Exploration of the Use of Bayesian Modeling of Gradients for Censored Spatiotemporal Data from the Deepwater Horizon Oil Spill

Details

• Personal Author:
  Banerjee S ; Carlin BP ; Engel LS ; Groth C ; Kwok RK ; Quick H ; Sandler DP ; Stenzel MR ; Stewart PA
• Description:
  This paper develops a hierarchical framework for identifying spatiotemporal patterns in data with a high degree of censoring using the gradient process. To do this, we impute censored values using a sampling-based inverse CDF method within our Markov chain Monte Carlo algorithm, thereby avoiding burdensome integration and facilitating efficient estimation of other model parameters. We illustrate use of our methodology using a simulated data example, and uncover the danger of simply substituting a space- and time-constant function of the level of detection for all missing values. We then fit our model to area measurement data of volatile organic compound (VOC) air concentrations collected on vessels supporting the response and clean-up efforts of the Deepwater Horizon oil release that occurred starting April 20, 2010. These data contained a high percentage of observations below the detectable limits of the measuring instrument. Despite this, we were still able to make some interesting discoveries, including elevated levels of VOC near the site of the oil well on June 26th. Using the results from this preliminary analysis, we hope to inform future research on the Deepwater Horizon study, including the use of gradient methods for assigning workers to exposure categories. [Description provided by NIOSH]
• Subjects:
  Environmental Exposure Environmental Pollution Occupational Health Safety Volatile Organic Compounds
• Keywords:
  Author Keywords: Gaussian Process Censored Data Environmental Pollution Exposure Assessment Gradients Hierarchical Modeling Markov Chain Monte Carlo Models Oil Spills Spatiotemporal Data Statistical Analysis VOCs Volatile Organic Compounds

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• ISSN:
  2211-6753
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Minnesota ; Missouri ; North Carolina ; OSHA Region 3 ; OSHA Region 4 ; OSHA Region 5 ; OSHA Region 7 ; OSHA Region 9 ; Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  166-179
• Volume:
  9
• NIOSHTIC Number:
  nn:20055688
• Citation:
  Spat Stat 2014 Aug; 9:166-179
• Contact Point Address:
  Harrison Quick, Department of Statistics, University of Missouri, Columbia, MO 65211, United States
• Email:
  quickh@missouri.edu
• Federal Fiscal Year:
  2014
• Performing Organization:
  University of California, Los Angeles
• Peer Reviewed:
  True
• Start Date:
  20130901
• Source Full Name:
  Spatial Statistics
• End Date:
  20170831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:d1a3a42df67145b50c23f08377938c909abd99193cb5e4f6f57cff122f3a3ca5dfe80344920aa3c704036c4920f91cfc99a9373c3439ea2bba636c1234939e0e
• Download URL:
  https://stacks.cdc.gov/view/cdc/213478/cdc_213478_DS1.pdf
• File Type:
  [PDF - 2.27 MB ]