Uncertainty in multi-media fate and transport models: a case study for TNT life cycle assessment.
Uncertainty in multi-media fate and transport models: a case study for TNT life cycle assessment.
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多媒体命运和运输模型的不确定性：TNT 生命周期评估的案例研究。
DOI：
10.1016/j.scitotenv.2014.06.061
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
M. Chappell
中科院分区：
文献类型：
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作者：
Michael L. Mayo;Zachary A. Collier;Vu Hoang;M. Chappell
生命周期评估（LCA）是决策者用来帮助评估各种工业过程的相对环境影响的一种评估方法。尽管许多 LCA 方法仍然对不确定的输入数据敏感，这可能会降低其结果的效用，但对组成 LCA 模型所产生的不确定性仍然知之甚少。在这里，我们开始通过评估参数值不确定性影响 SimpleBox 2.0 命运和传输模型的程度来解决这个问题，该模型是许多 LCA 生态毒理学影响类别的支柱。使用两种蒙特卡罗型采样方法评估了手榴弹生产中涉及的三种化学品的稳态浓度值的离散度：甲苯、2,4-二硝基甲苯 (2,4-DNT) 和 2,4,6-三硝基甲苯 (TNT)。首先一次随机采样一个参数，然后随机探索参数空间的局部补丁。我们证实，全球温度对模型结果的总体方差有主要影响，其幅度最多跨越约 8 个十年。这些结果与之前针对整个 LCA 方法获得的结果一致。 LCA方法迭代地进行计算；因此，减少单一组成部分（例如命运和运输模型）的错误可能会提高其作为决策辅助手段的性能和效用。
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.