FINAL APPROVAL OF THESIS Master of Science in Occupational Health
Noise Exposure Assessment of an Ohio Farm Family: A Pilot Project
Submitted by
Melisa Kay Witherspoon
In partial fulfillment of the requirements for the degree of Master of Science in Occupational Health
Date of Defense:
March 17, 2006
Major Advisor Sheryl Milz, Ph.D., C.I.H.
Academic Advisory Committee Michael S. Bisesi, Ph.D. Farhang Akbar, Ph.D.
Dean, College of Graduate Studies Keith K. Schlender, Ph.D. NOISE EXPOSURE ASSESSMENT OF AN
OHIO FARM FAMILY: A PILOT PROJECT
Melisa Kay Witherspoon
Medical University of Ohio
2006 DEDICATION
I would like to dedicate this work to the Miller family, whose support has made this work possible; to Rachel Miller who provided invaluable assistance during the collection of data; to my major advisor, Dr. Sheryl Milz for her understanding, guidance, and expertise; and to Dr. Michael Bisesi, who made my enrollment in the program possible.
ii ACKNOWLEDGMENTS
I would like to acknowledge the following for their contributions: Dr. Sheryl
Milz, major advisor; Dr. Michael Bisesi and Dr. Farhang Akbar, committee members; Mary Alderman; April Ames for data collection assistance, reviews and support. Finally, I would like to thank CDC/NIOSH for providing grant funding for tuition.
iii TABLE OF CONTENTS
DEDICATION...... ii
ACKNOWLEDGMENTS ...... iii
TABLE OF CONTENTS...... iv
INTRODUCTION ...... 1
LITERATURE REVIEW ...... 3
MATERIALS ...... 33
METHODS...... 38
RESULTS ...... 43
DISCUSSION...... 65
CONCLUSIONS...... 71
REFERENCES ...... 72
APPENDIX A. DEFINITIONS...... 87
APPENDIX B: FORMS ...... 93
APPENDIX C. TABLES ...... 102
ABSTRACT...... 110
iv INTRODUCTION
Overview
Farming has long been recognized as a hazardous occupation, but has been excluded from many of the health and safety regulations applied to other industries. The exclusion is in part due to the lobbying efforts of national, state, and county agricultural groups (McCullagh, 2002). The exclusion of agriculture from regulations does not imply that fewer hazards are present; in fact, a large number of hazards are present in the agricultural workplace. Included among these is elevated noise levels and related health effects, specifically noise induced hearing loss (NIHL). An estimated 323,000 American agricultural workers are exposed to noise levels above an 8-hour time-weighted average of
85 decibels measured on the A scaled [85 dB(A) TWA (8h)] (Berger et al., 2000).
Statement of Problem
Exposure assessments have been difficult for agricultural workers and farm operations. To date many studies have been performed to measure noise exposure through both subjective measurements and collection of data with direct reading instruments. In a review of literature performed by McCullagh
(2002) only one study was identified which conducted integrated monitoring
(Dennis and May 1995).
1 Farming presents a special circumstance for occupational health monitoring for three reasons. First, the work involves a variety of tasks and is carried out at many workplaces. Second, the farm workforce is unique due to its inclusion of both younger and older workers. Finally, farming is an industry where the workers typically live at the workplace. This provides the potential for exposure not only to the worker, but also to all family members.
Purpose and Significance
This pilot project involved exposure monitoring of agricultural operations in a way not known to have been conducted before. Monitoring was conducted for both farmers and family members using dosimeters for an extended period to determine noise exposure in the agricultural setting.
Objectives
The goals of this pilot project were to:
Characterize the noise exposure of the farm worker and residents while
performing farm work and during other occupational or non-occupational
activities;
Assess the compliance of participants with the monitoring strategy;
Determine the applicability and user preference of three time activity
diaries; and,
Assess the quality of self-reported activities.
2 LITERATURE REVIEW
Overview
Farming has been recognized as a hazardous occupation for centuries.
Schenker (1996) prepared a history of agricultural occupational health and identified concerns with regard to farm workers’ (threshers) health as early as
1555. Later, in the 1700s, Ramazzini recognized the hazards of agriculture.
However, a lapse in focus on agricultural health has occurred: “… the field of occupational health has been driven, since the late 18th century, by the urban engines and industries of the industrial revolution, and this focus on heavy industry has continued to the present time”(Schenker, 1996). Indeed, Schenker reports that during the early 1800s farming was viewed as “a healthy alternative to the filth and pollution of the industrialized cities, and to the hazards of long work hours and terrible working conditions in the factories.” While this may have been true in the 1800s, since then, regulations have altered the industrial workplace and created a safer environment, but these regulations are absent or less strict in agriculture.
Even with the lack of regulatory intervention, farming has changed significantly. For instance, the introduction of the internal combustion engine and mechanization of farming created major changes in the way farming is performed and increased the exposure of farmers to noise (Matthews, 1968). The population involved in farming also has changed. The percentage of the United
3 States population involved in agriculture has decreased over the years and is now less than 2% of the population. In 2001, there were 1,559,800 self-employed farmers, 490,000 unpaid farm workers, and 873,300 hired workers on farms in the United States (National Agricultural Statistics Service, 2005b). Farm sizes and types also have undergone transformation in light of the changes in agriculture. Most farms (1,910,000) in the United States are family or individually owned, but there are less farms now than historically have been present (U.S.
Census Bureau, 2006). The number of U.S. farms has fallen to 2.11 million in
2004 (NASS, 2005b).
These national changes also are reflected in local farming statistics. In
Ohio, there were 77,797 farms in 2002, a decrease of 1% since 1997. The average farm consisted of 187 acres, and 78.34% of Ohio’s farm land was used for cropland. These farms were operated by 43,488 farmers whose primary occupation was farming and 34,309 operators who ran the farm as a secondary occupation. The average age of farm operators in Ohio was 58.3 years in 2002
(NASS, 2002). In 1999, the national average age of farm operators was 54.3 and the average tenure at the present farm location was 20.1 years (NASS, 1999).
Agriculture activities do not only expose farm operators to potential hazards, but also exposes their families and hired workers to these hazards. The risks to children and elderly persons, as well as workers between the ages of 18 and 65, years makes agriculture unique with regard to occupational health (from
Von Essen and McCurdy, 1998).
4 Farmer Exposure Assessment
Agriculture is an occupation which demands that the worker perform a large number of tasks in a variety of locations and during all types of weather events. This variety in work conditions and tasks makes exposure assessments of farmers difficult. Therefore, assessments have often been carried out by questionnaire or telephone interview (Nieuwenhuijsen et al., 1996). Kerr et al.
(2003) reported that only three studies document noise measurement of farm equipment at several agricultural work sites (Holt et al., 1993, Dennis and May,
1995, Pessina and Guerretti, 2000).
Cordes and Foster Rea (1991a) identified three characteristics that make farming differ from other occupations: worker traits and behavior, work setting, and organizational structure. Farmers generally do not work in an area with a group of people, they live where they work and work in a variety of different settings, and typically do not have a hierarchical structure or administrative support system. McBride et al. (2003) reported that 75% of farm workers surveyed performed at least three different tasks each day. These traits compound the problems of conducting farmer exposure assessments.
Farmers’ exposure durations and work schedules are not necessarily similar to those of other occupations. Their work is seasonal and may continue until tasks are completed rather than a specific time of day. Hwang et al. (2001) surveyed 1,622 farmers and found that 54% of those surveyed reported working, 5 on average, more than 8 h per day on the farm. Among those surveyed, the median lifetime exposure to farm equipment was 13,620 h and to farm noise above 85 dB(A), 8 h daily, was 4.7 yr. During certain seasons (i.e.,, sowing or harvesting), Weston (1963) found that drivers may operate tractors for 8 to 12 h a day for several weeks at a time. According to the National Agricultural Statistics
Service (NASS) (2005a), farm workers worked an average of 42 h per week during the week of October 9-15, 2005. Self-reported hours worked on the farm ranged from 1 to 80 h per week in another study (Carpenter et al., 2002).
It is not only the long hours and variety of tasks that make agricultural exposure assessment difficult, but also the potential for synergistic effects and uniqueness of each operation. Other potential hazards associated with farming include organic dust, ultraviolet light, herbicides and pesticides, stress, musculoskeletal trauma, vibration, heat and cold, zoonoses, and groundwater contamination from fuel supply or other chemical releases (Emanuel, 1990;
Schenker, 1996; Cordes and Foster Rea, 1988; McDonald, 2000). Due to the variety of equipment used at each farm and differing maintenance issues, each farm presents a set of unique conditions. Solecki (1995) confirmed this by demonstrating that the actual dose to farmers is dependent upon the type of tractor used, work load, and working time.
Comparison of farmer exposure levels to occupational compliance limits is also more difficult due to the nature of farming. Noise hazard analysis for agricultural activities may be better suited to using a weekly equivalent noise
6 level, rather than daily, due to the variety of operations (Pessina and Guerretti,
2000). Farmers may be exposed to continuous noise throughout the workday during their various tasks, except for meal breaks. These long work days were found to contribute to Occupational Safety and Health Administration (OSHA) action level exceedances (Dennis and May, 1995).
Solecki (2000) also identified the need to adequately address the duration of exposure when evaluating noise and farmers in a study of 30 farmers conducted over 1 yr. The use of mean total monthly time of exposure showed that the highest periods of exposure for Polish farmers were during harvesting, when field and transport tasks are performed more frequently. The results confirmed that the individual time values had a lot of variability and work requirements changed with seasons (i.e., harvesting) both with regard to hours worked per day and days worked per month. Therefore, 1 mo was used as a work period in the study.
Different types of farms (i.e., cash grain or livestock) expose workers to different hazards. Field crop farm operators reported spending 60% of their time working in the fields and spending 20% (median) of their time performing noisy jobs. Field crop operators spent the most time working in noisy areas and the most time operating tractors (18% median) when compared to fruit and nuts, livestock, nursery, vegetable, or mixed farm operations in California
(Nieuwenhuijsen et al., 1996). The type of tractor used was also dependent upon the type of farm. Tractors used by field crop operators were reportedly more
7 likely to have a cabin than tractors used at other farms (Nieuwenhuijsen et al.,
1996).
Other potential problems encountered during farmer exposure assessments have included securing volunteers and reliability of self-reported data. Depczynski et al. (2005) had to use a snowball sampling method for farm machinery measurements in Australia. Although advertisements in local newspapers were used to attempt to secure volunteers, no respondents were yielded from this method. Their study also employed measuring exposure by activity or equipment, due to the implausibility of a “typical day’s” monitoring including all tasks performed by the farmers. While surveying is a frequently used method in farmer studies, Kerr et al. (2003) found that self-reported hearing loss was not a good indicator of actual hearing loss.
Agricultural Regulations
In the United States, the farming workforce is not regulated in the same manner as other industries. Often agriculture is excluded from regulatory standards. This exclusion is in part due to the lobbying efforts of national, state, and county agricultural groups (McCullagh, 2002). The sheer size of a farm’s work force may also provide relief from regulatory oversight. Approximately 97% of US farms employee less than 11 employees, and are therefore exempt from
OSHAs injury and illness log reporting requirements, and are not required to provide personal protective equipment (PPE) for use when handling pesticides
(Carpenter et al., 2002). 8 Because farms and farmers are not regulated, this may generate a different attitude among farm operators than that of managers in other industries.
A survey of 2,483 farms located in six Midwestern states found 42% of the farmers felt responsible only for themselves with regard to safety responsibility
(Carpenter et al., 2002). Farmers also may have a different attitude regarding use of PPE, possibly because of the lack of regulatory intervention. The use of
PPE on farms was found to be “woefully inadequate” by Carpenter et al. (2002).
Training on the proper use of PPE was also low among the farms surveyed. Only
27% of respondents had received any formal training in PPE use. It is notable that 90% of the farms included in the survey employed less than three laborers and were exempt from OSHA regulations.
In 1991, the Texas Agriculture Hazard Communication law was the only regulation requiring health and safety training for farm workers (Cordes and
Foster Rea, 1991b). The law was passed in 1987, became effective in 1988, and was only applicable to farms using certain chemicals. The law also required the
Texas Department of Agriculture and the Texas Cooperative Extension to provide pesticide safety training for farm workers in some counties (Texas
Department of Agriculture, 2005).
Noise Regulations
The first regulation enacted by the United States Federal government to deal with noise was promulgated in 1969 by the Department of Labor. After
Congress enacted the Occupational Safety and Health Act in 1970, the National 9 Institute of Occupational Safety and Health (NIOSH) was given power to research and develop noise criteria. In 1971, the OSHA noise regulation became applicable to all industries engaging in interstate commerce. The regulation was amended in 1981, but still does not cover most agricultural operations (Berger,
2000). Although agricultural operations are not included in the OSHA rule, noise exposure data are typically compared to available standards, including OSHA and NIOSH, since the standards were developed to be protective of human health.
The OSHA regulations provide a permissible exposure level (PEL) for workers. OSHA’s Occupational Noise Exposure rule (29CFR 1910.95) requires that employees be protected against the effects of noise exposure above an 8 h time-weighted average (TWA) of 90 decibels (dB) with an exchange rate of 5, measured on the A scale of a standard sound level meter at slow response. This corresponds to a PEL of 90 dB(A) 8h-TWA. A hearing conservation program is required by OSHA for employees exposed to noise at or above 85 dB(A) 8h-
TWA. Employers also must provide hearing protectors to all workers exposed to
8h-TWA noise levels of 85 dB(A) or above. At that level, the use of hearing protection is optional. However, if the noise exposure is greater than 90 dB 8h-
TWA, employees must wear hearing protection. OSHA also has set standards for the maximum (max) root mean square sound pressure (115 dB (A)) and the peak
(Lpeak (max)) permissible sound pressure level (140 dB) (OSHA, 2000).
10 As previously stated, NIOSH has been charged with developing noise criteria. These criteria are known as recommended exposure limits (RELs). In
1998, NIOSH reevaluated their criteria for noise exposure levels, which were originally developed in 1972. The 1998 recommendations state:
“The new risk assessment reaffirms support for the 85-dB(A) REL.
With a 40-year lifetime exposure at the 85-dB(A) REL, the excess
risk of developing occupational noise induced hearing loss (NIHL)
is 8%—considerably lower than the 25% excess risk at the 90-
dB(A) permissible exposure limit (PEL) currently enforced by the
Occupational Safety and Health Administration (OSHA) and the
Mine Safety and Health Administration (MSHA).”
“NIOSH previously recommended an exchange rate of 5 dB for the
calculation of time-weighted average (TWA) exposures to noise.
However, NIOSH now recommends a 3-dB exchange rate, which is
more firmly supported by scientific evidence.” (NIOSH, 1998)
Although not a regulatory agency, the American Conference of
Governmental Industrial Hygienists (ACGIH) has established threshold limit values (TLVs) for noise. The recommended TLVs for noise are the same as the
NIOSH RELs [85 dB(A) 8h-TWA with a 3 dB exchange rate]. The ACGIH TLVs are “conditions under which it is believed that nearly all workers may be
11 repeatedly exposed without adverse effect on their ability to hear and understand speech” (ACGIH, 2005).While the TLVs do not purport to protect all workers from the effects of noise exposure, they should protect the median population from NIHL greater than 2 dB over 40 yr of occupational exposure (ACGIH, 2005).
Rural Noise
The countryside has historically been viewed as a quiet place to live.
However, this too may be changing with the changing face of agriculture. Lines, et al. (1994) performed a survey of United Kingdom residents of both rural and urban settings. The purpose of their survey was to identify noise sources or activities that were considered annoying by the respondents. While they found that domestic, road vehicle, and aircraft sounds were the most common sources of offensive noise, they reported that agricultural noise resulted in 10 complaints per 100 survey respondents. Of the agricultural noise, the most common complaint source was bird scarers. Farm machinery and transport operations combined generated an approximately equal number of complaints as bird scarers. Agricultural noise was cited as being offensive due to the fact that it occurred at night and interfered with sleep, more so than any other offensive noise reported.
Noise Induced Hearing Loss
It is well understood that noise can produce hearing loss. An estimated 28 million people in the United States are deaf or hard of hearing (National Institute
12 on Deafness and other Communication Disorders [NIDCD], 1996) and NIHL is the most common occupational disease (NIOSH, 1999). The NIHL also is the second most frequently self-reported occupational illness or injury (NIOSH,
1999). This has prompted hearing to be one of the objectives for improving health cited by Healthy People 2010 (United States Department of Health and
Human Services [USDHHS], 2000).
The effects of noise exposure are not limited to hearing loss. Sprince et al.
(2003) reported finding a significant association between fall-related farm injuries and self-reported difficulty hearing normal conversation. Other nonauditory effects of noise include sleep disruption, decreased productivity, increased heart rate, increased blood pressure, dilation of the pupil and eye, startle reaction, and other cardiovascular and psychophysiological system changes (Berger et al.,
2000).
Hearing loss affects quality of life and can contribute to other health and safety issues such as increased fatigue and loss of concentration after short periods of exposure (Hutchings and Vasey, 1964). It may also become a social handicap due to an inability to effectively communicate and to misinterpret speech. Persons with hearing loss could also potentially suffer a loss in earning power (Berger et al., 2000).
A number of prior studies have reported farmers had higher rates of NIHL than other occupations (Lierle and Reger, 1958, Thelin et al., 1983, Broste et al.,
1989) and that an increase in years of farming resulted in increased rates of
13 NIHL (Plakke and Dare, 1992). By the age of 30, approximately 25% of male farmers have a communication handicap due to hearing loss. By age 50, this value rises to 50% (Karlovich et al., 1988). The risk of hearing loss may be due to a number of factors including, but not limited to, exposure duration, use or absence of hearing protective devices, and exposure to high levels of noise.
Of the farmers surveyed by Carpenter et al. (2002), 29% reported definite hearing loss, while 15% reported some loss of hearing. However, only 4% of those surveyed wore hearing aids. Self-reported hearing loss occurred in 22% of farmers interviewed as part of a study performed by Hwang et al., (2001). The study used part of the data from the New York State Farm Family Health and
Hazard Surveillance Project (NYS FFHHS) and included interviews of 1,622 New
York farmers. Of farmers surveyed by Gomez et al. (2001), 47% suffered from
NIHL.
When compared to office workers and construction workers, farmers are at a greater risk of NIHL, especially at 2,000 and 4,000 Hz (Thelin et al., 1983;
Kerr et al., 2003). Marvel et al. (1991) compared New York dairy farmers and rural non-farmers and reported that hearing loss determined through audiometric testing was higher in farmers (65%) than non-farmers (37%) in the higher frequencies. Hearing loss at high frequencies among farmers also was reported by May et al. (1990), and Plakke and Dare (1992).
In contrast, a comparison between farmers and non-farmers who visited the same Agri-Fair reported no significant difference (Thelin et al., 1983). Demers
14 and Rosenstock (1991) found that workers compensation claims were higher for agricultural workers in all categories except hearing loss. Brackbill, et al. (1994) found a difference in the prevalence of hearing loss between farmers and other
US workers only in the over 65 yr age range after age adjustment was performed. Finally, a retrospective cohort study conducted in the Marshfield
Epidemiologic Study Area reported that hearing loss was less prevalent among farm residents than non-farm residents (Greenlee, 2005).
Kerr et al. (2003) performed a study of 150 farmers comparing self- reported hearing loss and measured hearing loss. They found that 67% of farmers exhibited hearing loss as confirmed by audiometric testing (hearing loss was defined as loss greater than 25 dB in either ear). Similar to May et al.’s findings (1990), the left ear was more susceptible to hearing loss, especially at
2,000 Hz. The prevalence of hearing loss in the left ear was hypothesized to be the result of turning the left ear toward the engine on tractors when watching implements pulled by the tractors and driving trucks with the drivers’ side window down (Kerr et al., 2003).
Hearing loss also may be affected by the sex, age, and residency status.
Hwang et al. (2001) found that males were at a significantly higher risk for self- reported hearing loss than females working on farms, as were owner/operators
(35%) as compared to residents (17%) and workers (15%). With regard to type of farm, they found that livestock farms had the highest rate of reported hearing loss
(34%) and dairy farms had the lowest rate (18%). The risk of hearing loss in
15 younger farmers was demonstrated by Thelin et al. (1983) who found that 16.8% of farmers between the ages of 25 and 64 had high frequency hearing loss.
Plakke and Dare (1992) performed a study comparing audiometric tests results of 30 Iowa farmers and 30 controls. They found that the farmers’ hearing was significantly worse than the white collar worker controls. Statistically significant differences were found in the 35-44 aged group and the 45-54 aged group, but not in the 25-34 aged group. Farmers’ hearing, compared to controls, was significantly poorer with age. Audiometric testing in Iowa crop and livestock farmers and white collar workers reported that 56% of the farmers had some hearing loss compared to 23% of the non-farmers (McMahon and Urbain, 1988).
The problem of NIHL and farming is not restricted to the United States.
Kumar et al. (2005) performed a study of tractor driving and hearing loss on farmers in India and found that of 50 tractor drivers, four self-reported hearing problems. However, audiograms demonstrated that 24 of the farmers had high frequency hearing loss. Among 50 farmers who did not drive tractors, only 14 showed high frequency hearing loss based on audiogram analysis. The left ear was more frequently affected than the right ear.
In New Zealand, a noise exposure survey of 586 farm workers found median Leq exposure between 84.8 and 86.8 dB(A) (McBride et al., 2003).
However, 10 of the 60 measurements obtained exceeded the 90 dB(A) Leq permissible noise exposure level for New Zealand. Important risk factors for hearing loss were age, driving tractors without cabs, and working with metal.
16 They concluded that the majority of farmers were at a moderate risk for hearing loss and a significant minority was at high risk.
A study performed in Japan by Miyakita and Ueda (1997) evaluated the age adjusted number of workers in Japanese industries with NIHL. They reported that 8.2% of workers in agriculture and forestry had more than 40 dB hearing loss at 4 kHz. Solecki (2002, 2003, 2005) also has performed a number of studies in
Poland evaluating the potential for hearing loss.
Solecki (2003) determined that the percentage of farmers suffering from hearing impairment greater than 30 dB due only to noise was 9.4% in a study of
Polish private farmers. The mean annual exposure level of the farmers was 89.1 dB. In a separate study a statistically significant difference in hearing loss was identified between private farmers and manual and office workers not occupationally exposed to noise greater than 70 dB (Solecki, 2002). More recently in a study of 16 family farms, the mean level of exposure to noise for the whole year for farmers was calculated to be 90.5 dB(A) (Solecki, 2005). The study was conducted at dairy cattle breeding farms with tractors, and involved activity diaries maintained during the entire year of 2003 and dosimetric measurements of noise emitted by machinery and equipment during selected tasks.
17 Hearing Protection Use Among Farmers
As noted, hearing protection use among farmers is low. Karlovich et al.
(1988) studied farmers over a period of 5 yr. They found that a small percentage of those surveyed wore hearing protective devices, 18 to 25% of men and 9 to
15% of women. Hearing loss, when adjusted for age and sex, also was greater in the farmers than hearing loss compared to control values from literature.
Most of the farmer operators interviewed by Nieuwenhuijsen et al. (1996) who reported working in a noisy environment did not use any ear protection
(57.8%). The number of field crop operators who worked in a noisy environment and never wore ear protection was slightly lower, 47.5%. The remaining field crop operators working in noisy conditions were divided among those who wore ear protection less than half of the time (13.9%) or more than half the time
(38.7%). Generally, farm operators who were exposed to noise for a larger percent of their time were more likely to wear hearing protection (Nieuwenhuijsen et al., 1996).
More recently, Schenker et al., (2002) performed a telephone interview survey of California farmers and found that of the farmer operators responding, the average time spent around noisy machinery was 10% of their day.
Approximately 72% of respondents reported being in a noisy environment at least 5% of their day. Yet of those, hearing protection was only worn in the noisy conditions at least half of the time by 22.8% of the respondents. Hearing protection was never or rarely worn by 56.3% of those surveyed. The survey
18 responses may not be indicative of the true level of use of hearing protection.
McBride et al. (2003) found that reported hearing protection use collected by survey was higher than the actual amount of hearing protection observed.
Noise Exposure to Farmers
Self-reporting by farmers has shown that farmers are exposed to high noise levels (Karlovich et al., 1988; Broste et al., 1989; Marvel et al., 1991;
Knobloch and Broste, 1998; Wilkins et al., 1998; Hwang et al., 2001). However, only 61% of farmers surveyed by Carpenter et al. (2002) reported working in noisy situations where they had to shout to be heard. Self-reported noisy situations did not correlate with other activities that produce excessive noise, such as chain saws. Chain saw use was found to be commonplace, 82% of surveyed farmers used them. Other potentially noisy work included welding
(70%) and metal work such as cutting, grinding, and/or pounding (80%).
Carpenter et al.’s (2002) survey of Midwestern farmers also evaluated frequency of exposure and use of protective equipment. The frequency of working in noisy situations varied from less than once per month (24%), once per month (22%), once per week (33%), to once per day (21%). However, use of
PPE was low. Earplugs were reportedly never or rarely worn by 72% and earmuffs were rarely or never worn by 78% of the respondents.
A survey of cash grain farmers in Ohio reported that of 1,700 respondents,
61 million cumulative lifetime hours of noise exposure were reported (Wilkins et
19 al., 1998). The average years of operation was 31 with more than 115 days per year, and nearly 20,000 lifetime hours. Of the noisy activities performed by the farmers, 80% reported using chain saws, nearly all reported operating a cabless tractor, and 70% had operated a combine. The exposure that accounted for the most hours was operation of cabless tractors (54%) with total tractor operation consisting of 70% of cumulative lifetime hours. Even with this extent of exposure,
65.6% of the respondents never wore hearing protection, and only 9.8% reported using hearing protection more than half of the time.
A combination of exposure time and equipment used must be considered when studying potential causes of hearing loss in farmers. Jones and Oser
(1968) performed sound level measurements on 20 special farm equipment items and found that 13 generated greater than 85 dB for three octave bands
(0.5, 1, and 2 kHz). More recently, Depczynski et al. (2005) evaluated 56 types of machinery used in farming during 298 tasks or activities. In addition to collecting data at the level of the operators’ ear, they also collected data at locations where bystanders would likely be present. Common noise hazards identified by the study included firearms, tractors without cabs, workshop tools, small motors such as chainsaws and pumps, manual handling of pigs, shearing sheds, older cabbed tractors, and heavy machinery such as harvesters. Chainsaws produced the highest average noise levels [106 dB(A)] and pumps produced the second highest at 100 dB(A).
20 Dennis and May (1995) demonstrated the importance of exposure time when evaluating noise exposure for farmers. Data were collected from dairy farmers with personal dosimeters. The dosimeters were worn by the farmers for at least one full work day and a log of activities was maintained by the farmer.
The length of exposure time was 13.03 h (mean +/- 2.23 h). The median noise level was 80 dB(A). However, due to the length of exposure time the time weighted average was higher than the actual average (83 dB(A) versus 86 dB(A)
(Dennis and May, 1995).
Animals also can be a source of a noisy environment for farmers. Pig houses have been shown to generate noise levels between 95 and 104 dB(A) during feeding (Kristensen and Gimsing, 1988). Many farms are diverse and while the main farm production may be cash grain crops, some animals may be housed at the farm.
Children and Farming
Children can be exposed to the hazards of farming through a variety of circumstances including living on the farm, visiting the farm, or working on a farm. The NASS (2000) reported that there were 2,952,600 farm workers and
2,062,300 of those were family workers (self-employed or unpaid). Cordes and
Foster Rea (1991a) reported that an estimated 25% of the agricultural work force was children less than 16 yr of age.
An estimated 1,264,000youth lived on U.S. farm operations in 1998
(Myers and Hendricks, 2001). Add to that the 666,500 youth who do not live on 21 farms but were hired to work on a farm in 1998 (Myers and Hendricks, 2001), and it is easy to see that over 2 million youth less than 20 yr of age may be exposed to farm safety hazards each year (Adekoya and Pratt, 2001; Perry,
2003). Another group of potentially exposed children includes those who visit farms. As bystanders, these children have been identified as an at risk group
(Lee et al., 2002).
Regardless of the circumstance, these children may be exposed to the same or similar hazards as their adult counterparts through either farm work or through bystander activities. In response to the risks of children in agriculture, the
NIOSH Childhood Agricultural Injury Prevention Initiative works to develop a comprehensive health and safety program to prevent injuries from work and non work-related exposures to children in agriculture. Machinery is one of the greatest risks to youth on American farms and accounts for 36% of fatalities to youth less than 20 yr. Half of the farm machinery related deaths are caused by tractors (Rivara, 1985). Children less than 5 yr of age are not exempt from the hazards; 30% of farm machinery-related deaths were children less than 5 yr of age (Adekoya and Pratt, 2001).
Due to the potential risks to children, the National Childrens Center for
Rural and Agricultural Health (NCCRAH) has set as a goal the development of exposure limits specific for children working in agriculture. NIOSH also has responded to the risk and consequences associated with children working in agriculture and had funded over 30 research studies as of 2001 (Lee et al.,
22 2002). Still, a review of the MEDLINE database from 1990 to 2002 performed by
Perry (2003) did not identify any epidemiologic data on occupational skin disease or noise-induced hearing loss for farm youth.
While other industries restrict the use of child labor, a double standard applies to agricultural child labor, possibly because of economic requirements
(Rivara, 1985). Cordes and Foster Rea (1991b) identified the need for child labor laws to limit children from being exposed to dangerous work. Children under 18 are not permitted to perform hazardous work in other occupations, but at the age of 16 and above children are allowed to perform hazardous agricultural work. The
Fair Labor Standards Act (FLSA) exempts children under 12 from working on other farms, but not from work on family owned farms (United States Department of Labor [USDOL], 1938).
Agricultural child labor regulations are less restrictive than those for other occupations with regard to hours of work permitted. In accordance with the
FLSA, children over the age of 12 are allowed to work unlimited hours outside of school in agriculture. This differs from the restrictions placed on non-agricultural work, which limits working hours to 18 during school weeks. In a non-agricultural setting children under the age of 14 are forbidden from working. This is not true for the agricultural setting, where children as young as 10 and 11 can work limited hours performing non-hazardous jobs. None of these restrictions apply to children working for their guardians on family farms.
23 Regardless of regulatory standards, enforcement is difficult in the rural setting. Even though children less than 15 are not legally allowed to operate tractors, recent studies have shown tractor operation occurs at younger ages. A survey of Kentucky youth confirmed the use of tractors by children. Browning et al. (2001) identified the mean age of beginning solo tractor operations was 11.4 yr for boys and 12 yr for girls. The age of solo operation ranged from 5 to 17 yr.
Even if children are not operating tractors, they may be exposed as a passenger.
More than half (57.8%) of 10-15 yr olds and 48.9% of 16-18 yr olds were passengers on tractors. Males operated tractors more often (average 30 d per year) than females (average 5 d per year).
Clearly, children are around farm machinery. Although the injuries and fatalities reported may not be a direct result of children operating equipment, children do operate farm machinery. A survey of Wisconsin youth working in agriculture revealed that 84% of the students involved in farm work activities operated a tractor. Of the tractors operated by the youth, only 5 of 590 had cabs.
The study also found that over half of the students began operating tractors before the age of 10 and only 2% consistently used hearing protection devices with 74% reporting never using hearing protection (Knobloch and Broste, 1998).
Marlenga et al. (2001) surveyed parents or guardians of 1,138 children ages 7 through 16. They found that the most common farm activity the children were involved with was animal care, with nearly two-thirds of the children performing animal care chores. Tractor operation was less common, 12.2% of
24 the children operated tractors with implements and 18.9% performed other tractor operations. The percentages of children operating tractors with implements climbed steadily with age. For instance, of males aged 7-9 only 5.3% operated tractors with implements, compared to 60.5% of males aged 16.
Females were less likely to operate tractors; although by age 16, 26.5% of the females were operating tractors with implements.
A survey of Washington youth (Bonauto et al., 2003) revealed that 50.1% of children worked and of those who worked, 64% worked in agriculture. Work started at an early age for many of the children with 13.9% starting agricultural work between the ages of 1 and 8, 37.2% starting between 9 and 12, and 48.9% starting between 13 and 19. Children who lived on farms were more likely to start work at a younger age (1-8 yr). Most of the children residing on farms worked less than 20 h per wk (86%), while 47.5% of the non-resident teenagers worked more than 21 h per wk. The results of Bonauto et al.’s study revealed that 23.1% worked with machinery and 28.6% worked with animals. The majority of the respondents worked in harvesting/picking of vegetables (66.8%).
Of 445 students interviewed who lived and worked on farms, 94% reported using a tractor. Tractor use was 61% for those who lived on a farm but had minimal participation in chores and farming operations (Broste et al., 1989).
A study by Marlenga et al. (2004) found that over half of the tractors operated by children in the US and Canada were not equipped with roll over protection.
25 The effect of childhood exposure to noisy operations on farms has been documented. Broste et al. (1989) performed a study of hearing loss among high school students in Wisconsin and concluded that teenage children who work on farms had an increased prevalence of mild hearing loss. Evidence of hearing loss was present in 71% of those students who worked and lived on farms, and in
74% of those who did not live on farms but worked on farms. Gregg (1972) reported that 15-20% of new students entering the University of South Dakota had some hearing loss and hypothesized that the rural setting of the surrounding area and the young age at which children may being operating farm equipment may have contributed to the prevalence of hearing loss. Varchol et al. (1998) also found that children living on farms were at an increased risk of hearing loss in the higher frequencies.
In 1992, Wolfenden et al. identified another potential danger to children’s hearing. They discovered that while farmers reported being safety cautious about high risk items such as firearms, electric tools, and chemicals, they were less cautious about high noise levels. This led them to conclude that children’s exposure to high noise levels and lack of protective equipment should make hearing a target issue for farm safety groups.
Tractors and Noise
Tractors are an integral part of farming work equipment and are used for a variety of tasks including hauling, moving equipment, pulling implements, and powering other equipment through the power take-off (PTO) shaft or belt. The 26 invention of the tractor led to the mechanization of farming and changed the work characteristics of farming as an occupation. In the 1870s, the first tractors produced operated on steam power. Gasoline powered tractors were first sold in
1902. The peak year of tractor manufacturing was 1951, with 564,000 units made. Since that time, the design of tractors has changed considerably to add more comforts for the operator (White, 2001).
As early as 1937, studies were performed which suggested tractor noise was the main cause of hearing loss in tractor operators (Bunch, 1937). In 1956,
Bell reported on the potential effects of noise on tractor drivers, which included deafness, auditory fatigue, decrease in well being and morale, higher accident rates, and increased absenteeism and labor turnover.
Sound levels generated by older tractors, when they were new, has been documented. In 1964, Hutchings and Vasey studied tractor noise and reported sound levels of 100 to 113 dB(A) at the hearing zone of the operator. A study of
58 new tractors performed by Jones and Oser (1968) revealed that 90% of the tractors had sound levels in excess of 85 dB with an average of 103.5 dB.
Simpson and Deshayes (1969) reported on the University of Nebraska’s tractor noise study. The study of 55 tractors reported that sound levels at the operators’ ear when standing and sitting ranged from 97 dB to 114 dB. Similar noise levels were reported for other farm machinery such as combines.
A survey conducted in New South Wales found tractor noise levels ranged from 92 to 106 dB. In this study, they noted that operation of the tractors without
27 a muffler generated an increase in noise level, mostly in the middle and high frequencies. The operation without a muffler did not, however, always increase the overall sound level (Weston, 1963). Older agricultural tractors currently in use may not be properly maintained as evidenced by Pessina and Guerretti (2000) who found that only 50% of used tractors had working exhausts.
Measurements of noise associated with newer tractors also have been performed. Broste et al. (1989) measured noise at the ear level of the driver in 31 tractors. They found that all except one of the tractors generated noise greater than 85 dB at full throttle. More than 95 dB were generated by six of the tractors studied. Dewangan et al. (2005) more recently demonstrated that tractors still have the potential to generate harmful noise. They found that sound levels near the engines of tractors were 92 dB(A) and sound levels at the operators ear during various field operations ranged from 80.3 to 92.3 dB(A). Sound pressure levels increased with increased engine speed and were dependent upon the type of implement used. University Extension documents (Bean, 1991; Baker, 1993) also have reported sound intensity levels for tractors, farm equipment and power saws at 100 dB and noise levels within an acoustically insulated tractor cab at 85 dB.
Tractors are generally designed to have a 10 yr limit of technical obsolescence (Pessina and Guerretti, 2000). Newer acoustically engineered tractor cabs can provide protection to the operator. However, earlier designed
28 cabs did not always reduce sound levels. In fact, tractor cabs in the 1960s were found to generate additional noise (Mathews, 1968).
Not all farmers benefit from the improved engineering in new equipment.
May et al. (1990) reported that anecdotal observations indicated that tractors used by their cohorts may have been used for more than 20 yr. Pessina and
Guerretti (2000) reported that of 60 used tractors included in their study, tractor ages ranged from 4 to 30 yr and over half of the tractors were 10 to 20 yr old.
Further proof of the use of older tractors was reported by Cordes and Foster Rea
(1991b). Although tractors manufactured after 1976 in the United States have optional rollover protective structures, an estimated 3 million tractors were still in use that lacked the devices in 1991. Of 564 high school students who drove a tractor, only 7 of the respondents exclusively drove tractors with cabs (Broste et al., 1989).
The difference in noise exposure between tractors with and without cabs has been documented. Depczynski et al. (2005) found that the range of sound levels from tractors ranged from 15 to 93 dB(A), with tractors with cabs being the quietest and tractors without cabs being the loudest. They also determined that use of the radio during tractor operation increased the sound level by 3 to 5 dB.
Tractors with original quiet cabs generated average noise levels of 84.0 dB(A), with a range of 72 to 90 dB(A). Tractors without original cabs generated noise levels averaging from 91 to 95 dB(A) with a maximum of 101 dB(A). Some equipment was identified as increasing the amplitude of sound waves, such as
29 non-original cabs with large flat surfaces or improperly added windscreens or roofs (Pessina and Guerretti, 2000). In a sample of 155 tractors, 75% of those without cabs and 18% of those with cabs generated sound levels in excess of 90 dB(A) (Holt et al., 1993).
Kumar et al. (2005) measured noise levels on 51 tractors used in India.
The sound levels for some tractor models exceeded 100 dB(A) and all were greater than 90 dB(A). None of the tractors in the study had cabs and most were reportedly in use beyond their expected economic life. They also surveyed farmers who drove tractors and reported that the range of tractor driving hours per year was 100 to 5,000 (median 1,000 h, standard deviation 757 h).
Cabs alone do not fully protect the operator. A study conducted in Poland where all tractors had cabs, found that tractor drivers’ hearing was significantly worse than their cohorts who lived in rural areas and did not work in noisy jobs.
Additional analysis showed that younger tractor operators (under 35) had a statistically significant difference between hearing loss and period of employment that was not present in the older age group. Hearing loss of at least 20 dB in the highest frequency ranges (3 to 6 kHz) was present in 56% of the farmers
(Solecki, 1998).
Not all studies have supported the findings that cabless tractors result in increased noise exposure. Nieuwenhuijsen et al. (1996) did not find a significant association between self-reported noise exposure and the absence of an enclosed cabin on tractors used by farm operators. However, they did find that
30 tractor driving was one of the strongest predictors of dust and noise exposure and that field crop farm operators spent the largest percentage of time operating a tractor.
Aside from the absence or presence of a cab, there are other variables of tractor design that affect noise levels. For example, differences occur between wheeled tractors and track tractors. Tasks performed, engine speed, and load also must be taken into consideration. In a study performed by Dennis and May
(1995) the average sound level generated by 41 tractors was 80 dB(A) with a range of 70 to 92 dB(A) at idle speed. However, when the throttle was increased to common usage levels the mean increased to 92 dB(A) with a range of 74 to 98 dB(A). Lierle, et al. (1958) evaluated 11 tractors and measured sound pressure levels of 100 dB(C) when working and 97.5 dB(C) when idling. They also found the use of implements increased the sound pressure level by as much as 9 dB(C). Solecki (1995) also determined that the highest noise dose was derived from activities that placed an excessive load on the tractor engine.
Matthews (1968) performed noise measurements at the ear of the operator for tractors and other farm equipment under varying conditions. The majority of the measurements were made during normal agricultural tasks at commercial farms. Generally, tractor noise ranged from 74 to 108 dB(A), combine noise ranged from 86 to 98 dB(A), chain saw noise ranged from 106 to
120 dB(A) and livestock buildings ranged from 59 to 103 dB(A). Higher noise levels were associated with tractors with fitted cabs and by track laying tractors.
31 Horsepower did not correlate with the noise generated, but engine speed did.
The loudest tractor studied was a track laying tractor with a cab and the quietest tractor type in the study was a wheeled tractor without a cab.
Matthews (1968) also evaluated the type of activities performed with tractors. Plowing generated the highest noise levels [range 84 to 102 dB(A)], and hauling trailers or manure spreaders were the second loudest [range 77 to 97 dB(A)]. Cultivating generated noise levels ranging from 74 to 93 dB(A). Drilling and rolling noise levels ranged from 80 to 95 dB(A) and making silage or hay was generally quieter [range 74 to 91 dB(A)]. Eight miscellaneous tasks were measured including spraying and orchard spraying. Orchard spraying generated noise levels of 106 dB(A) and spraying ranged from 86 to 91 dB(A).
32 MATERIALS
Activity Logging
In order to correlate noise exposure with task or activity, participants were asked to complete an activity log. Prior to initiation of monitoring, three activity logs were developed for use. The activity logs were printed and bound in a book approximately 3” x 5” for ease of carrying. To correlate activities with times, all participants and researchers were provided with watches that were synchronized to the dosimeter times. All activities were divided into the following tasks:
. Animal Work
o Cleaned
o Fed
o Groomed
o Load/Unload
o Milk
o Move
o Treat
. Cleaning
o Grain Bin
o Milk Parlor
o Service Alley
. Handpicking
33 o Ground
o Tree
. Repair/Maintenance
o Clearing
o Building
o Yards
o Tractor
o Other Equipment
. Handling
o Bags/Sacks
o Small Grain/Pellet
o Rectangular Bales
o Silage
. Machine Operation
o Till/Plow
o Plant
o Spray/Fertilize
o Front End Loader
o Manure
o Mow
o Harvest
o Tractor on Road
34 o Other Vehicle
o Power Tools
o Haying Operations
. Other Farm Activities
. Non-Farm
o School
o Work
o Other
Each log book included space to describe any “other” activities. Logs were separated to identify tasks for each 15 min interval of the day and identical logs were maintained by observers. In addition to task information, the following general information was gathered:
. Waking time
. Retiring time
. Whether or not the dosimeter was worn each day
. Reasons why the dosimeter was not worn
. Use of hearing protection
. Use of headphones.
Three approaches were taken to the diaries. Diary 1 was divided by 15 min time blocks where the participant was asked to insert the code for each activity. A listing of activities and codes was provided at the end of the book. Diary 2 had
35 multiple pages that listed each of the identified tasks on one axis and time on another axis. The participant was asked to insert at “x” at the start and stop time of each activity. Diary 3 also had multiple pages listing the identified tasks.
However, blank columns were present to allow the participant to write in a start and stop time for each activity. Sample pages from each book are included in
Appendix B.
Equipment
Noise measurements were collected with Larson Davis SparkTM Model
Number 705+ noise dosimeters. The Larson Davis SparkTM 705 with attached
MPR001 combined preamplifier, 3/8" microphone cable and connector, is a Type
2 combination personal noise dosimeter and personal noise exposure meter. The detector accuracy of the dosimeters for true root mean square was less than 0.7 dB error from 40 to 143 dB. The dosimeters were factory calibrated on
September 13, 2005. They also were calibrated daily with a Larson Davis 114 dB calibrator. Windscreens were placed over the microphones during the entire monitoring period.
The SparkTM 705+ dosimeters worn by the participants were pre- programmed and could not be altered by the participants. Researchers operated a Larson Davis SparkTM 706RC, which was used to remotely control the programming of each 705+ and to download and temporarily store data.
Additionally, the Larson Davis noise and vibration software, BlazeTM , was used for downloading, storage, and manipulation of the logged data. 36 Area noise monitoring was conducted with a Quest 2700 Sound Level
Meter, Serial Number HU6060099. The Quest 2700 was factory calibrated on
August 8, 2004. The Quest 2700 with attached Model 056-852 preamplifier, removable precision 1/2'’ prepolarized condenser microphone, is a Type 2 sound level meter. The sound level meter is accurate to 0.7 dB at 25oC and within 1.0 dB over the temperature range of -10oC to +50oC. A windscreen, model number
WS-7, was placed over the microphone during monitoring.
37 METHODS
Participant Recruiting
Participants were recruited through distribution of an informational flyer. A copy of the flyer is included as Appendix A. Multiple copies of the flyer were sent to agricultural extension offices in the counties included in the study area. Other potential participants, identified through personal contacts, were directly sent a flyer.
In order to participate in the project, a family had to meet the following requirements:
. Be residents of a farming household;
. Operate a farm that included cash grain activities, but could include other
activities (such as livestock);
. Have a minimum of three persons from the household willing to
participate; and,
. Have all participants be of at least school age, able to read, write, and tell
time.
Monitoring Procedures
Participants were asked to wear a noise dosimeter during their waking hours each day. Monitoring was conducted during 3 wk, over a period of 5 mo during 2004. Monitoring periods consisted of 1 wk during planting season, 1 wk
38 during harvesting season, and 1 wk during growing season. The participants were instructed as to the proper use of the dosimeters and placement of the microphone was periodically checked by the on-site observer.
The dosimeters were programmed to perform data logging during the entire 24 h period each day using 2 separate channels simultaneously. The 2 channels were programmed for OSHA compliance comparison (channel 1) and for NIOSH/ACGIH comparison (channel 2). A summary of the parameters for each channel are outlined in Table I.
Table I. Dosimeter Settings
Parameter Channel 1 Channel 2 Exchange Rate 5 3 Threshold 80 dB(A) 80 dB(A) Criterion Level 90 dB(A) 85 dB(A) Criterion Duration 8 h 8 h RMS Weight A weighting A weighting Peak Weight C weighting C weighting Detector Slow Slow Gain 0 0 Sample Interval 60 s 60 s
39 Data Analysis
Data were exported from the BlazeTM program into Microsoft Office, Excel spreadsheets. Once imported, the activity log book data were added to the corresponding times. This allowed for sorting and manipulation of the data to:
. Identify and eliminate data from times when the dosimeter was not worn;
. Identify and eliminate data from times when the dosimeter did not record
data;
. Sort data by task;
. Group farming tasks to obtain an 8h-TWA for the participants farm work
day;
. Group all other occupational and non-occupational tasks to obtain an 8h-
TWA for the participants other activities;
. Sort data by equipment used to obtain an 8h-TWA for time spent
operating each piece of equipment;
. Sort data by task and obtain an 8h-TWA for time spent at each task; and,
. Compare participant activity codes to researcher activity codes and
determine the percent of time the activity codes were the same.
The BlazeTM software program then was used to calculate equivalent- continuous sound level and equivalent time weighted averages. In order to calculate equivalent time weighted averages for each activity and each equipment use time period, the start and stop times for each activity were used in
40 BlazeTM to identify run times which were included in the calculations. The equivalent-continuous sound level (Leq) was calculated by:
T 2 2 1 P t Leq 10 Log 2 dt dB 10 T T1 Po
where:
Leq = equivalent-continuous sound level
P(t) = instantaneous, frequency weighted, sound pressure in pascals
Po = reference sound pressure, μPa
T = measurement period or run time (T = T2-T1)
The time weighted average (TWA) was calculated by:
T 2 L AS 1 q TWA q log dt 10 T 10 T1
where:
TWA = time weighted average
q = exchange rate constant
exchange rate = 3, q = 10
exchange rate = 5, q = 16.61
LAS = frequency (A) and exponential time (SLOW) weighted sound level in
dB
T = measurement period or run time (T = T2-T1)
41 The 8 h equivalent time weighted average (8h-TWA) was calculated by for both periods less then and greater than the criterion duration:
T TWA q 8hTWA log10 T C
where:
TWA = time weighted average
q = exchange rate constant
exchange rate = 3, q = 10 for 8h-TWAACGIH
exchange rate = 5, q = 16.61 for 8h-TWAOSHA
TC = criterion duration of 8 h
T = measurement period or run time (T = T2-T1)
The dose was calculated by:
TWALc/ q Dose 100T /Tc 10
where:
TWA = time weighted average
q = exchange rate constant
exchange rate = 3, q = 10
exchange rate = 5, q = 16.61
TC = criterion duration of 8 hours
T = measurement period or run time (T = T2-T1)
TC = criterion duration of 8 h
Lc = criterion level in dB 42 RESULTS
Participant Descriptions
The family consisted of two adults and four children, with one resident working on the farm full time. Three family members participated in the study.
Subject 1 was the farm operator and worked on the farm full time. Subject 2 worked full time as a teacher and assisted on the farm part time. Subject 3 was a high school student who assisted on the farm part time.
Compliance with the Monitoring Strategy
Tables II, III, and IV present data for the hours the dosimeter was worn and data were recorded and the hours each participant was monitored by a researcher. Subject 1 wore the dosimeter for an average of 7.13 h per d during planting, 9.99 h per d during harvesting, and 11.45 h per d during growing season. Subject 2 wore the dosimeter for an average of 11.37 h per d during planting, 10.6 h per d during harvesting, and 7.08 h per d during growing season.
Subject 3 wore the dosimeter for an average of 3.07 h per d during planting, 6.53 h per d during harvesting, and 8.17 h per d during growing season. Overall, the farmer complied with dosimeter use instructions the most, wearing the dosimeter an average of 10.00 h per d for the entire project. Subject 2, the adult who worked off of the farm, wore the dosimeter the most days, 20 out of 22. The
43 compliance rate for daily wear for subject 2 was 9.72 h per d. The dosimeter was worn the least by subject 3, the student.
Monitoring was limited to observation of agricultural activities. Subject 1, the farmer, was monitored for the most hours (average 2.51 h per d during the project).
44 Table II. Subject 1: Dosimeter Use
Planting Season Day Hours Worn Hours Farmed Hours Monitored 1 10.83 5.98 0.00 2 15.40 10.48 9.08 3 0.00 0.00 0.00 4 0.00 0.00 0.00 5 0.62 0.00 0.00 6 11.25 4.73 1.37 7 11.78 9.93 12.80 Average 7.13 4.45 3.32 Growing Season Day Hours Worn Hours Farmed Hours Monitored 1 11.77 4.00 0.00 2 12.52 3.22 0.00 3 11.48 4.98 0.00 4 10.52 2.48 0.00 5 11.75 3.47 0.00 6 9.08 4.48 0.00 7 13.00 0.00 0.00 Average 11.45 3.23 0.00 Harvesting Season Day Hours Worn Hours Farmed Hours Monitored 1 9.03 5.72 7.87 2 10.27 5.38 11.60 3 13.75 1.48 1.50 4 13.85 5.47 6.35 5 7.97 4.47 0.25 6 13.52 4.33 1.93 7 11.52 5.97 0.00 8 0.00 0.00 0.00 Average 9.99 4.10 4.21 All Weeks Average 10.00 3.94 2.51 Monitoring was limited to farming activities
45 Table III. Subject 2: Dosimeter Use
Planting Season Day Hours Worn Hours Farmed Hours Monitored 1 10.13 0.00 0.00 2 16.60 0.00 0.00 3 10.65 0.00 0.00 4 13.38 0.00 0.00 5 3.65 0.00 0.00 6 12.27 0.00 0.00 7 12.88 0.00 0.00 Average 11.37 0.00 0.00 Growing Season Day Hours Worn Hours Farmed Hours Monitored 1 0.00 0.00 0.00 2 12.95 0.00 0.00 3 13.36 0.00 0.00 4 10.97 0.00 0.00 5 0.00 0.00 0.00 6 7.03 0.00 0.00 7 5.25 0.00 0.00 Average 7.08 0.00 0.00 Harvesting Season Day Hours Worn Hours Farmed Hours Monitored 1 3.55 0.00 0.00 2 15.55 2.27 2.03 3 13.28 0.00 0.00 4 14.75 0.00 0.00 5 8.40 0.00 0.00 6 14.97 0.00 0.00 7 12.20 0.00 0.00 8 2.07 0.00 0.00 Average 10.6 0.28 0.25 All Weeks Average 9.72 0.10 0.09 Monitoring was limited to farming activities
46 Table IV. Subject 3: Dosimeter Use
Planting Season Day Hours Worn Hours Farmed Hours Monitored 1 7.43 0.00 0.00 2 6.02 0.00 0.00 3 0.00 0.00 0.00 4 0.00 0.00 0.00 5 0.00 0.00 0.00 6 0.00 0.00 0.00 7 8.02 0.00 0.00 Average 3.07 0.00 0.00 Growing Season Day Hours Worn Hours Farmed Hours Monitored 1 8.02 0.00 0.00 2 7.85 0.00 0.00 3 7.68 0.00 0.00 4 8.18 0.00 0.00 5 7.77 0.00 0.00 6 7.18 1.00 0.00 7 10.50 0.00 0.00 Average 8.17 0.14 0.00 Harvesting Season Day Hours Worn Hours Farmed Hours Monitored 1 0.00 0.00 0.00 2 16.15 0.00 0.00 3 8.50 0.00 0.00 4 2.33 2.32 2.33 5 2.20 2.18 2.20 6 2.77 2.75 0.00 7 8.28 0.00 0.00 8 11.97 0.00 0.00 Average 6.53 0.28 0.57 All Weeks Average 5.95 0.38 0.21 Monitoring was limited to farming activities
47 Quality of Self-Reported Activities
In order to determine the quality of self-reported activities, a comparison of participant codes and researcher codes was made. The percentage of codes that were the same for the researcher and participant was calculated. Table V presents the percentage of times that researcher and participant codes were identical for those subjects and days during which monitoring was performed.
Due to the fact that worker monitoring was limited to farming activities, monitoring was not conducted during growing season based on expected activities reported by the farmer.
48 Table V. Reported Activity Agreement
Log Book Percent (%) Style Season Subject Day Agreement 3 Planting 1 2 50.46 3 Planting 1 6 65.85 3 Planting 1 7 83.19 Average 66.50 2 Harvesting 1 1 85.81 2 Harvesting 1 2 62.99 2 Harvesting 1 3 0.00* 2 Harvesting 1 4 65.90 2 Harvesting 1 5 0.00 2 Harvesting 1 6 43.97 Average 43.11 2 Harvesting 2 2 100.00 Average 100.00 2 Harvesting 3 4 86.00 2 Harvesting 3 5 0.00 Average 43.00 * = no data recorded by participant during monitoring period
Monitoring was only conducted while two of the log books were used, books 2 and 3. Six days of harvesting season were monitored for subject 1 with book 2. During those 6d, on average the participant code matched the researcher code 43.11% of the time. Monitoring was conducted for 29.5 h during the harvesting week. During planting week subject 1 was monitored for a total of
23.25 h and researcher and participant codes matched 66.5% of the time.
However, a comparison of matching codes over the total time monitored during each season revealed percent agreement results were similar. Book 3 matched
69.39% of the time and book 2 matched 64.72% of the time. This is due to the
49 fact that 2d of short term monitoring (less than 2 h) occurred in harvesting season when no matching code data occurred.
Log book 3 allowed the participant to fill in start and stop times for the activities performed, while log book 2 allowed participants to identify start and stop times with check marks made in columns of 30 min intervals. Participant codes and researcher codes for subject 1 matched more frequently when book 3 was used.
Characterization of Noise Exposure During Farming
Comparison to OSHA Criteria
To evaluate the participants’exposure to noise for comparison to the
OSHA criteria, all data were normalized to an 8h-TWA using a 5 dB exchange rate and criterion level of 90 dB(A). After the data were normalized, the results were compared to the OSHA action level of 85 dB(A) TWA and the OSHA-PEL of
90 dB(A) TWA. None of the days of monitoring, for any of the subjects, exceeded the OSHA-PEL of 90 dB(A) for an 8h-TWA.
The results of exposure monitoring during harvesting season are presented in Table VI. Subject 1, the full time farmer, did not work any days in excess of 8 h and spent a total of 32.82 h working on the farm during the week.
Subject 1’s 8h-TWA exposure exceeded the OSHA action level for 3 d during harvesting. Per OSHA, workers covered by the regulation that are exposed to 85 dB(A) as an 8h-TWA or higher must be included in a hearing conservation
50 program. Farm work performed by Subjects 2 and 3 never exceeded the OSHA-
PEL or OSHAs action level for inclusion in a hearing protection program [85 dB(A) with a 5 dB exchange rate].
Table VI. Harvest Season Farming Activity Noise Exposure
Exposure Leq DoseOSHA 8h-TWAOSHA Subject Day Duration (h) dB(A) % dB(A)* 1 1 5.72 86.8 51.1 85.2 1 2 5.38 86.6 61.5 85.7 1 3 1.48 67.5 1.4 59.3 1 4 5.47 87.0 85.4 88.9 1 5 4.47 79.0 21.3 78.8 1 6 4.33 81.2 22.0 79.1 1 7 5.97 68.5 0.8 55.3 2 2 2.27 76.9 7.0 70.8 3 4 2.32 79.1 24.6 79.9 3 5 2.18 80.7 29.7 81.2 3 6 2.75 79.2 24.7 79.9
*8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level (Channel 1 results) Bold = exceeds OSHA action level
The results of exposure monitoring during planting season are presented in Table VII. Subjects 2 and 3 did not perform any farm tasks during the week of monitoring. On 4 d subject 1 performed farm work and on one of those days the
OSHA action level was exceeded. It was during planting season that the longest work day was encountered, at 10.48 h. Of the 4 d when farm work was performed, the average number of hours farmed per day was 7.47. Subject 1 spent 31.12 hours farming during the week.
51 Table VII. Planting Season Noise Exposure During Farm Work
Exposure Leq DoseOSHA 8h-TWAOSHA Subject Day Duration (h) dB(A) % dB(A)* 1 1 5.98 80.5 29.5 81.2 1 2 10.48 81.0 40.0 83.4 1 6 4.73 79.8 29.6 81.2 1 7 9.93 84.9 90.5 89.3
*8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level (Channel 1 results) Bold = exceeds OSHA action level
The results of exposure monitoring during growing season are presented in Table VIII. Subject 2 did not perform any farm tasks during this week and subject 3 spent a total of 1 h farming during the week. Subject 1 worked on the farm 6 of the 7 d for a total of 22.63 h. Neither of the participants were exposed to noise levels in excess of the OSHA action level during any day of farm work.
Table VIII. Growing Season Farming Activity Noise Exposure
Exposure Leq DoseOSHA 8h-TWAOSHA Subject Day Duration (h) dB(A) % dB(A)* 1 1 4.00 69.1 2.7 64.0 1 2 3.22 70.1 16.3 66.6 1 3 4.98 73.3 4.5 65.1 1 4 2.48 76.6 4.7 59.5 1 5 3.47 69.4 3.4 65.6 1 6 4.48 71.6 5.0 68.4 3 6 1.00 72.8 4.2 67.1
*8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level (Channel 1 results) Bold = exceeds OSHA action level 52 Comparison to ACGIH/NIOSH Criteria
To evaluate the participants’ exposure to noise for comparison to the
ACGIH/NIOSH criteria, all data were normalized to an 8h-TWA using a 3 dB exchange rate and criterion level of 85 dB(A). After the data were normalized, the results were compared to the ACGIH-TLV and NIOSH-REL of 85 dB(A) TWA.
The results of exposure monitoring during harvesting season are presented in Table IX. Subject 1’s farm work activities exceeded the TLV on 4 of
7 d. Subject 3, the student, performed farm work during harvest season for 2.11 h during which the TLV was exceeded. Subject 2 participated in farming during this week, however those activities did not generate noise exposure levels in excess of the ACGIH-TLV.
53 Table IX. Harvest Season Farming Activity Noise Exposure
Exposure Leq DoseACGIH 8h-TWAACGIH Subject Day Duration (h) dB(A) % dB(A)* 1 1 5.72 86.8 156.4 86.9 1 2 5.38 86.6 359.7 90.6 1 3 1.48 67.5 4.1 71.2 1 4 5.47 87.0 461.4 91.6 1 5 4.47 79.0 74.4 83.7 1 6 4.33 81.2 123.8 85.9 1 7 5.97 68.5 1.7 67.3 2 2 2.27 76.9 44.8 81.5 3 4 2.32 79.1 74.0 83.7 3 5 2.18 80.7 111.1 85.5 3 6 2.75 79.2 81.9 84.1
*8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level (Channel 2 results) Bold = exceeds ACGIH TLV
The results of exposure monitoring during planting season are presented in Table X. Subjects 2 and 3 did not perform any farm tasks during the week of monitoring. On 4 d subject 1 performed farm work and on 2 of those days the
ACGIH-TLV was exceeded.
Table X. Planting Season Noise Exposure During Farm Work
Exposure Leq DoseACGIH 8h-TWAACGIH Subject Day Duration (h) dB(A) % dB(A)* 1 1 5.98 80.5 95.2 84.8 1 2 10.48 81.0 125.5 86.0 1 6 4.73 79.8 85.7 84.3 1 7 9.93 84.9 298.6 89.8
*8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level (Channel 2 results) Bold = exceeds ACGIH TLV 54 The results of exposure monitoring during growing season are presented in Table XI. Subject 2 did not perform any farm tasks during this week and subject 3 spent a total of 1h farming during the week. Subject 1 worked on the farm 6 of the 7d. Neither of the participants who farmed were exposed to noise levels in excess of the ACGIH-TLV during any day of growing season.
Table XI. Growing Season Farming Activity Noise Exposure
Exposure Leq DoseACGIH 8h-TWAACGIH Subject Day Duration (h) dB(A) % dB(A)* 1 1 4.00 69.1 6.9 73.4 1 2 3.22 70.1 7.9 74.0 1 3 4.98 73.3 12.7 76.0 1 4 2.48 76.6 3.0 69.8 1 5 3.47 69.4 8.7 74.4 1 6 4.48 71.6 12.8 76.1 3 6 1.00 72.8 18.1 77.6
*8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level (Channel 2 results) Bold = exceeds ACGIH TLV
Characterization of Noise Exposure at Work and School
Comparison to OSHA Criteria
To evaluate the participants’ exposure to noise for comparison to the
OSHA criteria, all data were normalized to an 8h-TWA using a 5 dB exchange
55 rate and criterion level of 90 dB(A). After the data were normalized, the results were compared to the OSHA action level of 85 dB(A) TWA and the OSHA-PEL of
90 dB(A) TWA. Subject 2 was a full time teacher and Subject 3 was a high school student. Tables XII and XIII summarize their noise exposure during those activities. Subjects 2 and 3 were not exposed to noise levels in excess of the
OSHA action level during work (teaching) or school activities.
Table XII. Subject 2 at Work Exposure
Exposure Leq DoseOSHA 8h-TWAOSHA Season Day Duration (h) dB(A) % dB(A)* Harvest 2 7.98 75.6 10.2 73.5 Harvest 3 7.23 71.1 3.2 65.2 Harvest 6 8.98 75.7 10.6 73.8 Harvest 7 6.98 79.8 20.3 78.5 Planting 1 6.98 79.0 16.2 76.9 Planting 2 5.48 73.5 6.1 69.8 Planting 7 6.97 72.7 4.7 68.0 Growing 2 6.98 74.7 7.8 71.6 Growing 3 6.98 62.6 0 22.8 Growing 4 6.98 70.8 2.9 64.6
*8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level (Channel 1 results) Bold = exceeds OSHA action level
56 Table XIII. Subject 3 at School Exposure
Exposure Leq DoseOSHA 8h-TWAOSHA Season Day Duration (h) dB(A) % dB(A)* Harvest 2 7.48 76.7 6.2 69.9 Harvest 3 7.48 76.6 7.4 71.3 Harvest 7 7.75 74.2 6.2 69.9 Harvest 8 7.50 70.8 2.6 63.8 Planting 1 7.92 74.3 5.7 69.3 Planting 2 6.00 73.5 4.3 67.3 Planting 7 8.00 75.7 8.6 72.3 Growing 1 8.00 74.7 7.5 71.3 Growing 2 7.83 73.8 5.8 69.5 Growing 3 7.67 74.2 6.5 70.2 Growing 4 8.17 74.6 7.5 71.3 Growing 5 7.75 74.2 7.2 71.1
*8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level (Channel 1 results) Bold = exceeds OSHA action level
Comparison to ACGIH/NIOSH Criteria
To evaluate the participants’ exposure to noise for comparison to the
ACGIH/NIOSH criteria, all data were normalized to an 8h-TWA using a 3 dB exchange rate and criterion level of 85 dB(A). After the data were normalized, the results were compared to the ACGIH-TLV and NIOSH-REL of 85 dB(A) TWA.
The results of exposure monitoring during non-farm activities are presented in Tables XIV and XV. Subjects 2 and 3 were not exposed to noise levels in excess of the ACGIH-TLV at their off farm activities.
57 Table XIV. Subject 2 at Work Exposure
Exposure Leq DoseACGIH 8h-TWAACGIH Season Day Duration (h) dB(A) % dB(A)* Harvest 2 7.98 75.6 29.5 79.7 Harvest 3 7.23 71.1 7.9 74.0 Harvest 6 8.98 75.7 30.8 79.9 Harvest 7 6.98 79.8 80.8 84.1 Planting 1 6.98 79.0 61.6 82.9 Planting 2 5.48 73.5 17.5 77.4 Planting 7 6.97 72.7 14.5 76.6 Growing 2 6.98 74.7 22.6 78.5 Growing 3 6.98 62.6 0.0 46.9 Growing 4 6.98 70.8 7.0 73.5
*8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level (Channel 2 results) Bold = exceeds ACGIH TLV
Table XV. Subject 3 at School Exposure
Exposure Leq DoseACGIH 8h-TWAACGIH Season Day Duration (h) dB(A) % dB(A)* Harvest 2 7.48 76.7 35.2 80.5 Harvest 3 7.48 76.6 42.7 81.3 Harvest 7 7.75 74.2 19.9 78.0 Harvest 8 7.50 70.8 7.5 73.8 Planting 1 7.92 74.3 19.9 78.0 Planting 2 6.00 73.5 20.0 78.0 Planting 7 8.00 75.7 32.1 80.1 Growing 1 8.00 74.7 21.6 78.3 Growing 2 7.83 73.8 17.8 77.5 Growing 3 7.67 74.2 19.8 78.0 Growing 4 8.17 74.6 22.8 78.6 Growing 5 7.75 74.2 22.8 78.6
*8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level (Channel 2 results) Bold = exceeds ACGIH TLV
58 Characterization of Noise Exposure by Farming Task
Subject 1 recorded a total of 11 tasks during the project and the researcher recorded eight tasks for Subject 1. The minimum number of tasks recorded in a given day when farming was performed was one. A maximum of four tasks per day were recorded by the farmer. The researchers recorded up to five tasks in 1d. Tables B-I,B-II, and B-III in Appendix B present individual data for each task event and the coding system used. Tables XVI and XVII summarize the range of noise levels associated with each task performed by Subject 1, the farm operator.
59 Table XVI. 8h-TWAs by Task Based on Researcher Coding
Exposure No. of Lmax Lpeak(max) Duration (h) Task 8h-TWAOSHA 8h-TWAACGIH Events Range in dB(A) Range in dB(C) All Events Range in dB(A) Range in dB(A) 5 4.87 Other Farm 61.5-82.9 72.3-88.9 100.6-111.2 128.4-134.3 Machinery Operation – 5 2.34 Other Vehicle 52.5-58.5 65.9-70.1 97.9-100.6 120.8-127.1 Machinery Operation – 4 1.6 Tractor on Road 57-70.6 68.5-80.8 88.6-108.7 125.2-135.3 Machinery Operation - 3 7.57 Harvesting 72-80.4 77.9-83.8 104.2-105.2 127.4-132.7 Machinery Operation - 3 2.82 Planting 63.4-78.1 73.8-85.1 97.7-108.2 132.1-134.7 Machinery Operation – 5 14.8 Till/Plow 71.2-88.9 77.7-91.7 101.2-113.4 126.2-137.1 Repair/Maintenance of 1 2.13 Other Equipment 70.7 78.5 107.6 124.8 1 0.27 Dumping water 55.4 68.0 101.0 121.8
8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level 8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level
60 Table XVII. 8h-TWAs by Task Based on Participant Coding
Exposure No. of Lmax Lpeak(max) Duration (h) Task 8h-TWAOSHA 8h-TWAACGIH Events Range in dB(A) Range in dB(C) All Events Range in dB(A) Range in dB(A) 3 11.21 Other Farm 65.6-73.8 74.4-79.8 97.5-103.1 123.3-131.2 Machine Operation – 1 0.98 70.1 78 104 136.2 Power Tools Machinery Operation – 9 24.77 54-81.1 65.8-84.7 51.8-111.5 119.1-135.4 Other Vehicle Machinery Operation – 2 0.71 65.8-68.4 75.8-78.3 98.7-104.7 123.4-134.7 Tractor on Road Machinery Operation - 2 10.48 80.6-86.2 83.7-85.1 104.7-107.6 132.7-134.5 Harvesting Machinery Operation - 2 2.75 73.1-74.8 80.0-80.7 99.7-105.4 117.9-132.3 Planting Machinery Operation – 4 13.73 70.4-87.8 77.4-91.3 101.9-113.4 130.4-137.1 Till/Plow Handling –Small 1 0.48 53.8 65.6 91.6 121.3 Grain/Pellets 1 0.48 Handling –Bags/Sacks 55.3 67.9 94.8 124.0 Repair/Maintenance of 5 11.93 49.8-64.0 63.9-73.4 99.4-103.8 124.5-136.5 Other Equipment Animal Work – 1 0.48 42.8 61.7 100.6 127.7 Load/Unload
8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level 8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level
61 Comparison to OSHA Criteria
No individual task exceeded the OSHA-PEL. However, tilling/plowing as recorded by both the researcher and participant did have events that exceeded
OSHA action level. One harvesting event, as recorded by the participant exceeded the OSHA action level. The same harvesting event, as recorded by the researcher did not exceed 85 dB(A).
Comparison to ACGIH/NIOSH Criteria
Tilling/plowing, planting, and other farm activities recorded by the researcher exceeded the ACGIH-TLV. In contrast, only harvesting and tilling/plowing events recorded by the participant exceeded the ACGIH-TLV.
Characterization of Noise Exposure by Farm Equipment
Table XVIII summarizes the noise levels associated with noted periods of use for equipment. Sound level measurements were collected from the equipment during various operating scenarios. Tables of the data and equipment codes are included as Tables B-IV and B-V in Appendix B. In contrast to prior tractor studies, only 1 of the 28 measurements exceeded 90 dB(A). This measurement was collected during planting activities.
62 Comparison to OSHA Criteria
None of the equipment used, in any combination, exceeded the OSHA-
PEL during any event. One equipment combination, the White tractor with disc, exceeded the OSHA action level.
Comparison to ACGIH/NIOSH Criteria
Tractor operation with a disc, regardless of tractor used, and tractor operation with a chisel plow did exceed the ACGIH-TLV. Two events were recorded with the tractor manufactured by White pulling the disc. One of the events exceeded the ACGIH-TLV and the other did not. One event was recorded with the White tractor and a mulcher and disc in tandem. This event did not exceed the ACGIH-TLV.
63 Table XVIII. 8h-TWAs by Farm Equipment Based on Researcher Coding
Exposure No. of Lmax Lpeak(max) Duration (h) Equipment 8h-TWAOSHA 8h-TWAACGIH Events Range in dB(A) Range in dB(C) All Events Range in dB(A) Range in dB(A) 3 1.57 Semi-truck 44.5-53.2 61.2-66.2 67.8-91.2 113.1-118.8 White tractor with 1 1 mulcher and disc 75.6 82.3 106.3 129.8 1 4.07 White tractor with disc 86.2 88.4 102.5 137.1 White tractor with 2 2.53 chisel plow 54.7-83.7 67.7-88.9 100.6-111.2 125.8-129 Massey Ferguson 1 0.65 tractor with disc 75.9 85.0 113.4 127.9 Massey Ferguson 1 0.07 tractor 49.5 64.6 94.5 118.8 2 6.4 Combine 80.4-80.7 83.7-83.8 104.2-104.7 132.5-132.7 Pick-up truck with 2 0.12 wagon 18.0-46.0 43.7-61.9 84.4-95.8 116.4-188.8 1 0.22 Pick-up truck 65.7 77.3 97.9 132.7 Mulcher and disc 1 1.42 (tractor not identified) 71.5 77.8 103.4 130.4 Water tank (tractor or 1 0.48 vehicle not identified) 69.5 80.5 108.7 135.3 1 4.98 Semi on road 81.0 84.7 107.9 135.4 1 2.48 White tractor 78.6 82.1 99.5 126.2 Wagon (tractor not 1 0.07 identified) 56.4 70.2 95.4 116.4
8h-TWAOSHA = normalized 8h-TWA using 5 dB exchange rate and 90 dB criterion level 8h-TWAACGIH = normalized 8h-TWA using 3 dB exchange rate and 85 dB criterion level
64 DISCUSSION
Overview
In general, the findings of this project confirm that noise exposure levels did not exceed the OSHA-PEL for any of the participants on any given day or during any given task. However, during certain farming activities and days of monitoring, the ACGIH-TLV was exceeded. The full-time farmer performed farming tasks on 17 of the 22 d of monitoring. The ACGIH-TLV was exceeded on six of those days, or 35.29% of the time. The OSHA 8h-TWA requiring inclusion in a hearing protection program for regulated employees [85 dB(A)] was exceeded on 6 d, or 23.53% of the time.
Summary of Results
Compliance with the monitoring program was high among the participants.
Subject 3, the student, wore the dosimeter on 17 of the 22 d. Even with the limited number of days worn, Subject 3 wore the dosimeter an average of 5.95 h per d for the 22 d. Use of the dosimeter was limited by after school sporting activities. Subject 2, the full time teacher, wore the dosimeter on 20 of 22 d, with an average of 9.72 h per day. Even though neither of these participants were regularly monitored, they complied with the requirements of the study. Subject 1, the full time farmer, wore the dosimeter on 19 of 22 d and wore the dosimeter an
65 average of 10.0 h per day. The dosimeter was not worn at all on three of the monitoring days.
The high compliance rate among the participants and that fact that none of the participants dropped out of the program indicates that the monitoring methodology is feasible. Even though participation was not 100% for any of the subjects, extending the monitoring period over 3 w allowed for a large volume of data to be collected. The length of monitoring also eliminated the potential for a complete loss of data due to weather conditions.
Due to the intrusive nature of the constant observation, monitoring periods were fewer than originally planned. Although the observations periods were reduced to primarily farm activities, sufficient data were collected to meet the goals of the study. Coding of non-farming tasks required less detail than farming tasks, therefore it is likely that the reported activities of the subjects during school and work accurately reflected those activities.
The analysis of time activity data suggests that farmer surveys and time activity logs may not be indicative of the true exposure times or activities. Indeed, data logged by the farmer yielded different 8h-TWAs for activities and identified different activities as producing higher exposure levels. While the differences between researcher and participant codes were not so great as to result in
OSHA-PEL exceedances, the OSHA hearing protection program action level of
85 dB(A) 8h-TWA and the ACGIH-TLV exceedances were different.
66 Anecdotal information obtained during the study revealed that completion of the log book was often done at the end of the day or during break periods.
This would explain the variation between participant and research start and stop times. It is also probable that short term activities, especially those less than 15 min in duration, would be excluded from the farmers’ reports. The observer, unobstructed by other work requirements, could maintain a more complete diary of activities.
While no one log book was preferred by all of the participants, the inconvenience of constantly logging activities was noted. A more convenient means of recording activities may improve researcher and participant agreement.
Likewise, elimination or recording by participants who are being observed may make the monitoring methodology more appealing to the participants.
It is notable that none of the participants wore hearing protection during the study. This information is similar to other findings from studies that have evaluated hearing protection use (Carpenter et al., 2002; Karlovich et al., 1998;
Nieuwenhuijsen et al., 1996).
Even though the noise exposure levels of the participants did not exceed
90 dB(A) 8h-TWA OSHA, farming activities frequently exceed the ACGIH-TLV. It is notable that these exceedances occurred during some relatively short exposure durations. The exposure durations of activities with ACGIH-TLV exceedances ranged from 2.18 to 10.48 h. Only two of the seven events over 85
67 dB(A) 8h-TWA NIOSH occurred on workdays lasting greater than 8 h. Both of those were during the planting season.
In contrast with Weston’s (1963) findings, machinery operation never exceeded 8 h during any work day. However, there were notable differences among the seasons. Since winter wheat was one of the crops raised by the farmer, planting and harvesting operations were performed during consecutive weeks and often overlapped. During growing season, when no tractor operation was conducted, none of the daily 8h-TWAs exceeded the ACGIH-TLV, nor did any of the task specific 8h-TWAs.
The highest sound level measurement obtained was taken inside of a tractor which was idling [96 dB(A)]. The second highest was taken outside of the combine while corn was being offloaded to wagons. Sound level measurements ranged from 96.1 to 75.1 dB(A), which was obtained at the semi-truck while idling.
Similar to McBride et al.’s (2003) findings, the farm operator typically performed more than one task per day and often performed three or more tasks per day. Only one task, tilling/plowing, exceeded the ACGIH-TLV more than half of the time (60%) as coded by the researcher. Only two other tasks, other farm work (20% of events) and planting (33% of events) exceeded the ACGIH-TLV.
The calculated NIOSH 8h-TWAs demonstrate the lack of agreement between researcher and participant coding, since participant coding reported two tilling/plowing and one harvesting event with NIOSH 8h-TWAs over 85 dB(A).
68 None of the activities or equipment monitored generated maximum levels in excess of 115 dB(A) or peak levels in excess of 140 dB(C).
Unlike the findings of Dennis and May (1995), the exposure period of the farm operator never exceeded 11 h per day. It is also notable that during any work week observed, the farmer’s exposure time did not exceed 40 h. Off farm activities, school and work, did not result in noise exposures greater than the
OSHA or NIOSH action levels.
Significance
To date, this is the only known study conducted in an agricultural setting where noise exposure data were collected using dosimeters for a family. The findings of this study demonstrate that the monitoring protocol can be accomplished, although actual dosimeter use by the participants may be lower than requested. The extended use of the dosimeters (use of a 3 w monitoring period) ensured that regardless of weather conditions or other situations, an adequate amount of data were collected.
Limitations
This project was limited by the small sample size. However, as a pilot project, the ability and efficiency of data collection were as important as the data collected.
One of the difficulties of the project was the balancing the observation times and need for collection of data with the privacy needs of the participants. A
69 balance was struck by limiting most monitoring to actual farm work events and locating the observer in a general work area, out of the way of the worker. Sound level measurements were collected only during times that were convenient for the worker. Although the project was initially expected to span over several months, all data were collected in a relatively short period (3 mo). The planting and harvesting observation weeks were consecutive, which may have affected the level of participation. Anecdotal reports suggested that initial use of the dosimeter was not problematic, however, as the monitoring period extended to the second week, it became more inconvenient.
Recommendations for Future Studies
Although the monitoring strategy did produce results, better results may be gained through alteration of activity logging protocol. These findings do support the findings of previous studies, in that farmer workers, including children, are exposed to noise in excess of the ACGIH-TLV. Therefore, it is recommended that future studies include children working and residing on farms.
Future studies would benefit from improved activity logging, improved or altered recruiting methods, and a larger study group.
70 CONCLUSIONS
Noise exposure on farms does contribute to the total noise exposure of residents. Although the family members residing on the farm performed limited farm work, the adolescent included in the study was exposed to noise levels in excess of the ACGIH-TLV. The farmer included in the study also performed tasks and had daily 8h-TWAs which exceeded the ACGIH-TLV. These findings confirm that if farmers were regulated as other occupations, farm workers would have to be included in a hearing protection program. Considering the reportedly limited use of hearing protection devices, farm worker exposure monitoring should be conducted on a larger scale to better evaluate noise exposure.
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86 APPENDIX A. DEFINITIONS
87 Decibel –(dB) dimensionless quantity based on the logarithm of the ratio of two power-like quantities L = 10 log (A/B) dB where L is level, and A and B are quantities related to power (Berger, 2000).
Exchange Rate –the trade-off relationship between an increase (or decrease) in sound level and the corresponding change allowed in exposure duration. When the sound level increases by the exchange rate (in dB) then the permissible duration is halved. When the sound level decreases by the exchange rate then the permissible duration is doubled (Berger, 2000).
Frequency –cycles per second (Berger, 2000)
Frequency Analysis –electronic filters can be used to separate sound into frequency bands so measurements can be made in specific frequency bands. It is then possible to measure only the sound in a given frequency band using any time weighting or the time-average sound level. For environmental noise, measurements are usually made in octave or one-third octave bands. Octave- band or one-third octave band data or criteria are understood to be unweighted unless it is clearly stated otherwise. (ASTM E1779 and ASTM E1686)
Frequency Weightings –several frequency-weighting networks (filters) have been internationally standardized. These networks provide a better match between measured results and human perception. The two used most frequently are designated A-weighting and C-weighting. (ASTM E1686, 2003)
. A-weighting is the most commonly used. It is used when a single-number
overall sound level is needed. Results are expected to indicate human
88 perception or the effects of sound on humans. A-weighting accounts for
the reduced sensitivity of humans to low-frequency sounds, especially at
lower sound levels.
. C-weighting is sometimes used to evaluate sounds containing strong low-
frequency components. It was originally devised to approximate human
perception of high-level sounds. (ASTM E1686, 2003)
Hertz –(Hz) cycles per second (Berger, 2000)
Impulse Sound –a brief, intrusive sound, such as that associated with a tire blowout, operation of a power press, or the discharge of a firearm. One definition of an impulse is an event having a rise time not more than 35 milliseconds (ms) to peak, and a duration of not more than 500 ms to 20 dB below peak. Impulse sound also includes repetitive events occurring at rates of 20 or less per second.
When the repetition rate falls between 10 and 20 per second, the perception could be that of a steady tone, and it would be measured by a sound level meter as such. (ASTM E1779, 2004)
Loudness, (sone), –that attribute of auditory sensation in terms of which sounds may be ordered on a scale extending from soft to loud. (ASTM E1686, 2003)
Maximum Level—the highest meter reading using the frequency weighting and time weighting required by the measurement procedure or plan. (ASTM E1779,
2004)
Peak Sound Pressure Level –A peak indicator measures the true peak level of a very short duration signal. It is preferred over impulse weighting to measure
89 sounds of less than 1 second, such as a gunshot or impact. It is not normally used to measure steady sounds or slowly varying sounds. A peak detector responds to the absolute positive or negative value of the waveform rather than its effective or “root mean square” value. Peak detectors can respond to a sound pulse and provide an accurate reading in less than 50 microseconds (µs). In normal use, a peak measuring instrument will hold its indication for ease of reading until reset or will store it in a memory for later reference. Although there are certain applications where A or C frequency weightings are used, it is most common to use the peak level unweighted. (ASTM E1686, 2003)
Sound Exposure Level –(SEL) accumulated exposure to noise expressed by
LAE = LAeq,T + 10 log [T/T0] where T is the exposure duration in seconds, T0 is the reference duration of 1 second, and LAeq,T is the time-average sound level using the A measurement scale. (Berger, 2000)
Sound Pressure Level –(SPL) sound pressure level measured in dB is calculated by 20 log (P/Po) dB re 20 micropascals, where P is the measured root-mean-square (rms) sound pressure and Po is the reference rms sound pressure and the reference pressure in air is 20 micropascals. (Berger, 2000)
Time-Average Sound Level –(Leq) equivalent-continuous sound level, also called average sound level, during time period T, using a 3-dB exchange rate.
(Berger, 2000)
Time-Weighted Average Sound Level, TWA, (dB), –an indicator of hearing damage risk during a workday of any length expressed as an equivalent 8 hour
90 steady level. The TWA is not always based on an energy-equivalent or 3 dB exchange rate. Pertinent regulations specify an exchange rate indicating the number of decibels considered to double hearing damage risk. Such regulations also may specify computation based on sampled measurements of the A-slow- weighted sound level, and a threshold level below which sound levels are not included in the computation of the TWA. (ASTM E1686, 2003)
Time Weighting –Sound levels often vary rapidly. It is not practical or useful for a meter to indicate every fluctuation of sound pressure. When it is desired to record the variation in sound, the meter performs an exponential average time weighting that emphasizes the most recent sound. There are three meter time- weighting characteristics commonly used in sound measurements (slow, fast, and impulse). A time weighting is specified whenever used in a measurement.
(ASTM E1686, 2003)
. The slow weighting is the most commonly used time weighting. It
provides a slowly changing level indication that is easy to read and is often
specified in regulations.
. The fast weighting more closely responds to human perception of sound
variation. It provides a faster response to the instrument’s indicator to
changing sound levels. Fast response is often used for short duration
measurements such as motor vehicle drive-by tests.
. The impulse weighting allows a faster rise in indicated level than the fast
weighting but causes a slower decrease in indicated level than the slow
91 weighting. Originally developed in Germany, it is used in Canada to regulate the noise of firearms and pest control devices and some industrial noises. (ASTM E1686, 2003)
92 APPENDIX B: FORMS
93 Form A-1. Recruitment Flyer
RESEARCH VOLUNTEERS NEEDED
WHO:
The research project participants will be residents of farming households. The farm must include cash grain activities, but may include other activities (such as livestock). A minimum of 3 persons per household are needed to participate, with a maximum of 5 persons per household. Participants must be of at least school age, able to read, write, and tell time. Participating households will be provided gift certificates for volunteering ($50 for the first week, $75 for the second week, and $100 for the third week). WHAT:
The Ohio Farm Family Total Noise Exposure Assessment research project is being performed by the Medical College of Ohio. The purpose of the project is to evaluate occupational and non-occupational noise exposures of farm workers and residents. o Agricultural workers have one of the highest rates of hearing loss of all occupations. o Tractor noise has been documented at levels up to 113 decibels (dB), while exposure to noise levels greater than 90 dB is believed to injure hearing. WHEN:
The study will take place during three seasons; planting, growing, and harvesting. During each season one continuous week of monitoring would be performed. Ideally each participating family would be monitored during each season of one year. Participants would select the week during each season that monitoring would occur. WHERE:
Participants would go about their normal activities on and off of the farm. Field investigators will travel to the household each day to monitor activities and briefly interact with participants to collect data. HOW:
Noise exposure will be assessed with a noise dosimeter. o The dosimeter is approximately the size of a cellular phone, so it will not interfere with normal work routine. o The dosimeter only measures noise exposure as number of dB (it is not a voice recorder). Participants will need to wear the dosimeter during waking hours, with the microphone attached to the shirt below the ear and the dosimeter attached to the belt. Participants will also be asked to complete daily time activity checklists provided by MCO. CONTACT:
If interested or you would like more information contact Sheryl Milz, PhD, CIH at (419) 383-3976.
Principal Investigator: Sheryl A. Milz, PhD, CIH / IRB # 104198 / Version Date: July 16, 2004
94 Form A-2. Log Book 1
95 Form A-2. Log Book 1, Continued
96 Form A-2. Log Book 1, Continued
97 Form A-3. Log Book 2
98 Form A-3. Log Book 2, Continued
99 Form A-4. Log Book 3
Other Information
Instructions: FARM FAMILY 1. Wake up time ______TOTAL NOISE 2. Bed time ______3. Dosimeter worn all day? Y N 1. Next to each activity code record the start and 4. Start and stop time not worn stop times for each time the task is performed. EXPOSURE 2. Include an H for work by hand or an M for work by machine/power equipment, as necessary. ASSESSMENT 3. For any activity code that includes “other,” provide a description of the task in the space provided on the additional sheets in the back of the booklet
Example: 5. Reasons not worn
Start Stop H or M
ANIMAL
WORK
6. Hearing protection worn? Y N Cleaned 10:30AM 11:30AM M 7. Start and stop time hearing protection worn Fed
Cleaned animal pens with Bobcat from 10:30 to 11:30
100 Form A-4. Log Book 3, Continued
101 APPENDIX C. TABLES
102 Table B-I: Exposure Data by Task Based on Researcher Coding
8h-TWAOSHA 8h-TWAACGIH Season* Day Time (h) Participant Codes Researcher Code dB(A) dB(A) Lmax Lpeak (max) H 2 0.9 60/82 71 62.6 73 102.4 128.8 P 7 0.65 60/66 71 69.1 78.7 100.6 128.9 P 6 0.88 71 71 70.2 77.7 103.1 128.4 P 2 0.47 66 71 61.5 72.3 106.9 128.9 H 4 1.97 60 71 82.9 88.9 111.2 134.3 P 2 0.57 66/82 68 57.9 68.9 99.4 120.8 H 1 0.37 60/68 68 58.5 69.2 100.6 127.1 H 6 0.82 50/82 68 52.9 65.9 99.8 126.8 P 6 0.08 71 68 57.3 69.2 100.6 125.8 P 7 0.5 66 68 58.1 70.1 97.9 121.3 H 2 0.75 60/61 67 70.6 80.8 108.7 135.3 P 6 0.35 71 67 57 68.5 99.5 125.8 P 2 0.4 66 67 59.5 69.9 88.6 134.5 P 7 0.1 66 67 58.9 71.6 95.4 125.2 P 2 3.33 66/82 66 80.4 83.8 104.2 132.5 P 7 3.07 66 66 79.4 82.8 104.7 132.7 H 2 1.17 66 66 72 77.9 105.2 127.4 H 1 1.12 61/82 61 75.6 82.1 105.2 134.7 H 2 1.45 61/60 61 78.1 85.1 108.2 132.9 H 5 0.25 82 61 63.4 73.8 97.7 132.1 H 1 4.2 60/82 60 82.7 85.1 101.2 126.2 H 4 4.45 60/82 60 88.9 91.7 113.4 136.7 H 6 1.08 60/50 60 71.2 77.7 103.4 130.4 P 7 4.07 60 60 85.7 88 102.5 137.1 H 2 1 60 60 75.6 82.3 106.3 129.8 P 2 2.13 44/66 44 70.7 78.5 107.6 124.8 * H = harvesting, P = planting, G = Growing
103 Table B-II: Exposure Data by Task Based on Participant Coding
Time Participant Researcher 8h-TWAOSHA 8h-TWAACGIH Lpeak (max) Season* Day (h) Codes Code dB(A) dB(A) Lmax dB(C) G 3 1.48 81 none 49.5 63.4 99.4 127.3 G 5 1.98 71 none 65.6 74.4 97.5 123.3 G 6 7.5 71 none 69.3 76.5 101.4 132.1 P 6 1.73 71 none/67/68/71 73.8 79.8 103.1 128.4 H 5 0.98 69 none 70.1 78.0 104 136.2 P 1 8.23 68 none 81.1 84.7 107.9 135.4 P 6 0.73 68 none 69.6 75.8 96.3 126.1 H 7 5.48 68 none 54.0 65.8 94.3 124.9 G 2 1.23 68 none 65.8 73.5 99.2 126.7 G 3 1.48 68 none 60.6 74.2 111.5 125.8 G 4 2.48 68 none 59.5 69.8 98.3 131.3 G 5 1.48 68 none - - 51.8 - G 6 0.73 68 none 57.9 68.6 90.8 119.1 P 2 2.93 68 none/82 58.9 69.8 100.1 121.8 P 6 0.48 67 none 65.8 75.8 98.7 123.4 H 5 0.23 67 none 68.4 78.3 104.7 134.7 P 2 6.25 66 44/82 86.2 85.1 107.6 134.5 P 7 4.23 66 71/82/66 80.6 83.7 104.7 132.7 P 6 0.75 61 none 73.1 80.0 99.7 117.9 H 6 2 61 none 74.8 80.7 105.4 132.3 H 6 1.23 60 none/60 71.0 77.4 101.9 130.4 P 7 6.02 60 none/71 86.9 88.6 102.5 137.1 H 4 4.98 60 60/71/82 87.8 91.3 113.4 136.7 H 5 1.5 60 none 70.4 78.9 106.4 135.8 * H = harvesting, P = planting, G = Growing
104 Table B-II continued: Exposure Data by Task Based on Participant Coding
Time Participant Researcher 8h-TWAOSHA 8h-TWAACGIH Lpeak (max) Season* Day (h) Codes Code dB(A) dB(A) Lmax dB(C) H 5 0.48 51 none 53.8 65.6 91.6 121.3 H 6 0.48 50 60/68 55.3 67.9 94.8 124 P 1 0.5 44 none 51.8 67.0 100.8 128.1 G 1 4 44 none 64.0 73.4 99.4 136.5 G 2 1.98 44 49.8 63.9 100.8 124.5 G 3 3.48 44 59.3 71.3 103.8 133.6 P 2 1.97 44 none/44 62.3 72.7 102.4 131.9 H 7 0.48 13 42.8 61.7 100.6 127.7 * H = harvesting, P = planting, G = Growing
105 Table B-III: Numeric Coding System - Tasks
Numeric Numeric Activity Activity Numeric Code Activity Code Code 1. ANIMAL WORK 4. REPAIR/MAINT. 6. MACHINE OPERATION Cleaned 10 Clearing 40 Till/Plow 60 Fed 11 Building 41 Plant 61 Groomed 12 Yards 42 Spray/Fert. 62 Load/Unload 13 Tractor 43 Front end Loader 63 Milk 14 Other Equip. 44 Manure 64 Move 15 5. HANDLING Mow 65 Treat 16 Bags/Sacks 50 Harvest 66 2. CLEANING Small Grain/Pellet 51 Tractor on Road 67 Grain Bin 20 Rectangular Bales 52 Other Vehicle 68 Milk Parlor 21 Silage 53 Power Tools 69 Service Alley 22 Haying Operations 70 3. HANDPICK 7. OTHER-FARM 71 Ground 30 8. NON-FARM Tree 31 School 80 Work 81 Other 82
106 Table B-IV: Numeric Coding System - Equipment
Tractor Code Implement Code Other 0 Other 0 Combine - Corn head 1 Planter 1 Combine 2 Baler 2 MF 3 Wagon 3 White 4 Chisel Plow 4 Semi-Truck 5 Mulcher 5 Disc 6 Water Tank 7
107 Table B-V: Sound Level Measurements
SLM: Quest 2700, Serial Number HU6060099 Date of Last Manufacturer Calibration: 8/9/04 t
r # n n
r s o ) e t o s t e o l e k e l t A e e c t m s l u o t p c i m a a B e a s i d r o a l I d m t d D e r T T
p ( N a a T R n R I S m I 10/13/2004 16:35 1 3 1 61 87.1 N Planting, running 10/13/2004 16:36 2 3 1 61 86.1 Y planting 10/14/2004 11:42 1 3 1 61 88.6 Y N planting 10/14/2004 11:49 2 4 7 67 83.5 Y Y N haul water on road 10/14/2004 11:55 3 4 7 71 80.9 Y Y Y dumping water 10/14/2004 12:26 4 3 1 61 91.6 Y N planting 10/14/2004 12:49 5 0 0 71 83.3 N N pumping water offloading water, outside tractor - 10/14/2004 13:00 6 4 7 71 81.8 N N work area working fields, irrigator & mulcher in 10/14/2004 13:42 7 4 5 60 88.1 Y Y N tandem 10/14/2004 13:45 8 4 5 60 88.5 Y Y Y irrigator & mulcher in tandem 10/14/2004 13:46 9 4 5 60 88.4 Y Y N working fields 10/14/2004 14:07 10 4 5 60 87.9 Y Y N working fields 10/14/2004 18:16 11 2 66 88.1 Y N harvesting beans, cab door open
108 Table B-V continued: Sound Level Measurements
SLM: Quest 2700, Serial Number HU6060099 Date of Last Manufacturer Calibration: 8/9/04 t
r n n r s # o ) e
t o s t o l e k e t p A e e c t m s l u o t c i m a a B e a s i m d r o a l I d t d D e a r T T
p ( N a T S R n R I m I 10/16/2004 14:58 1 4 4 60 83.7 Y Y N plowing 10/16/2004 15:25 2 4 4 60 85.0 Y Y N plowing 10/16/2004 17:12 3 3 6 60 88.8 Y N deep rip field 10/16/2004 17:20 4 3 6 60 89.0 Y N deep rip field 10/16/2004 17:30 5 3 6 60 88.6 Y N deep rip field combining corn, 10/22/2004 18:36 1 1 66 88.2 Y N N door open emptying corn 10/22/2004 18:30 2 1 66 87.6 N N N into semi emptying corn 10/22/2004 18:31 3 1 66 87.2 N N N into semi emptying corn 10/22/2004 18:32 4 1 66 85.9 N N N into semi 10/22/2004 18:33 5 1 66 79.5 N N Y combine combine corn, 10/22/2004 18:40 6 1 66 86.7 Y N N door open combine corn, 10/22/2004 18:43 7 1 66 87.8 Y N N door open 10/27/2004 17:55 1 6 68 75.1 Y mack semi truck 10/27/2004 18:20 2 1 66 89.3 N offloading corn combining corn, 10/27/2004 18:47 3 1 66 88.9 N door open
109 ABSTRACT
Noise exposure assessment monitoring was conducted for a farm family during each of the three farm seasons, harvesting, growing, and planting. Sound exposure levels for each participant were compared to the OSHA-PEL and
ACGIH-TLV. None of the participants exposure, during any task exceeded the
OSHA-PEL of 90 dB(A) 8h-TWA. The adolescent in the study was exposed to noise in excess of the ACGIH-TLV during one farming event. The farmer was exposed to noise in excess of the ACGIH-TLV on 7 of 22 farming days and during various tasks. Hearing protection was not used by any of the participants.
The findings confirm that if farmers were regulated as other occupations, farm workers would have to be included in a hearing protection program.
110