Browsing by Author "Koch, Jonas"

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Open Access
Simulation, identification and characterization of contaminant source architectures in the subsurface
(2014) Koch, Jonas; Nowak, Wolfgang (Jun.-Prof. Dr.-Ing.)
Improper storage and disposal of non-aqueous-phase liquids (NAPLs) has resulted in widespread subsurface contamination, threatening the quality of groundwater as freshwater resource. Contaminants with low immiscibility and solubility in the aqueous phase, remain as a separate phase. They dissolve into the groundwater and spread within the aquifer over long periods of time, before the contaminants are fully depleted. Due to their typically high toxicity, even low concentrations in groundwater may pose high risks on ecosystems and human health. The spatial distribution of contaminants in the subsurface (i.e., the contaminant source architecture, CSA for short) is highly irregular and not precisley predictable. Yet, the complex and uncertain morphology of CSAs and its interactions with uncertain aquifer parameters and groundwater flow have to be accounted for and need to be resolved at the relevant scale to maintain adequate prediction accuracy. The abundance of contaminated sites and difficulties of remediation efforts demand decisions to be based on a sound risk assessment. To this end, screening or investigation methods are applied. These methods assess which sites pose large risks, which ones can be left to natural attenuation, which ones need expensive remediation, and what remediation approach would be most promising. For this, it is important to determine relevant characteristics or impact metrics, such as geometric characteristics of the unknown CSA , total mass, potential mass removal by remediation, emanating dissolved mass fluxes and total mass discharge in past and future, predicted source depletion times, and the possible impact on drinking water wells, and thus on human health. The same characteristics are also important for designing monitoring or remediation schemes. Due to sparse data and natural heterogeneity, this risk assessment needs to be supported by adequate predictive models with quantified uncertainty. These models require an accurate source zone description, i.e., the distribution of mass of all partitioning phases in all possible states, mass-transfer algorithms, and the simulation of transport processes in the groundwater. Due to limited knowledge and computer resources, a selective choice of the relevant processes for the relevant states and decisions on the relevant scale is both sensitive and indispensable. Thus, it is an important research question what is a meaningful level of model complexity and how to obtain a physically and statistically consistent model framework. Almost every estimate of the desired impact metrics will be uncertain due to the typical uncertainty that is inherent in any process description in a heterogeneous subsurface environment, and due to the complex and non-linear interdependencies between aquifer parameters, CSA, groundwater velocities, and mass transfer. Thus, stochastic methods are indispensable because they can provide reasonable error bars and allow the involved stakeholders to take decisions in proportion to the posed risks of contaminated sites. In order to restrict this huge uncertainty, field data need to be assimilated by inverse models. To this end, concentration observations possess promising information on CSA geometries, transport processes, and aquifer parameters. Revealing these valuable information, however, requires an efficient inverse model that is again physically and stochastically consistent. In particular, the identification of CSAs has to cope with non-unique problems, non-linear interdependencies, and enhanced mixing and plume deformation in a heterogeneous environment. The overall goal of this thesis is to provide a sound basis for rational decisions that arise in the assessment of contaminated sites. Therefore, three theses are postulated in the following, for which their significance and validity is demonstrated throughout this work. 1.) The model framework must at least account for the heterogeneity of aquifers, the irregularity of flow fields, realistic and thus complex-shaped CSAs, the three-dimensionality of natural systems, adequate physical interlinkages of the key parameters at the adequate spatial and temporal scales, and it must at least treat the uncertainty of aquifer parameters and of the CSA. 2.) Joint identification of CSAs and aquifer parameters based on concentration observations can be achieved via non-linear and non-unique Bayesian inversion. An accurate and efficient inverse method for this task can be by obtained by applying the method of adjoint states and utilizing the linearity of the transport equation. 3.) The enhanced mixing of dissolved DNAPL and the solute plume deformation in heterogeneous aquifers significantly influences the inference quality of CSAs from downstream concentration observations. Knowledge on the driving processes of enhanced mixing allows to chose adequate measurement designs.