Hydrobiogeophysics: Linking geo-electrical properties and biogeochemical processes in shallow subsurface environments

Microbially mediated reactions drive (bio)geochemical cycling of nutrients and contaminants in shallow subsurface environments. Environmental forcings exert a strong control on the timing of reactions and the spatial distribution of processes. Spatial and temporal variations in electron acceptor and donor availability may modulate nutrient/contaminant turnover. Characterizing the preferential spatial-zonation of biologically driven reactions, and quantifying turnover rates is hindered by our inability to access the subsurface at the spatial and temporal resolution required to capture reaction kinetics. Typical subsurface sampling methods generate discrete spatial datasets as a function of the prohibitive cost and operational challenges of borehole installation and core sampling, coupled with sparse temporal datasets due to intermittent sampling campaigns. In order to improve our ability to access biogeochemical information within the subsurface, without the need for destructive and intrusive sampling, non-invasive geophysical techniques (a comparatively inexpensive alternative) have been proposed as a means to characterize subsurface reactive compartments and locate zones of enhanced microbial activity and the timing of their development. The challenge lies in linking electrical responses to specific changes in biogeochemical processes. In this thesis, I assess the potential and suitability of spectral induced polarization (SIP) and self-potential (SP) / electrodic potential (EP) derived geo-electrical signals to detect, map, monitor and quantify microbially mediated reactions in partially- and fully-saturated heterogeneous porous media (i.e., soil). I build on existing literature delineating the sensitivity of SIP, SP and EP to biogeochemical processes and both qualitatively and quantitatively link geo-electrical signal dynamics to specific microbial processes at the experimental scale. I address the monitoring of complex, coupled processes in a well-characterized near-natural system, and combine reactive transport models (RTMs) with single-process reactive experiments (reduced complexity), to isolate diagnostic signatures of specific reactions and processes of interest. In Chapter 2, I begin by monitoring biogeochemically modulated geo-electrical signals (SIP and EP), in the variably (and dynamically) saturated reactive zone within the capillary fringe of an artificial soil system. SIP and EP responses show a clear dependence on the depth-distribution of subsurface microbes. Dynamic SIP imaginary conductivity (σ'') responses are only detected in the water table fluctuation zone and, in contrast to real conductivity (σ') data, do not exhibit a direct soil moisture driven dependence. Using multiple lines of evidence, I attribute the observed σ'' dynamics to microbially driven reactions. Chapter 2 highlights that continuous SIP and EP signals, in conjunction with periodic measurements of geochemical indicators, can help determine the location and temporal variability of biogeochemical activity and be used to monitor targeted reaction zones and pathways in complex soil environments. Building on the findings from Chapter 2, that biomass distribution and activation strongly modulate SIP responses, in Chapter 3 and 4, I focus on isolating the geo-electrical contribution of microbes themselves. In Chapter 3, I couple geochemical data, a biomass-explicit diffusion reaction model and SIP spectra from a saturated sand-packed (with alternating layers of ferrihydrite-coated and pure quartz sand) column experiment, inoculated with Shewanella oneidensis, and supplemented with lactate and nitrate. The coupled RTM and geo-electrical data analysis show that imaginary conductivity peaks parallel simulated microbial growth and decay dynamics. I compute effective polarization diameters, from Cole-Cole modeling derived relaxation times, in the range 1 – 3 µm; two orders of magnitude smaller than the smallest quartz grains in the columns, suggesting that polarization of the bacterial cells directly controls the observed chargeability and relaxation dynamics. In Chapter 4, I address the lack of experimental validation of biomass concentrations in Chapter 3. I present a measurement-derived relationship between S. oneidensis abundance and SIP imaginary conductivity, from a microbial growth experiment in fully saturated sand-filled column reactors. Cole-Cole derived relaxation times highlight the changing surface charging properties of cells in response to stress. The addition of concurrent estimates of cell size allow for the first measurement-derived estimation of an apparent Stern layer diffusion coefficient for cells, which validates existing modelled values and helps quantify electrochemical polarization during SIP-based monitoring of microbial dynamics. The relaxation time results from Chapter 4 suggest that bacterial cell surface charge is modified in response to nitrite toxicity-induced stress. In Chapter 5, I present a biomass-explicit reactive transport model, which integrates nitrite-toxicity, as a key modulator of the energy metabolism of S. oneidensis, to predict the rates of nitrate and nitrite reduction. I validate the model with results from two separate experiments (at different experimental scales): (1) a well-mixed batch suspension and (2) the flow-through reactor experiment from Chapter 4. The incorporation of toxicity-induced uncoupling of catabolism and anabolism in the reactive term predicts the observed delay in biomass growth, facilitated by endogenous energy storage when nitrite is present, and consumption of these reserves after its depletion. The model is further validated by the close agreement between the trends in imaginary conductivity and simulated biomass growth and decay dynamics. Finally, in Chapter 6, I apply the RTM-SIP integrative framework from Chapters 3 and 5 to develop quantitative relationships between SIP signals and engineered nanoparticle concentrations. Therein, SIP responses measured during injection of a polymer-coated iron-oxide nanoparticle suspension in columns packed with natural aquifer sand are coupled to output from an advective-dispersive transport model. The results highlight the excellent agreement between simulated nanoparticle concentrations within the columns and SIP signals, suggesting that polarization increases proportional to increasing nanoparticle concentration. The results from Chapter 6, introduce the possibility of quantitative SIP monitoring of coated metal-oxide nanoparticle spatial and temporal distributions. Overall, my results show the applicability of SIP and EP to map and monitor the spatial zonation of biogeochemical hotspots and to detect their temporal activation. By coupling RTMs with geo-electrical datasets, I highlight the direct control that polarization of microbial cells exerts on SIP signals in biotic systems. Furthermore the measurement-derived SIP-biomass quantitative relationship provides a first attempt to directly measure in situ biomass density, using geo-electrical signals as a proxy. I show that geo-electrical signal dynamics (Cole-Cole relaxation time) can be used to inform processes within RTMs. Finally, the implementation of the combined modeling and electrical monitoring approach, to the case of engineered nanoparticles, confirms SIP’s suitability to monitor colloid transport in the environment and highlights considerations for method optimization

Does It Pay Off to Explicitly Link Functional Gene Expression to Denitrification Rates in Reaction Models?

Environmental omics and molecular-biological data have been proposed to yield improved quantitative predictions of biogeochemical processes. The abundances of functional genes and transcripts relate to the number of cells and activity of microorganisms. However, whether molecular-biological data can be quantitatively linked to reaction rates remains an open question. We present an enzyme-based denitrification model that simulates concentrations of transcription factors, functional-gene transcripts, enzymes, and solutes. We calibrated the model using experimental data from a well-controlled batch experiment with the denitrifier Paracoccous denitrificans. The model accurately predicts denitrification rates and measured transcript dynamics. The relationship between simulated transcript concentrations and reaction rates exhibits strong non-linearity and hysteresis related to the faster dynamics of gene transcription and substrate consumption, relative to enzyme production and decay. Hence, assuming a unique relationship between transcript-to-gene ratios and reaction rates, as frequently suggested, may be an erroneous simplification. Comparing model results of our enzyme-based model to those of a classical Monod-type model reveals that both formulations perform equally well with respect to nitrogen species, indicating only a low benefit of integrating molecular-biological data for estimating denitrification rates. Nonetheless, the enzyme-based model is a valuable tool to improve our mechanistic understanding of the relationship between biomolecular quantities and reaction rates. Furthermore, our results highlight that both enzyme kinetics (i.e., substrate limitation and inhibition) and gene expression or enzyme dynamics are important controls on denitrification rates

Denitrification‐Driven Transcription and Enzyme Production at the River‐Groundwater Interface: Insights From Reactive‐Transport Modeling

Molecular‐biological data and omics tools have increasingly been used to characterize microorganisms responsible for the turnover of reactive compounds in the environment, such as reactive‐nitrogen species in groundwater. While transcripts of functional genes and enzymes are used as measures of microbial activity, it is not yet clear how they are quantitatively related to actual turnover rates under variable environmental conditions. As an example application, we consider the interface between rivers and groundwater which has been identified as a key driver for the turnover of reactive‐nitrogen compounds, that cause eutrophication of rivers and endanger drinking water production from groundwater. In the absence of measured data, we developed a reactive‐transport model for denitrification that simultaneously predicts the distributions of functional‐gene transcripts, enzymes, and reaction rates. Applying the model, we evaluate the response of transcripts and enzymes at the river‐groundwater interface to stable and dynamic hydrogeochemical regimes. While functional‐gene transcripts respond to short‐term (diurnal) fluctuations of substrate availability and oxygen concentrations, enzyme concentrations are stable over such time scales. The presence of functional‐gene transcripts and enzymes globally coincides with the zones of active denitrification. However, transcript and enzyme concentrations do not directly translate into denitrification rates in a quantitative way because of nonlinear effects and hysteresis caused by variable substrate availability and oxygen inhibition. Based on our simulations, we suggest that molecular‐biological data should be combined with aqueous geochemical data, which can typically be obtained at higher spatial and temporal resolution, to parameterize and calibrate reactive‐transport models.Plain Language Summary: Molecular‐biological tools can detect how many enzymes, functional genes, and gene transcripts (i.e., precursors of enzyme production) associated with a microbial reaction exist in a sample from the environment. Although these measurements contain valuable information about the number of bacteria and how active they are, they do not directly say how quickly a contaminant like nitrate disappears. Nitrate, from agriculture and other sources, threatens groundwater quality and drinking water production. In the process of denitrification, bacteria can remove nitrate by converting it into harmless nitrogen gas using specialized enzymes. The interface between rivers and groundwater is known as a place where denitrification takes place. In this study, we use a computational model to simulate the coupled dynamics of denitrification, bacteria, transcripts, and enzymes when nitrate‐rich groundwater interacts with a nearby river. The simulations yield complex and nonunique relationships between the denitrification rates and the molecular‐biological variables. While functional‐gene transcripts respond to daily fluctuations of environmental conditions, enzyme concentrations and genes are stable over such time scales. High levels of functional‐gene transcripts therefore provide a good qualitative indicator of reactive zones. Quantitative predictions of nitrate turnover, however, will require high‐resolution measurements of the reacting compounds, genes, and transcripts.Key Points: We simulate the distributions of functional‐gene transcripts and enzymes related to denitrification at the river‐groundwater interface. Functional‐gene transcripts respond quickly to diurnal fluctuations of substrate and oxygen concentrations. Substrate limitation and oxygen inhibition impede the direct prediction of denitrification rates from transcript or enzyme concentrations.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659https://doi.org/10.5281/zenodo.6584591https://gitlab.com/astoeriko/nitrogenehttps://doi.org/10.5281/zenodo.6584641https://gitlab.com/astoeriko/adrpyhttps://doi.org/10.5281/zenodo.5213947https://github.com/aseyboldt/sunod