• Abstract and Figures
• Public Full-text

Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Caroline P. Groth IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Sudipto Banerjee Gurumurthy Ramachandran July, 2017 c Caroline P. Groth 2017 ALL RIGHTS RESERVED Acknowledgements I would like to thank members of the GuLF STUDY including Mark Stenzel, Patri- cia Stewart, Dale Sandler (PI), Richard Kwok (Co-PI), Lawrence Engel, Aaron Blair, Tran Huynh, Harrison Quick, Sudipto Banerjee, Gurumurthy Ramachandran, Wendy McDowell, Caitlin Roush, and Susan Arnold. I would especially like to thank my primary advisor Sudipto Banerjee for his support and encouragement throughout my dissertation. Without his support, this would not have been possible. I would also like to thank Mark Stenzel and Patricia Stewart for their efforts co- ordinating the exposure assessment effort. Their expertise in industrial hygiene and chemistry allowed us to develop the models enclosed in this dissertation. Additionally, I would like to thank Harrison Quick for teaching me Bayesian statistics my first year and acting like a mentor to me over the years. I would also like to especially thank Lynn Eberly for serving as a research mentor for me while my advisors have been away. Finally, I would like to thank the participants in the GuLF STUDY. Without their participation in this study, this research would not be possible. This research was supported by the Intramural Research Program of the NIH, Na- tional Institute of Environmental Health Sciences (Z01 ES102945) and the NIH Com- mon Fund. Sudipto Banerjee and Gurumurthy Ramachandran acknowledge support from their grant CDC/NIOSH 01OH010093. The authors declare no conflict of inter- est relating to the material presented in this dissertation. Its contents, including any opinions and/or conclusions expressed, are solely those of the authors. I also acknowledge the University of Minnesota's Super Computing Institute (MSI) for the use of their computing resources for parts of this dissertation. i Abstract In April 2010, the Deepwater Horizon oil rig caught fire and sank, sending approx- imately 5 million barrels of oil into the Gulf of Mexico over the ensuing 3 months. Thousands of workers were involved in the response and cleanup efforts. Many harm- ful chemicals were released into the air from crude oil, including total hydrocarbons (THC), benzene, toluene, ethylbenzene, xylene, hexane (BTEXH), and volatile organic compounds (VOCs). NIEHS's GuLF STUDY investigators are estimating the exposures the workers experienced related to their response and cleanup work and evaluating as- sociations between the exposures and detrimental health outcomes. My research focuses on developing statistical methods to quantify airborne chemical exposures in response to this event and to other settings in environmental health. Fac- tors complicating the exposure estimation include analytical method and data collection limitations. All analytical methods used to measure chemical concentrations have a limit of detection (LOD), or a threshold below which exposure cannot be detected with the analytical method (measurements below the LOD are called censored measurements). However, even these low exposures must be assessed to provide accurate estimates of exposure. Similarly, due to the scope of this event, it was not possible to take measure- ments in all scenarios where workers were involved in the response. Therefore, we must develop methods that allow us to estimate exposures under these limitations. In this dissertation, I introduce a strategy that uses chemical linear relationships to inform exposure estimates. We describe a Bayesian linear model for quantifying exposure while accounting for censoring in both a chemical predictor and a response. We further expand this model to quantify exposure in multiple EGs. Then, I describe a multivariate Bayesian linear model used to quantify exposures under various amounts of LOD censoring in the chemical response and multiple chemical predictors. We assess our model's performance against simpler models at a variety of censoring levels using WAIC. We apply our model to assess vapor exposures from measurements of volatile substances in crude oil on the Ocean Intervention III taken during the Deepwater Horizon oil spill response and cleanup. i Next, I explain how we used a database of over 26 million VOC measurements to supplement information in THC and BTEXH. I discuss the methods we used to convert this large VOC database into a exposure metric that could be compared with THC exposure. Then, I describe how we used the VOC exposure metrics to estimate THC and BTEXH exposure when VOC information was available but THC/BTEXH measurements were unavailable. Finally, I expand the Bayesian linear framework to a spatial setting that allows us to estimate exposure for particular areas in the Gulf of Mexico while accounting for values below LOD in both the response and predictor of interest. We also investigate imputation strategies designed to allow us to estimate exposure to our chemical predictor (providing input to our model) so we can better estimate our chemical response. I conclude with a brief description of our current investigation of environmental exposures during the Deepwater Horizon response and cleanup efforts. ii Contents Acknowledgements i Abstract i List of Tables viii List of Figures xiii 1 Introduction 1 1.1 Deepwater Horizon Background . .1 1.2 Chemical Exposures of Interest . .1 1.3 Exposure Assessment . .2 1.3.1 Individual Variability . .2 1.3.2 Data Collection . .2 1.3.3 Limits of Detection . .3 1.4 Determinants of Exposure . .4 1.4.1 Time Periods . .4 1.4.2 Areas . .5 1.4.3 Vessels . .6 1.4.4 Job Titles . .7 1.4.5 Exposure Groups . .7 1.5 Preview . .8 2 Bivariate left-censored Bayesian model for predicting exposure: Pre- liminary analysis of worker exposure during the Deepwater Horizon iii oil spill 9 2.1 Exposures during the Deepwater Horizon Oil Spill . 10 2.1.1 Left Censoring Statistical Methods . 12 2.2 Statistical Methods . 13 2.2.1 Posterior Predictive Model Comparisons . 16 2.3 Simulation Study . 18 2.3.1 Methods . 18 2.3.2 Results . 20 2.4 Results: Illustrative Example of Estimation of Xylene Exposure during the Deepwater Horizon Oil Spill . 23 2.5 Discussion . 29 2.5.1 Importance of Censored Data . 29 2.5.2 Results from the Deepwater Horizon Oil Spill . 30 2.5.3 General Discussion . 31 3 Multivariate left-censored Bayesian model for predicting exposure us- ing multiple chemical predictors 33 3.1 Introduction . 33 3.2 Background . 34 3.2.1 Multicollinearity . 35 3.3 Statistical methods . 36 3.3.1 Posterior inference . 39 3.3.2 Posterior predictive model comparisons: WAIC . 41 3.4 Simulation studies . 44 3.4.1 Data development . 44 3.4.2 Model comparison simulation . 45 3.4.3 Coverage simulation . 49 3.5 Preliminary analysis: Deepwater Horizon oil spill response and clean-up efforts . 53 3.5.1 Model comparison 1 . 55 3.5.2 General results . 56 3.6 Discussion . 58 iv 4 Supplementing information in THC and BTEXH using a database of 26 million VOC area observations collected during the Deepwater Horizon oil spill clean-up 60 4.1 Methods for Supplementing Information in THC and BTEXH Using the VOC Database . 61 4.1.1 Chemical and Sampling Background . 61 4.1.2 Big Data Methods . 62 4.1.3 Results: Discoverer Enterprise ................... 68 4.1.4 Instrument Hourly AMs . 68 4.1.5 Daily Averages and Relationship between THC and VOC . 72 4.1.6 Discussion . 72 4.2 ROV and Marine Vessel Results . 74 4.2.1 Development of VOC Prediction Set . 74 4.2.2 Predicting Daily THC Exposure . 75 4.2.3 Predicting Daily BTEXH Exposure . 76 4.2.4 Pairing TWAs with Latitude/Longitude Information . 80 5 Left-censored Areal Bayesian Spatial Model for Predicting Chemical Exposures using a Linear Relationship with a Left-censored Chemical Covariate 82 5.1 Statistical and Scientific Background . 83 5.1.1 Scientific Background . 83 5.1.2 Statistical Background . 84 5.2 Statistical Methods . 84 5.3 Simulation Study 1 . 86 5.3.1 Data Set Up . 87 5.3.2 Methods . 91 5.3.3 Results . 91 5.4 Estimating Areas with Missing Data Methods . 95 5.5 Simulation Study 2: Areas with Missing Data . 96 5.5.1 Model Comparison Methods . 97 5.5.2 Results . 98 v 5.6 Xylene Exposures During the Deepwater Horizon Oil Spill Response and Cleanup . 104 5.6.1 Spatial Data Preparation . 104 5.6.2 Comparison of Methods . 109 5.6.3 Results . 112 5.6.4 Discussion of Results and Future Directions within the Deepwater Horizon Spatial Analysis . 113 5.7 Discussion . 115 6 Conclusion and Discussion 117 6.1 Summary . 117 6.2 Limitations and Assumptions . 118 6.2.1 GuLF STUDY Assumptions . 118 6.3 Future Research . 119 6.3.1 Spatial Research . 119 6.3.2 Big Data Research . 119 6.4 Implications of the GuLF STUDY . 120 Bibliography 121 Appendix A. Acronyms 125 A.1 Acronyms . 125 Appendix B. Appendix for Chapter 2 127 B.1 Oil Weathering . 127 B.2 Linear Relationship Background . 128 B.3 RJAGS Model . 129 B.4 Openbugs Model . 130 B.5 Illustrative Example from EGs on the DDIII : Results with Inverse-gamma Variance Priors . 131 B.5.1 Prior and Model Specification . 131 B.5.2 Figures . 132 B.6 Uniform Prior Supplementary Materials . 139 vi B.6.1 Simulation Study: Results with Uniform Priors on Standard De- viations . ..

Load more

  177

Copy Link