Development of a noise metric for assessment of exposure risk to complex noises

Noise Exposure and Evaluation at Tire- Changing Facilities

Thirty (30) personal noise-exposure samples were collected on 20 tire-changing and repair technicians in three tire-changing facilities to determine their personal noise exposures and to estimate the maximum number of tire changes that could be performed without exceeding occupational exposure limits. Of the 30 projected 8-h time-weighted average noise samples, none exceeded the Occupational Safety and Health Administration's Permissible Exposure Limit, 1 (3%) exceeded the Occupational Safety and Health Administration's Action Level, and 18 (60%) exceeded the American Conference for Governmental Industrial Hygienists Threshold limit Value of 85 dBA, indicating the need for a hearing loss prevention program. The average shift time for the technicians was 6 h and 42 min and the average number of tire changes was 18. Based on the projected 8-h noise exposure 95% upper confidence limits, the estimated maximum number of tires that could be changed without exceeding the Occupational Safety and Health Administration's noise action level was 32 tires, the permissible exposure limit greater than 40 tires, and the American Conference of Industrial Hygienists' Threshold Limit Value was less than 20 tires. In addition, area noise samples of tire-changing equipment were taken with a sound-level meter to identify the noise sources that contributed to the tire technicians' exposures. The air ratchet, tire-changing machine, and tire-bead seater were measured at noise levels >85 dBA, increasing the risk of noise-induced hearing loss to the technicians.

Comparison of two dose-response relationship of noise exposure evaluation results with high frequency hearing loss

Background:

Complex noise and its relation to hearing loss are difficult to measure and evaluate. In complex noise measurement, individual exposure results may not accurately represent lifetime noise exposure. Thus, the mean L_{Aeq,8 h} values of individuals in the same workgroup were also used to represent L_{Aeq,8 h} in our study. Our study aimed to explore whether the mean exposure levels of workers in the same workgroup represented real noise exposure better than individual exposure levels did.

Methods:

A cross-sectional study was conducted to establish a model for cumulative noise exposure (CNE) and hearing loss in 205 occupational noise-exposed workers who were recruited from two large automobile manufacturers in China. We used a personal noise dosimeter and a questionnaire to determine the workers’ occupational noise exposure levels and exposure times, respectively. A qualified audiologist used standardized audiometric procedures to assess hearing acuity after at least 16 h of noise avoidance.

Results:

We observed that 88.3% of workers were exposed to more than 85 dB(A) of occupational noise (mean: 89.3 ± 4.2 dB(A)). The personal CNE (CNEp) and workgroup CNE (CNEg) were 100.5 ± 4.7 dB(A) and 100.5 ± 2.9 dB(A), respectively. In the binary logistic regression analysis, we established a regression model with high-frequency hearing loss as the dependent variable and CNE as the independent variable. The Wald value was 5.014 with CNEp as the independent variable and 8.653 with CNEg as the independent variable. Furthermore, we found that the figure for CNEg was more similar to the stationary noise reference than CNEp was. The CNEg model was better than the CNEp model. In this circumstance, we can measure some subjects instead of the whole workgroup and save manpower.

Conclusions:

In a complex noise environment, the measurements of average noise exposure level of the workgroup can improve the accuracy and save manpower.

Kurtosis corrected sound pressure level as a noise metric for risk assessment of occupational noises

Current noise guidelines use an energy-based noise metric to predict the risk of hearing loss, and thus ignore the effect of temporal characteristics of the noise. The practice is widely considered to underestimate the risk of a complex noise environment, where impulsive noises are embedded in a steady-state noise. A basic form for noise metrics is designed by combining the equivalent sound pressure level (SPL) and a temporal correction term defined as a function of kurtosis of the noise. Several noise metrics are developed by varying this basic form and evaluated utilizing existing chinchilla noise exposure data. It is shown that the kurtosis correction term significantly improves the correlation of the noise metric with the measured hearing losses in chinchillas. The average SPL of the frequency components of the noise that define the hearing loss with a kurtosis correction term is identified as the best noise metric among tested. One of the investigated metrics, the kurtosis-corrected A-weighted SPL, is applied to a human exposure study data as a preview of applying the metrics to human guidelines. The possibility of applying the noise metrics to human guidelines is discussed.

Application of the kurtosis statistic to the evaluation of the risk of hearing loss in workers exposed to high-level complex noise

OBJECTIVE

Develop dose-response relations for two groups of industrial workers exposed to Gaussian or non-Gaussian (complex) types of continuous noises and to investigate what role, if any, the kurtosis statistic can play in the evaluation of industrial noise-induced hearing loss (NIHL).

DESIGN

Audiometric and noise exposure data were acquired on a population (N = 195) of screened workers from a textile manufacturing plant and a metal fabrication facility located in Henan province of China. Thirty-two of the subjects were exposed to non-Gaussian (non-G) noise and 163 were exposed to a Gaussian (G) continuous noise. Each subject was given a general physical and an otologic examination. Hearing threshold levels (0.5-8.0 kHz) were age adjusted (ISI-1999) and the prevalence of NIHL at 3, 4, or 6 kHz was determined. The kurtosis metric, which is sensitive to the peak and temporal characteristics of a noise, was introduced into the calculation of the cumulative noise exposure metric. Using the prevalence of hearing loss and the cumulative noise exposure metric, a dose-response relation for the G and non-G noise-exposed groups was constructed.

RESULTS

An analysis of the noise environments in the two plants showed that the noise exposures in the textile plant were of a Gaussian type with an Leq(A)8hr that varied from 96 to 105 dB whereas the exposures in the metal fabrication facility with an Leq(A)8hr = 95 dB were of a non-G type containing high levels (up to 125 dB peak SPL) of impact noise. The kurtosis statistic was used to quantify the deviation of the non-G noise environment from the Gaussian. The dose-response relation for the non-G noise-exposed subjects showed a higher prevalence of hearing loss for a comparable cumulative noise exposure than did the G noise-exposed subjects. By introducing the kurtosis variable into the temporal component of the cumulative noise exposure calculation, the two dose-response curves could be made to overlap, essentially yielding an equivalent noise-induced effect for the two study groups.

CONCLUSIONS

For the same exposure level, the prevalence of NIHL is greater in workers exposed to non-G noise environments than for workers exposed to G noise. The kurtosis metric may be a reasonable candidate for use in modifying exposure level calculations that are used to estimate the risk of NIHL from any type of noise exposure environment. However, studies involving a large number of workers with well-documented exposures are needed before a relation between a metric such as the kurtosis and the risk of hearing loss can be refined.