Gaps in toxic industrial chemical model systems: Improvements and changes over past 10 years

Abstract

To assess the hazards of the releases of toxic industrial chemicals (TICs) to the atmosphere, comprehensive model systems are often used, which begin with the scenario definition and end with an estimate of health risk. In 2008 and 2010, the US Department of Homeland Security and Defense Threat Reduction Agency sponsored reports that identified knowledge gaps in TIC modeling. The current paper discusses which of the knowledge gaps were satisfactorily resolved in the past 10 years by new theoretical and experimental research, such as the 2010 and 2015–2016 Jack Rabbit field experiments. For example, the linked source emissions and transport and dispersion (T&D) models have been shown, in comparisons with Jack Rabbit II observations, to not have large mean biases. Consequently, the T&D models are less likely to be the cause of model system overpredictions of casualties observed after large TIC accidental releases, such as the Festus, Macdona, and Graniteville chlorine railcar incidents. It may be that the deposition models and/or the health effects models still need improvement. In addition to comments on the knowledge gaps identified 10 years ago, a few new knowledge gaps are addressed, such as indoor T&D and deposition, and estimating the magnitude of the saturation deposition value for various substrates and chemicals.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 16, 2021
Source ID
10.1002/prs.12289

Entities

People

  • Joseph Chang
  • Rachel Batt
  • Simon Gant
  • Steven R. Hanna
  • Thomas Mazzola
  • Thomas O. Spicer

Organizations

  • Defense Threat Reduction Agency
  • Health and Safety Executive
  • RAND Corporation
  • Science Applications International Corporation
  • United States Department of Homeland Security
  • University of Arkansas

Tags

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Environmental Engineering.
  • Organizational Process Management (OPM).