| 基于iTOUGH2的生物降解模型全局敏感性时变分析 |
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| 引用本文： | 杜建雯,施小清,徐红霞,吴吉春. 基于iTOUGH2的生物降解模型全局敏感性时变分析[J]. 水文地质工程地质, 2020, 47(2): 35-42. DOI: 10.16030/j.cnki.issn.1000-3665.201902037 |
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| 作者姓名： | 杜建雯 施小清 徐红霞 吴吉春 |
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| 基金项目： | 国家自然科学基金项目（41730856；41672229） |
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| 摘 要： | Monod动力学方程被广泛应用于描述地下水中有机污染物微生物降解过程。由于Monod方程参数众多，采用敏感性分析可识别参数的重要程度，有助于参数反演和理解微生物降解过程。已有敏感性分析一般仅关注敏感性的整体平均值及其随空间的变化，很少考虑敏感性随时间的变化。以一个好氧生物降解甲苯的一维砂柱试验为例，基于iTOUGH2采用Morris法和Sobol’法对降解过程参数及试验条件参数开展全局敏感性时变分析。研究结果发现，由于微生物好氧降解能力随时间先提高后减弱，导致降解过程参数的敏感性相应地随时间先增加后减少。最大基质降解速率k的Sobol’一阶敏感性指数在试验早期小于10%，中期最大为62%，晚期减至49%。参数间的相互作用效应随时间先增大后减小。k的参数间相互作用效应根据Sobol’总敏感性指数与一阶敏感性指数的差值表征，该值在试验早期和晚期近乎为0，中期达6%。通过敏感性以及参数间相互作用效应的时变分析发现，试验晚期的观测数据对模型过程参数的敏感性较大以及相互作用效应较小，因此选择试验晚期数据更有利于降解过程参数的反演识别。同时由于试验条件参数在早期敏感性较大，为避免试验条件控制不当导致的观测数据误差增大，试验早期应较中期和晚期更严格控制试验条件。
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| 关 键 词： | 全局敏感性分析 敏感性时变分析 好氧生物降解 相互作用效应 参数反演 试验设计 |
| 收稿时间： | 2019-02-27 |
Temporal variation of global sensitivity analysis for biodegradation model using iTOUGH2 |
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| Abstract: | Monod kinetic equation is widely used to describe the microbial biodegradation process of organic groundwater contaminants. Due to a large number of parameters in the Monod equation, sensitivity analysis can be used to identify the importance of the parameters, which is helpful for parameter inversion and microbial biodegradation process understanding. However, most existing sensitivity analyses only focus on average sensitivity value and space variation, seldom considering sensitivity over time. In this paper, a one-dimensional sand-column experiment of toluene aerobic biodegradation was taken as an example. Based on iTOUGH2 global sensitivity analysis (GSA), we used Morris and Sobol’ methods to analyze degradation process parameters and experimental parameters changing with time. The results show that the aerobic degradation ability of microbes first increases and then decreases over time, which leads to the same trend for the degradation process parameters sensitivity. Sobol’ Index of the maximum substrate degradation rate k varies from less than 10% at early-stage to at most 62% at middle-stage and decreases to 49% at late-stage. The parameter interaction effect also varies similarly to sensitivity. We used the differences between Sobol’ Total Sensitivity Index and Sobol’ Index to describe parameter k’s parameter interaction effect, which in this case are both at around 0% for early and late stages and rises to 6% at middle-stage. Through these time-varying analyses of sensitivity and parameter interaction effect, we find that observations in the late-stage of the experiment are more sensitive to degradation process parameters and the parameter interaction effect in the late-stage is smaller, so selecting the observations in the late-stage is more beneficial for the degradation process parameters inversion. Besides, to avoid the possible increase of observations error,the one caused by improper experimental control, the experimental conditions should be more strictly controlled in the early-stage than in the middle and late stages because the experimental parameters are more sensitive in the early-stage. |
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