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Environmental health exposures to airborne chemicals often originate fromchemical mixtures. Environmental health professionals may be interested inassessing exposure to one or more of the chemicals in these mixtures, but often,exposure measurement data are not available, either because measurementswere not collected/assessed for all exposure scenarios of interest or because someof themeasurementswere below the analytical methods' limits of detection (i.e.,censored). In some cases, based on chemical laws, two or more componentsmay have linear relationships with one another, whether in single or multiplemixtures. Although bivariate analyses can be used if the correlation is high, correlationsare often low. To serve this need, this paper develops a multivariateframework for assessing exposure using relationships of the chemicals presentin these mixtures. This framework accounts for censored measurements in allchemicals, allowing us to develop unbiased exposure estimates.We assessed ourmodel's performance against simpler models at a variety of censoring levels andassessed our model's 95% coverage.We applied our model to assess vapor exposurefrom measurements of three chemicals in crude oil taken on the OceanIntervention III during the Deepwater Horizon oil spill response and cleanup.

Introduction The 2010 Deepwater Horizon (DWH) disaster was the largest marine oil spill in U.S. history. (1) An estimated 4.9 million barrels of crude oil were discharged into the Gulf [...], Background: During the 2010 Deepwater Horizon (DWH) disaster, response and cleanup workers were potentially exposed to toxic volatile components of crude oil. However, to our knowledge, no study has examined exposure to individual oil spill-related chemicals in relation to cardiovascular outcomes among oil spill workers. Objectives: Our aim was to investigate the association of several spill-related chemicals [benzene, toluene, ethylbenzene, xylene, n-hexane (BTEX-H)] and total hydrocarbons (THC) with incident coronary heart disease (CHD) events among workers enrolled in a prospective cohort. Methods: Cumulative exposures to THC and BTEX-H across the cleanup period were estimated via a job-exposure matrix that linked air measurement data with self-reported DWH spill work histories. We ascertained CHD events following each worker's last day of cleanup work as the first self-reported physician-diagnosed myocardial infarction (MI) or a fatal CHD event. We estimated hazard ratios (HR) and 95% confidence intervals for the associations of exposure quintiles (Q) with risk of CHD. We applied inverse probability weights to account for bias due to confounding and loss to follow-up. We used quantile g-computation to assess the joint effect of the BTEX-H mixture. Results: Among 22,655 workers with no previous MI diagnoses, 509 experienced an incident CHD event through December 2019. Workers in higher quintiles of each exposure agent had increased CHD risks in comparison with the referent group (Q1) of that agent, with the strongest associations observed in Q5 (range of HR = 1.14-1.44). However, most associations were nonsignificant, and there was no evidence of exposure-response trends. We observed stronger associations among ever smokers, workers with Conclusions: Higher exposures to volatile components of crude oil were associated with modest increases in risk of CHD among oil spill workers, although we did not observe exposure-response trends. https://doi.org/10.1289/EHP11859

Introduction The 2010 Deepwater Horizon (DWH) disaster was the largest marine oil spill in U.S. history (National Commission on the BP Deepwater Horizon Oil Spill and Offshore Drilling 2011). An [...], Background: During the 2010 Deepwater Horizon (DWH) disaster, controlled burning was conducted to remove oil from the water. Workers near combustion sites were potentially exposed to increased fine particulate matter [with aerodynamic diameter [less than or equal to] 2.5 [micro]m (P[M.sub.2.5])] levels. Exposure to P[M.sub.2.5] has been linked to decreased lung function, but to our knowledge, no study has examined exposure encountered in an oil spill cleanup. Objective: We investigated the association between estimated P[M.sub.2.5] only from burning/flaring of oil/gas and lung function measured 1-3 y after the DWH disaster. Methods: We included workers who participated in response and cleanup activities on the water during the DWH disaster and had lung function measured at a subsequent home visit (n = 2,316). P[M.sub.2.5] concentrations were estimated using a Gaussian plume dispersion model and linked to work histories via a job-exposure matrix. We evaluated forced expiratory volume in 1 s (FEV1; milliliters), forced vital capacity (FVC; milliliters), and their ratio (FEV1/FVC; %) in relation to average and cumulative daily maximum exposures using multivariable linear regressions. Results: We observed significant exposure-response trends associating higher cumulative daily maximum P[M.sub.2.5] exposure with lower FEV1 (p-trend = 0.04) and FEV1/FVC (p-trend = 0.01). In comparison with the referent group (workers not involved in or near the burning), those with higher cumulative exposures had lower FEV1 [-166.8 mL, 95% confidence interval (CI): -337.3, 3.7] and FEV1/FVC (-1.7, 95% CI: -3.6, 0.2). We also saw nonsignificant reductions in FVC (high vs. referent: -120.9, 95% CI: -319.4, 77.6; p-trend = 0.36). Similar associations were seen for average daily maximum P[M.sub.2.5] exposure. Inverse associations were also observed in analyses stratified by smoking and time from exposure to spirometry and when we restricted to workers without prespill lung disease. Conclusions: Among oil spill workers, exposure to P[M.sub.2.5] specifically from controlled burning of oil/gas was associated with significantly lower FEV1 and FEV1/FVC when compared with workers not involved in burning. https://doi.org/10.1289/EHP8930

Introduction The Deepwater Horizon (DWH) drilling rig explosion in April 2010 resulted in the largest marine oil spill in U.S. history (National Commission on the BP Deepwater Horizon Oil Spill [...], BACKGROUND: The 2010 Deepwater Horizon disaster led to the largest ever marine oil spill. Individuals who worked on the spill were exposed to toxicants and stressors that could lead to adverse effects. OBJECTIVES: The GuLF STUDY was designed to investigate relationships between oil spill exposures and multiple potential physical and mental health effects. METHODS: Participants were recruited by telephone from lists of individuals who worked on the oil spill response and clean-up or received safety training. Enrollment interviews between 2011 and 2013 collected information about spill-related activities, demographics, lifestyle, and health. Exposure measurements taken during the oil spill were used with questionnaire responses to characterize oil exposures of participants. Participants from Gulf states completed a home visit in which biological and environmental samples, anthropometric and clinical measurements, and additional health and lifestyle information were collected. Participants are being followed for changes in health status. RESULTS: Thirty-two thousand six hundred eight individuals enrolled in the cohort, and 11,193 completed a home visit. Most were young (56.2% [less than or equal to] 45 years of age), male (80.8%), lived in a Gulf state (82.3%), and worked at least 1 day on the oil spill (76.5%). Workers were involved in response (18.0%), support operations (17.5%), clean-up on water (17.4%) or land (14.6%), decontamination (14.3%), and administrative support (18.3%). Using an ordinal job exposure matrix, 45% had maximum daily total hydrocarbon exposure levels [greater than or equal to] 1.0 ppm. CONCLUSIONS: The GuLF STUDY provides a unique opportunity to study potential adverse health effects from the Deepwater Horizon oil spill.

The GuLF Long-term Follow-up Study (GuLF STUDY) is investigating potential adverse health effects of workers involved in the Deepwater Horizon (DWH) oil spill response and cleanup (OSRC). Over 93% of the 160 000 personal air measurements taken on OSRC workers were below the limit of detection (LOD), as reported by the analytic labs. At this high level of censoring, our ability to develop exposure estimates was limited. The primary objective here was to reduce the number of measurements below the labs' reported LODs to reflect the analytic methods' true LODs, thereby facilitating the use of a relatively unbiased and precise Bayesian method to develop exposure estimates for study exposure groups (EGs). The estimates informed a job-exposure matrix to characterize exposure of study participants. A second objective was to develop descriptive statistics for relevant EGs that did not meet the Bayesian criteria of sample size ≥5 and censoring ≤80% to achieve the aforementioned level of bias and precision. One of the analytic labs recalculated the measurements using the analytic method's LOD; the second lab provided raw analytical data, allowing us to recalculate the data values that fell between the originally reported LOD and the analytical method's LOD. We developed rules for developing Bayesian estimates for EGs with >80% censoring. The remaining EGs were 100% censored. An order-based statistical method (OBSM) was developed to estimate exposures that considered the number of measurements, geometric standard deviation, and average LOD of the censored samples for N ≥ 20. For N < 20, substitution of ½ of the LOD was assigned. Recalculation of the measurements lowered overall censoring from 93.2 to 60.5% and of the THC measurements, from 83.1 to 11.2%. A total of 71% of the EGs met the ≤15% relative bias and <65% imprecision goal. Another 15% had censoring >80% but enough non-censored measurements to apply Bayesian methods. We used the OBSM for 3% of the estimates and the simple substitution method for 11%. The methods presented here substantially reduced the degree of censoring in the dataset and increased the number of EGs meeting our Bayesian method's desired performance goal. The OBSM allowed for a systematic and consistent approach impacting only the lowest of the exposure estimates. This approach should be considered when dealing with highly censored datasets. [ABSTRACT FROM AUTHOR]

The Deepwater Horizon (DWH) drilling unit explosion above the Macondo oil well on 20 April 2010 caused the release of approximately 4.9 million barrels (779 million L) of oil into the Gulf of Mexico. As part of a larger spill response and clean-up effort, approximately 1.84 million gallons (6.81 million L) of chemical dispersants COREXIT™ EC9500A and COREXIT™ EC9527A were applied to the resultant oil slicks through spraying on the water surface by plane and by vessel and through injection at the release source near the seabed. The GuLF STUDY is investigating the health effects of workers involved in the oil spill response and clean-up after the DWH explosion, and estimates of possible exposure to chemical dispersants were needed. Exposures were estimated to the volatile components of COREXIT™ EC9500A [petroleum distillates, hydrotreated light, and propylene glycol (PG)] and of COREXIT™ EC9527A [2-butoxyethanol (2-BE) and PG] using two of AIHA IHMOD2.0© mathematical modeling tools along with the dispersants' chemical and physical properties. Monte Carlo simulations were used to reflect uncertainty in input parameters with both the two-box, constant emission model and the near and mid field plume model for indoor and outdoor activities, respectively. Possible exposure scenarios considered various evaporation rates, sizes of the dispersant pool, wind speeds, and ventilation rates. For the two-box model, mean near field exposure estimates to 2-BE ranged from 0.9 to 5.7 ppm, while mean far field estimated exposures ranged from 0.3 to 3.5 ppm. Estimates of mean near field plume model exposures ranged from 0.01 to 3.7 ppm at 2.5 ft from the source, and <0.01 to 0.3 ppm at 10 ft from the source. Estimated exposures to PG were approximately 10% of the calculated 2-BE exposures and exposures to petroleum distillates about 40% higher than the 2-BE estimates. Results indicate that compared with current occupational exposure guidelines, overexposure to petroleum distillates and PG probably did not occur in our study, but under some conditions, for short periods, exposure to 2-BE may have exceeded the limits for peak exposures. These estimates were developed for use in job-exposure matrices to estimate exposures of workers having contact with dispersant vapors for the GuLF STUDY. [ABSTRACT FROM AUTHOR]

The GuLF STUDY, initiated by the National Institute of Environmental Health Sciences, is investigating the health effects among workers involved in the oil spill response and clean-up (OSRC) after the Deepwater Horizon (DWH) explosion in April 2010 in the Gulf of Mexico. Clean-up included in situ burning of oil on the water surface and flaring of gas and oil captured near the seabed and brought to the surface. We estimated emissions of PM2.5 and related pollutants resulting from these activities, as well as from engines of vessels working on the OSRC. PM2.5 emissions ranged from 30 to 1.33e6 kg per day and were generally uniform over time for the flares but highly episodic for the in situ burns. Hourly emissions from each source on every burn/flare day were used as inputs to the AERMOD model to develop average and maximum concentrations for 1-, 12-, and 24-h time periods. The highest predicted 24-h average concentrations sometimes exceeded 5000 µg m−3 in the first 500 m downwind of flaring and reached 71 µg m−3 within a kilometer of some in situ burns. Beyond 40 km from the DWH site, plumes appeared to be well mixed, and the predicted 24-h average concentrations from the flares and in situ burns were similar, usually below 10 µg m−3. Structured averaging of model output gave potential PM2.5 exposure estimates for OSRC workers located in various areas across the Gulf. Workers located nearest the wellhead (hot zone/source workers) were estimated to have a potential maximum 12-h exposure of 97 µg m−3 over the 2-month flaring period. The potential maximum 12-h exposure for workers who participated in in situ burns was estimated at 10 µg m−3 over the ~3-month burn period. The results suggest that burning of oil and gas during the DWH clean-up may have resulted in PM2.5 concentrations substantially above the U.S. National Ambient Air Quality Standard for PM2.5 (24-h average = 35 µg m−3). These results are being used to investigate possible adverse health effects in the GuLF STUDY epidemiologic analysis of PM2.5 exposures. [ABSTRACT FROM AUTHOR]

The GuLF STUDY is investigating health outcomes associated with oil spill-related chemical exposures among workers involved in the spill response and clean-up following the Deepwater Horizon disaster. Due to the lack of dermal exposure measurements, we estimated dermal exposures using a deterministic model, which we customized from a previously published model. Workers provided information on the frequency of contact with oil, tar, chemical dispersants applied to the oil spill and sea water, as well as the use of protective equipment, by job/activity/task. Professional judgment by industrial hygienists served as a source of information for other model variables. The model estimated dermal exposures to total hydrocarbons (THC), benzene, ethylbenzene, toluene, xylene, n -hexane (BTEX-H), polycyclic aromatic hydrocarbons (PAHs), and dispersants in GuLF DREAM units (GDUs). Arithmetic means (AMs) of THC exposure estimates across study participants ranged from <0.02 to 5.50 GDUs for oil and <0.02 to 142.14 GDUs for tar. Statistical differences in the estimates were observed among the AMs of the estimates for some broad groups of worker activities over time and for some time periods across the broad groups of activities. N-Hexane had ranges similar to THC for oil exposures (e.g. AMs up to 2.22 GDUs) but not for tar (up to 5.56 GDUs). Benzene, ethylbenzene, toluene, and xylene, in contrast, were characterized by higher exposure levels than THC for oil (AMs up to 12.77, 12.17, 17.45, and 36.77 GDUs, respectively) but lower levels than THC to tar (AMs up to 3.69, 11.65, 42.37, and 88.18 GDUs, respectively). For PAHs, the AMs were as high as 219.31 and 587.98 for oil and tar, respectively. Correlations of these seven substances to each other were high (>0.9) for most of the substances in oil but were lower for some of the substances in tar. These data were linked to the study participants to allow investigation of adverse health effects that may be related to dermal exposures. [ABSTRACT FROM AUTHOR]

Objectives Our objectives were to (i) determine correlations between measurements of THC and of BTEX-H, (ii) apply these linear relationships to predict BTEX-H from measured THC, (iii) use these correlations as informative priors in Bayesian analyses to estimate exposures. Methods We used a Bayesian left-censored bivariate framework for all 3 objectives. First, we modeled the relationships (i.e. correlations) between THC and each BTEX-H chemical for various overarching groups of measurements using linear regression to determine if correlations derived from linear relationships differed by various exposure determinants. We then used the same linear regression relationships to predict (or impute) BTEX-H measurements from THC when only THC measurements were available. Finally, we used the same linear relationships as priors for the final exposure models that used real and predicted data to develop exposure estimate statistics for each individual exposure group. Results Correlations between measurements of THC and each of the BTEX-H chemicals (n = 120 for each of BTEX, 36 for n -hexane) differed substantially by area of the Gulf of Mexico and by time period that reflected different oil-spill related exposure opportunities. The correlations generally exceeded 0.5. Use of regression relationships to impute missing data resulted in the addition of >23 000 n -hexane and 541 observations for each of BTEX. The relationships were then used as priors for the calculation of exposure statistics while accounting for censored measurement data. Conclusions Taking advantage of observed relationships between THC and BTEX-H allowed us to develop robust exposure estimates where a large amount of data were missing, strengthening our exposure estimation process for the epidemiologic study. [ABSTRACT FROM AUTHOR]

The NIEHS GuLF STUDY is an epidemiologic study of the health of workers who participated in the 2010 Deepwater Horizon oil spill response and clean-up effort. Even with a large database of approximately 28 000 personal samples that were analyzed for total hydrocarbons (THCs) and other oil-related chemicals, resulting in nearly 160 000 full-shift personal measurements, there were still exposure scenarios where the number of measurements was too limited to rigorously assess exposures. Also available were over 26 million volatile organic compounds (VOCs) area air measurements of approximately 1-min duration, collected from direct-reading instruments on 38 large vessels generally located near the leaking well. This paper presents a strategy for converting the VOC database into hourly average air concentrations by vessel as the first step of a larger process designed to use these data to supplement full-shift THC personal exposure measurements. We applied a Bayesian method to account for measurements with values below the analytic instrument's limit of detection while processing the large database into average instrument-hour concentrations and then hourly concentrations across instruments on each day of measurement on each of the vessels. To illustrate this process, we present results on the drilling rig ship, the Discoverer Enterprise. The methods reduced the 26 million measurements to 21 900 hourly averages, which later contributed to the development of additional full-shift THC observations. The approach used here can be applied by occupational health professionals with large datasets of direct-reading instruments to better understand workplace exposures. [ABSTRACT FROM AUTHOR]

BACKGROUND: During the 2010 Deepwater Horizon (DWH) disaster, controlled burning was conducted to remove oil from the water. Workers near combustion sites were potentially exposed to increased fine particulate matter [with aerodynamic diameter =2.5µm (PM[sub 2.5])] levels. Exposure to PM[sub 2.5] has been linked to decreased lung function, but to our knowledge, no study has examined exposure encountered in an oil spill cleanup. OBJECTIVE: We investigated the association between estimated PM[sub 2.5] only from burning/flaring of oil/gas and lung function measured 1–3 y after the DWH disaster. METHODS: We included workers who participated in response and cleanup activities on the water during the DWH disaster and had lung function measured at a subsequent home visit (푛=2,316). PM[sub 2.5] concentrations were estimated using a Gaussian plume dispersion model and linked to work histories via a job-exposure matrix. We evaluated forced expiratory volume in 1 s (FEV1; milliliters), forced vital capacity (FVC; milliliters), and their ratio (FEV1/FVC; %) in relation to average and cumulative daily maximum exposures using multivariable linear regressions. RESULTS: We observed significant exposure–response trends associating higher cumulative daily maximum PM[sub 2.5] exposure with lower FEV1 (푝-trend=0.04) and FEV1/FVC (푝-trend=0.01). In comparison with the referent group (workers not involved in or near the burning), those with higher cumulative exposures had lower FEV1 [-166.8mL, 95% confidence interval (CI): -337.3, 3.7] and FEV1/FVC (-1.7, 95% CI: -3.6, 0.2). We also saw nonsignificant reductions in FVC (high vs. referent: -120.9, 95% CI: -319.4, 77.6; 푝-trend=0.36). Similar associations were seen for average daily maximum PM[sub 2.5] exposure. Inverse associations were also observed in analyses stratified by smoking and time from exposure to spirometry and when we restricted to workers without prespill lung disease. CONCLUSIONS: Among oil spill workers, exposure to PM[sub 2.5] specifically from controlled burning of oil/gas was associated with significantly lower FEV1 and FEV1/FVC when compared with workers not involved in burning. [ABSTRACT FROM AUTHOR]