基于iTOUGH2的生物降解模型全局敏感性时变分析
Temporal variation of global sensitivity analysis for biodegradation model using iTOUGH2
-
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。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.
-
-
[1] [1]邓亚平,郑菲,施小清,等.多孔介质中DNAPLs运移行为研究进展[J].南京大学学报(自然科学版),2016,52(3):409-420. [DENG Y P, ZHENG F, SHI X Q, et al. Review on the transport of dense non-aqueous phase liquids in porous media [J]. Journal of Nanjing University (Natural Sciences), 2016,52(3):409-420.(in Chinese)]
[2] [2]胥思勤,王焰新.土壤及地下水有机污染生物修复技术研究进展[J].环境保护,2001,29(2):22-23. [XU S Q, WANG Y X. Bioremediation of organic contaminated soil and groundwater [J]. Engineering Protection 2001,29(2):22-23.(in Chinese)]
[3] [3]AZUBUIKE C C, CHIKERE C B, OKPOKWASILI G C. Bioremediation techniques-classification based on site of application: principles, advantages, limitations and prospects [J]. World Journal of Microbiology and Biotechnology, 2016,32(11):1-18.
[4] [4]安永磊.原位生物修复硝基苯污染地下水微生物群落结构及修复效能[D].长春:吉林大学,2012. [AN Y L. The microbial community structure and remediation efficiency in in-situ bioremediation of nitrobenzene contaminated groundwater [D]. Changchun: Jilin University, 2012.(in Chinese)]
[5] [5]MONOD J. The growth of bacterial cultures[J]. Annual Review of Microbiology,1949,3(1):371-394.
[6] [6]郑春苗, BENNETT G D.地下水污染物迁移模拟[M]. 2版.北京:高等教育出版社,2009:58-66. [ZHENG C M, BENNETT G D. Applied contaminant transport modeling[M]. 2nd ed.Beijing: Higher Education Press, 2009:58-66.(in Chinese)]
[7] [7]JUNG Y, BATTISTELLI A. User’s guide for biodegradation reactions in TMVOCBio [R]. Office of Scientific and Technical Information (OSTI), 2017.DOI: 10.2172/1377850.
[8] [8]LACROIX E, BROVELLI A, HOLLIGER C, et al. Evaluation of silicate minerals for pH control during bioremediation: Application to chlorinated solvents [J]. Water Air & Soil Pollution, 2012, 223(5):2663-2684.
[9] [9]CONARD S R. Modeling DNAPL pool dissolution: sensitivity analysis, inhibition kinetic effects, and intermediate-scale flow cell experiment evaluation [D]. Michigan Technological University, 2016:74-84.
[10] [10]MOHAMED M, HATFIELD K. Dimensionless parameters to summarize the influence of microbial growth and inhibition on the bioremediation of groundwater contaminants [J]. Biodegradation, 2011,22(5):877-896.
[11] [11]郑菲,施小清,吴吉春,等.苏北盆地盐城组咸水层CO2地质封存泄漏风险的全局敏感性分析[J].高校地质学报,2012,18(2):232-238. [ZHENG F, SHI X Q, WU J C, et al. Global sensitivity analysis of leakage risk for CO2 geological sequestration in the saline aquifer of yancheng formation in Subei basin [J]. Geological Journal of China Universities,2012,18(2):232-238.(in Chinese)]
[12] [12]宋晓猛,孔凡哲,占车生,等.基于统计理论方法的水文模型参数敏感性分析[J].水科学进展,2012,23(5): 642-649. [SONG X M, KONG F Z, ZHAN C S,et al. Sensitivity analysis of hydrological model parameters using a statistical theory approach [J]. Advances in Water Science,2012,23(5): 642-649.(in Chinese)]
[13] [13]郑菲,施小清,吴吉春,等.深部咸水层CO2地质封存数值模拟参数的全局敏感性分析——以苏北盆地盐城组为例[J].吉林大学学报:地球科学版,2014,44(1): 310-318. [ZHENG F, SHI X Q, WU J C, et al. Global parametric sensitivity analysis of numerical simulation for CO2 geological sequestration in saline aquifers: a case study of Yancheng formation in Subei basin[J]. Journal of Jilin University (Earth Science Edition), 2014,44(1): 310-318.(in Chinese)]
[14] [14]WAINWRIGHT H M, FINSTERLE S, ZHOU Q L, et al. Modeling the performance of large-Scale CO2 storage systems: a comparison of different sensitivity analysis methods[J]. International Journal of Greenhouse Gas Control, 2013,17:189-205.
[15] [15]CACUCI D. Sensitivity & uncertainty analysis, volume I[M]. Boca Raton: Chapman and Hall/CRC, 2003.
[16] [16]WAINWRIGHT H M, FINSTERLE S, JUNG Y, et al. Making sense of global sensitivity analyses [J]. Computers & Geosciences, 2014, 65:84-94.
[17] [17]WAINWRIGHT H M, FINSTERLE S. Global sensitivity and data-worth analyses in iTOUGH2: user’s guide [R]. Office of Scientific and Technical Information (OSTI), 2016.DOI: 10.2172/1274412
[18] [18]PIANOSI F, BEVEN K, FREER J,et al. Sensitivity analysis of environmental models: a systematic review with practical workflow [J]. Environmental Modelling & Software,2016,79(6):214-232.
[19] [19]UDDAMERI V, HERNANDEZ E A, SINGARAJU S. A successive steady-state model for simulating freshwater discharges and saltwater wedge profiles at Baffin Bay, Texas [J]. Environmental Earth Sciences, 2014,71(6):2535-2546.
[20] [20]CVETKOVIC V, SOLTANI S, VIGOUROUX G. Global sensitivity analysis of groundwater transport [J]. Journal of Hydrology, 2015,531(1):142-148.
[21] [21]KUMAR D, SINGH A, JHAR K, et al. A variance de composition approach for risk assessment of groundwater quality[J]. Exposureand Health, 2019,11(2): 139-151.
[22] [22]MAHMOUDI E, HLTER R, GEORGIEVA R, et al. On the global sensitivity analysis methods in geotechnical engineering: a comparative study on a rock salt energy storage[J]. International Journal of Civil Engineering, 2019, 17(1):131-143.
[23] [23]SHAHKARAMI P, LIU L C, MORENO L, et al. Radionuclide migration through fractured rock for arbitrary-length decay chain: analytical solution and global sensitivity analysis [J]. Journal of Hydrology, 2015,520:448-460.
[24] [24]SAAD B M, ALEXANDERIAN A, PRUDHOMME S, et al. Probabilistic modeling and global sensitivity analysis for CO2, storage in geological formations: a spectral approach [J]. Applied Mathematical Modelling, 2018,53:584-601.
[25] [25]刘波,肖红飞,杨亚刚.基于Morris法的巷道围岩变形全局敏感性模拟分析[J].矿业安全与环保,2018,45(1):102-106. [LIU B, XIAO H F, YANG Y G. Global sensitivity simulation analysis of roadway surrounding rock deformation based on morris method[J]. Mining Safety and Environmental Protection, 2018,45(1):102-106.(in Chinese)]
[26] [26]罗跃,叶淑君,吴吉春,等.地面沉降模型的参数全局敏感性[J].浙江大学学报(工学版),2018,52(10):2007-2013. [LUO Y, YE S J, WU J C,et al.Global sensitivity analysis of parameters in land subsidence model [J]. Journal of Zhejiang University (Engineering Science), 2018,52(10):2007-2013.(in Chinese)]
[27] [27]BEA S A, WAINWRIGHT H, SPYCHER N, et al. Identifying key controls on the behavior of an acidic-U(VI) plume in the Savannah River Site using reactive transport modeling [J]. Journal of Contaminant Hydrology,2013,151:34-54.
[28] [28]VALSALA R, GOVINDARAJAN S K. Co-colloidal BTEX and microbial transport in a saturated porous system: numerical modeling and sensitivity analysis [J]. Transport in Porous Media,2019, 127(2): 269-294.
[29] [29]张梦琳,胡立堂,程莉蓉,等.基于iTOUGH2的地热模型资料价值分析方法[J].地质科技情报,2018,37(3):235-241. [ZHANG M L, HU L T, CHENG L R,et al. Evaluation method of data worth of geothermal model using iTOUGH2 [J]. Geological Science and Technology Information, 2018,37(3):235-241.(in Chinese)]
[30] [30]MORRIS M D. Factorial sampling plans for preliminary computational experiments [J]. Technometrics, 1991,33(2):161-174.
[31] [31]SOBOL I M. Sensitivity estimates for nonlinear mathematical model [J]. Mathematical Modeling and Computational Experiment, 1993,1:407-414.
[32] [32]FINSTERLE S. iTOUGH2 user’s guide [R]. Lawrence Berkeley National Laboratory LBNL-40040, Berkeley, CA, 2010.
[33] [33]MACQUARRIE K T B, SUDICKY E A, FRIND E O. Simulation of biodegradable organic contaminants in groundwater 1.Numerical formulation in principal directions[J]. Water Resources Research, 1990,26(2):207-222.
[34] [34]PRUESS K. TOUGH2-A general purpose numerical simulator for multiphase fluid and heat flow [R]. Office of Scientific and Technical Information (OSTI), 1991.DOI: 10.2172/5212064
[35] [35]LEE S J, MCPHERSON B J, VASQUEZ F G. Leakage pathway estimation using iTOUGH2 in a multiphase flow system for geologic CO2 storage[J]. Environmental Earth Sciences, 2015,74(6):5111-5128.
[36] [36]WELLMANN J F, FINSTERLE S, CROUCHER A. Integrating structural geological data into the inverse modelling framework of iTOUGH2 [J]. Computers & Geosciences, 2014,65:95-109.
[37] [37]王景瑞,胡立堂,尹文杰.iTOUGH2反演模型在地下水模拟中的应用[J].水文地质工程地质,2015,42(1):35-41. [WANG J R, HU L T, YIN W J. Application of iTOUGH2 to groundwater modeling [J]. Hydrogeology & Engineering Geology, 2015,42(1):35-41.(in Chinese)]
-
期刊类型引用(2)
1. 宋美钰,施小清,康学远,吴吉春. DNAPL场地污染通量升尺度预测的敏感性分析. 地质科技通报. 2023(02): 327-335 . 百度学术
2. 沈晓芳,万玉玉,王利刚,苏小四,董维红. 基于多相流数值模拟的某石油污染场地地下水中VOCs自然衰减过程识别及能力评估. 地学前缘. 2021(05): 90-103 . 百度学术
其他类型引用(3)
计量
- 文章访问数: 430
- HTML全文浏览量: 93
- PDF下载量: 329
- 被引次数: 5