Using a probabilistic approach in an ecological risk assessment simulation tool: Test case for depleted uranium (DU)

Ming Fan, Tepwitoon Thongsri, Lisa Axe, Trevor A. Tyson

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A probabilistic approach was applied in an ecological risk assessment (ERA) to characterize risk and address uncertainty employing Monte Carlo simulations for assessing parameter and risk probabilistic distributions. This simulation tool (ERA) includes a Window's based interface, an interactive and modifiable database management system (DBMS) that addresses a food web at trophic levels, and a comprehensive evaluation of exposure pathways. To illustrate this model, ecological risks from depleted uranium (DU) exposure at the US Army Yuma Proving Ground (YPG) and Aberdeen Proving Ground (APG) were assessed and characterized. Probabilistic distributions showed that at YPG, a reduction in plant root weight is considered likely to occur (98% likelihood) from exposure to DU; for most terrestrial animals, likelihood for adverse reproduction effects ranges from 0.1% to 44%. However, for the lesser long-nosed bat, the effects are expected to occur (>99% likelihood) through the reduction in size and weight of offspring. Based on available DU data for the firing range at APG, DU uptake will not likely affect survival of aquatic plants and animals (<0.1% likelihood). Based on field and laboratory studies conducted at APG and YPG on pocket mice, kangaroo rat, white-throated woodrat, deer, and milfoil, body burden concentrations observed fall into the distributions simulated at both sites.

Original languageEnglish (US)
Pages (from-to)111-125
Number of pages15
JournalChemosphere
Volume60
Issue number1
DOIs
StatePublished - Jun 2005

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Environmental Chemistry
  • Chemistry(all)
  • Pollution
  • Health, Toxicology and Mutagenesis

Keywords

  • Bioavailability
  • Depleted uranium
  • Ecological risk assessment
  • Monte Carlo simulation

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