OSRC workers performed a range of jobs/tasks, from stopping the leak to administrative support, with different exposure profiles (Table 1 ). Initially, jobs and tasks were the basis of a preliminary exposure assessment. Due to the weathering of the oil, vessel, vessel type, location, and time periods were later identified as possible determinants of individual exposure levels. The ultimate goal of the GuLF STUDY is to have quantitative exposure estimates for total hydrocarbons (THC) and BTEX-H (benzene, toluene, ethylbenzene, xylene, hexane) as these oil-related chemicals comprised most of the air measurements taken during the spill and are generally considered to be the more toxic components. Exposure estimates for dispersants and particulates from burning were also desired because of their association with some health effects and because of concerns raised by the public. An ordinal job–exposure matrix (JEM) was developed based on jobs or tasks/vessel or vessel type/location/time period to estimate THC exposures for study participants (Stewart et al., in press ). THC is a composite of the volatile chemicals from the oil and, as such, can be thought of as a surrogate for the “OSRC oil experience.” In the development of the questionnaire and the ordinal and quantitative JEMs, study industrial hygienists (IHs) relied on BP measurement data and their accompanying documentation, federal and BP contractor reports, numerous other spill-related documents, and interviews with key personnel managing the OSRC effort and some workers.
The exposure section of the enrollment interview was structured to capture detailed information about the participants’ OSRC activities and served as the link to the JEM. Participants provided the start/stop dates for any OSRC work and then for each OSRC job/task queried, start/stop dates, average number of days worked/week, average number of hours worked/day, use of personal protective equipment, and dermal contact with chemical agents. Participants also provided information on heat stress and other work-related exposures and on sleeping quarters.
More than 28,000 full-shift, personal air monitoring samples were collected on workers by BP contractors to characterize exposure to OSRC chemicals from April 2010 through June 2011. Because multiple chemicals were analyzed on each sample, 160,000 measurements were available on THC, BTEX-H, and other toxicants. A large proportion of these measurements was below the reported limits of detection when analyzed based on occupational exposure limits. When these monitoring data were recalibrated by one of the BP contractors and the study IHs to reflect the analytical methods’ limits of detection, it was possible to quantify levels below the initially reported LODs. The effort substantially decreased the amount of censored data; for example, THC censored data went from 80% to ~ 20%. The proportion of censored data for the other chemicals was still relatively high (~ 70%) but was substantially lower than the original 95% censoring. We evaluated strategies for dealing with censored data and developed methods to leverage the censored data on THC to develop estimates for other BTEX-H chemicals (Huynh et al. 2014 (link), 2016 (link); Quick et al. 2014 (link)).
Our team of experienced IHs used the recalculated air measurement data to identify factors associated with exposure levels to characterize exposures: jobs/tasks, vessel/vessel type, location, and time period. Unique combinations of these factors were identified that were expected to have similar distributions of THC exposure. The measurement data were used to determine average THC exposures for each job or task/vessel or vessel type/location/time period combination (n = 2,385 “exposure groups”), which was translated to ordinal values (1–7). The resulting JEM was linked to the OSRC work reported in the questionnaire to estimate THC exposures for each participant in the cohort. Different metrics can be developed for different exposure–response scenarios and assumptions. For example, we estimated the maximum exposure by identifying the maximum level across all estimates assigned to an individual to create a person-specific maximum exposure metric. Exposure averages (mean or median) within and across jobs/tasks or in specific time periods (e.g., before the well was capped) or locations also can be developed.
Specific questionnaire responses were also used to identify, based on tasks, vessels, locations, and dates, workers with likely exposure to dispersants (yes/no) and to particulates (low, medium, high) from burning of oil. Quantitative exposure estimates for inhaled THC and specific chemicals (e.g., BTEX-H) are being developed, as are semiquantitative estimates of dermal exposure, estimates for dispersants, and estimates for particulate matter from burning.
The exposure section of the enrollment interview was structured to capture detailed information about the participants’ OSRC activities and served as the link to the JEM. Participants provided the start/stop dates for any OSRC work and then for each OSRC job/task queried, start/stop dates, average number of days worked/week, average number of hours worked/day, use of personal protective equipment, and dermal contact with chemical agents. Participants also provided information on heat stress and other work-related exposures and on sleeping quarters.
More than 28,000 full-shift, personal air monitoring samples were collected on workers by BP contractors to characterize exposure to OSRC chemicals from April 2010 through June 2011. Because multiple chemicals were analyzed on each sample, 160,000 measurements were available on THC, BTEX-H, and other toxicants. A large proportion of these measurements was below the reported limits of detection when analyzed based on occupational exposure limits. When these monitoring data were recalibrated by one of the BP contractors and the study IHs to reflect the analytical methods’ limits of detection, it was possible to quantify levels below the initially reported LODs. The effort substantially decreased the amount of censored data; for example, THC censored data went from 80% to ~ 20%. The proportion of censored data for the other chemicals was still relatively high (~ 70%) but was substantially lower than the original 95% censoring. We evaluated strategies for dealing with censored data and developed methods to leverage the censored data on THC to develop estimates for other BTEX-H chemicals (Huynh et al. 2014 (link), 2016 (link); Quick et al. 2014 (link)).
Our team of experienced IHs used the recalculated air measurement data to identify factors associated with exposure levels to characterize exposures: jobs/tasks, vessel/vessel type, location, and time period. Unique combinations of these factors were identified that were expected to have similar distributions of THC exposure. The measurement data were used to determine average THC exposures for each job or task/vessel or vessel type/location/time period combination (n = 2,385 “exposure groups”), which was translated to ordinal values (1–7). The resulting JEM was linked to the OSRC work reported in the questionnaire to estimate THC exposures for each participant in the cohort. Different metrics can be developed for different exposure–response scenarios and assumptions. For example, we estimated the maximum exposure by identifying the maximum level across all estimates assigned to an individual to create a person-specific maximum exposure metric. Exposure averages (mean or median) within and across jobs/tasks or in specific time periods (e.g., before the well was capped) or locations also can be developed.
Specific questionnaire responses were also used to identify, based on tasks, vessels, locations, and dates, workers with likely exposure to dispersants (yes/no) and to particulates (low, medium, high) from burning of oil. Quantitative exposure estimates for inhaled THC and specific chemicals (e.g., BTEX-H) are being developed, as are semiquantitative estimates of dermal exposure, estimates for dispersants, and estimates for particulate matter from burning.
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