Experimental and Numerical Investigations of Changes in Flow and Solute Transport Processes in Porous Media Affected by Bioclogging

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Experimental and numerical investigations of changes in flow and solute transport processes in porous media affected by bioclogging

Seifert, Dorte; Jakobsen, Rasmus

Publication date: 2005

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Citation (APA): Seifert, D., & Jakobsen, R. (2005). Experimental and numerical investigations of changes in flow and solute transport processes in porous media affected by bioclogging. Kgs. Lyngby: DTU Environment.

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Experimental and Numerical Investigations of Changes in Flow and Solute Transport Processes in Porous Media Affected by Bioclogging

Dorte Seifert

Institute of Environment & Resources

Experimental and Numerical Investigations of Changes in Flow and Solute Transport Processes in Porous Media Affected by Bioclogging

Dorte Seifert

Ph.D. Thesis May 2005

Institute of Environment & Resources Technical University of Denmark Experimental and Numerical Investigations of Changes in Flow and Solute Transport Processes in Porous Media Affected by Bioclogging

Cover: Torben Dolin & Julie Camilla Middleton Printed by: DTU tryk Institute of Environment & Resources ISBN 87-89220-95-1

The thesis will be available as a pdf-file for downloading from the institute homepage on: www.er.dtu.dk

Institute of Environment & Resources Library Bygningstorvet, Building 115, Technical University of Denmark DK-2800 Kgs. Lyngby

Phone: Direct: (+45) 45 25 16 10 (+45) 45 25 16 00 Fax: (+45) 45 93 28 50 E-mail: [email protected] Preface

The present thesis “Experimental and Numerical Investigations of Changes in Flow and Solute Transport Processes in Porous Media Affected by Bioclogging” has been submitted as a part of the requirements for the Ph.D. degree at the Technical University of Denmark (DTU).

The study was performed at Environment & Resources (E&R) DTU from September 2000 to March 2005. Associate Professor Peter Engesgaard, Geological Institute, University of Copenhagen, acted as the principal adviser, and provided the needed inspiration, support and encouragement throughout the study. The adviser at E&R DTU Associate Professor Rasmus Jakobsen assisted by thorough and involved perusal of the thesis.

A two-month visit from the master student Paulo Herrera from University of Illinois at Urbana-Champaign (UIUC) at DTU, and my later visit to Al Valocchi’s group at UIUC resulted in a beneficial collaboration. During my research stay at Cornell University, New York, I joined the bioclogging group of Philip Baveye, where I profited from working with the Post Docs Martin Thullner and Katsutoshi Seki.

The study was funded by the Technical University of Denmark.

The papers are not included in this www-version but may be obtained from the Library at the Institute of Environment & Resources, Bygningstorvet, Building 115, Technical University of Denmark, DK-2800 Kgs. Lyngby ([email protected]).

Lyngby, March 2005

Dorte Seifert

Abstract

Tracer studies in laboratory column and sandbox experiments were performed to investigate the processes of bioclogging. Model simulations of the experiments investigated the coupling between the bioclogging process, microbial migration, and attachment/detachment of biomass, and examined the effect of these processes on the flow pattern and solute transport in porous media. Tracer tests conducted in three column and three sandbox experiments revealed that the flow pattern, and hereby solute transport, changed due to bioclogging from being uniform to non-uniform exemplified through increasing hydrodynamic dispersion, diffusive mass transfer between the mobile phase and the immobile biomass, and development of preferential flow paths. The bulk hydraulic conductivity decreased by up to three orders of magnitude in the 5 cm long columns, and by two orders of magnitude in the 44 cm long sandbox. The most significant reduction in the hydraulic conductivity occurred quickly (within the first 20 days of the experiments). The changes in tracer transport revealed a reduction in the porosity down to 70%-80% of the initial value, i.e., only 20-30% of the pore space was filled with biomass. These observations indicate that bioclogging is controlled by biomass in micro-colonies plugging the pore throats, as biofilm models have revealed that a significantly larger amount of biomass as a biofilm (>90% of the porosity) is needed to produce the observed decrease in the hydraulic conductivity. The sandbox experiments revealed that microorganisms migrated upgradient toward the substrate source with a velocity of about 0.5 cm/day. Model simulations using a numerical bioclogging model, which was extended to include migration of microorganisms by random motility and chemotaxis, were able to reproduce the observed microbial migration and the subsequently upgradient bioclogging. In the model it was assumed that microorganisms migrated in pores with stagnant water or close to the sand grain surface where the pore water velocity is reduced. In the model simulations it was necessary to assume that the reduced pore water velocity affecting the microorganisms was 1% of the average pore water velocity. The migration of microorganisms is sensitive to the detachment rate of biomass from the solid to the aqueous phase, which depends on the substrate concentration. Sandbox experiments with different substrate concentrations revealed a faster microbial migration with a higher substrate concentration.

Resumé

Laboratorieforsøg i kolonner og strømningsrender blev udført for at undersøge bioclogging processer. Modelsimuleringer af forsøgene blev brugt til at undersøge koblingen mellem bioclogging, mikrobiel bevægelse og sorption/desorption af biomasse samt virkningen af disse processer på strømningsmønsteret og stoftransporten i porøse medier. Sporstofforsøg udført i tre kolonne- og tre strømningsrendeforsøg viste at bioclogging ændrer strømningsmønsteret, og herved stoftransporten, fra at være ensartet til ”ujævn” illustreret med stigende hydrodynamisk dispersion, stoftransport via diffusion mellem den mobile fase og den immobile biomasse og udvikling af præferentielle strømningsveje. Den hydrauliske ledningsevne, K, formindskedes med op til tre størrelsesordner i de 5 cm lange kolonner. Faldet i den samlede K-værdi over de 44 cm lange strømningsrender var på to ca. størrelsesordner. Den mest signifikante reduktion i den hydrauliske ledningsevne skete hurtigt (indenfor de første 20 dage af forsøgene). De observerede ændringer in sporstof transport gav en reduktion i porøsiteten på ned til 70%-80% af den initiale værdi, dvs. 20-30% af porevolumenet var fyldt med biomasse. Disse observationer indikerer, at bioclogging bliver styret af biomasse i mikro-kolonier, der tilstopper porehalsene, idet biofilmsmodeller har vist at en betydelig større mængde biomasse som en biofilm (>90% af porøsiteten) er nødvendig for at producere det observerede fald i den hydrauliske ledningsevne. Forsøgene i strømningsrenden viste, at mikroorganismerne bevægede sig opstrøms mod substratkilden med en hastighed på ca. 0.5 cm/dag. Modelsimuleringer med en numerisk bioclogging model, som blev udvidet til at inkludere migration af mikroorganismer ved tilfældig tumle bevægelse og kemotaksi, var i stand til at reproducere den observerede mikrobielle migration og den følgende opstrøms bioclogging. I modellen blev det antaget, at mikroorganismerne migrerede i pore med stillestående vand eller tæt på overfladen af sandkornene, hvor porevandshastigheden er reduceret. Det var i modelsimuleringerne nødvendigt at antage, at den reducerede porevandshastighed, mikroorganismerne blev påvirket af, udgjorde 1% af den gennemsnitlige porevandshastighed. Bevægelsen af mikroorganismer er følsom overfor desorptionen af biomasse fra den faste til vandige fase, som er afhængig af substrat koncentrationen. Strømningsrendeforsøgene med forskellige substrat koncentrationer udviste en hurtigere mikrobiel bevægelse ved højere substrat koncentration.

III

Table of Contents

1. Introduction...... 1 2. Objectives...... 4 3. Bioclogging experiments...... 4 4. Results and discussion of bioclogging experiments...... 6 4.1. Hydraulic conductivity...... 6 4.2. Tracer tests...... 7 5. Modeling of bioclogging experiments...... 8 5.1. Analytical modeling of tracer tests in column experiment ...... 8 5.2. Numerical modeling of sandbox experiments ...... 9 6. Conclusions ...... 9 7. Perspectives...... 10 References ...... 11

Papers 1. Use of Tracer Tests to Investigate Changes in Flow and Transport Properties due to Bioclogging of Porous Media – a Review and New Experiments

2. Numerical Investigation of Microbial Migration by Random Motility and Chemotaxis in Bioclogging Experiments

3. Experimental Investigation of Bioclogging in a 2D Sandbox by use of Dye Tracer Tests

4. Numerical Investigation of Bioclogging Experiments in a 2D Sandbox

1 Introduction

1. Introduction When analyzing flow and transport in the subsurface, the properties of a porous medium such as hydraulic conductivity, porosity and dispersivity are often assumed to be constant in time. However, many physical, chemical and biological processes can alter the porous medium properties. Filtration of suspended solids in the porous medium is the most important and most investigated reason for physical clogging. An example is artificial recharge where surface water containing small particles is re- charged into the groundwater aquifer, either by infiltration or pumping. Chemical processes altering the porous medium properties are for example precipitation or dissolution of minerals when water with different geochemical properties are mixed. Dissolution of minerals makes the porous medium more permeable, whereas precipitation of minerals clogs the porous medium. Biological clogging occurs when microorganisms, e.g., bacteria or fungi attached to the soil, grow on an organic carbon source. Growth of microorganisms involves dividing and multiplying of microorganisms and production of extracellular polysaccharides (EPS). The increasing amount of biomass, i.e., microorganisms and EPS, occupies a part of the pore space, and hence changes the properties of the porous medium. Also the microbial production of low soluble gases, e.g., N2 by denitrification or CH4 by fermentation, can cause en- trapment of gas bubbles in the porous medium that further reduces the effective porosity. A thorough description of the different types of clogging can be found in Baveye et al. (1998) and Rinck-Pfeiffer (2000). In this thesis the focus will be on bioclogging. Bioclogging is likely to occur in smaller or larger extent when microbes are supplied with a high concentration of an organic compound and sufficient electron acceptor, e.g., in reactive barriers/biocurtains, during natural attenuation or enhanced bioremediation of pollutants in the groundwater, at artificial recharge of surface or wastewater into the groundwater, and microbiologically enhanced oil recovery. In most of these cases bioclogging is detrimental to the process, but in a few cases bioclogging can be beneficial. With natural attenuation, where indigenous microbes degrade an organic pollutant, bioclogging will potentially alter the properties of the porous medium close to the substrate source. The decreased permeability can cause the water flow to be diverted around the bioclogged zone, and result in a misinterpretation of the remediation status as observed concentrations will change if a monitoring well is located in or in the vicinity of the bioclogged zone. In these cases the changes due to bioclogging can often only be noticed from tracer tests and will have very little effect on the hydraulic head. Therefore, in real-world natural attenuation of a pollutant, bioclogging of the porous medium is rarely observed due to very sparse monitoring. An example of possible bioclogging during natural attenuation is seen from the reactive transport modeling of the Vejen landfill plume in Denmark, which has been extensively moni- tored for research purposes. Chloride distribution showed a splitting of the plume and indicated a low permeable zone just downgradient the landfill though drilling results showed a homogeneously sand aquifer (Brun, 1997; Brun et al., 2002). The low

1 1 Introduction permeable zone could be due to chemical and biological clogging from over 20 years of leakage from the landfill with a very high concentration of dissolved organic carbon, as both significant reduction in the redox potential and change in the geochem- istry were observed. Other studies have clearly demonstrated an intensive clogging from landfill leakage (e.g., Paksy et al. 1998; Fleming et al., 1999; Fleming and Rowe, 2004). Enhanced bioremediation of an organic pollution, where injection of microbes, electron acceptor or cometabolic substances enhance the degradation of a pollutant, im- plies a high potential for bioclogging. The most pronounced bioclogging will occur close to the injection well, where the optimal conditions for the microorganisms are present. This can cause clogging of the screen in the injection well or the zone adja- cent to the well, which will reduce the capacity of the injection well, and hereby, the effect of the bioremediation (e.g., Taylor and Jaffé, 1991; McCarty and Semprini, 1993; McCarty et al., 1998). Artificial recharge of surface water and wastewater into the groundwater are used for, e.g., conservation of the groundwater resource and as water treatment. Labora- tory column experiments using infiltration of surface and pretreated wastewater revealed a considerably decrease in the infiltration rate due to clogging (e.g., Okubo and Matsumoto, 1979; Albrechtsen et al., 1998; Rinck-Pfeiffer at al., 2000). The clogging was a combination of physical clogging by suspended solids and bioclogging due to degradation of organic compounds. After about a month the infiltration rate in the sand columns had decreased to less than 1%, and the artificial recharge system was no longer operational. A case where bioclogging with advantage can be utilized to control and remove an organic pollutant is in reactive barriers, also called biocurtains. The barriers are cre- ated by injection of microorganisms and growth media into the subsurface through an array of injection wells. Growth of the microorganisms reduces the hydraulic conductivity of the soil and creates a low permeable barrier. The barrier can control and limit the transport and enhance the degradation of the pollutants (Trefry et al., 1998; Hyndman et al., 2000; Komlos et al., 2004). Modeling reveals that heteroge- neities in the hydraulic conductivity can significantly change the residence time in the barrier (Benner et al., 2001), resulting in preferential flow through the barrier and hereby reduce the treatment efficiency. Another beneficial use of bioclogging is in microbiologically enhanced oil recovery. Oil in high permeable zones in, e.g., sandstone is easily recovered by displacement with water, leaving behind the oil in the low permeable zones. By introducing microorganisms to sandstone cores bioclogging of primarily the high permeable zones results in an improved oil recovery from the low permeable zones (e.g., Torbata et al., 1986; Zhang et al., 1992; Lappan and Fogler, 1994). To be able to prevent undesirable bioclogging and exploit the beneficial effects of bioclogging, it is necessary to understand the changes in flow and transport properties of the bioclogged medium. Several porous media column experiments have been

2 1 Introduction performed to observe the changes in the hydraulic conductivity (e.g., Frankenberger et al., 1979; Okubo and Matsumoto, 1983; Vandevivere and Baveye, 1992a+b; Seki et al., 1998), and some studies have furthermore used tracer tests in bioclogging column experiments to examine the changes in solute transport properties (Taylor and Jaffé, 1990a; Holm, 2000; Bielefeldt et al., 2002a+b). Taylor and Jaffé (1990a) performed tracer tests by extracting samples at 4-6 locations in the columns once at the end of experiment, whereas Holm (2000) and Bielefeldt et al. (2002a+b) performed tracer tests 2-6 times during the bioclogging experiments. From the column experiments a decrease of up to 3 orders of magnitude in the hydraulic conductivity, and an increase in the dispersivity of a factor of 2-1000 within a few weeks to several months were found. Besides, Holm (2000) observed indications of development of preferential flow paths with tracer diffusing between the mobile zone and immobile biomass. The effect of bioclogging from a substrate point source has been investigated in sandbox experiments (Kildsgaard and Engesgaard, 2002; Thullner et al., 2002b; Seki et al., 2004). In all experiments the tracer was diverted around the bioclogged zone with no significant changes in hydraulic conductivity. Different injection approaches revealed different biomass distributions. Injection of substrate through the injection point and electron acceptor through the background flow resulted in bioclogging in two long strips down through the sandbox, i.e., in the mixing zone of the two solu- tions (Thullner et al., 2002b). Injection of both substrate and electron acceptor through the injection point resulted in a more continuous zone affected by bioclogging downgradient the injection point (Kildsgaard and Engesgaard, 2002; Seki et al., 2004). Bioclogging experiments in two-dimensional micromodels were performed to enable continuous observations of the morphology and distribution of biomass in the pores (Dupin and McCarty, 2000). Biomass growth in a combination of biofilm and aggregates was observed primarily in the part of the micromodel close to the substrate injection. At times biomass was sloughed off resulting in sudden increase in the hydraulic conductivity. Motile microorganisms were observed to be flushed downgradient when located in the centre of the pores, whereas an upgradient migration of motile microorganisms close to the pore walls was observed. Experiments reveal that the changes in flow and solute transport due to bioclogging are affected by many processes. Numerical modeling of bioclogging experiments may help in understanding the many interacted and coupled processes taking place when microorganisms grow on a substrate, attach to and detach from the solid phase, are transported depending on the water flow and the substrate gradient, and alter the porosity and hydraulic conductivity. Pore network models have been developed, e.g., to investigate the relation between the reduction in porosity and the reduction in hydraulic conductivity due to biomass (Dupin et al, 2001; Thullner et al., 2002a). Both models revealed that assuming biomass growth in aggregates resulted in a significant larger reduction of the hydraulic conductivity than if the same amount of biomass was distributed uniformly as a biofilm. These findings are important in order to understand the effect of microbial

3 1 Introduction morphology on bioclogging, but also predictable, as biomass in aggregates can com- pletely clog a pore without filling it, whereas a pore needs to be filled with biofilm to obtain the same degree of clogging. The model results correspond with the findings of Vandevivere et al. (1995), in which different relations between porosity and hydraulic conductivity were compared with experimental observations. Reactive transport models including bioclogging have been developed and used for simulating bioclogging experiments (Holm, 2000; Kildsgaard and Engesgaard, 2001, Thullner et al. 2004). The one-dimensional model of Holm (2000) was reasonably capable of predicting results of column experiments, i.e., measurements of the substrate, electron acceptor and aqueous biomass concentrations, and the decrease in hydraulic conductivity. The models of Kildsgaard and Engesgaard (2001) and Thull- ner et al. (2004) were used to simulate the sandbox experiments with a point substrate source. The models were calibrated by fitting to the observed dye tracer distribution, and again reasonable results were obtained.

2. Objectives The objective of this study has been to investigate changes in porous media properties due to bioclogging. An increased understanding of the processes can help in exploiting the beneficial effects and reducing the disadvantages of bioclogging. Bioclogging of a porous medium has been investigated by experimental and numerical methods, where changes in hydraulic head and tracer tests are used to assess the changes in porous media properties and flow pattern. The focus in the experiments was to use simple continuous measurements of the changes of the hydrodynamics of the flow system, whereas a microbiological study of bioclogging was beyond the scope of this study. The main results of this study has been reported in four scientific papers enclosed in this thesis, and the present report summarizes and relates the results from the papers.

3. Bioclogging experiments The experimental part of the study was performed in laboratory columns and sandboxes packed with sand, Figure 1 and 2, (Seifert and Engesgaard, 2005a; 2005c). Part of or all the sand was inoculated with a mixed culture of microorganisms obtained from a wastewater treatment plant. In most studies pure cultures of microorganisms have been used, as it is easier to obtain good measures of a pure culture than a mixed. As it was not the intention to study the biomass per se, a mixed culture was found to better reflect actual field conditions (Clement et al., 1997).

4 3 Bioclogging experiments

Fig. 1. Pictures of the column experiment setup. Two out of three columns shown.

Fig. 2. Pictures of the setup for the three sandbox experiments.

5 3 Bioclogging experiments

Several trial runs of the sandbox experiment showed the need of initially separating the microorganisms from the substrate, as the microorganisms migrated towards the substrate source with the risk of clogging the inlet chamber mesh, and contaminating the pumps and solution bottles. For that reason the inoculated microorganisms were initially located 10 cm downgradient the substrate injection in the sandbox experiments. Microbial migration probably also took place in the columns, though it was not possible to observe these problems in the column experiments. By changing the solution bottles as often as possible (mostly once a day) the risk of undesirable substrate consumption in the bottles was reduced. Preliminary column experiments in 20 cm long columns (reported in Engesgaard et al., 2005) showed that the most severe bioclogging occurred within the first few centimeters from the substrate injection, and the following column experiments were therefore performed in 5 cm long column. The development of bioclogging was observed as changes in the hydraulic conductivity and in the flow pattern, which were continuously tracked by measuring the hydraulic head gradient throughout the experiments, and carrying out tracer tests every 2-4 days. In the column experiments the tracer concentration was measured at the outlet of the columns, and in the sandbox experiments the two-dimensional dye tracer distribution was recorded by a digital camera.

4. Results and discussion of the bioclogging experiments 4.1 Hydraulic conductivity The bulk hydraulic conductivity, K, in the columns was observed to decrease up to three orders of magnitude over the 5 cm, with about an order of magnitude further decrease in the first centimeter of the columns within the first 20 days, i.e., the initial severe bioclogging occurred primarily in the first part of the column. Hereafter a period with increasing values and a period with oscillating values was observed. This was assumed to be a result of sloughing of biomass in the upgradient end, and subse- quent attachment and growth further downgradient, as the hydraulic conductivity of the next 2 cm of the columns was observed to decrease in this period (Seifert and Engesgaard, 2005a). In the sandbox experiments the bulk hydraulic conductivity had, after 26 days, decreased down to 2-5% of the initial value over the 44 cm long sandbox. Assuming a 5 cm zone of bioclogging with a decrease in the K-value as found for the column experiments within the first 20 days, and the rest of the sandbox unaffected by bioclogging, resulted in bulk K- values for the sandbox in the same range as the measured value (Seifert and Engesgaard, 2005c). This indicates an initially fast substrate degradation where only a small part of the sandbox was affected by bioclogging, as found for the 20 cm long columns. Relations between the bulk hydraulic conductivity, K, and the mobile porosity frac- tion, E = Tmobile/Ttotal, was used to estimate the changes in the bulk porosity from the

6 4 Results and discussion of bioclogging experiments measurements of the bulk hydraulic conductivity. Several K-E-relations have been developed assuming different microbial morphology (reviewed in Seifert and Enges- gaard, 2005a). The K-E-relations were developed on the assumption that the considered zone was homogeneously bioclogged, with identical porosity and hydraulic conductivity changes for the whole zone. This requirement is assumed to be met in numerical modeling with a discretization on a millimeter scale, but using the K-E- relations on larger scales to estimate bulk values of a heterogeneously bioclogged zone (e.g., for a column or a sand box) may not give entirely correct results. Still, in Vandevivere et al. (1995), Vandevivere (1995), and Thullner et al. (2002a) the K-E- relations were compared with experimental results from column experiments of 5-8 cm (Cunningham et al., 1991; Vandevivere and Baveye, 1992c), where the hydraulic conductivities were measured over the entire length of the column. In Cunningham et al. (1991) the biomass concentration was measured from images taken through a microscope, which did not detect any obvious differences in biofilm thickness between the up- and downgradient ends of the columns. In Vandevivere and Baveye (1992c) the biomass concentration was computed by mass balance for the whole column. However, assuming the biomass to be homogeneously distributed in the entire column is inconsistent with all other bioclogging experiments showing that the upgradient part of the columns is significantly more affected by bioclogging than the downgradient part. Using the K-E-relations on different scales necessitates the relations to be adjusted for the scale on consideration. With the relation of Thullner et al. (2002a), as the only one, it is possible to change the parameters (primarily the threshold value for the relative mobile porosity, Emin) for fitting to bulk K values. The relation is given by:

3 2 K(E) § E Emin · § E Emin · a ¨ ¸ 1 a ¨ ¸ (1) K ini © 1 Emin ¹ © 1 Emin ¹ where a is a fitting parameter and Emin = Tmin/Ttotal, with Tmin as the minimum mobile porosity, which means that Emin is the threshold value for mobile porosity, i.e., the value of the minimum relative mobile porosity at which the hydraulic conductivity reaches zero. In the current study Equation (1) was used in both the column and sandbox experiments to estimate bulk porosities, where only a part of the porous medium was affected by bioclogging, resulting in bulk porosities down to 75-80% of the initial value. 4.2 Tracer tests The breakthrough curves from the about 30 tracer tests in each column experiment were divided into four groups covering different phases of bioclogging: first an initial phase of 3-8 days with no significant changes in the tracer transport, followed by about 40 days with a small increase in dispersion, then 60-90 days with a severe increase in dispersion, and at last a post disinfection phase where the dispersivity was

7 4 Results and discussion of bioclogging experiments observed to decrease as a result of declogging of the columns due to starvation and disinfection with chlorine (Seifert and Engesgaard, 2005a). The sandbox experiments only covered the first and partly the second phases found from the column experiments. Dye tracer tests in the sandbox were used to visualize the changes in the solute transport in these two phases. Within the first 3 days no significant changes in the dye tracer distribution was observed, which corresponds to the initial phase found in the column experiments. Hereafter a development of preferential flow paths was observed (Seifert and Engesgaard, 2005c). If the tracer breakthrough had been measured in the outlet of the sandbox, the development of preferential flow paths would appear as an increase in dispersion as found for the second phase in the column experiment. Two different substrate injection modes were used in the sandbox experiment: One with a short injection line covering one third of the width of sandbox, and one covering the whole width. The decrease in the hydraulic conductivity for the two setups was similar, but the tracer distributions revealed large differences in the biomass distribution between the two injection modes. In the two experiments with the long line source, the biomass grew in a large zone covering the entire width of the sandbox, whereas in the experiment with the short line source a correspondingly smaller part of the sandbox was affected by bioclogging, and the flow was diverted around this zone. The porosity decrease in the lower part of the sandbox was estimated from the tracer propagation and the K-E-relations, and a discrepancy between the two methods was explained with a development of three-dimensional effects in the sandbox, where the tracer was assumed to be diverted around a more bioclogged zone in the center of the sandbox. Up to 50% of the increase in tracer propagation was estimated to be caused by the three-dimensional effects in the sandbox, resulting in a bulk porosity decrease down to about 70-80% of the initial value. In the sandbox experiments the location of the microorganisms and the substrate injection were initially separated with 10 cm of sterilized sand. Changes in the dye tracer distribution with development of preferential flow paths upgradient the initial location of the microorganisms indicated an upgradient migration of microorganisms towards the substrate source. After 3-4 days of lag time the upgradient migration was found to be about 0.5 cm/day.

5. Modeling of the bioclogging experiments 5.1 Analytical modeling of tracer tests in the column experiments The breakthrough curves from the column experiments were analyzed by an analytical one-dimensional dual porosity model (Seifert and Engesgaard, 2005a). The dual porosity model was used to investigate the development of preferential flow paths and immobile zones of biomass. Bulk porosities obtained from the K-E-relation was used as input parameters to the model, whereas the dispersion and mass transfer

8 5 Modeling of bioclogging experiments coefficients were estimated from the model by inverse optimization. The dispersivity was found to increase one order of magnitude, and the mass transfer coefficient, given as the Damkohler number, was found to be constant, approximately Z = 45, indicating a fast mass transfer between the mobile phase and immobile biomass. Comparison of the changes in hydraulic conductivity and dispersivity showed that the initial steep decrease in the K-value did not significantly change the solute transport parameters as only a small bioclogged part of the columns resulted in the large changes in the K-value. Later in the experiment the biomass affected a larger part of the column, and the dispersivity increased significantly due to an increased amount of biomass in the columns. 5.2 Numerical modeling of sandbox experiments The sandbox experiments were modeled by a numerical bioclogging model developed by Kildsgaard and Engesgaard (2001) and extended by Seifert and Engesgaard (2005b) to include random motility and chemotaxis. In the model it was assumed that microorganisms migrated in pores with stagnant water or close to the sand grain surface where the pore water velocity was reduced. In the model simulations it was necessary to assume that the reduced pore water velocity affecting the microorganisms was f = 1% of the average pore water velocity, where f is defined as the pore water velocity reduction factor. The model was calibrated by comparing with the bulk hydraulic conductivity, and the tracer distribution, including the estimated porosity changes, and the upgradient migration of the microorganisms (Seifert and Engesgaard, 2005d). The primary calibration parameters were the biomass density and the microbial migration parameters. Using a biomass density in the range of what others have used for modeling purposes, and values of the random motility and chemotaxis parameters about an order of magnitude higher than the values found in the literature, it was possible to fit all observations reasonably well. The model was not able to predict the small-scale changes in the tracer front and the pronounced development of preferential flow paths. This was assumed to be due to a randomness of the bioclogging not included in the model. The model results revealed that the description of the detachment coefficient in the model should be improved from being a simple constant first order coefficient to include the substrate concentration and pore water velocity, to be able to model the microbial migration, sloughing of biomass, and development of preferential flow paths.

6. Conclusions The main conclusions of the study are: 1. Investigations of various relations between the hydraulic conductivity and the porosity, a comparison with results from column and sandbox experiments, and modeling of sandbox experiments revealed that the equation assuming micro- colonies of Thullner et al. (2002a) gave the best result.

9 6 Conclusions

2. Column experiments revealed that bioclogging occurred in several phases; an initial phase with no significant changes in transport properties; a bioclogging phase showing minor changes in dispersivity and a significant reduction in the hydraulic conductivity; and a rearranging phase there growth of micro-colonies deeper in the columns caused an altering in tracer transport from being uniform to more non-uniform with an effect of diffusive mass transfer of the tracer between the mobile phase and the immobile biomass. The mass transfer of tracer between the mobile phase and the biomass was found to be fast, but was still important for the solute transport. 3. The sandbox experiment with different substrate injection modes revealed repro- ducibility in the hydraulic conductivity, but very different pictures of the tracer distribution. These experiments showed that measuring only the K-value would not necessarily reflect the changes in flow pattern. 4. The sandbox experiments also revealed that the microorganisms were migrating upgradient towards the substrate source. Numerical investigations of the bioclogging experiments, where the microbial migration mechanisms, random motility and chemotaxis, were incorporated into an existing numerical bioclogging model, were able to explain the observations. The microorganisms were estimated to migrate at a rate of 0.5 cm/day towards the substrate source. 5. The bioclogging experiment in columns and a sandbox showed indications of three-dimensional effects from bioclogging with development of preferential flow paths. This and the complexity of controlling growth and migration of microorganisms considerably complicate the performance of bioclogging experiments and the selection of appropriate measuring methods.

7. Perspectives The present study provides some answers to the processes of bioclogging, but many more questions and possibilities of new experimental methods arose during the study. The three-dimensional effects from bioclogging with development of preferential flow paths are difficult to quantify in one- and two-dimensional experimental setups. After ending an experiment, columns and sandboxes can be dismantled to observe the final three-dimensional distribution of, e.g., biomass and tracer. However, only continuously non-destructive measurements of the biomass and tracer distribution can give a picture of the development of bioclogging of a porous medium. This can be performed by x-ray techniques using a synchrotron to obtain a sufficient high resolution to be able to distinguish between sand gains, water and biomass (e.g., Gilbert et al., 1999; Lai et al., 2000; Lawrence et al., 2003). Several conditions needed to be considered: the radiation intensity at which the biomass suffers less damage from the radiation, use of special strain of microorganisms, and use of a dopent in the water phase, to be able to distinguished between the phases.

10 7 Perspectives

The simple chemotactic model used in this study assuming the microorganisms to be affected by a reduced pore water velocity could be improved by a better description of the pore water velocity reduction factor, f. The present model assume all microorganisms to be affected the same way by the pore water, though Dupin and McCarty (2000) showed that the microorganisms were either flushed downgradient with the pore water in the centre of the pores or migrated upgradient towards the substrate close to the pore walls. An improved model, where f is a function of, e.g., the substrate gradient, can combine these observations. If the substrate gradient is less than a certain threshold value f is equal to one, i.e., the microorganisms are transported with the pore water. If the substrate gradient is higher than the threshold value, f is less than one, i.e., the pore water velocity affecting the microorganisms is reduced and the microorganisms have the ability to migrate upgradient towards the substrate source. Besides, further experimental investigations of upgradient migration of microorganisms in porous media is needed to understand the migration processes of random motility and chemotaxis, and to be able to achieve better estimates the migration parameters. Modeling of bioclogging is extremely sensitive to the biomass density and the relation between the relative porosity, E, and the hydraulic conductivity, K. Biomass density has been estimated with several orders of magnitude difference (e.g., Ritt- mann and McCarty, 1980a+b; Characklis et al., 1982; Clement et al., 1996; Holm, 2000), and is dependent on the microbial strain, production of EPS, etc. The biomass density controls the volume of the produced biomass, and hereby the change in porosity. The connection between the porosity and the hydraulic conductivity is deter- mined by the K-E-relations, and thus the biomass density has an effect on hydraulic conductivity. Further investigations on the K-E-relations, the parameter values of the relation of Thullner et al. (2002a), and the connection with the biomass density are needed together with an analysis of using these relations on different scales.

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