normalized to a nominal 8-hour working day; _{0} = 1 year; _{0} is the reference sound pressure level in

To analyze the effect of kurtosis on NIPTS, we grouped the data according to the noise kurtosis value (β_{N}) that each worker was exposed to. The kurtosis group should be divided so that the mean NIPTS of workers within this group is significantly different from that of other groups. The participants were partitioned into one of three groups based on the kurtosis value of their noise exposure:

K_{1}: 3≤β_{N}≤10;

K_{2}: 10<β_{N}≤50;

K_{3}: β_{N}>50

Based on our analysis of individual noise data collected from more than 3,000 workers, the kurtosis of industrial noise can be as high as about 1,000. More details on the kurtosis grouping described above are available in the

The kurtosis adjustment was calculated according to _{Aeq,8h} and log_{10}(β_{N}/3) as independent variables, the coefficient λ was calculated by multiple linear regression model:
_{0} is the NIPTS-intercept; _{1} and _{2} are the regression coefficients representing the change in NIPTS relative to a one-unit change in _{Aeq.8h} and _{10}_{N}_{0}, _{1,} and _{2} that minimizes ε, and λ=_{2}_{1}. The dependent variable is actual NIPTS_{346}, i.e., the average of actual NIPTS at 3, 4, and 6 kHz. The model was validated by comparing the difference between actual NIPTS_{346} and estimated NIPTS_{346} (with or without kurtosis adjustment) using the

Noise exposure level (L_{Aeq,8h}), duration of exposure, kurtosis, age, and sex were summarized in _{346} and the difference between the actual NIPTS_{346} and the ISO 1999 predicted NIPTS_{346} were analyzed using a mixed model where the NIPTS_{346} or the NIPTS_{346} difference served as the dependent variable, and noise level (L_{Aeq,8h}), kurtosis, and their interaction served as independent variables. The group means for noise level and kurtosis, and their 95% confidence interval (CI) were calculated. A significance level of p<0.05 was applied to the overall test for all factors and their interaction. Pairwise comparisons were processed among NIPTS_{346} and kurtosis groups. For all pairwise comparisons, Bonferroni adjustment was applied in evaluating significance. The analyses were performed using IBM® SPSS Statistics (version 22).

The 1,297 participants in the control group had no history of exposure to high-level workplace noise. They are factory office workers, technology company programmers, and health care workers working in environments with noise levels below 70 dBA.

Data were collected on 2,601 workers exposed to various industrial noises. The workers were classified into three groups according to the kurtosis value of noise they were exposed to.

Some perspective on the relationship between NIPTS_{346} and L_{Aeq,8h} can be obtained by plotting actual NIPTS_{346} for each noise exposure level (from 70 to 95 dBA). _{Aeq,8h} range.

The effects of different kurtosis categories are evident when the noise exposure level (L_{Aeq.8h}) measurements are collapsed into 1-dB bins, and the mean noise level within each bin is plotted against the mean actual NIPTS_{346} for that bin. These effects are shown in _{Aeq.8h,} and the ordinate is the mean NIPTS_{346} for the data points belonging to a specific kurtosis category within the 1-dB bin. The figure clearly shows a positive relationship between the L_{Aeq.8h} and NIPTS_{346} for each kurtosis category. Because the data shown in _{Aeq,8h}, and NIPTS_{346}, a logistic function that would allow nonlinear and nearly linear descriptions of the data of the form NIPTS=a/[1+e^{(b-LAeq)/c}] was chosen to describe the results of the three kurtosis categories. For the appropriately set parameters a, b, and c, this relation allows NIPTS to approach a positive number close to zero as L_{Aeq,8h} approaches 0 dBA, and NIPTS to a ceiling value as L_{Aeq,8h} approaches a high level (e.g., greater than 95 dBA). Note: As can be seen in _{1} group (solid black circles), due to the small sample size of L_{Aeq.8h} when it is less than 80 dBA, there are not enough samples in the 1-dB interval, so the samples of L_{Aeq.8h} in the 70–79 dBA region are divided into two groups to ensure a certain number of samples in each group. The samples of L_{Aeq,8h} in the range of 70~75 dBA were averaged to get an average point, and the data samples in the range of 76~79 dBA were averaged to get another data point, as shown in _{3} group (hollow red circles in _{Aeq,8h} less than 79 dBA in the K_{3} group. For the K_{2} group (hollow green circles in

Initially, the regression analyses used the average NIPTS at 3, 4, and 6 kHz as the dependent variable, with age, sex, duration, L_{Aeq,8h,} and log_{10}(β_{N}/3) as the independent variables. As mentioned above, actual NIPTS of each noise-exposed worker were obtained by subtracting normal median hearing threshold levels by age- and sex-matched populations of the control group. As a result, the correlation between NIPTS and age or sex was reduced. The inclusion of age and sex as independent variables did not significantly improve the model fitting using the multiple linear regression analysis. The data points in _{Aeq.8h} and NIPTS_{346} functions under each kurtosis category but smoothened out the influence of exposure duration on exposure duration multiple linear regression. Consequently, the inclusion of the duration as an independent variable did not significantly improve the model’s performance. Eventually, L_{Aeq.8h} and log_{10}(β_{N}/3) were used as independent variables of the multiple linear regression equation, controlling for the effects of age, sex, and exposure duration. _{Aeq,8h} as the exposure variable and the other using the kurtosis-adjusted L_{Aeq,8h}. It is clear from _{Aeq,8h} alone (Model 1 in ^{2} = 0.75, whereas the kurtosis-adjusted model (Model 2 in ^{2} = 0.88 (an increase of 0.13 over the R^{2} value in Model 1). The difference in R^{2} between the two models is significant (p < 0.001). This significant change in the overall model fit indicates that the model attribution of hearing loss has an important change from L_{Aeq,8h} to kurtosis-adjusted L_{Aeq,8h} (i.e.,_{Aeq,8h} can significantly improve the accuracy of noise-induced hearing loss assessment. Using the human data collected in China, the coefficients b_{1} and b_{2} in Model 2 were obtained as 0.56 and 3.64, respectively. Consequently, the adjustment coefficient can be calculated as λ=b_{2}/b_{1}=6.50.

The kurtosis-adjusted L_{Aeq,8h} (i.e., _{346}, and the results were compared to those from unadjusted L_{Aeq,8h}. According to the above multiple linear regression results, the following equation was used for

The NIPTS_{346} of each individual noise-exposed worker was estimated by ISO prediction formula (_{Aeq,8h} (un-adjusted) or _{346} using L_{Aeq,8h} or _{346}, respectively. The mixed model analysis showed that there was a significant adjustment effect (df=1, F = 346.6, p < 0.001), and kurtosis by adjustment interaction effect (df=2, F = 40.3, p < 0.001) on the NIPTS_{346} difference. The estimated marginal mean (EMM) for each group is summarized in

_{346} for each kurtosis level before and after kurtosis adjustment. The results show that, for un-adjusted L_{Aeq.8h}, the ISO 1999 formula underestimates NIPTS_{346} by an average of 3.72 dB for kurtosis group K_{1}; by 6.35 dB for group K_{2}; 10.24 dB for group K_{3}. After the noise levels (L_{Aeq,8h}) were adjusted for kurtosis using _{1}; within 0.08 dB for group K_{2}; and within −0.96 dB for group K_{3}. _{Aeq.8h}. The EMM of underestimated NIPTS_{346} for each kurtosis level after kurtosis adjustment using λ=4.02 was also shown in _{1}; by 2.8 dB for group K_{2}; and by 4.5 dB for group K_{3}. While kurtosis-adjusted L_{Aeq.8h} using λ=4.02 could correct underestimations of NIHL due to complex noise exposure to a certain extent, its correction degree is insufficient for human data.

One method to analyze the effect of kurtosis on NIHL is to make a reasonable clustering of data according to the kurtosis values of noise exposed by individual workers and then compare the differences of NIPTS_{346} in each data class under a similar noise level. The data used in this study between 85–95 dBA are the same as those used in the previous study (_{346} obtained from these four groups. For group _{346} was 11.5 dB, which was significantly lower than the 13.4 dB in group _{346} between group _{346} were significantly smaller than that of group _{346} in group _{346} in group _{1}) ；10<_{2}), and _{3}). The EMMs of NIPTS_{346} in these three groups were 11.5, 13.4 dB, and 16.6 dB, respectively. The NIPTS_{346} differences among these three groups were statistically significant, with _{1}- K_{2} group pair, _{1}- K_{3} and K_{2}- K_{3} group pairs. Based on the above kurtosis classification, the combined effects of noise level and kurtosis on high-frequency NIPTS were analyzed, and the Goley model was studied.

_{Aeq,8h}. The kurtosis value of a work type in _{10}(β/3). _{N}≤ 50 accounts for the majority, such as stamping, drilling, casting, metal processing, etc. In this study, the highly impulsive complex noises (β_{N}>50) mainly existed in the workplaces of wood processing, nail gunning, and assembly in various manufacturing plants (including automobile, furniture, and electronic machinery manufacturers). It is worth noting that many work types have a wide range of kurtosis values, some spanning two kurtosis categories, some even three kurtosis categories. Examples include stamping, drilling, casting, metalworking, etc., with kurtosis values ranging from 7 to 86. The kurtosis and level of noise received by individual workers can largely depend on such factors as the position of work, the frequency of tool use, and the intensity of background noise. Therefore, the kurtosis of the noise exposure of individual workers should be calculated according to the actual noise exposed for each worker.

In this study, most workers did not wear hearing protection devices, and a small population of workers with high noise exposure (L_{Aeq}~95 dBA) may wear devices. A recent study of 385 workers at an automobile manufacturing plant in China (

Based on the data analysis of 2601 workers in this study, 19.9% of workers were exposed to steady-state noise (3≤_{N}≤10), 59.1% of workers to complex noise with low or moderate impulsive components (10<_{N}≤50), and 21% of workers to complex noise with high kurtosis (β_{N}>50). Because non-Gaussian complex noise is common in the manufacturing industry, and the current noise standards (e.g.,

A logistic function was used to fit the dose-response data shown in

_{Aeq,8h} and NIPTS_{346} using logistic function fitting in each kurtosis category (coefficient of determination R^{2}>0.9 for all three curves). For the sake of discussion, the three equations reflecting L_{Aeq,8h} and NIPTS_{346} were named after the kurtosis category, which is the K_{1} curve, K_{2} curve, and K_{3} curve, respectively.

The K_{1} curve (the black line) in _{346}), and its fitting curve equation is _{346} can be calculated when L_{Aeq,8h} is at a specific level. Considering the situation when L_{Aeq,8h} =75 dBA, where the calculated value of NIPTS_{346} is 3.9 dB, this is very close to the actual value (i.e., 3.7 dB at L_{Aeq.8h}=74 dBA) in _{Aeq,8h} = 78 dBA, the calculated NIPTS_{346} is 5.4 dB, showing an increased hearing shift (hearing loss) at the high frequencies, which is consistent with _{Aeq,8h} equal to 75 dBA (at 4 kHz); However, NIPTS is not predicted until the exposure level reaches 78 dBA. When L_{Aeq,8h}=80 dBA, the calculated value of NIPTS_{346} will be 6.6 dBA according to the K_{1} curve. The exposure level of 80 dBA was set as the action level (need to wear hearing protection devices) by the European Union (Directive 2003/10/EC). When the L_{Aeq,8h} equals 85 dBA, the calculated NIPTS_{346} is 9.9 dB. High-frequency hearing loss is apparent at this level. This level was set as recommended exposure level (REL) by the U.S. National Institute for Occupational Safety and Health (

The K_{2} curve (the green line) in _{346}). The equation of this curve is: _{Aeq,8h}=70 dBA, the calculated value of NIPTS_{346} is 4.7 dB. When the L_{Aeq,8h} is 75 dBA, the calculated value of NIPTS_{346} is 7 dB. From _{346} values of group K_{2} were all about 7 dB within the range of 72–77 dBA, which is near twice the magnitude of the shifts at this level in group K_{1}. When the L_{Aeq,8h} equals 80 dB, the calculated value of NIPTS_{346} is 9.6 dB, indicating that the non-Gaussian complex noise had begun to produce significant NIPTS_{346} at this exposure level. It is worth noting that when the exposure level of complex noise is 80 dBA, and the kurtosis value was greater than ten and less than 50, the high-frequency hearing loss caused by complex noise is comparable to that induced by continuous steady-state noise at 85 dBA (NIPTS_{346} = 9.6 vs. 9.9 dB). Therefore, for complex noise (β>10), the NIOSH noise exposure REL and OSHA Action Level may need to be lowered from 85 dBA to 80 dBA in the United States and elsewhere. It’s worth noting that _{1} and K_{2} curves is that they converge L_{Aeq,8h} increases. When L_{Aeq,8h} ≥100 dB, the difference of NIPTS_{346} between the curves is only 0.3 dB. This convergence suggests that hearing loss from complex noise with moderate kurtosis values (10<_{Aeq,8h} was less than 100 dBA, especially in the range of 70–90 dBA, for a fixed exposure level, the NIPTS_{346} in group K_{2} was significantly higher than that in group K_{1}.

The K_{3} curve (the red line) in _{346 and} complex noise with high kurtosis values (β>50). The fitting curve equation is as follows: _{Aeq,8h} is equal to 70 dBA, the calculated value of NIPTS_{346} is 3.8 dB. When L_{Aeq,8h} = 75 dBA, the calculated NIPTS_{346} is 6.5 dB. It is worth noting that the K_{3} curve and K_{2} curve intersect around L_{Aeq,8h}=78 dBA. When the noise level is greater than 78 dBA, the NIPTS_{346} difference between groups K_{3} and K_{2} becomes larger and larger. Especially when L_{Aeq,8h} ≥85 dBA, the NIPTS_{346} in group K_{3} was significantly higher than that in groups K_{2} and K_{1}. At the range between 85 and 95 dBA, the higher the noise level, the greater the difference in NIPTS_{346}. According to the equations of the three fitting curves, it can be found that when L_{Aeq.8h} is greater than 100 dBA, the NIPTS_{346} difference between K_{3} group and K_{1}/K_{2} group tends to be stable (about 4 dB).

It is worth mentioning that, as we pointed out in our previous study, kurtosis is an adjunct metric to energy in the evaluation of NIHL (_{Aeq,8h} is below 70 dBA, neither continuous noise nor complex noise can produce significant NIPTS_{346}. Therefore, we can infer that the noise level of L_{Aeq,8h}=70 dBA is the “threshold” for the effect of kurtosis. When L_{Aeq,8h}<70 dBA, the value of kurtosis does not have an impact on NIHL evaluation.

Animal and epidemiological studies have shown that the temporal structure of noise (kurtosis) plays a vital role in NIHL evaluation (_{Aeq,8h}) using kurtosis and derived the adjustment coefficient of λ=4.02 from an analysis of chinchilla noise-exposure data. However, it is essential to note that the NIHL results observed in chinchillas are different from those observed in humans, where chinchillas are more susceptible to developing hearing loss following noise exposures. Therefore, the adjustment coefficient (λ) obtained from the chinchilla noise data does not necessarily apply to humans. As a comparison, we directly applied λ=4.02 to workers’ data, and the results are shown in _{2} group’s underestimation was reduced from the original average of 6.35 dB to 2.8 dB, the underestimation degree of K_{3} group was reduced from the original average of 10.24 dB to 4.5 dB, but the degree of hearing damage caused by noise with high kurtosis value was still vastly underestimated.

Since human hearing is not as sensitive as that of chinchilla, it can be seen from adjustment formula (7) that to suffer a fixed NIHL, the adjustment coefficient of the human model should be larger than that of chinchilla. In other words, humans need to receive more noise energy than chinchillas do to suffer a comparable NIHL. Using data collected from the industrial and non-noise population in China and the ISO 1999 prediction formula for NIPTS, we derived an optimum adjustment coefficient (λ=6.5) that could be applied practically to protect the hearing of workers by using Goley’s correction formula. As can be seen from _{Aeq,8h} by kurtosis, (1) For workers exposed to steady-state noise (_{1}), the underestimation of NIPTS_{346} by ISO 1999 decreased significantly from 3.72 dB to 1.23 dB; (2) For workers exposed to complex noise with medium kurtosis (10<β_{N}≤50, group K_{2}), the underestimation of NIPTS_{346} by ISO 1999 decreased significantly from 6.35 dB to 0.08 dB. It is clear that after kurtosis adjustment, ISO 1999 was able to accurately predict high-frequency hearing loss of workers in the K_{2} group. Considering that most occupational noises belong to this type of non-Gaussian complex noise (59.1% of the total number of workers exposed to this type of noise in our collected data), the adjustment of kurtosis to L_{Aeq} is of great significance for the correction of the ISO 1999 prediction formula. (3) For workers exposed to complex noise with high kurtosis (_{N}>50, group K_{3}), the underestimation of NIPTS_{346} by ISO 1999 decreased significantly from 10.24 dB to −0.96 dB. This result shows that kurtosis has a significant adjustment on L_{Aeq} with greater impulsive content (β>50), although the overall adjustment effect is slightly over-adjusted (about 1 dB overestimation for NIPTS_{346}). It is worth pointing out that in the Introduction of the

The reason for choosing 3–6 kHz for investigating NIPTS in this study is that hearing loss initially occurs mainly in this frequency range under stable noise exposure conditions. Therefore, from the perspective of hearing protection, we should study the dose-response relationship in the frequency band where it is the most sensitive to NIHL and find the optimal kurtosis adjustment algorithm to evaluate NIHL better to prevent hearing loss to the greatest extent. However, the NIPTS produced by complex noise may have different trajectories in frequency from continuous noise. In addition, NIPTS of other test frequencies (e.g., 1,2,8 kHz) should also be studied, as these bands are important for speech recognition and understanding. The above topics are beyond the scope of this study and will be of great significance as future research work.

In the formulation and revision of ISO1999 over the years, researchers have taken into account the different effects of impulsive noise and steady-state noise on hearing. Therefore, in the ISO1999:1971, it was pointed out that a correction of 10-dB should be added on the basis of the measured L_{Aeq} for impulsive noise. In

Meanwhile, we should pay attention to the application scope of kurtosis. Since the kurtosis metric is an adjunct to energy in the evaluation of trauma from complex noise exposure, the validity of kurtosis depends on the noise exposure level. If the equivalent energy level of the noise exposure is low (e.g., less than 70 dB), it will not contribute to hearing loss no matter how high the value of kurtosis is. On the other hand, if the peak level of an impulse noise exceeds 140 dB SPL, the mechanisms of hearing damage include both mechanical and strains. The use of kurtosis would be questionable because there are no data about its effectiveness in this area. In order to greatly reduce the dose-response bias due to the wearing of hearing protection devices, the noise exposure range of this study was 70–95 dBA. In addition, due to the insufficient sample size at 70–78 dBA (especially for K_{1} and K_{3} groups), more data are needed to explore the relationship between kurtosis and energy interaction in this region.

The database in this study was collected from a population of Chinese workers. There can be concern about the extent to which the results are applicable to populations of other non-Chinese ethnicities.

In this study, the combined effects of noise exposure level and kurtosis on NIHL were analyzed by using data collected from 2,601 Chinese workers exposed to various industrial noises in comparison to non-noise exposed workers (n=1,297). The Goley model was re-investigated, and the adjustment coefficient, i.e., λ, was recalculated. The following conclusions can be addressed:

Because non-Gaussian complex noise is present in a wide range of industries, the temporal characteristics of noise (i.e., kurtosis) must be considered when evaluating occupational NIHL.

For non-Gaussian complex noise (β>10), NIHL may occur when L_{Aeq} is greater than 70 dBA, and NIHL is pronounced when L_{Aeq} is larger than 80 dBA. Therefore, any singular occupational REL will be insufficient to protect the hearing of workers unless kurtosis adjustment is applied.

A kurtosis-adjusted L_{Aeq,8h} with an adjustment coefficient of 6.5 allows a more accurate prediction of high-frequency NIHL in the region of 70 dBA≤L_{Aeq}≤95 dBA, which is very important for the hearing protection of workers exposed to various complex noises.

M.B.Z. designed and performed the investigation, analyzed data, and wrote part of the original draft; X.J.G conducted the field investigation, subject selection and interview, data evaluation, and quality control; W.J.M. and C.A.K. validated the project and methodology, reviewed and edited the manuscript; X.S. was responsible for project administration, validate the results of data analysis, and manuscript review; W.J.H. and J.S.L conducted project supervision and provided the discussion; W.G. conducted the field investigation and quality control; W.Q. designed and supervised the project, analyzed data, wrote and edited the manuscript. All authors discussed the results and implications and commented on the manuscript at all stages.

The authors wish to thank those who agreed to participate in the study for their time, interest, and cooperation. Drs. David Byrne, Thais Morata, and Christa Themann from the U. S. National Institute for Occupational Safety and Health, Dr. Alice Suter, and Mr. Barry Lempert provided helpful critiques of the manuscript. This work was sponsored by Grant 200-2015-M-63857, 200-2016-M-91922 from the National Institute for Occupational Safety and Health, USA; Grant N00014-17-1-2198 from Office of Naval Research, USA; Zhejiang province key research and development project (2015C03039), China; Zhejiang Provincial Program for the Cultivation of High-level Innovative Health Talents, China; Health Commission of Zhejiang Province (2019KY057), China. Grant No. 81771936 from National Natural Science Foundation, China; The Program of Occupational Health Risk Assessment of China NIOHP (Grant No. 13103109000160004); Preliminary Program on Occupational Health Standards of China NIOHP (No. 2021 10102); and Jiangsu Provincial Outstanding Medical Academic Leader and Innovation Team (CXTDA2017029) from the Province Health Commission of Jiangsu Province, China.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention.

The authors have no conflicts of interest to disclose.

Scatter plot showing noise-induced hearing loss (NIPTS_{346}) as a function of noise exposure level L_{Aeq,8h}. The kurtosis value ranges are K_{1}:3≤_{N}≤10; K_{2}: 10<_{N}≤50; K_{3}: _{N}>50.

Scatterplot showing noise-induced hearing loss (NIPTS_{346}) as a logistic function of noise exposure level L_{Aeq,8h}, and kurtosis category using 1-dB noise-level bins. The kurtosis value ranges are K_{1}:3≤_{N}≤10; K_{2}: 10<_{N}≤50; K_{3}: _{N}>50.

The estimated marginal mean of underestimated NIPTS_{346} by _{1}:3≤β_{N}≤10; K_{2}: 10<β_{N}≤50; K_{3}: β_{N}>50.

The estimated marginal mean of NIPTS_{346} at each kurtosis category. (A) Four kurtosis categories (_{1}, K_{2}, and K_{3}) used in the current study. Error bars: standard error. n: number of workers in the kurtosis category.

Selected values of the statistical distribution of hearing threshold levels in decibels of the control group according to frequency classified by age and sex.

Frequency (Hz) | Hearing Threshold (dB) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Age | |||||||||||||||

20 | 30 | 40 | 50 | 60 | |||||||||||

Percentages | |||||||||||||||

10 | 50 | 90 | 10 | 50 | 90 | 10 | 50 | 90 | 10 | 50 | 90 | 10 | 50 | 90 | |

| |||||||||||||||

500 | 3 | 6 | 10 | −1 | 6 | 12 | 0 | 6 | 13 | 1 | 8 | 15 | 3 | 11 | 17 |

1000 | 1 | 6 | 12 | −2 | 4 | 11 | −2 | 6 | 15 | 1 | 7 | 15 | 2 | 9 | 17 |

2000 | −2 | 4 | 11 | −2 | 4 | 12 | −2 | 6 | 15 | 0 | 8 | 18 | 2 | 13 | 24 |

3000 | 1 | 5 | 12 | −2 | 6 | 12 | −2 | 8 | 19 | 1 | 11 | 12 | 5 | 15 | 32 |

4000 | −3 | 6 | 12 | −3 | 5 | 12 | −3 | 8 | 18 | 4 | 13 | 26 | −2 | 15 | 41 |

6000 | 3 | 12 | 25 | 4 | 13 | 25 | 4 | 17 | 28 | 10 | 23 | 44 | 14 | 28 | 57 |

| |||||||||||||||

500 | 2 | 6 | 12 | −1 | 5 | 11 | 0 | 6 | 13 | 2 | 8 | 15 | 4 | 12 | 25 |

1000 | 0 | 6 | 12 | −2 | 4 | 11 | −2 | 5 | 13 | 0 | 7 | 17 | 4 | 12 | 27 |

2000 | −1 | 5 | 12 | −2 | 5 | 12 | −2 | 5 | 13 | 2 | 9 | 18 | 5 | 14 | 27 |

3000 | −2 | 5 | 12 | −2 | 5 | 13 | −2 | 6 | 16 | 1 | 10 | 18 | 5 | 19 | 34 |

4000 | −3 | 4 | 12 | −4 | 3 | 12 | −4 | 5 | 15 | −1 | 8 | 17 | 3 | 19 | 34 |

6000 | 7 | 13 | 22 | 3 | 13 | 21 | 3 | 15 | 27 | 8 | 19 | 33 | 11 | 31 | 52 |

Age is grouped in 10-yr intervals; that is, “30” represents ages 25–34 yrs, etc.

A breakdown of typical noise sources, gender, average age, noise exposure level, and exposure duration corresponding to workers in three noise kurtosis categories.

Kurtosis category | Typical noise sources | Participants | Noise exposure | |||
---|---|---|---|---|---|---|

Male (n) | Female (n) | Age (yrs) | Duration (yrs) | L_{Aeq} (dBA) | ||

3≤β_{N}≤10 | Spinning, weaving, pulping | 377 | 140 | 36.4±9.4 | 9.9±7.6 | 88.6±4.6 |

10<β_{N}≤50 | Punching, stamping, metalworking, heat treating, assembly, drilling | 1125 | 412 | 36.3±9.0 | 9.7±7.8 | 87.2±5.1 |

β_{N}>50 | Woodworking, nail gunning, assembly | 463 | 84 | 34.6±9.9 | 6.5±6.5 | 87.9±4.8 |

Note: Age, duration, and L_{Aeq}: mean±1 s.d.

Results of regression models using L_{Aeq,8h} and kurtosis-adjusted L_{Aeq,8h} to estimate NIPTS_{346}.

Coefficients B | λ (b_{2}/b_{1}) | B Lower 95% | B Upper 95% | |||
---|---|---|---|---|---|---|

_{346}=b_{0}+b_{1}L_{Aeq,8h} | N/A | R^{2}=0.75 | F=182.20 | |||

Intercept | −36.25 | −10.02 | <0.0001 | −43.30 | −29.02 | |

L_{Aeq} | 0.57 | 13.50 | <0.0001 | 0.49 | 0.66 | |

_{346}=b_{0}+b_{1}L_{Aeq,8h}+b_{2}log_{10}(β_{N}/3) | 6.50 | R^{2}=0.88 | F=225.81 | |||

Intercept | −38.64 | −15.41 | <0.0001 | −43.66 | −33.62 | |

L_{Aeq} | 0.56 | 19.31 | <0.0001 | 0.50 | 0.62 | |

log_{10}(β_{N}/3) | 3.64 | 8.22 | <0.0001 | 2.79 | 4.40 |

Note: The effects of age, sex, and duration were controlled in the models.

The estimated marginal means and standard errors of NIPTS_{346} difference between the actual measured NIPTS_{346} and the ISO 1999 predicted NIPTS_{346} for before-and-after-adjustment (BAA) and kurtosis by BAA groups.

Effect | Group | Estimated Mean | Standard Error | 95% Confidence Interval | |
---|---|---|---|---|---|

Lower Bound | Upper Bound | ||||

BAA | Un-adjusted (UA) | 6.77 | 0.26 | 6.27 | 7.27 |

Kurtosis-adjusted (KA) | 0.03 | 0.26 | −0.47 | 0.53 | |

Kurtosis × BAA^{+} | K_{1} × KA | 1.23 | 0.51 | 0.23 | 2.23 |

K_{1} × UA | 3.72 | 0.51 | 2.73 | 4.72 | |

K_{2} × KA | 0.08 | 0.29 | −0.50 | 0.66 | |

K_{2} × UA | 6.35 | 0.29 | 5.77 | 6.92 | |

K_{3} × KA | −0.96 | 0.49 | −1.93 | 0 | |

K_{3} × UA | 10.24 | 0.49 | 9.27 | 11.21 |

p value for NIPTS_{346} difference between KA and UA is <0.001.

p values for NIPTS_{346} difference between (K_{i} × KA) and (K_{i} × UA) pairs (

Note: BAA stands for “before-and-after-adjustment”.

The kurtosis distribution information of some work types in the manufacturing industry [n: the number of workers investigated in the kurtosis analysis in the corresponding work type; Correction value = 6.5*log_{10}(β/3), where the β is the mean of the kurtosis values of all the workers in that work type].

Industry | Work type | Kurtosis (β) | Correction (dB) | Un-adjusted L_{Aeq.8h} (dBA) | n |
---|---|---|---|---|---|

_{1}
_{N} | |||||

Textile mill | Spinning |
| 109 | ||

Weaving |
| 49 | |||

Knitter |
| 84 | |||

Mechanist |
| 14 | |||

Spandex | Winding |
| 52 | ||

Papermill | Defibrinating |
| 7 | ||

Pulping |
| 28 | |||

Rewinder |
| 11 | |||

_{2}
_{N} | |||||

Auto brake pad manufactory | Assemblyman |
| 57 | ||

Machining |
| 100 | |||

Auto Parts Manufactory | Thread rolling |
| 41 | ||

Depositing |
| 19 | |||

Tapping |
| 14 | |||

Numerical control machine |
| 6 | |||

Spot welding |
| 11 | |||

Lathe worker |
| 21 | |||

Drawing wire |
| 16 | |||

Packing |
| 33 | |||

Sorting |
| 38 | |||

Automotive Fasteners | Electroplating |
| 31 | ||

Cold heading |
| 60 | |||

Polishing |
| 10 | |||

Heat treatment |
| 31 | |||

Automatic lathe work |
| 16 | |||

Baby carriage manufactory | Punch |
| 42 | ||

Stamping |
| 85 | |||

Commercial vehicle body factory | Craneman |
| 25 | ||

Spot welding |
| 23 | |||

Electric welder |
| 79 | |||

Electrical appliance factory | Stretching |
| 12 | ||

Sanding |
| 9 | |||

Electrical appliance factory | Forming |
| 8 | ||

Assemblyman |
| 18 | |||

Final assembly plant for automobiles | Machining |
| 9 | ||

Assemblyman |
| 221 | |||

Hardware factory | Sand blast |
| 8 | ||

Stamping |
| 20 | |||

Benchwork |
| 11 | |||

Heavy truck engine factory | Casting |
| 10 | ||

Hydroelectric | Drilling |
| 15 | ||

Cold operating |
| 8 | |||

Modelling |
| 10 | |||

Iron and steel plant | Steel rolling |
| 41 | ||

Finishing |
| 21 | |||

Loading |
| 8 | |||

Machinery | Grinding |
| 13 | ||

Metal processing |
| 7 | |||

Machinery & electric | Assemblyman |
| 147 | ||

_{
N
}
| |||||

Electrical appliance | Wiring |
| 10 | ||

Furniture manufactory | Frame nailing |
| 51 | ||

Woodworking |
| 23 | |||

Assemblyman |
| 12 | |||

Nail gunning |
| 213 | |||

Grid structure | Assemblyman |
| 26 | ||

Kitchen & bath manufacturing | Assemblyman |
| 13 |