Literature DB >> 25037048

Uncertainty in multi-media fate and transport models: a case study for TNT life cycle assessment.

Michael Mayo1, Zachary A Collier1, Vu Hoang2, Mark Chappell3.   

Abstract

Life cycle assessment (LCA) is an evaluation method used by decision-makers to help assess the relative environmental impacts of various industrial processes. Despite that many LCA methods remain sensitive to uncertain input data, which can reduce the utility of their results, uncertainty arising from constituent LCA models remains poorly understood. Here, we begin to address this problem by evaluating the extent to which parameter-value uncertainty affects the SimpleBox 2.0 fate and transport model, which serves as a backbone for many LCA ecotoxicological impact categories. Two Monte Carlo type sampling methods were used to evaluate dispersion in steady-state concentration values for three chemicals involved in grenade production: toluene, 2,4-dinitrotoluene (2,4-DNT), and 2,4,6-trinitrotoluene (TNT). Parameters were first sampled stochastically one-at-a-time, then by randomly exploring a local patch of the parameter space. We confirmed that global temperatures contribute primarily to the overall variance of model results, which at most spanned approximately 8 decades in magnitude. These results are consistent with previous results obtained for the whole of the LCA method. LCA methods carry out calculations iteratively; a reduction in the error of a single component, such as the fate and transport model, may therefore improve its performance and utility as a decision-making aid. Published by Elsevier B.V.

Entities:  

Keywords:  Fate and transport model; Life cycle assessment; Sensitivity analysis; SimpleBox; Trinitrotoluene; Uncertainty

Mesh:

Substances:

Year:  2014        PMID: 25037048     DOI: 10.1016/j.scitotenv.2014.06.061

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Data-Driven Method to Estimate Nonlinear Chemical Equivalence.

Authors:  Michael Mayo; Zachary A Collier; Corey Winton; Mark A Chappell
Journal:  PLoS One       Date:  2015-07-09       Impact factor: 3.240

2.  Building geochemically based quantitative analogies from soil classification systems using different compositional datasets.

Authors:  Mark A Chappell; Jennifer M Seiter; Haley M West; Brian D Durham; Beth E Porter; Cynthia L Price
Journal:  PLoS One       Date:  2019-02-19       Impact factor: 3.240

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.