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

doi:10.13745/j.esf.sf.2021.2.14

地学前缘 ›› 2021, Vol. 28 ›› Issue (5): 90-103.DOI: 10.13745/j.esf.sf.2021.2.14

• 地下水污染监测与模拟 • 上一篇下一篇

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

沈晓芳¹^,²(), 万玉玉³, 王利刚⁴, 苏小四²^,³, 董维红²^,³^,^*()

1.吉林大学建设工程学院, 吉林长春 130000
2.吉林大学水资源与环境研究所, 吉林长春 130000
3.吉林大学地下水资源与环境教育部重点实验室, 吉林长春 130000
4.中国昆仑工程有限公司吉林分公司, 吉林吉林 132100

收稿日期:2020-05-24 修回日期:2020-08-05 出版日期:2021-09-25 发布日期:2021-10-29
通信作者: 董维红
作者简介:沈晓芳(1996—),女,博士研究生,地质工程专业。E-mail: shenxf18@mails.jlu.edu.cn
基金资助:
国家重点研发计划项目(2018YFC1800901);吉林大学博士研究生交叉学科科研资助计划项目(101832020DJX073)

Multiphase flow modeling of natural attenuation of volatile organic compounds (VOCs) in a petroleum contaminated sit

SHEN Xiaofang¹^,²(), WAN Yuyu³, WANG Ligang⁴, SU Xiaosi²^,³, DONG Weihong²^,³^,^*()

1. College of Construction Engineering, Jilin University, Changchun 130000, China
2. Institute of Water Resources and Environment, Jilin University, Changchun 130000, China
3. Ministry of Education Key Laboratory of Groundwater Resources and Environments, Jilin University, Changchun 130000, China
4. Jilin Branch of China Kunlun Engineering Co., Ltd., Jilin 132100, China

Received:2020-05-24 Revised:2020-08-05 Online:2021-09-25 Published:2021-10-29
Contact: DONG Weihong

摘要/Abstract

摘要：

监测自然衰减(monitoring natural attenuation,MNA)技术是目前普遍认可的去除地下水中挥发性有机污染物(volatile organic compounds,VOCs)的技术。但受其修复周期长、监测费用昂贵等因素的影响,实地开展MNA技术修复污染场地具有一定的局限性。基于此,本研究运用多相流数值模拟手段识别了某石油污染场地内典型VOCs污染物(苯、甲苯、萘)在地下水中的自然衰减过程并评估了其自然衰减能力。结果表明:采用TMVOC所建立的多相流数值模拟模型能较好地预测和识别VOCs在地下水中的衰减规律;在研究区中,苯、甲苯和萘由于理化性质差异,在地下水中的污染羽分布特征不同,其自然衰减过程受挥发、吸附和生物降解作用的影响程度也不同;挥发和生物降解作用对VOCs自然衰减的影响程度均为苯>甲苯>萘,而吸附作用对VOCs自然衰减的影响程度为萘>甲苯>苯;在污染源被阻断的前提下,苯、甲苯和萘分别在泄漏发生7.0、6.5和6.0年后通过自然衰减达到理想去除效果。本文研究成果可以为水文地质条件类似的VOCs污染场地MNA修复方案的制定和修复效果评估提供理论支撑。

关键词: VOCs, 自然衰减, 多相流数值模拟, 地下水

Abstract:

The monitored natural attenuation (MNA) technology has become a generally recognized technology for remediation of VOCs. However, this technology, owing to the long remediation period and expensive monitoring costs, has certain limitations in real world applications. In this study, we use multiphase flow numerical simulation to identify the natural attenuation process and assess the attenuation capacity for typical VOCs (benzene, toluene, naphthalene) in a petroleum contaminated site. The results show that the multiphase flow numerical model established by TMVOC can predict the attenuation laws of VOCs in groundwater. In the study area, the pollution plumes of benzene, toluene and naphthalene in groundwater have different distribution patterns due to the differences in their physicochemical properties, and the natural attenuation process is affected to various degrees by volatilization, adsorption and microbial degradation. The effects of volatilization and biodegradation on VOC remediation are benzene > toluene > naphthalene, while the effect of adsorption is naphthalene > toluene> benzene. Assuming the pollution source is blocked, benzene, toluene and naphthalene are predicted to achieve ideal remediation through MNA at 7.0, 6.5 and 6.0 years, respectively, after oil leak. The above simulation results can provide theoretical support for the implementation and evaluation of MNA technology in remediation of VOC contaminated sites under similar hydrogeological conditions.

Key words: VOCs, natural attenuation, multiphase flow numerical simulation, groundwater

中图分类号:

X523

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[J]. 地学前缘, 2021, 28(5): 90-103.

SHEN Xiaofang, WAN Yuyu, WANG Ligang, SU Xiaosi, DONG Weihong. Multiphase flow modeling of natural attenuation of volatile organic compounds (VOCs) in a petroleum contaminated sit[J]. Earth Science Frontiers, 2021, 28(5): 90-103.

图/表 14

图2 模型概化示意图

Fig.2 Conceptualization of the model

图1 场地平面图、地下水流场及采样点分布图

Fig.1 Planar diagram of the pollution site, showing the groundwater flow field and sampling point distribution

表1 含水层水文地质参数

Table 1 Hydrogeological parameters of the aquifer

参数	干密度^*/(kg·m^-3)	孔隙度^*	绝对渗透率(水平)/m²	绝对渗透率(垂直)/m²
黏土	2 600	0.31	4.0×10^-14	1.0×10^-14
细砂	2 650	0.31	8.0×10^-11	8.0×10^-10
中粗砂	2 660	0.30	4.0×10^-10	1.0×10^-10
含砾中粗砂	2 670	0.30	4.0×10^-10	1.1×10^-10
砂砾石	2 660	0.32	8.0×10^-10	5.0×10^-10

图3 含水层初始静水压强分布(a)和水位拟合曲线(b)

Fig.3 Distribution of initial hydrostatic pressure in the aquifer (a) and plots of simulated (red) and measured (black) water levels at each well location (b)

表2 污染物理化性质参数以及自然衰减参数

Table 2 Summary of physicochemical properties and natural attenuation parameters of contaminants

有机物名称	分子式	标准沸点/K	摩尔质量/ (g·mol^-1)	临界温度/K	临界压强/bar	临界体积/ (cm³·mol^-1)	密度/ (g·cm^-3)	溶解度常数 (摩尔分数)	分配系数 K_oc/ (m³·kg^-1)	分配系数 K_d/ (mL·g^-1)	生物降解衰变常数 /s^-1	介质中的有机碳分数
苯	C₆H₆	353.2	78.114	562.2	48.2	259	0.878 6	4.11×10^-4	0.089 1	0.06	4.0×10^-8
萘	C₁₀H₈	217.9	128.17	748.4	40.5	431	1.162 0	4.86×10^-6	1.29	2.04	2.4×10^-8	0.159%
甲苯	C₇H₈	383.8	92.14	591.8	41.0	316	0.866 0	1.01×10^-4	0.273	1.007	2.0×10^-8

表3 模拟情景条件设置以及模拟目的

Table 3 Simulation scenarios and purpose

情景	条件设置	模拟目的
情景一	石油以定速率3.21×10^-5 kg/s泄漏6个月后切断污染源	了解VOCs在地下环境中的迁移距离以及分布特征,为分析情景二、三、四中各自然衰减过程对VOCs的迁移转化过程的影响提供参考
情景二	以情景一的输出结果作为初始条件,模拟在考虑挥发、溶解作用条件下,VOCs在多相间的迁移	分析挥发、溶解作用对VOCs自然衰减过程的影响
情景三	以情景一的输出结果作为初始条件,模拟在考虑吸附作用的条件下,VOCs在多相间的迁移	分析吸附作用对VOCs自然衰减过程的影响
情景四	以情景一的输出结果作为初始条件,模拟在考虑生物降解作用的条件下,VOCs在多相间的迁移转化	分析生物降解作用对VOCs自然衰减过程的影响
情景五	在综合考虑挥发、吸附和生物降解作用的情况下,模拟场地内VOCs的自然衰减过程	模拟场地内苯、甲苯和萘达到理想修复效果所需的时间

图4 苯、甲苯、萘实测浓度与计算浓度随距离变化对比结果

Fig.4 Comparison of measured (black) and simulated (red) concentrations of benzene, toluene and naphthalene at each well location

图5 NAPL相苯、甲苯和萘在包气带和饱水带中的摩尔分数分布

Fig.5 Mole fraction distribution of NAPL phase benzene,toluene and naphthalene in aeration and saturation zones

图6 苯、甲苯和萘在液相和气相中的摩尔分数分布

Fig.6 Mole fraction distribution of benzene, toluene and naphthalene in liquid or gas phase

图7 苯、甲苯和萘在气相和液相中摩尔分数的变化

Fig.7 Variation of benzene, toluene and naphthalene mole fractions in gas or liquid phase

图8 地下水中苯、甲苯和萘在考虑吸附作用和未考虑吸附作用条件下摩尔分数分布

Fig.8 Mole fraction distribution of benzene, toluene and naphthalene in groundwater with or without adsorption effects

图9 地下水中有无生物降解作用条件下苯、甲苯和萘的浓度历时曲线

Fig.9 Concentration-time curves for benzene, toluene and naphthalene in groundwater with or without biodegradation

图10 地下水中苯、甲苯和萘在泄漏停止1年和3年后污染羽分布

Fig.10 Distribution of benzene, toluene and naphthalene plumes in groundwater one (left panel) and three-year (right panel) after oil leak stopped

图11 地下水中苯、甲苯和萘在石油泄漏后浓度变化

Fig.11 Changes of benzene, toluene and naphthalene concentrations in groundwater after oil leak

参考文献 43

[1]	夏雨波, 杨悦锁, 杜新强, 等. 石油污染场地浅层地下水MNA原位修复潜能及微生物降解效益评估[J]. 吉林大学学报(地球科学版), 2011, 41(3):831-839.
[2]	王玉梅, 党俊芳. 油气田地区的地下水污染分析[J]. 地质灾害与环境保护, 2000, 11(3):271-273.
[3]	李在平, 陈鸿泉, 林辉山, 等. 自然衰减应用于石油碳氢化合物污染整治之探讨[J]. 台湾土壤及地下水环境保护协会简讯, 2005(17):3-9.
[4]	SABER A N, ZHANG H F, CERVANTES-AVILÉS P, et al. Emerging concerns of VOCs and SVOCs in coking wastewater treatment processes: distribution profile, emission characteristics, and health risk assessment[J]. Environmental Pollution, 2020, 265:114960. DOI URL
[5]	张佰丰. 挥发性有机物(VOCs)的排放及治理技术综述[J]. 科技展望, 2016, 26(21):169.
[6]	陈小华, 杨青, 孙从军, 等. 典型地下水浅埋区加油站渗漏污染潜势分析及高潜势验证[J]. 环境科学研究, 2013, 26(11):1171-1177.
[7]	SHRIVASTAVA M, LOU S, ZELEYUK A, et al. Global long-range transport and lung cancer risk from polycyclic aromatic hydrocarbons shielded by coatings of organic aerosol[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(6):1246-1251.
[8]	PAN Y, ZENG X K, XU H X, et al. Assessing human health risk of groundwater DNAPL contamination by quantifying the model structure uncertainty[J]. Journal of Hydrology, 2020, 584:124690.
[9]	DONG W H, CAO Z P, LI M L, et al. Natural attenuation of naphthalene along the river-bank infiltration zone of the Liao River, Shenyang, China[J]. Journal of Contaminant Hydrology, 2019, 220:26-32. DOI URL
[10]	THORNTON S F, NICHOLLS H C G, ROLFE S A, et al. Biodegradation and fate of ethyl tert-butyl ether (ETBE) in soil and groundwater: a review[J]. Journal of Hazardous Materials, 2020, 391:122046.
[11]	PENG R H, FU X Y, ZHAO W, et al. Phytoremediation of phenanthrene by transgenic plants transformed with a naphthalene dioxygenase system from Pseudomonas[J]. Environmental Science & Technology, 2014, 48(21):12824-12832. DOI URL
[12]	钟毅, 李广贺, 张旭, 等. 污染土壤石油生物降解与调控效应研究[J]. 地学前缘, 2006, 13(1):128-133.
[13]	FERGUSON D K, LI C, JIANG C Q, et al. Natural attenuation of spilled crude oil by cold-adapted soil bacterial communities at a decommissioned High Arctic oil well site[J]. Science of the Total Environment, 2020, 722:137258. DOI URL
[14]	HARITASH A K, KAUSHIK C P. Biodegradation aspects of polycyclic aromatic hydrocarbons (PAHs): a review[J]. Journal of Hazardous Materials, 2009, 169(1/2/3):1-15. DOI URL
[15]	王浩, 陆垂裕, 秦大庸, 等. 地下水数值计算与应用研究进展综述[J]. 地学前缘, 2010, 17(6):1-12.
[16]	ADADEVOH J S T, OSTVAR S, WOOD B, et al. Modeling transport of chemotactic bacteria in granular media with distributed contaminant sources[J]. Environmental Science & Technology, 2017, 51(24):14192-14198. DOI URL
[17]	PROMMER H, BARRY D A, ZHENG C. MODFLOW/MT3DMS-based reactive multicomponent transport modeling[J]. Groundwater, 2003, 41(2):247-257. DOI URL
[18]	GAGANIS P, KJELDSEN P, BURGANOS V N. Modeling natural attenuation of multicomponent fuel mixtures in the vadose zone: use of field data and evaluation of biodegradation effects[J]. Vadose Zone Journal, 2004, 3(4):1262-1275. DOI URL
[19]	SOOKHAK LARI K, JOHNSTON C D, RAYNER J L, et al. Field-scale multi-phase LNAPL remediation: validating a new computational framework against sequential field pilot trials[J]. Journal of Hazardous Materials, 2018, 345:87-96. DOI URL
[20]	SOOKHAK LARI K, DAVIS G B, RAYNER J L, et al. Natural source zone depletion of LNAPL: a critical review supporting modelling approaches[J]. Water Research, 2019, 157:630-646. DOI URL
[21]	PRUESS K, BATTISTELLI A. TMVOC, a numerical simulator for three-phase non-isothermal flows of multicomponent hydrocarbon mixtures in saturated-unsaturated heterogeneous media[R]. Oak Ridge: Office of Scientific and Technical Information(OSTI), 2005.
[22]	SOOKHAK LARI K, JOHNSTON C D, RAYNER J L, et al. Field-scale multi-phase LNAPL remediation: validating a new computational framework against sequential field pilot trials[J]. Journal of Hazardous Materials, 2018, 345:87-96. DOI URL
[23]	GIRAUD Q, GONÇALVÈS J, PARIS B, et al. 3D numerical modelling of a pulsed pumping process of a large DNAPL pool: in situ pilot-scale case study of hexachlorobutadiene in a keyed enclosure[J]. Journal of Contaminant Hydrology, 2018, 214:24-38. DOI URL
[24]	BATTISTELLI A. Modeling multiphase organic spills in coastal sites with TMVOC V.2.0[J]. Vadose Zone Journal, 2008, 7(1):316-324. DOI URL
[25]	王颖, 陈雷, 杨洋, 等. 基于TMVOC的地下水位波动带苯系物迁移转化模拟[J]. 环境科学研究, 2020, 33(3):634-642.
[26]	PRUESS K. The TOUGH codes: a family of simulation tools for multiphase flow and transport processes in permeable media[J]. Vadose Zone Journal, 2004, 3(3):738-746.
[27]	PRUESS K, OLDENBURG C M, MORIDIS G J. TOUGH2 user’s guide version 2[R]. Oak Ridge: Office of Scientific and Technical Information(OSTI), 1999.
[28]	FINSTERLE S, DOUGHTY C, KOWALSKY M B, et al. Advanced vadose zone simulations using TOUGH[J]. Vadose Zone Journal, 2008, 7(2):601-609. DOI URL
[29]	LAMICHHANE S, BAL KRISHNA K C, SARUKKALIGE R . Polycyclic aromatic hydrocarbons (PAHs) removal by sorption: a review[J]. Chemosphere, 2016, 148:336-353. DOI URL
[30]	REID R C, SHERWOOD T K, STREET R E. The properties of gases and liquids[J]. Physics Today, 1959, 12(4):38-40.
[31]	KARICKHOFF S W, BROWN D S, SCOTT T A. Sorption of hydrophobic pollutants on natural sediments[J]. Water Research, 1979, 13(3):241-248. DOI URL
[32]	BATTISTELLI A. Modeling biodegradation of organic contaminants under multiphase conditions with TMVOCBio[J]. Vadose Zone Journal, 2004, 3(3):875-883. DOI URL
[33]	杜建雯, 施小清, 徐红霞, 等. 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.
[34]	XU T F. Incorporating aqueous reaction kinetics and biodegradation into TOUGHREACT: applying a multiregion model to hydrobiogeochemical transport of denitrification and sulfate reduction[J]. Vadose Zone Journal, 2008, 7(1):305-315. DOI URL
[35]	赵琪, 苏小四, 左恩德, 等. 某石油烃污染场地包气带介质及含水介质TPH污染特征[J]. 科技导报, 2015, 33(7):25-29.
[36]	焦珣, 苏小四, 吕航. 某石油类污染场地地下水石油烃生物降解的地球化学证据[J]. 地质科学, 2012, 47(2):499-506.
[37]	彭建平, 邵爱军. 基于Matlab方法确定VG模型参数[J]. 水文地质工程地质, 2006, 33(6):25-28.
[38]	TRACY J C. A practical guide to groundwater and solute transport modeling[J]. Eos, Transactions American Geophysical Union, 1996, 77(44):434. DOI URL
[39]	American Society for Testing and Materials. Standard guide for risk-based corrective action applied at petroleum release sites: E1739(95)[S]. West Conshohocken: ASTM, 2015.
[40]	付建民, 赵振洋, 陈国明, 等. 液相管道流量与压力对小孔泄漏速率的影响[J]. 石油学报, 2016, 37(2):257-265.
[41]	DONG W H, ZHANG P, LIN X Y, et al. Natural attenuation of 1, 2, 4-trichlorobenzene in shallow aquifer at the Luhuagang’s landfill site, Kaifeng, China[J]. Science of the Total Environment, 2015, 505:216-222. DOI URL
[42]	WANG J B, WANG C, HUANG Q Y, et al. Adsorption of PAHs on the sediments from the Yellow River Delta as a function of particle size and salinity[J]. Soil and Sediment Contamination: an International Journal, 2015, 24(2):103-115. DOI URL
[43]	卫生部. 生活饮用水卫生标准[J]. 经济管理文摘, 2006(11):36-38.

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

Multiphase flow modeling of natural attenuation of volatile organic compounds (VOCs) in a petroleum contaminated sit

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