Table 1.
Overview of the exposure assessment process
| Steps | Reference | Comment |
|---|---|---|
| Questionnaire development | ||
| Reviewed air measurement data for determinant information to develop exposure groups that could link to questions in the questionnaire | Stenzel, Groth, Huynh et al., 2021 | Due to limited job title information in measurement database, interview questionnaire focused on work activities |
| Developed dermal exposure questions | Stewart et al., 2021 | Input data for GuLF DREAM (below) |
| Exposure groups (EGs) | ||
| Reviewed and coded measurement data for exposure determinants that were identifiable for study participants | Stenzel, Groth, Huynh et al., 2021 | Based on exposure determinants, primarily on job-activity-task, location, and time (Supplementary Tables S1 and S2, available at Annals of Work Exposures and Health online). |
| Exposure estimation | ||
| THC and BTEX-H | ||
| Investigated effect of censorship of measurements, small sample sizes, high variability, and multiple LODs and distributions on various statistical approaches. Selected estimation model | Huynh et al., 2014, 2016 | Selected Bayesian methods to develop exposure statistics: selected N ≥ 5 and % censoring ≤80 as criteria for exposure estimation of an EG |
| Recalculated the personal monitoring data to reflect the analytic LOD | Stenzel, Groth, Banerjee et al., 2021 | Overall censoring decreased from 93 to 60% |
| Excluded inappropriate personal measurements and measurements of other non-study related chemicals | Stenzel, Groth, Banerjee et al., 2021 | Decreased total number of measurements (160 000; 143 000 THC and BTEX-H) to 135 000 THC and BTEX-H measurements |
| Developed methodology to predict exposures using Bayesian univariate and bivariate models from personal measurements | Groth et al., 2017, 2018 | Used Bayesian univariate model to develop THC exposure statistics; the bivariate model with THC to develop BTEX-H exposure statistics |
| Estimated priors for Bayesian analyses | Groth, Huynh et al., 2021 | Used correlations between THC and BTEX-H personal measurements based on high-level exposure determinants for priors |
| Developed personal exposure estimates | For rig workers (Huynh et al., 2021a); other water workers (Huynh et al., 2021b); and land workers (Huynh et al, 2021c) | For all EGs with N ≥ 5 and % censoring ≤80, developed AMs, GMs, GSDs, and 95%iles and their 95% CIs for a JEM for each of THC and BTEX-H |
| Developed methods to estimate EGs with measurements that did not meet estimation criteria | Stenzel, Groth, Banerjee et al., 2021, Stewart et al., this paper, SM | 1) Relaxed rule of <80% censoring if number of measurements substantially exceeded 5 for AMs, GMs, GSDs, and 95%iles and their 95% CIs 2) Further relaxed rules when censoring and N criteria were not met3) Where N was <5, used the concept of sister ships, sister states and sister time periods 4) Used order-based statistical method (probability Z-scores) if N ≥ 20 and censoring = 100% for AMs, GMs, and GSDs and 95%iles 5) Used a substitution method if N ≥ 5 to <20 and censoring=100% for AMs, GMs, GSD, and 95%iles Inserted into JEM |
| Developed methods to estimate EGs with 0 to <5 measurements | Stenzel, Groth, Banerjee et al., 2021 | Used similar EGs (‘sister’ rigs, broad rig job groups, ‘sister’ states, ‘sister’ time periods) for AM, GM, GSD and 95%ile estimates. Inserted into JEM |
| Reviewed >26 000 000 area VOC measurements and summarized | Groth, Banerjee et al., 2021 | Reduced number of measurements to ~22,000 VOCs hourly vessel estimates |
| Developed THC full-shift equivalent estimates from VOCs hourly vessel estimates based on THC:VOCs relationship. Calculated THC means from original THC measurements and converted THC estimates by vessel-time period. Applied bivariate method to develop BTEX-H estimates | Ramachandran et al., 2021 | Increased the number of vessel-days available for estimation by 60% for ROV and response marine vessels |
| PM2.5 | ||
| Estimated emissions from information in literature; modeled air concentrations from emissions, AERMOD and other input parameters; estimated air concentrations by averaging air concentrations across broad areas of the Gulf and days | Pratt et al., 2021 | Quantitative PM2.5 air concentration estimates of AMS and GSDS for 18 areas across the Gulf of Mexico and 4 Gulf coastal states for TP1b for JEM |
| Dispersants | ||
| Analyzed dispersant-related exposure situations. Applied AgDisp model with input parameters for aerosols from aerial and vessel spraying | Arnold et al., 2021 | Air concentration estimates of AMs, GMs, GSDs dispersant aerosol exposures. Analysis suggested low probability of exposure, generally low levels and low duration of exposure. |
| Modeled vapor exposures using two-box and Plume models with Monte Carlo simulations for dispersant handling or being in an area with dispersants | Stenzel, Arnold et al., 2021 | Air concentration estimates of dispersant vapor exposures for AMs GSDs, and 95th percentiles, and their 90% confidence intervals for the JEM. Generally low levels. |
| Oil mist | ||
| Applied professional judgment by 2 industrial hygienists and achieved consensus for pressure washing or wave action sources | This paper | Ordinal estimates of oil mist exposure (scale 0–4) for JEM |
| Dermal Exposures (THC, BTEX-H, PAHs, dispersants) | ||
| Reviewed dermal estimation models and recent dermal exposure studies to modify previously published dermal assessment model | Gorman Ng et al., 2021 | Developed GuLF DREAM model |
| Applied study participant responses and professional judgment by industrial hygienists as input parameters to model | Stewart et al., 2021 | Participant-specific quantitative estimates of dermal exposures |
N, number; CI, credible interval; JEM, job-exposure matrix; SM, supplementary materials. TP1b, 15 May, 2010–15 July 2010.