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Immobilisation of SRB on different support materials Basu, Onita 2000

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Immob i l i sa t ion o f S R B on Di f ferent Suppor t Mater ia ls by Onita Basu B.A.Sc, The University of British Columbia, 1995 A thesis submitted in partial fulfilment of the requirements for the degree of Master of Applied Science in the Faculty of Graduate Studies (Department of Civil Engineering, Environmental Engineering Programme) We accept this thesis as conforming to the required standard T H E UNIVERSITY OF BRITISH COLUMBIA November, 1999 ©Onita Basu, 1999 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head o f my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department o f _ The University of British Columbia Vancouver, Canada Date NovKrritt. /""P^J^?^ ABSTRACT The attachment and growth of sulphate reducing bacteria to solid supports, under autotrophic conditions, made from different materials was studied in this project. The study of the immobilisation of S R B to different support surfaces was conducted in two parts. In the first study, glass beads, ceramic beads, molecular sieve, Teflon/plastic pieces, and zeolite supports were used. The purpose of this study was to determine i f the selected analytical techniques were appropriate methods to monitor the biomass and sulphate concentrations present in the batch flasks. Both the turbidimetric and methylthymol blue methods of sulphate analysis are considered valid techniques to monitor sulphate concentrations. T K N is considered an appropriate method for enumerating S R B biomass, whereas measurements using the total solids and protein led to erroneous results. In the second study, foam, basalt, Ringlace and alginate beads were used as immobilisation surfaces. The amount of biomass immobilised on the different materials was monitored and compared to the total biomass in the system in an effort to quantify which support would be suitable for an immobilised bioreactor system. In order of decreasing immobilisation, compared to freely suspended biomass, is alginate beads (84%) > foam (79%) > Ringlace (37%), while the biomass on the basalt was below the detection limit of the T K N analysis. A study of S R B growth in the complex and defined media showed that S R B were able to grow in both nutrient mediums. However, the specific activity of the S R B in the complex media was greater than that in the defined media, 0.097 and 0.015/h, respectively. The C O 2 uptake was first initiated in the defined media solution at a rate of 1.81xl0" 0 5 mol C 0 2 / ( L . h ) , while the uptake of C O 2 in the complex media was initiated after approximately 150 hours at a rate of 0.38xl0" 0 5 mol C 0 2 / ( L . h ) . i i TABLE OF CONTENTS ABSTRACT II LIST OF TABLES VI LIST OF FIGURES VIII NOMENCLATURE X ACKNOWLEDGMENTS XII CHAPTER 1: INTRODUCTION 1 1.1 MOTIVATION 2 1.2 LAYOUT OF THE THESIS 3 CHAPTER 2: LITERATURE REVIEW 5 2.1 ACID ROCK DRAINAGE 6 2.2 TREATMENT OPTIONS 10 2.2.1 Chemical Treatment 10 2.2.2 Passive Treatment _ _ 14 2.2.3 Ex-situ Biological Processes 15 2.3 SRB OVERVIEW 19 2.3.1 The Sulphur Cycle ______ _ 19 2.3.2 Distribution of SRB 20 2.3.3 Cultivation and Media 20 2.3.4 Electron Donors 21 2.3.5 Hydrogen Utilising SRB Species 26 2.3.6 Inhibition of SRB 29 2.4 IMMOBILIZATION OF BACTERIA TO SURFACES ; .31 2.4.1 Biofilm Formation 34 2.4.2 SRB Biofilm Quantification .... _ _ 37 2.4.3 Cell Growth Kinetics in a Batch System __ 38 2.4.4 Reactor Selection _ 40 2.5 SUMMARY 42 2.5.1 Selection of Support Materials 43 2.7 THESIS OBJECTIVES 44 iii CHAPTER 3: METHODS AND MATERIALS 46 3.1 OVERVIEW OF EXPERIMENTS _ . 46 3.2 COMPARISON OF SRB GROWTH IN DIFFERENT MEDIA 47 3.2.1 SRB Growth . .....47 3.2.2 Nutrient Solutions 48 3.2.3 Temperature _ 50 3.2.4 Cultivation 50 3.3 GROWTH ON SUPPORT MATERIALS 51 3.3.1 Preparation of Growth Surfaces 52 3.3.2 SRB Growth 52 3.4 C 0 2 UPTAKE EXPERIMENTS 57 3.5 ANALYTICAL METHODS 59 3.5.1 Total Solids and Volatile Solids 59 3.5.2 Total Kjeldahl Nitrogen (TKN) Assay for Biomass Determination ; 60 3.5.3 Total Protein (DC Bio-RadAssay) 61 3.5.4 Sulphate Analysis 62 3.5.5 Gas Analysis/C07 Monitoring 64 3.5.6 Scanning Electron Microscope Imaging 67 CHAPTER 4: RESULTS AND DISCUSSION 68 4.1 SRB GROWTH IN DIFFERENT NUTRIENT MEDIA 68 4.1.1 Nutrient Solution Tests 68 4.1.2 Comparison ofMVH, MVH2, MVH3 Nutrient Solutions ____ 69 4.1.3 CO2 Monitoring 73 4.2 SET LGROWTH ON SUPPORT MATERIALS: GLASS, MOLECULAR SIEVE, CERAMIC BEADS, TEFLON AND ZEOLITE 78 4.2.1 Solids .....78 4.2.2 Growth Curves using TKN Measurements 80 4.2.3 Sulphate Reduction 84 4.2.4 CO2 Monitoring 87 4.2.5 Discussion of Set 1 Support Surfaces '__ 90 4.3 SET 2:GROWTH ON SUPPORT MATERIALS: FOAM, BASALT, RINGLACE, AND ALGINATE BEADS ..... 9 3 4.3.1 Growth Curves using TKN and Protein Measurements 93 4.3.2 SRB Growth on Support Materials 100 4.3.3 Scanning Electron Microscope Images 103 4.3.4 Sulphate Reduction 108 4.4 SUMMARY 1 1 1 CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 115 5.1 CONCLUSIONS 115 5.2 RECOMMENDATIONS 117 REFERENCES 118 APPENDIX A: RAW AND CALCULATED DATA 125 V LIST OF TABLES Table 2.1: Examples of A R D Water Quality _ 7 Table 2.2: K S P Values of Various Metals in Water (pH = 7.0) 11 Table 2.3: Ground Water Analysis of Budelco Zinc Refinery 16 Table 2.4: Budelco Fu l l Scale Plant Results (initial trials) 18 Table 2.5: Summary of Carbon/Energy Sources used in Previous Studies 23 Table 2.6: Hydrogen Util ising S R B 27 Table 2.7: Temperature and p H Conditions used in Previous Studies 28 Table 2.8: Surface Immobilised Support Materials used in Anaerobic Studies 33 Table 2.9: Comparison of Different Reactors with Immobilisation Surfaces 41 Table 3.1: Nutrient Solutions 49 Table 3.2: S R B Growth Surfaces 51 Table 4.1: Addit ion of Yeast and/or Bactopeptone to Nutrient Solutions 69 Table 4.2: Blackening as an indication of Activi ty in Nutrient Solutions 69 Table 4.3: T K N values for the S R B in three M V H solutions 70 Table 4.4: Summary of Nutrient Solution T K N and Sulphate Results 71 Table 4.5: Y i e l d Coefficients of S R B with Sulphate as electron acceptor 73 Table 4.6: C 0 2 Uptake Experiment - Final T K N Values 74 Table 4.7: Change in CO2 level during Nutrient Solution Experiments 77 Table 4.8: Total Solids Results 78 Table 4.9: Set 1 Experiment Specific Growth Rates and Doubling Times 83 Table 4.10: Molecular Sieve and Control Comparison of Total Solids and T K N data 84 Table 4.11: Summary of Sulphate Reduction Results in Set 1 Experiments 86 Table 4.12: T K N based Specific Growth Rates and Total Biomass Growth in Set 2 Experiments 95 Table 4.13: Comparison of Specific Growth Rates and Yie ld Coefficients 96 Table 4.14: Total Protein Assay Results for Control, Foam, Basalt and Ringlace 97 Table 4.15: Summary of Biomass Growth based on the Protein Assay 100 Table 4.16: Summary of Biomass Growth based on the T K N Assay 100 Table 4.17: S R B Growth in Solution vs. on Support for Set 2 Experiments Table 4.18: Sulphate Reduction Data for Set 2 Experiments LIST OF FIGURES Figure 1.1: Relationship between biosorption and microbe metabolism 1 Figure 2.1: Sulphide Precipitation Diagram 12 Figure 2.2: Metal Hydroxide Precipitation Diagram 13 Figure 2.3: Wetlands for A R D Treatment 14 Figure 2.4: Paques U A S B Process Pilot Plant .....17 Figure 2.5: Microbial Mediated Sulphur Cycle 19 Figure 2.6: Ethanol vs. Hydrogen as Electron Donor Source for S R B 24 Figure 2.7: Gas Lift Reactor used by V a n Houten 25 Figure 2.8: Substrate Conversion and Dilution Rate in an Immobilised Ce l l System 32 Figure 3.1: Nutrient Solution Selection Process 48 Figure 3.2: Sampling Procedure F low Diagram 55 Figure 4.1: First Order Growth Comparison of M V H Nutrient Solutions 71 Figure 4.2: C 0 2 Uptake in M V H Nutrient Solution 75 Figure 4.3: C 0 2 Uptake in M V H 2 Nutrient Solution 75 Figure 4.4: C 0 2 Uptake in M V H 3 Nutrient Solution 76 Figure 4.5: Total Solids Results for the Set 1 Experiment Support Surfaces 79 Figure 4.6: Control and Glass Bead Support Growth Curves 81 Figure 4.7: Teflon and Zeolite Support Growth Curves 81 Figure 4.8: Molecular Sieve and Ceramic Bead Support Growth Curves 82 Figure 4.9: Sulphate Reduction with Glass Support 86 Figure 4.10: Sulphate Reduction with Teflon and Zeolite Supports 87 Figure 4.11: C 0 2 depletion curves for Set 1 Experiments 88 Figure 4.12: Ringlace, and Alginate Bead Support T K N Growth Curves 94 Figure 4.13: Foam and Basalt Support T K N Growth Curves 95 Figure 4.14: Growth Results for Foam and Basalt based on T K N and Protein Measurements 98 Figure 4.15: Growth Results for Control and Ringlace based on T K N and Protein Measurements 99 Figure 4.16: Comparison of S R B immobilised on support ( T K N ) 101 Figure 4.17: Comparison of S R B biomass in solution and immobilised on support 102 Figure 4.18: S E M Foam 1 104 Figure 4.19: S E M Foam 2 104 Figure 4.20: S E M Foam 3 105 Figure 4.21: S E M Alginate Bead 1 105 Figure 4.22: S E M Alginate Bead 2 106 Figure 4.23: S E M Ringlace 1 106 Figure 4.24: S E M Basalt 1 107 Figure 4.25: S E M Basalt 2 107 Figure 4.26: Sulphate Reduction with Alginate Beads and Ringlace Supports 109 Figure 4.27: Sulphate Reduction with Foam and Pumice Supports 109 NOMENCLATURE Aco2 = area of the CO2 peak Acomp = A N 2 = area o f N2 peak d = diameter (m) D = F / V = dilution rate (h"1) F = volumetric flowrate (m3/h)) ko = zero order rate constant (kg/m /s) K s = substrate conversion rate (mg/L) Ksp = solubility product r]T = total effectiveness factor (dimensionless) P = protein concentration (mg/mL) PF = final protein concentration (mg/mL) Pi = initial protein concentration (mg/mL) R = AC02/Acomp = ratio of CO2 peak area to the peak area of N2+CO2 r = the volumetric rate of reaction (kg/m 3/s), rs = -dS/dt = the rate of substrate consumed (kg/m /s) rx = dx/dt = the rate of biomass produced (kg/m Is), s.a. = surface area (m ) SD = standard deviation S = substrate concentration (mg/L) Si = initial substrate concentration (mg/L) SF = final substrate concentration (mg/L) [SO4] = sulphate concentration (mol/L). T = organic nitrogen mass (u.g) Ti = initial organic nitrogen mass (p,g) Tp = final organic nitrogen mass (p-g) t = time td = (In 2)/u, = doubling time (hours) x Nomenclature (con't) u. = specific growth rate (time"1). Pmax = maximum specific growth rate (time"1) V = volume of solution in the batch flasks (L) V = volume in the reactor (m ) V s = sample digestion volume (mL). x = biomass concentration (kg/m 3) X = the mass of biomass (mg) X m = immobilised cell concentration (mg/L) YS04 = molar growth yield = (g biomass/ mol sulphate) Y x s = biomass yield coefficient (mg biomass/mg substrate) ACKNOWLEDGMENTS I would like to thank my supervisors Susan Anne Baldwin (Bioresource Engineering) and Jim Atwater (C iv i l Engineering) for their insightful comments during the course of this project. Many thanks to Paula Parkinson and Susan Harper from the C i v i l Engineering Environmental Lab for their cheerful technical support and assistance with the T K N analysis and CO2 sampling, as well as Phing Lau in Bioresource Engineering for his help with the automated sulphate analysis. I would also like to thank Elaine Humphreys in Biological Sciences for the advice on how to prepare samples for the S E M , Jim Mackenzie in Physics for assisting me with the S E M equipment, and David Jez in Electrical Engineering for his invaluable computer support and friendship. The funding for this project was provided through a research grant from N S E R C . x i i Introduction CHAPTER 1: INTRODUCTION Wastewaters containing high levels of toxic metals, sulphate and a low p H can negatively impact the environment. The treatment of these aqueous streams is important to protect the natural environment and human health. Treatment processes include chemical precipitation, reverse osmosis and ion exchange, however these treatment methods may be too expensive to be economically attractive. Therefore it is important to find a more economically attractive alternative, and bioremediation may provide this option. Removal of metals from wastewaters through cell adsorption, with dead biomass, has been studied as a possible treatment technique for removing metals from wastewater (Eccles, 1995, Mattuschka and Straube, 1993). However, both l iving and dead biomass can accumulate metals. L iv ing cells can assist in the removal of heavy metals through cell wall adsorption, intracellular accumulation, and extracellular polymeric substance (EPS) as well as through precipitation. Whereas dead biomass accumulates metals strictly through the process of cell wall adsorption. Living or dead cells Biosorption Ce l l sui binding 'face Independent of metabolism Living cells Intracellular accumulation EPS accumulation or precipitation Function of metabolism Figure 1.1: Relationship between biosorption and microbe metabolism. (Source: Chang et. al., IAWQ Conference, 1998) 1 Introduction Binding of metals to the cell wall is independent of the metabolic traits of the organism while intracellular accumulation, EPS accumulation and precipitation are functions of metabolism (refer to Figure 1.1). Therefore, a treatment technique, which utilises l iving biomass, has the additional advantages of intracellular metal accumulation, as well as EPS metal accumulation/precipitation. A process that can take advantage of l iving biomass w i l l have a greater ability to remove metals than a biological process relying strictly on cell adsorption. In particular, sulphate reducing bacteria (SRB) reduce sulphate to hydrogen sulphide, which reacts with heavy metals in solution to form insoluble metal sulphide precipitates. This process also has the potential to recover metal sulphides from the effluent, which allows for recycling of the metal sulphides to the front end of metal refining processes, recovery of the sulphides as a saleable products, or simply as the isolation of hazardous sludge (Barnes et. al., 1991). A s well , this biological process may be more economical than more traditional chemical treatment methods of heavy metal, high sulphate containing effluent streams; since metal sulphide precipitates tend to be denser and produce less sludge volume than metal hydroxide sludge making them less expensive to transport (Rowley et. al., 1994). 1.1 Motivation A n immobilised growth system prevents bacterial washout at high flowrates, and provides a larger surface area for bacterial colonisation, resulting in the potential for increased rates of removal. Previous work on attachment of S R B to surfaces has mainly dealt with heterotrophic growth conditions (Maree and Strydom, 1985, Bass et. al., 1996, Kolmert et. al., 1997). Energy sources for heterotrophic growth of S R B are present in two main forms: organic waste, and bulk chemicals. Both of these types of energy sources may lead to competition between S R B and other bacteria, such as methanogens; whereas S R B can outcompete methanogens under autotrophic conditions. A s well , the use of organic waste may lead to secondary pollution concerns while the growth of S R B under autotrophic conditions (CO2/H2) does not cause secondary pollutants. Although one study did compare S R B immobilisation under autotrophic conditions, it only compared two materials, pumice and basalt (Van Houten et. al., 1994). This study looks at the growth of S R B to different 2 Introduction types of surfaces, under autotrophic conditions. The intention was to select suitable materials that were rapidly colonised by S R B , and would support high cell densities. In addition, the materials selected should not inhibit the sulphate reduction. In this project, various support media were examined for their potential to promote rapid bacterial colonisation by determining which physical properties seemed to encourage bacterial adhesion. The following media were selected for comparison: glass beads, molecular sieve, ceramic beads, Teflon-plastic, zeolite, foam, basalt, Ringlace and calcium-alginate immobilised beads. The selection process for these materials is discussed in Chapter Two. 1.2 Layout of the Thesis Chapter Two covers the literature review section of the thesis. In this section the general environmental conditions that cause acid rock drainage ( A R D ) w i l l be discussed. Examples of the chemical properties of A R D w i l l be given, followed by a discussion of chemical treatment for A R D ; as well as the advantages of metal sulphide precipitates compared to metal hydroxide precipitates. A discussion of biological treatment methods for A R D w i l l next be presented, followed by an overview on S R B . This section covers the general distribution of S R B and aims at explaining the growth conditions selected in this study. The last part of the literature review w i l l include a section on bacterial immobilisation and biofilm formation, specific to anaerobic systems. This w i l l be followed by a summary of the literature review, with the purpose of pointing out some of the gaps in that available data that this project hopes to f i l l . This w i l l be followed by the objectives of the thesis. In Chapter Three the methods and materials of the experiments w i l l be discussed. This w i l l include how the S R B were cultured and handled, as well as describing in detail how each experiment was performed, including the experimental protocols for each. The theory on the analysis of the various tests w i l l also be covered in this section of the thesis. 3 Introduction In Chapter Four the results w i l l be presented and discussed. This includes the results of biomass concentration versus time on the support materials and in solution as well as the total biomass. The results of the protein assay are compared with the results from the T K N assay. The measured specific growth rates w i l l be presented and compared with values reported in literature. Results of sulphate reduction w i l l be presented and compared with reported values. The CO2 uptake rates in the defined and complex media w i l l be stated. The S E M images of the bacteria attached to the surfaces are presented. A s well , the experimental procedures w i l l be reviewed for their suitability in this project. In Chapter Five the conclusions made from this project and recommendations for future work w i l l be presented. The appendices include the raw and calculated data from the experiments. 4 Literature Review C H A P T E R 2: L I T E R A T U R E R E V I E W Heavy metals can be found in acid rock drainage, bottom ashes and flue ashes from incineration processes, and electro-plating or circuit board processing industrial effluent. As well, metal refining sites that process metal sulphides often have groundwater contaminated with both heavy metals and sulphate (Barnes et. al., 1991). Elevated levels of sulphates are present in acid rock drainage due to bacterial oxidation of pyrite. In industrial effluents, sulphates are present from spent sulphuric acid (Van Houten et. al., 1994, Maree and Strydom, 1985, 1987). Sulphate levels in chemical industry effluent can range from 200-50,000 mg/L (Van Houten, 1996). One of the most conventional treatment methods for the removal of heavy metals is chemical reduction using reducing agents such as sodium sulphide or lime treatment (Sengupta, 1993). Lime treatment produces large volumes of metal hydroxides mixed with gypsum. The large volumes of sludge produced are not recyclable and have increasing disposal costs as waste management laws become stricter (Fujie et. al., 1994, Rowley et. al., 1994). As a result, there is an increasing demand for economically viable and environmentally sound alternatives for managing acid rock drainage and other sulphate containing waste streams. Although many types of effluents contain high levels of sulphates and metals, ARE) from mine sites is one of the largest sources. In the United States over 17,000 km of streambeds are affected by ARD (Elliot et. al., 1998). In Canada, there are approximately 10,000 abandoned mines and 6,000 abandoned tailing sites; and an estimated 875 million tonnes of mining wastes capable of causing ARD (CIELAP, 1996). Because mining has such a large impact, ARD will be considered in more detail in the literature review. The treatment of similar effluents from other sources, while not considered here would be similar. 5 Literature Review 2.1 Acid Rock Drainage A R D from mining operations originates from drainage from underground tunnels, surface runoff from open pit mines, and drainage from waste rock and tailings deposits. A R D is caused by the exposure of sulphide ores, mainly pyrite, to air and water which results in the production of acid and high levels of sulphates and dissolved metals in water. Sulphide minerals normally lie beneath the earth where air and water penetration is minimised; under these conditions, the acid generation process has little effect on the ground water. However, when the sulphide containing rocks are exposed to air and water through processes like mining, the acid generation process increases (Sengupta, 1993). Both chemical and bacterial reactions are involved in the production of A R D . The exclusion of air or moisture to the exposed rocks w i l l stop the acid generation process. Inhibiting bacterial activity can also slow down the rate of acid generation. The quantity of A R D produced is restricted by the amount of acid neutralising minerals present. Calcite, CaC03, is the most common acid consuming mineral; and it consumes 1-2 moles H /mole C a C 0 3 , through the production H C 0 3 " and H 2 C 0 3 (Sengupta, 1993). In the first step of the reaction process (that produces A R D ) , iron sulphide is oxidised to dissolved iron (ferrous), sulphate and hydrogen ions. The formation of the hydrogen ions decreases the p H of the water. I f enough oxygen is present in the water, the ferrous ion then oxidises to ferric iron. When the p H of the environment is greater than about 3.5, the ferric iron w i l l precipitate out as iron hydroxide while further decreasing the p H . A n y ferric iron that does not precipitate out can facilitate the oxidation of more iron sulphide, thereby generating more ferrous iron, sulphate and hydrogen ions. Certain iron and sulphur oxidizing bacteria species can accelerate the rate of the reaction by increasing the rate of the ferrous-iron oxidation step. The most commonly associated bacteria with A R D generation are Thiobacillus ferrooxidans. They can increase the rate of acid formation by up to a factor of five (Sengupta, 1993). The reactions which cause A R D are shown below (Sengupta, 1993): 6 Literature Review F e S 2 + 7 /20 2 + H 2 0 -> F e 2 + + 2 S 0 4 2 " + 2 H + (2.1) F e 2 + + I/2O2 + H + <-> F e 3 + + 1/2H 2 0 (2.2) F e 3 + + 3 H 2 0 <-> Fe(0H )3i + 3 H + (2.3) F e S 2 + 14Fe 3 + + 8 H 2 0 -> 15Fe 2 + + 2 S 0 4 2 " + 1 6 H + (2.4) Overall Reaction: FeS 2 + 15/40 2 + 7/202 -> F e ( O H ) 3 i + 2 S 0 4 2 " + 4 H + (2.5) A c i d rock drainage from the tailings and waste rock produced by mining operations are often low in p H and high in metal concentrations due to microbial, chemical and hydrological processes that act on the waste. The acid environment tends to mobilise metals which are usually toxic to biota, in addition the acidic conditions are less favourable than near-neutral p H growth conditions for many organisms (Ledin and Pederson, 1996). Examples of the chemical properties of A R D are given in Table 2.1. Table 2.1: Examples of ARD Water Quality Measurement Seepage from an Seepage from a Mine Water from an (mg/L) except pH Abandoned Silver Mine Waste Underground Uranium Mine Rock Pile Copper Mine Tailings Pond (British Columbia) (British Columbia) (Ontario) pH 2.0 2.8 3.5 Sulphate 7440 7650 1500 Acidity 14600 43000 — Iron 3200 1190 10.6 Manganese 5.6 78.3 6.4 Copper 3.6 89.8 16.5 Aluminium 588 359 — Lead 0.67 2 0.1 Cadmium 0.05 0.5 •0.143 Zinc .11.4 53.2 28.5 Arsenic 0.74 25 0.05 Nickel 3.2 8.0 0.06 (Source: Sengupta, 1993) 7 Literature Review Once a mine site is decommissioned, the problems associated with A R D do not stop. This is because acidic drainage is still produced by rainwater and groundwater infiltrating the mine site. Two brief site descriptions of decommissioned mines with A R D drainage are given below. The purpose of describing the sites is to show the levels of contaminants contained within their respective drainage waters and to illustrate that these waters require treatment. It is the existence of such mining sites that helps provide the motivation for looking at economical ways to treat A R D . The Berkley Pit The Berkley Pit is perhaps one of the best examples of A R D in North America. It is an abandoned open pit mine in Butte, Montana. Before 1983, it produced over 20 bil l ion lbs. of copper, 4.9 bi l l ion lbs. of zinc, 3.7 bil l ion lbs. of manganese, and 2.9 mil l ion oz of gold. Since 1982, the pit has been filling with water. The Berkley Pit is the highest acid producing mine in the United States. The pit water has a p H = 2.7 near the surface of the water, a sulphate concentration of 4850 mg/L and concentrations of metals ranging from 433 mg/L copper, 202 mg/L total iron, 212 mg/L zinc, and 153 mg/L magnesium (Sengupta, 1993). These levels w i l l increase with depth in the pit, partially due to the formation of hydroxide species which form solid particles and sink toward the bottom of the pit; as they fall any ions adsorbed to the hydroxide floe w i l l sink as well . About 40% of the water flow into the Berkley Pit is from underground mines while the remaining 60% stems from surface sources. B y the early 1990's, the water depth in the pit was 213 meters. A t its present rate of water infiltration, the pit is expected to start overflowing in 2011. Clearly, the Berkley Pit is an example of a mine site that requires a major practical process for the treatment of acidic pit water before it starts overflowing and damaging the surrounding environment. The Britannia Mine The Britannia mine is considered to be the worst source of metal contamination in North America by Environment Canada (Vancouver Sun, 1996). The Britannia mine is located 8 Literature Review 50 k m north of Vancouver, B C . Approximately 48,000,000 tonnes of ore was mined for copper, silver, zinc, and gold between 1905-1973. Unlike the Berkley Pit, the Britannia mine is mainly underground with over 160 km of mine shafts and tunnels. There are three main access portals to the mine: 2200, 2700, and 4100 adits. The 2200 and 2700 were sealed off at the time of the mine closure to route mine drainage to a single outlet that would discharge at depth into Howe Sound. Currently all three portals have some drainage that discharges to the surrounding environment. The drainage from the 2700 portal is not considered an environmental problem. The 2200 portal has flow rates ranging from 0-10,000,000 L / d , with copper levels up to 120 mg/L, zinc 50 mg/L, cadmium 0.4 mg/L, iron 60 mg/L, aluminium 74 mg/L and sulphate concentrations ranging from 200 to 2000 mg/L with a p H of 2 to 4. The 2200 drains into a freshwater creek and raises the copper level to toxic concentrations for fish. The 4100 portal drainage ranges from 4,000,000 to 40,000,000 l/d, with copper and zinc levels from 12-28 mg/L, cadmium 0.1 mg/L, and iron and aluminium 30 mg/L. The sulphate concentration ranges from 1200-1800 mg/L with a p H of 3 to 4. Despite it's reputation as one the worst metal contaminated sites in North America, along with the adverse impact the drainage water has on aquatic life in local stream beds, as well as pilot plant tests run by a local Vancouver Company (1996) looking at both lime treatment and a combined chemical/biological treatment, there was still no active treatment occurring at the Britannia Mine as of September 1999 (Rowley et. al., 1997). 9 Literature Review 2.2 Treatment Options Preventing the formation of A R D is the preferred option to actually treating A R D . In newer mines this is facilitated by the use of covers, the addition of chemicals to the waste rock pile, and subaqueous disposal (Ledin and Pederson, 1996, Sengupta, 1993). However, in cases where these methods have not been implemented it is necessary to treat the A R D directly. This is most commonly accomplished through the addition of chemicals to raise the p H of the mine water and precipitate out the metals as hydroxides. Other, more recent methods include the use of natural and artificial wetlands which take advantage of bacterial sulphate reduction, and microbial metal accumulation; and the use of sulphate reducing bacteria bioreactors (Ledin and Pederson, 1996, Barnes et. al., 1991, Webb et. al., 1998). The use of chemical treatment as an option and some of the limits associated with this method are compared below to the potential of biological treatment with S R B . A brief comment on the use of wetlands w i l l be mentioned followed by a review of a S R B bioreactor treatment system that is being used to treat the groundwater of a metal refining site. 2.2.1 Chemica l Treatment Chemical treatment of mine waters involves the additions of chemicals such as lime, limestone, sodium carbonate and/or sodium hydroxide. This raises p H o f the water and results in the metals precipitating out as metal-hydroxides. The disadvantage to this method is that large volumes of sludge are produced composed mainly of calcium sulphate and metal hydroxide. In general, lime neutralisation is popular because (Murdock et. al., 1994): • it can treat a wide range of acidities and flowrates, • it is easy to handle and inexpensive, • it is a proven technology with moderate operating costs. 10 Literature Review The general precipitation reactions for metal hydroxides are: M 2 + + SO42" + C a 2 + + 2 0 H " + H 2 0 -> MrOH)^ + CaSO>2H 2 Oi (2.6) 2 M 3 + + 3 S 0 4 2 " + 3 C a 2 + + 6 0 H ' + H 2 0 -> 2M(OH ) 3 i + 3CaS0 4 .2H 2 c4 (2.7) In the above reactions, it can be observed that both metal hydroxides and calcium sulphate dihydrate (gypsum) precipitate out of solution. The additional precipitation of gypsum increases the volume, and cost, o f sludge that requires disposal. The precipitation of metal hydroxides is a function of the solubility of the metal species, which is affected by p H . Metals such as F e 3 + , S n 2 + , and H g 2 + w i l l readily precipitate at low p H (3-4), while others such as A l 3 + , C u 2 + , and P b 2 + precipitate at a slightly higher p H (5-6). A n even higher p H is required to precipitate metals such Fe , Z n , C d and N i . The solubility of various metal sulphides as a function of p H is shown in Figure 2.1, and the solubility of various metal hydroxides as a function of p H is shown in Figure 2.2. These figures indicate that it might be possible to remove some metals more readily as sulphides (including C d , C u , Fe, Pb, N i , A g , and Zn) at lower p H values. Table 2.2: KsP Values of Various Metals in Water (pH = 7.0) Metal Ksp, as a hydroxide Ksp, as a sulphide Cadmium 2x10-"* 3.6x10"" Copper 2 x l 0 - 1 9 8.5 x l O - 3 6 Iron 1.1 xlO" 3 6 (Fe3+)/2 x lO - 1 5 (Fe 2 +) 3.7 xlO" 1 9 Lead 2.5 x lO - 1 6 7 x l 0 - 2 7 Nickel 2x l0" 1 6 2x l0" 2 1 Silver . . . 2 X K T 4 9 Zinc 4.5 x l O - 2 3 1.2 xlO" 2 3 Source(Brady and Humiston, 1986, Sawyer, McCarty et al., 1978) Compared in Table 2.2 are the Ksp values of various metal hydroxides and metal sulphides at neutral p H . The Ksp value of sulphide precipitates is generally lower than that of the respective metal hydroxides, this indicates that the sulphide precipitates are less soluble than 11 Literature Review their metal hydroxide precipitates. For instance, the solubility of Cd(OH)2 and CdS in pure water can be calculated as follows: Cd(OH) 2 <-> C d 2 + + 20F1 Ksp = 2 x l 0 ' 1 4 (2.8) 2x10" 1 4 = X (2X) 2 [Cd 2 + ] = X = ((2xl0" 1 4 ) /4) 1 / 3 = 1.7xl0" 5 M = 1.9 mg/L CdS <r> C d 2 + + S 2- Ksp = 3 .6x l f J 2 9 (2.9) 3 . 6x l0 ' 2 9 = X (2X) 2 [Cd 2 + ] = X = ((3.6xl0" 2 9 ) /4) I / 3 = 2.1X10" 1 0 M = 2.3 x l O ' 5 mg/L It is clear, from the above calculations that the level of cadmium in solution after precipitation with sulphide is significantly less than its hydroxide counterpart. 0 2 4 6 8 10 12 14 PH Figure 2.1: Sulphide Precipitation Diagram. (Source: Peters and K u , 1985) 12 Literature Review I I I !_ O «• N n i » < Figure 2.2: Metal Hydroxide Precipitation Diagram. (Source: Monhemius, A . J . , 1977) 13 Literature Review A s mentioned earlier, many traditional treatment techniques for removal of heavy metals from effluent streams rely on the formation of metal hydroxide precipitates. However, in systems where sulphate is available, the formation of metal sulphides over metal hydroxides has many advantages (De Vegt et. al., 1995, Rowley et. al., 1994, Hammack et. al., 1994): • Metal sulphides form more rapidly than metal hydroxides, and form a denser sludge. • Metal sulphides are less soluble (lower Ksp) than metal hydroxides. • Metal sulphide precipitates are more stable over a wider p H range than metal hydroxides. 2.2.2 Passive Treatment The use of natural and constructed wetlands, which take advantage of natural microbial processes, is a passive biological option for the treatment of A R D . Wetlands are stagnant, anoxic ponds that contain a variety of plant species such as cattails and mosses (Ledin and Pederson, 1996). Metals can be immobilised by sulphate reducing bacteria and/or by plant root uptake. In addition, the A R D tends to be neutralised and is diluted within the wetland. A schematic of a wetland type treatment is shown in Figure 2.3. WETLANDS Positive effects t A "natural" way of treating mine waste drainage •'_ + Sulphate reduction may take place + Acid and metals are removed — > ° f t Potential risks -The accumulated/precipitated metals may be mobilized, e. g. by microbial iron reduction or chemical processes • Sensitive to pulses of high metal concentration Figure 2.3: Wetlands for A R D Treatment. (Source: Ledin and Pederson, 1996) Wetlands can increase the alkalinity of A R D and remove metals through plant uptake, sorption onto organic material, metal hydrolysis, and biological sulphate reduction. Wetlands 14 Literature Review are considered a low cost, passive treatment method but the utilisation of wetlands may be limited in colder areas where bacterial processes are inhibited at low temperatures. Wetlands are most effective for treating low flow A R D that is not highly acidic but still contains high levels of metals (Hackl, 1997). 2.2.3 Ex-situ Biological Processes The advantages of metal sulphide precipitates over metal hydroxide precipitates were discussed in Section 2.2.1 from a chemical perspective. However, a biological process, which converts sulphate to sulphides would prevent the need to add sulphate as a chemical to remove metals (Uhrie et. al., 1996). In fact, the possibility of a biological reactor containing S R B to treat A R D has been studied previously (Turtle et al., 1969, Maree and Strydom, 1985, 1987, Barnes et. al., 1991). These systems all used complex organics as a carbon source for the bacteria present. More recently, the potential of S R B bioreactors using autotrophic growth conditions has been studied (Du Preez et. al., 1992, V a n Houten et. al., 1994, 1996). However, these studies were all completed at bench scale level. The Paques U A S B Process, described in this section, is the first full scale system to utilise a S R B bioreactor for the treatment of heavy metal, high sulphate containing effluent (Barnes et al., 1991, De Vegt and Buisman, 1995). The Paques UASB Process Sulphate reducing bacteria have been used to treat groundwater contaminated with heavy metals and high sulphate levels from a zinc refinery in Budelco, the Netherlands. The composition of the groundwater at the Budelco refinery site is shown in Table 2.3. A s mentioned previously, the S R B w i l l convert the sulphate to sulphide, which w i l l then react with the metals in solution and precipitate out as a metal sulphide. The overall reaction can be represented as: Metal Sulphate + Carbon Substrate - » Metal Sulphidel + C 0 2 + H 2 0 + Biomass (2.10) 15 Literature Review This process uses an upflow anaerobic sludge blanket ( U A S B ) reactor to treat the groundwater. It has been shown to withstand large changes in the influent composition and can rapidly recover from process upsets. The metal sulphide sludge produced from this system can be recycled to the front end of the metal refinery to recover both the metals and convert the sulphide to saleable sulphur. The bioreactor is capable of operation under non-sterile conditions since the sulphide produced by the S R B is an inhibitor for other microorganisms. The main competitor with S R B is methanogens. However, at the high sulphate levels present in the Budelco groundwater, the S R B can outcompete the methanogens helping to control their numbers. Table 2.3: Ground Water Analysis of Budelco Zinc Refinery Component Concentration (mg/L) Sulphate 1300 Zinc 135 Cadmium 1.5 Copper 0.8 Cobalt 0.1 Iron 4 Calcium 320 Ammonium 1 (Source: Barnes et. al., 1991) Shown in Figure 2.4 is a schematic of the biological treatment process. The process includes an influent buffer tank, feed tanks for nutrients, ethanol, alkali and a flocculent. A n inline mixer is included to promote mixing of the various feeds with the influent before entering the reactor. A n inline heater exchanger is used to test the process at different temperatures. A stripper is included to remove hydrogen sulphide from the gas product stream and soluble sulphide from the aqueous stream. The flocculent is required to prevent bacterial washout at residence times less than 30 hours. It promotes the formation of bacterial floes, which w i l l have a heavier mass than free suspended cells, allowing for an increased flowrate through the system (shorter residence time) before washout occurs. 16 Literature Review The process uses ethanol as a feed source for the S R B . S R B do not completely oxidise ethanol, and acetate is a by-product of this reaction. Although S R B can then oxidise acetate to CO2, the degradation is at a slower rate than that of ethanol leading to an accumulation of acetate in the system. A t high concentrations of acetate, S R B growth becomes inhibited. To overcome this problem the Budelco site encouraged the growth of methanogens, which can completely degrade the acetate. The methanogen growth was accomplished by adding methanol to the system, as methanogens w i l l outcompete S R B for this food source. W M D SUKWC BUNKO RfACTOR RQASTD1 Figure 2.4: Paques U A S B Process Pilot Plant. (Source: Barnes et. al., 1991) The groundwater p H is around 4.5, so an alkali tank is used to help maintain the p H near neutral in the reactor since S R B have an optimal growth at around p H 7.5, although they are capable o f growth from around p H 5-9. The carbonate and sulphide byproducts produced by S R B also help to provide buffering to the system. 17 Literature Review Shown in Table 2.4 are the results from the full scale demonstration plant. The Paques U A S B process demonstrates that S R B can be used to effectively treat contaminated groundwater from the refinery. The final plant design includes a tilted plate settler and a sand filter to provide final polishing to the effluent and lower the final zinc concentration to 0.05-0.15 mg/L. Table 2.4: Budelco Full Scale Plant Results (initial trials) Component Influent (mg/L) Effluent (mg/L) Percentage Removal % Sulphate 1200 160 86.7 Zinc 230 0.3 98.7 Cadmium 1.2 <0.01 99.2 Iron 54 2 96.3 Lead 9 .02 99.8 Copper 2.1 0.03 98.6 Cobalt 0.2 0.02 90 (Source: De Vegt and Buisman, 1995) The two main problems encountered in this system were the formation of acetate as a byproduct of S R B ethanol oxidation and bacterial washout. Methanogens were introduced into the system to counteract the first problem. The other problem encountered using the sludge blanket system was overcome by adding a flocculent to the system in order to decrease bacteria washout at residence times below 30 hours. 18 Literature Review 2.3 S R B Overview 2.3.1 The Sulphur Cycle The three oxidation states of sulphur found in nature are -2 (sulphides), 0 (elemental sulphur), and +6 (sulphate). The change in oxidation states of sulphur is mainly mediated through microbial processes. Shown in Figure 2.5 is the microbial mediated sulphur cycle as well as the aerobic and anaerobic environments required for each stage to occur. Bacteria Figure 2.5: Microbial Mediated Sulphur Cycle. (Source: Van Houten, 1996) The majority of sulphur is found in the form of sulphate and sulphide in minerals such as gypsum, CaS04.2H20, and pyrite, FeS2, or iron sulphide. These minerals are mainly present in sediments and rocks, below the surface of the earth. Sulphide is present in the following equilibrium states: H2S <-> HS" <-> S2" (2.11) low pH neutral pH high pH 19 Literature Review Below a p H of 6, H2S predominates, while above a p H of 6, HS" and S " are the main species. The latter are highly soluble in water, whereas H2S is not and easily volatilises into the gas phase and is typically recognised for its unpleasant rotten egg like odour. Under aerobic conditions at neutral p H , sulphide rapidly oxidises to sulphur, although the reaction is catalysed by the presence of sulphur oxidizing bacteria. Since this reaction takes place spontaneously, bacterial mediated steps usually occur when H2S is rising from sediment and crosses the anoxic/oxic boundary. Elemental sulphur is chemically stable but can be oxidised by sulphur oxidizing bacteria to sulphate. The sulphate ion is chemically very stable, under normal environmental conditions, and does not reduce easily. However, under anaerobic conditions, sulphate reducing bacteria can reduce sulphate ions to sulphide (Madigan et. al., 1996). 2.3.2 Distribution of SRB S R B can be found in a vast variety of places including soils, fresh water, marine water, hot springs and geothermal areas, oi l and gas reservoirs, estuarine muds, sewage, corroding iron, and sheep rumen. They can tolerate temperatures ranging from -5 to 75°C and p H values ranging from 5 to 9 (Perry, 1995). In fact, S R B activity has been noted in A R D with p H values of 3-4, although it is possible that the S R B were actually in microniches with higher p H values (Widdel, 1988, Ell iot et. al., 1998). Most S R B can also utilise sulphite and thiosulphite as electron acceptors in addition to sulphate (Cypionka and Pfennig, 1986). Some S R B have been observed using nitrate and fumarate as electron acceptors under sulphate limiting conditions (Widdel, 1988). 2.3.3 Cultivation and Media A l l S R B are anaerobic bacteria and most are gram negative (Middleton and Lawrence, 1977). Growth of S R B occurs in the absence of oxygen, although they may survive a temporary exposure to oxygen and then become active again i f anaerobic conditions are reestablished 20 Literature Review (Widdel, 1988). Gram-negative mesophilic S R B can be grown in a defined media without complex nutrients such as yeast extract or peptone, although they can be used to stimulate the growth of a number of S R B species (Widdel and Bak, 1992). It is recommended that S R B be kept in the dark to prevent the growth of photosynthetic sulphur bacteria; as these bacteria can alter the redox potential of the system to the disadvantage of the S R B . Also , some S R B are light sensitive and display inhibited growth during exposure (Widdel and Bak, 1992). 2.3.4 Electron Donors S R B fall into two general categories: incomplete and complete oxidisers. Complete oxidisers reduce organic compounds to CO2 and incomplete oxidisers reduce organic compounds to acetate and CO2 (Odom, 1993). S R B and methanogens often compete with one another for the use of electron donors as they can both utilise many of the same electron donors (Song et. al., 1988). However, S R B can often outcompete methanogens under limiting electron donor availability conditions (Vroblesky et. al., 1996). S R B can utilise a variety of compounds as electron donors, and, in general use sulphate as a terminal electron acceptor. Electron donor sources are usually restricted to low molecular weight organic compounds such as acetate, lactate, ethanol and butyrate. Often these compounds are the fermentative products from other anaerobic bacterial processes but some species can also grow with hydrogen. The main groups of electron donors utilised by S R B are (Widdel, 1988): • Lactate • Hydrogen • Formate • Acetate • Propionate • Butyrate and Higher Straight-Chain Fatty Acids • Branched Chain Fatty Acids • Monovalent Acids • Dicarboxylic Acids • Hydrocarbons (lactate, ethanol). 21 Literature Review The reactions, of the most common selected electron sources can be summarised as follows (VanHouten, 1996): Butyrate + 1 / 2 S 0 4 2 " -> 2Acetate + !/ 2HS + y&f A G 0 = -27.8 (2.12) 4 H 2 + S 0 4 2 " + 2 H + -> 4 H 2 0 +H 2 S A G 0 = -38.1 (2.13) Acetate + S 0 4 2 " -> 2 H C 0 3 " +HS" A G 0 = -47.6 (2.14) Lactate + VSSO^" -> Acetate + H C 0 3 " + V2RS + Vdt A G 0 = -80.0 (2.15) Ethanol + lASOf -> Acetate + V 2 H S + V£? + H 2 0 A G 0 = -80.0 (2.16) (G° values from Thauer, 1977) A s shown above, ethanol is more energetically favourable for the S R B than hydrogen, and as such S R B may grow faster with ethanol as a feed substrate. However, other concerns that need to be considered when selecting an electron donor are the cost of the electron donor/sulphate removed and the formation of any secondary pollutants that may need to be treated (Du Preez et. al., 1992). Carbon and Energy Sources Listed in Table 2.5 are carbon and energy sources that have been studied for industrial applications using S R B . These sources can be divided up into two main groups: organic waste material and bulk chemicals. Organic waste materials are not considered a suitable carbon and electron source as these materials can introduce additional pollution to the heavy metal contaminated wastewater. A R D does not contain organic compounds, and so the addition of organic waste wi l l require a secondary treatment process to remove any remaining pollutants in order to produce a clean effluent (Van Houten, 1996). A study using sewage sludge, molasses, pulp m i l l wastewater, had difficulty in removing all of the C O D from the wastewater (Maree and Strydom, 1985). Organic waste is also comprised of a variety of compounds, such as alcohols, and lower fatty acids. These compounds are used by S R B , methanogens, and acetogens, which may result in competition between these three bacteria types in the reactor (Van Houten, 1996). 22 Literature Review Table 2.5: Summary of Carbon/Energy Sources used in Previous Studies Carbon/Energy Source Comments Source Organic Waste Material Sugar, Molasses, Pulp M i l l Molasses may be cheaper to use Maree and Strydom, Wastewater, Sewage Sludge than lactate or ethanol. 1985,1987 Difficulty in removing COD to Groudeva and Groudev, required levels. 1994 Mixed carbon sources: organic May need to use a pre-fermentation Visser et. al., 1993 pollutants with high COD step, mixed populations of bacteria Fujie et. al., 1994 (-2500 mg/L) + yeast extract present. + sugar Bulk Chemicals Acetate Bypasses the ethanol degradation Allaoui and Forster, step limited by slow degradation of 1994 acetate. Slower growth than with ethanol Ethanol and methanol Acetate forms as a byproduct of Barnes et. al., 1991 (90% EtOH/10%MeOH) ethanol degradation, which is then Rowley et. al., 1994 further converted to CO2, C H 4 and methane (through the addition of methanogens). 0.8% Lactate ~ - Barnes et. al., 1991 Diels et. al., 1991 0/20/80% CO/CO2H2 SRB outcompete methanogens for Du Preez et. al., 1992 5/15/80% CO/CO2H2 hydrogen. No toxic affects if use Van Houten et. al., 1994, 20/0/80% C O / C Q 2 H 2 CO2/H2 only. 1996 Bulk chemicals, are more advantageous to use than organic waste, primarily because there is no secondary pollution that requires treatment. Both ethanol and lactate represent substrates that support fast growth of S R B . (Widdel and Hansen, 1991, Postgate, 1994). However, both substrates are rapidly oxidised to acetate. This represents two disadvantages, firstly, acetate is toxic at high concentration to S R B and it is oxidised at a slower rate than either, ethanol and lactate. Therefore, it is possible that the environment w i l l become toxic to the S R B . Secondly, methanogens use acetate, and competition between the two species may arise (Van Houten, 1996). Another possible bulk chemical source is mixtures of C O , CO2 and H2, also known as synthesis gas, which has been demonstrated to be a viable carbon and electron donor source (Du Preez et. al., 1992, V a n Houten et al., 1994). S R B have been shown to outcompete methanogens for hydrogen (Vroblesky, 1996). It has also been demonstrated that for larger scale reactors CO2/H2 mixtures become more cost effective than using an ethanol based 23 Literature Review energy source (refer to Figure 2.6); this was accomplished by comparing the cost of H 2 and ethanol required to treat a theoretical waste stream (Van Houten, 1996). The following assumptions were made in this comparison: a 1:1 and 2:3 ratio of moles ethanol /mole sulphate reduced, that greater than -10 kmol sulphate/hour were to be treated, and that 20 kmol/hour ethanol and 80 kmol/hour hydrogen were required. Synthesis gas can be obtained as an industrial off-gas from (1) the heating plants of steam and methane industries, (2) the partial oxidation of fuel o i l , and (3) from coal gasification. (Du Preez et. al., 1992): 120 100 o (!) o E 5= Q to o •••-ethanol (1:1) ethanol (2:3) hydrogen 10 20 kmol SO<27h 30 Figure 2.6: Ethanol vs. Hydrogen as Electron Donor Source for SRB. (Source: Van Houten, 1996) Synthesis Gas Van Houten completed a Ph.D. thesis on biological sulphate reduction using synthesis gas in a gas lift reactor (Van Houten, 1996). The reactor configuration is shown in Figure 2.7. This project was divided into three main studies. In the first two parts, H 2 / C 0 2 were used as the carbon and energy source, while the last part o f the study looked at the effects o f C O on S R B activity in the gas lift reactor. 24 Literature Review The first part studied the growth of S R B on hydrogen and carbon dioxide (80/20% v/v). The purpose of this study was to investigate the feasibility of sulphate reduction in a gas-lift (expanded bed) reactor using immobilised biomass grown on H2/CO2 (80/20% v/v). The effect of free H2S on biomass growth was also investigated. Pumice and basalt were compared for their potential as immobilisation surfaces, during operation of the reactor. N o biofilm growth was noted on basalt particles, although the S R B were found to form a stable biofilm on the pumice within 15 days of operation, under turbulent flow conditions. This was attributed to difference in the surfaces of the basalt and pumice. The pumice had large pores, which the S R B were able to colonise and then spread out across the pumice surface. In contrast, the lack o f deep pores on the basalt, thus as the bacteria attempt to adhere to the basalt surface, high turbulence and abrasion from other particle knocks the bacteria from the basalt surface. S R B growth was found up to free H 2 S concentrations of 450 mg/L; below this concentration a maximum sulphate conversion of 1250 mg/(L.h) was obtained with values ranging from 583 - 1250 mg/(L.h). Figure 2.7: Gas Lift Reactor used by Van Houten (Source: Van Houten, 1996) 25 Literature Review The second study investigated the affect of p H on S R B activity, as well as examining the bacterial morphology in the reactor. Bioreactor operation was possible within a p H range of 5.5-8.0 with an optimal p H of 7.5, within this p H range the sulphate conversion rates varied between 4.2-2.1 g S 0 4 / g biomass per day within this p H range. The bacterial types in the reactor were found to consist of Desulfovibrio species, and Acetobacterium species. Since synthesis gas, consists of H2, CO2, and C O , the third study investigated the affects of carbon monoxide on sulphate reduction. The H2 level was kept constant in the reactor while the CO2 and C O levels were changed. Inhibition of S R B activity was noted at 5%CO, which resulted in a decrease in sulphate reduction from 12-14 g S0 4 / (L .d) to 6-8 g S0 4 / (L .d) . Increasing the C O levels up to 20% was not found to further decrease the sulphate reduction rate, it was also found that adding a recycle loop to the reactor increased the conversion up to 10gSO 4 / (L .d ) . 2.3.5 Hydrogen Utilising SRB Species S R B can grow both autotrophically and heterotrophically on hydrogen. For instance Desulfovibrio species require acetate as a carbon source while Desulfobacterium species can use CO2 as the carbon source (Van Houten, 1996). Desulfovibrio are usually curved and often motile, they can use H2 but require acetate in addition to CO2 as a carbon source for growth. Desulfobulbus species are generally oval shaped although some types form slender rods. They can grow with H2 as an electron donor and C 0 2 as their carbon source. Desulfobacterium species are a nutritionally versatile completely oxidizing S R B , capable of autotrophic growth. They range in shape from rods to ovals to nearly spherical shaped cells. A summary of hydrogen utilising S R B is listed in Table 2.6. 26 Literature Review Table 2.6: Hydrogen Utilising SRB Species Morphology Width (im Length um Optimum Temp, °C Hydrogen Utiliser Spore Formers 1 Desulfotomaculumn nigrificans Rod 0.5-0.7 2-4 55 + + orientis Rod, slightly curved 0.7-1 3-5 37 + + ruminis Rod 0.5-0.7 4-6 37 + + 1 Desulfovibrio desulphuricans Vibrio 0.5-0.8 1.5-4 30-36 + -vulgaris Vibrio 0.5-0.8 1.5-4 30-36 + -gigas africanus Large Vibrio Vibrio 0.8-1 0.5-0.6 6-11 2-3 30-36 30-36 + + salexigens Vibrio 0.5-0.8 2-3 30-36 + -' Desulfobacter hydrogenophilus curvatus Rod Vibrio 1-1.3 0.5-1 2-3 1.7-3.5 28-32 28-32 + +, poorly 'Desulfobacterium autotrophicum Oval 0.9-1-3 1.5-3 20-26 + -macestii Rod 0.7 1.9-2 35 + -i . . . ntacini Spheroid 1.5 3 29 + -2 Desulfosarcina variabilis Oval or Rod 1-1.5 1.5-2.5 33 + -(Source: 1 Widdel, 1988,z Widdel and Bak, 1992 ) Few microbial species can use hydrogen for growth, they include sulphur reducers, denitrifying bacteria, methane bacteria and homoacetogenic bacteria (Widdel, 1988). Methanogens and S R B both compete for hydrogen in anaerobic systems. However, in systems with sufficient sulphate (non-limiting), S R B w i l l outcompete the methanogens as they have a higher substrate affinity for hydrogen. S R B can also maintain the hydrogen threshold value below that able to be utilised by methanogens (Lens and Visser, 1998). Desulfovibrio species can grow relatively fast on hydrogen, Desulfotomaculumn species, and Desulfobus species can also grow on hydrogen but at slower rates than the Desulfovibrio species (Schink, 1988). 27 Literature Review Temperature, pH, and Micron utrients Shown in Table 2.7 is a summary of some of the conditions utilised in previous studies for S R B growth, including temperature and p H . A l l studies required that nitrogen and phosphorous be present in the nutrient solution to ensure growth of the bacteria. A s well , a low redox potential varying between -100 to -350 m V was noted as a requirement for S R B growth. Table 2.7: Temperature and p H Conditions used in Previous Studies Temperature, °C p H Population and *Redox Potential Nutrients Added Source T = 15-40 5-9 SRB and Mixed N >5%, Barnes et. al., E(SRB)=- 100 mV P>0.2%ofthe 1991 E (Mixed) = - 300 mV ethanol consumed T = 20-38 7-7.7 Mixed Population E (Mixed) = -350 mV N,P, trace metals Dudney et. al., 1995 3.5 Mixed Population N,P Groudeva and Groudev, 1994 T = 20-38 7.0 SRB raw sewage sludge Maree and Strydom, 1985, 1987 no control N / A SRB E(SRB) = -150mV gypsum Revis et. al., 1989 T = 30 5-8 SRB N,P Rowley et. al., 1994 Room 7.0 SRB Organics, N,P Uhrie et. al., temperature E (SRB) = -300 mV (SHE) 1995 T = 30 7.0 SRB N,P Van Houten et al., 1994 T = 3 0 ± 1 6.8 Mixed populations (SRB, SRB+M, M) N,P Visser et. al., 1993 *Mixed refers to a mixed SRB and methanogen population, unless indicated all redox potential are compared to a standard calomel electrode; N, P refer to ammonium salts and phosphate salts, respectively. Experiments with S R B have been run successfully with temperatures ranging from 15-40 °C. The optimal growth temperature for a mixed population of S R B , under neutral p H conditions, has been demonstrated to be approximately 32 °C, refer to Figure 2.7 (Maree and Strydom, 1987). 28 Literature Review 2.3.6 Inhibi t ion of S R B Hydrogen Sulphide Inhibition Hydrogen sulphide is a byproduct of the sulphate reduction process performed by S R B . The H2S is also toxic to the bacteria and can cause growth inhibition as well as cell death. It is assumed that the undissociated H2S, not HS" or S2", causes the inhibition as only neutral molecules can pass through the cell membrane (Lens and Visser, 1998) Another advantage to using H 2 and CO2 is it provides gas stripping capability of H2S which helps to maintain the sulphide level at nontoxic concentrations to the S R B , thus increasing conversion rates of sulphate (Van Houten et. al., 1994). To avoid reaching an inhibitory sulphide level, the carbon source can also be controlled to adjust the conversion rate (Kolmertet. al., 1997). 29 Literature Review Metal Inhibition Although the reduction of sulphate to sulphide by S R B can be used to remove heavy metals from contaminated water by the formation of metal sulphide precipitates, high metal concentrations can still cause inhibition of S R B bacteria. Concentrations of copper, chromium, and cyanide have been reported to inhibit S R B growth (Postgate, 1984, Song et. al., 1988). S R B inhibition has been found at copper concentrations > 70 - 130 mg/L, with an IC50 estimated at 100 mg/L. Chromium concentrations > 130 mg/L caused a 15% decrease in activity compared to a control with no chromium. S R B were found to be sensitive to cyanide, with concentrations > 5 - 50 mg/L causing a reduction in activity. The IC50 value for cyanide was estimated at 20 mg/L cyanide (Song et. al., 1988). Toxic limits reported in different studies vary, possibly due to varying levels of sulphide and alkalinity present, which may affect the quantity of heavy metal precipitation. Differences in p H , temperature, and residence times in reactors may also affect the results (Postgate, 1984). It has been reported that the local precipitation of metal sulphides can increase the resistance of organisms to the toxic affects of the metals and it may be possible under long term studies to increase the resistance of S R B to high metal effluent streams (Revis et. al., 1989). 30 Literature Review 2.4 Immobilisation of Bacteria to Surfaces The purpose of immobilising S R B on a surface is to increase the biomass concentration in a reactor and to prevent cell washout. Immobilisation of cells refers to the restriction of cell mobility within a defined space and is categorised by two main types: cell entrapment and surface immobilisation. Cel l entrapment is when the bacteria are enclosed within a space, while surface immobilisation occurs when the bacteria attach to the surface of a support material. A n immobilised system can, in theory, promote higher substrate conversion rates at higher flowrates than are possible in nonimmobilised systems. Because a continuously stirred bioreactor system (well mixed) w i l l have the same effluent stream composition as the liquid in the reactor, freely-suspended cells are constantly withdrawn from the vessel in the effluent stream. Therefore, an immobilised cell system with a concentration of cells, X M , not removed from the system may have a higher substrate conversion rate at higher dilution rates, D . The relationship between the dilution rate, steady state substrate concentration and the immobilised cell concentration in a C S T R can be represented by (Doran, 1995): Equation 2.1 / / m a x 5 _ D(Sl~S)YxS Ks + S~ (Si - S)Yxs + TJXXM Where p m a x = maximum specific growth rate (h"1), Ks = substrate conversion rate (mg/L), Yxs = biomass yield coefficient (mg biomass/mg substrate), X M = immobilised cell concentration (mg/L), D = F / V = dilution rate (h"1) (where F= volumetric flowrate and V = volume in the reactor), r\j = total effectiveness factor (dimensionless), and accounts for the extent to which a reaction is affected by internal mass transfer for immobilised cells. Therefore it can be considered as the ratio of the rates of reaction of immobilised cells to free cells. S = substrate concentration (mg/L). 31 Literature Review Shown in Figure 2.8 is the relationship for a C S T R with immobilised cells and without (i.e. X M = 0), at steady state, with p . m a x = 0.1/h, K s = 0.00lg/L, Y x s = 0.05g/g and Si = 0.008 g/L. The plot of substrate conversion rate versus dilution rate shows that for X M > 0, D can be operated at much higher values than D C R IT ICAL for the suspended cell system. In fact, even when the system is mass transfer limited at T|T = 0.3, the substrate conversion rates are much higher than the suspended cell system. X M = 0.1 g T1T=1.0 X M = 0.1 g L-1 T1T = 0.3 I M M O B L L I S E D - C E L L C H E M O S T A T M S U S P E N D E D - C E L L C H E M O S T A T ^CRITICAL 0.2 0.3 0,4 Dilution rate, DCh*1) Figure 2.8: Substrate Conversion and Dilution Rate in an Immobilised Cell System. (Source: Doran, 1995) Immobilisation may result in high substrate conversions at high dilution rates. However, this is dependent on the ability of the bacteria to colonise the support surface present in the reactor system. Not all supports are colonised to the same degree. Many different surfaces 32 Literature Review have been studied to encourage anaerobic microbial colonisation of surfaces in order to allow for improved treatment capability in reactor systems. Listed in Table 2.8 are some of the surfaces used for immobilisation. Also included is any observations made by the authors on how well the support was colonised by the bacteria and any noticeable improvement in substrate conversion rates. Table 2.8: Surface Immobilised Support Materials used in Anaerobic Studies Support Support Properties Comment Source Sand Pumice Sintered Glass (methanogens) Sand (d=0.7-1.0 mm) Pumice (d=3-4 mm, p=0.431 mL/g) Sintered glass (d=3-3.5 mm, p=0.084 mL/g) Expanded Bed Reactor: Porous media achieves quicker and better colonisation than nonporous media. Biomass able to colonise an internal structure as well as external, recover quicker from inhibitory substances. Allaoui and Forster, 1994 Crushed Bunter Sandstone (SRB) Sand (M= 0, 0.01,0.1, and 1 gram) Batch System: More sulphide produced in systems with a support surface than without (1>0.1>0.01>0) (2.5 mM for the system with 1 gram of support and 1.4 mM for the control with no support). Bass et. al., 1996 Foam Nylon Foam (porosity 90-96%, pore d=0.064-0.085 cm) Nylon (pore d=0.085 cm) Packed Bed Reactors: Foam was fund to consistently retain more biomass than nylon. Nylon was found to be less subject to degradation under mechanical abrasion. Bolte et. al., 1986 Foam Zeolite Glass Beads (Methanogens) Zeolite (d=5mm) Glass (d=5mm) Foam (pore d=2.21, 0.42, and 0.27 mm) Packed Bed: Better biofilm development observed with foam than zeolite. No biofilm development observed on glass beads. Huysman et. al., 1983 Mine Gob Material No support (SRB) N/A Batch System: Heavy metals, except for Ni, were removed to below detection limit (0.1 ppm) in the case of the colonised mine gob material as compared to nonsupport system. Kim and Cha, 1997 Poraver Plastic carriers Poraver - foam glass particles Plastic carriers - (two types not specified) Packed Bed and Suspended Carrier: Packed bed system more robust than suspended carriers, which had a decreased capacity during the experiments. Kolmert et. al., 1997 Hard Stone Quartz sand Plastic No Support (SRB) Hard Stone (d=l cm, and finely crushed) Quartz Sand (d=2 mm) Expanded Bed: Higher rate of sulphate removal in system with hard stone than nonsupport system. No noticeable improvement with quartz sand or plastic support system. Maree and Strydom, 1985, 1987 Pumice Basalt (SRB) Pumice (d=0.2-0.5 mm) Basalt (d=0.29 mm) Gas Lift Reactor: Growth of bacteria started in pores of particles followed by surface coating. Van Houten, 1996 33 Literature Review A s mentioned earlier, immobilisation falls under two general categories: surface attachment and cell entrapment. The supports listed in Table 2.8 are examples of surface attachment. Alginate beads can be used as a cell entrapment technique to immobilise S R B . Alginate beads have a pore size within its gel matrix from 5-200 nm in diameter. A s well immobilisation with alginate beads has been shown to be a safe, fast and versatile technique (Smidsrod and Skjak-Braek, 1990). 2.4.1 B io f i lm Format ion The following section describes some of the properties of biofilm formation that were considered in the selection of support materials for this project. Biofilms form as the result of adsorption, the production of extracellular polymeric substances, EPS , and growth. Generally, the bacteria uniformly distribute throughout the EPS matrix because the EPS causes biofilms to be gel-like, transport processes tend to control the microbial behaviour in the biofilm (Characklis, 1991). Many bacteria produce large quantities of EPS and quickly adsorb to a variety of surfaces while others gradually become adsorbed to surfaces over long periods of time. These differences may be due to nutritional conditions, the nature of the substratum surface, and previous macromolecular adhesion to the surface (Characklis, 1991). Nutrient limiting conditions have been suggested as a way to stimulate EPS production and encourage bacterial growth on surfaces (Zobell, 1943). Previous macromolecular adhesion may affect the surface charge of the material and also affect the ability of bacteria to adhere to a surface (Characklis, 1991). Surface Adsorption Bacteria have a net negative charge at acidic and neutral p H levels. This creates an inherent preference for materials that have a net positive charge. However, there are many diffusional restraints and chemical interactions that must be overcome before adsorption occurs: 34 Literature Review "It is generally conceded that, while the main body of the bacterial cell does not make direct contact with the substratum surface, adsorption is mediated by a process of bridging to the substratum by fine extracellular structures capable of overcoming the repulsion effects by a combination of Brownian displacement, chemical bonding, dipole interactions, and hydrophobic interactions" (Characklis, 1991). Bacterial adhesion is affected by the hydrophilic and hydrophobic nature of the surface. One study found that bacterial adhesion was more predominant on hydrophilic surfaces (slightly negative charge) than hydrophobic (positively charged) surfaces when the surface tension of the suspending medium is less than the surface tension of the bacteria. In the reverse case, the opposite is observed (Absolom et. al., 1983). Another study which coated pieces of the foam with P V C to decrease its hydrophobic properties found that this decreased the amount of biomass adhering to the surface, suggesting that the more hydrophobic the material the better it is for colonisation (Huysman et. al., 1983). Some authors have observed increased rates of activity in systems with support material than in systems without support (Bass et. al., 1996, K i m and Cha, 1997, Maree and Strydom, 1985, 1987). It has been postulated that in a batch system a support material provides a greater surface area for growth, which can result in an increase in the amount of active bacteria present and account for observed increases in activity (Bass et. al., 1996). Surface Roughness Microbial colonisation on a surface tends to increase with increasing surface roughness (Geesey and Costerton, 1979, Baker, 1984, Characklis, 1984). Some authors have also reported finding attached microbes mostly concentrated in crevices as opposed to smooth surfaces (Geesey and Costerton, 1979, Beeftink and Staugaard, 1986, Meraz, 1995). Whether or not the surface irregularities serve as anchoring points for bacterial adhesion or whether the surface roughness encourages adsorption is still under debate (Schink, 1988). 35 Literature Review Pore Size The pore size of the support surface must be suited for bacterial colonisation. For instance, activated carbon has a large internal surface area, ranging from 500-1600 m /g, but most of it cannot be colonised by bacteria since most of the pores are smaller than 50 nm in diameter (Schink, 1988). It has been postulated that the optimal size of surface micropores that promote bacterial colonisation, is 5 times the length of the bacteria (Messing, 1982). Three types o f foam, zeolite, and glass beads were used in a comparative study to monitor the colonisation of surfaces by methanogens (Huysman et. al., 1983). The foams were highly porous and contained pore sizes of 0.27mm, 0.43 mm and 2.21 mm. The zeolite, contained pores in the nm range, while the glass was non porous. The foam systems were colonised rapidly, followed by some surface attachment in the system with zeolite and no colonisation in the glass system. The study found that both the 0.43 mm and 0.27 mm foams had better bacterial colonisation than a foam with a mean pore diameter o f 2.21mm, with the former showing a slightly better performance than the smaller pore sized foam (Huysman et al, 1983). Both pumice and basalt have been studied as possible supports for S R B , with the pumice support material showing a better biofilm formation than the basalt (Van Houten, 1996). The pumice particles were observed to have a very porous, sponge-like structure, while the basalt has a rough surface but lacks deep pores. The preference for the bacteria to colonise the pumice over the basalt, which had negligible biofilm formation, may be due to the more porous structure under turbulent conditions. The S R B can easily enter the pores of the pumice where the liquid turbulence is less and the bacteria have time to adhere to the surface. The S R B have little chance to adhere to the basalt surface due to liquid turbulence and direct collisions with other basalt pieces which can knock the bacteria off the surface. 36 Literature Review 2.4.2 SRB Biofilm Quantification Biomass quantification in bacterial studies is often accomplished by using one or more of the following techniques: gravimetric analysis, enumeration techniques, optical density, and protein determination. Gravimetric analysis relies on measuring the weight of a sample. This is either accomplished by taking a wet weight or dry weight sample. A wet weight sample involves monitoring the increase in weight of a known sample volume over the time of the experiment. A n increase in weight is expected to be due to an increase in biomass. A dry weight, or total solids, involves drying a known sample at 103 °C and then weighing it - this is to avoid errors associated with water retention in the sample. Enumeration of bacterial numbers can be determined using the membrane filter (MF) or the multiple tube fermentation (MTF) techniques. In the M F technique a number of serial dilutions are made. These dilutions are then passed through a 0.45 urn filter paper, which is small enough to retain the bacteria. The bacteria (on the filter paper) are then contacted with a solid agar solution that contains nutrients for growth and incubated. After incubation the number of colonies formed can be counted and the concentration in the original sample determined. The M T F technique is based on the principle of dilution to extinction. In this test, a number of serial dilutions are made. The next step involves transferring a 1 m L sample from each of the serial dilutions to five test tubes containing a suitable culture medium with an inverted tube inside. The presence of gas is taken as a positive indication of growth. The number of positive to negative samples is compared to an M P N table to estimate the number of bacteria present. These enumeration techniques are very labour intensive and require anaerobic conditions for the S R B to grow. The optical density method is a fast technique that measures the optical density of a sample. A n increase in optical density is attributed to increasing biomass. However, the optical density readings w i l l be skewed in a study with S R B due to interference from metal sulphide precipitates. 37 Literature Review Another way to determine the biomass of a sample is to measure the concentrations of the cell constituents, such as protein, organic carbon, or organic nitrogen. These methods rely on either comparing the sample to a known standard or by measuring the total amount of the constituent in a sample. In the former case, such as with proteins, absorbance measurements of the sample are taken and compared to a standard curve of a known protein. In the latter case, i f the molecular weight of the bacteria is known, the biomass can be determined by comparing the measured amount of the cell constituent, such as total organic carbon, to the known molecular weight. Microscopy can also be used to quantify biomass. Microscopy is a useful tool as it can be used to monitor the development of a biofilm to a surface. It is possible to use phase contrast imaging in conjunction with a light microscope to enumerate bacteria that grow in monolayers. Microscope imaging also allows for observation of the types of bacterial species present in the biofilm. A s S R B do not form monolayers, light microscopy was discarded as a method of enumeration. However, scanning electron microscope imaging is a useful tool for observing where the S R B adhere to surfaces as well as to observe the morphological characteristics of the bacteria. The organic nitrogen, total solids and protein determinations were considered as possible methods to measure the biomass in the system, while scanning electron microscopy was used to observe where the bacteria adhered to the support surfaces. These techniques are discussed in more detail in the Methods and Materials Chapter of the thesis. 2.4.3 Cell Growth Kinetics in a Batch System In a batch culture, the rate of cell growth changes based on which phase of cell growth is occurring in the batch system: the lag phase, growth phase, stationary phase and death phase. The lag phase begins immediately after inoculation, during this stage the rate of growth is close to zero as the cells adapt to their new environment. 38 Literature Review The exponential growth phase: after the lag phase, the rate of growth increases and continues until the stationary phase. If the growth rate is exponential this w i l l appear as a straight line on a plot of In (biomass) versus time. In this stage the rate of cell division is determined by their ability to process nutrients. The stationary phase: as the nutrients in the batch system become limiting or as inhibitory products accumulate, the growth rate slows until the population remains stationary. In this phase either the cells have exhausted the substrates required for growth and/or the rate of cell death is equal to that of cell growth. The death phase: The nutrients in the system have been exhausted, or the environmental conditions in the batch system no longer support growth. In this phase the rate of cell death is greater than cell growth. In a closed system where growth is the only factor affecting the biomass concentration, the rate of cell growth during the growth phase can be described as: Equation 2.2 dx rx = — = ux dt Where: rx = dx/dt = the rate of biomass produced (kg/m Is), x = biomass (kg/m ) u. = the specific growth rate (s"1). If the specific growth rate is constant, it can be integrated as follows: Equation 2.3 j—dx = u jdt X°X t = 0 hue = \nxo + jut 39 Literature Review From the above equation, a plot of In x versus time gives a straight line with slope u,, the specific growth rate. 2.4.4 Reactor Selection Once a preliminary batch study has been completed, and a suitable immobilisation surface for S R B determined, the next step is to continue the studies under continuous flow conditions in a reactor. Various reactor designs have been studied for the removal of sulphate and/or heavy metals from wastewater. A summary of some of the reactor designs, considered to date, along with potential advantages, is listed in Table 2.9. O f the reactors listed in Table 2.9, the membrane bioreactor and the gas-lift (expanded bed) type reactor appear to have interesting potential. It has been reported that most bioreactor systems require some degree of nutrient addition to ensure the viability of the cells (Barnes et. al., 1991, Diels et. al., 1991, V a n Houten et. al., 1994, Dudney et. al., 1995). Due to the set up of a membrane reactor, a lower addition of nutrient to the cells may be possible since there is direct feed o f the nutrients to the biofilm and the microbial community. However, it is unclear i f the membrane surface provides the optimum conditions necessary to maximise removal of heavy metals and sulphides from wastewater streams. Biomineralization is a good tool for lowering metal levels in effluents to less than 1 ppm. The membrane allows the bacteria to remove the metals from one stream while the cells are kept viable by another nutrient stream. This separation of streams allows treatment of water with minimal consumption of nutrients like carbon and phosphate (Diels et. al., 1991). The dynamic development of the system is controlled by the diffusion reaction for both the substrate and the inhibiting product. A t low membrane thickness the substrate can easily diffuse through the membrane and the metabolic products produced in the later stages control the system. A t a higher thickness, the diffusion of the substrate decreases and substrate gradients appear, which results in controlling the system during the initial phase (Lefebvre et. al., 1997). 40 Literature Review Positive results have been reported in both packed and expanded bed type reactors (Maree and Strydom, 1987, Groudeva and Groudev, 1994, Al laoui and Forster, 1994, V a n Houten et. al., 1994). Similarly, the gas uplift reactor with pumice particles has been proven to work successfully in the removal of heavy metals and sulphates from effluent streams (Van Houten et. al., 1994). The advantage of the fluidised bed over a packed bed reactor is that channelling is minimised, the drawback is they can be more complicated to design. Table 2.9: Comparison of Different Reactors with Immobilisation Surfaces Technique Advantages Source 1. Expanded Bed • porous media (pumice and sintered glass) had better Allaoui and Reactors colonisation than non porous media (sand). Forster, 1994 2. Tubular • metals recuperated on glass beads, Diels et. al., Membrane • separation of nutrient and effluent stream, 1991 Reactor and Flat • Cd recovery 99.9% Sheet Reactor • combination of membrane immobilisation and nutrient (Asymmetric) diffusion keeps bacteria in stationary phase promoting Zifron Membrane biomineralization of heavy metals, (Symmetric) • better results were achieved with the symmetric membrane. 3. P V A Membranes • metal removal by biosorption. Grappelli et. al., 1995 4. Packed Bed • optimum pH 7 but buffering capacity of effluent allowed Groudeva Reactor workable conditions at 3.5, and Groudev, • removal of heavy metals down to ppb level, 1994 • glass beads used as packing material, • it took 5 months to obtain maximum SRB activity. 5. Packed Bed • 90% removal of sulphate with hard stone vs. 40% with Maree and Reactor sludge blanket, Strydom, • hard stone media yielded best results. 1985,1987 6. Gas-Uplift • biofilm stable under turbulent flow conditions, Van Houten Reactor (SRB • open structure of pumice assists in microbe attachment. et. al., 1994 population only) 41 Literature Review 2.5 Summary The literature review section of this thesis covered the environmental conditions associated with A R D . The chemical treatment of A R D , which relies on raising the p H of the A R D to form metal hydroxide precipitates, was compared to the advantages of precipitating metals as sulphides. This was extrapolated further to the potential use of a biological process using S R B to treat A R D . A n example of a current process using a S R B bioreactor to treat heavy metal contaminated effluent was illustrated. The two main problems encountered in this system were the formation of acetate as a byproduct of ethanol oxidation from the S R B and bacterial washout. Methanogens were introduced into the system to counteract the first problem and a flocculent was added to the system to overcome the second. A review of the general conditions required to cultivate S R B was also included in the literature review. The selection of growth conditions were (1) deciding upon a suitable carbon source and energy source, (2) determining a nutrient media that promoted bacterial growth based on that carbon and energy sources, (3) the p H of the nutrient media, and (4) the incubation temperature of the bacteria. In general low molecular weight organic sources are suitable carbon and energy sources for S R B . However, these organic sources may not always be readily available at an industrial site and they may not be cost effective. In addition, i f organic sources are introduced into a system as a feed source for S R B , any unconsumed organics w i l l require treatment prior to discharge. Based on this data, CO2/H2 were selected as the carbon and energy sources for the S R B , experiments were designed to determine the appropriate nutrient media and the temperature selected for the experiments was 32±1 °C, under neutral p H conditions. A n immobilised growth system allows bacteria to remain fixed on the media and prevent bacterial wash out at high dilution rates; this allows for a large mean cell-residence times with short hydraulic retention rates. The Paques U A S B Process used a reactor with freely suspended cells and also encountered problems with bacterial washout at residence times below 30 hours. In order to overcome this problem a flocculent was added to the system in order to promote bacterial granulation and prevent washout. Another way to combat this 42 Literature Review issue would be to use a support surface, or attached growth system, that would allow for bacterial colonisation, and prevents bacterial washout at higher flow rates. While many studies have looked at immobilisation of bacteria to surfaces, only a limited number of studies have looked at S R B immobilisation. In particular there is a shortage of data on the attachment of S R B to surfaces under autotrophic conditions. 2.5.1 Selection of Support Materials Previous work on S R B indicates that porous structured media is better colonised than nonporous media (Diels et. al., 1990, Al laoui and Forster, 1994, V a n Houten et. al., 1994,). Other properties that have been noted to encourage bacterial adhesion or bacterial growth are the surface charge and hydrophobicity, surface roughness, and surface area (Huysman et. al., 1983, Schink, 1988, Characklis, 1991, Bass et. al., 1996,). Based on these factors, and the desire to study a large number of different types of materials available at low cost. The following support materials were used in this study: foam, basalt, zeolite, glass beads, alginate beads, Teflon, Ringlace, molecular sieve, and ceramic beads. Foam was selected as a highly porous material because porous material is better colonised than nonporous material (Allaoui and Forster, 1994, Huysman et. al., 1983). It was also selected as a low cost support material. Glass beads were selected as they have a net positive surface charge which was thought to encourage bacterial adhesion. Both the basalt and the zeolite were selected as low cost, natural rock formations with rough surfaces. The ceramic beads and molecular sieve were chosen due to availability and to see i f additional surface area encouraged bacterial growth. Ringlace is a commercial product sold as a biomass support used in aerobic wastewater treatment. We wanted to see i f S R B would attach to it. The Teflon-plastic pieces were chosen because Teflon is hydrophobic in nature and may encourage biofilm attachment. Finally, the alginate beads were selected to see i f entrapped cells would have a higher sulphate reduction rate than surface immobilised cells. 43 Literature Review 2.6 Thesis Objectives Immobilised growth systems prevent bacterial washout at high flowrates and provide a larger surface area for bacterial colonisation (resulting in the potential for increased rates of removal). Previous work with attachment of S R B to growth surfaces has mainly looked at heterotrophic growth conditions (Maree and Strydom, 1985, Bass et. al., 1996, Kolmert et. al., 1997). One study looked at immobilisation of pumice and basalt under autotrophic conditions, but not other materials (Van Houten et. al., 1994). The use of waste organics as the carbon and energy source for bacteria can lead to secondary pollution and bulk chemicals such as ethanol may not be as cost effective as the growth of S R B under autotrophic conditions (CO2/H2) (Van Houten, 1996, D u Preez et. al., 1992). The focus of this study is to look at the ability of S R B to attach to different types of growth surfaces, under autotrophic conditions, in order to determine what types of growth surfaces promote rapid bacterial colonisation for S R B . The central objective of this thesis is to compare attachment and growth of sulphate reducing bacteria to solid supports made from different materials. In order to obtain this objective the following sub-objectives were addressed (1) the selection of a suitable medium for autotrophic growth of S R B , (2) the development of a technique for quantifying biomass concentrations on the support materials and in suspension, (3) the measurement of S R B growth on different support materials. Static batch cultures were used instead of a continuous flow bioreactor due to time constraints. In order to accomplish these objectives the following parameters were measured: biomass growth on the support materials and in solution, CO2 uptake, and sulphate reduction. S E M images were used to determine how and where the S R B preferentially ( if any) colonised the different surfaces. B y measuring the increase in biomass, it is possible to determine the specific growth rates of the bacteria in the different systems. Measuring the biomass also allows for the determination of immobilised compared to freely suspended bacteria. The nutrient uptake rate ( C 0 2 ) in the system was monitored to observe i f the rate of bacterial growth was affected by changes in the media composition. The sulphate reduction rate was 44 Literature Review measured to determine the reaction order in an autotrophic system. A s well , the sulphate reduction data and the biomass growth were used to determine the growth yield, Y S O 4 , in the different systems. 45 Methods and Materials CHAPTER 3.0: METHODS AND MATERIALS 3.1 Overview of Experiments During this project three experiments were conducted: (1) the comparison of S R B growth in different nutrient media, (2) growth of S R B on nine different support materials and (3) monitoring the rate of CO2 uptake by S R B . This chapter w i l l describe the experimental procedures and the analytical methods that were used during this project. The comparison of S R B growth in different media was done so as to select a nutrient solution that supported growth under autotrophic conditions. The ratio of yeast extract and bactopeptone in the nutrient solution was investigated to see i f the S R B would grow in a defined media under autotrophic conditions. The biomass growth, sulphate reduction and the rate of CO2 uptake were monitored in these experiments. The second set of experiments was broken up into two sections: set 1 and set 2. One of the main objectives of the set 1 experiments was to establish the analytical techniques to be used to quantify the biomass in the system as well as to measure the sulphate reduction rate of the S R B . In the set 2 experiments, the biomass growing on the support and in solution, respectively, was quantified. The sulphate reduction rate was also measured in these experiments. The yield, YSO4, was calculated based on the mass of biomass produced per mole of sulphate reduced. The second set o f growth experiments was conducted over two weeks during which we did not want the nutrients to become limited. The purpose of the CO2 experiments was to measure the CO2 uptake rate to estimate the time at which CO2 needed to be recharged to the growth bottles. A secondary objective was to monitor the CO2 in both complex and defined media to observe i f there was any difference in the uptake rates. The accompanying sections outline the individual procedures and, where required, the basic principles involved in performing the experiments. 46 Methods and Materials 3.2 Comparison of SRB Growth in Different Media The initial experiments consisted of establishing a suitable nutrient solution for S R B growth under autotrophic conditions. Six types of nutrient media were prepared and 1 m L of inoculum was added to the batch flasks. Complete blackening in the flask was taken as an indicator of active bacterial growth, and was considered to have occurred when the solution in the flasks was completely black and opaque after the vial was shaken. Once a suitable nutrient solution was established (based on the time for complete blackening to occur), successive experiments were performed to grow the bacteria in a defined media instead of a complex media. Again S R B growth was monitored by observing the length of time required for complete blackening to occur in the batch flasks. N o quantitative measurements were gathered for this section of the experiments. 3.2.1 S R B G r o w t h A S R B mixed culture grown under mixed conditions suitable for both heterotrophic and autotrophic growth was obtained from the Biomet Mining Corporation, Vancouver, Canada. A n inoculum was prepared from this culture by growing under batch conditions for 3-5 days prior to inoculation of the batch flasks used in the experimental procedures. The cultures were grown under autotrophic conditions. Initially the S R B culture was taken directly from the stock and grown in a complex media, either the Biomet, ethanol, or Modified Van Houten ( M V H ) media respectively. The set 2 experiments were carried out in a defined media based on the M V H nutrient solution. The complex media all contained yeast extract and/or bactopeptone, which tend to promote faster growth but can cause interference with protein tests since these chemicals contain protein as well . A defined media that doesn't contain yeast extract or bactopeptone, w i l l cause less interference when quantifying the biomass samples. 47 Methods and Materials 3.2.2 Nutrient Solutions Six nutrient solutions, described in Table 3.1, were prepared and tested for growth of S R B . The solutions were prepared with distilled water and autoclaved for 15 minutes at 121 °C. A l l chemicals except bactopeptone, yeast extract, and thioglycolic acid were added to the solution before autoclaving. Once the solution had cooled to room temperature, 1 M HC1 was used to adjust the p H level to 7. The p H meter calibration was checked each time with p H 4 buffer solution. A Mettler Toledo Model 465 p H probe was used to adjust the p H of the nutrient solution. The nutrient media selection process for the 2 sets of experiments is shown in Figure 3.1. M E D I A S E tL E C T O N SRB Inoculum from BIOMET BIOMET Recipe (complex media) Ethanol Recipe (complex media) MVH Recipe (complex media) •r Selected for use in Set 1 Experiments SRB Inoculum grown in MVH i 1 MVH2 Recipe (+yeast) MVH3 Recipe (no yeast} {no bactopeptone) MVH4 Recipe (+baetopeptone) Selected for use in Set 2 Experiments Figure 3.1: Nutrient Solution Selection Process 48 Methods and Materials Once the nutrient media was prepared, 40 m L was transferred to a 160 m L batch flask and 1 m L of inoculum was added (refer to the Inoculation Protocol on page 53 for further details). The samples were placed in an incubator at 31 °C and the bottles were monitored daily, for up to 10 days, for blackening in the flasks, which was taken as an indication of S R B activity. The Modified V a n Houten ( M V H ) nutrient media is a simplified version of another nutrient media used to growth S R B under autotrophic conditions (Van Houten et. al., 1994). The M V H media is the same except for the stock salt solution and the exclusion of a vitamin solution and a trace element solution. The vitamin and trace elements solutions require an additional 21 chemicals but only compromises 2.2 m L of the 1 litre volume (Stams, 1992). Communication with both Michael Rowley of the Biomet Min ing Corporation and Susan Baldwin, respectively, indicated that a simpler media could be prepared which would still allow for growth of the S R B ; as such the M V H solution was prepared and tested for S R B growth. Table 3.1: Nutrient Solutions Ingredient Biomet+ Ethanol MVH"" MVH2 MVH3 MVH4 Amount added (g) (made up to 1000 mL in dH20) Bactopeptone 0.012 0.216/0.10 0.1 Yeast Extract 0.013 1.03 0.204/0.10 0.1 Thioglycolic Acid 0) 10 mL 5 mL 5 mL 5 mL 5 mL Ascorbic Acid (2) 10 mL N a 2 S 0 4 4.953 4.953 4.953 4.953 K H 2 P 0 4 0.065 0.409 0.409 0.409 0.409 NFLCl 0.297 0.297 0.297 0.297 MgCl 2 . 6H 2 0 — — 0.091 0.091 0.091 0.091 CaCl 2 .2H 2 0 — 0.120 0.120 0.120 0.120 N a 2 H P 0 4 — — 0.524 0.524 0.524 0.524 KC1 — 0.385 0.385 0.385 0.385 N a H C 0 3 — — 1.209 1.209 1.209 1.209 Stock Salt Solution (3) — — 100/50 mL 50 mL 50 mL 50 mL Ethanol — 1.20 mL Methanol — 0.13 mL (NH4) 2S0 4 0.163 1. Thioglycolic Acid made up as 3.5 g/350 mL dH20 2. Ascorbic Acid made up as 10 gi L dH20 3. Stock Salt Solution: 3.5 g NH4C1, 0.60 g KH 2 P0 4 , 10.00 g FeS04.7H20, 18.35 g MgCl2.6H20, 6.75 g CaCl2.2H20 made up to 1L with dH 20. (Sources: + Michael Rowley, personnel communication, 1998, ++ Van Houten et. al., 1994) 49 Methods and Materials 3.2.3 Temperature The temperature of the batch flask experiments were maintained at 31 ± 2 °C by placing the batch flasks in temperature controlled incubators. A Blue M dry type bacteriological incubator and a N e w Brunswick Scientific Innova 4230 incubator were used for these experiments. 3.2.4 Cul t iva t ion When cultivating S R B with an H2/CO2 mixture it is recommended to leave a headspace of 2/3 to 3/4 o f the total volume. The amount of inoculum to add should be around 1% (v/v) for faster growing S R B species and up to 5-10% (v/v) for slower growing species. Enrichment cultures should be transferred at least twice into new medium before use to gradually dilute away non S R B and the transfer volume should be kept between 1-10% (v/v) (Widdel and Bak, 1992). Based on this 40 ml of nutrient solution was added to 160 m L Kimble Bottles to allow for 75%) headspace, and 1 m L o f inoculum was added to the 40 m L nutrient solutions (2.5% v/v). 50 Methods and Materials 3.3 Growth on Support Materials The purpose of these experiments was to evaluate the potential of a wide variety of materials to support growth of sulphate reducing bacteria. The batch growth experiments attempt to quantify the bacterial density in solution and on the growth supports provided in the batch flasks. Two sets of experiments were performed: set 1 and set 2. In set 1, the following support materials were used: molecular sieve, glass beads, ceramic beads, Teflon, and zeolite and the following analyses were performed, total and volatile solids; % C 0 2 in headspace, T K N and sulphate concentration. In set 2, the following support materials were used: foam, basalt, calcium alginate beads, and Ringlace. In these experiments the biomass was separated into two samples: the growth in suspension and that attached to the support materials. The following analyses were conducted: T K N , sulphate, and protein. A s well , scanning electron microscope ( S E M ) images were also captured to determine how densely and where the bacteria were colonising the various growth surfaces. Table 3.2: S R B G r o w t h Surfaces Growth Surface Experiment Comment Glass Beads (smooth) Set 1 Sphere, diameter =3 mm, surface area =0.0033m2/g Davison Molecular Sieve Set 1 Sphere, diameter =3 -5 mm Fisher Scientific Ceramic Beads Set 1 Sphere, diameter =5 mm Zeolite Set 1 10+ mesh size Canmark Ltd Teflon/Plastic Pieces Set 1 Disc, diameter =20 mm, width= 3mm each disc, cut into quarters. Basalt Set 2 Spheroid, diameter =3-5 mm, Ocean Construction Supplies Ltd length =7-11 mm, surface area =3.68 m 2/g Polyurethane Foam Set 2 Cube, side =15 mm, density =40 kg/m J, surface area =0.184 m 2/g Ringlace Ringlace Products Ltd. Set 2 Thread-like, length =150 mm section Thread width = 100 um 9902 N E Glisan, Portland Oregon Calcium Alginate Beads Set 2 Sphere, diameter = 3 mm 51 Methods and Materials 3.3.1 Preparat ion of G r o w t h Surfaces The selection process, for the growth surfaces used in this project, was discussed in Section 2.6.1: Selection of Support Materials. Listed in Table 3.2 are the various growth surfaces used in the first and second set of support material experiments during this project. A l l growth surfaces were treated in the same manner except for the calcium alginate beads (see preparation below). The method for preparing the calcium alginate beads was adapted from two separate papers (Kueck and Armitage, 1984, Santoyo et. al.,1996). Preparation of Growth Surfaces 1. Wash growth surface in dilute nitric acid. 2. Rinse growth surface with tap water. 3. Autoclave growth surface (15 min @ 121 °C). 4. Rinse growth surface with tap water. 5. Autoclave growth surface (15 min @ 121 °C). 6. Dry and keep in oven at 40 °C until required (up to 3 days). Preparation of Calcium Alginate Beads 1. Prepare 0.2 M C a C l 2 solution and cool in fridge. 2. A d d 1 m L thioglycolic acid to 200 m L distilled H 2 0 . 3. A d d 8 g sodium alginate to the 200 m L distilled H 2 0 (4% w/v). 4. M i x at 35 °C until well dissolved and no clumps are left in solution. 5. A l l o w to cool to room temperature. 6. A d d 40 m L of S R B inoculum to sodium alginate solution under a N 2 head with constant stirring. 7. Pump alginate/SRB mixture through a 20 gauge needle to add beads dropwise into the 0.2 M CaCl2 solution with continuous slow stirring (this takes about 8 hours). 8. A l l o w beads to harden in a fridge overnight. 3.3.2 S R B G r o w t h Media: The media selected is based on a nutrient solution described by V a n Houten and is composed of the following per 1000 m L of distilled water: N a 2 S 0 4 4.95 g, N a 2 H P 0 4 . 2 H 2 0 0.524 g, K H 2 P 0 4 0.41 g, N H 4 C 1 0.30 g, KC1 0.38 g, M g C l 2 . 2 H 2 0 0.10 g, C a C l 2 . 2 H 2 0 0.11 g, N a H C 0 3 1.2 g, thioglycolic acid 5 m L , bactopeptone 0.10 g, yeast extract 0.10 g, and stock 52 Methods and Materials salt solution 50-100 m L . The stock salt solution contains per 2000 m L of distilled water: NH4CI 7g, K H 2 P 0 4 1.2 g, FeSO 4 . 7H 2 0 20 g, C a C l 2 . 2 H 2 0 13.5 g, M g C l 2 . 6 H 2 0 36.7 g (Van Houten et al., 1994). In some cases the bactopeptone and yeast extract were omitted from the media preparation. Set I Experiments Bacteria Culture: Bacteria were cultured in 40 m L of nutrient medium in 120 m L serum bottles with rubber stoppers. The bottles were inoculated with 1 m L of inoculum pipetted directly from a 3 day old batch culture grown in 40 m L of the M V H solution. The cultures were grown under autotrophic conditions with a 8 0 % H 2 - 2 0 % C O 2 atmosphere, and incubated at 31 °C. The C 0 2 was injected in to the flasks through the rubber stoppers with a 60 m L Luer-Lok disposable syringe and 20 gauge needle. Set 2 Experiments Bacteria Culture: Bacteria were cultured in 40 m L of nutrient medium in 160 m L Kimble bottles with black screw caps and a butyl rubber septum. The method for adding the bacteria to the bottles was adapted from the Hungate Technique for the preparation and use of media under anaerobic conditions (Hungate, 1969). The batch flasks contained 1 m L of inoculum (except in the case o f calcium alginate beads, in which case 4 m L of inoculum was immobilised in the beads) pipetted directly from a 5 day old batch culture grown in 40 m L of M V H 2 solution. The cultures were grown under autotrophic conditions with a 7 5 % H 2 -2 5 % C 0 2 atmosphere, and incubated at 31 °C. The C 0 2 was injected in to the flasks through the butyl rubber septum with a 50 m L Hamilton Gas Tight syringe and 24 gauge needle. Inoculation Protocol 1. Autoclave bottles for 15 minutes at 121 °C. 2. I f required, add growth surface for S R B (refer to Preparation of Growth Surfaces for more detail). 3. A d d 40 m L of the appropriate nutrient solution to each vial , M V H for set 1 experiments and M V H 2 for the set 2 experiments. 4. Nitrogen purge the vial (with the nutrient solution) for 5 minutes. 53 Methods and Materials 5. Hydrogen purge the vial for 5 minutes. 6. A d d 1 m L of S R B inoculum after about 3-4 minutes of hydrogen purging. 7. Cover the vial opening with parafilm to minimise air entering the vial . 8. Quickly stopper the vial once the hydrogen purge is complete. 9. Flush 50 m L gas tight syringe twice with CO2. 10. Inject CO2 into the vial . Inject 25 cc CO2 for set 1 and 30 cc CO2 for set 2. 11. Check that the incubator temperature is set to 31 °C. 12. Place the batch flasks in the incubator. 13. Conduct the following tests TSS, V S S , T K N , protein, sulphate, and S E M prep as required. The protocols for these tests are outlined in the analysis sections of this chapter. Experimental Procedure One of the purposes of the set 1 experiments was to determine how long the experiments should be run. Three experimental runs were completed during this set of experiments. The length of each run was 9 days, 5 days and 7 days, respectively. In the first run, samples were collected daily between days 3-9, in the second run, samples were collected each day of the experiment, while in the third run samples were collected on each day o f the experiment except for day 2. The first set of experiments demonstrated that adequate results could be obtained within 7 days using a complex media. Since the set 2 experiments were conducted in a defined media, the duration of the experiment was increased to 14 days. Samples were collected on days 1,3,5, 8, 11, and 14 of the growth experiments. In the case of the Ringlace support, there was a lack of available material and samples were collected on days 8, 11, and 14 only. The data collected for the control on day 1 was assumed to also represent the data for the Ringlace on day 1 as these values for the experiment are measurements of the inoculum biomass concentration and the initial sulphate present in the solution. The batch growth experiments performed involved sacrificial sampling in order to determine the total biomass in the system. On any given day of sampling two or three bottles were selected at random for analysis. The nutrient solution in each vial was filtered through a 0.22 urn Mill ipore filter paper. The general handling procedure required to gather the samples for analysis is demonstrated in Figure 3.2. 54 Methods and Materials Both the biomass in solution and on the support was collected. This was accomplished by first collecting the biomass in the filtrate on filter paper and then collecting the biomass on the support on a different filter paper. The filtrate was collected, preserved with 2 M HC1 and placed in a 4 °C fridge for later sulphate analysis. The collected material on the filter paper was rinsed with a weak acid solution, followed by two rinses with distilled water. The filter paper and solids was then placed in a desiccator. The material on the support materials in the batch flasks was then also collected on filter paper. This involved adding distilled water to the flask, vigorously shaking it, and then filtering the water. This process was repeated until the water added to the flask remained clear after shaking. Filter Sample SRB in 160 mL Kimble Bottles Collect Filtrate Collect Solids (Suspension or Support) Vacuum Adjust to pH 2 with 2 MHO Store in Fridge TKN Analysis Protein Analysis at 4°C + Sulfate Analysis Figure 3.2: Sampling Procedure Flow Diagram The filter paper with solids was placed in a desiccator. Once all the sampling was complete and the filter paper with the solids had dried they were prepared for protein and/or T K N digestion. The filter papers were placed in digestion tubes and 10 m L 0.5 M N a O H was added, the samples were vortexed and placed in a block digester for 90 minutes at 85 °C. In 55 Methods and Materials most cases the filter paper was completely digested after the 90 minutes and the solids resuspended in the N a O H . Two m L of the sample was removed and placed in small labelled glass vials for protein testing. Ten m L of T K N digestion reagent (200 m L H2SO4 and 134 g K 2 S O 4 dissolved in 1000 m L distilled water) was then added to the digestion tubes and the samples further digested for 6.5 hours. After digestion, the samples were allowed to cool to room temperature, then diluted to 100 m L with distilled water, the samples were placed in the fridge at 4 °C until a T K N analysis was performed. 56 Methods and Materials 3.4 CO2 Uptake Experiments The change in the level of CO2 in the gas headspace of the batch flasks was monitored over time in complex and defined M V H nutrient solutions. The initial and final biomass concentrations ( T K N values) were also measured. Two types of experiments were conducted. One experiment monitored the change in CO2 in bottles with and without support in the complex media, while the other experiment compared the CO2 uptake rate in the complex and defined nutrient solutions. The purpose of the first experiment was to determine i f recharging of the flasks with CO2 would be required during the support studies, while the purpose of the second experiment was to observe i f the type of nutrient media present in the bottles affected the CO2 uptake rate. In the first experiment, bottles were prepared with glass beads, Teflon pieces, molecular sieve, ceramic beads, zeolite and a control in the M V H nutrient solution, bottles were prepared in duplicate for each day of sampling. Each bottle contained 40 m L of nutrient solution and 1 m L of inoculum. The experiment was run for 6 days and samples were collected once daily for the duration of the experiment. The CO2 in the bottles was measured using the Hamilton-Fisher Gas Partitioner Model 29. The second experiment consisted of three separate runs, in the first run, three bottles with foam, basalt, and a control, respectively, were prepared in duplicate with M V H 2 as the nutrient solution. Each bottle contained 40 m L of nutrient solution and 1 m L of inoculum. This experiment was run for 160 hours before being stopped due to temperature fluctuation problems with the incubator and a blockage that developed in the Shimadzu T O C Analyser Model TOC-500. The second run consisted of two bottles (without support) of M V H 2 and M V H 3 nutrient solutions, respectively, each prepared in triplicate. Each bottle contained 40 m L of nutrient solution and 1 m L of inoculum. Samples were collected 3 times a day for the first 12 days of the experiments, and 1-2 times a day for the duration of the experiment. A new calibration curve was prepared each time samples were collected. The M V H 2 and M V H 3 test was run for 800 hours. The third run consisted of bottles of M V H nutrient solution (without support) prepared in duplicate with one control bottle prepared as well . 57 Methods and Materials Each bottle contained 40 m L of nutrient solution and 1 m L of inoculum, except for the control to which no inoculum was added. Samples were collected 3 times daily for the first 150 hours and then 1-2 times daily for duration of the test, this experiment was run for 350 hours. A new calibration curve was prepared each time samples were collected. 58 Methods and Materials 3.5 Analytical Methods 3.5.1 Total Solids and Volatile Solids Total and volatile solids were measured in the set 1 experiments to monitor the increase in biomass over time. Total solids are considered the dry solids mass (contents) of the sample after drying at 103 °C, while volatile solids are considered the fraction of the total solids that volatilises at 500 °C. The difference between the total solids and the volatile solids would be the inorganic solids contents of the sample. Dry weight measurements were conducted on filtered and washed samples. Initially 5 m L of sample was removed filtered and weighed, later the total medium volume in each flask was filtered and washed. Dilute nitric acid was used in an attempt to acidify and resuspend the metal sulphides; allowing them to pass through the filter paper, the acid wash was then followed by a rinse with distilled water. The samples were placed overnight in an oven at 103 °C, followed by storage in a desiccator until the weight was recorded. I f the volatile solids were also being measured the sample was then placed in a Linberg Muffle Furnace at 500 °C for 1 hour, allowed to cool and then weighed. The sampling procedures utilised were adapted from the Section 2540: Solids in Standard Methods (Standard Methods, 1995). The Total and Volatile Solids Sampling Protocol outlines the method for sampling both total and volatile solids while the Total Solids Sampling Protocol was used when only total solids was measured. Total and Volatile Solids Sampling Protocol 1. Pre-fire a ceramic crucible in a muffle furnace at 500 °C, for at least 1 hour. 2. A l l o w the crucible to cool to room temperature; either in the muffle furnace or in a crucible holding oven (set at ~ 30 °C). 3. Weigh the empty crucible. 4. Vigorously shake, by hand, the batch flasks. 5. Remove 5 m L of sample from the batch flasks with a wide bore pipette. (Repeat to collect 2 samples from each vial being sampled). 6. Place the crucible in the oven at 103 °C for 1 hour. 59 Methods and Materials 7. Remove the crucible and let cool to room temperature. 8. Weigh the crucible. 9. Place the crucible in the muffle furnace at 500 °C for 1 hour. 10. A l l o w the crucible to cool to room temperature; either in the muffle furnace or in a crucible holding oven (set at 30 °C). 11. Weigh the crucible. Total Solids Sampling Protocol 1. Prefire ceramic or aluminium crucible in 500 °C oven for at least 1 hour. 2. Filter sample through 0.22 um membrane filter paper. 3. Rinse with one wash of dilute acid. 4. Rinse twice with distilled water. 5. Fire samples in 103 °C oven overnight. 6. Store in a desiccator until ready to record sample weight. 7. Record sample weight. 3.5.2 Total Kjeldahl Nitrogen (TKN) Assay for Biomass Determination Organic nitrogen was used as an indirect correlation to the biomass in the batch flasks. Organic nitrogen is considered to be organically bound nitrogen in the trinegative state and as such does not actually include all organic nitrogen compounds, but does include most types of organic nitrogen. It does include proteins and peptides, nucleic acids, urea and many synthetic organic materials. From an analytical perspective, organic nitrogen and ammonia can be determined together and are usually referred to as "kjeldhal nitrogen". (Standard Methods, Section 4500: Nitrogen (Organic), 1995). The total organic nitrogen ( T K N ) was measured with a Lachat Autoanalyzer QuikChem 800. T K N measures the inorganic and organic nitrogen in a system by converting all organically bound nitrogen, nitrates to ammonia. The level of ammonia in the sample is then measured as indication of the total nitrogen in the system. In this experiment the T K N value is taken to represent only the organically bound nitrogen and can be used as an indirect correlation to the total biomass in the system. A l l organic nitrogen is assumed bound within the cells of the bacteria being measured. Nitrate and ammonia that is present in the nutrient media wi l l be water soluble. 60 Methods and Materials Since the solution is filtered, followed by a slightly acidic water wash and then two washes with distilled water all of the nitrate, and ammonia that is in solution is assumed to be washed off the cells and through the filter paper. A n y remaining nitrogen content is therefore based on organically bound nitrogen. To confirm this theory, nutrient solutions with no bacteria were rinsed through the filter papers and carried through the T K N analysis. N o ammonia was expected to remain on the filter paper, since it is water soluble - the T K N values from these tests were used as blanks for the results, with the values being subtracted from the T K N values obtained from the sample results. The T K N digestion protocol was adapted from Technicon Industrial Systems (1975), while the T K N analysis was adapted from a procedure provided by Lachat (1976). TKN Sampling and Preparation Protocol 1. Place sample with dry filter paper into T K N digestion tubes. 2. A d d 10 m L T K N digestion reagent. 3. A d d 1-2 boiling chips to each digestion tubes. 4. Place samples in the digestion block. 5. Set low temperature dial at 120 °C for 3 hours. 6. Set high temperature dial at 350 °C for 3.5 hours. 7. A l l o w samples to cool to room temperature. 8. Dilute T K N samples to 100 m L . 9. Place samples and standards in sampling tubes for autoanalyser. 3.5.3 Tota l Protein ( D C B i o - R a d Protein Assay) The Bio-Rad D C protein assay is a colourmetric assay for protein concentration and is based on the Lowry Assay (Lowry et. al., 1951). It provides an indirect measurement of the biomass in the system. This assay was selected, as it is suitable for measuring proteins that have been solubilised in sodium hydroxide solutions up to 0.5 M N a O H . The D C Bio-Rad assay has been modified from the original Lowry Assay to allow for maximum colour development in 15 minutes and with a colour change of not more than 5% in one hour. 61 Methods and Materials This is a two step procedure and is based on the reaction of protein in the samples with an alkaline copper tartrate solution and Fol in reagent, which is a phosphomolybodate complex. In the first step, copper reacts with and binds to the protein in the alkaline media. In the second step, the copper treated proteins reduce the Fol in reagent (Lowry et. al., 1951). The reduction of the Fol in reagent results in the loss of oxygen atoms, which is the cause of the subsequent colour change observed. Maximum absorbance occurs at 750 nm and minimum absorbance at 405 nm. The total protein preparation and sampling procedure has been adapted from instructions provided by Bio-Rad. Total Protein Preparation and Sampling Protocol 1. Place sample with dry filter paper into digestion tubes. 2. A d d 10 m L 0.5 M N a O H to digestion tubes. 3. Place samples in digestion block. 4. Set low temperature dial at 90 °C for 1.5 hours. 5. A l l o w samples to cool to room temperature. 6. Remove 2 m L of samples for protein test (use remaining for T K N test). Testing (adapted from the Bio-Rad Procedure): 1. Resuspend bovine gamma globulin protein standard in 0.5 M N a O H . 2. Pipette 100 p L o f standard or sample into clean, dry test tube (if necessary, dilute samples by adding 50 p L of sample with 50 p L NaOH) . 3. A d d 500 p L reagent A (an alkaline copper tartrate solution) and mix well . 4. A d d 4.0 m L reagent B (dilute Fol in Reagent) and mix well . 5. Incubate at room temperature for 15 - 20 minutes (note that the colour remains relatively constant from 1 5 - 6 0 minutes with about a 5% decrease in colour over this time period). 6. Measure O D at 750 nm. 7. Determine concentration of protein standard by plotting standards vs. OD750 and then comparing with OD750 from samples. 3.5.4 Sulphate Analysis Two techniques were used to measure sulphate: the barium chloride turbidmetric method and the methylthymol blue colourimetric method. 62 Methods and Materials Turbidimetric Method Sulphate concentrations were measured from procedure outlined in Section 4500 SO4 E : Turbidimetric Method (Standard Methods, 1995). The optical density was measured using a Mil t ron Roy Spectronic 20D spectrophotometer. In this method, sulphate ions are precipitated in an acetic acid medium with barium chloride. The sulphate reacts with the barium forming barium sulphate that appears as a white cloudy suspension in solution. The absorbance value is then recorded at 420 nm and compared to the absorbance values recorded for sulphate standards to determine the sulphate concentration in the samples. Methylthymol Blue Method Sulphate concentrations were measured using a Lachat Autoanalyzer QuikChem 8000. The method employed was based on colourimetric changes measured at 460 nm. The analysis method was adapted from written instructions provided by the Lachat Company (1994). A s well as procedures outlined in Section 4500 SO4 F: Automated Methylthymol Blue Method (Standard Methods, 1995) In this analysis, barium is reacted with methylthymol blue ( M T B ) in an ethanol solution to form a blue complex. The sample, containing sulphate, is next reacted with the ethanol bar ium-MTB solution. The sulphate displaces the M T B forming barium sulphate and uncomplexed M T B . Sodium hydroxide is added to the solution to raise the p H , which allows for the measurement of the uncomplexed M T B that is grey in colour. A summary of the reactions is shown below: Barium + M T B —• (Bar ium-MTB) complex (3.1) (Bar ium-MTB) complex + sulphate ->• (Barium-sulphate) C Om Piex + M T B (3.2) M T B + N a O H -> M T B g r e y + p H t (3.3) 63 Methods and Materials Since one mole of sulphate directly displaces one mole of M T B , the change in colour monitored provides a direct correlation to the sulphate concentration in the sample. Preparation and Collection of Sulphate Samples 1. Filter sample through 0.22 um Mil l ipore (Durapore) filter paper. 2. Collect filtrate in 15 m L test tube. 3. Adjust filtrate to p H 2 with concentrated HC1. 4. Store in fridge at 4 °C until testing (not to exceed 28 days). 5. Dilute samples before measuring sulphate concentration (approximately200x using the turbidmetric method and lOOx using the methylthymol blue method). 6. Prepare standards in the range of 0-100 ppm sulphate. 3.5.5 Gas Ana lys i s /C02 Mon i to r ing The Fisher-Hamilton Gas Partitioner (CA# 11-127) was used to monitor the CO2 in flasks of the set 1 experiments with molecular sieve, ceramic beads, Teflon, zeolite, and the glass beads. The Shimadzu T O C Analyser Model TOC-500 was used to monitor the CO2 for the nutrient uptake experiments. CO2 Measuring Technique To use the gas partitioner 0.5 ml of sample is injected using a 1 m L Hamilton Luer-Lok gas tight syringe into the analyser. To use the T O C analyser 25, 50 Or 100 p L of samples is injected using a 25 or 100 p L Hamilton Luer-Lok gas tight syringe into the analyser. The CO2 values for the gas partitioner are quantified by comparing the total peak area to the C 0 2 peak area, providing a percentage value of CO2 in the sample. The CO2 values for the T O C analyser are quantified by comparing the CO2 peak area to a standard curve produced by injecting known amounts of C 0 2 into the analyser. 64 Methods and Materials Fisher-Hamilton Gas Partitioner Model 29 The gas partitioner is a gas chromatograph that has been specifically designed to quantitatively measure substances which, are in the gas phase at room temperature. The gas partitioner has a dual-column, dual-detector chromatograph that can separate and monitor carbon dioxide, oxygen, nitrogen, methane, hydrogen sulphide and carbon monoxide. A continuous flow of helium gas is used as the carrier gas to sweep the samples through the two columns. The columns are packed to selectively hinder the passage of various components in the sample, resulting in the separation of the gases and elution through the system at different times. A detector senses and produces an electrical signal as each component is eluted through the columns. The electrical signal is sent to a recorder where it is recorded as a measurable peak. The height of the peak is proportional to the gas concentration; since a component w i l l always emerge at the same time from the column, the elution time can be used to characterise a particular gas. Shimadzu TOC Analyser Model TOC-500 The total organic carbon (TOC) analyser combusts samples to C 0 2 and water at different temperatures and measures the CO2 produced. It measures total carbon (TC), inorganic carbon (IC), volatile organic carbon ( V O C ) and total organic carbon (TOC). The samples for T C analysis are passed through an oxidation catalyst and heated to 680 °C, whereas for the determination of IC, samples are passed through a reaction tube (without a catalyst) at 150 °C. T O C is the difference in value between the T C and the IC measurement. V O C are composed of low boiling point organic carbons, which evaporate at temperature below about 90 °C. The IC reaction tube can be used to determine the V O C content i f the sample is already wel l characterised. The T O C analyser utilises an infrared (IR) detector to measure the C 0 2 concentration in the gas. Monatomic molecules such as N2, O2 and H2, which may be present in the gas phase do not absorb IR energy. The amount of IR energy absorbed by the C 0 2 present in a sample is 65 Methods and Materials proportional to the gas density and hence concentration of the CO2 in the sample, according to the Beer-Lambert Law. A calibration curve was established by injecting known volumes of CO2 into the T O C analyser and recording the resultant peak area. A syringe was used to withdraw a known volume of gas from the headspace of the sample vials and then injected into the T O C analyser, the resultant area was compared to the calibration curve to establish the volume of CO2 in the headspace of the batch flasks. The CO2 calibration curve was produced by injecting known volumes (and hence concentrations) of CO2 into the analyser. Medical grade C 0 2 was used to establish the CO2 calibration curve. A section of tubing was attached to the C 0 2 cylinder; the tubing was then clamped onto a gas blown bubble. The glass bubble had a rubber septum attached to the top and two stopcocks on either side, tubing was attached to both stopcocks, and one section connected to the gas cylinder and the other placed in a flask with water. Once an adequate flowrate was established - a syringe was pushed through the septum, flushed with CO2 and then filled to a known volume. TOC Analyser CO2 Injection Protocol 1. Open the carrier air valve and adjust flow to 150 mL/min . 2. Switch the M a i n Furnace Oven button to O N . 3. A l l o w the equipment 1 hour to warm up. 4. Open the C 0 2 regulator valve and observe gas bubble percolating through water. Adjust the gas flow until the bubbles are steady but not causing violent bubbling. 5. Flush gas tight syringe with C 0 2 and then f i l l slightly greater than required volume of C 0 2 . 6. Just before injecting - expel excess CO2 from syringe. 7. Prepare a standard calibration curve by injecting known volumes (15,10,5, 0 uL) of CO2 into the T O C analyser of C 0 2 . Inject each volume in duplicate, or until reproducible areas result. 8. Remove samples form incubator. 9. Inject syringe carefully through the butyl rubber septum of the batch flask and then pump syringe twice. F i l l syringe to just past 25 uL. 10. I f the area reading is less than the area reading for the 5 uL standard, repeat step 9 using a 50 uL gas sample. 66 Methods and Materials 11. Just before injecting - expel excess gas from syringe. 12. Inject sample into the T O C analyser 3.5.6 Scanning Elect ron Microscope Imaging The scanning electron microscope images provide the opportunity to visually observe how the sulphate reducing bacteria are adhering to and colonising the different support media being examined. The S E M generates images by shooting a beam of electrons at the surface to be imaged. Electrons are then either backscattered or given off (secondary electrons) by the gold coated specimen. The beam is slowly scanned across the specimen until an area has been imaged by the microscope (Postek et. al., 1980). A Hitachi S-4100 F E S E M (field emission scanning electron microscope) was used to capture the photos. Before a sample is placed in a S E M it must be prepared properly. This includes fixing the sample, drying the sample, and coating the sample with a conductive material. The fixation protocol for preparing the S E M samples was established with the help of Elaine Humphreys in Biological Sciences (Humphreys, 1999). Preparation of Samples (Fixation Protocol) 1. Warm up fixing solution, 2.5 M glutaraldehyde solution in a 0.1 M cacodylate buffer at p H 7.0, to 32 °C. 2. Place samples in fixing solution for 30 minutes. 3. Wash samples with 0.1 M cacodylate buffer 3 times for 5 minutes each. 4. Place samples in 0.1 M OSO4 solution and allow to sit for 30-60 minutes. (The OSO4 is extremely toxic and should be handled strictly in a fumehood and with gloves). 5. Rinse 1-2 times with distilled H2O. 6. Dehydrate the sample with successive ethanol solutions of 30%, 50%, 70%, 85%, 95%, and three times with 100% ethanol. Each wash is 5 minutes in duration. 7. Critical Dry Point samples with CO2. 8. Sputter coat samples with gold. 67 Results and Discussion CHAPTER 4: RESULTS AND DISCUSSION 4.1 SRB Growth in Different Nutrient Media 4.1.1 Nutr ient Solution Tests The nutrient solution tests were divided up into two sections - initially a suitable complex nutrient solution was tested that would allow for active autotrophic growth of S R B in a time period of about 3 days. This nutrient solution was used in the first set of support experiments. The second set o f nutrient solution tests, compared defined media for S R B growth. Six nutrient solutions were compared in total. Growth of S R B was considered to have occurred i f blackening was observed in the growth vials. The first set (set 1) of support material experiments was carried out in the M V H nutrient solution, while the second set (set 2) of support material experiments was carried out in the M V H 2 nutrient solution. A typical complex nutrient solution for heterotrophic growth of S R B would consist of: 0.5 g/L K H 2 P 0 4 , 1 g/L NH4CI, 1 g/L C a S 0 4 , 2 g/L M g S 0 4 - 7 H 2 0 , 3.5 g/L sodium lactate, 1 g/L yeast extract, 0.1 g/L ascorbic acid, 0.1 g/L thioglycolic acid, 0.5 g/L FeS04-7H20 (Postgate, 1984). Initially three complex nutrient solutions were compared: the Biomet, Ethanol and M V H recipes. A complete description of these nutrient solutions can be found in Table 3.1: Nutrient Solutions. Blackening was observed within 3 days with the M V H solution, 5 days with the ethanol solution and only minimal growth was noticed after 21 days with the Biomet solution. The M V H solution was selected as the most suitable nutrient solution for the set 1 experiments since blackening was observed more rapidly in this nutrient solution than the others, as well the ethanol solution was discarded since it provides a readily available carbon source other than C 0 2 for the S R B . Although the goal of the project was to study the growth of S R B under autotrophic conditions, the ethanol media was initially included to confirm the 68 Results and Discussion viability of the obtained culture in case no growth was observed with the M V H and Biomet recipes. The second set o f nutrient experiments compared the growth of S R B in more defined modifications of the M V H media. Solutions were made up with yeast extract only, bactopeptone only and one solution with neither yeast extract nor bactopeptone (as indicated in Table 4.1: Addit ion of Yeast and/or Bactopeptone to Nutrient Solutions). The purpose of the defined media is to exclude carbon sources other than CO2 to the S R B , and to minimise the amounts of yeast extract and bactopeptone, which may interfere with the protein assay used during the set 2 experiments. Table 4.1: Addition of Yeast and/or Bactopeptone to Nutrient Solutions MVH MVH2 MVH3 MVH4 Yeast + Bactopeptone + - + + + indicates addition of substance - indicates substance omitted Table 4.2: Blackening as an indication of Activity in Nutrient Solutions MVH MVH2 MVH3 MVH4 Observation* Rapid blackening by day 3 Some blackening by day 4-5, complete blackening by day 6-7 Rapid blackening by day 3 Rapid blackening by day 3 *all tests performed in triplicate Based on the results shown in Table 4.2, S R B can grow adequately without the addition of yeast or bactopeptone as nutrients. A s expected, the defined media required more time before blackening occurred. 4.1.2 Comparison of M V H , MVH2, M V H 3 Nutrient Solutions The observation of blackening in the nutrient solution tests provided a visual determination of growth in the different media but did not allow a quantitative comparison between the growth differences in the M V H , M V H 2 and M V H 3 nutrient solutions. In order to do this biomass samples were collected and analysed for T K N to provide an indication of how quickly the S R B grew with and without the presence of yeast extract and bactopeptone. The 69 Results and Discussion M V H 4 solution was not studied further at this point since it is more common to add yeast extract to nutrient solutions and there was no observed differences in activity between it and the M V H 3 solution. The initial and final T K N values for the three nutrient solutions are listed in Table 4.3. Shown in Figure 3.1 are the plots of the natural logarithm of T K N versus time; it can be noticed that the solution with the greater amount of yeast and bactopeptone had a faster initial growth. Table 4.3: T K N values for the SRB in three M V H solutions M V H M V H 2 M V H 3 (ugN) SD (ugN) SD (ugN) SD Initial T K N 45 <20+ 11 <2(T 11 Final T K N * 785 181 25 256 11 Initial TKN values are based on the values from the blank which had greater values than initial MVH2, and MVH3 TKN values The M V H 2 and M V H 3 solutions were allowed to run for 1.5 months and T K N values were also measured at this times, the values were 402 and 369 ug N respectively. These samples were not run in duplicate however, so the confidence interval cannot be stated for these numbers. Based on the approximate error (4-11%) in the final values, shown in Table 4.3, the final error could be expected to be about ± 44 u.g N . The addition of yeast and bactopeptone has been noted to act as jump start for the S R B growth but is not strictly required (Widdel and Bak, 1992). The final values measured are as expected, based on this, since the M V H nutrient solution with the complex media had the fastest specific growth. It also produced the largest concentration of biomass, which may be due to extra available carbon present in the yeast extract and bactopeptone. The M V H 3 solution has yeast extract (but no bactopeptone) and a final T K N value of 256 u.g N while the M V H 2 solution has a final T K N of 181 u.g N on day 14. The higher value for the M V H 3 compared to the M V H 2 solution may due to the addition of the yeast extract. 70 Results and Discussion 0 2 4 6 8 10 12 14 Time (days) Figure 4.1: First Order Growth Comparison of MVH Nutrient Solutions. Note: The specific growth of each solution was taken from the slope between points 1 and 2. The values determined were 0.097, 0.015, and 0.013/h for the MVH, MVH2, and MVH3 solutions, respectively. Table 4.4: Summary of Nutrient Solution TKN and Sulphate Results T K N T T = T F - T , Biomass, X T ( d S 0 4 / d t ) / X T Yield, YSO4 u g N mg mg/(L.h.mg) g X T /mol S 0 4 reduced M V H 740 6.50 2.31 8.47 M V H 2 161 1.41 2.41 5.59 M V H 3 236 2.07 0.85 10.06 Sulphate dSO^dt Initial Final Reduced mg/(L.h) mg/L mg/L molx IO - 0 4 M V H 15.04 ±2.79 4117 2318 7.68 M V H 2 3.42 ±0.75 3405 2812 2.53 M V H 3 1.75 ±0.44 3360 2878 2.06 The T K N and sulphate reduction data for the three nutrient solutions are summarised in Table 4.4. The final T K N values follow the expected trend with M V H > M V H 3 > M V H 2 . The biomass is calculated based on the assumed mass fraction of nitrogen in one mole of 71 Results and Discussion biomass. The chemical formula is represented by CH1.8O0.5N0.2 (Roels, 1983). The conversion from pg N to mg Biomass then is: Equation 4.1 T J . T V K T , A A f24.6^ ( l m g ^ mg Biomass = TKN (pg N) x 1 2.8 1000 pg The specific sulphate reduction rate was determined by dividing the change in sulphate over time by the biomass concentration. This normalises the data between the systems, since a higher initial inoculum concentration would result in a greater amount of sulphate reduced. The values for M V H and M V H 2 solutions are similar ranging from 2.31-2.42 mg/(L.h.mg biomass); the value for the M V H 3 solution is lower and has a value o f 0.85 mg/(L.h.mg biomass). Another method of determining the activity of the bacteria is to establish the growth yield, Y S O 4 , this is the mass of biomass produced per mol of sulphate reduced. The growth yield can be shown as: Equation 4.2 (XF-XI) Yso4 = ([SOA]I-[SO*]F)XV Where Y S O 4 = molar growth yield (g biomass/ mol sulphate), X = the mass of biomass (g), V = the volume of solution in the batch flasks (L), and [SO4] = the sulphate concentration (mol/L). The yield coefficients determined range from 5.59-10.06 g/mol S 0 4 . These values correspond well with previously published research, which had yield coefficients ranging from 4-12.2 g/mol SO4 in systems using hydrogen as the electron donor (Badziong and Thauer, 1978, Cypionka and Pfennig, 1986). Slightly higher values, ranging from 11-13.5 g/mol SO4 have been reported in systems using a defined lactate nutrient media, as shown in Table 4.5. 72 Results and Discussion Table 4.5: Yield Coefficients of SRB with Sulphate as electron acceptor Species Growth Yield, Yso4 g /mol S 0 4 pH Nutrient Type Continuous or Batch Source Desulfovibrio vulgaris (Marburg) 4-5 7.2 Hydrogen, Acetate and C 0 2 Continuous Badziong and Thauer, 1978 Desulfovibrio vulgaris (Marburg) 8.3 6.5 Hydrogen, Acetate and C 0 2 Continuous Badziong and Thauer, 1978 Desulfovibrio vulgaris (Marburg) 13.5 7.1 Defined Lactate Sulphate Medium Batch Ingvosen and Jorgensen, 1984 Desulfovibrio 11.0 7.1 Defined Lactate Batch Ingvosen and sapovorans Sulphate Medium Jorgensen, 1984 Desulfovibrio •12.0 7.1 Defined Lactate Batch Ingvosen and salexigens Sulphate Medium Jorgensen, 1984 Desulfotomaculumn orientis 6.6-7.5 6.95 Hydrogen Basal Mineral Medium w/ lmmol acetate/L Batch Klemps et. al., 1985 Desulfotomaculumn orientis 8.5-12.2 6.85 Hydrogen and CO2, Basal Mineral Medium Continuous Cypionka and Pfennig, 1986 4.1.3 CO2 M o n i t o r i n g The CO2 level in the headspace of the bottles was monitored, with the Shimadzu T O C analyser, over time to observe the rate of decrease in CO2 over the duration of the experiment. A decrease in CO2 was taken as an indication that the S R B were utilising it as a carbon source for cell synthesis. A previous study, using radioactively labelled CO2, showed that in a batch system with H2 as the electron donor and CO2 available as the carbon source, 90% of the cell carbon could be attributed to C 0 2 (Klemps et. al., 1985). Complex nutrients such as yeast extract and bactopeptone while not required for growth can stimulate it, by providing extra micronutrients and carbon for cell synthesis (Widdel and Bak, 1992). Thus, it might be expected that the nutrient solution with the yeast extract and bactopeptone w i l l show a faster rate of C 0 2 uptake. The CO2 uptake rates for the M V H , M V H 2 and M V H 3 nutrient solutions are shown in Figure 4.2, Figure 4.3, and Figure 4.4, and as predicted, the systems with the bactopeptone and/or yeast extract did display faster rates of CO2 uptake. 73 Results and Discussion Interestingly, however is that there appears to be a large lag phase in the complex nutrient solution ( M V H ) of about 150 hours before the CO2 level begins to decrease. A similar lag phase from about 100-200 hours is also noticed with the M V H 3 experiments, while a shorter lag phase, between 50-75 hours, is apparent with the M V H 2 nutrient solution. It is likely that the S R B first utilise the most readily available carbon source, which is provided by the yeast and/or bactopeptone present in the M V H and M V H 3 solutions. The M V H 2 solution has no other sources of carbon present and in order to grow the S R B must utilise the CO2 available. A s Figure 4.2, Figure 4.3 and Figure 4.4 also show the rate of CO2 uptake, was initiated in the order of M V H > M V H 3 > M V H 2 . The CO2 experiments confirm that complex nutrients can stimulate growth but also show they are not necessary for growth to occur. A s well , it should be noted that CO2 measurements were taken from replicate bottles on each day of sampling. Both the M V H and M V H 2 replicates had similar uptake rates, respectively. However, in the case of the M V H 3 replicates, 2 of the 3 prepared bottles had similar uptake rates, while the third bottle had a slightly longer lag phase and a slower uptake rate. It is possible that a smaller volume of S R B inoculum was added to this bottle, which might account for the slightly lower uptake rate. Table 4.6: C 0 2 Uptake Experiment - Final T K N Values Solution Time* (hours) T K N X(mg) u g N SD M V H 400 4.9 558 25 M V H 2 900 3.5 402 35 M V H 3 900 3.2 369 42 T i m e at which samples were measured The T K N values were measured at the end of the experiments, this corresponds to 400 hours for the M V H solution and 900 hours for the M V H 2 and M V H 3 solutions. The results are listed in Table 4.6. The S R B growth in the M V H solution had the highest biomass concentration at 4.9 mg, which is attributed to the additional carbon in the yeast and bactopeptone. The M V H 3 and M V H 2 values are similar 3.2 and 3.5 mg, respectively. 74 Results and Discussion o o 0.002 i i 200 Time (Hours) Figure 4.2: C0 2 Uptake in MVH Nutrient Solution. Note: T = 31 °C, the C0 2 uptake starts at around 150 hours and decreases at a rate of 1.81 xlO" mol C02/(L.h). The error bars represent the standard deviation determined in the calibration curve on each day of sampling. 0.012 0.01 ^ 0.008 o u 0.006 0.004 0.002 l -1- *P X T 1 * 1 X • X " - i - ^ t . * 100 200 300 400 500 Time (Hours) 600 700 800 900 Figure 4.3: C0 2 Uptake in MVH2 Nutrient Solution. Note: T = 31 °C, the C0 2 uptake starts between 50-75 hours and decreases at a rate of 0.38 xlO mol C02/(L.h). The error bars represent the standard deviation determined in the calibration curve on each day of sampling. 75 Results and Discussion 0.011 0.003 0.001 0 100 200 300 400 500 600 700 800 900 Time (Hours) Figure 4.4: C0 2 Uptake in MVH3 Nutrient Solution. Note: T = 31 °C, the CO2 uptake starts at about 100 hours for the lower curve at a rate of 1.12 xlO'05 mol C02/(L.h), while the C0 2 uptake starts at about 200 hours for the upper curve at a rate of and decreases at a rate of 0.75 xlO"05 mol C02/(L.h). The error bars represent the standard deviation determined in the calibration curve on each day of sampling. It is not possible to report the values in terms of a Y ie ld Coefficient, YCO2, since not all of the carbon utilised in the systems with complex media would have come from the CO2 available. In particular, both the M V H and M V H 3 solution had other sources of carbon available due to the addition of the yeast extract and bactopeptone. It is interesting to note that the overall change in the C 0 2 was similar for the different nutrient solutions (refer to Table 4.7). However, it must be pointed out that the following assumptions were made: the final time CO2 activity was observed for both the M V H and M V H 2 solutions corresponds to the last time a sample was taken and it was assumed that CO2 uptake stopped at that time. If the experiments had been run for a longer period of time the change in CO2 would likely be greater than the values reported. A s well , as no other data measuring C 0 2 uptake rates was found in literature it was not possible to compare the CO2 uptake rates to other data. 76 Results and Discussion Table 4.7: Change in C 0 2 level during Nutrient Solution Experiments Solution R a t e o f C 0 2 Uptake mol COz/(L.h) xlO 0 5 Initial Time C O 2 Activity Observed (h) Final Time C 0 2 Activity Observed (h) Change in C02(mol/L) M V H 1.81 ±0.24 150 350 0.004 M V H 2 0.38 ±0.03 50 800 0.003 M V H 3 1.12 ±0.04 100 500 0.004 M V H 3 0.75 ± 0.03 200 675 0.004 77 Results and Discussion 4.2 Set 1: Growth on Support Materials: Glass, Molecular Sieve, Ceramic Beads, Teflon and Zeolite The main purpose of the set 1 experiments was to determine i f the selected analytical techniques were appropriate methods to monitor the biomass and sulphate concentrations present in the batch flasks. This was accomplished by comparing the results to previously published data. 4.2.1 Solids Total solids were measured in one experiment with glass beads and a control. In a second experiment total solids was measured for the following supports: glass, molecular sieve, ceramic beads and zeolite. Both experiments were carried out with the complex M V H nutrient solution. The first experiment involved collecting 5 m L samples from the 40 m L nutrient solution, the total biomass was estimated by multiplying the sample weight (expressed in mg/mL) by the total sample volume. The total solids were observed to increase over the first 6 days but then decreased on the 7 t h day of sampling (refer to Table 4.8). The overall change in the total solids is small and it is unclear i f any growth is occurring. Table 4.8: Total Solids Results Time (days) Control (No Support) Glass Beads Solids (mg) SD Solids (mg) SD 3 46.1 0.4 45.2 0.1 4 48.6 0.2 47.4 0.2 6 52.5 0.4 49.3 0.4 7 44.0 0.1 44.8 0.3 In the second experiment, the entire volume of the sample was filtered, instead of just 5 mL. This was to remove any error in the total solids that may arise due to a lack of homogeneity in the sample volume collected for analysis. A s shown in Figure 4.5, the total solids data was scattered and an increase in biomass over time was not observed. Volatile solids measurements, were also monitored during this experiment, however the numbers were 78 Results and Discussion below the method detection limit (0.01 mg). The standard deviations for the solids data plotted in Figure 4.5 ranged from 0.4-10 mg, also indicating that the data should be considered with scepticism. 45 0 1 2 3 4 5 6 7 8 Time (days) Figure 4.5: Total Solids Results for the Set 1 Experiment Support Surfaces. The procedure for collecting the solids samples is outlined in Section 3.4.2. The collected solids samples were washed with dilute nitric acid to acidify and resuspend the metal sulphides; allowing them to pass through the filter paper. The main reason for the error in the data may have to do with the amount of mass collected, compared to the weights of the crucibles. Ceramic crucible were used for these experiments and weighed between 18-20 grams each while the final dry weight of the sample was in the order of 0.01-0.04 grams, making it difficult to obtain an accurate weight for the S R B samples. The problems here were also encountered during another project utilising S R B (Pierre Berube, personal communication, 1998). To compensate for this problem, aluminium crucibles, which are lighter in weight, could be used. Although the total volume in the flask was filtered to collect the biomass, a larger initial inoculum could be used to increase the amount of biomass in the sample. 79 Results and Discussion 4.2.2 Growth Curves using T K N Measurements T K N was used as an indirect correlation to the biomass in the batch flasks. The increase in the natural logarithm of T K N over time for the set 1 experiments is shown in Figure 4.6 (control and glass beads), Figure 4.7 (Teflon and zeolite) and Figure 4.8 (molecular sieve and glass beads). The error bars displayed in the figures are based on the standard deviation of the samples. The results from this portion of the experiments show an increase in biomass, or organic nitrogen, over time. The nutrient solution for the set 1 experiments was the M V H solution. B y plotting the natural logarithm of the T K N value over time, the specific growth rate, u., can be determined. A straight line is expected from this type of plot during the exponential growth phase of the S R B (as discussed in the Literature Review Section: Cel l Kinetics in a Batch System). The specific growth rates and doubling times of the S R B determined in this experiment are listed in Table 4.9. The data for the molecular sieve and the ceramic beads support materials appears to follow a first order trend over the duration of the experiment with covariance values of 0.99 and 0.99, respectively, for the first order trend lines fitted to the data. This indicates that the substrates are in excess and that the bacteria are in the growth phase. The covariance values for the first order fits to the zeolite and Teflon data are 0.99 and 0.93, respectively. The M V H control follows a linear trend for the first days and then appears to plateau indicating the bacteria have become substrate limited and are in the stationary phase. The glass support follows a similar trend as the control. The specific growth rates for the control and glass are each based on their respective first two data points as this appears to be the exponential growth phase for these systems - in these cases monitoring more samples would have been desirable to confirm the rapid increase in initial T K N . 80 Results and Discussion 6.5 5.5 Z A S 3.5 Figure 4.6: Control and Glass Bead Support Growth Curves. Note: T=31 °C, grown in complex MVH nutrient solution, u. = 0.096 and 0.089 h"1 for the control and glass beads, respectively. 7 6.5 6 1 4 T .^^*n y = 0.3031x +5.3484 T E F L 0 N R2 = 0.9302 ^ ^ ^ ^ " ^ A ^ y = 0.2933x +4.3505 ^ Zeolite R2 = 0.9972 • Teflon • Zeolite • 0 1 2 3 4 5 6 Time (days) Figure 4.7: Teflon and Zeolite Support Growth Curves. Note: T=31 °C, grown in complex MVH nutrient solution, n = 0.0126 and 0.0122 h"1 for the Teflon and zeolite, respectively. 81 Results and Discussion 0 1 2 3 4 5 6 7 Time (days) Figure 4.8: Molecular Sieve and Ceramic Bead Support Growth Curves. Note: T=31 °C, grown in complex MVH nutrient solution, u = 0.0057 and 0.0034 h"1 for the molecular sieve and ceramic beads, respectively. The specific growth rate, p., was determined from the following equation: Equation 4.3 l n X - l n X o t The doubling time for the control (system with no support) was determined to be 7.2 hours while for the support systems the values range from 7.7 to 206 hours. Batch culture studies with pure S R B species report specific growth values ranging from 0.013 to 0.15/hour, the control in this experiment was established as 0.096 which is within the range of data collected from previous studies (Badziong and Thauer, 1978, Robinson and Tiedge, 1984, Klemps et. a l , 1985). 82 Results and Discussion Table 4.9: Set 1 Experiment Specific Growth Rates and Doubling Times Support u td (h^) (h)_ M V H (control) 0.096 7.2 Glass Beads 0.089 7.7 Teflon 0.0126 54.9 Zeolite 0.0122 56.7 Molecular Sieve 0.0057 122 Ceramic Beads 0.0034 206 *based on T K N values The glass bead system had a doubling time of 7.7 hours, which is similar to that of the control. The Teflon and zeolite appear to have impeded growth with doubling times calculated between 54.9-56.7 hours, while the molecular sieve and ceramic beads support materials appear to have strongly impeded the growth of the SRB with doubling times of 122 and 206 hours respectively. Total Solids Sampling vs. TKN Data for Biomass Determination Both T K N data and total solids data was collected for the 7 day experiment with the following supports: molecular sieve, ceramic beads, and glass beads. The solids data is based on the negative difference method in that the final value is subtracted from an initial value while the T K N data is based strictly on a positive result (measurement of TKN). A comparison of the data for the control and molecular sieve is shown in Table 4.10. In all cases with the T K N data a general increase in measurement was recorded over the duration of the experiment, in comparison a consistent increase in solids was not observed with the total solids method. 83 Results and Discussion Table 4.10: Molecular Sieve and Control Comparison of Total Solids and TKN data Time Total Solids Biomass* TKN (days) (mg) (mg) (MgN) Control 0 . . . 0.4 45 1 15.6 4.0 456 3 15.0 6.8 771 5 10.3 7.4 847 6 10.0 6.9 785 Molecular Sieve 1 19.3 3.4 382 3 42.4 — _._ 4 30.1 5.3 608 5 . . . 5.9 671 6 26.0 6.5 741 *based on T K N values 4.2.3 Sulphate Reduct ion Sulphate results for the glass, Teflon/plastic, and zeolite supports and a nonsupport control are presented here. The results for the molecular sieve and ceramic beads have been omitted from this section. Sulphate samples were collected for the molecular sieve and ceramic bead support systems, however equipment problems with the Lachat analyser delayed the testing of these particular samples beyond the recommended storage of 28 days by nearly a month. When these samples were analysed no change in the sulphate concentrations was measured. The sulphate measurements for the control and glass support were determined using the turbidimetric method while the Teflon and zeolite support system sulphate concentrations were determined with the Lachat analyser. Plots of the sulphate reduction that occurred in the support systems with glass, zeolite and Teflon show an overall decrease in sulphate reduction but do not follow a linear trend pattern. The control appears to demonstrate a linear decrease in sulphate with time, as is demonstrated in Figure 4.9, the covariance, r2=0.94 for the fitted trend line; although the first data point was excluded as an outlier from this trend line. The decrease in sulphate concentration with the glass support system is higher on days 4 and 5 than on day 3, because 84 Results and Discussion this was also observed in the control, the data from day 3 was considered to be an outlier and was not included in the results shown in Table 4.11. N o sulphate measurements were taken on the first 2 days of sampling as the sulphate concentration was not expected to decrease during this time period. However, samples should have been taken and in subsequent experiments sulphate samples were taken at the start of the runs. Figure 4.10 shows the plots of the Teflon and zeolite support systems. Both the Teflon and zeolite support systems appears to exhibit an initial lag phase where little sulphate reduction is occurring for the first four days and then drops off sharply on the fifth. The error bars in Figures 4.9 and 4.10 are based on standard deviations. The sulphate reduction data is summarised in Table 4.11; since it was not possible to fit a trend line to the sulphate reduction curves, other than the control, the growth yield, YSO4, is based on the difference between the initial and final sulphate concentration values. The calculation may be carried out as shown: Equation 4.4 XT (XF-XI) ASO* [SO*]i-[SO*]F In order to used equation 4.3, the initial and final sulphate concentration must be converted from mg/L to moles, this is accomplished by using the following equation: Equation 4.5 mols SO4 = mg SO* mol 96.06 g x lg 1000 mg xV 85 Results and Discussion Table 4.11: Summary of Sulphate Reduction Results in Set 1 Experiments Support T K N Sulphate T T = T , r T I Biomass Yield, Yso4 Initial Final Reduced u g N mg g X T /mol S 0 4 reduced mg/L mg/L mols x 10"04 M V H 740 6.50 8.47 4117 2318 7.68 Glass 681 5.98 16.24 3649 2785 3.69 Teflon 820 7.20 15.98 3552 2477 4.51 Zeolite 253 2.22 4.88 3414 2346 4.56 A s mentioned in section 4.1.2 the growth yield, YSO4, is one way to analyse the data and expresses the biomass produced in the system with respect to the amount of sulphate reduced. The Y s o 4 values range from 4.88-16.16.24 g/mol S 0 4 , which is similar to values reported in literature which range from 4-13.5 g/mol SO4 (Badziong and Thauer, 1978, Ingvosen and Jorgensen, 1984, Cypionka and Pfennig, 1986). 4500 Figure 4.9: Sulphate Reduction with Glass Support. Note: T = 31 °C, the error bars are based on standard deviations and the experiment was conducted with the complex MVH nutrient solution. 86 Results and Discussion 4500 1500 Time (days) Figure 4.10: Sulphate Reduction with Teflon and Zeolite Supports. Note: T = 31 °C, the error bars are based on standard deviations and the experiment was conducted with the complex MVH nutrient solution. 4.2.4 C 0 2 Monitoring The gas partitioner was used to monitor the C 0 2 in the set 1 batch experiments. The purpose of the gas partitioner was to attempt to observe i f the C 0 2 level decreased in the headspace of the bottles. This was in an attempt to provide a rough confirmation that the bacteria were utilising the C 0 2 , as well as to determine the length of time before all the C 0 2 was utilised in the flasks. This would provide an indication of when an experiment should be stopped or when C 0 2 should be recharged to the flasks in the experiment. Two separate experiments were run using the gas partitioner; one with a control and the following support surfaces: glass beads, molecular sieve, and zeolite. The second experiment was conducted with Teflon and zeolite as support surface. The gas partitioner measures the following gases: C 0 2 , 0 2 , Nitrogen, C O and H 2 S ; it does not measure the hydrogen gas present in the sample mixture. The y-axis on the graph is the 87 Results and Discussion ratio of CO2 to the gases in the head space, except hydrogen, in the flask and is simply calculated as: Equation 4.6 Acoi K = (AC02 + Aomp) Where R = ratio of CO2 peak area to the peak area of N2+CO2 Aco2 = area of the CO2 peak ACOMP = combined peak area of N2, O2, C O and H2S present in the headspace, as only N2 was present, ACOMP = AN2-Two peak areas are used in this calculation, as the gases pass through the first column in the gas partitioner, an initial peak (referred to as the composite peak, ACOMP) o f all the gases, except the carbon dioxide is detected. The second peak that passes through the first column is the carbon dioxide peak. The gases then pass through a second column, which separates out the gases from the composite peak, and are detected by another sensor. The carbon dioxide passes through this column without detection. 0.8 0 1 2 3 4 5 6 7 Time (days) Figure 4.11: CO2 depletion curves for Set 1 Experiments. 88 Results and Discussion The scatter in the data observed in Figure 4.11 is due to the following problems: • ACOMP is not constant between samples since it was not injected in a consistent fashion during the preparation of the batch experiments. This is considered to be one of the main problems with the data scatter. • Since ACOMP was assumed to be constant no standard CO2 injections were run with the gas partitioner, this was a mistake. A s CO2 standards would have compensated for the error in ACOMP because then it would have be possible to directly compare the CO2 area with that of the standards. • A leak was detected in the second column of the gas partitioner after the experiments were completed. Although, the data used was from the first column, the leak in the second column resulted in the need to use a higher flowrate than recommended which may have decreased the equipment detection sensitivity. The only general observation that can be made from Figure 4.11 is that except for the zeolite support, all o f the other systems seemed to show some decrease in CO2, which may be taken as an indication that it was being utilised. The lack of any noticeable activity in the zeolite flask may indicate that some component of the zeolite had an inhibiting affect on S R B growth or possibly that the system was contaminated with an outside carbon source that the S R B utilised. Both reasons appear to have some validity since growth was observed in the flask but at much more limited quantities compared to the other systems (refer to Table 4.11). The data for the glass beads is scattered. Good trends are observed for the batch flasks with no support, ceramic beads, molecular sieve, and Teflon. It appears as though these support systems have a slightly faster decrease in CO2 compared to the flasks with no support. A s noted above, the CO2 data in the set 1 experiments appears to show a general decreasing trend for the most of the systems, except for the batch experiments with zeolite. In general, however the data is quite scattered. The amount of CO2 injected into the set 1 bottles was 25 cc into 80 m L of headspace this corresponds to approximately 31% CO2. After 5 days, we 89 Results and Discussion see that the ceramic beads, molecular sieve, and Teflon values are approaching zero. This data does not correspond well with the M V H CO2 data, which still had measurable levels of C 0 2 after 400 hours (16 days). 4.2.5 Discussion of Set 1 Support Surfaces In the initial phases of this project, the following support surfaces were used: glass beads, molecular sieve, ceramic beads, Teflon/plastic pieces, and zeolite. During this portion of the project the total biomass in the system was measured while the fraction of biomass on the different supports compared to that in solution was not determined. Only visual observations were made at this point to determine i f any of the supports seemed to provide a suitable surface for bacterial adhesion. Glass beads have been used in some previous experiments as surfaces for bacterial colonisation with success (Allaoui and Forster, 1994, Diels et. al., 1990, Groudeva and Groudev, 1990). Another study, however, found that no colonisation occurred while using glass beads as an immobilisation surface (Huysman et. al., 1983). The data obtained from the glass beads shows an increase in biomass over time based on the T K N test. However, less metal sulphide precipitates were observed adhering to the glass beads than with other surfaces, and were easily removed by gently shaking the flasks. The glass bead are not porous, which may also limit the amount of biomass that can attach to the glass surface, whereas the study by Diels (1990) used sintered glass beads which have a porous structure. The growth yield, 16.24, was higher than that determined for control. Although the data indicates an increase in biomass over time with the zeolite support, no significant concomitment precipitation of metal sulphides was observed. A s well , the increase in biomass was significantly less than that measured in the other systems. A relatively low growth yield, 4.88 g/mol SO4, was observed. Other natural rock types including septolite, pumice, basalt, crushed stones, and zeolite have been examined before (Huysman et. a l , 1983, Maree and Strydom, 1985, 1987, Al laoui and Forster, 1994, V a n Houten et. al., 1994). In previous studies, although success was found with the growth of 90 Results and Discussion S R B using septolite, pumice and hard stone, a stable biofilm was not observed when basalt or zeolite was used. The results obtained indicate that zeolite surface does not encourage bacterial growth and is not considered to be a suitable support surface for the S R B . Both the data for the ceramic beads and molecular beads appears fairly good in terms of increase in T K N . A s well , both materials appeared to have blackening on their surfaces, possibly due to the porous nature of both materials, allowing for good bacterial adhesion and growth. However, both materials are fairly brittle and were observed to fracture during the batch experiments; resulting in a slight disintegration of the beads over the short time period (5-7 days) in which the experiment was run. It is likely that the beads would degrade further in longer term studies, especially in a continuous flow reactor where more turbulent conditions would exist, thus they are not considered as suitable immobilisation surfaces for S R B growth. The small Teflon/plastic pieces had an increasing biomass trend observed from the T K N data gathered. A s well , good biofilm coating was noticed on the Teflon/plastic pieces, with an apparent initial preference for the Teflon side over the plastic side. A previous study examining the potential of using a plastic support for S R B immobilisation had less successful results (Maree and Strydom, 1985). However, bacteria have shown a preference for adhering to hydrophobic materials (Abolsom et. al., 1983, Huysman et. al., 1987). The hydrophobic properties of the Teflon may encourage bacterial adhesion. The growth yield in this batch system, 15.98 g/ mol SO4, was greater than that determined for the control. Review of Set 1 Experiments One of the primary objectives of the set 1 experiments was to become familiar with and validate the techniques used to monitor the biomass in the system. This was accomplished by comparing calculated data to published data in the literature. A secondary objective was to visually observe i f any of the materials appeared to show promising results for bacterial adhesion. The solids test was discarded as a method to measure biomass due to large differences between duplicate samples and difficulty in obtaining an increase in the total solids measurements over the time in which the experiments were conducted. The T K N 91 Results and Discussion analysis was considered valid as a general increase in T K N was observed over the duration of the experiments (as expected), further, and more importantly, the specific growth rates, u., determined corresponded well with literature values. A s well both the turbidimetric and methythymol blue methods of sulphate analysis were considered valid, as the calculated growth yields, Y S O 4 , were similar to values reported in the literature. O f the two methods used to measure sulphate, the methylthymol blue method is less labour intensive since it is run on an automated analysis, and as such it was used as the technique to measure sulphate in the set 2 experiments. 92 Results and Discussion 4.3 Set 2: Growth on Support Materials: Foam, Basalt, Ringlace, and Alginate Beads The immobilisation materials used in the set 2 experiments were foam, basalt, Ringlace, and alginate beads. The nutrient solution for the set 2 experiments was the M V H 2 solution. It should be noted that although the Teflon support from the set 1 experiments showed promising results as an immobilisation surface, it was not included in the set 2 experiments, which quantified the amount of biomass attached to the surface, due to a lack of available equipment. A s in the set 1 experiments, T K N was used as an indirect correlation to the amount of biomass in the batch flasks. In addition, a protein analysis was also run on the samples in a secondary attempt to monitor the increase in biomass during the experiments. The purpose of the protein assay was to provide a check of the T K N estimated biomass concentrations. The biomass determined from the T K N assay were based on the theoretical molecular weight of the bacteria, while the protein biomass concentrations were compared to a Bovine gamma globulin standard. A comparison of the results obtained by measuring T K N and protein are presented below. The amount of biomass immobilised on the different materials was monitored and compared to the total biomass in the system in an effort to quantify which support would be suitable for an immobilised bioreactor system. The sulphate reduction rate in the systems was also monitored and expressed in terms of the growth yield, YS04. 4.3.1 G r o w t h Curves using T K N and Protein Measurements Total TKN Plots of the natural logarithm of T K N versus time are shown in Figure 4.12 (Ringlace and alginate beads) and Figure 4.13 (foam and basalt). The error bars displayed in the figures are based on the standard deviations of the samples. The specific growth rates and doubling times of the S R B determined in this experiment are listed in Table 4.12. The trends for the alginate beads and the control have linear portions and then flatten out during the stationary phase of S R B growth after days 8 and 5, respectively. The data for the Ringlace indicates an increasing biomass trend with no apparent decrease in the growth rate. 93 Results and Discussion It should be noted that the alginate bead support system has a higher initial T K N concentration (as shown in Figure 4.12) compared to the initial T K N value found in the other support systems in the experiment, including the control. This is due to method of inoculation, in the other growth experiment systems 1 m L of S R B inoculum was added to each batch flask, whereas in the alginate system 4 m L of S R B inoculum was encapsulated within the beads (as described in Chapter 2: Methods and Materials). This resulted in a much higher initial concentration of S R B in the alginate support system. 7.0 2.0 J , , , , _ _ , , , 1 0 2 4 6 8 10 12 14 16 Time (days) Figure 4.12: Ringlace, and Alginate Bead Support TKN Growth Curves. Note: T = 31 °C, grown in defined MVH2 nutrient solution, p = 0.0041, 0.015, 0.0064 h"1 for the alginate beads, control and Ringlace, respectively, error bars are based on standard deviations. 94 Results and Discussion 12 14 16 0 2 4 6 8 10 Time (days) Figure 4.13: Foam and Basalt Support TKN Growth Curves. Note: T = 31 °C, grown in defined MVH2 nutrient solution, p = 0.007 and 0.006 h"1 for the foam and basalt, respectively, error bars are based standard deviations. Table 4.12: TKN based Specific Growth Rates and Total Biomass Growth in Set 2 Experiments T K N Support I1, td T T = T F - T , Biomass, X T (hr"1) (hr) (ugN) (mg) Control 0.015 46 161 1.41 Foam 0.0070 99 275 2.42 Ringlace 0.0066 104 159 1.40 Basalt 0.0062 112 166 1.46 Alginate Beads 0.0041 170 334 2.93 The covariance value for the first order trend line fitted to growth phase portion of the alginate bead and Ringlace systems is 0.99 and 0.86. The foam and basalt support systems show a linear increase over the length of the experiment. First order trend lines fitted to the data have covariance values of 0.97 for the foam, and 0.94 for the basalt. While a covariance value of 1 represents a perfect linear fit, the covariance values calculated seem to reasonably support the assumption of a first order trend, with the possible exception of the Ringlace support system. 95 Results and Discussion The specific growth rates for the control is based on the differences between only the two data points on days 1 and 5 for the control, since the control system exhibited a large increase in T K N between these points followed by a levelling out, thus it was not possible to fit a first order trend line to this data. Previous studies, under autotrophic conditions, with various S R B have been performed to establish the specific growth coefficient and yield data with respect to sulphate reduction. This data is summarised in Table 4.13. The specific growth for the control was 0.015/h, which fits within the data reported in the literature. Table 4.13: Comparison of Specific Growth Rates and Yield Coefficients Species Specific growth, Mo" 1 ) Batch vs. Continuous pH Notes Author Desulfovibrio vulgaris 0.15 Batch 6.5-7.2 H 2 / C 0 2 , and acetate Badziong and Thauer, 1978 Desulfovibrio 0.05 Batch 6.7 H 2 /C0 2 wi th Robinson and sp. Gil minerals and vitamins added Tiedge, 1984 Desulfotomaculumn orientis 0.077 Batch 6.9-7.0 H 2 / C 0 2 , basal mineral medium Klemps, Cypionka, et al 1985 Desulfotomaculumn orientis 0.013-0.044 Continuous 7 H 2 / C 0 2 , basal mineral medium, Sulphate limited Cypionka and Pfennig, 1986 Desulfotomaculumn orientis 0.09 Continuous 7 H 2 / C 0 2 , basal mineral medium, non-limited Cypionka and Pfennig, 1986 The specific growth for the control was 0.015/h, which fits within the data reported in the literature, refer to Table 4.15, albeit on low side. The specific growth for the foam, Ringlace, and basalt systems, 0.0070/h, 0.0066/h and 0.0062/h, respectively, were considerably lower than the reported values. The alginate system had the lowest specific growth at 0.004/h. This is not surprising since the nutrients needed to diffuse through two layers, the liquid media and the solid beads before reaching the S R B , as well the electron donor and carbon need to diffuse through the gas, liquid and solid phases. The media used in the literature included trace vitamins, while the defined nutrient media used in this experiment did not, this 96 Results and Discussion difference may have contributed to the specific growth of the S R B reported in the literature generally being higher than the values calculated in this study. Total Protein The BioRad D C protein assay was used as an additional indirect method to monitor the increase in biomass over time during the course of the experiment. One of the objectives in using the protein assay was to confirm the trends obtained from the T K N assay. The protein data does tend to follow the same general trend lines as the T K N data with increasing protein concentrations over time, however the error (based on 95%CI) associated with the results is relatively large decreasing confidence in them. The protein data for the alginate beads is not included, as the alginate beads did not digest during the digestion step used in preparing the samples for analysis. Table 4.14: Total Protein Assay Results for Control, Foam, Basalt and Ringlace Time Control Foam Basalt Ringl ace (days) mg/mL SD mg/mL SD mg/mL SD mg/mL SD 0 0.135 0.013 0.154 0.039 0.154 0.039 0.135 0.013 5 0.372 0.014 0.329 0.143 0.295 0.096 — — 8 0.341 0.013 0.409 0.157 0.320 0.163 0.437 0.087 11 0.419 0.044 0.127 0.046 0.404 0.464 0.478 0.077 14 0.339 0.165 0.240 0.201 0.334 0.486 0.638 0.099 97 Results and Discussion Shown in Figure 4.14 and Figure 4.15 is a comparison between the determined biomass values based on both the T K N and protein analyses. The biomass concentration is calculated by assuming that 55% of the total biomass is protein and is calculated as: Equation 4.7 mg Biomass = protein (mg/ml) x Vs x 1 mg Biomass 0.55 mg protein Where V s = the digestion volume (10 mL). Both the protein and T K N converted values show a general increasing trend, however the protein based biomass values are much higher than the biomass values that were calculated from the T K N data. Time (days) Figure 4.14: Growth Results for Foam and Basalt based on T K N and Protein Measurements. Note: the values in the table are expressed in mg Biomass and the error bars shown are based on standard deviations. 98 Results and Discussion "SB E si 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 r * r l -14 I Control-tkn 0.18 1.05 1.16 0.84 1.59 ORinglace-tkn 0.18 0.20 1.03 1.57 • Control-protein 2.43 6.77 6.21 7.62 6.16 • Ringlace-protein 2.43 7.95 8.69 11.60 Time (days) Figure 4.15: Growth Results for Control and Ringlace based on T K N and Protein Measurements. Note: the values in the table are expressed in mg Biomass and the error bars shown are based on standard deviations. Finally, the biomass produced per mole of sulphate reduced can be calculated from both the T K N and protein data. This data is summarised in Table 4.15 and Table 4.16. In the case o f the protein-based values, the observed yields are high, ranging from 13.22-63.96 g biomass/ mol S O ^ except for the foam, which has a yield of 4.80 g biomass/mol SO4. It would appear that, in general, the protein assay overpredicted the biomass concentration. The biomass was determined by comparing the measured protein values to a Bovine gamma globulin standard. It is possible that the ratio of proteins in the standard is lower than that present in the S R B , which would account for the higher than expected results. The T K N based values on the other hand range from 5.07 - 9.77 g/mol. A s mentioned earlier, the literature values for the yield coefficient have been quoted between 4-12.2 g/mol under similar growth condition (refer to Table 4.5), based on this it is likely the T K N values are a better representation than the protein values for determining the biomass. 99 Results and Discussion Table 4.15: Summary of Biomass Growth based on the Protein Assay Protein Support PT—PF-PI Biomass, X T (dS04/dt)/XT Yield, Y mg/ml mg mg/(L.h)/mg g Xj/mol S0 4 reduced M V H 2 0.204 3.71 0.923 14.65 Basalt 0.180 3.27 0.484 13.22 Foam 0.086 1.56 2.209 4.80 Ringlace 0.503 9.15 0.130 63.96 Alginate — — — — Beads Table 4.16: Summary of Biomass Growth based on the TKN Assay TKN Support Ti—Tp-Tj Biomass, X T (dS04/dt)/XT Yield, Yso4 u g N mg mg/(L.h)/mg g XT/mol S0 4 reduced Control 161 1.41 2.42 5.59 Ringlace 159 1.40 0.85 9.77 Basalt 166 1.46 1.09 5.89 Foam 275 2.42 1.46 7.28 Alginate 334 2.93 1.56 5.07 Beads 4.3.2 S R B G r o w t h on Support Mater ia ls Since the growth yield values, YSO4, calculated using the protein values did not match well with the literature values, the determination of the S R B growth on the support materials was based on the T K N data collected. The main objective of monitoring the growth on the support materials in the second set of experiments was to show the differences between the amount of immobilized biomass compared to that in free suspension. This data is summarised in Table 4.17, the % o f S R B on the support materials in decreasing order is alginate beads>foam>Ringlace>basalt. Figure 4.16 shows the increase of bacteria adhering to the alginate beads, foam, basalt, and Ringlace support materials, as expected the alginate beads values significantly higher than the others since the alginate beads were encapsulated with S R B from the start of the experiment. 100 Results and Discussion 300 \ ' ^ 250-1 E w 200-+ P 150 * Alginate-Support Foam-Support Ringlace-Support Basalt-Support Time (days) Figure 4.16: Comparison of SRB immobilised on support (TKN) The foam has a highly porous structure, which may encourage the colonisation of S R B within the pore spaces, accounting for the high percentage (79%) of S R B on the foam support (Huysman et. al., 1983). The Ringlace had 37% adhesion and the results for the basalt were below the method detection limit of the T K N analyser. The Ringlace is a series of thread like material strung together all of which have a smooth surface, the lack of surface roughness and open pore spore spaces for the S R B to colonise may account for reason it has a lower surface adhesion % compared to the foam. The basalt had a rough surface with small crevices, which the S R B could colonise, and was expected to yield good results. However, the lack of large deep pores may have impeded the bacterial colonisation (Van Houten et. al., 1994). Shown in Figure 4.17 is the comparison of the amount of biomass in support versus solution for the foam, basalt, Ringlace, and alginate beads. 101 Results and Discussion Table 4.17: SRB Growth in Solution vs. on Support for Set 2 Experiments Support Total ugN SD % in Solution % on Support MVH2 181 25 100 0 Basalt 189 137 100 MDL* Ringlace 179 25 63 37 Foam 298 35 2! 79 Alginate- 586 221 16 84 Beads * the basalt TKN values for the support were below the method detection limit of the Lachat Autoanalyzer (a) (c) 14 Time (days) 14 Time (days) (b) 350 300 250 z M 200 3-s 150 100 50 0 (d) 1 Foam-Solutioni I Foam-Support m m Time (days) i i 14 3 Basalt-Solution I Basalt-Support 1 14 Time (days) Figure 4.17: Comparison of SRB biomass in solution and immobilised on support. Note: The above figure shows that the SRB encapsulated within the alginate beads had the highest immobilisation, followed by the foam, and Ringlace, while biomass was not measured on the basalt support. The error bars are based on standard deviations. 102 Results and Discussion 4.3.3 Scanning Elect ron Microscope Images The purpose of taking the S E M images was to (1) attempt to verify the biomass results in terms of which surfaces had the highest calculated % of bacterial adhesion and (2) to comment on the how the S R B appeared to be adhering or colonising the various surfaces. S E M images were taken for the foam, basalt, Ringlace, and alginate bead supports. The S E M images support the T K N data with more colonies of bacteria observed adhered to the surfaces as follows: foam > Ringlace > basalt. The S R B were encapsulated within the alginate beads and alginate beads which had been cut in half also showed colonies of bacteria, S E M images were also taken of the surface of alginate beads and these showed S R B adhering to outer surface. The foam has some of the most interesting images (refer to Figure 4.18, Figure 4.19, Figure 4.20). Bacteria were observed attached to the surface of the foam, as well as in floes inside the pore spaces. These later structures appeared to be attached to the foam via strands of what are assumed to be extracellular polymeric structures (EPS). It was also interesting to note that more S R B colonies and E P S formation was observed between the foam matrix than actually adhered to the surface. These observations are in agreement with a similar study using foam as a support for methanogens (Bolte et. al., 1986). The alginate beads were observed to have limited growth on the surface of the beads (refer to Figure 4.21) and large colonies of S R B within the beads (refer to Figure 4.22). This was expected since the S R B inoculum was encapsulated within the bead, the S R B observed on the surface of the bead are attributed to a bacterial colony expansion forcing single cells to the bead surface (Wijffels, 1994). The Ringlace S E M images (refer to Figure 4.23) show that small colonies of S R B adhering to the surface but no large groups were found, possibly due to a lack of niches the S R B could colonise. The basalt support had a rough surface with small pores or surface pockets, individual S R B were observed adhered to the surface with the occasional colony found within small surface pockets. The S E M images confirms that S R B were adhering to the basalt but at overall concentrations too small to be enumerated using the T K N assay (refer to Figure 4.24). 103 Results and Discussion Figure 4.18: S E M Foam 1. Note: (a) Foam Support, bar 100 um, (b) SRB on section of foam, taken from inside cross-section, bar 10 um. Figure 4.19: S E M Foam 2. Note: (a) Magnified section of Figure 4.18 (b) above, bar 1 urn, (b) bacterial growth found on outside edge piece of foam, bar 10 um. 104 Results and Discussion (a) (b) Figure 4.20: S E M Foam 3. Note: (a) Outside edge of foam with apparent web-like bacterial growth, bar 100 urn, (b) magnified view of upper right hand web structure, bar 100 um. (a) (b) Figure 4.21: S E M Alginate Bead 1. Note: (a) 2500X Magnification of Alginate Bead Surface, bar 10 um The area highlighted by the box is shown magnified in (b) 8000X Magnification of surface. Some SRB have been forced to the bead surface. 105 Results and Discussion (a) (b) Figure 4.23: S E M Ringlace 1. Note: (a) 40X Magnification of Ringlace, bar 750 fim, (b) 700X Magnification of Ringlace, lighter areas, indicated by the arrows, denote areas with SRB, bar 42.9 | im. 106 Results and Discussion Figure 4.24: S E M Basalt 1. Note: (a) 400X Magnification of Basalt, bar 10 um (b): 4500X Magnification of Basalt, notice the sulphate reducing bacteria, bar 1 um. Figure 4.25: S E M Basalt 2. Note: (a) Surface pockets found on Basalt Support were occasionally found to have colonies of bacteria, bar 100 urn. (b) Left Hand Side Depression from 'a ' Note the bacterial colony and the larger elongated and short rods of SRB, bar 10 um. 107 Results and Discussion Although the exact types of S R B present in the inoculum was not known at the start of the study it appears as though it may be a mixed population of Desulfotomaculumn species, which range in size from 2-6 um. The S E M images show that mainly short vibrio type S R B (1-2 um in length) were observed on the support but occasionally longer rods (up to 6 um in length) were also observed. Further, only a few S R B species are capable of mesophilic, autotrophic growth on strictly CO2/H2, of which Desulfotomaculumn is one of these species and it is the only group, which also falls within the size range observed. 4.3.4 Sulphate Reduction The sulphate concentration was monitored over two weeks with basalt, foam, Ringlace and alginate beads acting as support surfaces. Figure 4.26 and Figure 4.27 shows plots of the sulphate concentration over time, and the error bars are based on standard deviations. These experiments were carried out in the M V H 2 nutrient media. The sulphate reductions all appear to follow zero order kinetics although the rates vary from 1.27-4.58 mg/(L.h), as shown in Table 4.18. The calcium alginate beads system appears to have the most rapid sulphate removal. Since this system has the highest bacterial concentration (due to the method of inoculation, as discussed earlier) these results were expected. The control also has a higher rate of sulphate reduction compared to the Ringlace although the former has lag phase before the reduction begins. The foam also has an apparently high sulphate reduction while little activity is noted in the basalt system. Zero order trend lines were fitted to the data, the Ringlace covariance value is 0.78, while the covariance values for the alginate beads and control were 0.92 and 0.91, respectively. The covariance values for the zero order trend lines for the basalt and foam support reduction data were 0.86, and 0.90. These values would appear to support the justification for fitting a linear trend line to this data, with the possible exception of the Ringlace support system. 108 Results and Discussion Control y = -82.194x +3894.8 R 2 = 0.9114 Ringlace y = -30.484x + 3420.4 R 2 = 0.7773 10 12 Time (days) • Ringlace • Alginate A Control 14 16 Figure 4.26: Sulphate Reduction with Alginate Beads and Ringlace Supports. Note: T = 31 °C, the error bars are based on standard deviations and the experiment was conducted with the defined MVH2 nutrient solution. • Foam • Basalt 0 2 4 6 8 10 12 14 16 Time (days) Figure 4.27: Sulphate Reduction with Foam and Pumice Supports. Note: T = 31 °C, the error bars are based standard deviations and the experiment was conducted with the defined MVH2 nutrient solution. 109 Results and Discussion Table 4.18: Sulphate Reduction Data for Set 2 Experiments Support T K N Sulphate T i — T p - T j Biomass, X T Yield, YSO4 dSOVdt Initial Final Reduced u g N mg g X T /mol S 0 4 reduced mg/(L.h) mg/L mg/L mols xlO"4 Control 161 1.41 5.59 3.4210.75 3405 2812 2.53 Basalt 166 1.46 5.89 1.58 ±0.33 4176 3596 2.48 Foam 275 2.42 7.42 2.46 ±0.32 3787 3024 3.26 Ringlace 159 1.40 9.77 1.27 ±0.48 3383 3048 1.43 Alginate 334 2.93 5.07 4.58 + 0.78 3560 2205 5.78 Beads A s shown in Table 4.18, the sulphate reduction rate in decreasing order was alginate beads>control> foam>basalt> Ringlace. A s mentioned earlier, the alginate support system had a larger concentration of S R B than the others, thus it was expected to have the highest sulphate reduction rate. The control exhibited a greater sulphate reduction rate compared to foam despite a higher biomass concentration in the foam system, while the basalt and Ringlace, which had comparable biomass concentrations to the control, also exhibited lower sulphate reduction rates. Sulphate Kinetics In a closed, batch system, when the reaction rate is independent of the reaction concentration, the reaction obeys zero order kinetics and the rate w i l l be constant at all times during the reaction. This is expressed as: Equation 4.8 -dS . r = ko dt where r = the volumetric rate of reaction (kg/m /s), dS/dt = represents the change in concentration over time, and ko is the zero order rate constant (kg/m /s). 110 Results and Discussion B y integrating the equation 4,7, we can see that a plot of S versus time w i l l give a straight line with a slope -1^: Equation 4.9 A previous study found that at sulphate concentrations > 30 mg/L at T = 25-31 °C, the growth of acetate utilising S R B was independent of the Sulphate concentrations, with a reduction rate of 12 mg/(L.h) sulphate in a batch system (Middleton and Lawrence, 1977). While another study also found that sulphate reduction followed zero order kinetics (Maree and Strydom, 1987). Based on this, the sulphate reduction data collected in this experiment was plotted as sulphate concentration versus time and fitted to linear trend lines. Both the complex media and defined media controls appeared to agree with the zero order assumption. In general, most of the support systems from the second set of experiments also appear to follow zero order kinetics, with the possible exception of the Ringlace. However, it is difficult to tell i f the sulphate reduction data from the first set of experiments also follows zero order kinetics. 4.4 Summary Two types of experiments were conducted during this project (1) a comparison of S R B growth under autotrophic growth in a complex and defined media, and (2) the immobilisation of S R B on different support materials. The complex nutrient media solution contained both yeast extract and bactopeptone, which have been shown to facilitate faster growth with S R B (Widdel and Bak, 1992). However, the yeast extract may interfere with the protein analysis and T K N analysis that were conducted in these experiments. A s such, it was desirable to see i f growth could also be accomplished in a defined media without yeast extract or bactopeptone. A study of S R B growth in the complex So t = o S = So-kot 111 Results and Discussion and defined media showed that while the S R B did grow in the defined media, although the specific growth of the S R B was much higher in the complex media than the defined media, 0.097 and 0.015/h, respectively. This translates to a doubling time of 7 hours compared to 46 hours. In conjugation with monitoring the increase in biomass over time, the CO2 uptake rate of the S R B was also monitored. Interestingly, the CO2 uptake rate was first initiated in the defined media solution before the complex media solution. It is possible that the S R B in the complex nutrient media first utilised any organic carbon available from the addition of the yeast extract and bactopeptone. A s well the complex media solution has a higher final biomass value compared to the defined media solution, 6.50 and 1.41 mg biomass, respectively. This also supports the idea that carbon available from the yeast extract and bactopeptone was being utilised by the S R B . The study of the immobilisation of S R B to different support surfaces was conducted in two parts. In the first study, glass beads, ceramic beads, molecular sieve, Teflon/plastic pieces, and zeolite supports were used. None of these materials proved to be suitable surfaces for S R B attachment, except possibly the Teflon. In the second study, foam, basalt, Ringlace and alginate beads were used as immobilisation surfaces. Teflon could have been included in these studies, except there was a lack of bottle available and it was decided that the other materials had priority. In the first study the following parameters were monitored: total solids, total T K N , CO2 uptake and sulphate reduction; whereas in the second study: the T K N , and protein both in solution as well as on the supports, and sulphate reduction were measured. The CO2 uptake studies during the support experiments were considered invalid as an inconsistent amount of nitrogen (as an inert gas) was initially added to the flasks, which was supposed to be used as a comparison to the change in C 0 2 levels. Both T K N and total solids were monitored in the first study, however the total solids did not increase consistently over the time of the experiment and the V S S level was too small to be measured. The T K N values on the other hand did show an increasing level over the time of the experiment. Plots of the T K N versus time appeared to generally follow first order kinetics, with the specific growth values calculated for the control, glass beads, Teflon and zeolite generally falling within the 112 Results and Discussion values reported in literature, p = 0.012 - 0.096/h compared to values of p = 0.013-0.15/h reported (Robinson and Tiedge, 1984, Klemps et al., 1985, Badziong and Thauer, 1978). The specific growth values calculated for the molecular sieve and ceramic bead systems however, where an order of magnitude lower than that reported in literature. The specific growth values calculated for the second set of experiments, which were conducted in the defined media, were generally lower than that determined in the first set of experiments with the complex media. Ranging from p = 0.004/h for the alginate beads to 0.015/h for the control. The lower specific growth values were expected, based on the results obtained during the nutrient solution experiments. Since the first set of experiments only monitored the total increase in biomass it was not possible to quantify which support was a better immobilisation surface for growth. However, based on final T K N numbers (as an indirect measurement of biomass) and visual observations, the Teflon support may be a good support surface for S R B . The final biomass in the Teflon system was similar to that in the control, 7.20 and 6.50 mg, respectively. A s well biofilm was noticed to be adhering to the Teflon, and it was not easily removed by simply shaking the flask. It has been postulated that hydrophobic surfaces, may encourage bacterial adhesion (Bolte et al., 1986, Huysman et al., 1983). Neither the glass beads nor the zeolite were observed to have good biofilm development on their surfaces and while biofilms were noticed to be adhering to the ceramic beads and molecular sieve, both of these materials were observed to be fracturing. A s such, they would probably disintegrate, or crumble, rapidly in a reactor system. In the second set of experiments with support, the amount of biomass on the surfaces was also quantified. In order of decreasing biomass attached to the surface compared to free suspended biomass was alginate beads (84%)>foam (79%)>Ringlace(37%), while the biomass on the basalt was below the detection limit of the T K N analysis. The results for the foam and alginate beads were as expected. The S R B were encapsulated within the alginate beads and thus S R B were immediately adhered to this immobilisation surface. A s such, any biomass in solution was due to either rupturing of the bead walls due to colony expansion or 113 Results and Discussion single cell release to the surface of the beads (Wijffels, 1994). Porous media have been found to be better colonised than nonporous media (Huysman et. al., 1983, Al laoui and Forster, 1994, V a n Houten et. al., 1994,). A s such the foam was expected to have better results than either the Ringlace or basalt. It has also been noted that microbial colonisation on surfaces tends to increase with increasing surface roughness (Geesey and Costerton, 1979, Baker, 1984, Characklis, 1984). Thus, it was expected that the basalt would perform better than the Ringlace. However the opposite was observed, with approximately, 37% of the biomass adhered to the support in the Ringlace system, while no measurable biomass was found on the basalt system. It has been postulated that the basalt surface lacked sufficiently deep pores, and crevices, for good bacterial colonisation (Van Houten, 1996). While the hydrophobic properties of the Ringlace support are not known, it is possible that this was a contributing reason for the observed bacterial adhesion. If these experiments were to be repeated, it would be recommended that all bottles be prepared in triplicate for each day of sampling. During this experiment, samples were generally prepared in duplicate, however in some cases there was a large discrepancy between the data obtained during the same day. For instance, in the basalt support system, on day 14 two samples were collected for the T K N measurements, one value was 672 u.g N while the other was 448 u.g N . A third sample would have weighted the average to the more appropriate T K N value. The results from the total solids and the protein analysis did not agree well with the results of the T K N analysis, although the protein results did show a general increasing biomass trend, which supports the T K N results. A Bovine gamma globulin ( B G G ) standard was used in the protein assay to determine the biomass content o f the S R B , a more appropriate standard would be a purified protein sample of S R B , however this was not available. It is possible that the B G G standard contains a different ratio of proteins than the S R B , which could account for the results being higher than expected. Both the turbidimetric and methylthymol blue sulphate analysis methods appeared to yield good results. The turbidimetric samples were analysed manually while the methythymol blue samples were analysed automatically using flow injection analysis. From a time perspective, the methythymol blue method is significantly faster, as approximately 60 samples/hour could be processed whereas approximately 10 samples/hour were processed using the manual method. 114 Conclusions and Recommendations CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions The primary objective of this thesis was to compare the immobilization of S R B to different types of support surfaces, while a secondary objective was monitor the growth of S R B in a complex and defined nutrient media under autotrophic conditions. The objectives were accomplished by monitoring the biomass growth and sulfate reduction in the different support systems, monitoring CO2 uptake, and with the use of S E M images. The conclusions can be listed as follows: • The immobilization of S R B was greatest in the alginate beads followed by the foam support, and Ringlace. N o significant quantities of biomass were measured on the basalt surfaces. The percentage of immobilized biomass compared to that in free suspension after 14 days of study was 84% for the alginate beads, 79% for the foam, and 37% for the Ringlace. • The alginate beads had the highest level of immobilized cells as expected, since the S R B were growing within the individual beads. The S R B found in solution was attributed to single cell release and colony eruption of bacteria due to growth at the bead surface. • The immobilization of the S R B on the foam was also high and is attributed to a bacterial preference for colonizing porous structures, and adhering to hydrophobic materials. • N o quantifiable levels of biomass were measured on the basalt support, despite its rough surface, while a small percentage of biomass was quantified on the Ringlace, it is unclear what properties of the Ringlace, encouraged the bacterial adhesion. 115 Conclusions and Recommendations • Porosity and hydrophobicity were found to encourage bacterial adhesion more than surface roughness. • The complex media control was found to have the largest overall specific growth rate, p = 0.096/h, when compared to the specific growth rates determined for both the defined and complex nutrient support systems. The specific growth rate for the defined media control, p = 0.015/h was less than that calculated for the Ringlace support system, p. = 0.023/h, but greater than the p values calculated for alginate beads, foam and basalt supports, which were also grown in a defined media. • The sulfate reduction was found to follow zero order kinetics for the defined and complex nutrient media controls, as well as the support systems with Ringlace, foam, alginate beads, and the basalt. However, the sulfate reduction data for the glass beads, Teflon, and zeolite support systems did not follow the expected trend. • The molar growth yield, YSO4 values for the complex and defined nutrient media were 8.47 and 5.59 g biomass/mol SO4 reduced, respectively. The YS04 values for the S R B bacteria cultivated in the complex media support systems varied from 4.88-16.24 g/mol, while the YSO4 values for the defined media support systems were 5.07, 5.89, 7.28, and 9.77 g/mol for the alginate beads, basalt, foam, and Ringlace, respectively. These values compare wel l data published in literature, which range from 4-13.5 g/mol (see Table 4.5). • The nutrient solution studies confirmed that S R B can grow under autotrophic conditions in a defined media, however the complex media was found to have a faster rate of CO2 uptake compared to the defined media, 1.81xl0" 0 5 and 0.38xl0" 0 5 mol C0 2 / (L .h ) , respectively. A s well , the C 0 2 uptake rate for the intermediate nutrient solution, which had no bactopeptone but did contain yeast extract, was 1.12-0.75 x l O " 0 5 mol C 0 2 / ( L . h ) . • Monitoring T K N was found to be a valid tool for indirectly estimating the S R B biomass. A s well , the complex media did not interfere with the T K N analysis when using the designed experimental protocols. 116 Conclusions and Recommendations • Although the protein measurements followed the same general increasing biomass trends as the T K N measurements, the calculated growth yields using the protein values were higher than expected. The Bovine gamma globulin protein standard is not appropriate for monitoring S R B biomass. 5.2 Recommendations The main objective of this project was to determine a suitable immobilization surface for bacterial colonization. This support surface could be used in future reactor studies to compare the ability of the S R B to treat high sulfate, high metal containing effluent at different flow rates. Based on the data gathered from this project, either a hydrophobic, porous structured support (such as foam), or a cell entrapment type immobilization surface would be suitable for future reactor studies. 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Wageningen University. 87. Zobell , C . E . (1943). Journal of Bacteriology 46: 39-56. 124 APPENDIX A: RAW AND CALCULATED DATA Sulphate Raw Data: Control and Glass Beads Turbimetric Method (Manual) Sample#1 Sample#2 Sample#1 Sample#2 Sample#1 Sample#2 Description Day % Transmittance Sulphate (mg/L) Sulphate (mg/L) (1 mL in 200 mL) (numbers diluted by 2 (dilution corrected) Control 3 83.8 83.8 15.37 15.37 3073 3073 Control 4 78.0 78.0 20.58 20.58 4117 4117 Control 6 79.6 80.4 19.14 18.42 3829 3685 Control 7 81.6 82.8 17.35 16.27 3469 3253 Control 9 87.8 88.2 11.77 11.41 2354 2282 (1 mL in 200 mL) Glass Beads 3 85.0 14.29 2858 Glass Beads 4 80.8 80.4 18.06 18.42 3613 3685 Glass Beads 6 80.2 81.4 18.60 17.53 3721 3505 Glass Beads 7 86.0 86.6 13.39 12.85 2678 2570 Glass Beads 8 85.4 85.4 13.93 13.93 2786 2786 Sulphate Measurements and Standard Deviations Control Glass Beads Control Glass (days) Sulphate (mg/L) SD SD 3 3073 2858 0 — 4 4117 3649 0 50.9 6 3757 3613 101.7 152.6 7 3361 2624 152.6 76.3 9 2318 2786 50.9 0 1 2 Error Analysis of Control 1 m b 1 2 2 sem seb 1 -361.063 5735.096 3 r2 sey 2 67.19033 453.2236 4 F df 3 0.935227 242.2582 5 ssreg ssresid 4 28.87707 2 5 1694767 117378.1 where: m = slope b= y-intercept sem = error in the slope seb = error in the y-intercept r2=variance sey = error in the y value F= F statistic, or the F-observed value df = degrees of freedom ssreg= regression sum of squares ssresid = ressidual sum of squares Sulphate Raw Data: Control and Glass Beads Sulphate Standard Curve(Manual Method) Percentage Sulphate (mg/L Transmittance 0 Too 4 96 8 93.4 20 79 40 56 Sulfate Concentratin vs. % Transmittance c 2 40 5 5 20 \ , , , , 1 0 10 20 30 40 50 Sulfate Concentration (ppm) Sulphate Raw and Calculated Data: Teflon and Zeolite (numbers diluted by 35) Data Sample#1 Sample#2 Sample#1 Sample#2 Bottle* Date/98 Peak Area Sulphate (ppm) T#0 day 0 27-Nov 11528960 101.49 Z#0 day 0 27-Nov 11112039 97.54 T#1 day 1 28-Nov 12205466 107.91 Z#1 day 1 28-Nov 11248103 11574247 98.83 101.92 Z#3 day 2 29-Nov 11527168 11560755 101.48 101.79 Z#2 day 2 29-Nov 11172941 10933043 98.12 95.84 T#2 day 2 29-Nov 12394138 12407962 109.70 109.83 T#3 day 2 29-Nov 12286208 13257088 108.68 T#4 day 3 30-Nov 13265626 117.97 T#5 day 3 30-Nov 10496320 91.70 Z#4 day 3 30-Nov 11692134 103.04 Z#5 day 4 1-Dec 10551412 92.22 T#7 day 4 1-Dec 11002349 96.50 T#6 day 4 1-Dec 12289044 108.70 Z#7 day 5 2-Dec 7898445 67.05 T#8 day 5 2-Dec 8836038 75.95 T#9 day 5 2-Dec 7864115 66.73 Sulphate Measurements and Standard Deviations (corrected for dilution) Teflon Zeolite Teflon Zeolite Time Sulphate (ppm) S.D. S.D. 0 3552 3414 1 3777 3513 77 2 3829 3476 22 100 3 3669 3606 281 4 3591 3228 302 5 2497 2347 228 Sulphate Raw and Calculated Data: Teflon and Zeolite Sulphate Standard Curve (Methylthymol Blue Method) Sulphate (ppm) Peak Area 77 9084135 55 5881908 40 5176756 24 3900570 16 3284531 0 0 Sulfate Calibration Curve 1.00E+07 -| S042- Standard (ppm) 129 Sulphate Raw Data: Basalt and Foam April 19- May 2/99 Experiment Bottle* Desc Sample#1 Sample#2 Sampling Day Sulphate Concentration (ppm) numbers diluted by 100 3 Basalt 5 38.63 37.89 5 Basalt 8 37.50 6 Basalt 14 37.07 36.54 7 Basalt 3 39.67 40.01 8 Basalt 11 35.96 36.87 10 Basalt 3 39.05 38.88 11 Basalt 14 35.02 35.21 12 Basalt 5 36.70 37.05 13 Basalt 8 38.73 38.54 14 Basalt 11 dropped 21 Basalt blank 43.93 43.67 31 foam 14 24.80 24.61 33 foam 11 32.00 31.95 34 foam 3 37.24 37.58 35 foam 8 34.76 33.86 36 foam 11 dropped 39 foam 8 36.37 36.65 42 foam 5 37.33 39.85 43 foam 14 34.44 34.91 44 foam nsrb blank 38.19 37.88 45 foam nsrb blank 37.16 46 foam 5 36.41 35.96 47 foam 14 32.48 70 control blank 37.57 37.37 71 control blank 37.52 36.53 72 Basalt blank 38.28 41.18 73 foam blank 38.26 Sulphate Measurments (corrected for dilution) Foam Basalt Foam Basalt Day (Sulphate ppm) S.D C. 1=0.95% S.D. .1. =0.95% 0 3787 4177 50 49 263 195 3 3741 3940 24 — 53 52 5 3739 3757 174 170 86 85 8 3541 3825 133 130 66 75 11 3198 3641 3 — 64 — 14 3025 . 3596 514 451 100 139 130 Sulphate Raw Data: Basalt and Foam Error Analys is - Foam Error Analysis - Basalt 1 2 1 2 1 -58.9 4163 -38.0 4082 2 7.56 76.2 7.81 65.0 3 0.984 50.7 0.855 90.7 4 120 2 23.7 4 5 309174 5147 194644 32915 1 2 1 m b 2 sem seb 3 r2 sey 4 F df 5 ssreg ssresid where: m = slope b= y-intercept sem = error in the slope seb = error in the y-intercept r2=variance sey = error in the y value F= F statistic, or the F-observed value df = degrees of freedom ssreg= regression sum of squares ssresid = ressidual sum of squares Sulphate Raw Data: MVH3, MVH2, Alginate, Ringlace Batch Subset 1:June/July/99 Experiments Sample#l Sample#2 Bottle # Desc Sampling Day Sulfate Concentration (ppm) 10 MVH3 5 35.4 34 12 MVH3 5 35.5 35.3 13 MVH3 8 31.8 31.6 14 MVH3 14 29.6 29.7 15 MVH3 11 28.7 30.6 16 MVH3 8 32.6 32.7 17 MVH3 11 30 30.5 18 MVH3 5 32.4 33.2 19 MVH3 1 33.4 33.8 21 MVH3 14 27.8 28 23 MVH3 11 30 30.5 30 Alginate 14 25 24 31 Alginate 11 26.5 27.2 32 Alginate 5 30.9 31.7 33 Alginate 8 27.9 27.8 36 Alginate 5 26.7 26.8 37 Alginate 11 13.6 14.6 40 Alginate 5 28.9 29.2 41 Alginate 8 24.8 23.8 42 Alginate 14 19.4 19.8 44 Alginate 0 35.5 35.7 45 Alginate 11 23.1 23.2 47 Alginate 0 numbers diluted by 100 50 MVHII 0 32.8 33.2 51 MVHII 0 35 35.2 52 MVHII 5 34.5 35.1 53 MVHII 14 29.7 29.6 56 MVHII 8 33 33.4 57 MVHII 8 32.4 33.7 60 MVHII 14 26.9 27.4 61 MVHII 11 29.5 29.2 62 MVHII 11 28.9 29.3 63 MVHII blank 31 30.4 64 MVHII blank 31.1 30.7 65 MVHII 11 27.5 26.7 67 MVHII 14 27.2 27.9 71 72 73 74 75 78 Ringlace Ringlace Ringlace Ringlace Ringlace Ringlace 8 11 14 8 14 11 33.3 28 30 32.9 31 30.2 31.4 28.7 30.6 32.6 30.3 31.5 Sulphate Raw Data: MVH3, MVH2, Alginate, Ringlace Sulphate Measurements (ppm) (corrected for dilution) (days) 1 5 8 11 14 Ringlace 3383 3255 2960 3048 Ca-Beads 3560 2903 2608 2137 2205 MVH2 3405 3480 3313 2852 2812 MVH3 3360 3430 3218 3005 2878 Sulphate Standard Deviations and Confidence Limits 0 5 8 S_D C.l=0.95% S.D C.l=0.95% S.D C.l=0.95% Ringlace 32 — — — 82 80 Ca-Beads 14 — 205 164 209 205 MVH2 123 120 42 — 56 55 MVH3 28 — 131 105 56 54 11 14 S.D C.l=0.95% S.D C. 1=0.95% Ringlace 156 153 43 42 Ca-Beads 588 470 286 281 MVH2 114 91 123 99 MVH3 71 57 101 99 Sulphate Raw Data: MVH3, MVH2, Alginate, Ringlace Excel Linear Error Analysis Error Analysis - MVH2 sulfate reduction 1 2 1 2 1 -82.2 3895 1 m b 2 18.1 182.6 2 sem seb 3 0.9 121.6 3 r2 sey 4 20.6 2.0 4 F df 5 304017 29566 5 ssreg ssresid Error Analysis - Ringlace where: m = slope 1 2 b= y-intercept 1 -28.5 3397 sem = error in the slope 2 11.6 113.8 seb = error in the y-intercept 3 0.7 112.3 r2=variance 4 6.0 2.0 sey = error in the y value 5 75368 25219 F= F statistic, or the F-observed value df = degrees of freedom Error Analysis - Alginate ssreg= regression sum of squares 1 2 ssresid = ressidual sum of squares 1 -109.995 3540 • 2 18.6 167.7 3 0.9 188.5 4 35.0 3.0 5 1243770 106569 Errot Analysis - M V H 3 1 2 1 -42.3346 3508.21012 2 10.52715 94.9780363 3 0.843524 106.735169 4 16.17222 3 5 184240.3 34177.1887 Total Solids and TKN Values for MVH Control, Ceramic Beads, Molecular Sieve, and Glass Beads Day 0=Oct 28/98 Crucible Crucible Crucible w Batch V KN (ug N) (before) (after) Filter (g) Filter+Solids otal Solids#l Total Solid Sample ID (g) (g) (g) (g) (g) (g) C#l 470 day 1 26.8951 26.8949 0.1084 27.0423 0.0388 0.0390 M#l 382 day 1 28.1811 28.1807 0.1096 28.31 0.0193 0.0197 G#l 383 day 1 25.1775 26.1771 0.1086 26.3019 1.0158 0.0162 V#l 456 day 1 28.4446 28.4443 0.1086 28.5688 0.0156 0.0159 C#2 765 day 3 19.8794 19.8799 0.1043 20.0208 0.0371 0.0366 C#3 766 day 3 18.9048 18.9043 0.105 19.0474 0.0376 0.0381 M#2 690 day 3 28.7936 28.7936 0.105 28.941 0.0424 0.0424 M#3 dropped day 3 26.2075 0.1039 G#2 732 day 3 27.6373 0.1031 27.7617 0.0213 V#2 771 day 3 18.8199 18.8198 0.1048 18.9397 0.015 0.0151 C#4 739 day 4 18.5684 18.5687 0.1084 18.6992 0.0224 0.0221 C#5 717 day 4 18.9525 18.953 0.1067 19.0747 0.0155 0.015 M#4 588 day 4 17.9152 17.9162 0.1043 18.0511 0.0316 0.0306 M#5 629 day 4 19.6196 19.6199 0.1043 19.7524 0.0285 0.0282 G#3 664 day 4 18.8339 18.8343 0.1039 18.9577 0.0199 0.0195 V#3 day 4 18.6791 18.679 0.107 18.7922 0.0061 0.0062 C#6 638 day 5 18.3688 18.3689 0.1078 18.49 0.0134 0.0133 M#6 672 day 5 19.0019 19.002 0.107 19.301 0.1921 0.192 G#6 601 day 5 17.8225 17.8228 0.1087 17.9552 0.024 0.0237 V#4 848 day 5 18.9989 18.9993 0.108 19.1172 0.0103 0.0099 C#8 710 day 6 18.5305 18.513 0.1086 18.6636 0.0245 0.042 MS 748 day 6 18.9526 18.9537 0.1086 19.0872 0.026 0.0249 V#5 785 day 6 18.2195 18.2203 0.1082 18.3377 0.01 0.0092 Glass 722 day 6 18.8625 18.8631 0.1085 18.9863 0.0153 0.0147 C#7 798 day 7 18.8488 18.8497 0.1094 18.9909 0.0327 0.0318 C#9 685 day 7 18.7466 18.7505 0.109 18.8731 0.0175 0.0136 V#6 839 day 7 19.0823 19.0833 0.1086 19.1993 0.0084 0.0074 Glass 42 day 0 No Support 45 day 0 Note: Total Solids#1 = (Crucible with Filter +Solids)-(Filter+Crucible wt before firing) Note: Total Solids#2 = (Crucible with Filter +Solids)-(Filter+Crucible wt after firing) C=Ceramic Beads M=Molecular Sieve G=Glass Beads V=Control 135 Total Solids and TKN Values for MVH Control, Ceramic Beads, Molecular Sieve, and Glass Beads Total Solids Day Ceramic Beads Molecular Sieve Glass Beads Control 1 0.039 0.039 0.019 0.020 0.016 0.016 0.016 3 0.037 0.037 0.042 0.042 0.021 0.015 0.015 4 0.019 0.019 0.030 0.029 0.020 0.020 0.006 0.006 5 0.013 0.013 0.192 0.192 0.024 0.024 0.010 0.010 6 0.024 0.042 0.026 0.025 0.015 0.015 0.010 0.009 7 0.017 0.014 0.008 0.007 Summary of TKN Results Day Ceramic Mol Sieve Glass Control 0 42 45 1 470 382 383 456 3 765 690 732 771 4 728 608 663 5 638 671 600 847 6 710 748 723 785 Standard Deviations Ceramic Mol Sieve Day SD SD 1 3 0.69 4 -16.14 5 16.14 28.50 6 7 79.99 TKN Values for Teflon and Zeolite TKN (ug N) Batch VI Day Run 1 Run 2 V#1 0 44 45 V= Control (No Support) T#0 0 52 T= Teflon Z#0 0 78 Z = Zeolite Z#1 1 103 T#1 1 263 T#2 2 348 354 T#3 2 408 Z#2 2 140 Z#3 2 130 T#4 3 475 T#5 3 738 754 Z#4 3 196 V#2 4 513 T#6 4 791 T#7 4 603 Z#7 5 331 T#8 5 832 T#9 5 890 894 Total TKN Measurements Day Teflon Zeolite Control 0 52 78 45 1 263 103 2 370 135 3 656 196 4 697 513 5 872 331 Standard Deviation Day Teflon Zeolite ~0 1 2 33 7 3 156 4 132 5 35 TKN Values for MVH, Basalt, and Foam TKN (ug N) April/99 Experiments TKN Results Run 1 Run2 Run 1 Run 2 Vial No Desc Sampling Day Solution Solution Support Support Filter 3 basalt 5 389.55 327.93 whatman 5 basalt 8 485 368 whatman 6 basalt 14 671.8 408.98 whatman 7 basalt 3 20.43 1.9 millipore 8 basalt 11 543.9 415.41 whatman 10 basalt 3 45.72 48.38 1.7 millipore 11 basalt 14 473 359 whatman 12 basalt 5 446.74 371.1 whatman 13 basalt 8 521.74 446 whatman 14 basalt 11 447 377 whatman 31 foam 14 503 465 658 whatman 33 foam 11 533 501 whatman 34 foam 3 34.31 35.13 21.69 millipore 35 foam 8 496.77 527.3 whatman 36 foam 11 514 whatman 39 foam 8 491.79 509.3 502.74 whatman 42 foam 5 435.13 422.55 whatman 43 foam 14 88 93 241 248 millipore 46 foam 5 424 410.83 456 whatman 60 srb April 19 (Day 0) 33.87 millipore 62 srb April 19 (Day 0) 12.22 millipore 65 pumice blank 383 409 whatman 70 foam blank 448 443 whatman 71 whatman blank 386 whatman 72 millipore blank 0 millipore 138 T K N Values for MVH, Basalt, and Foam Total T K N Measurements (ug N) Day Foam Basalt 0 23.0 23.0 3 56.4 40.0 5 -28.4 -24.3 8 116.4 120.4 11 149.5 112.5 14 297.9 189.4 Note: TKN Values based on TKN less value of filter blanks for Whatman or Millipore Total T K N Standard Deviations and Confidence Intervals Foam Basalt Day SD Cl=95% SD Cl=95% 0 15.3 21.2 15.3 21.2 3 c 0.6 0.8 15.4 17.5 O 8 12.7 14.4 65.8 64.5 11 9.2 12.7 71.4 69.9 14 35.4 28.3 137.2 134.5 T K N Measurements (Solution vs Support) (ug N) Foam Foam Basalt Basalt Day Solution Support Solution Support 0 23.0 0.0 23.0 0.0 3 34.7 21.7 38.2 0.0 8 46.3 70.1 120.4 0.0 11 85.0 64.5 112.5 0.0 14 63.3 234.7 189.4 0.0 T K N Measurements (Solution vs Support) Standard Deviations and Confidence Limits 0 3 8 SD CI=95% SD CI=95% SD CI=95% Foam-Solution 15.3 21.2 0.6 0.8 3.5 4.9 Foam-Support --- -« --- --- 12/7 14.4 Basalt-Solution 15.3 21.2 15.4 17.5 26.0 29.4 Basalt-Support — — 0.1 0.2 55.2 62.4 11 14 SD CI=95% SD . CI=95% Foam-Solution — — 15.2 14.9 Foam-Support 9.2 12.7 4.9 6.9 Basalt-Solution 68.5 95.0 140.6 194.8 Basalt-Support 27.2 37.6 — — T K N Values for MVH3, MVH2, Calcium Alginate, Ringlace TKN (ug N) June 14-July 3/99 Experiments Batch Subset 1 Run 1 Run 2 Run 1 Run 2 Label # Desc Sampling Day Solution Solution Support Support 10 MVH3 5 82 82 12 MVH3 5 100 103 13 MVH3 8 148 151 14 MVH3 14 261 268 15 MVH3 11 156 160 16 MVH3 8 95 96 17 MVH3 11 114 117 18 MVH3 5 96 98 21 MVH3 14 242 251 23 MVH3 11 145 151 30 Alginate Beads 14 37 37 418 433 31 Alginate Beads 11 8 9 512 494 33 Alginate Beads 8 121 119 393 409 36 Alginate Beads 5 29 29 341 352 40 Alginate Beads 5 21 23 443 452 41 Alginate Beads 8 130 135 382 502 42 Alginate Beads 14 152 157 545 563 44 Alginate Beads 0 0 0 275 289 45 Alginate Beads 11 176 178 414 424 47 Alginate Beads 0 59 62 157 164 50 MVHII 0 2.0 3.9 51 MVHII 0 1.6 2.2 52 MVHII 5 118 121 53 MVHII 14 180 184 56 MVHII 8 87 89 57 MVHII 8 159 192 60 MVHII 14 151 153 61 MVHII 11 80 79 62 MVHII 11 102 110 63 MVHII blank 0.0 0.0 64 MVHII blank 19 20 65 MVHII 11 100 107 67 MVHII 14 206 210 T K N Values for MVH3, MVH2, Calcium Alginate, Ringlace TKN (ug N) Batch Subset 1 Run 1 Run 2 Run 1 Run 2 Label # Desc Sampling Day Solution Solution Support Support 71 Ringlace 8 34 35 5 6 72 Ringlace 11 70 71 28 30 73 Ringlace 14 80 81 51 54 74 Ringlace 8 26 27 0 0 75 Ringlace 14 151 138 79 82 78 Ringlace 11 86 91 45 48 90 Foam 14 88 93 241 248 Millipore blank 0 0 Millipore blank 0 0 TKN Values for MVH3, MVH2, Calcium Alginate, Ringlace Total T K N Measurements (ug N) (days) 0 5 8 11 14 Ringlace 20.0 — 33.3 117.3 179.0 Ca-Beads 251.5 422.5 547.8 553.8 585.5 MVH2 20.0 120.0 131.8 96.3 180.7 MVH3 20.0 94.0 122.5 140.5 255.5 note: TKN = 20 was used as day 0 values based on blanks ran from MVH2 solutions Total T K N Standard Deviations and Confidence Limits (days) 0 5 8 SD CI=95% SD CI=95% SD CI=95% Ringlace — — . . . — 15.3 10.6 Ca-Beads 114.3 79.2 202.3 140.2 161.9 112.2 MVH2 11.3 7.8 2.1 2.9 52.3 51.2 MVH3 11.3 5.8 9.2 7.4 31.2 30.6 11 14 SD CI=95% SD CI=95% Ringlace 24.3 16.8 36.3 25.1 Ca-Beads 209.4 145.1 220.7 153.0 MVH2 13.5 10.8 25.1 20.1 MVH3 20.0 16.0 11.4 11.2 T K N Measurements (Solution vs Support) (ug N) 0 5 8 11 14 Ringlace - solution — — 30.5 79.5 112.5 Ringlace • support — — 2.8 37.8 66.5 Ca-Beads • solution 30.25 25.5 126.3 92.8 95.8 Ca-Beads - support 221.25 397 421.5 461.0 489.8 TKN Measurements (Solution vs Support) Standard Deviation and Confidence Limits 0 5 8 SD CI=95% SD CI=95% SD CI=95% Ringlace -support — — — — 4.7 4.6 Ringlace - solution — — — — 3.2 3.1 Alginate - support 35T5 343 4~! 4~0 1~5 TA Alginate - solution 70.4 69.0 58.6 57.4 54.8 53.7 11 14 SD CI=95% SD CI=95% Ringlace -support 10.6 10.4 37.3 36.6 Ringlace - solution 10.2 10.0 16.3 15.9 Alginate - support 93.9 92.0 67.9 66.5 Alginate - solution 49.2 48.2 74.8 73.3 Protein Raw Data: Basalt, and Foam April Experment Protein OD reading 750 nm modified Lowry Assay Vial # Desc Sampling Standard Protein Absorbance Values Biomass (mg/ml) Solution day 3 Basalt 5 whatman 1 0.502 0.504 0.506 0.831 0.844 0.858 5 Basalt 8 whatman 1 0.512 0.514 0.502 0.899 0.913 0.831 6 Basalt 14 whatman 1 0.496 0.504 0.506 0.790 0.844 0.858 7 Basalt 3 millipore 2 0.4 0.402 0.4 0.164 0.175 0.164 8 Basalt 11 whatman 1 0.496 0.498 0.5 0.790 0.803 0.817 10 Basalt 3 millipore 2 0.41 0.406 0.406 0.222 0.198 0.198 11 Basalt 14 whatman 1 0.538 0.536 0.536 1.077 1.064 1.064 12 Basalt 5 whatman 1 0.492 0.492 0.498 0.762 0.762 0.803 13 Basalt 8 whatman 1 0.494 0.492 0.492 0.776 0.762 0.762 14 Basalt 11 whatman 1 0.486 0.482 0.484 0.721 0.694 0.707 15 Basalt 8 whatman 1 0.526 0.532 0.52 0.995 1.036 0.954 20 Basalt 11 whatman 1 0.526 0.528 0.526 0.995 1.009 0.995 Support 3 Basalt 5 whatman 1 0.526 0.522 0.526 0.995 0.968 0.995 5 Basalt 8 whatman 1 0.488 0.48 0.48 0.735 0.680 0.680 6 Basalt 14 whatman 1 0.418 0.416 0.418 0.255 0.241 0.255 7 Basalt 3 millipore 2 0.518 0.504 0.504 0.849 0.767 0.767 8 Basalt 11 whatman 1 0.4 0.396 0.396 0.132 0.104 0.104 10 Basalt 3 millipore 2 0.524 0.528 0.52 0.883 0.907 0.860 11 Basalt 14 whatman 1 0.498 0.5 0.498 0.803 0.817 0.803 12 Basalt 5 whatman 1 0.504 0.5 0.502 0.844 0.817 0.831 13 Basalt 8 whatman 1 0.524 0.52 0.516 0.981 0.954 0.927 14 Basalt 11 whatman 1 0.516 0.516 0.516 0.927 0.927 0.927 15 Basalt 8 whatman 1 0.516 0.516 0.52 0.927 0.927 0.954 20 Basalt 11 whatman 1 143 Protein Raw Data: Basalt, and Foam Protein OD reading 750 nm modified Lowry Assay Desc Sampling Standard Protein Absorbance Values Biomass (mg/ml) Solution 31 foam 14 whatman 1 0.494 0.496 0.488 0.776 0.790 0.735 33 foam 11 whatman 1 34 foam 3 millipore 2 0.412 0.404 0.404 0.233 0.187 0.187 35 foam 8 whatman 2 0.502 0.516 0.51 0.756 0.837 0.802 36 foam 11 whatman 2 39 foam 8 whatman 1 0.534 0.53 0.532 1.050 1.023 1.036 42 foam 5 whatman 1 0.51 0.516 0.512 0.886 0.927 0.899 43 foam 14 millipore 1 44 foam 14 whatman 1 0.504 0.502 0.496 0.844 0.831 0.790 45 foam 14 whatman 1 0.48 0.48 0.482 0.680 0.680 0.694 46 foam 5 whatman 0.482 0.486 0.484 0.640 0.663 0.651 Support 31 foam 14 whatman 1 0.528 0.53 0.518 1.009 1.023 0.940 33 foam 11 whatman 1 0.5 0.496 0.496 0.817 0.790 0.790 34 foam 3 millipore 0.41 0.404 0.404 0.222 0.187 0.187 35 foam 8 whatman 1 0.5 0.506 0.504 0.817 0.858 0.844 36 foam 11 whatman 0.524 0.522 0.524 0.883 0.872 0.883 39 foam 8 whatman 1 0.524 0.526 0.526 0.981 0.995 0.995 42 foam 5 whatman 1 0.5 0.502 0.5 0.817 0.831 0.817 43 foam 14 millipore 1 44 foam 14 whatman 1 0.464 0.462 0.46 0.570 0.557 0.543 45 foam 14 whatman 1 0.502 0.5 0.5 0.831 0.817 0.817 46 foam 5 whatman 1 0.508 0.51 0.508 0.872 0.886 0.872 60 srb 0 0.34 ! 0.404 0.408 0.412 0.159 0.186 0.214 62 srb 0 0.34 1 0.398 0.398 0.4 0.118 0.118 0.132 71 filter blank whatman 1 0.496 0.478 0.48 0.712 72 filter blank millipore 2 0.372 0.376 0.374 0.001 144 Protein Raw Data: Basalt, and Foam Total Protein Measurements (mg/ml) (day) 0 3 5 8 11 14 Basalt 0.154 1.026 0.295 0.320 0.404 0.334 Foam 0.154 0.401 0.329 0.409 0.127 0.240 note: if values is less than blank taken as zero Total B iomass Standard Deviations and Conf indence Limits 0 3 5 8 SD Cl=95% SD Cl=95% SD Cl=95% SD Cl=95% 0*039 (1032 0!06l o"036 O096 0.054 0.163 0.092 0.039 0.032 0.034 0.027 0.143 0.081 0.157 0.073 11 14 SD Cl=95% SD Cl=95% Basalt 0.464 0.235 0.486 0.275 Foam 0.046 0.036 0.201 0.093 Basalt Foam 145 Protein Raw Data: Basalt, and Foam Protein Standard Curves Standard Curve 1 mg/mL Protein Absorbance Values Standard Curve 2 mg/mL Protein Absorbance Values 0 0.73 1.46 0.376 0.498 0.598 0.376 0.496 0.58 0.376 0.497 0.589 0 0.73 1.46 0.371 0.51 0.62 0.364 0.502 0.618 0.3675 0.506 0.619 Standard Curve 1 0.2 0.4 0.6 0.8 1 1.2 1.4 Gamma Gobulin Protein (mg/ml) 1.6 Standard Curve 2 0.2 0.4 0.6 0.8 1 1.2 Gamma Gobulin Protein (mg/mL) 1.4 1.6 146 Protein Raw Data: MVH2, MVH3, Ringlace, and Alginate Beads Batch Subset 1 (Absorbance @ 750 nm) Bottle# Desc Sampling Day Std Curve Protein Absorbance Values Biomass (mg/ml) 10 MVH3 5 1 0.418 0.418 0.418 0.313 0.313 0.313 12 MVH3 5 2 0.41 0.408 0.408 0.172 0.157 0.157 13 MVH3 8 1 0.44 0.438 0.438 0.470 0.456 0.456 14 MVH3 14 1 0.434 0.434 0.438 0.427 0.427 0.456 15 MVH3 11 16 MVH3 8 2 0.42 0.418 0.422 0.487 0.458 0.515 17 MVH3 11 1 0.45 0.442 0.442 0.542 0.485 0.485 18 MVH3 5 1 0.416 0.416 0.416 0.298 0.298 0.298 19 MVH3 0 2 0.392 0.394 0.398 0.086 0.115 0.172 21 MVH3 14 1 0.418 0.42 0.422 0.313 0.327 0.341 22 MVH3 0 2 0.376 0.378 0.378 -0.143 -0.115 -0.115 23 MVH3 11 1 0.426 0.428 0.426 0.370 0.384 0.370 Solution 30 Alginate 14 1 0.424 0.42 0.422 0.356 0.327 0.341 31 Alginate 11 32 Alginate 8 1 0.428 0.424 0.424 0.384 0.356 0.356 33 Alginate 5 1 0.394 0.394 0.392 0.141 0.141 0.127 36 Alginate 5 1 0.442 0.438 0.442 0.485 0.456. 0.485 37 Alginate 11 1 0.498 0.502 0.498 0.885 0.914 0.885 40 Alginate 5 1 0.43 0.426 0.43 0.399 0.370 .0.399 41 Alginate 8 1 0.434 0.432 0.43 0.427 0.413 0.399 42 Alginate 14 1 0.434 0.438 0.436 0.427 0.456 0.442 44 Alginate 0 2 0.402 0.4 0.229 0.200 45 Alginate 11 1 0.468 0.466 0.464 0.671 0.656 0.642 47 Alginate 0 2 0.424 0.422 0.42 0.544 0.515 0.487 Note: The alginate beads would not digest using the divised protocal thus no support values were calculated 50 MVHII 0 2 0.406 0.404 0.406 0.143 0.129 0.143 51 MVHII 0 2 0.406 0.402 0.143 0.115 52 MVHII 5 2 0.44 0.436 0.438 0.387 0.358 0.372 53 MVHII 14 2 0.402 0.404 0.404 0.115 0.129 0.129 56 MVHII 8 1 0.42 0.424 0.42 0.327 0.356 0.327 57 MVHII 8 1 0.422 0.424 0.422 0.341 0.356 0.341 60 MVHII 14 1 0.444 0.44 0.442 0.499 0.470 0.485 61 MVHII 11 1 0.436 0.434 0.436 0.442 0.427 0.442 62 MVHII 11 1 0.43 0.43 0.42 0.399 0.399 0.327 63 MVHII blank 64 MVHII blank 65 MVHII 11 1 0.418 0.418 0.458 0.458 67 MVHII 14 1 0.432 0.432 0.43 0.413 0.413 0.399 147 Protein Raw Data: MVH2, MVH3, Ringlace, and Alginate Beads Vial No. Desc Sampling Day Std Curve Protein Absorbance Values Biomass (mg/ml) Solution 71 Ringlace 8 1 0.398 0.398 0.398 0.170 0.170 0.170 72 Ringlace 11 1 0.418 0.418 0.416 0.313 0.313 0.298 73 Ringlace 14 1 0.416 0.416 0.416 0.298 0.298 0.298 74 Ringlace 8 1 0.42 0.422 0.426 0.327 0.341 0.370 75 Ringlace 14 dropped 78 Ringlace 11 lost Support 72 Ringlace 11 1 0.396 0.4 0.398 0.155 0.184 0.170 73 Ringlace 14 1 0.438 0.438 0.436 0.456 0.456 0.442 74 Ringlace 8 1 0.4 0.4 0.398 0.184 0.184 0.170 75 Ringlace 14 1 0.406 0.406 0.406 0.227 0.227 0.227 78 Ringlace 11 lost Total Protein Measurements (mg/ml) (days) 0 5 8 11 14 Ringlace 0.135 0.437 0.478 0.638 MVH2 0.135 0.372 0.341 0.419 0.339 MVH3 0.124 0.258 0.474 0.439 0.382 Total Protein Standard Deviations and Confidence Limits (days) 0 5 8 SD CI=95% SD CI=95% SD CI=95% Ringlace 0.013 0.087 0.057 MVH2 0.013 0.011 0.014 0.016 0.013 0.010 MVH3 0.044 0.049 0.072 0.047 0.024 0.019 (days) 11 14 SD Cl=95% SD CI=95% Ringlace 0.0765 0.0612 0.0993 0.0649 MVH2 0.0438 0.0303 0.1648 0.1142 MVH3 0.0738 0.0590 0.0617 0.0494 148 Protein Raw Data: MVH2, MVH3, Ringlace, and Alginate Beads Protein Standard Curves Standard Curve 1 Standard Curve 2 mg/L Protein Absorbance Values mg/L Protein Absorbance Values 0 0.374 0.374 0.373 0 0.38 0.382 0.378 0.71 0.47 0.476 0.476 0.73 0.5 0.496 1.41 0.572 0.572 0.57 1.46 0.58 0.586 Standard Curve 1 0.7 -. 0.6 4 P 0.3 o 02 0.1 -0 J , , , ; 1 , , 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Gamma Gobulin Protein mg/mL Standard Curve 2 0.7-, E £ 0.3 o O 0.2 0.1 0 I 1 1 1 1 1 1 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Gamma Globulin Protein mg/mL Molecular Sieve, Ceramic Beads, Glass Beads, Control - C 0 2 Raw Data Sample Size Injected = 0.5 mL (unless stated otherwise) Date = Oct 29/98 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 43.004 0.17 4487 2 M#1 2 31.282 0.33 3264 2 composite Run 1 3 6 0.59 626 3 C02 Day 1 4 2.616 1.04 273 1 02 5 17.098 1.89 1784 1 N2 Total 100 5947 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 31.228 0.12 2126 2 M#1 2 32.065 0.3 2183 3 composite Run 2 3 7.902 0.55 538 1 C02 Day 1 4 3.775 1.01 257 1 02 5 25.029 1.82 1704 1 N2 Total 100 4682 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 21.738 0.11 1281 2 M#1 2 350551 0.28 2095 3 composite Run 3 3 9.248 0.54 545 1 C02 Day 1 4 4.31 0.98 254 1 02 5 29.153 1.82 1718 1 N2 Total 100 4612 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 28.059 0.13 1949 2 V#1 2 30.809 0.3 2140 3 composite Run 1 3 16.153 0.55 1122 1 C02 Day 1 4 3.513 1.02 244 1 02 5 21.466 1.86 1491 1 N2 Total 100 4997 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 17.616 0.13 1036 2 V#1 2 33.022 0.3 1942 3 composite Run 2 3 19.333 0.55 1137 1 C02 Day 1 4 3.809 1 224 1 02 5 26.22 1.84 1542 1 N2 Total 100 4845 150 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 13.21 0.13 709 2 junk V#1 2 33.11 0.3 1777 3 composite Run 3 3 20.738 0.55 1113 1 C02 Day 1 4 4.397 1.01 236 1 02 5 28.454 1.82 1532 1 N2a Total 100 4658 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 12.664 0.11 762 2 G#1 2 34.103 0.27 2052 3 composite Run 1 3 17.65 0.52 1062 1 C02 Day 1 4 5.684 0.98 342 1 02 5 29.899 1.81 1799 1 Total 100 5255 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 18.728 0.11 1337 2 G#1 2 32.736 0.28 2337 3 composite Run 3 3 15.955 0.53 1139 1 C02 Day 1 4 5.477 1 391 1 02 5 27.105 1.81 1935 1 N2 Total 100 5802 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 18.9825 0.16 1276 2 G#1 2 32.282 0.34 2170 3 composite Run 4 3 16.201 0.58 1089 1 C02 Day 1 4 5.281 1.04 355 1 02 5 27.254 1.85 1832 1 N2 Total 100 5446 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 2.001 0.04 107 2 C#1 2 39.125 0.14 2092 2 Run 1 3 29.811 0.32 1594 3 composite Day 1 4 6.751 0.57 361 1 C02 5 2.431 1.05 130 1 02 6 19.88 1.85 1063 1 N2 Total 100 3148 151 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 1.628 0.05 73 2 C#1 2 30.821 0.13 1382 2 Run 2 3 31.869 0.31 1429 3 composite Day 1 4 8.095 0.56 363 1 C02 5 2.966 1.02 133 1 02 6 24.621 1.84 1104 1 N2 Total 100 3029 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 2.876 0.04 131 2 C#1 2 28.452 0.15 1296 2 Run 3 3 32.448 0.33 1478 3 composite Day 1 4 8.342 0.58 380 1 C02 5 2.942 1.06 134 1 02 6 24.94 1.85 1136 1 N2 Total 100 3128 October 31/98 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 0.908 0.04 76 2 C#2 2 8.46 0.14 708 2 Run 1 3 44.151 0.32 3695 3 composite Day 3 4 1.219 0.61 102 1 C02 5 2.294 1.08 192 1 02 6 42.968 1.93 3596 1 N2 Total 100 7585 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 0.97 0.07 81 2 C#2 2 7.996 0.15 668 2 Run 2 3 43.991 0.33 3675 3 composite Day 3 4 1.161 0.61 97 1 C02 5 2.61 1.09 218 1 02 6 43.273 1.93 3615 1 N2 Total 100 7605 152 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 0.415 0.04 30 2 C#2 2 0.968 0.08 70. 2 Run 3 3 5.63 0.12 407 2 Day 3 4 44.736 0.31 3234 3 composite 5 1.162 0.59 84 1 C02 6 3.168 1.08 229 1 02 Total 7 43.92 1.92 3175 1 N2 6722 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 0.506 0.03 40 2 C#3 2 11.603 0.12 918 2 Run 1 3 43.415 0.31 3435 3 composite Day 3 4 0.784 0.59 62 1 C02 5 2.793 1.09 221 1 02 6 40.9 1.89 3236 1 N2 Total 6954 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 0.506 0.04 31 2 C#3' 2 1.045 0.08 64 2 Run 2 3 3.299 0.12 202 3 Day 3 4 45.566 0.31 2790 1 composite 5 0.947 0.58 58 1 C02 6 2.809 1.08 172 1 02 Total 7 45.827 1.92 2806 1 N2 5826 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Bead 1 1.386 0.07 88 2 C#3 2 2.741 0.13 174 3 Run 3 3 45.825 0.33 2909 1 composite Day 3 4 0.914 0.61 58 1 C02 5 2.914 1.09 185 1 02 6 46.219 1.92 2934 1 N2 Total 6086 153 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 1.01 0.03 64 2 M#2 2 8.962 0.08 568 2 Run 1 3 43.61 0.31 2764 3 composite Day 3 4 4.071 0.59 258 1 C02 5 42.348 1.9 2684 1 N2 5706 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 0.542 0.04 38 2 M#2 2 10.923 0.12 766 2 Run 2 3 43.79 0.31 3071 3 composite Day 3 4 3.821 0.59 268 1 C02 5 40.924 1.92 2870 1 N2 6209 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 0.318 0.03 19 2 M#2 2 9.579 0.06 573 2 Run 3 3 44.266 0.3 2648 3 composite Day 3 4 3.828 0.58 229 1 C02 5 42.009 1.88 2513 1 N2 5390 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 0.695 0.03 43 2 M#3 2 11.632 0.07 720 2 Run 1 3 43.15 0.31 2671 3 composite Day 3 4 3.99 0.59 247 1 C02 5 40.533 1.9 2509 1 N2 5427 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 1.193 0.03 78 2 M#3 2 12.439 0.13 813 2 Run 2 3 42.901 0.32 2804 3 composite Day 3 4 4.207 0.6 275 1 C02 5 39.259 1.92 2566 1 N2 5645 154 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 0.867 0.03 55 2 M#3 2 12.541 0.13 796 2 Run3 3 42.634 0.32 2706 3 composite Day 3 4 4.222 0.6 268 1 C02 5 39.735 1.92 2522 1 N2 5496 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 1.096 0.04 66 2 ??? V#2 2 14.395 0.14 867 2 ??? Run 1 3 32.276 0.34 1944 3 composite Day 3 4 23.825 0.61 1435 1 C02 5 28.408 1.93 1711 1 N2 5090 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 1.239 0.04 83 2 V#2 2 20.287 0.14 1359 2 Run 2 3 30.408 0.33 2037 3 composite Day 3 4 21.585 0.6 1446 1 C02 5 26.482 1.93 1774 1 N2 5257 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) No Support 1 1.277 0.04 78 2 V#2 2 13.47 0.14 823 2 Run3 3 33.028 0.33 2018 3 composite Day 3 4 24.664 0.6 1507 1 C02 5 27.5614 1.93 1684 1 N2 5209 Date = Nov 1/98 Day 4 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 2.413 0.03 142 2 M#4 2 2.77 0.14 163 2 Run 1 3 47.171 0.33 2776 3 composite Day4 4 2.379 1.09 140 1 02 5 45.268 1.86 2664 1 N2 Total 5580 155 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 2.291 0.13 131 2 M#4 2 48.575 0.32 2778 3 composite Run 2 3 2.868 1.08 164 1 02 Day4 4 46.267 1.86 2646 1 N2 5588 Total Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 1.715 0.02 90 1 M#4 2 50.2 0.3 2635 1 composite Run 3 3 48.085 1.84 2524 1 N2 Day4 5159 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 2.539 0.06 150 2 M#5 2 3.588 0.13 212 2 Run 1 3 46.378 0.33 2740 3 composite Day4 4 3.825 0.6 226 1 C02 5 2.59 1.08 153 1 02 6 41.08 1.86 2427 1 N2 Total 5546 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 4.146 0.16 254 2 M#5 2 47.968 0.32 2939 3 Run 2 3 3.787 0.6 232 1 composite Day4 4 2.579 1.06 158 1 02 5 41.521 1.88 2544 1 N2 Total 2934 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Molecular Sieve 1 2.102 0.03 132 2 M#5 2 2.818 0.12 177 2 Run 3 3 46.768 0.32 2937 3 composite Day 4 4 3.631 0.6 228 1 C02 5 3.041 1.08 191 1 02 6 41.64 1.86 2615 1 N2 Total 5971 156 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 1.485 0.05 76 2 G#3 2 6.469 0.13 331 2 Run 1 3 32.265 0.31 1651 3 composite Day4 4 28.454 0.59 1456 1 C02 5 31.327 1.88 1603 1 N2 Total 4710 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 1.784 0.03 98 2 junk G#3 2 0.673 0.08 37 2 Run 2 3 3.786 0.12 208 2 Day4 4 33.018 0.31 1814 3 composite 5 28.813 0.59 1583 1 C02 6 31.926 1.86 1754 1 N2 Total 5151 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Glass Beads 1 1.776 0.03 103 2 G#3 2 0.535 0.09 31 2 Run 3 3 4.795 0.13 278 2 Day4 4 32.58 0.31 1889 3 composite 5 28.613 0.58 1659 1 C02 6 31.701 1.86 1838 1 N2 Total 5386 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 1.581 0.03 136 2 C#4 2 4.151 0.13 357 2 Run 1 3 45.983 0.33 3955 3 composite Day4 4 0.918 0.61 79 1 C02 5 2.814 1.07 242 1 02 6 44.553 1.88 3832 1 N2 Total 8108 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 1.266 0.03 106 2 C#4 2 4.406 0.13 369 2 Run 2 3 47.444 0.32 3973 3 composite Day4 4 0.908 0.6 76 1 C02 5 3.129 1.06 262 1 02 6 42.847 1.86 3588 1 N2 Total 7899 157 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 1.237 0.03 119 2 C#4 2 7.534 0.15 725 2 Run 3 3 44.882 0.33 4319 3 composite Day4 4 0.707 0.62 68 1 C02 5 3.793 1.09 365 1 02 6 41.848 1.89 4027 1 N2 Total 8779 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 2.726 0.07 233 2 C#5 2 4.704 0.17 402 2 Run 1 3 45.717 0.36 3907 3 composite Day4 4 0.62 0.64 53 1 C02 5 4.154 1.11 355 1 02 6 42.078 1.9 3596 1 N2 Total 7911 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 1.602 0.03 146 2 C#5 2 10.916 0.15 995 2 Run 2 3 44.355 0.34 4043 3 composite Day4 4 0.603 0.62 55 1 C02 5 3.928 1.1 358 1 02 6 38.596 1.88 3518 1 N2 Total 7974 Sample Data Peak Number Area % Retention time Area BC Gas Batch V (min) Ceramic Beads 1 5.925 0.16 490 2 C#5 2 47.582 0.34 3935 3 composite Run 3 3 0.762 0.61 63 1 C02 Day4 4 3.724 1.08 308 1 02 5 42.007 1.9 3474 1 N2 7780 November 2/98 Day 5 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 0.695 0.03 76 2 C#6 2 9.214 0.14 1007 2 Run 1 3 45.475 0.33 4970 3 composite Day 5 4 0.393 0.61 43 1 C02 5 4.429 1.06 484 1 02 6 39.793 1.83 4349 1 N2 9846 158 Molecular Sieve, Ceramic Beads, Glass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 0.884 0.03 93 2 C#6 2 5.734 0.09 603 2 Run 2 3 46.234 0.32 4862 3 composite Day 5 4 0.418 0.61 44 1 C02 5 3.718 1.06 391 1 02 6 43.011 1.82 4523 1 N2 9820 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 1.923 0.07 247 2 C#6 2 6.33 0.16 813 2 Run 3 3 45.235 0.35 5810 3 composite Day 5 4 0.413 0.63 53 1 C02 5 4.617 1.08 593 1 02 6 41.482 1.86 5328 1 N2 11784 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 1.237 0.04 115 2 M#6 2 11.037 0.15 1026 2 Run 1 3 45.009 0.34 4184 3 composite Day 5 4 2.291 0.62 213 1 C02 5 2.915 1.08 271 1 02 6 37.511 1.85 3487 1 N2 8155 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 1.388 0.08 119 2 M#6 2 4.655 0.14 399 2 Run 2 3 46.669 0.33 4000 3 composite Day 5 4 2.368 0.62 203 1 C02 5 3.512 1.08 301 1 02 6 41.407 1.82 3549 1 N2 8053 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 1.639 0.02 138 2 M#6 2 5.298 0.13 446 2 Run 3 3 46.431 0.32 3909 3 composite Day 5 4 2.91 0.61 245 1 C02 5 2.174 1.05 183 1 02 6 41.549 1.81 3498 1 N2 7835 Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 0.933 0.02 71 2 V#4 2 5.413 0.13 412 2 Run 1 3 37.538 0.31 2857 3 composite Day 5 4 21.101 0.59 1606 1 C02 5 35.015 1.81 2665 1 N2 7128 Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 1.58 0.02 118 2 V#4 2 5.331 0.13 398 2 Run 2 3 38.387 0.32 2866 3 composite Day 5 4 21.23 0.59 1585 1 C02 5 33.472 1.84 2499 1 N2 6950 Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 1.089 0.03 88 2 V#4 2 9.874 0.13 798 2 Run 3 3 36.884 0.32 2981 3 composite Day 5 4 19.203 0.6 1552 1 C02 5 32.95 1.81 2663 1 N2 7196 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 0.621 0.06 44 2 G#4 2 9.086 0.13 644 2 Run 1 3 34.283 0.32 2430 3 composite Day 5 4 23.49 0.59 1665 1 C02 5 32.52 1.81 2305 1 N2 6400 160 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 8.521 0.12 665 2 G#4 2 34.739 0.31 2711 2 composite Run 2 3 23.552 0.59 1838 3 C02 Day 5 4 33.188 1.82 2590 1 N2 7139 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 11.363 0.13 938 2 G#4 2 35.239 0.32 2909 2 composite Run 3 3 21.333 0.59 1761 3 C02 Day 5 4 32.065 1.84 2647 1 N2 7317 November 3/98 Day 6 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 1.176 0.06 98 2 ??? C#8 2 3.241 0.14 270 2 ? Run 1 3 47.065 0.3 3921 3 composite Day 6 4 4.513 0.95 376 1 02 5 44.004 1.61 3666 1 N2 7963 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 1.501 0.06 125 2 ??? C#8 2 2.785 0.14 232 2 ? Run 2 3 46.987 0.3 3917 3 composite Day 6 4 5.27 0.95 439 1 02 5 43.457 1.62 3620 1 < N2 7976 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Ceramic Beads 1 2.347 0.06 210 2 C#8 2 3.946 0.14 353 2 Run 3 3 47.15 0.31 4218 3 composite Day 6 4 4.941 0.95 442 1 02 5 41.616 1.6 3723 1 N2 8383 161 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 1.934 0.13 169 2 M#7 2 48.724 0.3 4258 2 composite Run 1 3 1.19 0.55 104 3 C02 Day 6 4 4.726 0.94 413 1 02 5 43.426 1.61 3795 1 N2 8570 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 5.122 0.14 484 2 M#7 2 49.751 0.3 4701 2 composite Run 2 3 0.868 0.56 82 3 C02 Day 6 4 5.958 0.96 563 1 02 5 38.3 1.61 3619 1 N2 8965 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Molecular Sieve 1 1.178 0.05 110 2 M#7 2 1.873 0.14 175 2 Run 3 3 46.601 0.3 4353 3 composite Day 6 4 0.749 0.56 70 1 C02 5 6.777 0.95 633 1 02 42.822 1.62 4000 N2 9056 Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 11.587 0.15 939 2 V#5 2 39.832 0.3 3228 2 composite Run 1 3 13.388 0.55 1085 3 C02 Day 6 4 3.43 0.94 278 1 02 5 31.762 1.61 2574 1 N2 7165 Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 1.439 0.04 113 2 V#5 2 10.445 0.14 820 2 Run 2 3 39.613 0.31 3110 3 composite Day 6 4 13.693 0.55 1075 1 C02 5 3.312 0.95 260 1 02 31.499 1.64 2473 N2 6918 162 Molecular Sieve, Ceramic Beads, G lass Beads, Control - C02 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch V (min) No Support 1 4.826 0.14 349 2 V#5 2 41.98 0.3 3036 2 composite Run 3 3 15.169 0.55 1097 3 C02 Day 6 4 3.498 0.95 253 1 02 5 34.527 1.62 2497 1 N2 6883 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 6.145 0.15 519 2 ??? G#5 2 40.658 0.31 3434 2 composite Run 1 3 13 0.56 1098 3 C02 Day 6 4 4.914 0.96 415 1 02 5 35.283 1.62 2980 1 N2 7927 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 3.02 0.13 247 2 G#5 2 40.523 0.29 3314 2 composite Run 2 3 13.928 0.54 1139 3 C02 Day 6 4 3.962 0.94 324 1 02 5 38.567 1.62 3154 1 N2 7931 Sample Data Peak Number Area % Retention time Area BC Batch V (min) Glass Beads 1 5.42 0.11 460 2 G#5 2 39.767 0.28 3375 2 composite Run 3 3 11.465 0.53 973 3 C02 Day 6 4 4.902 0.93 416 1 02 5 38.447 1.6 3263 1 N2 8027 163 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 19.732 0.13 780 2 T#0 2 33.392 0.31 1320 3 composite Run 1 3 32.355 0.59 1279 1 C02 Day 0 4 14.521 1.79 574 1 N2 5 3173 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 11.345 0.13 398 2 T#0 2 21.009 0.31 737 2 composite Run 2 3 14.31 0.32 502 3 composite Day 0 4 36.887 0.6 1294 1 5 16.448 1.79 577 1 3110 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 24.009 0.12 1018 2 T#0 2 32.123 0.31 1362 3 composite Run 3 3 30.425 0.59 1290 1 Day 0 4 13.443 1.79 570 1 5 3222 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 28.892 0.16 1119 Z#0 2 30.648 0.34 1187 composite Run 1 3 40.46 0.62 1567 Day 0 2754 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 17.582 0.14 541 Z#0 2 30.452 0.32 937 composite Run 2 3 51.966 0.6 1599 Day 0 2536 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 15.584 0.15 482 2 Z#0 2 32.396 0.34 1002 3 composite Run 3 3 52.021 0.62 1609 1 Day 0 2611 164 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 34.988 0.13 1776 2 Z#1 2 30.871 0.33 1567 3 composite Run 1 3 34.141 0.63 1733 1 Day 1 3300 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 0.669 0.02 24 2 Z#1 2 0.752 0.05 27 22 Run 2 3 19.304 0.13 693 3 Day 1 33.398 0.33 1199 1 composite 45.877 0.63 1647 2846 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 16.941 0.11 524 2 Z#1 2 34.465 0.32 1066 3 Run 3 3 48.594 0.61 1503 1 composite Day 1 2569 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 38.4 0.15 1145 2 T#1 2 21.472 0.34 808 3 composite Run 1 3 40.128 0.64 1510 1 Day 1 2318 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 27.145 0.13 794 2 T#1 2 23.045 0.33 674 3 composite Run 2 3 49.812 0.63 1457 1 Day 1 2131 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 35.648 0.14 1350 2 T#1 2 22.815 0.34 864 3 composite Run 3 3 41.537 0.64 1573 1 Day 1 2437 165 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 28.131 0.11 1195 2 T#2 2 24.411 0.31 1037 3 Run 1 3 33.781 0.6 1435 1 Day 2 13.677 1.68 581 1 3053 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 24.609 0.13 1022 2 T#2 2 25.692 0.33 1067 3 Run 2 3 34.674 0.61 1440 1 Day 2 15.025 1.68 624 1 3131 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 26.696 0.12 1106 2 T#2 2 23.727 0.31 983 3 Run 3 3 35.071 0.6 1453 1 Day 2 14.506 1.66 601 1 3037 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 19.28 0.11 970 2 Z#2 2 31.962 0.31 1608 3 Run 3 3 34.824 0.6 1752 1 Day 2 13.934 1.66 701 1 4061 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 23.403 0.11 1099 2 Z#2 2 29.77 0.31 1398 3 Run 4 3 33.22 0.59 1560 1 Day 2 13.607 1.68 639 1 3597 166 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 0.671 0.03 36 2 Z#3 2 29.601 0.12 1589 2 Run 1 3 27.385 0.32 1470 3 Day 2 30.756 0.61 1651 1 11.587 1.71 622 1 3743 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 0.525 0.03 30 2 Z#3 2 0.56 0.05 32 2 Run 2 3 32.354 0.14 1850 2 Day 2 27.632 0.35 1580 3 28.227 0.63 1614 1 10.703 1.71 612 1 3806 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 0.497 0.03 26 2 Z#3 2 0.612 0.06 32 2 Run 3 3 26.864 0.13 1405 2 Day 2 28.585 0.34 1495 3 31.778 0.62 1662 1 11.663 1.68 610 1 3767 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 20.395 0.12 1220 2 T#4 2 32.029 0.31 1916 3 Run 1 3 25.493 0.6 1525 1 Day 3 22.083 1.68 1321 1 4762 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 26.759 0.15 1848 2 T#4 2 31.161 0.33 2152 3 Run 2 3 22.256 0.62 1537 1 Day 3 19.823 1.71 1369 1 5058 167 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 10.071 0.12 525 2 T#4 2 35.603 0.31 1856 3 Run 3 3 28.045 0.6 1462 1 Day 3 26.28 1.68 1370 1 4688 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 13.62 0.12 1204 2 T#5 2 42.215 0.31 3997 3 Run 1 3 9.072 0.6 802 1 Day 3 5.656 1.09 500 2. 26.437 1.7 2337 7636 3 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 5.996 0.12 461 2 T#5 2 47.679 0.31 3666 3 Run 2 3 11.016 0.6 847 1 Day 3 6.334 1.09 487 2 28.976 1.69 2228 7228 3 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Teflon 1 8.349 0.11 696 2 T#5 2 46.905 0.31 3910 3 Run 3 3 9.465 0.6 789 1 Day 3 6.322 1.13 527 2 28.959 1.69 2414 3 7640 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 18.133 0.12 606 2 Z#4 2 31.658 0.31 1058 3 Run1 3 50.209 0.6 1678 1 Day 3 2736 Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 43.738 0.16 2609 2 Z#4 2 28.533 0.34 1702 3 Run 2 3 27.728 0.62 1654 1 Day 3 3356 168 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention time Area BC Batch VI (min) Zeolite 1 20.472 0.11 712 2 Z#4 2 30.88 0.3 1074 3 Run 3 3 48.649 0.59 1692 1 Day 3 2766 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 4.802 0.11 603 2 T#6 49.243 0.31 6183 3 Run 2 3.592 0.6 451 1 Day 4 8.386 1.1 1053 2 33.976 1.66 4266 3 11953 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 12.075 0.11 1414 2 T#6 55.884 0.31 6544 3 Run 3 3.365 0.61 394 1 Day 4 28.676 1.66 3358 1 10296 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 10.103 0.1 758 2 T#7 44.942 0.31 3372 3 Run1 12.662 0.6 950 1 Day 4 4.092 1.09 307 2 28.202 1.68 2116 3 6745 169 Teflon and Zeolite C 0 2 Raw Data Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 11.353 0.12 859 2 T#7 44.343 0.31 3355 3 Run 2 12.741 0.6 964 1 Day 4 3.873 1.12 293 2 27.69 1.68 2095 3 6707 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 10.371 0.09 889 2 T#7 7.07 0.19 606 2 Run 3 40.877 0.3 3504 3 Day 4 0.467 0.48 40 1 9.449 0.59 810 1 4.281 1.08 367 2 27.485 1.66 2356 3 7077 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Zeolite 0.93 0.03 40 2 Z#7 30.792 0.14 1325 2 Run 1 23.658 0.35 1018 3 Day 5 44.62 0.65 1920 1 2938 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Zeolite 1.086 0.03 43 2 Z#7 23.662 0.14 937 2 Run 2 24.672 0.34 977 3 Day 5 50.581 0.65 2003 1 2980 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Zeolite 1.36 0.02 54 2 Z#7 27.884 0.13 1107 2 Run 3 23.476 0.34 932 3 Day 5 47.28 0.65 1877 1 2809 170 Teflon and Zeolite C02 Raw Data Sample Data Peak Number Area % Batch VI Retention Time (min) Area BC Teflon 0.498 0.03 80 2 T#8 5.509 0.15 885 2 Run 1 43.552 0.35 6997 3 Day 5 3.753 0.66 603 1 6.305 1.24 1013 2 40.383 1.84 6488 3 15101 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 0.449 0.03 63 2 T#8 6.212 0.13 872 2 Run 2 48.351 0.34 6787 3 Day 5 4.937 0.65 693 1 7.345 1.22 1031 2 32.706 1.79 4591 13102 3 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 0.573 0.02 87 2 T#8 10.572 0.15 1605 2 Run 3 47.48 0.36 7208 3 Day 5 3.979 0.67 604 1 7.061 1.21 1072 2 30.334 1.83 4605 3 13489 Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 0.265 0.02 42 2 T#9 5.208 0.13 824 2 Run 1 44.135 0.33 6983 3 Day 5 3.356 0.64 531 1 6.99 1.21 1106 2 40.046 1.8 6336 14956 3 Sample Data Peak Number Area % Retention Time Area BC Batch VI Ojjin) Teflon 0.4 0.03 58 2 T#9 7.487 0.14 1085 2 Run 2 48.737 0.34 7063 3 Day 5 3.533 0.65 512 1 8.018 1.2 1162 2 31.824 1.82 4612 3 13349 171 Teflon and Zeolite C02 Raw Data Sample Data Peak Number Area % Retention Time Area BC Batch VI (min) Teflon 4.57 0.12 719 2 T#9 45.068 0.33 7091 3 Run 3 2.638 0.63 415 1 Day 5 6.985 1.2 1099 2 40.74 1.8 6410 3 15015 172 Carbon Dioxide Analys is : No Support, G lass Gas Partitioner R=Aco2/(Aco2+composite) Day No Support Glass Beads 1 0.344 0.369 0.385 0.341 0.328 0.334 3 0.425 0.415 0.428 4 0.469 0.466 0.468 5 0.360 0.356 0.356 0.407 0.404 0.377 6 0.252 0.257 0.265 0.242 0.256 0.224 Day No Support Glass Beads Average SD Average SD 1 0.366 0.021 0.334 0.007 3 0.422 0.006 4 0.467 0.001 5 0.358 0.002 0.396 0.016 6 0.258 0.007 0.241 0.016 Carbon Dioxide Analys is : Teflon and Zeolite Gas Partitioner R=Aco2/(Aco2+composite) Day Teflon Zeolite 0 0.492 0.486 0.486 0.569 0.631 0.616 1 0.651 0.684 0.645 0.525 0.579 0.585 2 0.470 0.460 0.478 0.521 0.527 2 0.529 0.505 0.526 3 0.443 0.417 0.441 0.613 0.493 0.612 3 0.167 0.188 0.168 4 0.068 0.057 4 0.220 0.223 0.188 5 0.079 0.093 0.077 0.654 0.672 0.668 Day Teflon Zeolite Avg SD Avg SD 0 0.4883 0.0033 0.6053 0:0322 1 0.6602 0.0206 0.5630 0.0329 2 0.4695 0.0093 0.5219 0.0097 3 0.3039 0.1425 0.5726 0.0691 4 0.2344 0.0823 5 0.0831 0.0083 0.6646 0.0098 Carbon Dioxide Analysis: Ceramic Beads, Molecular Sieve Gas Partitioner R=Aco2/(Aco2+composite) Day Ceramic Beads Molecular Sieve 1 0.185 0.203 0.205 0.161 0.198 0.206 3 0.027 0.026 0.025 0.085 0.080 0.080 3 0.018 0.020 0.020 0.085 0.089 0.090 4 0.020 0.019 0.016 0.000 0.000 0.000 4 0.013 0.013 0.016 0.076 0.072 0.000 5 0.009 0.009 0.009 0.048 0.048 0.059 6 0.000 0.000 0.000 0.024 0.017 0.016 Day Ceramic Beads Molecular Sieve Average SD Average SD 1 0.1972 0.0109 0.1884 0.0242 3 0.0226 0.0038 0.0849 0.0044 4 0.0161 0.0026 0.0224 0.0383 5 0.0089 0.0003 0.0519 0.0061 6 0.0000 0.0000 0.0189 0.0043 Sample of Gas Partitioner Data Note: this particular sample has no C02 present. The combined peak is present at 0.29 minutes. The peak at 0.12 minutes is from a flux in the air pressure in the column as the sample is injected. CHANNEL, R '"""BUECT" 1 8 / 0 5 , 5 8 P»1 t 43 : 91 02 Peak N2 Peak 1 0 / 8 5 / 9 8 0 1 : 4 3 : 8 1 CH= " R " PS= -1. FIuE' 1. METHOD 8. RUM 5 INDEX 5 PERK* ftREHJS RT BEER EC 1 3. .445 8. 12 2 8 8 0 2 2 . 49 , 0 ? 8 0. 23 2849 %<, 3 • 7 . 324 1 . 8 3 469 S I 4 3 9 . 5 5 2 - ± , 32 2296 0 1 TOTAL IM. 5885 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data noon noon noon midnight 8:00 A M 4:00 PM midnight 24-May 25-May 26-May 26-May 27-May 27-May 27-May Solution Sample MVH2 1 18660 19266 18780 17410 18732 15559 17716 MVH2 2 17656 18658 18243 16655 18128 15854 17530 MVH2 3 18160 18408 18316 16611 17448 15456 17201 MVH3 4 15393 17524 16913 15286 16607 14262 16100 MVH3 5 15268 16394 17202 16049 16679 15008 16634 MVH3 6 15748 17185 16792 15484 16348 14565 15927 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 47846 15 6.24 48181 10 4.16 33142 10 4.16 33142 5 2.08 15870 5 2.08 15866 0 0.00 0 45922 46966 43259 45985 46327 42706 30918 31219 27966 29767 31219 27973 15274 14909 13715 15191 15136 14401 0 0 0 43413 38949 43676 44587 38990 44041 30134 25622 27229 30451 26452 28725 15353 12811 14393 15031 13226 14719 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: May 24 noon 7749 90 y=mx+b May 25 noon 7361 -98 y=C02 concentration May 26 noon 7529 -305 x=peakarea May 26 midnight 6888 -271 m=slope May 27 8am 7038 471 b=intercept May 27 4pm 6245 26 May 27 midnight 6988 -233 Nutrient Solution: C 0 2 Uptake Experiments Raw Data . Peak Area Data 8:00 A M 4:00 PM Midnight 8:00 A M 4:00 PM Midnight 8:00 A M 4:00 PM 28-May 28-May 28-May 29-May 29-May 29-May 30-May 30-May Solution Sample MVH2 1 17342 18861 15396 16087 18375 20569 16011 19732 MVH2 2 17026 18556 15075 15336 17777 20046 15584 19066 MVH2 3 16813 17280 15153 16338 17647 19961 14936 19632 MVH3 4 14872 16753 13598 12730 14388 15405 11844 13858 MVH3 5 15973 17367 15498 15246 17137 19526.5 15329 18831 MVH3 6 15347 15876 14777 14592 16185 17389 13438 16707 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 42326 45415 40592 15 6.24 42513 45723 40221 10 4.16 27877 30723 26849 10 4.16 28402 30513 27132 5 2.08 14146 15767 13214 5 2.08 14516 14794 13352 0 0.00 0 0 0 39876 45579 51607 39969 51136 40330 45980 51227 38848 50597 27392 30531 33692 26312 33270 27353 30778 34335 26312 33227 13202 15254 16799 14023 15776 13660 15237 16584 13550 16799 0 0 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: May 28 8am 6775 96 y=mx+b May 28 4pm 7303 93 y=C02 concentration May 28 midnight 6495 -106 x=peak area May 29 8am 6444 137 m=slope May 29 4pm 7343 12 b=intercept May 29 Midnight 8271 -316 May 30 8am 6257 406 May 30 4pm 8189 -513 176 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data Midnight 8:00 A M 4:00 PM Midnight 8am 4:00 PM 8am 4:00 PM 30-May 31-May 31-May 31-May 1-Jun 1-Jun 2-Jun 2-Jun Solution Sample MVH2 1 18810 15924 18043 17470 15243 — 16160 15391 MVH2 2 18800 15398 18870 17399 16118 — 16480 15115 MVH2 3 18602 15021 17857 16945 15071 — 15914 14477 MVH3 4 13743 10525 12919 12477 10489 10987 10973 10352 MVH3 5 18808 15020 18322 17524 15894 15995 16882 15532 MVH3 6 16235 13142 15095 14603 12836 13177 12977 12132 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 50616 38078 47107 15 6.24 49657 38953 47767 10 4.16 32804 25513 31657 10 4.16 32726 25513 31657 5 2.08 16077 13190 16453 5 2.08 16093 12865 16255 0 0.00 0 0 0 46563 41122 42419 44509 40776 47221 40696 41083 44529 40860 31799 28305 28260 30935 26661 31111 27477 28260 30277 27137 15028 13696 14000 14611 13580 15658 13668 13451 14691 13950 0 0 0 0 0 Linear Fit Data to C02 Standards Where: y=mx+b y=C02 concentration x=peak area m=slope b=intercept m=slope b=intercept May 30 Midnight May 31 8am May 31 4pm May 31 Midnight June 1 8am June 1 4pm June 1 Midnight June 2 8am June 2 4pm 8068 6257 7725 7549 6575 6542 7186 6515 -487 -517 -477 -147 123 1044 26 50 177 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Solution MVH2 MVH2 MVH2 Sample 1 2 3 4:00 PM 3-Jun Peak Area Data 4:00 PM 4-Jun 15007 17752 15189 17420 15107 16982 Midnight 4-Jun 14671 14741 14508 4:00 PM 5-Jun 4:00 PM 8-Jun 20569 16058 19257 15418 19690 15006 4:00 PM 9-Jun 4:00 PM 10-Jun 15107 15348 14946 15073 14341 14290 MVH3 4 9600 11251 9188 12895 9285 8149 8244 MVH3 5 15138 17904 14773 19990 15371 14528 14943 MVH3 6 11935 13043 10933 15061 9895 9069 9469 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 41002 50069 40607 56233 46215 43129 45532 15 6.24 41420 49999 39583 56798 46879 43259 44954 10 4.16 27711 32781 27040 38906 31417 29241 30590 10 4.16 27678 33431 26511 38612 31806 28637 29919 5 2.08 13964 16192 14060 19032 15606 14572 15702 5 2.08 14039 15350 13746 19829 15293 14598 14810 0 0.00 0 0 0 0 0 0 0 Linear Fit Data to C02 Standards Where: y=mx+b y=C02 concentration x=peak area m=slope b=intercept m=slope b=intercept June 3 4pm June 4 4pm June 4 midnight June 5 4pm June 8 4pm June 9 4pm June 10 4pm 6588 8093 6377 9042 7487 6910 7238 197 -597 338 531 49 137 122 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data Solution Sample MVH2 1 MVH2 2 MVH2 3 4:00 PM 9:00 PM 11-Jun 14-Jun 13058 13951 13842 14441 12987 10690 4:00 PM 4:00 PM 16-Jun 18-Jun 12788 8801 13139 8884 12588 12091 10:00 A M 10:00 A M 19-Jun 20-Jun 13649 13790 14816 13701 14456 12965 MVH3 4 7923 4847 6758 6657 7499 27839 MVH3 5 13429 12635 11183 10719 11439 38520 MVH3 6 8489 7279 3408 5614 7213 21845 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 42670 41145 15 6.24 42115 41159 10 4.16 28250 28061 10 4.16 28575 5 2.08 14360 13876 5 2.08 14321 14628 0 0.00 0 0 40909 45056 42497 41353 40919 43504 43392 41176 27258 28095 28841 28116 26486 27453 29119 28496 13987 14178 14181 14280 13702 13951 14291 14224 0 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: June 11 4pm 6847 2^33 y=mx+b June 14 9pm 6578 465 y=C02 concentration 16-Jun-99 6523 65 x=peakarea June 18 4pm 7096 -697 m=slope June 19 10am 6902 5 b=intercept June 20 10am 6596 428 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data 10:00 A M 10:00 A M 10:00 A M 5:00 PM Midnight 5:00 PM 21-Jun 22-Jun 23-Jun 25-Jun 25-Jun 26-Jul Solution Sample MVH2 1 12730 13173 13779 13201 — 27104 MVH2 2 13738 13880 14493 14360 — 28949 MVH2 3 13122 13883 14156 13311 — 27305 vol (uL) 25 25 25 25 . . . 50 MVH3 4 29568 29889 29848 30621 — 15153 MVH3 5 41127 37645 38680 37162 — 18281 MVH3 6 22819 23638 23945 27761 . . . 13795 vol (uL) 25 25 25 100 50 M V H 7 14414 14592 14727 M V H S 14375 14824 15056 vol (uL) 25 25 25 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 43099 42972 43703 43287 45515 45515 15 6.24 43033 44144 43562 43613 45324 45300 10 4.16 29401 28839 29600 29104 30299 29988 10 4.16 29275 29719 29340 28687 29558 29244 5 2.08 14393 14796 14744 14698 14212 15068 5 2.08 14015 14376 14344 14700 14181 15068 0 0.00 0 0 0 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: June 21 10am 6939 3 y=mx+b June 22 10am 6983 78 y=C02 concentration June 23 10am 7007 59 x=peak area June 25 5pm 6941 122 m=slope June 25 Midnight 7353 -635 b=intercept June 26 5pm 7259 -143 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data Midnight 9:00 A M Midnight 10:00 A M 4:00 PM Midnight 9:00 A M Midnight 26-Jun 27-Jun 27-Jun 28-Jun 28-Jun 28-Jun 29-Jun 29-Jun Solution Sample MVH2 1 — — 27179 — — — — — MVH2 2 . . . — 28391 — — — . . . . . . MVH2 3 — — 26669 50 . . . — — ::: — MVH3 4 — — — 13943 — — — . . . MVH3 5 — — — 18296 — — — . . . MVH3 6 . . . — — 12550 50 — — — — M V H 7 14858 14943 15040 14357 14451 13858 14098 14339 M V H 8 15376 14777 15427 14803 14602 14388 14434 14769 25 25 25 25 25 25 25 25 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 44550 45184 46364 15 6.24 44550 45717 46279 10 4.16 30175 29728 30184 10 4.16 30175 30015 30376 5 2.08 15109 15216 15111 5 2.08 15109 15216 15111 0 0.00 0 0 0 42853 43886 43037 44058 41574 43123 43467 42874 42607 42556 28667 28872 28554 28530 27767 28202 28783 28554 28777 27937 14202 14278 14230 13976 14322 14220 14278 14230 13976 14722 0 0 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: June 26 Midnight 7133 232 y=mx+b June 27 9am 7262 -25 y=C02 concentration June 27 Midnight 7432 -295 x=peak area June 28 10am 6892 -107 m=slope June 28 4pm 7015 -217 b=intercept June 28 Midnight 6889 -67 June 29 9am 6979 -324 June 29 Midnight 6688 278 181 Nutrient Solution: C 0 2 Uptake Experiments Raw Data Peak Area Data • 9:00 A M Midnight 10:00 A M Midnight 9:00 A M Midnight Noon Midnight Solution Sample 30-Jun 30-Jun 1-Jul 1-Jul 2-Jul 4-Jul 5-Jul 5-Jul M V H 7 14383 14799 15144 14555 14312 18290 12292 13856 M V H 8 14658 15015 15422 14990 14550 17910 12809 13119 vol (uL) 25 25 25 25 25 25 25 25 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 41165 42812 42904 42124 41680 49665 39985 41965 15 6.24 41444 42438 43709 42013 41008 50157 39634 41328 10 4.16 27594 28767 28880 28823 27066 32742 26475 27530 10 4.16 27052 28126 29668 28724 27133 32974 26576 27602 5 2.08 14043 14329 14663 14725 14522 17227 13440 14139 5 2.08 13792 14506 14543 14770 14114 17459 13753 13855 0 0.00 0 0 0 0 0 0 0 0 Linear Fit Data to C02 Standards m=slope b=intercept Where: June 30 9am 6598 59 y=mx+b June 30 Midnight 6813 133 y=C02 concentration July 1 10am 6940 165 x=peak area July 1 Midnight 6702 558 m=slope July 2 9am 6559 259 b=intercept July 4 Midnight 7918 370 July 5 Noon 6349 199 July 5 Midnight 6656 40 Peak Area Data Noon Midnight Noon Noon Noon Solution Sample 6-Jul 6-Jul 8-Jul 9-Jul 10-Jul M V H 7 14040 10864 9296 7663 9696 M V H 8 14589 12283 10220 7889 9422 vol (uL) 25 25 25 25 25 Standards (C02) Injected Cone. vol (uL) mol* 10-07 15 6.24 43926 45479 39574 34701 43972 15 6.24 43819 45343 39531 34652 45612 10 4.16 29138 30039 26880 22827 30682 10 4.16 29361 30163 26907 23617 31284 5 2.08 14632 14704 13475 11898 15279 5 2.08 14423 14731 13025 12287 15567 0 0.00 0 0 0 0 0 Linear Fit Data to C02 Standards Where: y=mx+b y=C02 concentration x=peak area m=slope b=intercept m=slope b=intercept July 6 Noon July 6 Midnight July 8 Noon July 9 Noon July 10 Noon 7040 7310 6352 5512 7174 -59 -285 121 344 476 182 Nutrient Solution: C02 Uptake Experiments Raw Data C02 Concentration determined from Peak Area Data Concentration (mol/L) Solution Sample 24-May 25-May 26-May 26-May 27-May 27-May 27-May noon noon 10:00AM Midnight 8:00 A M 4:00 PM Midnight (Hours) 0 24 48 60 68 76 84 1 0.00959 0.01026 0.01014 0.01027 0.01038 0.00995 0.01027 MVH2 2 0.00907 0.00997 0.00985 0.00983 0.01004 0.01014 0.01017 3 0.00933 0.01001 0.00989 0.00980 0.00965 0.00988 0.00998 (Hours) 0 24 48 60 68 76 84 4 0.00790 0.00924 0.00915 0.00903 0.00917 0.00912 0.00935 MVH3 5 0.00783 0.00940 0.00930 0.00948 0.00921 0.00960 0.00966 6 0.00808 0.00918 0.00908 0.00915 0.00902 0.00931 0.00925 Solution Sample 28-May 28-May 28-May 29-May 29-May 29-May 30-May 8:00 A M 4:00 PM Midnight 8:00 A M 4:00 PM Midnight 8:00 A M (Hours) 92 100 108 116 124 132 140 1 0.01018 0.01028 0.00955 0.00990 0.01000 0.01010 0.00998 MVH2 2 0.01000 0.01011 0.00935 0.00943 0.00968 0.00985 0.00970 3 0.00987 0.00941 0.00940 0.01006 0.00961 0.00981 0.00929 (Hours) 92 100 108 116 124 132 140 4 0.008723614 0.009125037 0.008440468 0.007816312 0.007831419 0.007602531 0.007312162 MVH3 5 0.009373637 0.00946133 0.009610683 0.009377961 0.009328946 0.009595641 0.009540136 6 0.009004051 0.008644696 0.009166617 0.008972032 0.008810341 0.008561971 0.008331212 Solution Sample 30-May 30-May 31-May 31-May 31-May 1-Jun 1-Jun 4:00 PM Midnight 8:00 A M 4:00 PM Midnight 8:00 A M 4:00 PM (Hours) 148 156 164 172 180 188 196 1 0.00989 0.00957 0.01051 0.00959 0.00934 0.00920 MVH2 2 0.00956 0.00956 0.01017 0.01002 0.00930 0.00973 3 0.00984 0.00946 0.00993 0.00949 0.00906 0.00909 (Hours) 148 156 164 172 180 188 196 4 0.00702 0.00705 0.00706 0.00694 0.00669 0.00631 0.00608 MVH3 5 0.00945 0.00957 0.00993 0.00973 0.00936 0.00960 0.00914 6 0.00841 0.00829 0.00873 0.00806 0.00782 0.00773 0.00742 Nutrient Solution: C02 Uptake Experiments Raw Data C02 Concentration determined from Peak Area Data Concentration (mol/L) Solution Sample 1-Jun 2-Jun 2-Jun 3-Jun 4-Jun 4-Jun 5-Jun Midnight 8:00 A M 4 pm 4:00 PM 4:00 PM midnight 4:00 PM (Hours) 204 212 220 244 268 276 292 1 0.00898 0.00942 0.00899 0.00907 0.00899 0.00886 MVH2 2 0.00916 0.00925 0.00910 0.00890 0.00903 0.00828 3 0.00884 0.00886 0.00905 0.00869 0.00889 0.00848 (Hours) 204 212 220 244 268 276 292 4 0.00609 0.00633 0.00571 0.00586 0.00555 0.00547 MVH3 5 0.00938 0.00951 0.00907 0.00914 0.00905 0.00861 6 0.00721 0.00742 0.00713 0.00674 0.00665 0.00643 Solution Sample 8-Jun 9-Jun 10-Jun 11-Jun 14-Jun 16-Jun 18-Jun 4:00 PM 4:00 PM 4:00 PM 4:00 PM 9:00 PM 4:00 PM 4:00 PM (Hours) 364 388 412 436 513 556 580 1 0.00855 0.00867 0.00841 0.00776 0.00820 0.00780 0.00535 MVH2 2 0.00821 0.00857 0.00826 0.00822 0.00850 0.00802 0.00540 3 0.00799 0.00822 0.00783 0.00772 0.00622 0.00768 0.00721 (Hours) 364 388 412 436 513 556 580 4 0.00493 0.00464 0.00449 0.00476 0.00266 0.00410 0.00415 MVH3 5 0.00819 0.00833 0.00819 0.00798 0.00740 0.00682 0.00644 6 0.00526 0.00517 0.00517 0.00510 0.00414 0.00205 0.00356 Solution Sample 19-Jun 20-Jun 21-Jun 22-Jun 23-Jun 10:00 A M 10:00 A M 10:00 A M 10:00 A M 10:00 A M (Hours) 598 622 646 670 694 1 0.00791 0.00810 0.00734 0.00750 0.00783 MVH2 2 0.00858 0.00805 0.00792 0.00791 0.00824 3 0.00837 0.00760 0.00756 0.00791 0.00805 (Hours) 598 622 646 670 694 4 0.00434 0.00416 0.00426 0.00427 0.00425 MVH3 5 0.00663 0.00577 0.00593 0.00538 0.00551 6 0.00418 0.00325 0.00329 0.00337 0.00341 Nutrient Solution: C02 Uptake Experiments Raw Data C02 Concentration determined from Peak Area Data Concentration (mol/L) Solution Sample 25-Jun 25-Jun 26-Jul 26-Jun 27-Jun 27-Jun 28-Jun 5:00 PM Midnight 5:00 PM Midnight 9:00 A M Midnight 10:00 A M (Hours) 749 756 773 780 789 804 814 1 0.00754 — 0.00751 — — 0.00739 MVH2 2 0.00821 — 0.00802 — — 0.00772 3 0.00760 — 0.00756 — — 0.00726 (Hours) 749 756 773 780 789 804 814 4 0.00439 — 0.00421 — — — 0.00408 MVH3 5 0.00534 — 0.00508 — — — 0.00534 6 0.00398 — 0.00384 — — — 0.00367 (Hours) 0 7 24 31 40 55 65 M V H 7 0.00823692 0.00828 0.00819 0.00820 0.00825 0.00825 0.00840 8 0.00821444 0.00841 0.00838 0.00849 0.00815 0.00846 0.00865 Solution Sample 28-Jun 28-Jun 29-Jun 29-Jun 30-Jun 30-Jun 1-Jul 4:00 PM Midnight 9:00 A M Midnight 9:00 A M Midnight 10:00 A M (Hours) 71 79 88 103 112 127 137 M V H 7 0.00836 0.00809 0.00827 0.00841 0.00868 0.00861 0.00863 8 0.00845 0.00839 0.00846 0.00867 0.00885 0.00874 0.00879 Solution Sample 1-Jul 2-Jul 4-Jul 5-Jul 5-Jul 6-Jul 6-Jul Midnight 9:00AM Midnight Noon Midnight Noon Midnight (Hours) 151 160 223 235 247 259 271 M V H 7 0.00835 0.00857 — 0.00762 0.00830 — 0.00610 8 0.00861 0.00872 — 0.00794 0.00786 — 0.00688 Solution Sample 8-Jul 9-Jul 10-Jul Noon Noon Noon (Hours) 307 331 355 M V H 7 0.00578 0.00531 0.00514 8 0.00636 0.00548 0.00499 Nutrient Solution: C02 Uptake Experiments Raw Data Excel Linear Error Analysis 1 2 where: m = slope 1 m b b= y-intercept 2 sem seb sem = error in the slope 3 r2 sey seb = error in the y-intercept 4 F df r2=variance 5 ssreg ssresid sey = error in the y value F= F statistic, or the F-observed value df = degrees of freedom ssreg= regression sum of squares ssresid = ressidual sum of squares M V H Linear analysis+ 1 2 1 -1.813E-05 0.01166129 2 2.3779E-06 0.00063357 3 0.90639963 0.00046964 4 58.1023074 6 5 1.2815E-05 1.3233E-06 +fitted through averaged values from Sample 7 and 8 MVH2 Linear analysis* 1 2 1 -3.847E-06 0.01012257 2 3.1836E-07 0.00012779 3 0.80224951 0.00045692 4 146.04759 36 5 3.0491E-05 7.5159E-06 •fitted through averaged values from Samples 1, 2, and 3 MVH3 Linear analsysis++ 1 2 1 -1.196E-05 0.00958546 2 4.3123E-07 0.00011881 3 0.97095264 0.00027507 4 768.810338 23 5 5.8171E-05 1.7403E-06 -H-fitted through averaged values from Samples 5 and 6 MVH3 Linear analsysis** 1 2 -7.453E-06 0.01093825 3.0839E-07 0.00011696 0.96529116 0.00024712 584.033153 21 3.5666E-05 1.2824E-06 ** fitted through values from Sample 4 Nutrient Solution: C02 Uptake Experiments -TKN Raw Data TKN (ug N) Bottle # Description Sample #1 Sample #2 1 MVH2 376 388 2 MVH2 371 385 3 MVH2 436 456 4 MVH3 347 357 5 MVH3 411 430 6 MVH3 328 339 7 MVH 568 585 8 MVH 526 552 Descriptive Statistics using Excel 97 Analysis Package MVH MVH2 MVH3 568 376 347 526 371 411 585 436 328 552 388 357 385 430 456 339 MVH MVH2 MVH3 Mean 558 Mean 402 Mean 369 Standard Erro 13 Standard Error 14 Standard Error 17 Median 560 Median 387 Median 352 SD 25 SD 35.2 SD 41.7 Sample Varia 630 Sample Variance 1238.8 Sample Variance 1738.7 Kurtosis -0.2 Kurtosis -1.1 Kurtosis -1.4 Skewness -0.5 Skewness 1.0 Skewness 0.8 Range 59 Range 85 Range 102 Minimum 526 Minimum 371 Minimum 328 Maximum 585 Maximum 456 Maximum 430 Count 4 Count 6 Count 6 C.l.= 95.0% 40 C.l.= 95.0% 37 C.l.= 95.0% 44 187 

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