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Carbon Cycling in Subterranean Estuaries and Implications for Oceanic Fluxes

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Title:
Carbon Cycling in Subterranean Estuaries and Implications for Oceanic Fluxes
Creator:
Pain, Andrea J
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[Gainesville, Fla.]
Florida
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University of Florida
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english
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1 online resource (166 p.)

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Doctorate ( Ph.D.)
Degree Grantor:
University of Florida
Degree Disciplines:
Geology
Geological Sciences
Committee Chair:
MARTIN,JONATHAN BOWMAN
Committee Co-Chair:
BIANCHI,THOMAS S
Committee Members:
JAEGER,JOHN M
OGRAM,ANDREW V
FRANK,KATHRYN I

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Subjects / Keywords:
biogeochemistry -- carbon -- carbonate -- estuary -- fluorescence -- groundwater -- siliciclastic -- subterranean
Geological Sciences -- Dissertations, Academic -- UF
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bibliography ( marcgt )
theses ( marcgt )
government publication (state, provincial, terriorial, dependent) ( marcgt )
born-digital ( sobekcm )
Electronic Thesis or Dissertation
Geology thesis, Ph.D.

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Abstract:
Subterranean estuaries (STEs) are biogeochemically active zones where fresh groundwater and saline pore water mix. Biogeochemical reactions are fueled by organic carbon (OC), and produce the greenhouse gases carbon dioxide (CO2), methane (CH4), and remineralized nutrients. Reactions modify the chemical composition of submarine groundwater discharge (SGD), which alters coastal ocean chemical compositions through delivery of solutes. The extent of reactions should depend on STE hydrogeology, which regulates groundwater flow and provides solid phase reactants for biogeochemical reactions. To constrain the impacts of SGD on coastal C cycling, I characterize STEs representing two hydrogeologic end members: a carbonate karst STE, located in Puerto Morelos, Quintana Roo, Mexico, where numerous submarine springs act as point sources for SGD, and a siliciclastic STE located in Indian River Lagoon, FL, which is the site of widely distributed groundwater seepage. Yucatan hydrogeology leads to rapid groundwater flow rates relative to reaction kinetics and near conservative mixing of OC. In contrast, Florida STEs have long groundwater residence times and microbial activity causes colored dissolved organic matter (CDOM) concentrations to increase to several times the concentration of background values. OC characterization via fluorescence and PARAFAC modeling reveals increases in the proportion of labile protein-like OC with salinity at both sites, reflecting mixing of terrestrial organic matter with fresh marine OC. At both sites, remineralization of OC modifies SGD composition and produces CO2. The Yucatan calcium carbonate (CaCO3) aquifer dissolves and partially buffers excess CO2, but buffering may be limited by dissolution kinetics. Indian River Lagoon STEs produce both CO2 and CH4 but limited amounts of CaCO3 in sediment reduces CO2 buffering. Differences between Yucatan and Florida STEs suggest that more organic carbon and CO2 discharges from siliciclastic than carbonate STEs, and SGD from these systems may impact coastal carbon cycling differently. These results highlight the role of STE hydrogeology for carbon and nutrient cycling, altering SGD composition. Although STEs appear important to coastal C and nutrient budgets, heterogeneity in STE hydrogeology complicates estimates of global fluxes. ( en )
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In the series University of Florida Digital Collections.
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Includes vita.
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Includes bibliographical references.
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This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (Ph.D.)--University of Florida, 2017.
Local:
Adviser: MARTIN,JONATHAN BOWMAN.
Local:
Co-adviser: BIANCHI,THOMAS S.
Statement of Responsibility:
by Andrea J Pain.

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CARBON CYCLING IN SU BTERRANEAN ESTUARIES AND IMPLICATIONS FO R OCEANIC FLUXES By ANDREA J PAIN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2017

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2017 Andrea J Pain

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To my parents

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4 ACKNOWLEDGMENTS I thank my advisor, Jon Martin, for the support and enthusiasm for all things carbon, and my lab mates for assistance in the field, lab, and intellectual discussions. I acknowledge the Water Institute Graduate Fellowship cohort of 2013 for their thoughtful contributions, discussions and shared love of cochinita, to the Dixie Motel in Cocoa, FL for their hospitality and for never failing to provide colorful field experiences. I would also like thank my family for their continued support throughout this process. This research was supported by funding provided by the National Science Foundation and the St.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ .......... 9 ABSTRACT ................................ ................................ ................................ ................... 11 CHAPTER 1 BIOGEOCHEM ISTRY OF CARBONATE VERSUS SILICICLASTIC SUBTERRANEAN ESTUARIES AND IMPLICATIONS FOR CARBON CYCLING: A REVIEW ................................ ................................ ............................ 13 Role of Submarine Groundwater Discharge in Coastal Carbon Cycle .................... 13 Carbonate Versus Siliciclastic STEs ................................ ................................ ....... 14 Hydrological Impact on Biogeochemical Reactions ................................ .......... 15 Mineralogical Impact on Carbon Cycling ................................ .......................... 18 Feedbacks with Biogeochemical Cycling of Nutrients and Metals .................... 19 Summary and Implications ................................ ................................ ...................... 23 2 OR GANIC CARBON QUANTITY AND QUALITY ACROSS SALINITY GRADIENTS IN CONDUIT VERSUS DIFFUSE FLOW DOMINATED SUBTERRANEAN ESTUARIES ................................ ................................ ............. 31 In troduction ................................ ................................ ................................ ............. 31 Methods ................................ ................................ ................................ .................. 35 Study Locations ................................ ................................ ................................ 35 Sample Colle ction ................................ ................................ ............................ 36 Laboratory Methods ................................ ................................ ......................... 38 Modeling ................................ ................................ ................................ ........... 38 Results ................................ ................................ ................................ .................... 41 PARAFAC Results ................................ ................................ ........................... 41 Organic Carbon Concentrations and Conservative Mixing Mode l Results ....... 41 PARAFAC Component Abundance with Salinity ................................ .............. 43 Discussion ................................ ................................ ................................ .............. 44 Organic Carbon Dyn amics and Sources ................................ .......................... 45 Organic Carbon Quality Across Salinity Gradients ................................ ........... 46 Distribution of Residuals and Implications for Controls of Biogeochemical Reactions ................................ ................................ ................................ ...... 47 Conclusions ................................ ................................ ................................ ............ 50 3 ORGANIC INORGANIC CARBON FEEDBACKS IN A CARBONATE KARST AQUIFER AND IMPLICATIONS F OR NUTRIENT AVAILABILITY ......................... 58

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6 Introduction ................................ ................................ ................................ ............. 58 Study Location ................................ ................................ ................................ ........ 60 Methods ................................ ................................ ................................ .................. 63 Field Methods ................................ ................................ ................................ ... 63 Laboratory Met hods ................................ ................................ ......................... 64 Modeling ................................ ................................ ................................ ........... 65 STE conservative mixing models ................................ ............................... 65 Surface water organic carbon mass balance ................................ ............. 66 Results ................................ ................................ ................................ .................... 67 Salinity and Biogeochemical Parameters ................................ ......................... 67 Organic Carbon Character and Distribution ................................ ...................... 69 Discussion ................................ ................................ ................................ .............. 70 Terrestrial Sources and Processing of Organic Carbon ................................ ... 71 Biogeochemical processing in the STE ................................ ............................ 73 Implications for STE Nutrient Sources and Sinks ................................ ............. 76 Impact on Surface Water Carbon Cycling ................................ ........................ 78 Conclusions ................................ ................................ ................................ ............ 81 4 BIOGEOCHEMICAL CONTROLS OF GREENHOUSE GAS PRODUCTION AND SEQUESTRATION IN SILICICLASTIC SUBTERRANEAN ESTUARIES ...... 91 Introduction ................................ ................................ ................................ ............. 91 Carbon Dioxide and Carbonate Equilibria ................................ ........................ 92 Methanogenesis and Carbonate Equilibria ................................ ....................... 95 Methods ................................ ................................ ................................ .................. 97 Sample Collection ................................ ................................ ............................ 98 Laboratory Methods ................................ ................................ ......................... 99 Data Processing ................................ ................................ ............................. 100 Dissolved gas concentrations ................................ ................................ .. 100 Modeling ................................ ................................ ................................ .. 102 Results ................................ ................................ ................................ .................. 102 Dissolved Gas Concentrations and Carbonate Chemistry .............................. 102 Distribution of Redox Reactions ................................ ................................ ..... 103 CH 4 Concentrations and Oxidation ................................ ................................ 104 Alk: DIC Ratios Compared to Reaction Stoichiometries ............................. 105 Discussion ................................ ................................ ................................ ............ 105 Impacts on CO 2 Concentrations and DIC speciation ................................ ...... 106 Lower salinity portions of STEs (salinity < 15) ................................ ......... 106 Higher salinity portions of STEs (salinity > 15) ................................ ......... 109 Alk: DIC ratios ................................ ................................ ....................... 109 Redox and Mineralogical Controls of Dissolved Gas Concentrations ............. 111 Implications for CO 2 and CH 4 Fluxes ................................ .............................. 113 Conclusions ................................ ................................ ................................ .......... 114 5 SUMMARY AND CONCLUDING REMARKS ................................ ....................... 131 Impacts Due to Flow ................................ ................................ ............................. 131

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7 Impacts Due to Aquifer Solid Material ................................ ................................ ... 133 Concluding Remarks ................................ ................................ ............................. 134 APPENDIX A PARAFAC MODEL ................................ ................................ ............................... 136 B YUCATAN WATER CHEMISTRY DATA ................................ .............................. 147 C INDIAN RIVER LAGOON WATER CHEMISTRY DATA ................................ ....... 149 LIST OF REFERENCES ................................ ................................ ............................. 152 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 166

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8 LIST OF TABLES Table page 1 1 Hydrologic properties of carbonate and silicicl astic subterranean estuaries ...... 26 1 2 Reduction half reactions coupled with oxidation of organic matter .................... 26 2 1 PARAFAC component matches as identified via OpenFluor. ............................. 52 2 2 R 2 and p value for correlations between PARAFAC components in PARAFAC model ................................ ................................ ............................... 52 3 1 Chemical characterization of conservative mixing model end members ............ 82 3 2 Water chemistry parameters of terrestrial water, near shore springs, and seawater. ................................ ................................ ................................ ............ 83 3 3 R 2 between PARAFAC components in Yucatan samples ................................ ... 84 3 4 Mass balance of terrestrial PARAFAC components based on salinity ................ 84 4 1 Impact of redox pathways and biogeochemical reactions on alkalinity and DIC. ................................ ................................ ................................ .................. 116 4 2 DIC concentrations compared to concentrations produced from reactions ... 117 4 3 Alk concentrations compared to concentrations produced from reactions ..... 120 A 1 Complete list of samples included in PARAFAC model.. ................................ .. 137 B 1 Yucatan water chemistry parameters for geochemical modeling in PHREEQc 148 C 1 Indian River Lagoon w ater chemistry input parameters for geochemical modeling in PHREEQc. ................................ ................................ .................... 150

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9 LIST OF FIGURES Figure page 1 1 Ghyben Herzberg relationship demonstrating the depth of a freshwater lens overlying saline groundwater.. ................................ ................................ ............ 27 1 2 Conceptual models of subterranean estuaries. ................................ .................. 28 1 3 Map of locations of piezometer transects at Indian River Lag oon Florida. ......... 29 1 4 Maps of Yucatan study site and sampling locations. ................................ .......... 30 2 1 Five component PARAFAC model for subterranean estuary samples. .............. 53 2 2 Cross plots of salinity versus DOC, total CDOM, and ORP ............................... 54 2 3 Residuals of salinity ba sed conservative mixing models for DOC and total CDOM.. ................................ ................................ ................................ .............. 55 2 4 Relative PARAFAC component abundance versus salinity. ............................... 56 2 5 Conceptual model of CDOM concentrations versus salinity in STEs. ................. 57 3 1 Conceptual model of organic matter sources to Yucatan STE ........................... 85 3 2 Salinity versus solute concentration in Yucatan STE ................................ .......... 86 3 3 Salinity versus CDOM and PARAFAC component abundance in Yucatan STE .. ................................ ................................ ................................ .................. 87 3 4 Cross plot of Ca versus DIC residuals in Yucatan STE water samples. ............. 88 3 5 Cros s plots of salinity versus N:P and Ca:P molar ratios in Yucatan STE ......... 89 3 6 Conceptual model of biogeochemistry within Yucatan STE. .............................. 90 4 1 Relationship between dissolved gas concentrations and salinity STE site s .... 123 4 2 Carbonate chemistry versus salinity at Indian River Lagoon STEs ................. 124 4 3 Contour plots of salinity, DOC, redox species, and dissolv ed gases at Indian River Lagoon STEs . ................................ ................................ ........................ 125 4 4 Distribution of salinity DIC, Alk, and Ca between seepage faces. ............. 126 4 5 CH 4 concentrations versus 13 C CH 4 signatures and CH 4 oxidation ............... 127 4 6 Alk and DIC compared to Alk: DIC ratios produced by reactions .. ............ 128

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10 4 7 Alk:DIC ratios modeled by conservative mixing model compared to measured values ................................ ................................ ................................ .............. 129 4 8 Relationships between DIC and Alk estimated via salinity based conservative mixing models. ................................ ................................ ............. 130

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11 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy CARBON CYCLING IN SU BTERRANEAN ESTUARIES AND IMPLICATIONS FO R OCEANIC FLUXES By Andrea J Pain December 2017 Chair: Jonathan Martin Major: Geolog y Subterranean estuaries (STEs) are biogeochemically active zones where fresh groundwater and saline pore water mix. Biogeochemical reactions are fueled by organic carbon (OC), and produce the greenhouse gases carbon dioxide (CO 2 ), methane (CH 4 ), and remineralized nutrients. Reactions modify the chemical composition of s ubmarine groundwater discharge (SGD), which alters coastal ocean chemical compositions through delivery of solutes. The extent of reactions should depend on STE hydrogeology, which regulates groundwater flow and provides solid phase reactants for biogeoche mical reactions. To constrain the impacts of SGD on coastal C cycling, I characterize STEs representing two hydrogeologic end members: a carbonate karst STE, located in Puerto Morelos, Quintana Roo, Mexico, where numerous submarine springs act as point sou rces for SGD, and a siliciclastic STE located in Indian River Lagoon, FL, which is the site of widely distributed groundwater seepage. Yucatan hydrogeology leads to rapid groundwater flow rates relative to reaction kinetics and near conservative mixing of OC. In contrast, Florida STEs have long groundwater residence times and microbial activity causes colored dissolved organic matter (CDOM)

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12 concentrations to increase to several times the concentration of background values. OC characterization via fluorescence and PAR AFAC modeling reveals increases in the proportion of labile protein like OC with salinity at both sites reflecting mixing of terrestrial organic matter with fresh marine OC. At both sites, r emineralization of OC modifies SGD compositio n and produces CO 2 The Yucatan calcium carbonate (CaCO 3 ) aquifer dissolves and partially buffers excess CO 2 but buffering may be limited by dissolution kinetics. Indian River Lagoon STEs produce both CO 2 and CH 4 but limited amounts of CaCO 3 in sediment r educes CO 2 buffering. Differences between Yucatan and Florida STEs suggest that more organic carbon and CO 2 discharges from siliciclastic than carbonate STEs and SGD from these systems may impact coastal carbon cycling differently These results highlight the role of STE hydrogeology for carbon and nutrient cycling, altering SGD composition. Although STEs appear important to coastal C and nutrient budgets, heterogeneity in STE hydrogeology complicates estimates of global fluxes.

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13 CHAPTER 1 BIOGEOCHEMISTRY O F CARBONATE VERSUS SILICICLASTIC SUBTERRANEAN ESTUARIES AND IMPLICATIONS FOR CARBON CYCLING : A REVIEW Role of Submarine Groundwater Discharge in Coastal Carbon Cycle The coastal ocean is a dynamic component of the global carbon cycle, and may serve as a si gnificant CO 2 source or sink depending on the relative magnitudes of carbon fixation via primary productivity versus organic carbon remineralization (Cai, 2011) Coastal carbon cycling is magnified in surface estuaries where fresh and saltw ater mix and enhance organic carbon remineralization reactions. Reactions are enhanced due to delivery of suspended matter from riverine input, which increases organic carbon remineralization rates, while associated light attenuation decreases primary prod uctivity (Abril and Borges, 2004) In the marin e C cycle, only estuaries are typically net sources of CO 2 while continental shelves and the open ocean are net CO 2 sinks (Cai, 2011) On a global scale, estuaries comprise only 0.3% of ocean surface area but contribute more CO 2 to the atm osphere than is fixed by continental shelves (7.3% of ocean surface area), and one third the amount that is fixed in the open ocean (92.5% of ocean surface area; Cai, 2011) A freshwater saltwater mixing zone, analogous to surface water estuaries, occurs where freshwater in coastal aquifers mixes with marine pore wat er. This zone, known as the subterranean estuary ( STE; Moore, 1999) contributes freshwater and terrestrial solutes to the coastal zone via submarine groundwater discharge (SGD; Lambert and Burnett, 2003) SGD is comprised of both fresh and saline components, similar to surface estuaries, and the composition of SGD depends on the composition of fresh and saltwater end members but may be strongly modified by biogeochemical re actions within the STE (Moore, 1999) While groundwater input volumes are typically more

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14 poorly constrained than those from riverine discharge or surface water runoff, combined fresh and marin e SGD fluxes may be equivalent to or greater than riverine input in some regions, and are therefore critical to coastal biogeochemical budgets (Burnett et al., 2006; McCoy and Corbett, 2009) Similar to surface estuaries, biogeochemical processing occurs in STEs, which is enhanced by their low water:rock ratio. These low ratios result in enhanced availability of reactive solid phase material such as organic matter and mineral phases, and terminal elec tron acce ptors, such as Fe and Mn oxides which may be used to remineralize organic carbon While most studies quantifying SGD fluxes have focused on nutrients and metals (Slomp and Van Cappellen, 2004; Roy et al., 2013a; Null et al., 2014) estimates of carbon fluxes indica te that SGD may be a source of CO 2 (Sadat noori et al., 2016; Liu et al., 2017) dissolved inorganic carbon (Dorsett et al., 2011; Szymczycha et al., 2013) methane (CH 4 ; Bugna et al., 1996; Dulaiova et al., 2010; Lecher et al., 2015) and organic carbon (Suryaputra et al., 2015; Yang et al., 2015) SGD may therefore be an important source of carbon and reaction products of organic carbon reminer alization, which include greenhouse gases (CO 2 and CH 4 ), and nutrie nts. These fluxes, however, should depend on biogeochemical processing of carbon in in subterranean estuaries prior to discharge. Carbonate Versus Siliciclastic STEs While subterranean estuaries are active sites of biogeochemical reactions, variability in mineralogical and hydrogeological properties impact the range and magnitude of reactions that can be expected to occur, and may determine the net impact of reactions on carbon cycling. Subterranean estuaries may be broadly separated into two hydrological end members: those dominated by widely distributed diffuse flow, as is typical where groundwater flows through siliciclastic sediments (Martin

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15 et al., 2007; Spiteri et al., 2008 a ) versus conduit flow, which is typical of karstic carbonate aquifers (Null et al., 2014) Most studies to date have focused on fluxes from siliciclastic STEs because of their predominance in heavily populated regions of the U.S. East Coast (Cai et al., 2003; Michael et al., 2005; Spiteri et al., 2008 a ; Gonneea and Charette, 2014) and Europe (Jankowska et al., 1994; Szymczycha et al., 2012) In karstic groundwater systems most freshwater input to the coast occurs as SGD because high hydraulic conductivity leads to rapid infiltration of precipitation which limits surface water sources and runoff (Fleury et al., 2007) Ca r bonate karst coastlines are found throughout the Caribbean (Hernndez Terrones et al., 2011; Nul l et al., 2014; Young et al., 2017) Gulf Coast of Florida (Brooks, 1961; Corbett et al., 1999; Swarzenski et al., 2001) and Mediterranean regions (Fleury et al., 2007) but have received relatively less attention than siliciclastic systems. While both types of STEs are provide important solute fluxe s to coastal oceans, there are distinct differences between the hydrological properties of siliciclastic versus carbonate STEs that determine groundwater flow rates and residence time, while mineralogical differences impact biogeochemical feedbacks in carb on cycling. Hydrological Impact on Biogeochemical Reactions The location of the freshwater saltwater interface in coastal systems can be described by the Ghyben Herzberg relationship (Verruijt, 1968) This relationship describes the hydrostatic balance of a freshwater lens floating on denser saltwater which is maintained through freshwater recharge (Fig. 1 1),

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16 ( 1 1 ) where z is the depth of the freshwater lens below sea level, h is the height of the water table above sea level, and f and s represent the density of fresh and saltwater, respectively (Eq. 1 1) The depth of th e base of the freshwater lens can be estimated as the ratio of the density of freshwater over the difference in densities between salt and freshwater (approximately ~40 for fresh water (S=0) and average ocean water (S = 35) ) multiplied by the elevation of the water table above sea level ( Eq. 1 1, Fig. 1 1) The position of the freshwater saltwater interface is controlled by hydrologic factors including groundwater recharge rates, which increases hydraulic head, as well as hydraulic conductivity, which regulates groundwater flow. Silicicla stic and karst carbonate aquifers possess different hydrological properties that impact their flow dynamics (Fig. 1 2). Differences in hydrologic properties should affect biogeochemical reactions because of their control on residence time as well as delive ry of solutes and removal of reaction products. For instance, compared to siliciclastic aquifers (Fig. 1 2a), carbonate aquifers may have orders of magnitude higher hydraulic conductivity (Fig. 1 2 b ; Table 1 1), which prevents the development of high hydra ulic gradients and leads to lower residence time of groundwater in karst aquifers, particularly in conduits When hydraulic head decreases relative to sea level, high hydraulic conductivity of karst aquifers allows surface seawater to intrude into conduits for meters to kilometers inland ( Fig. 1 2b; Beddows et al., 2007) In contrast, lower hydraulic conductivity and slower groundwater flow in siliciclastic systems leads to higher groundwater residence times (Michael et al., 2005) Because of the differences in water residence time in siliciclastic versus carbonate aquifers, reaction dynamics should

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17 vary between these aquifer end members, and may le ad to systematic variations in the chemical composition of SGD. Relationships between reaction kinetics and transport rates are expressed through the Damkohler number Da, which is the ratio of the reaction rate to the transport rate : ( 1 2 ) Where Da is high, reactions may be expected to approach thermodynamic equilibrium because the reaction occurs quickly relative to transport. The differences in transport rates between carbonate an d siliciclastic aquifers may impact biogeochemical reactions by altering Da. An important chemical parameter that regulates the type and kinetics of biogeochemical reactions in groundwater systems is the redox potential of pore waters. Redox potential is r elated to the availability of terminal electron acceptors (TEAs) used by microbial communities to remineralize organic carbon, and in marine systems predominantly include oxygen, nitrate, manganese, iron, and sulfate (Froelich et al., 1979) The relative energy yield of reactions declines along the redox ladder, represented sequentially in Table 1 2. For instance, oxic respiration yi elds more energy than any of the other TEAs and thus occurs preferentially. Once oxygen is depleted, less energetically favorable reactions will occur, in the order of nitrate reduction, iron reduction, sulfate reduction, and methanogenesis, though redox z ones may overlap. Because the suite of redox reactions occurs sequentially according to the energy yield of reactions, redox potential predominantly depends on the rate of replenishment of TEAs compared to the rate of organic carbon remineralization, and thus should depend on groundwater residence time (Spiteri et al., 2008 b ) For instance, Slomp and

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18 Van Cappellen ( 2004) suggested that, given equivalent inputs of organic carbon and terminal electron acceptors, aquifers with hydrological parameters that reduce groun dwater residence time (e.g. high recharge and flow rates) are more likely to maintain oxic conditions than aquifers with low recharge and flow rates. Redox potential affects microbial N cycling (Santoro, 2009; G onneea and Charette, 2014) as well as phosphorus mobilization (Slomp and Van Cappellen 2004) because of NO 3 TEA and from the production of NH 4 and PO 4 during org a nic carbon remineralization Mineralogical Impact on C arbon Cycling Apart from differences in hydrologic characteristics, and their control on biogeochemical reactions, siliciclastic and carbonate STEs impact carbon cycling differently because of feedbacks from reactions with the different aquifer solids. In particular, CO 2 concentrations are altered by feedbacks between organic carbon remineralization and carb onate mineral dissolution. These feedbacks are initiated when remineralization of organic matter generates carbonic acid (H 2 CO 3 ), CH 2 O + O 2 CO 2 + H 2 O H 2 CO 3 ( 1 8 ) which decreases the pH of pore waters : H 2 CO 3 HCO 3 + H + CO 3 2 + 2H + ( 1 9 ) The dissociation of carbonic acid (H 2 CO 3 ) into HCO 3 and CO 3 2 depends predominantly on ambient pH. At standard oceanic pH (8.2), about 88% of dissolved inorganic carbon is present as HCO 3 while 11% is CO 3 2 and 1% is dissolved CO 2 and H 2 CO 3 (Sarmiento and Gruber, 2006) When contributions of CO 2 via Eq. 1 8 cause d issolved CO 2 concentrations to increase beyond the partial pressure of atmospheric CO 2 currently at approximately 400 ppm, outgassing can occur and water will become a source of atmospheric CO 2

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19 However, increased acidity in water drives Eq. 1 8 to the left, reducing the concentration of CO 3 2 ions and causing undersaturation of CaCO 3 minerals, which dissolve according to : CaCO 3 Ca 2+ + CO 3 2 ( 1 10 ) However, because this reaction impacts the speciation of C in Eq. 1 9, it is often represented as: CaCO 3(s) + CO 2(g) + H 2 2+ + 2HCO 3 ( 1 11 ) This reaction illustrates that the dissolution of CaCO 3 consumes one molecule of atmospheric CO 2 and produces 2 moles of HCO 3 and is therefore a net sink of atmospheric CO 2 and the combined effects of aerobic respiration and carbonate dissolution lead to no net change in atmospheric C concentrations Therefore, the presence of carbonate minerals may limit increases in partial pressure of CO 2 dissolved in the water (P CO 2 ) derived from organic carbon remineraliza tion This buffering represents a negative feedback between C released via organic matter remineralization and P CO 2 of pore waters, thus impacting the status of waters as either C O 2 sources or C O 2 sinks. Feedbacks with Biogeochemical Cycling of Nutrients and Metals In surface waters, carbon cycling depends on the availability of macronutrients such as nitrogen (N), phosphorus (P) and micronutrients such as iron (Fe) due to nutrient requ irements of primary producers. Marine primary producers typically fix c arbon and nutrients at an average C:N:P ratio of 106:16:1 (Redfield, 1934) Carbon and nutrient cycling in STEs may therefore impact carbon fixation in surface waters when SGD contains elevated concentrations of nutrients. For instance, phosphorus

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20 delivery via SGD has been linked to increased primary productivity near submarine springs in the Yucatan (Carruthers et al., 2005) while enhanced SGD derived nutrient fluxes to the Gulf of Mexico have been linked to red tides and harmful algal blooms (Hu et al., 2006) These local observations suggest that nutrient enriched SGD may trigger large s cale shifts in ecological structures and significantly impact net primary productivity (Lyons et al., 2014) Compared to nitrogen, which has few significant mineral reservoirs (Holloway and Dahl gren, 2002) multiple feedbacks between phosphorus concentrations and mineral stability in siliciclastic and carbonate STEs suggest that it could be coupled to carbon cycling. Phosphorus is often the limiting nutrient for primary productivity in coastal marine environments, and has been cited as the ultimate limiting nutrient for primary productivity in marine ecosystems over geologic timescales (Toggweiler, 1999; Tyrrell, 1999) In the ocean, the largest source of P is riverine input of particulate matter and dissolved P species and up to 99% of particulate P and 25% of dissolved phosphate delivered by rivers are buried in deltas and continental shelves (Paytan and McLaughlin, 2007) As the main repository of oceanic P, s ediment P cycling often plays an important role in controlling the concentration of P in overlying waters. Dominant forms of P in sediment include organic P, phosphate adsorbed to Fe oxide or carbonate minerals, and P stored in apatite minerals. Diagenetic interactions between these reservoirs affect the rate and extent by which P can be remobilized and returned to the water column (Ruttenberg and Berner, 1993; Koch et al., 2001) Depending on these interactions, sediment can be a net source if reactions lead to net liberation of P from sedimentary reservoirs so that it can be transported from STEs to surfac e waters, but

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21 may also be P sinks if reactions lead to net decreases in dissolved P concentration due to precipitation or adsorbtion to minerals (Short et al., 1990; Jensen et al., 1998) In siliciclastic sediments, cycling of organic carbon, iron, and phosphorus are coupled due to the impact of redox status on the stability of Fe oxide miner als, which form sorption interactions with dissolved phosphate (PO 4 3 ). Fe oxide minerals may be reduced under anoxic conditions during microbial remineralization of organic matter (Table 1 2; Eq. 1 5). Reduction of iron from Fe(III) to Fe(II) strongly inc reases its solubility and results in the dissolution of solid Fe oxyhydroxides and the liberation of sorbed P (Caraco et al., 1989; Blomqvist et al., 2004; Roy et al., 2012) Once liberated, dissolved Fe and P are mobile and may be transported through sediment and to surface waters via advective or diffusive transport processes, where th ey can be consumed by primary producers. Re precipitation of iron oxide or iron sulfide minerals may occur, however, depending on the saturation state of these minerals in pore waters. When iron oxides re precipitate near the sediment water interfa ce, they may re sorb phosphorus (Chambers et al., 1990; Linkhorst et al., 2017) However, in marine systems where sulfate reduction generates sulfide, iron sulfide minerals may precipitate. These minerals have very low sorption potential for phosphorus, and their precipit ation will not impact P fluxes from sediment. The conversion of Fe oxides to Fe sulfides is noted to increase P fluxes from sediment in marine systems, and has been proposed as a mechanism to explain the predominance of N limitation in marine environments as a consequence of greater P inputs (Blomqvist et al., 2004)

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22 In sediments predominantly composed of carbonate minerals, strong associations between P and CaCO 3 minerals and subsequent conversion into more stable apatite phases cause it to become a limiting nutrient for primary productivity (DeKanel and Morse; 1978; Rosenfeld, 1 979; Short, 1985; Millero, 2001). The high affinity of PO 4 for carbonate mineral surfaces is evident in the distribution of dissolved PO 4 and N:P ratios in carbonate versus siliciclastic systems. For instance, p ore waters in low CaCO 3 sediments along tempe rate coasts typically have higher PO 4 concentrations and lower N:P ratios than shallow marine CaCO 3 domi nated sediments, which have lower PO 4 concentrations and higher N:P ratios (Lapointe et al., 1992) Because organic carbon remineralization is coupled with CaCO 3 dissolution in carbonate syst ems, sorbed P may be liberated and transported t o surface waters. Enhanced delivery of P due to CaCO 3 dissolution may lead to enhanced primary productivity where P is limiting (Carruthers et al., 2005) The coupling of the above reactions in both siliciclastic and carbonate STEs depends on the kinetics of reactions relative to transport rate. For instance, while organic carbon remineralization kinetics largely depend on the quality of organic substrates and availability of terminal electron acceptors (Arndt et al., 2013) dissolution and precipitation kinetics of CaCO 3 minerals depend on the degree of over or under saturation and reactions are slow when waters are close to saturation (Morse and Arvidson, 2002) Therefore, where D a is small (reaction rate is slow relative to transport rate) rea ctions may not reach thermodynamic equilibrium. In the case of carbonate STEs where CO 2 generation may be buffered by CaCO 3 dissolution, a low Da number

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23 may reduce the extent of buffering and water may still serve as a source of atmospheric CO 2 from organi c C remi neralization. Summary and Implications Hydrological and mineralogical differences between siliciciclastic and carbonate STEs may lead to significant variations in biogeochemical reactions involving carbon and nutrients, and may therefore systemati cally differ in the impact of SGD on surface water carbon budgets. While organic carbon remineralization generates carbonic acid (Eq. 1 8), only sediments with carbonate minerals may buffer pore waters through CaCO 3 dissolution and cause CO 2 to be sequeste red as HCO 3 or CO 3 2 Moreover, feedbacks between carbon and phosphorus cycling are likely to be more important in carbonate systems versus siliciclastic due to the affinity of P for CaCO 3 minerals and the increased likelihood of P limitation in carbonate systems. While sediment mineralogy may control the extent of biogeochemical feedbacks impacting the C cycle, systems may only be expected to reach chemical equilibrium when the rate of water transport is slow compared to reaction kinetics. Since carbonate and siliciclastic systems have widely variable hydrological properties (Fig. 1 2) with higher hydraulic conductivity and flow rates in carbonate systems, the hydrological characteristics of STEs may control the impact of biogeochemical processing on SGD composition by impacting the redox potential of water as well as the dissolution of CaCO 3 minerals following organic carbon remineralization. I address the impacts of biogeochemical reactions on carbon processing in carbonate versus siliciclastic STEs by c omparing two STEs representing hydrogeologic end members. The siliciclastic end member is represented by Indian River Lagoon on the east coast of Florida (Fig. 1 3), where groundwater flows through siliciclastic

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24 sediments as diffuse discharge (Martin et al., 2007; Smith et al., 2008; Dorsett et al., 2011; Roy et al., 2013a) The carbonate end member is represented by submarine springs offshore of the Yucatan peninsula (Fig. 1 4), Mexico, where groundwater discharges to the coast through conduits in the carbonate karst aquifer (Valle Levinson et al., 2011; Parra et al., 2014; Parra et al., 2015; Young et al., 2017) Hydrological properties and magni tudes of SGD between field sites are given in Table 1 1. I address variations in carbon processing by first evaluating organic carbon sources and processing with respect to the impact on redox potential across salinity gradients in both types of STEs (Ch. 2). I then explore relationships between organic carbon remineralization, carbonate mineral dissolution, and phosphorus dynamics at the Yucatan field site (Ch. 3), and discuss greenhouse gas (CO 2 and CH 4 ) production and sequestration in siliciclastic syste ms as represented by the Indian River Lagoon field site (Ch. 4). In order to assess the change in the chemical composition of submarine groundwater discharge due to reactions that occur in the freshwater saltwater mixing zone, I employ salinity based conse rvative mixing models. These mixing models assume deviations from a line drawn between these two points reflect changes in composition due to reactions. Deviations depend on the position of the line and thus on the definition of fresh and saltwater end mem bers. This approach is frequently employed in environments where water sources with distinct chemistries mix, including surface water (Guo et al., 2007; Spencer et al., 2007; Markager et al., 2011) and subterranean estuaries (Beck et al., 2007; Sanders et al., 2012; Gonneea et al., 2014) However, uncertainty in end member selection results in mixing models that may over

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25 or under estimate the magnitudes of reactions. In mixing models employed throughout this dissertation, I define the fresh end member as the freshest subterranean estuary sample collected at each sampling location and during each sampling time, and use compos itions of surface lagoon water as the saltwater end member. This approach is taken to assess biogeochemical changes that occur where freshwater and salt mix and assumes that the freshest subterranean estuary sample is representative of the freshwater flowi ng to the subterranean estuary. However, while composition of surface seawater is approximately constant over the width of seepage faces, chemical gradients may develop in fresh groundwater that result in a high degree of variability in water samples of si milar salinity. Where this is the case (e.g. Fig. 4 2), results of mixing models are sensitive to end member selection. While I am aware of the impact that end member values have on determining residual values, my general interest is in looking at overall trends in a qualitative way to determine the sign and not necessarily the absolute magnitude of processes. Use of conservative mixing models for quantitative assessments such as m ass balance would require more rigorous determination of the uncertainty of e nd members.

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26 Table 1 1. Hydrologic properties of carbonate and siliciclastic subterranean estuaries in this study. Location Type Fresh SGD (m 3 km 1 yr 1 ) Marine SGD (m 3 km 1 yr 1 ) Total SGD (m 3 km 1 yr 1 ) Hydraulic Conductivity (m/s) Yucatan Peninsula Carbonate karst 8.6 (1) --1x10 0 to 110 1 (2) 0.7 3.9 (3) --0.5 (4) ----112 (5) Indian River Lagoon Siliciclastic 0.05 2.5 (6) 320 (6) 1x10 6 (7) (1) Hanshaw and Back (1980) (2) Gonzle z Herrera et al. (2002) (3) Smith et al. (1999) (4) Hernndez Terrones et al. (2011) (5) Null et al. (2014) (6) Martin et al. (2007) (7) Zimmermann et al. (1985) Table 1 2. Reduction half reactions coupled with oxidation of organic matter (CH 2 O + H 2 O CO 2 + 4H + + 4e ). Relative energy yield is reported with respect to aerobic respiration and is taken from Lovley and Chap elle (1995) Reactions modified from Stumm and Morgan (1996) Equation Reduction reaction Relative energy yield 1 3 Aerobic respiration O 2(g) + 4H + + e 2H 2 O 100 1 4 Nitrate reduction NO 3 + 10H + + 8 e NH 4 + + 3H 2 O 93 1 5 Iron reduction FeOOH (s) + HCO 3 + 2H + + e FeCO 3(s) + 2H 2 O 84 1 6 Sulfate reduction SO 4 2 + 9H + + 8e HS +4H 2 O 6 1 7 Methanogenesis CO 2(g) + 8H + + 8e CH 4(g) + 2H 2 O 3

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27 Figure 1 1. Ghyben Herzberg relationship demonstrating the depth of a freshwater lens overlying saline groundwater. The border between fresh and saltwater is represented as a sharp interface over which no mixing occurs. This schematic assumes static conditions (no flo w), but flow is required to maintain the position of the freshwater lens and necessitates the development of submarine groundwater discharge at a seepage face. Modified from Ve rruijt ( 1968)

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28 Figure 1 2. Conceptual models of subterranean estuaries. (a) Siliciclastic STEs with diffuse flow through porous sediments. The subterranean estuary, or mixing zone between fresh and saltwater end members, occurs across the entire freshwater saltwater interface. Because it is not a sharp interface, it is represente d as a dashed line. Permanent piezometers installed in sediment are used to sample across the seepage face. Modified from Martin et al. ( 2007) (b) Carbonate karst STE w ith point discharge through conduits. Mixing occurs within conduits that have much higher hydraulic conductivity than the carbonate matrix.

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29 Figure 1 3. Map of locations of piezometer transects at Indian River Lagoon Florida. (a) Indian River Lagoon is situated on the east coast of Florida. (b) T ransects are located in the central portions of Indian River Lagoon (EGN and RWP) and Banana River Lagoon (BRL).

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30 Figure 1 4. Maps of Yucatan study site and sampling locations. (a) L ocation of stu dy site on Yucatan peninsula. (b) Location of sampling poin ts including inland cenotes. (c) Nearshore sampling sites and extent of lagoon bounded by reef crest.

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31 CHAPTER 2 ORGANIC CARBON QUANTITY AND QUALITY ACROSS SALINITY GRADIENTS IN CONDUIT VERSUS DIFFUSE FLOW DOMINATED SUBTERRANEAN ESTUARIES Introduction Subterranean estuaries (STEs) occur where fresh groundwater and saline pore water mix in coa stal aquifers and are zones of active biogeochemical transformation of ecologically relevant solutes such as nutrients, metals, and carbon (Moore, 1999) While STEs are analogous to surface es tuaries, longer residence times in STEs and lower water:rock ratios should allow for a greater range of biogeochemical reactions to occur in part due to the remineralization of organic carbon, which depletes terminal electron acceptors and lowers redox po tential (Slomp and Van Cappellen, 2004) These transformations could alter fluxes of t errestrial solutes in submarine groundwater discharge (SGD) when delivered to the coastal ocean. SGD has long been recognized as a source of freshwater and terrestrial solutes to the coastal zone (Slomp and Van Cappellen, 2004; Windom et al., 2006; Martin et al., 2007; Roy et al., 2013b; Kwon et al., 2014) but the link between biogeochemical processing in STEs, variations in hydrogeology, and fluxes from SGD is poorly known The distribution of redox reactions in STEs are driven by the distributions of terminal electron acceptors (TEAs) that microbes use to re mineralize organic carbon (Slomp and Van Cappellen, 2004) In STEs, intensity of organ ic carbon processing should vary with salinity which may be regarded as a tracer for the proportion of fresh and saltwater sources in the mixing zone. This co variance would result from two processes in STEs, specifically changes in the relative proportio n of terrestrial versus marine organic matter, and changes in the concentrations of terminal electron acceptors available to remineralize organic carbon. In addition to the biogeochemical control,

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32 physical controls related to flow rates through the STEs sh ould also impact distribution of redox reactions and resulting changes in pore water compositions in an d fluxes of solutes from STEs. This chapter evaluates the relative effect of these three processes on potential solute fluxes from STEs. The kinetics of organic carbon reminerali zation are impacted by its reactivity which relates to the ease with which microbes can metabolize organic compounds. In nature, o rganic carbon reactivity lies along a spectrum from highly reactive to virtually inert under ambient environmental conditions The d istribution of organic carbon along this reactivity spectrum is thus more important to its remineralization dynamics than its bulk quantity (Berner, 1980; Arndt et al., 2013) T errestrial organic carbon is typically less reactive than marine organic carbon in part because of higher proportion of vascular plant material that is comprised of structural biomolecul es such as lignin and cutin These compounds a re typically less readily degraded than proteins and carbohydrates which are characterized by weaker peptide bonds and greater nutrient (nitrogen and phosphorus, N and P) content (Arndt et al., 2013) Moreover, labile fractions of terrestrial organic carbon may be degraded during delivery to the STEs through long flow paths within coastal aqui fers, resulting in a residual organic carbon fraction that is more recalcitrant than fresh terrestrial organic carbon (Hopkinson et al., 1998) On the other hand, marine organic matter contains a greater proportion of relatively more reactive compounds such as carbohydrates and proteins (Schwarzenbach et al., 2003) Marine organic carbon is also produced closer to the STE than terrestrial organic carbon and can be rapidly transported into the STE through mixing with saline water (Martin et al., 2006; Young et al., 2017) The relative freshness

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33 of marine organic matter in STEs may therefore be greater, and h ence more reactive, than terrestrial o rganic carbon in fresh groundwater and may affect the magnitude and rates of biogeochemical reactions in mixed marine and terrestrial water of STEs. Remineralization dynamics in STEs may also be a function of changes in terminal electron acceptor availabil ity. For example, surface saltwater contains high concentrations of oxygen (Young et al., 2017) and sulfate, while fresh water may contain high concentrations of nitrate (Kroeger et al., 2007; Kroeger and Charette, 2008) that may be used to re mineralize organic carbon (Froelich et al., 1979) I n freshwater with sufficient reactive organic carbon organic carbon remineral ization can commonly progress to methanogenesis because of low availability of other terminal electron acceptors, such as sulfate Because seawater is a major source of sulfate, i ncreased sulfate availability due to increases in salinity should enhance org anic carbon remineralization rates in methanogenic freshwater These effects have been observed in s altwater intrusion expe riments on methanogenic freshwater sediments, which demonstrated that organic carbon remineralization rates increase with the additio n of sulfate (Weston et al., 2011) Similarly, oxygen contained in surface seawater may increase remineralization rates in anoxic freshwater though the extent of this increase depends on saltwater transport rates. For instance, r apid aer obic remineralization rates lead to the consumption of oxygen within the first few millimeters of typical marine sediments when transport occurs via diffusion (Megonigal et al., 2005) Because it is consumed rapidly enhance d remineralization from oxygen would require rapid transport of surface seawater to deep portions of STE s via advection.

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34 Hy drogeological characteristics of STEs c ould also affect organic carbon processes in STEs. Two distinc t end member hydrologic settings occur in STEs and include diffuse seepage from porous media, for example in sand and gravel aquifers (Martin et al., 2007; Spiteri et al., 2008 a ) and point discharge from karstic conduit systems (Swarzenski et al., 2001; Null et al., 2014; Young et al., 2017) These two end membe r settings are characterized by different flow rates and thus residence time of water and reactants in the mixing zone and with aquifer solid materials. Here, I characterize changes in the quantity and quality of DOC along salinity gradients in two STEs th at represent these hydrogeologic end mem bers. One represents diffuse seepage from siliciclastic sediments (Indian River Lagoon, FL; Martin et al., 2007) and the other represents conduit flow through a carbonate karst aquifer (Yucatan Peninsula, Mexico; Null et al., 2014; Parra et al., 2014; Parra et al., 2015) I evaluate non conservative mixing behavior of organic carbon using dissolved organic carbon (DOC) concentrations and characterization of colored dissolved organic matter (CDOM) via fluorescence spectroscopy and PARAFAC analysis (Murphy e t al., 2013) DOC includes all organic molecules, while CDOM includes the fraction of total DOC that exh ibits chromophoric properties, and CDOM is therefore a subset of total DOC although the two are often collinear in coastal systems (Del Castillo, 2005) I link organic carbon processing with biogeochemical reactions via oxidation reduction potential (ORP), which is a measure of th e availability of terminal electron acceptors (TEAs) to respire organic carbon. Elevated ORP values indicate that fewer redox reactions have occurred, or more oxidized species have been delivered to the STE Because organic carbon is consumed in most micro bially mediated redox reactions, I use the distribution

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35 of organic carbon with salinity to depict the general controls of biogeochemical reactions within STEs and propose a conceptual model that describes reactive zones in STEs due to fresh and saltwater m ixing. Methods Study Locations Indian River Lagoon is located on the east coast of Florida (Fig. 1 3) and the Yucatan field site is located in a reef lagoon offshore of Puerto Morelos approximately 40 km south of Cancun in the state of Quintana Roo, Mexico (Fig. 1 4 ). These two sites are separated by only a few hundred kilometers and thus have similar climate regimes, including cool dry winter months with warm humid summer months and periodic impacts from tropical storms. Both sites are microtidal and have low wave energy as a result of barriers separat ing them from the ocean. However, the geological and hydrogeologic characteristics of sites settings differ. The Yucatan Peninsula is a karstic carbonate platform of Triassic to Holocene age, and is character ized by dissolutional secondary porosity. This secondary porosity generates high aquifer permeability and hydraulic conductivity, which limits surface water on the terrestrial portion of the peninsula and causes precipitation to recharge the aquifer throug h thin soil layers overlying the carbonate matrix. The aquifer discharges at the coast as SGD from submarine springs (~ 78.5% of the discharge at the Puerto Morelos lagoon ) with the remainder from diffuse seepage from the beach shore face (Beddows et al., 2007; Null et al., 2014) The springs can reverse flow during extreme high tides, storm surges, and wind set up allowing surface seawater to intrude into conduits (Parra et al., 2015; Young et al., 2017) and create a brackish mixing zone between fresh and saltwater that extends approximately 1 4 km inland (Beddows et al., 2007)

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36 Indian River Lagoon is located on the Atlantic coast of central Florida and spans approximately 250 km of coastline in three hydrological subunits: Mosquito Lagoon, Indian Riv er Lagoon, and Banana River Lagoon (Fig. 1 3 ). Three STEs in the Indian River and Banana River lagoon were included in this study, Eau Gallie North (EGN), Banana River Lagoon (BRL) and Riverwalk Park (RWP ). Sediments in these STEs are siliciclastic ranging from fine sand to clays, which allow groundwater seepage to occur at rates ranging from 0.02 to 0.9 m 3 /d per meter of shoreline at EGN (Martin et al., 2007) This slow seepage makes STE salinity gradients static ov er timescales of days to weeks in contrast to the rapid ex change of the Yucatan aquifer. However, seasonal variation in lagoon water salinity and fresh groundwater head are known to cause fluctuations in seepage face width (Roy et al., 2013) and storm driv en saltwater intrusion events can alter seepage face salinity for several months (Smith et al., 2008) Sample C ollection At the Yucatan site, water was sampled over a two week period in September 2014 from four submarine springs (Hol Kokol, Gorgos, Laja, and Pargos) that are distributed along ~5 km of shoreline in P uerto Morelos (Fig. 1 4 ). During the sampling periods, surface lagoon water periodically intruded into springs and caused variable salinity of vent discharge that serves as a conservative tracer to assess mixing, from which biogeochemical processing in the STE may be evaluated (Young et al., 2017). All spring samples were collected during spring discharge periods. To characterize freshwater inputs to the coastal zone, water samples were also collected from inland cenotes (water filled sinkholes), mangrove su rface water, and an inland well (Fig. 1 3 ). Indian River Lagoon samples were collected four times during fall (September October) and spring (May), starting in fall 2014 and ending spring 2016, at all three

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37 seepage faces (location s of fresh water discharge ) (Fig. 1 3 ). Seepage face widths varied between sites and extended about 20 m offshore at EGN, 35 m offshore at RWP, and 50 m offshore at BRL. Permanent multilevel piezometers (multisamplers; Martin et al., 2003) were installed at increasing distances offshore to permit recurring sampling at fixed sediment depths across the seepage faces (Fig. 1 3) Piezometers were installed in 2004 at EGN and between May 2014 September 2015 at RWP and BRL. Sampled piezometers were located 0, 10, 20 and 22.5 m offshore at EGN (EGN X ), 10, 20, and 35 m offshore at RWP (RWP X ) and 1, 11, 21, and 45 m offshore at BRL (BRL X ; Fig.1 4 ) where the value of X represents the distance offshore in meters Samples were collected by pumping water to the surface through 0.5 cm diameter flexible PVC tubing. In the Yucatan, tubes were installed at the spring openings by SCUBA diving, and by lowering the tubing from the surface to measured depth s within the cenotes and well. At Indian River Lagoon, the tubing was connected to multisampler p iezometer ports ( Fig. 1 2a; Martin et al., 2003). A YSI Pro Plus sensor was installed in an overflow cup in line with the tubing to measure salinity, temperature, pH, dissolved oxygen, and oxidation reduction potential (ORP) while pumping water. Once these parameters were stable, water samples were filtered through 0.45 m trace metal grade Geotech medium capacity disposable canister filters and collected and preserved in the field according to the specific solute. Samples for dissolved organic carbon (DOC) concentration and CDOM analysis were collected in amber borosilicate vials that were combusted at 550C prior to use. Although the < 0.45 m fraction also includes colloids, I refer to this size fraction as dissolved because both dissolved and colloidal organic carbon fractions are mobi le in groundwater. C hanges in the abundance

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38 and quality of the <0.45 M fraction should reflect changes in organic carbon processing due to end member mixing and in situ reactions such as remineralization (consumption) or p roduction from other organic carbon pools (e.g. particulate or sedimentary organic carbon). DOC concentration samples were acidified with hydrochloric acid to pH<2. Fluorescent DOC samples were not acidified and kept frozen until analysis within one month of collection. Laboratory M ethods DOC concentrations were analyzed on a Shimadzu TOC VCSN total organic carbon analyzer, and the coefficient of variance for check standards was less than 2%. Spectroscopic and fluorescence techniques were used to assess or ganic carbon quality Fluorescence measurements were collected on a Hitachi F 7000 Fluorescence Spectrophotometer to generate 3D Excitation Emission Matrices (EEMs). Scans were collected at 700 V and at excitation wavelengths ranging from 240 450 nm at 5 n m intervals, and emission wavelengths ranging from 250 550 nm at 2 nm intervals. Instrument specific effects were corrected from data utilizing a correction that accounts for difference in lamp intensity across the excitation emission wavelength range. Inn er filter effects due to organic carbon content were corrected with UV spectra according to methods outlined in Ohno (2002) An aliquot of the fluorescence sample was used to measure UV absorption on a Shimadzu 1800 UV Spectrophotometer, and UV absorption data were collected at 1 nm intervals from 240 550 nm. Modeling Sufficiently large sets of EEMs can be deconvolved using Par allel Factor Analysis (PARAFAC) into statist ically significant fluorophores that comprise colored dissolved organ ic mat ter (CDOM). Fluorophores represent distinct structural groups of

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39 organic matter that have fluorescent properties, and principally include humic acids, fulvic acids, and proteins (Murphy et al., 2013) The absolute and relative abundances sources, but also provide an indication of the quality of organic matter beca use proteins are typically more labile than humic or fulvic acids. Processing of EEMs for modeling with PARAFAC included pre processing of raw data and corrections for instrument specific effects, inner filter effects, masking to eliminate signals from fir st and second order Rayleigh scattering, and conversion to Raman Units PARAFAC modeling was achieved using the drEEM toolbox in Matlab version 2015b (MathWorks, 2015; Murphy et al. 2013b) To reduce collinearity between samples due to dilution effects and to better model low concentration samples, EEMs were normalized to total sample fluor escence intensity before modeling with PARAFAC. A total of 322 samples were included in the PARAFAC model (Appendix A lists full descriptions). The PARAFAC model was run with non negativity constraints and was split half validated, and model results were r everse normalized before being exported. The abundances of PARAFAC components are reported in Raman Units (R.U.) that are normalized to the fluorescence intensity of water to remove variability due to instrument drift. While Raman units are quantitative, t hey cannot be converted to molar units as the relationships between concentration and fluorescence intensity for each PARAFAC component is unknown. Knowledge of these relationships would require an instrument specific calibration for each component. As com ponents may not be physically or chemically separated from one another, molar conversion is not possible and therefore component abundance is discussed in terms of their relative concentrations. I report PARAFAC component

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40 abundances in both Raman Units and proportional to total fluorescence ( Eq. 2 1; %C1, %C2, etc.), calculated as component abundance (R.U.) divided by total CDOM (Eq. 2 2) ( 2 1 ) ( 2 2 ) for a PARAFAC model of i components, where C n represents one of the identified components To separate changes in organic carbon concentration due to end member mixing from changes due to reactions involving organic carbon I construct salinity based conservative mixin g models for DOC and total CDOM. I define the fresh end member of conservative mixing models as the freshest groundwater sample for each STE location at each sampling time, and the saline end member as the surface saltwater sample for each location at each sampling time. In most cases, the surface saltwater sample has the highest salinity in the dataset, however som e pore water samples at EGN have higher salinity than surface water during each sampling time. While sediment salinity fluctuates with surface water salinity, a time lag is in duced as salt diffuses into the STE from the lagoon that can induce short term salinity inversions in sediment (Martin et al., 2006) I quantify changes in concentration due to reactions by comparing measured concentrations to those predicted by two end member mixing, and assume deviations from conservative mixing represe nt biogeochemical alteration. ( 2 3 )

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41 When the measured concentration ([Measured]) is greater than the concentration predicted by conservative mixing model ([Mixing model]), reactions resulted in a net gain of the solute, while lower measured concentrations than mixing model conce ntrations indicate that reactions resulted in a net loss of the solute. I report deviations from conservative mixing in concentration units (mg/L for DOC and R.U. for CDOM), as well as the percent deviation from the conservative mixing line (Eq. 2 3 ) Resu lts PARAFAC R esults PARAFAC modeling results in a 5 component model that explains >99% of the variability (Fig. 2 1 ). Model components are compared to those previously identified in the literature via OpenFluor (http://www.openfluor.org/), where components are considered matches when a comparison of the excitation emission spectra between components yields an R 2 > 0.95 (Table 2 1). Components C1, C2, and C4 are characterized as terrestrial humic like, while C3 is microbial humic like and C5 is protein like. All components are significantly positively correlated, but the strength s of correlations vary The strongest relationships are between component C1 and C3 (R 2 =0.88) as well as between C1 and C5 (R 2 = 0.84), and between C2 and C4 (R 2 = 0.95; Table 2 2). O rganic Carbon Concentrations and Conservative Mixing Model Results Concentrations of organic carbon (both DOC and CDOM) are lower in the Yucatan than the Indian River Lagoon sites. Yucatan samples exhibit significant negative correlations between DOC and C DOM with salinity (DOC: r 2 = 0.92; p < 0.0001; CDOM: r 2 = 0.96; p < 0.0001 ), but ORP values display no significant correlation to salinity Fig. 2 2a ). The greatest DOC concentration in Yucatan water measures 3.5

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42 mg/L and occurs in the freshest sample (salinity=9.7), decreasing to between 0.5 1 mg/L at seawater salinity. CDOM follows a similar trend with the greatest value in the freshest sample, ~4 R.U that decrease s to ~0. 5 R.U. in the surface seawater sample. I n Indian River Lagoon at BRL, no significant relationship exists between salinity and DOC or total CDOM, but the maximum DOC concentration of 60 mg/L and highest CDOM abundance of 64 R.U occur in samples with salinity < 5 (Fig. 2 2b ) though one outlier e xists with a DOC concentration of 85 mg/L at a salinity of 10 (Fig. 2 2b) Surface lagoon water contains from 10 24 mg/L DOC and 2.5 4.0 R.U. CDOM At EGN, both total DOC and CDOM exhibit a significant positive correlation with salinity (DOC: r 2 = 0.47; p < 0.0001; CDOM: r 2 = 0.49; p < 0.0001) (Fig. 2 2c ). Freshwater (salinity <5) at EGN has lower DOC and CDOM content than surface saltwater, and ranges from 1.3 4.4 mg/L and 0.5 1.8 R.U., respectively. S urface saltwater ranges from 7.0 12.7 mg/L DOC and 2.8 4.1 R.U. CDOM. At RWP, DOC concentrations exhibit a significant (r 2 = 0.52; p < 0.0001) positive correlation with salinity while CDOM exhibits a significant negative correlation (r 2 = 0.38; p < 0.0001; Fig. 2 2d ). Freshwater (salinity<5) has DOC concentrat ions from 7.3 10.0 mg/L DOC and 3.2 8.2 R.U. CDOM. Saltwater ranges from 10.8 12.9 mg/L DOC and 2.3 2.8 R.U. CDOM. At all Indian River Lagoon sites, all pore water ORP values decrease with salinity and all are lower than surface water samples, which have v alues near zero. These correlations are significant at EGN (r 2 = 0.50, p<0.0001) and RWP (r 2 =0.75, p<0.0001) but not at BRL. P ore water and surface water samples display similar salinity organic carbon relationships over time Differences between measured and modeled DOC and CDOM concentrations as estimated with conservative mixing models are smaller in the Yucatan than the Indian

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43 River Lagoon samples (Fig. 2 3 ). The Yucatan samples show a net loss of up to 0.75 mg/L DOC ( 40% ) across the freshwater saltwate r mixing zone compared to the concentration expected from conservative mixing ( Fig. 2 3a) This loss coincides with a gain of 0.9 R.U. ( 45% ) of CDOM. The highest deviations from conservative mixing occur at intermediate salinity of approximately 20. For In dian River Lagoon sites measured CDOM and DOC concentrations are both greater and less than those predicted by conservative mixing. A t BRL a maximum enrichment of 65 mg/L DOC (350% ) occurs, as well as 55 R.U. (550% ) CDOM at salinities less than 10 (Fig. 2 3b ). At EGN, measured DOC concentrations are both higher and lower than those predicted by conservative mixing : measured DOC concentrations range between 8 mg/L ( 60% ) lower and up to 5 mg/L 90% greater than expected con servative mixing values ( Fig 2 3c ). These deviations occur both in fresh as well as in more saline portions of the salinity gradient. At RWP, measured DOC concentrations are generally higher than conservative mixing line in the fresher portion of the salinity gradient, and reach concentra tions up 1.5 mg/L (20%) higher than expected from conservative mixing. Measured DOC concentrations are lower than expected from conservative mixing in the saltier portion of the salinity gradient, and are up to 1 mg/L ( 10% ) lower than conservative mixing c oncentrations ( Fig. 2 3d ). Measured CDOM concentrations are generally higher than conservative mixing values, and reach up to 4 R.U. ( 70% ) greater than expected from conservativ e mixing at salinities less than 10. PARAFAC Component Abundance with Salinity PARAFAC components display similar quantitative relationships to salinity as those observed for total CDOM; however, the relative abundance of PARAFAC

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44 components differ with salinity (Fig. 2 4 ). At all locations, the C2 component exhibits significant nega tive correlations with salinity (Yucatan r 2 =0.57, p<0.0001; BRL r 2 =0.08, p<0.05; EGN r 2 =0.32, p<0.0001; RWP r 2 =0.77, p<0.0001), although the correlations are weak at BRL and EGN In contrast, t he C5 component exhibits significant positive correlations wit h salinity (Yucatan R 2 =0.49, p<0.001; BRL r 2 =0.54, p<0.0001; EGN r 2 =0.32, p<0.0001; RWP r 2 =0.44, p<0.0001). Except for the Yucatan site, the C3 component exhibits a weak but significant positive correlation with salinity (Yucatan R 2 =0.03; BRL R 2 =0.19, p<0. 001; EGN R 2 =0.32, p<0.0001; RWP R 2 =0.16, p<0.01). The other components have little consistent variations with salinity between sites. The C1 component decreases in relative abundance with salinity, though this relationship is only significant at Yucatan and EGN sites and is poor at all sites. The C4 component shows little correlation with salinity at all sites except for EGN which exhibits a significant ( R 2 = 0.33; p<0.0001) positive correlation with salinity. Discussion Concentrations of DOC and CDOM vary by an order of magnitude between sites, with lowest concentrations in the Yucatan and EGN, followed by RWP and BRL (Fig.2 2). Because organic carbon remineralization drives many biogeochemical reactions, t hese concentration differences may affect the magnitude of biogeochemical reactions in the STEs. The extent of reactions between STEs should also be impacted in a variety of ways due to their distinct flow regimes (Yucatan = conduit flow; Indian River Lagoon = widely distributed seepage). First, mixing occurs rapidly in the conduits in the Yucatan which could limit biogeochemical processing if mixing rates are faster than reaction rates. Second, karst ic conduits have a lower rock:water ratio than the primary (i.e., intergranular) porosity in karst terrains or porous media. Aquifer solid materials are

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45 important sites of sedimentary organic carbon storage as well as mineral phases used by microbial commu nities to remineralize organic carbon (e.g. iron oxide minerals), and provide a substrate for microbial communities to grow and catalyze biogeochemical reactions. To depict differences in organic carbon processing between STEs, I first discuss the distrib ution of DOC, CDOM, and deviations from values expected from conservative mixing based on variation in salinity I then assess general trends in the quality of organic carbon between sites as indicated from the PARAFAC modeling These results inform a conc eptual model that outline s zones of enhanced biogeochemical activity in STEs as inferred by changes to organic carbon concentrations, and by analogy, products of biogeochemical reactions in STEs (Fig. 2 5). Organic Carbon Dynamics and Sources In the Yucata n, the near linear relationship between salinity and DOC (r 2 =0.90) and total CDOM (r 2 >0.95; Fig. 2 2 a ) suggests that salt and fresh water mixing rate is high relative to reaction rates, and that dilution of OC rich groundwater with OC poor seawater is the primary control of the DOC and total CDOM concentrations This result contrasts with the distribution of DOC and CDOM at Indian River Lagoon sites, where weak correlations between salinity and DOC and CDOM (Fig. 2 2b d) reflect non conservative relationshi ps. A greater degree of non conservative behavior at Indian River Lagoon sites is further indicated by greater residuals conservative mixing models than at the Yucatan site. Residuals are greater at Indian River Lagoon in absolute concentration as well as relative change (% deviation Eq. 2 3 ). For instance, t he m aximum deviation of DOC concentrations from the conservative mixing line for the Yucatan is a 0.7 mg/L loss (40%), compared to gains of 65 mg/L (>350%) at BRL, 5

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46 mg/L (100%) at EGN, and 2 mg/L (20%) at RWP (Fig. 2 3a d). Similar trends are observed for CDOM. The maximum deviation of CDOM abundance from the conservative mixing line for the Yucatan is a gain of approximately 1 R.U. ( 40 %) compared to 55 R.U. ( 600% ) at BRL, 4 R.U. ( 300% ) at EGN, and 4 R.U. ( 90% ) at RWP. These changes reflect more organic carbon processing in Indian River Lagoon than Yucatan STEs and is likely a result of the differences in flow rates and water rock ratios. Because organic carbon dri ves redox reactions, which consume terminal electron acceptors (e.g. oxygen and nitrate, Table 1 2) and produce remineralized carbon (CO 2 CH 4 ) and nutrients (nitrogen and phosphorus, N and P), the greater degree of organic carbon processing at Indian Rive r Lagoon compared to Yucatan sites implies greater consumption of terminal electron acceptors as well as production of react ion products. The extent of these reactions may therefore be critical in determining the chemical composition of SGD. Organic Carbon Quality Across Salinity Gradients Despite differences in DOC concentrations among sites similarities in the relative abundance of PARAFAC components as a function of salinity reveal systematic variations in organic carbon quality (Fig. 2 4 ). In particula r, the relative abundance of C2, characte rized as terrestrial humic like, decreases with salinity, while the relative abundances of C3 (microbial humic like) and C5 (protein like) increase with salinity at all sites (Fig. 2 4 ). Decreasing terrestrial humic like organic matter with salinity would be expected from dilut ion of terrestrial groundwater through mixing with marine water. A ddition ally marine water appears to serve as a source of reactive proteins as demonstrated by th e relative increase of C5 wit h salinity Proteins may be produced through primary productivity in seawater or alternatively, through in situ microbial cell

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47 turnover or degradation of particulate or solid phase organic carbon within the STE (Blough and Del Vecchio, 2002) Because C3 is microbial humic in nature, it may more likely be derived from in situ production, while C5 may be derived from either surface water primary production or in situ STE re actions. Although the exact origins and reactivity of C3 and C5 cannot be determined from these data their relative abundances have implications distribution of biogeochemical reactions within the STE. T he increase in labile protein like C5 with salinity suggests that biogeochemical reactions may be intensifi ed on the saline side of STEs, particularly if organic carbon availability limits reactions However, e ven if organic carbon is not limiting, inputs of labile organic carbon should increase reminerali zation kinetics (Berner, 1980; Arndt et al., 2013) leading to more remineralization in saline portions of the STE. Because organic carbon remineralization is a component of many other biogeochemical reactions including nutrient and greenhouse gas production, the shift in organic carbon quality with salinity may impact fluxes of these solutes from SGD. Distribution of Residuals and Implicatio ns for Controls of Biogeochemical Reactions Despite differences in flow regime between Yucatan and Indian River Lagoon sites, reactions within STEs impact CDOM similarly and lead to its production within the freshwater saltwater mixing zone while DOC is c onsumed at the Yucatan site as well as at EGN (Fig. 2 3 a and c ). Production of CDOM may result from in situ degradation of particulate organic matter or microbial activity ( Blough and Del Vecchio, 2002) DOC may also be produced by similar mechanisms, although its consumption at Yucatan and EGN sites may reflect preferential remineralization of non chromophoric DO C. The mechanism for different signs of DOC and CDOM residua ls at the Yucatan and EGN

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48 sites are unknown but could relate to differences in the reactivity of CDOM versus non chromophoric DOC : CDOM may contain relatively more recalcitrant biomolecules than bulk DOC and is therefore may be less readily degraded than n on chromophoric portions (Blough and Del Vecchio, 2002) Given the consistent non conservative behavior of CDOM between sites, I use distribution of the non conservative CDOM as an indicator for enhanced biogeochemical activity to compare the magnitude and location s of reactions between STE sites. In all cases, the non conservative CDOM displays systematic variations with salinity that reflect the zones of enh anced biogeoc hemical activity. Enhanced biogeochemical activity suggests that a limiting reactant has been delivered to the zone and increases the intensity of reactions involving organic carbon. Therefore, the discussion below examines the distribution of CDOM residua ls along salinity gradients at STE sites to evaluate controls of enhanced biogeochemical reactions. In this framework, t he source of limiting reactants determines the location of organic carbon processing within the salinity gradient while the mixing rate relative to reaction rate determines the deviation from conservative mixing lines as a measure of the magnitude of the reactions (Fig. 2 5). Three of the four STE sites (Yucatan, BRL, and RWP) contain greater CDOM concentrations in freshwater compared to saltwater, and freshwater redox potential is low (Fig. 2 2). Low ORP and high CDOM concentrations indicate that organic carbon remineralization is limited by the availability o f terminal electron acceptors. Thus, enhanced remineralization following freshwa ter saltwater mixing may occur through the delivery of electron acceptors in the seawater, such as sulfate, to the fre shwater

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49 saltwater mixing zone (Fig. 1 5). Water containing organic carbon but lacking sulfate may be sufficiently reducing to support meth anogenesis (Weston et al., 2011) but methanogenesis is inhibited by sulfate concentrations > 1 mM (Whiticar and Schoell, 1986) Once sulfate mixes with reducing and low sulfate freshwater, methanogenesis should cease and sulfate reduction begin because of the greater energy yield of sulfate reduction relative to methanogenesis (Table 1 1). This shift in redox pathway appears to have occurred at RWP and BRL, where maximum CDOM residuals are located at the fresher portion of the salinity gradient and where delivery of sulfate would have the greatest impact on organic carbon remineralization (Fig. 2 5c). However, at the Yucatan STE, the freshest sample measured has a salinity of 10, which if simply diluted by sulfate free freshwater should over 8 mM of SO 4 2 and should inhibit methanogenesis. Moreover, sulfate concentrations at this salinity are far greater than organic carbon concentrations (up to 3 mg/ L or 0.25 mM), and sulfate should therefore not limit remineralization. Alternatively, the turbulent flow dynamics in karst conduits may allow for surface water to deliver oxygen up to several 10s of meters into conduits (Parra et al., 2015) In this case, rapid depletion of oxygen could enhance biogeochemical activity to a greater extent than sulfat e reduction because of the greater energy yield (Table 1 1). Reduction of dissolved oxygen by organic carbon remineralization has been found at the Yucatan site previously ( Young et al., 2017) and likely results at least in part to the enhanced reminerali zation observed at intermediate salinities here (Fig. 2 5b) EGN is distinct from other sites because freshwater has positive ORP values and contains lower organic carbon concentrations than saltwater. The low total CDOM an d

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50 DOC concentrations correspond w ith elevated ORP values which indicate that TEA availability is high (Fig. 2 2c ) This correspondence suggests that biog eochemical reactions at this site are carbon limited, rather than TEA limited, which allows aerobic conditions to be maintained in the fresh portion of the STE N on conservative behavior of CDOM at EGN results from biogeochemical reactions in the more saline p ortion of the STE where relatively labile marine organic carbon is delivered to the STE Because of its relatively higher content of protein like organic matter, as represented by PARAFAC component C5, the inputs of marine organic matter in saline portions of the STE may serve to further increase organic carbon remineralization in c arbon lim ited STEs Increased remineralization would result from both an increase in the quantity and the relative reactivity of organic carbon available to drive reactions (Fig. 2 5a) Conclusions T his study reveal s hydrogeological controls on the distributio n of organic ca rbon and the extent of its processing in subterranean estuaries. Organic carbon distribution in the Yucatan STE, characterized by conduit dominated flow, appears to be mostly controlled by end member mixing, though salinity based conservative mix ing models reveal some in situ production of CDOM coinciding with DOC consumption. This observation suggests that biogeochemical processing of carbon is sufficiently rapid to alter its concentrations with salinity, despite short timescales of mixing. In In dian River Lagoon STEs which are characterized by widely distributed seepage through siliciclastic sediments, organic carbon quantities are controlled by mixing as well as reactions These reactions can enhance DOC and CDOM concentrations several times gr eater than conservative mixing concentrations Despite these differences, all four sampled STEs exhibit s imilar variation s in organic carbon quality across salinity

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51 gradients where t he relative abundance s of terrestr ial humic like compounds (%C2) decrease s and protein like compounds (%C5) increases with salinity. Because proteins tend to be more reactive than humic acids, these findings may illustrate a change in the overall reactivity of organic carbon within STEs, in which reactivity increases with incre asing contributions of marine organic matter. This trend occurs regardless of the organic carbon concentrations in fresh vs. marine end members or the differences in hydrology inherent in karst carbonate versus siliciclastic systems. This finding highlight s the importance of organic carbon reactivity, which depends on its origins, to biogeochemical reactions in STEs. Because organic carbon remineralization drives many reactions that alter the concentration and speciation of solutes such as nutrients (Slomp and Van Cappellen, 2004; Gonneea and Charette, 2014) metals (Roy, et al., 2010; Johannesson et al., 2011) and carbon, the intensity and distribution of reactions with salinity may exert an important control on solute fluxes via SGD.

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52 Table 2 1. PARAFAC component matches as identified via OpenFluor Component Component Description C1 Terrestrial humic like, suggested as photo refractory (C2; Yamashita et al., 2010) C2 Terrestrial humic l ike, high molecular weight (C1; Kothawala et al., 2012) C3 Microbial humic like fluorescence (C2; Murphy et al., 2011) C4 Terrestrial humic (C3, Walker et al., 2009) C5 Protein like (C4; Cawley et al., 2012) Table 2 2. R 2 and p value for correlations between PARAFAC componen ts in PARAFAC model, n=322. All components are positively correlated. *p<0.0001 C1 C2 C3 C4 C1 -0.46* 0.88* 0.36* C2 0.46* -0.74* 0.95* C3 0.88* 0.74* -0.62* C4 0.36* 0.95* 0.62* -C5 0.84* 0.19* 0.66* 0.13*

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53 Figure 2 1 Five component PARAFAC model for subterranean estuary samples Components C1, C2 and C4 are characterized as terrestrial humic like, C3 is microbial humic like, and C5 is protein like (Table 2 1).

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54 Figure 2 2 Cross plots of salinity versus DOC, total CDOM, and ORP for ( a ) Yucatan, ( b ) BRL, ( d ) EGN, and ( d ) RWP sites. Closed data points represent groundwater samples and open data points represent surface water for the Yucatan samples, and for samples collected in Indian River Lagoon in September 2014 (black circle), May 2015 (black square), September 2015 (red circle) and May 2016 (red square)

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55 Figure 2 3 Residuals of s alinity based conservative mixing model s for DOC and total CDOM for ( a ) Yucatan, ( b ) BRL, ( c ) EGN, and ( d ) RWP sites. Deviations from residuals are reported in concentrations as well as percent deviation from the conservative mixing line. Closed data points represent groundwater samples and open data points represent surface water for the Yucatan samples, and for samples collected in Indian River Lagoon in September 2014 (black circle), May 2015 (black square), September 2015 (red circle) and May 2016 (red square).

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56 Figure 2 4 Relative PARAFAC component abundance versus salinity for ( a ) Yucatan, ( b ) BRL, ( c ) EGN, and ( d ) RWP sites. Closed data points represent groundwater samples and open data points represent surface water for the Yucatan samples, and for samples collected in Indian River Lagoon in Septemb er 2014 (black circle), May 2015 (black square), September 2015 (re d circle) and May 2016 (red square)

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57 Fig ure 2 5. Conceptual model of CDOM conce ntrations versus salinity for (a) Carbon limited STEs (representative of EGN) (b) Oxygen limited STEs (r epresentative of Yucatan) and (c) Sulfate limited STEs (re presentative of BRL and RWP). Graphs present hypothetical concentrations (solid black lines) compared to conservative mixing lines (dashed black lines) of solutes due to remineralization of a generic organic molecule, (CH 2 O) 106 (NH 3 ) 15 (H 3 PO 4 ). Black dots re present concentrations in freshwater and saltwater end members. The change in con centrations due to reactions are represented by R. The location of production on the salinity gradient is a function of the limiting reactant, while the absolute deviation fro m the conservative mixing line is determined by reaction rate relative to mixing. Expected changes in solute concentrations are indicated in panels for (a) organic carbon limited STEs, where both fresh and saltwater end members contain equivalent concentra tions of O 2 /NO 3 but low concentrations of nutrients. Remineralization in the saline portion due to labile organic carbon delivery increases NH 4 /PO 4 /CO 2 concentrations. (b) oxygen limited STEs, where freshwater is reducing (low O 2 /NO 3 ) and contains products of remineralization (NH 4 PO 4 CO 2 ). Delivery of O 2 by saltwater depletes O 2 and generates NH 4 PO 4 and CO 2 and (c) sulfate limited STEs, which is similar to (b) but processing occurs near the freshwater portion of the STE where sulfate concentrations be gin to increase.

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58 CHAPTER 3 ORGANIC INORGANIC CARBON FEEDBACKS IN A CARBONATE KARST AQUIFER AND IMPLICATIONS FOR NUTRIENT AVAILABILITY Introduction Carbonate aquifers are characterized by high permeability due to secondary porosity that results from the dis solution of calcium carbonate minerals, leading to the formation of karst features that allow rapid water infiltration to the groundwater table ( Fleury et al., 2007) Consequently, freshwater is predominantly stored as groundwater and surface runoff may be scarce or negligible. For coastal karst aquifers, these characteristics make submarine groundwater discharge (SGD) the predominant or sole source of terrestrial fresh water and solutes to the coastal zone (Fleury et al. 2007) and thus a critical component of coastal biogeochemical budgets. The terrestrial water is unlikely to discharge from subterranean estuaries (STE) without chemical modif ication along the flow paths. Thes e modifications are likely to have important impacts to budgets of biogeochemically important solutes in coastal zones (Moore, 1999; Slomp and Van Cappellen, 2004) Biogeochemical reactions in STEs are largely driven by organic c arbon remineralization (Ch. 2). This reaction l eads to the production of carbonic acid via hydration of the produced CO 2 (Froelich et al., 1979) I depict aerobic remineralizati on as a generic remineralization pathway below. Aerobic respiration is the most energetically favorable reaction and therefore proceeds when oxygen is available in sufficient concentrations: (CH 2 O) 106 (NH 3 ) 15 (H 3 PO 4 ) + 138O 2 106CO 2 + 16 HNO 3 + H 3 PO 4 + 122H 2 O ( 3 1 )

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59 CO 2 generated by remineralization induces further feedbacks as it hydrates to carbonic acid that dissociates to decrease th e pH of the water according to: CO 2 + H 2 O H 2 CO 3 HCO 3 + H + CO 3 2 + 2H + ( 3 2 ) The decreased pH leads to undersaturation with respect t o carbonate minerals which disso lve according to : CaCO 3(s) + CO 2(g) + H 2 O Ca 2+ + 2HCO 3 ( 3 3 ) While organic carbon remineralization and associated CaCO 3 dissolution are known to occur in carbonate aquifers (Gulley et al., 2011; Brown et al., 2014; Gulley et al., 2015) the re lative magnitude of these processes are unknown in STEs where mixing of terrestrial and marine organic carbon enhances biogeochemical reactions (Ch. 2). Because reactions within STEs modify the composition of SGD, the relative magnitudes of CO 2 sources (re mineralization, Eq. 3 1) and sinks (CaCO 3 dissolution, Eq. 3 3) may regulate surface water CO 2 fluxes to the atmosphere. Apart from producing CO 2 organic carbon remineralization also leads to the generation of nutrients (Eq. 3 1) producing CO 2 and inorg anic nitrogen (N) and phosphorus ( P ) at a ratio of 106:16:1, otherwise known as the Redfield Ratio (Redfield, 1934) This ratio aligns with the typical nutrient requirements of phot osynthetic algae in the ocean, and processes that alter the ratios of dissolved nutrients drive ecosystems toward nutrient limitation (Redfield, 1934) P is commonly limiting in carbonate settings due to a high affinity for sorptio n of PO 4 3 to carbonate mineral surfaces, which reduces dissolved P concentrations (DeJonge and Villerius, 1989) Co nversely, c arbonate dissolution driven by elevated CO 2 concentrations may increase P availability in coastal zones (Price and Herman, 1991; Price et al., 2010) Therefore, both carbonate

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60 dissolution and precipitation could alter fluxes of P from sediment to surface water (Short e t al. 1990) If P is a limiting nutrient, as is commonly the case in carbonate systems, liberation of P from organic carbon remineralization coupled with carbonate mineral dissolution may drive a negative feedback loop for surface water CO 2 concentrations: excess P delivered by SGD can drive primary productivity, which sequesters CO 2 as organic matter. While links between organic carbon remineralization, carbonate mineral saturation, and P availability are known to exist, the magnitude of th ese feedbacks within STEs and their impact on fluxes from SGD are unknown Here, I assess the relationships between the organic carbon processing and feedbacks with CO 2 and P concentrations in a carbonat e karst aquifer in Quintana Roo Mexico where groun dwater discharges to coastal lagoons through submarine springs. Previous work at this location indicates that both organic matter reactivity and terminal electron acceptor availability change along salinity gradients: t he proportion of protein like organic carbon increases with salinity (Ch. 2) as well as oxygen concentrations derived from surface water, which is rapidly consumed in spring vents during period of saltwater intrusion (Young et al., 2017) C onstrain ing coupled nutri ent and carbon transformations prior to discharge to the coastal ocean could improve understanding of carbon cycling and biogeochemical processes in carbonate karst STEs. Study Location The field site is located in a coastal lagoon offshore of Puerto Morel os approximately 40 km south of Cancun in the state of Quintana Roo, Mexico, on the Yucatan Peninsula ( Fig. 1 4 a). The Yucatan Peninsula is a karstic carbonate platform of Triassic to Holocene age, and is characterized by dissolutional secondary porosity. This

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61 secondary porosity generates high aquifer permeability and hydraulic conductivity, which limits surface water and causes groundwater to discharge at the coast as SGD from submarine springs while surface runoff is negligible (Gonzlez Herrera et al., 2002; Beddows et al., 2007) Groundwater is recharged as rainwater infiltrates thin soil layers overlying the carbonate m atrix, and most recharge occurs during the rain y season from June to October. Recharged water carries elevated P CO2 (the partial pressure of dissolved CO 2 ) from soil OC remineralization that aids dissolution (Gulley et al., 2016) Although surfa ce water is scarce, the region is dotted with dissolution features known as cenotes which expose the water table and may extend to saltwater below the freshwater lens (Schmitter Soto et al., 2002) Mangrove wetla nds, which fix large amounts of carbon, occupy ~2 km wide benches between Marine Isotope Stage (MIS) 5e high stand deposits (Fig. 1 4 b and 1 4 c; Blanchon et al. 2009) and coa stal dunes. Secondary porosity form long water filled cave systems that connect inland cenotes to offshore springs. Spring discharge to the coastal fringing reef lagoon is controlled in part by lagoon hydrodynamics. Some of the submarine springs reverse fl ow with elevated lagoon level at high tide and during wave and wind set up events (Parra et al., 2014; Parra et al., 2015) Saline and freshwater components of SGD have been separated using multiple methods including isotopic tracers ( 222 Ra), salinity, and silica m ixing models (Hanshaw and Back 1980; Hernndez Terrones et al. 2010; Nul l et al. 2014) Discharge varies seasonally due to increased recharge in the rainy season, but peak discharge lags peak precipitation by several months (Perry et al. 2003; Null et al. 2014) Null et al. (2014) estimated fresh SGD at Puerto Morelos at 29.3 m 3 day 1 per meter of shoreline, 78.5%

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62 of which was derived from spring discharge, with the remainder derived from diffuse seepage from the beach face. Beach face discharge originates from an unconfined surficial aquifer while spring discharge originates from a deep confined aquifer (Null et al., 2014) The lagoon is microtidal but tidal flushing regulates lagoon residence time, which averages 3 hours but can be as low as 0.35 hours following lagoon level setup follow ing storm swells (Coronado et al., 2007) Nutrient concentrations can be elevated in Yucatan groundwater from wastewater contamination (Metcalfe et al., 2011) Contamination can occur through diffuse run off as well as through point sources such as widely dist ributed individual septic tanks and disposal wells that inject untreated wastewater into the saline groundwater below the freshwater lens. Injected wastewater pollutants migrate upward into the freshwater lens, elevating the concentrations of contaminants, nutrients, and pathogens (Metcalfe et al., 2011) Domestic wastewater contamination also leads to heterogeneous nutrient concentrations in coastal freshwater wells (Hernandez Terrones et al., 2011). Wastewater contami nation of springs is reflected in elevated E. coli concentrations of spring discharge, suggesting some OC and nutrients in the STE may have an anthropogenic source. Nutrient concentrations in spring discharge are high compared to average lagoon water conce ntrations (Null et al., 2014) N:P ratios are greater than the Redfield ratio of 16 though P loads are several times higher than those from simi lar SGD systems such as Florida Bay (Hernndez Terrones et al., 2011; Null et al., 2014) Because P is a limiting nutrient, its delivery from SGD increases primary productivity around submarine springs, and groundwater P inputs are critical for ecosystem dynamics (Carruthers et al. 2005)

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63 Methods Field Methods Water was sampled over a two week period in September 2014 along a transect from cenotes ~21 km inland to the lagoon. A lthough these samples are not along a known flow line, each represents characteristics of water along a flow path from the recharge to discharge areas (conceptual model given in Fig. 3 1). Most samples were collected offshore including four submarine sprin gs (Hol Kokol, Gorgos, Laja, and Pargos) that are distributed along ~5 km of shoreline in Puerto Morelos (Fig. 1 4 c ). During the sampling periods, tides, wave setup, and storms elevated sea level and caused surface lagoon w ater to backflow into springs. Th is backflow caused variable salinity of vent discharge that serves as a conservative tracer to assess mixing, from which biogeochemical processing in the STE may be evaluated. All spring samples were collected as the springs discharged. A lagoon surface wa ter end member was collected in the lagoon outside the direct influence of discharging vents (approx. 1 km from shore; Fig. 1 4 c ). Inland water was collected from three cenotes (Cenote Siete Bocas, Cenote Zapote, and Cenote Kin Ha) approximately 21 km fro m the shoreline (Fig 1 4 b). These samples represent the primary recharge area of the Yucatan aquifer. S urface water was collected from a mangrove wetland that parallels the coastline along a 2 km wide bench between MIS stage 5e highstand deposits. This water represents a potential freshwater source and was sampled in April 2014 prior to the main sampling trip in September. Finally a near shore well (UNAM ), located approximately 100 m inland was sampled to represent groundwater from the dune field that parallels the coast. Although these sites are unlike to represent a single continuous flow path, we use these samples to estimate

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64 potential sources and transformation of solutes that flow to the STE sampled in the offshore wells (Fig. 3 1). Sampling was accomplished by pumping water to the surface through a 0.5 cm diameter flexible PVC tube from spring openings, cenotes and the well. A YSI Pro Plus sensor was installed in an overflow cup in line with the tubing to measure salinity, temperature, pH, dissolved oxygen, and oxida tion reduction potential (ORP). Once these parameters were stable, samples were collected after being filtered through 0.45 m trace metal grade Geotech medium capacity dispos able canister filt ers and preserved in the field. Samples for cations and anions were collected in HDPE bottles; cation samples were preserved with trace metal grade nitric acid (pH<2) while no preservative was added to anion samples. Samples for dissolved organic carbon (DOC) and fluorescent DOC analysis were collected in amber borosilicate vials that were combusted at 550C prior to use. DOC samples were acidified with hydrochloric acid to pH<2. Fluorescent DOC samples were not acidified and were frozen u ntil analysis within one month of collection. Nutrient samples were filtered directly into polypropylene vials with no preservative and frozen until analysis. DIC samples were filtered at 0.2 m directly into glass vials and sealed tightly with no headspac e. All other samples were kept chilled in the field (ice) and refrigerated in the laboratory until analysis. Laboratory Methods Anion and cation concentrations were measured on an automated Dionex ICS 2100 and ICS 1600 Ion Chromatograph, respectively. Erro r on replicates was less than 5%. Nutrient concentrations were analyzed on a Seal AA3 AutoAnalyzer. Error on replicates was less than 10%. DOC concentrations were analyzed on a Shimadzu TOC VCSN total organic carbon analyzer, and the coefficient of varianc e was less than 2%.

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65 DIC concentrations were measured on a UIC (Coulometrics) 5011 CO 2 coulometer coupled with an AutoMate Preparation Device. Samples were acidified and the evolved CO 2 was carried through a silver nitrate scrubber to the coulometer where t otal C was measured. Accuracy was calculated to be 0.1 mg/L. We depicted organic carbon character through the generation of excitation emission matrices (EEMs) via fluorescence spectroscopy, and modeled the results with PARAFAC analysis according to metho ds outlines in Chapter 2 Note that the relationship between concentration and fluorescence intensity is unknown but likely to be variable between components, therefore the proportion of fluorescence is not analogous to the relative concentration of compon ents, but only reflects relative changes in component abundance compared to the total amount of fluorescent matter. Modeling STE conservative mixing m odels Carbon chemistry is depicted through ge ochemically modeled parameters P CO2 (partial pressure of diss olved CO 2 ) and SI cal (calcite saturation index). SI cal is calculated to determine whether a system is at equilibrium with respect to calcite (CaCO 3 ) and is defined as the log of the ratio of the ion activity product (IAP) to the solubility product (K sp ) with respect to calcite. At equilibrium, the IAP should be equal to K sp and thus the saturation index should be 0. Under saturated systems will have negative SI cal values, and super saturated systems will have positive SI values. SI cal values indicate whe ther mineral dissolution is expected to occur. SI cal and P CO2 were calculated with geochemical modeling software PHREEQc (USGS) with the PHREEQc.dat database (Parkhurst, 1995) Input parameters included major cation and anion concentrations, pH, temperature, and DIC concentrati ons of water samples (Appendix B )

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66 To depict changes in STE chemistry due to reactions versus mixing, I constructed conservative mixing models between a saltwater end member (represented by surface lagoon water), and a freshwater end member, which I define as the freshest SGD sample measu red (Hol Kokol; Table 3 1). The mixing models wer e applied to reaction products including DIC, P CO2 SI cal Ca 2+ NH 4 + and PO 4 3 Mixing was assumed to be linear except for P CO2 and SI cal which are non linear (Langmuir, 1997) For these parameters conservative mixing models were constructed by using salinity to calculate the fraction of fresh and saltwater e nd members in each sample (Eq. 3 4 and 3 5), where f designates the proportional contr ibution of end members and S designates salinity. I modeled mixtures corresponding to each sample salinity in PHREEQc to determine the expected value to mixing. f freshSGD + f seawater = 1 ( 3 4 ) S Sample = f freshSGD *S freshSGD + f lagoon S lagoon ( 3 5 ) For all conservative mixing models, the residual between measured data and the conservative mixing value is considered to represent changes in chemistry due to reactions within the STE. Residual concentrations, indicating changes due to reactions, are indicated by symbols, which equals measured value minus the value expected solely from mixing (Eq. 3 6); positive values represent a gain and negative values represent a loss of the solute Solute = [Solute measured ] [Solute mixingmodel ] ( 3 6 ) Surface water organic carbon mass balance To evaluate the fate of terrestrially deriv ed OC in surface lagoon water, I constructed a mass balance for the surface lagoon water composition based on its

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67 measured salinity, assuming it was a mixture of average seawater and freshest SGD (Table 3 1) Seawater outside the lagoon was not sampled in this study, so average seawater was ass umed to have a salinity of 35. Salinity lagoon = f freshSGD *Salinity freshSGD + f seawater Salinity seawater ( 3 7 ) Total fresh SGD was split into SGD from su bmarine springs versus beach face SGD based on the results of Null et al. (2014), where spring SGD was 7 8 .5% of total fresh SGD and both SGD types were assumed to have the same salinity: f springSGD = 0.7 8 5*f freshSGD ( 3 8 ) Estimating the fraction of spring water is needed because it is assumed to be the only source of CDOM, because seawater organic carbon concentrations are typically low and beach face concentrations are likely to be insignificant compared to springs ( Chapelle et al. 2016) Solving for f freshSGD in Eq. 3 7 and inserting into Eq. 3 8 yields an expression estimating the proportion of lagoon surface water derived from spring SGD. Values of salinity and fluorescent OC content of lagoon water and fresh SGD end members were identical to those used in mixing models (Table 3 1). Results Salinity and Biogeochemical P arameters Terrestrial water increases in salinity with proximity to shore: cenotes have the lowest average salinity (0.72) followed by mangroves (1.60) and U NAM well (5.63) (Table 3 2 ). Fresh groundwater (cenotes and UNAM well) have low dissolved oxygen concentratio ns and negative ORP values although the UNAM well is more reducing than cenotes. Mangrove surface water has a positive ORP value and higher pH than the cenotes and UNAM well samples. SGD samples span a salinity range from 9.77 to 26.6

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68 and all are reducing. Lagoon surface water has the highest salinity sampled (32.7), which on average is 90% saturated with DO and has a positive ORP (Table 3 2). Within the STE, both measured and conservative mixing concentrations for DIC, P CO2 and NH 4 concentrations are inve rsely correlated with salinity, while measured and conservative mixing for SI cal and Ca concentration are positively correlated (Fig. 3 2a e) Although the conservative mixing model shows an inverse relationship between salinity and PO 4 concentrations, the measured PO 4 concentrations are not significantly related to salinity For STE samples, positive values of DIC, P CO2 Ca, NH 4 and PO 4 indicate they are enriched and reflect production within the STE while SI cal values are more undersaturated than w ould be expected based on simple mixing between the saline and fresh end members (Fig. 3 2c). Freshwater end members (UNAM well, cenotes, and mangroves) are distinct from STE samples in the concentrations of solutes in Fig. 3 2. Cenotes have higher DIC co ncentrations, lower P CO2 values, and higher SI cal values than other freshwater end members, while they contain lower concentrations of NH 4 and PO 4 relative to STE samples. Similarly m angrove samples are depleted in all solute concentrations relative to co nservative mixing relationships, except for DIC, SI cal or P CO2 for which no data are available for the mangrove site In contrast, the UNAM well falls close to a projection of the mixing line of STE samples for all solutes, though NH 4 concentrations are sl ightly elevated and PO 4 concentrations are slightly lower than the lower salinity extension of the conservative mixing line (Fig. 3 2).

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69 Organic Carbon Character and D istribution Total CDOM abundance in spring vent samples shows a strong negative correlatio n with salinity (Fig.3 3a). The CDOM is broken into various components by PARAFAC modelling, that is described in Chapter 2 and includes 3 22 samples from subterranean estuaries in the Yucatan as well as Indian River Lagoon, Florida. The five different PAR AFAC component abundances vary with salinity in STE samples as well as compared to freshwater sources (mangrove surface water, cenotes, and inland wells) (Fig. 3 3 b f). PARAFAC components are collinear for Yucatan samples, with R 2 values near 1 for all comparisons (Table 3 3). Terrestrial freshwater end members have variable component abundances: the UNAM well falls close to an extension of the salinity abundance observed in STE samples similar to the solute concentrations, while t he mangrove surface water sample has much higher and cenote freshwater samples have much lower abundances (Fig. 3 3a). Changes in organic matter composition are depicted by variation in the proportion of fluorescence attributable to each PARAFAC component For STE samples, most component abundances remain constant across the salinity gradient but considerable composition changes occur in the m ost saline samples (Fig.3 4b f) Except for component C5, the mangrove surface water and cenote differ from STE and UNAM well samples, where mangroves have relatively greater C1 and lower C2, C3 and C4 abundances, while cenotes have relatively higher C3 but lower C1 and C4 abundances. Utilizing the mass balance relationships outlined in Eq. 3 7 and 3 8 and based on th e salinity of lagoon surface water (32.7 compared to the salinity of standard seawater (35) and fresh SGD (9.77; Table 3 1), I estimate that SGD comprises 9% of surface lagoon water. If 78.5% of fresh SGD is derived from submarine springs with the

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70 remainde r from diffuse seepage at the beach face (Null et al., 2014), 6.5% of surface lagoon water is derived from submarine spring discharge. I assume that this spring discharge contributes all CDOM to surface water, and that surface water CDOM content is regulat ed by simple dilution of organic carbon rich SGD (Table 3 4). This assumption allows me to evaluate potential changes in CDOM from reactions in the lagoon through comparisons of the measured PARAFAC component in surface lagoon water to that predicted by d ilution of CDOM rich spring discharge. Based on this analysis, modeled concentrations are approximately two times greater than measured concentrations (indicating net consumption in surface water) for components C1 C4, but about one third the measured abun dance of C5 (indicating net production in surface water; Table 3 4). Discussion The following discu ssion addresses the magnitudes of feedbacks between organic carbon remineralization, carbonate mineral dissolution, and phosphorus concentrations in carbonat e karst STEs. I assess organic matter quality at two different locations: within the inland aquifer, and at discharge sites of the hydrologic system (Fig. 3 1). These discussions center on PARAFAC signatures and biogeochemical solute concentrations of end members I first discuss the sources and transformations of organic matter in the inland aquifer system by comparing the quantity and quality of organic carbon from terrestrial freshwater sources in a system wide flow model (e.g., Baker et al. 20 03; Baker and Spencer 2004; Lapworth et al. 2008) along with indicators of biogeochemical reactions (P CO2 SI cal Ca, NH 4 and PO 4 ). I then evaluate biogeochemical reactions within the freshwater saltwater mixing zone of the STE. Based on the ratios of solutes produced compared to the stoichiometries of reactions I

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71 describe a conceptual model of OC driven biogeochemical reactions within the STE Finally, I discuss implications for organic carbon fluxes to the lagoon and CO 2 fluxes to the atmosphere. Te rrestrial Sources and Processing of Organic Carbon Organic carbon in the STE is largely derived from terrestrial freshwater sources, as indicated by the strong negative correlations betwee n salinity and CDOM abundance (Fig. 3 3a ) and with DOC (Chapter 2, Fig. 2 2). While components are collinear (Table 3 3), the relative proportion of PARAFAC components varies with salinity in terrestrial freshwater end members (cenotes, mangrove surface water, and UNAM well), as well as offshore in STE samples and surface lagoon water (Fig. 3 3). These variations suggest changes in organic carbon quality that may impact the distribution and magnitudes of biogeochemical reactions during transport from terrestrial freshwater sources offshore and discharge as SGD. Possible sources of terrestrial OC in the STE could include water recharged directly through soils, infiltration from mangrove forests, and anthropogenic wastewater Although freshwater sampling points do not represent points along a specific flow paths in the con ceptual model presented in Fig. 3 1, they should represent characteristics o f coast parallel environments. Specifically, cenotes represent inland areas where precipitation recharges groundwater (Fig. 3 1a), mangroves represent the strip between the 5e high stand reef deposits and coastal dunes (Fig. 3 1b), and the UNAM well represents the coastal dunes (Fig. 3 1d). Inland groundwater recharge areas (characterized by cenotes), appear to contribute little OC and contains less organic matter than the well, mang roves, or water in the STE (Fig. 3 3a ). The mangrove wetlands have higher CDOM concentrations and relative C1 component abundances

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72 corresponding with lower relative abundances of C2, C3 and C4 than other freshwater sources (Fig. 3 3a e ). Mangrove organic carbon may be a source to the STE, because i ntertidal mangrove forests transpire sufficient water to create pore water brines that can alter SGD dynamics (McGowan and Martin, 2007) and similar salt concentration may set up density instability in the freshwater wetlands and cause downward infiltration into the groundwater flow path in our field area (Fig. 3 1b ). While no wastewater was sampled, disposal is unregulated in the region and known to contaminate freshwater sources (Hernndez Terrones et al. 2011; Metcalfe et al. 2011) The injection of wastewater below the halocline and its subsequent upward mixing (Metcalfe et al., 2011) indicates this source could also contribute to organic matter in the STE. While mangrove wetlands may contribute to terrestrial OC at the STE, the PARAFAC signature of mangrove wetlands is distinct from both the UNAM well and the STE by being dominated by C 1 with lower abundances of C2 C5 (Fig. 3 1b ). These distinctions suggest it is altered along the flow paths to the coast (Fig. 3 1c). Alteration is not simply caused by dilution with low OC groundwater, a process that would retain the relative abundance of PARAFAC components. Alteration of organic carbon composition through remineralization could modify both the quantity and quality of organic carbon and change PARAFAC component composition along freshwater flow paths. Remineralization is evidenced by elevated P CO2 and nutrient concentrations, and lower SI cal values in the UNAM well and STE samples compared to cenotes (Fig. 3 2b f). T he salinity and nutrient concentrations, and P CO2 and SI cal values of the UNAM well fall close to the expected conservative mixing (Fig 3 2 ), which suggests most alte ration

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73 occurs inland of location of the UNAM well (e.g. Fig. 3 1c ) and little additional alteration occurs as water flows from the dunes to the springs (e.g., between d and e on Fig. 3 1 ). Biogeochemical processing in the STE Organic carbon remineralizat ion between inland freshwater end members (cenotes, mangroves) and the STE is shown by increasing P CO2 and nutrient (NH 4 and PO 4 ) concentrations (Fig. 3 2). However, f urther remineralization within the freshwater saltwater mixing zone occurs within the STE as indicated by positive values of P CO2 NH 4 and PO 4 (Fig. 3 2). Additionally, carbonate mineral saturation indices within STE samples are lower than the conservative mixing line while Ca values are positive, suggesting that CaCO 3 dissolution also occurs (Fig. 3 2c and d). R emineralization may result from intrusion of oxygen containing surface lagoon water to the STE (Chapter 2; Young et al., 2017), which may enhance remineralization rates because oxygen produces the most energy of redox reactions (Chapter 2; Table 1 2). Alternatively, labile organic carbon, such as that depicted by the C5 component in PARAFAC modeling, is present in relatively high concentrations in lagoon water and could enhance remineralization reactions in the m ixing zone (Chapter 2). Given the evidence from conservative mixing models that both organic carbon remineralization and CaCO 3 dissolution occur within the STE, the following discussion focuses on the magnitude of carbon feedbacks as well as impact on P c oncentrations by comparing the change in solute concentration to that predicted by reaction stoichiometry (Eq. 3 1 and 3 3). I assume that inorganic C and P distributions are predominantly controlled by organic carbon remineralization, which I assume gener ate s C:N:P at the Redfield Ratio of 106:16:1 (Eq. 3 1), CaCO 3 dissolution, which produces

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74 Ca and DIC at a molar ratio of one (Eq. 3 3), and sorption interactions between P and CaCO 3 minerals, which has no fixed stoichiometry. Remineralization reactions in the STE generate CO 2 which decreases SI cal to values lower than those expected from conservative mixing (Fig. 3 2c). Enrichment of Ca ( Ca) by up to 1.3 mM above conservative mixing reflects carbonate dissolution within the mixing zone (Fig. 3 2d). Dissol ution of CaCO 3 minerals may occur due to fresh and saltwater end member mixing alone because of the cubic rather than linear dependence of SI cal on Ca concentrations (Plummer 1975; Smart et al. 1988; Sanford and Konikow 1989) However, in this case, mixing alone is unlikely to cause dissolution because mixing only gener ates positive SI cal values (Fig. 3 2c). OC remineralization, CO 2 production, and hydration to carbonic acid (Eq. 3 1 and 3 2) may reduce SI cal to negative values and drive dissolution (e.g., Gulley et al. 2014; Gulley et al. 201 5) This mechanism is more likely here, resulting from enhanced OC remineralization (Gulley et al., 2016) from the introduction of terminal electron acceptors or labile organic carbon with seawater. The net impact of coupled organic carbon remineralizat ion (Eq. 3 1) and CaCO 3 dissolution (Eq. 3 3) on CO 2 concentrations depends on the relative magnitudes of these two processes This may be assessed by comparing the proportions of DIC and Ca production ( DIC and Ca) within the STE, which are taken as the residual of salinity based conservative mixing models (Fig. 3 2a and 3 2c). OC remineralization produces CO 2 (E q. 3 1) while carbonic acid driven carbonate mineral dissolution is a CO 2 sink as it converts CO 2 to bicarbonate (E q. 3 3). Consequently, positiv e DIC residuals (Fig. 3 2a) result from both OC remineralization and carbonate mineral

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75 dissolution. However, positive P CO2 residuals (Fig. 3 2b) indicate that increases in DIC largely reflect OC remineralization rather than carbonate mineral dissolution I f only OC remineralization and carbonate mineral dissolution impact DIC and Ca concentrations, all residuals should plot within the shaded field in Fig. 3 4 bounded by the expected changes of molar DIC: Ca ratios due to these two reactions. However, nearly half the STE samples plot outside this field, indicating that additional re actions contribute Ca or consume DIC. Many additional reactions could contribute Ca or consume DIC including ion exchange processes, precipitation of carbonate miner als, outgassing of CO 2 and/or primary productivity. Although Ca concentrations in groundwater may increase due to ion exchange processes following saltwater intrusion (Sayles and Mangelsdorf, 1977) this process should be minor in carbonate aquifers, which have little cation exchange capacity. In addition, the Yucatan aquifer exhibits conservative mixing of Na + indicating little exchange occurs ( Price and Herman 1991) Calcite precipitation reduces Ca and DIC concentrations at a molar ratio of 1 and thus could not fractionate DIC/Ca residual ratios. Only precipitation of other metal carbonates (magnesite, siderite, dolomite etc.) would fractionate the Ca/DIC ratio Precipitation of these metals seems unlikely as little Fe is present in the systems and magnesite is close to saturation ( Whelan et al. 2011) The Ca/DIC disequilibrium in Fig. 3 4 more likely results from DIC lost from the system Such a loss could be due to outgassing of CO 2 as observed in other coastal carbonate aquifer systems ( Price and Herman, 1991) or from primary productivity Because t he freshest spring water has P CO2 values around 1.5 orders of magnitude higher than the atmospher ic P CO2 of 10 3.4 atm and all STE samples have P CO2 values

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76 higher than atmospheric CO 2 (Fig. 3 2b), outgassing is a plausible mechanism to reduce dissolved DIC concentrations but water would have to come into contact with surface water in order to be able to exchange with the atmosphere (Fig. 3 6). Alternatively, primary productivity would also reduce the DIC concentration without a ffecting the Ca concentration. The lack of lig ht in the STE requires DIC fixation by chemolithoautotrophic bacteria, a process wh ich has not been well studied in STE systems However, chemolithoautotrophic bacteria fix large amounts of carbon in anoxic, sulfidic cave systems such as in the Yucatan of fshore springs, as well as deep sea vents and could contribute to loss of DIC (Jannash, 1995; McCollum and Shock, 1997; Engel et al., 2004; Arndt et al., 2013) Implications for STE Nutrient Sources and Sinks Though organic carbon reminerali zation in STEs generates inorganic N and P (Eq. 3 1) and CaCO 3 dissolution may lead to liberation of sorbed P, the impact of these reactions on P delivery from SGD depends on their relative magnitudes compared to P sorption to CaCO 3 minerals. Th e extent of additional P sinks within the STE can be assessed by comparing NH4: PO4 ratios to the Redfield Raio, assuming all NH 4 and PO 4 are derived from organic carbon remineralization at the Redfield Ratio of 16:1 Production of nutrients from remineralization of organic carbon with an N:P ratio of 16 should produce NH 4 : PO 4 ratios of 16 if no additional sources or sinks of N or P alter ratios. R atios that are higher than 16:1 indicate an additional N source or P sink. Since N lacks a mineral source in carbonate aquifers and is largely derived from remineralization of organic N to NH 4 P sorption is a more likely candidate to explain high ratios (Santoro, 2010) Alternatively, if P is derived from Ca CO 3 dissolution, ratios

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77 should reflect that of carbonate rocks. The concentration of P contained in the Yucatan carbonate rocks is unknown, but Ca:P ratios in the Floridan carbonate karst aquifer range from 360 5700 (Price et al., 2008). This comparison ma y not be perfect because a source of P exists in the Floridan aquifer from the P rich siliciclastic Hawthorn Group rocks (Scott, 1988) which is missing from the Yucatan aquifer (Back and Hanshaw, 1970) With this caveat in mind, I assess sources and sinks of N and P through comparisons of measured N:P ratios, and residual N: P and Ca: P ratios to ratios typical o f organic matter and carbonate minerals (Fig. 3 5 ). Bulk N:P (NH 4 :PO 4 ) ratios suggest that nutrient s contained in fresh and saltwater end members are derived predominantly from organic carbon remineralization, because the N:P ratios of the freshwater end m ember is slightl y elevated above the Redfield ratio while that of the saltwater end member is approximately 16:1 (Fig. 3 5a). However, N:P ratios at intermediate salinities reach values of 400. This indicates that, while both nutrients are produced within the STE (Fig. 3 2e and f) the concentrations of P relative to N are considerably reduced likely due to sorption of P to CaCO 3 minerals Similar to bulk N:P ratios, N: P molar ratios are near Redfield ratios (16:1) in the freshwater end member and lago on surface water suggesting remineralization of marine organic matter as a likely source of excess N and P (Fig. 3 5b ). However, N: P ratios reach values of nearly 800 at intermediate salinity, suggesting a loss of P from the system because of the lack o f mineral N to cause the excess. High N: P value s compared to the Redfield Ratio suggest that the P concentrations are reduced in the discharging fresh water Losses could occur through sorption to carbonate mineral surfaces ( Price and Herman 1991; Price et al. 2010; Leader et al. 2007) Decreasing

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78 N:P and N: P ratios with salinity may reflect a reduction in the magnitude of the CaCO 3 sorption sink because d esorption of P from carbonate mi neral surfaces typically occurs at salinities above 90% of seawater values (Price et al. 2008) The salinity dependence of N:P and N: P ratios is consistent with this mechanism, where ion exchange at high salinity wou ld lead to desorption of P associated with carbonate minerals, thus decreasing both the N :P and Ca:P ratios in the STE. The desorption of P with increasing salinity does not liberate all sorbed P, however, because the N:P and N: P ratio remains above the Redfield r atio for most samples The retention of P within the carbonate aquifer lowers P delivery via SGD, and may impact surface water carbon cycling if P is a limiting nutrient (Fig. 3 6). Most Ca: P residuals are within the range of Floridan carbonat e rock values, except for the freshest water sample, which has a Ca:P ratio of over 15000 at a salinity of 10 (Fig. 3 5c ). While CaCO3 dissolution may be a source of P, it appears to be relatively minor here. For instance, since other evidence of organic c arbon remineralization exists (e.g. elevated P CO2 and NH 4 concentrations), remineralization should produce P at an N:P ratio of approximately 16:1. Because N:P ratios are much higher than this value in many STE samples, considerable P sorption to CaCO 3 minerals must occur. Therefore, even though CaCO 3 dissolution may contribute excess P, CaCO 3 appears to be a net sink of P in this setting due to sorption interactions. Impact on Surface Water Carbon Cycling The conceptual model of STE biogeochemical proc essing (Fig. 3 6 ) reflects chemical modifications of the discharging water, which should impact coastal biogeochemical budgets by providing a source of carbon and nutrients (Slomp and Van

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79 Cappellen 2004; Kim and Swarzenski 2010; Roy et al. 2013) Using a ma ss balance approach for lagoon water salinity (Eq. 3 7 and 3 8 ), I assess the fate of SGD derived organic carbon (CDOM) in surface lagoon water utilizing the measured versus modeled (mass balance) abundance s of PARAFAC components in lagoon water, and discu ss implications for lagoon surface water P CO2 values and fluxes to the atmosphere. For organic carbon, I estimate production or degradation of PARAFAC components in the lagoon and compare it with the measured abundances in lagoon water to assess its fate after being discharged from the STE. The salinity mass balance indicates lagoon water contains approximately 7% fresh SGD from springs and thus C1 C 5 in lagoon surface water should be 7% of the fresh SGD concentration, assuming negligible contributions fro m seawater However, modeled C1 to C4 abundances using this mass balance are more than 200% greater than measured abundances, indicating that significant loss of CDOM occurs following discharge (Table 3 4). In contrast, C5 is three times more abundant in l agoon surface water than predicted from the mass balance, suggesting an additional source in surface lagoon water, such as through production via photosynthesis. The relative depletion of C5 in SGD may also reflect preferential remineralization if it is more labile, or the lack of a source in the subterranean environment because it is isolated from sunlight and photosynthesis may not occur. Multiple mechanisms could reduce the C1 C4 abundances in surface seawater, including remineralization, coagulation/f locculation, or photo oxidation. Photo oxidation causes losses ranging from 50% in laboratory studies to 70% in natural systems, similar to our estimates but is a relatively slow process that operates on long the average residence time of the

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80 Puerto Morelos lagoon (~3 hours; Vodacek et al., 1997; Blough and Del Vecchio, 2002; Coronado et al., 2007) Therefore, rapid reactions such as microbial remineralization or coagulation/flocculation are likely more important removal mech anisms than photo oxidation. This mass balance suggests tha t the majority of terrestrial organic carbon discharged via SGD is remineralized in surface water within the lagoon residence time of three hours (Coronado et al., 2007) This rate of remineralization may be reflected in the P CO2 of l agoon surface (589 ppm, or logP CO2 = 3.23) which elevated above atmospheric values (400 ppm). The supersaturated P C O2 of the lagoon water suggests that SGD may impact atmospheric CO 2 fluxes either by CO 2 supersaturation of SGD itself or remineralization of terrestrial carbon delivered by SGD However, n utrients delivered by SGD could induce a negative feedback for atmo spheric CO 2 fluxes if increased availability of limiting nutrients (N or P) increase s primary productivity and fix es dissolved CO 2 The P CO2 of lagoon water is likely to vary over time, at diel and longer frequencies, depending on productivity, and thus to evaluate the total CO 2 flux would require longer term measurements than available from this study. Nonetheless, elevated P CO2 in the STE fr om biogeochemical processing indicates SGD could be an important source of CO 2 to the lagoon and atmosphere as suggested for STEs elsewhere ( Dorsett et al. 2011; Szymczycha et al. 2013) At the time of our sampling, primary productivity did not fix all additional CO 2 delivered by SGD because lagoon surface water remains supersaturated with respect to the atmosphere Because P app ears to be a limiting nutrient in this setting (e.g., Carruthers et al. 2005) the preferential retention of P within the STE may limit surface water primary productivity, and therefore limit the C fixation

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81 rate and drive surface lagoon water to be a source of CO 2 to the atmosphere during this sampling period. Conclusions Organic carbon in carbonate karst STEs drives biogeochemical reactions that generate nutrients and disso lved CO 2 and leads to carbonate mineral dissolution. Organic carbon is processed in the STE prior to discharge and leads to increased nutrient concentrations as well as carbonate mineral dissolution. Remineralization in the STE may be enhanced due to the i ntroduction of oxygen contained in surface lagoon water or the contribution of relatively labile organic carbon, here represented by PARAFAC component C5 Although P may be derived from both OC remineralization and carbonate mineral dissolution, elevated N: P and Ca: P ratios indicate that P sinks in the STE, such as s orption to the carbonate matrix, may reduce P fluxes in SGD Because N: P ratios are greater than the Redfield ratio, the sinks may cause P limitation in this type of carbonate coastal setting. OC remineralization in the STE also increases CO 2 concentrations of water that is discharged to the lagoon, and may contribute to supersaturation of lagoon water with respect to atmospheric CO 2 concentrations. Nutrient delivery by SGD could offset this CO 2 source by increasing C fixation via primary productivity, although P retention in the STE may limit the amount of primary productivity t hat may occur in surface waters

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82 Table 3 1. Chemical characterization of end members for conservative mixin g models Fresh SGD (Hol Kokol) Lagoon surface water Salinity 9.77 32.74 pH 7.21 8.08 ORP 205.2 83.4 D.O. (%) 6.2 89.3 DOC (M) 344 59 C1 (R.U.) 1.44 0.04 C2 (R.U.) 0.57 0.01 C3 (R.U.) 0.67 0.02 C4 (R.U.) 0.28 0.01 C5 (R.U.) 0.09 0.02 DIC (mM) 4.34 2.15 Log P CO2 1.81 2.95 SI cal 0.02 0.68 Ca 2+ (mM) 5.11 11.63 NH 4 + (M) 46.96 2.08 PO 4 3 (M) 0.54 0.04

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83 Table 3 2. Water chemistry parameters of terrestrial water, near shore springs, and seawater n Type Collection depth (m) Salinity Temp (C) DO (%)** pH ORP (mV) Cenotes 3 GW 2, 20, 20 0.72 0.04 25.33 0.87 21.6 9.0 7.12 0.08 84.27 12.98 Mangrove 1 SW 0.9 1.60 25.8 49.8 7.41 109.2 Well 1 GW 18 5.63 32 4.4 7.13 239.7 Hol Kokol 2 SGD -9.82 0.07 28.75 0.07 6.9 1.0 7.25 0.06 196.40 12.45 Gorgos 5 SGD -19.91 0.44 29.16 0.35 15.2 3.5 7.17 0.13 191.86 44.94 Laja 1 SGD -21.33 28.70 8.0 7.14 207.60 Pargos 7 SGD -23.43 1.87 29.64 0.44 15.1 7.0 7.28 0.11 86.27 92.41 Lagoon water 3 SW 2 32.74 30.1 89.3 8.08 83.4 Range shown is + 1 standard deviation. GW = groundwater, SW = surface water, SGD = submarine groundwater discharge. ** Percent saturation

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84 Table 3 3. R 2 between PARAFAC components in Yucatan samples C1 C2 C3 C4 C2 0.99 C3 0.99 0.99 C4 0.99 0.99 0.98 C5 0.94 0.94 0.93 0.96 Table 3 4. Mass balance of terrestrial PARAFAC components based on salinity Component Abundance in Fresh SGD (R.U.) Modeled Abundance in Surface Lagoon (R.U.) Measured Abundance in Surface Lagoon (R.U) Modeled/measured (%) C1 1.444 0.093 0.004 234% C2 0.567 0.036 0.014 258% C3 0.666 0.043 0.021 211% C4 0.284 0.018 0.008 221% C5 0.094 0.006 0.018 33%

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85 Figure 3 1 Conceptual model of a hypothetical flow line to the near shore springs delineating potential sources and sites of transformation of organic matter broken into five fluorescent components shown in the pie diagrams C onfining units depicted as horizontal brown lines with question marks may restrict water exchange and separate the aquifer into upper unconfined and lower confined portions, particularly near the shore (e.g., Null et al., 2014) ( a) Inland c enote s intersect the f reshwater lens. Aquifer recharge drives flow of groundwater toward coast. ( b) S urficial water at mangroves contains high co ncentrations of CDOM, possibly also including anthropogenic organic carbon in wastewater infiltrates the aquifer, increasing groundwa ter CDOM concentrations between cenotes and the shoreline Mangrove PARAFAC signature is dominated by C1 over other terrestrial components Injected wastewater may also contribute to groundwater chemistry ( c) Organic matter is remineralized and increases P CO2 and inorganic nutrient content of groundwater. ( d) W ater in the UNAM well is composed of groundwater mi xed with surface organic source. ( e) G roundwater flows offshore to be discharged in springs as SGD. PARAFAC signature of spring discharge is similar to UNAM well water. Freshwater mixes with saltwater prior to discharge in STE underlying freshwater lens, shaded in yellow. ( f) Lagoon surface water contains relatively more protein like C5 than all terrest rial groundwater sources or spring discharge. Note compression in the horizontal scale.

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86 Figure 3 2 Salinity versus (a) DIC, (b) log P CO2 (p<0.001), (c) SI cal (d) Ca 2+ (e) NH 4 + and (f) PO 4 3 (Left panel s ) and residuals from the mixing models (r ight panels). Filled circles represent SGD samples and lagoon surface water. Black lines represent conservative mixing between freshest SGD sample and surface lagoon water. Residuals are reported in same units as data. DIC data was not available for mangroves so no data for DIC, P CO2 and SI cal are reported

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87 Figure 3 3. Salinity versus (a) total CDOM and various PARAFAC components including (b) C1), (c) C2, (d) C3, (e) C4, and (f) C5. The total CDOM is the sum of the various components. The blac k dots represent the values for samples collected from the STEs and the open squares represent water from the cenotes, open diamonds water from the mangrove forest, and open triangles water from the well. These data represent the quantity (a) and quality (b f) of chromophoric organic carbon

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88 Figure 3 4. Cross plot of Ca versus DIC residuals in STE water samples. Arrows and fields represent DIC and Ca residuals if the main sources are organic carbon (OC) remineralization and CaCO 3 dissolution Salinity ranges of data points are color coded from low salinity (red) to high salinity (blue).

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89 Figure 3 5 Cross plots of salinity versus (a) m olar N:P ratios ( b) N: P ratios and (c) Ca: P ratios

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90 Figure 3 6 Conceptual model of STE biogeochemistry within STE. Black boxes represent solute reservoirs, solid arrows represent fluxes between reservoirs, dashed arrows represent transformations due to reactions. Arrow thickness indicates the magnitude of fluxes and transformations. Fresh gr oundwater delivers organic carbon to the STE. Surface seawater delivers oxygen. Organic carbon from fresh groundwater is remineralized with oxygen from surface saltwater to produce NH 4 PO 4 and CO 2 NH 4 is discharged in high concentrations in SGD due to lack of sink in the STE, while some P is retained due to Ca P sorption interactions, reducing SGD concentrations. CO 2 is consumed in CaCO 3 dissolution and outgassing, reducing SGD concentrations.

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91 CHAPTE R 4 BIOGEOCHEMICAL CONTROLS OF GREENHOUSE GAS PRODUCTION AND SEQUESTRATION IN SILICICLASTIC SUBTERRANEAN ESTUARIES Introduction Gradients of organic carbon quantity, quality, and terminal electron acceptor concentrations regulate the distribution of redox reactions in subterranean estuaries (STEs) and generate solutes that may be transported to surface waters via submarine groundwater discharge (SGD). A consequence of organic carbon remineralization reactions is the produ ction of greenhouse gases including carbon dioxide (CO 2 ) and methane (CH 4 ) which may be transported to surface waters and evade to the atmosphere. CO 2 and CH 4 are produced alongside well studied reactions that alter nutrient and metal concentrations. Howe ver, carbon fluxes from SGD have received relatively little attention compared to nutrients (Slomp and Van Cappellen, 2004; Kroeger and Charette, 2008; Spiteri et al., 2008 a ) and metals (Roy, M. et al., 2010; Whelan et al., 2011; Johannesson et al., 2011) S everal studies have found SGD t o be a significant sou rce of carbon to surface waters (Cai et al., 2003; Liu et al., 2012; Liu et al., 2017) though little consensus exists as to whether STEs are sources or sinks of CO 2 because interactions between remineralization reactions and sediment mineralogy may alter CO 2 concentrations and pore water buffering capacity (Cai et al., 2003; Liu et al., 2017) CH 4 fluxes from STEs have received relatively more attention than CO 2 because CH 4 is used as a quasi conservative tracer of SGD due to its typically high concentrations in groundwater com pared to surface water (Cable et al., 1996; Corbett et al., 2000; Dulaiova et al., 2010). Both CO 2 and CH 4 are generated during redox reactions (Table 4 1) and their concentrations in STEs should therefore in part be controlled by redox gradients that

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92 de pend on changes in terminal electron acceptor and organic carbon availability and reactivity (Chapter 2). In the case of CH 4 production occurs during organic carbon remineralization when other terminal electron acceptors have been depleted (Megonigal et al., 2005) Because seawater contains high concentrations of sulfate, methanogenesis does not occur in marine settings unless supplies of organic carbon are sufficiently high to deplete sulfate Methanogenesis is therefore a relatively more important process in freshwater settings where sulfate availability is low (Megonigal et al., 2005) Because sulfate reduction is energetically favorable compared to methanogenesis (Table 1 2), and because sulfate concentrations are highly correlated with salinity, v ariations in STE sul fate concentrations should regulate zones of methanogenesis. While CH 4 is known to be delivered in large quantities to surface water via SGD (e.g. Bugna et al. 1996; Borges et al., 2016) fewer studies have assessed the impact of methanogenesis on carbonate chemistry, though both methanogenesis and methane oxidation produce CO 2 (Table 4 1, Eq. 4 5 and 4 9). Studies using CH 4 as a tracer typically assume that it is quasi conservative (Bugna et al., 1996; Dulaiova et al., 2010; Lecher et al., 2015) but more recent assessments of CH 4 oxidation in coastal aquifers have observed that methanotrophy can consume a large proportion of CH 4 and strongly reduce fluxes to surface water (Schutte et al., 2016) Carbon Dioxide and Carbonate Equilibria Carbon dioxide is produced during all organic carbon remineralization reactions, including methanogenesis, bu t may be sequestered as HCO 3 or CO 3 2 depending on the carbonate chemistry of pore waters ( Eq. 4 10 ) ( Froelich et al., 19 79; Stumm and Morgan, 1996)

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93 CO 2 + H 2 O H 2 CO 3 H + + HCO 3 2H + + CO 3 2 ( 4 10 ) The degree of sequestration depends on buffering capacity, which is a function of total dissolved inorganic carbon (DIC) concentrations : DIC = [H 2 CO 3 *] + [HCO 3 ] + [CO 3 2 ] ( 4 11 ) and [H 2 CO 3 (aq) ] = [CO 2(aq) ] + [H 2 CO 3 (aq) ] ( 4 12 ) as well as pore water pH, and alkalinity ( Alk) (Egleston et al., 2010) DIC is defined as the sum of dissolved CO 2 including its hydrated form (H 2 CO 3 ) and dissociation products, bicarbonate (HCO 3 ) and carbonate (CO 3 2 ) ions (Eq. 4 11) Note that hydration of dissolved CO 2 (CO 2(aq) ) to H 2 CO 3 occurs more rapidly than dissolution of CO 2 in water, therefore the sum of dissolved CO 2 plus H 2 CO 3 is referred to as H 2 CO 3 (Eq. 4 12), because the distribution between dissolved CO 2 and H 2 CO 3 does not impact the speciation of DIC (Eq. 4 11). The speciation of DIC in Eq. 4 11 is predominantly a function of pH, where progressively higher pH (lower concentrations of H + ions) causes deprotonation of H 2 CO 3 and causes more DIC to be sp eciated as HCO 3 and CO 3 2 Alkalinity is a critical parameter in carbonate chemistry because it determines the acid neutralizing capacity of water, for example as carbonic acid concentrations increase following organic carbon remineralization. Increased alkalinity reduces the magnitude of decreases in pH as weak acid is added to water, and represents the excess of base (proton acceptors) over acids (proton donors). Proton acceptors form complexes with H + ions, which reduces concentration of free H + Alkal inity (Alk) may operationally be expressed as the sum of the most common base s (Eq. 4 13 ) or by the

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94 charge balance of all strong acids and bases that are unaffected by acid additions (Eq. 4 14 ; Sarmiento and Gruber, 2006) Alk = [HCO 3 ] + 2[CO 3 2 ] + [OH ] [H + ] [B(OH) 4 ] + minor bases ( 4 13 ) Alk = [Na + ] + [K + ] + 2[Mg 2+ ] + 2[Ca 2+ ] + minor cations (4 14 ) [Cl ] 2[SO 4 2 ] [Br ] [NO 3 ] minor anions For the carbonic acid system, the buffering capacity of water changes based on changes in the ratio of alka linity to DIC (Sabine et al., 2004) where a greater Alk:DIC ratio increases the buffering capacity of water, which allows more CO 2 to be dissolved and sequestered as HCO 3 and CO 3 2 Both DIC and alkalinity are altered by many diage netic reactions, which in turn alter buffering capacity. However, these reactions differ between carbonate or siliciclastic systems. In carbonate systems and in the open ocean, DIC and alkalinity are predominantly controlled by carbonate mineral (CaCO 3 ) d issolution and precipitation and atmospheric CO 2 concentrations (Sarmiento and Gruber, 2006) However, in siliciclastic settings where CaCO 3 minerals are less abundant, redox reactions, including organic carbon remineralization by different electron acceptors (Table 1 2) may play a larger role in regulating Alk:DIC ratios than CaCO 3 mineral dissolution or precipitation. DIC is produced from o rganic carbon remineralization because it produces dissolved CO 2 that become speciated as DIC (Eq. 4 11) according to pore water c hemistry, but also produces or consumes alkalinity depending on the remineralization pathway (Table 4 1). For instance, aerobic remineralization and methanogenesis produce DIC but no alkalinity, while denitrification, iron reduction, and sulfate reduction produce alkalinity and DIC at molar ratios of ~ 0.8, 8, and 1, respectively ( Table 4 1;

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95 Kuivila and Murray, 1984) The impact of redox pathway on Alk:DIC ratios is well known, and reaction stoichiometries are frequently used to determine the relative importance of the suite of diagenetic reactions changes in alkalinity a nd DIC, expressed as Alk: DIC (Berner et al., 1970; Davison and Woof, 1990; Chen and Wang, 1999; Cai et al., 2003; Thomas et a l., 2009; Liu et al., 2017) In general, the main sources of alkalinity in anaerobic marine sediments include denitrification, iron reduction, and sulfate reduction (Berner et al., 1970; Thomas et al., 2009; Liu et al., 2017) The impact of these reactions on carbonate equilibria in STEs should thus alter SGD CO 2 fluxes to surface waters in different amounts, which may in turn alter fluxes of CO 2 from surface water to the atmosphere, depending on processing in th e water column, and makes redox reactions a critical parameter in CO 2 fluxes in siliciclastic systems compared to carbonate systems. Methanogenesis and Carbonate Equilibria Methanogenesis is typically a minor redox pathway in marine sediments due to inhibi tion by sulfate, and is generally restricted to freshwater systems or organic rich marine sediments where sulfate may be entirely depleted (Whiticar and Schoell, 1986; Mitterer, 2010) Freshwater entering STEs s hould allow methanogenesis to occur, making methanogenesis in STEs more common than in saline marine sediments. However, its impact on carbonate chemistry across salinity gradients in STE sediments is not well known, though isolated studies suggest it coul d contribute significant DIC (Cai et al., 2003) Methane may be produced via two pathways, CO 2 reduction : CO 2 + 4H 2 CH 4 + 2H 2 O ( 4 15 )

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96 2CH 2 O + 2H 2 O 2CO 2 + 4H 2 ( 4 16 ) and acetate fermentation : CH 3 COOH CO 2 + CH 4 ( 4 17 ) CO 2 reduction (Eq. 4 15) is coupled with fermentation (Eq. 4 16) because H 2 is required in CO 2 reduction and is often the limiting reactant Because of this coupling, t he net impact of both pathways is to produce CO 2 and CH 4 at a molar ratio of 1. Methanogenesis has no impact on alkalinity, however, and results in a Alk: DIC ratio of 0 (Table 4 1, Eq. 4 5), and therefore should decrease the CO 2 buffering capacity of the pore waters since Alk:DIC ratios are proportional to buffering capacity (Sabine et al., 2004) Additionally, methane produced by either CO 2 reduction or acetate fermentation may subsequent ly be oxidized through aerobic CH 4 + 3O 2 CO 2 + 2H 2 O ( 4 18 ) or anaerobic pathways CH 4 + SO 4 HCO 3 + HS +H 2 O ( 4 19 ) Because anaerobic oxidation of methane (AOM) produces HCO 3 and reduces SO 4 2 to sulfide, it results in a Alk: DIC value of 3 because every mole of sulfate reduced increases alkalinity by 2 moles (Eq. 4 14), and HCO 3 is included in both DIC and alkalinity (Eq. 4 11 and 4 13). In contrast, aerobic oxidation results in a Alk: DIC ratio of 0 because no alkalinity is produced (Eq. 4 18). To assess the controls of carbonate c hemistry in siliciclastic STEs where alkalinity is likely to be determined by the distribution of redox reactions rather than CaCO 3 saturation, I report dissolved gas concentrations and redox sensitive solutes

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97 (NO 3 Fe(II), and HS ) at three siliciclastic STEs bordering I ndian River Lagoon, Florida (EGN, BRL, and RWP; Fig. 1 3). I isolate the impacts of reactions from those due to mixing by the use of salinity based conservative mixing models and consider that changes in CO 2 concentrations reflect producti on from organic carbon remineralization, but may also reflect changes in DIC speciation from alteration of pore water buffering capacity. If changes in buffering capacity results in changes in CO 2 concentrations, CO 2 concentrations should be altered as wel l the proportion of DIC as CO 2 and Alk:DIC ratios. Because reactions have disparate impacts on Alk:DIC ratios (Table 4 1), I assess which reac tions control gas distributions by comparing compare zones of changes in DIC and alkalinity to zones delineated by r eactions outlines in Table 4 1. I also compare the ratios of changes in DIC and alkalinity ( Alk: DIC) in STE samples to those of reaction stoichiometry. These results are used to evaluate controls of CO 2 concentrations in siliciclastic STEs, which is critical for determining the role of SGD on coastal carbon cycles. Methods I collected samples from multi level piezometers that had previously been installed at EGN, RWP and BRL sites (Fig. 1 3 ; see Chapter 1 for thorough site description). Briefly, p iezometers were installed in 2004 at EGN and during May 2014 September 2015 at RWP and BRL. At EGN, sampled piezometers were installed at 0, 10, and 20 m offshore (EGN 0, EGN 10, EGN 20), at RWP were 10, 20, and 35 m offshore (RWP 10, RWP 20, RWP 35) and at BRL were 1, 11, 21, and 45 m offshore (BRL 1, BRL 11, BRL 21, BRL 45; piezometer schematic illustrated in Fig. 1 2 ). Samples described in this study were collected in May, 2016

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98 Sample Collection Sampling was accomplished by pumping pore water to the surface through a 0.5 cm diameter flexible PVC tube attached to the multisampler ports. A YSI Pro Plus sensor was installed in an overflow cup in line with the tubing to measure salinity, temperature, pH, dissolve d oxygen (DO ), and oxidation reduction potential (ORP). Although instrumented with a DO sonde, hydrogen sulfide interfered with DO measurements, and thus I lack reliable DO concentration measurements. Once all of these parameters were stable, samples were filtered through 0.45 m trace metal grade Geotech medium capacity disposable canister filters into sample vials. Samples for cations and anions were collected in HDPE bottles; cation samples were preserved with trace metal grade nitric acid (pH<2) while n o preservative was added to anion samples. DIC samples were filtered at 0.2 m directly into glass vials and sealed tightly with no headspace. Redox sensitive solutes, Fe(II) and hydrogen sulfide, were measured on 0.45 m filtered water in the field imme diately after pumping from the multisampler tubing using colori metric methods. Fe(II) was measured using the ferrozine method (Stookey, 1970) Samples were measured in triplicate. Water was sampled from the flowing pore water stream and 1 mL of ferrozine was immediately added to 10 mL of sample and agitated to mix The absorbance of developed color was measured after 5 minutes of reaction with a Hach DR 890 portable colorimeter at 560 nm. Blanks were prepared with ferrozine and distilled water and measured before each triplicate pore water sample. Measured absorbance values were converted to concentrations with laboratory calibrations prepared with Fe(II) standards, utilizing an aliquot of the prepared ferrozine that was used in the field. Sampling for hydrogen sulfide occurred simultaneously with

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99 the Fe sampling and H 2 S was measured immediately in triplicate using the methylene blue method acco rding to US EPA methods outlined in Hach (2015). Precision is reported at 0.1 mg/L. Gas samples were collected via headspace extractions. Unfiltered water was p umped into the bottom of 500 mL bottles until they overflowed and immediately capped with rubber stoppers fitted with two 3 way inlet valves. 60 mL of water was extracted from one inlet and replaced with 60 mL of CO 2 free N 2 gas. Bottles were shaken f or 2 minutes to equilibrate headspace gas with water, and headspace gas was extracted and immediately injected into pre evacuated 60 ml glass serum bottles. Samples were stored at room temperature until analysis within one week of collection. Method check standards were collected by injecting gases of known concentrations of CO 2 and CH 4 into evacuated vials and treated identically to samples. Laboratory Methods Gas samples were analyzed for CO 2 and CH 4 concentrations, and 13 C CO 2 and 13 C CH 4 on a Picarro cavity ring down spectrometer. Carbon isotopic compositions are reported in reference to Vienna Pee Dee Belemnite (VPDB). Because hydrogen sulfide interferes with CO 2 concentrations and 13 C CO 2 measurements, sample gas was passed through an in line elemen tal copper scrubber before analysis (Malowany et al., 2015) The error on check stand ards was less than 10%. Anion and cation concentrations were measured on an automated Dionex ICS 2100 and ICS 1600 Ion Chromatograph, respectively. Error on replicate analyses was less than 5%. DIC concentrations were measured on a UIC (Coulometrics) 5011 CO 2 coulometer coupled with an AutoMate Preparation Device. Samples were acidified and

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100 the evolved CO 2 was carried through a silver nitrate scrubber to the coulometer where total C was measured. Accuracy was calculated to be 0.1 mg/L. Data Processing Dissolved g as c oncentrations Headspace dissolved gas concentrations are reported in dissolved raw ppm values to facilitate comparison with atmospheric gas concentrations as well as molar concentration s to allow for stoichiometric modeling. Conversion to mo lar units followed the methods outlined in Bastviken et al., (2004) To solve for the mo les of gas originally dissolved in solution, I first converted measured gas concentration (ppm) in headspace to moles: ( 4 20 ) Where n g equals the moles (n) of gas in the gaseous phase, P x is the measured partial pressure of CH 4 or CO 2 (atm), V g is the volume of headspace gas (L), R is the common gas constant (0.0821 L atm K 1 mol 1 ) and T is the temperature (K), here taken as 298.15 K (25C). The number of moles of gas dissolved in the aqueous phase (n aq ) is calculated by: ( 4 21 ) w here C aq is aqueous concentration and V aq is aqueous volume (500 mL minus 60 mL replaced by headspace gas to give a total volume of 440 mL) and K H constant (M atm 1 ). The value of K H was taken at 25C as 1.4x10 3 for CH 4 and as 3.5x10 2 for CO 2 (Lide and Frederikse, 1995) The final concentration of dissolved gas in water samples (C aq ) was then calculated as the sum of the number of moles of gas in aqueous and gaseous phases divided by the aqueous volume :

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101 ( 4 22 ) CH 4 oxidation was calculated using the isotopic method outlined in Mahieu et al. (2008 ) and Preuss et al. (2013) The fraction of oxidized methane (f ox ) in an open system is given by: ( 4 23 ) where E is the measured 13 C CH 4 val ue for each pore water sample, P is 13 C CH 4 of produced methane ox trans is a fractionation factor resulting from transportation of CH 4 While the exact value of P is unknown, diagenetic alteration of 13 C CH 4 values through oxidation or transport only enrich 13 C CH 4 signatures, therefore the value of P is take as the most depleted 13 C CH 4 signature per STE site, because it is likely the least impacted by diagenetic alteration. Literature ox range between 1.003 and 1.049. I calc ulate the fraction of oxidized methane with the largest fraction factor ( ox = 1.049 ) (Mahieu et al., 2008 ) which will give the minimum amount of CH 4 oxidation required to explain the observed variations in 13 CH 4, and thus is a conservative estimate for CH 4 oxidation. Literature trans vary from 1 for advection dominated systems to 1.0178 for diffusion dominated porous media ( Visscher et al., 2004; Mahieu et al., 2008; Preuss et al., 2013) Based on Roy et al., ( 2011 ), I assume that transport is advection trans = 1. The concentration of o xidized methane (CH 4 ox ) is derived by solving the set of equations: CH 4 produced = CH 4 measured + CH 4 ox ( 4 24 )

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102 CH 4 ox = f ox CH 4 produced ( 4 25 ) where the measured concentration of methane (CH 4 measured ) is considered to represent the original concentration of produced methane (CH 4 produced ) minus the oxidized portion CH 4 ox Modeling I used concentrations of major cations and anions, pH, temperature, and DIC concentrations to model the alkalinity and speciation of carbonate ions in PHREEQc using the PHREEQc database (Parkhurst, 1995) Alkalinity was estimated from the charge balance of the model input (Parkhurst, 1997) To assess the impacts due to mixing versus reactions in the STE, I construct ed salinity based conservative mixing models between the freshest STE sample at each site and surface saltwater. Mixing models assume that the freshest groundwater sample is representative of groundwater with the least impact from diagenetic reactions due to freshwa ter saltwater mixing in the STE. The models use lagoon water compositions as the saltwater end member. Results Dissolved Gas Concentrations and Carbonate Chemistry Dissolved CO 2 and CH 4 concentrations in STE pore water samples are elevated above surface water concentrations at all STE sites, and gas concentrations are higher in fresh portions of STEs (Fig. 4 1). CO 2 concentrations reach highest concentrations in freshest sampled of BRL (up to 120,000 ppm) and EGN (up to 46,000 ppm; Fig. 4 1a). The m aximum CO 2 concentration at RWP (22,000 ppm) occur s in a sample with salinity of approximately 10. Surface water CO 2 concentrations are elevated above atmospheric CO 2 concentrations (currently around 400 ppm) at all sites and measure 1030 ppm at

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103 BRL, 2130 ppm at EGN, and 1850 ppm at RWP (Fig. 4 1b) CH 4 concen trations are highest in freshwater portions of STEs at BRL and RWP, but in brackish portions of EGN (Fig. 4 1c) At BRL, CH 4 reaches concentrations of 29,000 ppm at BRL 1, while concentrations reach 58,960 ppm at RWP 20. At EGN, maximum CH 4 occurs at EGN 2 0 and reaches 390 ppm. Salinity at this pore water interval is 16.5. All STE samples and surface water samples have CH 4 concentrations higher than atmospheric concentrations of approximately 2 ppm (Fig. 4 1d). Conservative mixing models between freshest po re water and surface saltwater indicate that DIC, alkalinity, and Ca are predominantly produced at all three STE sites (Fig. 4 2a, b, and c). A high degree of variability of CO 2 concentrations with salinity occur at BRL 1 and EGN 0, which are indicated sep arately from other samples due to their distinct chemistries. At BRL 1, which is located at the mangrove colonized shoreline, CO 2 concentrations are greater than expected from conservative mixing, and the proportion of DIC as CO 2 increases while Alk:DIC ra tios decrease relative to salinity. At EGN 0, CO 2 concentrations are lower than conservative mixing, while the proportion of DIC as CO 2 decreases with salinity, and Alk:DIC ratios increase (Fig. 4 2d, g, and h). Apart from these changes in chemistry at EGN 0 and BRL 1, CO 2 concentrations generally decrease with salinity at all seepage faces, as well as the proportion of DIC as CO 2 while Alk:DIC ratios increase (Fig. 4 2d, g, and h). Distribution of Redox Reactions Salinity, ORP, and the distributions of re dox sensitive species are displayed in Fig. 4 3 ORP values are all negative except for the shoreline piezometer of EGN This site also contains up to 200 M nitrate, while maximum NO 3 concentrations are only 25 M at BRL and 2.5 M at RWP. Fe(II) is prese nt at highest concentrations near EGN

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104 20(40 M) and BRL 11 (35 M). Hydrogen sulfide (HS ) exhibits concentrations up to 60 M at EGN, directly overlying the zone of Fe(II) production BRL contains up to 120 M HS at the shoreline piezometer, which is col onized by mangroves, as well as shallow sediments of the piezometer 45 m offshore. At RWP, HS is produced in shallow sediments in piezometers 10 and 35 m offshore, as well as in deep sediments 35 m offshore. CH 4 concentrations at EGN reach maximum concentrations of 3 M at EGN 22.5 in brackish pore waters (salinity=22.4 ), but reach 200 M in freshwaters at BRL 1 and 400 M at RWP 20 CO 2 concentrations are highest at the nearshore piezometer for all seepage faces, and concentrations decrease in mag nitude from 6500 M at BRL 1600 M at EGN and 800 M RWP The difference between measured DIC, alkalinity and Ca concentrations and the salinity based conservative mixing lines depicted in Fig. 4 2a, b and c are expressed as DIC Alk and Ca (Fig. 4 4). Values of Alk compared to Ca, and NO 3 Fe(II) and HS concentrations indicate similarities in zones of alkalinity production and Ca values at EGN, while zones correspond more closely to HS conce ntrations at BRL and RWP (Fig. 4 3 ). CH 4 Concentration s and Oxidation At all seepage faces, 13 C CH 4 signatures are lowest where CH 4 concentrations are highest (Fig. 4 5a ). T he lowest 13 C CH 4 signature s at each seepage face are measured at I assume this low value represents the 13 C CH 4 value of methane production prio r to fractionation by oxidation because oxidation and transport of CH 4 by diffusion or advection enriches the residual CH 4 pool and leads to isotopically heavier values (Whiticar, 1999) The quantity

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105 of CH 4 oxidized is highest at low salinity samples, reaching maximum concentrations of 45 M at RWP, 30 M at BRL, and 8 M at EGN (Fig. 4 5b). Alk: DIC Ratios Compared to Reaction Stoichiometries Plots of Alk and DIC calculated from conservative mixing models compared to the ratios of Alk to DIC from biogeochemical reactions (Table 4 1) reflect which reaction may dominate the changes in pore water concentrations Most saline por e water samples from all sites plot slightly under the 1:1 line corresponding to values expected from sulfate reduction and denitrification (Fig. 4 6 ). Ratios of freshwater samples from EGN 0 and BRL 1 plot farther from the 1:1 line Alk : DIC ratios are c loser to th e value expected from CaCO 3 dissolution a t EGN 0 and Alk : DIC ratios are close to those expected from methanogenesis at BRL Most pore water Alk:DIC ratios are low compared to surface water values (Fig. 4 7). Comparison of observed Alk:DIC ratios to ratios predicted by conservative mixing between fresh and saltwater end members indicates that pore waters at EGN 0 and EGN 10 have relatively higher Alk:DIC ratios than expected from conservative mixing, while pore waters at BRL 1 have relativel y lower Alk:DIC ratios than those predicted by conservative mixing (Fig. 4 7). Discussion CO 2 and CH 4 concentrations elevated above surface water and atmospheric concentrations (Fig. 4 1) suggest that SGD is a source of both CO 2 and CH 4 to surface waters a nd the atmosphere. While freshwater appears to be one source of CO 2 and CH 4 for STEs due to negative relationships with salinity (Fig. 4 1) deviations from conservative mixing models indicate that concentrations are modified by reactions The

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106 relative magn itudes of these reactions s hould control changes in CO 2 concentrations and DIC speciation. Because of differences in chemical reactions in fresh compared to marine portions of the STE I first discuss controls of changes in CO 2 speciation in samples with s alinity <15, particularly at BRL and EGN where sharp chemical gradients with salinity lead to large changes in CO 2 concentrations. I subsequently discuss processes in portions of STEs where salinity >15. Because the overall impact of reactions on CO 2 fluxe s from SGD depends on net changes in alkalinity and DIC that result from a range of biogeochemical reactions, I compare the residuals of conservative mixing models of alkalinity and DIC ( Alk and DIC), to ratios expected based on each reaction s toichiomet ry shown in Table 4 1. These results elucidate he biogeochemical controls of CO 2 concentrations and DIC speciation in siliciclastic STEs, and may be used to predict potential impacts of SGD from siliciclastic STEs o n surface water carbon budgets. Impacts o n CO 2 Concentrations and DIC speciation Lower salinity portions of STEs ( s alinity < 15) Conservative mixing models indicate that reactions consume CO 2 at EGN 0 but produce CO 2 at BRL 1 and mid salinity samples at RWP (Fig. 4 2d). The largest differences in chemical composition from conservative mixing values occur at the EGN and BRL shoreline piezometers (EGN 0 and BRL 1). Along with changes in CO 2 concentrations the proportion of DIC as CO 2 decreases at E GN and incre ases at BRL and RWP, and the Alk:DIC ratios increase at EGN 0 and decrease at BRL 1 and RWP These changes suggest that deviation of CO 2 concentrations from the conservative mixing line is in part determined by changes in the buffering capacity of pore wat er, but

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107 buffering capacity increases at EGN, thus sequestering CO 2 as HCO 3 or CO 3 2 and decreases at BRL and RWP, causing increases in CO 2 concentrations. EGN 0 is distinct from other locations because it has positive ORP values and high concentrations o f NO 3 (Fig. 4 3) which may drive denitrification. Further offshore from EGN 0, NO 3 concentrations decrease along with ORP values suggest that NO 3 may be consumed by denitrification, but a more detailed assessment of N transformation is required to confirm the role of denitrification. Denitrification would produce alkalinity and DIC ( Alk: DIC) at a ratio of approximately 0.8 (Table 4 1; Eq. 4 1b). Because the Alk:DIC ratio in the freshwater end member of EGN is around 0.5 (Fig. 4 2h), denitrificati on could increase alkalinity relative to DIC and thus the pore water buffering capacity and promote sequestration of CO 2 as DIC. This could account for the increase in Alk:DIC ratios from the freshwater end member at EGN and contribute to the increase in A lk:DIC observed at EGN 0 ( ranging from 0.5 0.9; Fig 4 2h). Additional reactions may contribute, however, including CaCO 3 dissolution. Ca concentrations at EGN 0 are between 1 2 mM (Fig. 4 4). The distribution of Ca in the seepage face closely aligns with Alk (Fig. 4 4) and suggests that CaCO 3 dissolution is a source of alkalinity. Because CaCO 3 dissolution results in a Alk: DIC ratio of 2, well above the Alk:DIC ratio of the EGN freshwater end member, it could result in the increasing Alk:DIC ratios obs erved at EGN 0 and promote CO 2 sequestration. Reactions at the BRL shoreline piezometer (BRL 1) result in CO 2 concentrations (Fig. 4 2d) and proportion of DIC as CO 2 (Fig. 4 2g) elevated above values exp ected from conservative mixing. These reactions also result in Alk:DIC ratios lower than expected from conservative mixing (Fig. 4 2h). Alk:DIC ratios at BRL 1 are low (0.4 0.6)

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108 compared to the freshwater end member (0.85; Fig. 4 2h), which could reflect alteration by diagenetic reactions that produce a Alk : DIC ratio at 0.4 or lower. Elevated HS and CH 4 concentrations at BRL 1 suggest that sulfate reduction and methanogenesis occur, and are likely fueled by high organic carbon concentrations (Fig. 4 3). Sulfate reduction has a Alk: DIC ratio of approximat ely 1 (Table 4 1, Eq. 4 4), and therefore would not contribute to the decrease in Alk:DIC ratios from the freshwater end member value of 0.85. Methanogenesis produces DIC but not alkalinity, and could lower Alk:DIC ratios (Table 4 1, Eq. 4 5). However, the magnitude of CH 4 production at BRL 1 (100 200 M; Fig. 4 2e) is small compared to the CO 2 produced (2000 4000 M; Fig. 4 2d) and methanogenesis would therefore not provide all additional CO 2 and would only contribute to the reduction. Other reactions co uld include sulfide or iron oxidation, which may be catalyzed by mangrove roots (Lee, 1999) These reactions consume alkalinity but do not impact DIC (Table 4 1, Eq. 4 7 and 4 8), and thus could also lower Alk:DIC ratios a t BRL 1. Similar to BRL, methanogenesis or sulfate reduction coupled with sulfide oxidation could lead to the elevated CO 2 concentrations at mid salinities of RWP. Samples that plot above the conservative mixing line have salinities of approximately 10, and are also the shallowest samples at RWP 10 and RWP 20, which are located at depths of 60 cm and 20 cm below the sediment water interface, respectively. The high CH 4 concentrations at RWP (Fig. 4 3) suggest that methanogenesis contributes to the increase in Alk:DIC ratio and elevated CO 2 concentrations. Alternatively, since these samples are relatively shallow, iron and/or su lfide oxidation may also play a role. For instance, bioirrigation is known to mix surface water to depths of at least 25 35 cm at

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109 Indian River Lagoon (Martin et al., 2006) However, low Fe(II) concen trations relative to sulfide concentrations suggest that sulfide oxidation may contribute more than Fe oxidation. Decreases in Alk:DIC ratios from CH 4 oxidation is less likely to play a role because it contributes an Alk:DIC ratio of 3:1 (Table 4 1, Eq. 4 9 ). Higher salinity portions of STEs ( s alinity > 15) Sali ne portions of STEs (salinity > 15) have more homogeneous distributions of CO 2 DIC, and alkalinity than fresher portions, which are reflected in Alk: D IC ratios that cluster around the 1:1 line (Fig. 4 4). In general, Alk:DIC ratios increase from ratios found in the freshwater to values around or slightly greater than 1 in the most saline STE samples and in surface water. These increases in Alk:DIC ratios may be due to sulfate reduction, because the d istribution of DIC and Alk in the saline portions of BRL and RWP (Fig. 4 4) correspond to zones delineated by elevated sul fide concentrations (Fig. 4 3). S ulfate reduction has a Alk: DIC ratio of approximately 1, which is close to the Alk:DIC ratios observed in surface waters and in the most saline samples of STEs. Sulfate reduction is likely the dominant redox reaction in saline portions of STEs b ecause of low potential for aerobic re spiration (evidenced by negative ORP values ) low potential for denitrification due to low NO 3 concentrations, low Fe(II) concentrations compared to HS concentrations, and low CH 4 concentrations due to the inhibition of methanogenesis by sulf ate (Fig. 4 2 h). Alk: DIC r atios The likely controls of carbonate chemistry as delineated by the above discussion for EGN 0 (predominantly controlled by denitrification and CaCO 3 dissolution ), and BRL 1 (predominantly controlled by methanogenesis, sulfate reduction a nd sulfide

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110 oxidation ) are supported by comparison of Alk: DIC ratios to those predicted by reactions in Table 4 1 (Fig. 4 6). Samples from EGN 0 have Alk: DIC ratios greater than 1 (Fig. 4 6a), and plot between the Alk: DIC expected from denitrification and CaCO 3 dissolution. This high Alk: DIC ratio should promote the sequestration of CO 2 as HCO 3 or CO 3 2 A reduction of pore water buffering capacity at BRL 1 is shown by Alk: DIC ratios near zero, which would be expected from methanogenesis but could alternatively result from a combination of processes including sulfate reduction, methanogenesis, and iron/ sulfide oxidation (Fig 4 6a) All of these processes are plausible at BRL 1 considering elevated concentrations of CH 4 and HS (Fig. 4 3). At this site with lower buffering capacity would result in elevated CO 2 concentrations and possibly fluxes of CO 2 In contrast with freshwater samples of BRL and EGN, most saline samples plot near or slightly lower than the 1:1 line, or between the ratios expected from sulfate reduction and denitrification (Fig. 4 6b). Because low or non detectible NO 3 concentrations occur in all but EGN 0 samples, denitrification is unlikely to impart its ratio on saline samples. Moreover, up to 1.5 mM of DIC production a nd 2 mM of alkalinity production is observed for saline samples of BRL and EGN. This production of DIC via denitrification would require 1.2 mM of NO 3 assuming the stoichiometry of denitrification in Table 4 1, which is six times higher than the highest N O 3 concentrations measured at EGN 0. Therefore, saline Alk: DIC ratios are more likely controlled by sulfate reduction, although other processes, such as methanogenesis or sulfide oxidation, could also contribute.

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111 Redox and Mineralogical Controls of Dissolved Gas Concentrations As discussed above, the distribution of redox reactions controls carbonate chemistry because of variations in the production of DIC relative to alkalinity. Although biogeochemical processes in freshwater portions of STEs alter carbonate chemistry, particularly at BRL 1 and EGN 0, they have contrasting impacts on CO 2 concentrations. I hypothesize that th ese differences result from variability in the distribution of CaCO 3 in sediment, as well as differences in redox potential betw een EGN and BRL freshwater. Specifically, EGN 0 appears to have CaCO 3 mineral phases to dissolve and buffer CO 2 production while the CaCO 3 content of sediment may be lower at RWP and BRL Additionally, f reshwater at EGN has positive ORP values and contains NO 3 contrasting with BRL and RWP, where freshwater is reducing and supports methanogenesis. Redox potential may determine the impact of reactions on carbonate chemistry because denitrification has a Alk: DIC ratio of 0.8, greate r than most fresh water samples, and would increase the buffering capacity of pore waters. The relatively high redox potential at EGN 0 appears to be related to the concentration of organic carbon in freshwater, which is low at EGN compared to other sites (Fig. 4 3). Low or ganic carbon concentrations allow the persistence of terminal electron acceptors such as oxygen and nitrate due to lower organic carbon remineralization rates and would allow for denitrification to be a dominant redox pathway. Relatively low redox potenti als at BRL 1 may result in low Alk: DIC ratios due to the contributions of methanogenesis, which has a Alk: DIC ratio of 0, though sulfate reduction coupled with sulfide oxidation would result in low Alk: DIC. This coupling is plausible where CO 2 produc tion occurs at BRL in mangrove dominated shorelines and at RWP surficial sediments At these locations,

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112 sulfate reduction is evident due to elevated sulfide concentrations (Fig. 4 3), but are also sites of plausible sulfide oxidation which may be catalyzed by mangrove roots (Lee, 1999) or oxidation of sulfide via the introduction of oxygen to sediments through bioturbation (Martin et al., 2006) I quantify the chan ges in DIC and alkalinity due to the combination of reactions (denitrification, iron reduction, sulfate reduction, methanogenesis, methane oxidation, and CaCO 3 dissolution) using reaction stoichiometry (Table 4 1) in order to determine if the sum of these reactions yields a Alk: DIC ratio that corresponds to Alk: DIC ratios determined via conservative mixing models (Fig. 4 2a and b). For many samples, this stoichiometric analysis does not yield results similar to the magnitude or sign of DIC and Alk based on conservative mixing mode ls in Fig. 4 2 ( Tables 4 2 and 4 3, Fig. 4 8). These differences may be due to the lack of inclusion of iron and sulfide oxidation in the stoichiometric model which are known to occur at EGN sediments and likely occur at B RL and RWP, but require further geochemical modeling to estimate their relative importance. Additionally, uncertainty is introduced due to the use of salinity based conservative mixing models because of the considerable variability observed in composition of freshwater entering the STE, particularly at EGN and BRL. The selection of the freshest STE sample at each site as the freshwater end member allows an assessment of the impact of reactio ns using salinity as a tracer. However variability in freshwater c hemistry may result in mixing models that over or under predict the impacts of reactions. While quantitative assessments of the role of redox reactions in carbonate chemistry may not be ideal for this study setting due to the sensitivity of end member sel ection, the qualitative assessments based on Alk: DIC ratios suggest that

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113 specific reactions control carbonate chemistry. Moreover, the roles of these reactions (e.g. denitrification and CaCO 3 at EGN 0, and combined sulfate reduction, sulfide oxidation, a nd methanogenesis at BRL 1) are corroborated by the distribution of solutes in Fig. 4 3. Implications for CO 2 and CH 4 Fluxes Concentrations of CO 2 and CH 4 in all three STEs are orders of magnitude higher than surface water concentrations, and surface water concentrations are elevated above atmospheric concentrations (Fig. 4 1). Despite the apparent increased buffering capacity of pore water with increasing salinity, the STE is still a source of CO 2 as well as CH 4 to surface waters and likely to the atmosphe re. The impact of SGD on surface water chemistry depends on the difference between SGD Alk:DIC ratios and surface water Alk:DIC ratios. For instance, if STE samples have Alk:DIC ratios lower than surface water ratios, SGD should decrease the CO 2 buffering capacity of surface water. This decrease will limit uptake of anthropogenic atmospheric CO 2 (Egleston et al., 2010) or CO 2 from other sources such as organic matter remineralization (Liu et al., 2017) Most pore water in the Indian River Lagoon STEs have Alk:DIC r atios lower than surface waters (Fig. 4 7) because of low Alk:DIC ratios in the freshwater (Fig. 4 2h) compared to surface saltwater. Despite multiple diagenetic pathways (sulfate reduction, Fe reduction, denitrification) that produce Alk:DIC ratios at or above molar ratios of 1, the combined impact of reactions is insufficient to produce Alk:DIC ratios in por e waters that are greater than surface water values (Fig. 4 6). Higher DIC exports relative to alkalinity have been noted in aerobic siliciclastic STE s (L iu et al., 2017) and researchers concluded that SGD reduces the buffering capacity of surface water. In aerobic settings, this result is not surprising

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114 because aerobic respiration requires coupling with CaCO 3 dissolution to generate alkalinity and incr ease buffering capacity (Kuivila and Murray, 1984) The results suggest that SGD from siliciclastic STEs should decrease CO 2 buf fering capacity of surface water unless sufficient CaCO 3 is available to buffer pore waters from increased H 2 CO 3 concentrations (Ku ivila and Murray, 1984) This effect has been noted in other groundwater systems and CO 2 fluxes from carbonate aquifers may be several times lower than siliciclastic due to increased buffering capacity following CaCO 3 dissolution (Khadka et al., 2014) Conclusions The results of this study suggest that the distribution of redox reactions with salinity gradients in ST E s is critical for CO 2 and CH 4 concentrations Reactions in freshwater portions of STEs both increase and decrease the sequestration of CO 2 depending o n the redox status of pore waters. DIC: Alk ratios compared to reaction stoichiometries reveals that most brackish to saline pore waters are impacted by sulfate reduction with potential contributions of methanogenesis or sulfide oxidation, leading to Alk : DIC ratios slightly less than 1. Fresh pore waters exhibit a larger deviation from the 1:1 ratio, and reactions both increase and decrease the Alk:DIC ratios of STE pore waters. These results suggest that, despite the production of alkalinity through mul tiple anaerobic pathways, SGD from STEs lacking CaCO 3 minerals are likely to decrease the CO 2 buffering capacity of surface water, which could lead to greater CO 2 fluxes from surface waters to the atmosphere. Given the importance of estuaries and the coast al ocean in the global carbon cycle, carbon fluxes from SGD may represent an understudied but important net source or net sink of CO 2 Whether SGD serves as a

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115 net source of sink of CO 2 may be ultimately be determined by the abundance of CaCO 3 minerals ava ilable to buffer pore waters against CO 2 generated by organic carbon remineralization.

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116 Table 4 1. Impact of redox pathways and biogeochemical reactions on alkalinity and DIC. Equation Reaction 4 1 Aerobic respiration (CH 2 O) 106 (NH 3 ) 16 H 3 PO 4 + 138O 2 106CO 2 + 122H 2 O + 16HNO 3 + H 3 PO 4 (16)(NO 3 )/(106)(CO 2 )= 0.15 1 4 2 Denitrification (CH 2 O) 106 (NH 3 ) 16 H 3 PO 4 + 84.8HNO3 106CO 2 + 42.4N 2 + 148.4H 2 O + 16NH 3 + H 3 PO 4 +(84.8)(NO 3 )/(106)(CO 2 ) = 0.8 1 4 3 Iron reduction (CH 2 O) 106 (NH 3 ) 16 H 3 PO 4 + 424FeOOH + 848H + 106CO 2 + 742H 2 O + 424Fe +2 + 16NH 3 + H 3 PO 4 +(848)(H + )/(106)(CO 2 ) = 8 1 4 4 Sulfate reduction (CH 2 O) 106 (NH 3 ) 16 H 3 PO 4 + 53SO 4 2 + 106H + 106CO 2 + 106H 2 O +53H 2 S + 16NH 3 + H 3 PO 4 +(53*2)(SO 4 )/(106)(CO 2 ) = 1 1 4 5 Methanogenesis (CH 2 O) 106 (NH 3 ) 16 H 3 PO 4 53CO 2 + 53CH 4 + 16NH 3 + H 3 PO 4 0/53(CO 2 ) = 0 4 6 CaCO 3 dissolution CaCO 3 CO 3 2 + Ca 2+ (2*1)Ca 2+ /1(CO 3 2 ) = 2 4 7 Sulfide oxidation 2 H 2 S + 2O 2 SO 4 2 + 2H + (2*1)(SO 4 2 4 8 Fe oxidation 2 4Fe 2+ + O 2 + 6H 2 O 4FeOOH + 8H + (8)(H + 4 9 AOM CH 4 + SO 4 2 HCO 3 + HS + H 2 O (2*1)(SO 4 2 )+ 1(HCO 3 )/ 1(HCO 3 ) = 3 1 Chen and Wang, 1999 2 Anderson and Schiff, 1987

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117 Table 4 2. DIC concentrations compared to concentrations produced from denitrification (DN DIC; Eq. 4 2), iron reduction (FeR DIC; Eq. 4 3), sulfate reduction (SR DIC; Eq. 4 4), methanogenesis (Methane DIC; Eq. 4 5), methane oxidation (Meth ox DIC; Eq. 4 9) and CaCO 3 dissolution (CaCO 3 DIC; Eq. 4 6). All data are reported in M. Sample depths are cm below the sediment water interface. Piezometer Depth (cm) SAL (PSU) DIC DN DIC FeR DIC SR DIC Methane DIC Meth ox DIC CaCO 3 DIC BRL 1 20 6.13 3900 0 0 26 103 7 2300 BRL 1 30 2.68 4600 0 0 27 171 3 1300 BRL 1 60 2.65 5700 0 0 37 206 3 900 BRL 1 100 2.19 4300 0 0 68 109 9 800 BRL 11 20 1.7 500 2 9 0 4 3 2300 BRL 11 60 1.69 0 0 9 0 2 6 0 BRL 11 100 1.71 100 32 9 0 2 30 0 BRL 11 150 1.69 0 2 9 1 0 0 0 BRL 11 180 1.69 100 0 8 0 1 1 100 BRL 11 210 1.76 100 0 8 1 5 2 100 BRL 11 210 1.81 0 34 8 0 2 3 200 BRL 21 20 5.38 600 0 6 30 16 1 4100 BRL 21 60 3.57 200 10 8 20 31 1 5000 BRL 21 91 3.48 0 1 6 16 28 0 1800 BRL 21 150 1.73 200 0 5 1 0 0 300 BRL 21 200 1.72 100 0 6 1 5 1 0 BRL 21 250 1.72 100 0 7 1 3 1 100 BRL 45 20 24.56 1900 0 0 46 16 0 600 BRL 45 60 21.48 1700 0 0 45 20 1 1200 BRL 45 120 2.87 800 0 0 21 2 0 1500 BRL 45 50 2.49 700 0 0 1 1 0 1800 BRL 45 180 2.89 600 0 0 1 1 0 1100 BRL 45 210 3.07 500 0 0 0 1 0 1000 BRL 45 210 3.02 500 4 0 1 1 0 800 BRL 45 0 22.33 0 0 0 0 0 0 EGN 0 15 0.76 0 218 0 0 0 0 0 EGN 0 25 0.97 2400 250 0 0 0 0 0

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118 Table 4 2. Continued Piezometer Depth (cm) SAL (PSU) DIC DN DIC FeR DIC SR DIC Methane DIC Meth ox DIC CaCO 3 DIC EGN 0 95 1.14 1900 206 0 0 0 0 1800 EGN 0 115 1.09 2000 211 0 0 0 0 1800 EGN 10 15 16.2 600 0 1 4 0 0 1600 EGN 10 25 4.07 1000 0 2 2 0 0 1000 EGN 10 75 2.59 1300 0 3 0 0 0 2000 EGN 10 95 0.9 1200 0 0 0 0 0 1200 EGN 10 115 0.9 1200 0 3 0 0 0 1600 EGN 10 115 0.9 1200 0 3 0 0 0 1600 EGN 10 0 23.8 0 0 0 0 0 0 1600 EGN 20 7 25.17 0 0 0 2 0 0 0 EGN 20 15 24.47 200 0 0 14 0 0 0 EGN 20 25 24.35 700 0 1 22 0 0 200 EGN 20 55 22.39 1300 0 10 18 2 0 300 EGN 20 95 16.49 1800 0 9 4 3 0 400 EGN 20 115 16.01 1800 0 1 0 1 0 400 EGN 22.5 36 24.13 700 0 0 35 0 0 800 EGN 22.5 36 25.03 700 0 0 31 0 0 400 EGN 22.5 106 23.23 1600 0 10 1 3 0 200 EGN 22.5 156 22.52 1800 0 6 5 3 8 1000 RWP 10 60 9.05 1800 0 0 19 124 3 500 RWP 10 100 0.45 300 0 0 5 226 2 100 RWP 10 150 0.4 400 0 0 6 283 0 400 RWP 10 200 0.36 400 0 0 2 0 6 300 RWP 20 20 11.29 2200 4 1 11 51 6 100 RWP 20 60 0.5 400 0 0 3 312 7 2400 RWP 20 100 0.4 300 0 0 2 411 9 400 RWP 20 200 4 1100 0 0 3 288 5 100 RWP 20 250 0.36 400 0 0 4 261 0 100 RWP 35 10 24.36 5200 0 0 4 0 0 700 RWP 35 20 24.36 5200 0 0 37 1 0 0

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119 Table 4 2. Continued Piezometer Depth (cm) SAL (PSU) DIC DN DIC FeR DIC SR DIC Methane DIC Meth ox DIC CaCO 3 DIC RWP 35 30 24.26 5200 0 0 30 1 1 200 RWP 35 100 2.23 700 0 0 16 0 25 100 RWP 35 150 1.77 600 0 4 3 216 32 400 RWP 35 200 5.56 1200 0 0 9 114 43 400 RWP 35 200 5.53 300 0 0 28 89 0 1300 RWP 35 0 24.37 0 0 0 12 0 0 1000

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120 Table 4 3. A lk concentrations compared to concentrations produced from d enitrification (DN Alk; Eq. 4 2 ), ir on reduction (FeR Alk; Eq. 4 3 ), sul fate reduction (SR Alk; Eq. 4 4 ), metha nogenesis (Methane Alk; Eq. 4 5 ), methane oxidation (Meth ox Alk; Eq. 4 9 ) and CaCO 3 dissolution (CaCO 3 Alk; Eq. 4 6 ). All data are in eq/L. Sample depths are cm below the sediment water interface. Piezometer Depth (cm) SAL (PSU) Alk DN Alk FeR Alk SR Alk Methane Alk Meth ox Alk CaCO 3 Alk BRL 1 0.2 6.13 0 0 0 26 103 21 4500 BRL 1 0.3 2.68 300 0 1 27 171 8 2600 BRL 1 0.6 2.65 900 0 1 36 206 8 1700 BRL 1 1.0 2.19 800 0 1 67 109 27 1600 BRL 11 0.2 1.7 300 2 36 0 4 8 4500 BRL 11 0.6 1.69 100 0 35 0 2 17 0 BRL 11 1.0 1.71 0 26 36 0 2 90 0 BRL 11 1.5 1.69 0 2 35 1 0 0 0 BRL 11 1.8 1.69 0 0 34 0 1 4 100 BRL 11 2.1 1.76 100 0 32 0 5 7 300 BRL 11 2.1 1.81 200 27 33 0 2 9 300 BRL 21 0.2 5.38 500 0 22 30 16 3 8200 BRL 21 0.6 3.57 100 8 34 20 31 2 10000 BRL 21 0.9 3.48 100 1 22 15 28 0 3600 BRL 21 1.5 1.73 200 0 18 1 0 0 700 BRL 21 2.0 1.72 100 0 24 1 5 4 0 BRL 21 2.5 1.72 100 0 28 1 3 3 300 BRL 45 0.2 24.56 1400 0 0 45 16 0 1200 BRL 45 0.6 21.48 1200 0 0 45 20 2 2400 BRL 45 1.2 2.87 400 0 0 20 2 1 3000 BRL 45 0.5 2.49 300 0 0 1 1 1 3600 BRL 45 1.8 2.89 300 0 0 1 1 1 2200 BRL 45 2.1 3.07 100 0 0 0 1 1 1800 BRL 45 0.0 22.33 0 0 0 0 0 0 0 EGN 0 0.2 0.76 0 175 0 0 0 0 0 EGN 0 0.3 0.97 1000 194 0 0 0 1 0 EGN 0 0.8 1.11 2700 169 0 0 0 0 2000

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121 Table 4 3. Continued Piezometer Depth (cm) SAL (PSU) Alk DN Alk FeR Alk SR Alk Methane Alk Methox Alk CaCO 3 Alk EGN 0 1.0 1.14 2900 165 0 0 0 0 3600 EGN 0 1.2 1.09 2800 169 0 0 0 1 3500 EGN 10 0.2 16.2 800 0 4 4 0 0 3200 EGN 10 0.3 4.07 2100 0 7 2 0 0 1900 EGN 10 0.8 2.59 2400 0 14 0 0 0 4200 EGN 10 1.0 0.9 2400 0 1 0 0 0 2500 EGN 10 1.2 0.9 2500 0 12 0 0 0 3100 EGN 10 1.2 0.9 2400 0 13 0 0 0 3200 EGN 10 0.0 23.8 0 0 0 0 0 0 3200 EGN 20 0.1 25.17 0 0 0 2 0 0 0 EGN 20 0.2 24.47 100 0 0 14 0 0 100 EGN 20 0.3 24.35 700 0 5 22 0 0 400 EGN 20 0.6 22.39 1200 0 40 18 2 1 500 EGN 20 1.0 16.49 1700 0 37 4 3 1 700 EGN 20 1.2 16.01 1700 0 3 0 1 0 700 EGN 22.5 0.4 24.13 500 0 1 35 0 0 1600 EGN 22.5 0.4 25.03 600 0 0 31 0 1 900 EGN 22.5 1.1 23.23 1300 0 38 1 3 0 400 EGN 22.5 1.6 22.52 1800 0 22 5 3 25 2000 RWP 10 0.6 9.05 RWP 10 1.0 0.45 RWP 10 1.5 0.4 RWP 10 2.0 0.36 RWP 20 0.2 11.29 400 3 6 11 51 17 100 RWP 20 0.6 0.5 0 0 0 3 312 20 4800 RWP 20 1.0 0.4 200 0 0 2 411 28 800 RWP 20 2.0 4 200 0 0 3 288 16 200 RWP 20 2.5 0.36 600 0 0 4 261 0 200 RWP 35 0.1 24.36 0 0 0 4 0 1 1400 RWP 35 0.2 24.36 500 0 0 36 1 1 0

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122 Table 4 3. Continued Piezometer Depth (cm) SAL (PSU) Alk DN Alk FeR Alk SR Alk Methane Alk Methox Alk CaCO 3 Alk RWP 35 0.3 24.26 500 0 0 30 1 3 400 RWP 35 0.6 20 400 0 0 27 4 0 200 RWP 35 1.0 2.23 900 0 0 16 0 74 200 RWP 35 1.5 1.77 300 0 15 3 216 95 800 RWP 35 2.0 5.56 400 0 1 9 114 130 800 RWP 35 2.0 5.53 700 0 0 28 89 0 2600 RWP 35 0.0 24.37 800 0 0 12 0 0 2000

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123 Figure 4 1. Relationship between dissolved gas concentrations and salinity at each STE site. Panels a) and b) present CO 2 concentrations, and panels c) and d) present CH 4 concentrations. To facilitate comparison of low concentration samples, panels b) and d) present log concentrations. Open symbols represent surface water concentrations.

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124 Figure 4 2. Concentrations of solutes with salinity between Indian River Lagoon seepage faces, including ( a) DIC, b) alkalinity, c) Ca, d) CO 2 e) CH 4 f) pH g) proportion of DIC as CO 2 and h) Alk:DIC ratios Dotted lines represent conservative mixing between the freshest STE sample (indicated with white circle) and surface s eawater (indicated with white square) at each seepage face and are only shown when a mixing model was constructed Fresh and saltwater end members are indicated for CO 2 CH 4 and pH although no mixing model was constructed for these parameters Due to thei r distinct chemistries, samples from BRL 1 and EGN 0 are indicated in blue and orange, respectively.

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125 Figure 4 3 Distribution of salinity, DOC, redox species, and dissolved gases b etween Indian River Lagoon seepage faces. Note that the vertical and horizontal scales of each STE site are different.

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126 Figure 4 4 Distribution of salinity DIC, Alk, and Ca between Indian River Lagoon seepage faces. Note that the vertical and horizontal scales of each STE site are different.

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127 Figure 4 5. CH 4 concentrations and isotopic compositions at Indian River Lagoon sites. (a) CH 4 concentrations and 13 C CH 4 signatures and (b) the quantity of CH 4 oxidized versus salinity

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128 Figure 4 6 Alk and DIC compared to Alk: DIC ratios produced by reactions. The diagonal dotted black line represents a 1:1 line, while vectors represent ratios of Alk: DIC produced by reactions (SR= sulfate reduction). Panel (a) presents the full range of data, while (b) is scaled to depict the cluster of data points with salinity > 15 The size of data points corresponds to salinity. Modified from Liu et al. (2017).

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129 Figure 4 7 Alk:DIC ratios modeled by conservative mixing model (Alk:DIC (mix)) compared to Alk:DIC (measured). The size of data points represents sample salinity. Color coded dotted lines represent the surface water measured Alk:DIC ratio at each seepage face. Samples from EGN 0 and EGN 10, as well as BRL 1 are indicated due to their distinct chemistrie s from other STE samples.

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130 Figure 4 8. Relationships between DIC and Alk estimated via salinity based conservative mixing models (deviations from conservative mixing lines indicated in Fig. 4 2a and b) compared to that calculated from the net impact o f reactions. Reactions included in this calculation include denitrification, iron reduction, sulfate reduction, methanogenesis, methane oxidation, and CaCO 3 dissolution (data presented in Tables 4 2 and 4 3). The only significant relationship is between A lk estimates at EGN.

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131 CHAPTER 5 SU MMARY AND CONCLUDING REMARKS Biogeochemical processing in subterranean estuaries modifies the composition of submarine groundwater discharge, which is an important source of freshwater and terrestrial solutes to coastal zones. Comparison between carbon processing in siliciclastic STEs of Indian River Lagoon, FL and a karst carbonate STE in Quintana Roo, Mexico, demonstrates that the impacts of reactions on carbon transformations, and lik ely on carbon fluxes, vary due to aquifer hydrogeology. These differences may be classified into differences due to flow, which alters residence time within the freshwater saltwater mixing zone, and differences due to interactions with aquifer solid materi al. These variations may lead to considerable yet systematic variability in the composition of SGD derived from siliciclastic compared to carbonate karst aquifers. Impacts Due to Flow Carbonate karst STEs have high groundwater transport rate and low ground water residence time in the freshwater saltwater mixing zone compared to widely distributed siliciclastic systems. Lower residence time in carbonate karst STEs leads to relatively less organic carbon processing compared to siliciclastic STEs (Chapter 2) T he magnitudes of differences are considerable: compared to concentrations predicted by conservative mixing, STE processing increased CDOM by 40% in the Yucatan, while this increase was up to 600% at Indian River Lagoon. This finding suggests that fluxes of organic carbon per unit volume of SGD should be considerably higher from siliciclastic STEs with a lower water:rock ratio and greater potential to interact with sedimentary organic carbon pools, depending on the absolute volumes of discharge water, which will control solute loading. This finding also implies that fluxes of by

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132 products of remineralization, which include remineralized nutrients, metals, and greenhouse gases, should be greater per unit volume in siliciclastic systems compared to karst carbona te systems. The impact of STE hydrogeology on SGD fluxes depends on the concentrations of solutes in SGD as well as total SGD volume from the two end member systems. Many studies quantifying SGD fluxes have focused on the fresh component of SGD because it regenerated from marine sediments (Knee and Paytan, 2011) These techniques often multiply estimates of total fresh SGD, quantified via seepage meters, chemical tracers, water balance approaches, piezometers, or numerical methods (Burnett et al., 2006) by the concentration of a solute of interest prese nt in the fresh groundwater end member (McCoy and Corbett, 2009; Knee and Paytan, 2011) These methods do not account for transformation of groundwater within the subterranean estuary or the saline component of submarine groundwater discharge. Since the results of this study suggest that saltwater provides labile organic carbon substrates as well as terminal electron acceptors that drive biogeochemical reactions (Chapter 2), saline SGD is a critical component both in terms of volume (Table 1 1) and by impacting the chemical composition of SGD. I therefore compare the magnitudes of combined fresh and saline SGD between sample sites to determine the likely impact of STE reactions on chemi cal fluxes. C ombined fresh and saline SGD fluxes (commonly referred to as total SGD) between Indian River Lagoon sites and the Yucatan are similar in magnitude, with estimates of Indian River Lagoon SGD at approximately 320 m 3 km 1 yr 1 ( Martin et al.

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133 2007) while the Yucatan is estimated at approximately 112 m 3 km 1 yr 1 ( Null et al. 2014) While these SGD fluxes were calculated using different methods and the error may be high, they suggest that the widely distributed Indian River Lagoon system contributes equivalent or gr eater volumes of total SGD compared to the carbonate karst system of the Yucatan (Table 1 1). Because the concentrations of organic carbon, and likely other solutes, are higher from Indian River Lagoon than Yucatan STEs, overall SGD solute fluxes are likel y to be considerably greater for Indian River Lagoon compared to Yucatan site, and may be related to the higher degree of biogeochemical alteration of fresh and marine SGD due to higher residence times in the f reshwater saltwater mixing zone and the lower water:rock ratio that provides solid phase organic carbon and mineral phases to drive reactions. Impacts Due to Aquifer Solid Material Because biogeochemical reactions may involve aquifer solid material, STE hydrogeology also exerts an influence on the che mical composition of SGD. For instance, organic carbon remineralization leads to CO 2 generation in both Yucatan (Chapter 3) and Indian River Lagoon (Chapter 4) sites. Increased acidity leads to the undersaturation and dissolution of CaCO 3 minerals in the Y ucatan, but only in isolated locations of Indian River Lagoon, and SGD is a greater CO 2 source in part due to decreased buffering capacity of Indian River Lagoon pore waters. SGD from siliciclastic STEs may therefore be a greater source of CO 2 to surface w aters than that from carbonate karst STEs, and may cause surface waters to become sources of CO 2 to the atmosphere. However, the extent of CaCO 3 buffering in karst STEs may be limited by low water residence time that may prohibit water from reaching thermo dynamic

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134 equilibrium with respect to CaCO 3 minerals, particularly when the CaCO 3 saturation state of groundwater approaches equilibrium and dissolution kinetics become slower Concl uding Remarks The results of this study allow several conclusions to be made regarding the role of SGD in coastal carbon cycling as a function of aquifer hydrogeology. Siliciclastic STEs are likely to contribute more solutes, including DOC, CDOM, and CO 2 to surface waters than carbonate karst STEs, given equivalent volumes of total SGD. Reduced buffering capacity of SGD in siliciclastic STEs results from a lower availability of CaCO 3 minerals, and SGD from these systems is more likely to increase CO 2 fluxes to surface water and to the atmosphere. In carbonate karst systems, buf fering due to CaCO 3 dissolution may occur but could be limited by low reaction rates compared to the flow rate. Once delivered to surface water, a negative feedback for CO 2 concentrations may be induced due to SGD nutrient delivery that stimulates higher r ates of primary productivity. In the Yucatan, this mechanism appears to be reduced due to retention of P within the aquifer when P sorbs to CaCO 3 mineral surfaces. While not examined here, nutrient reten tion is less likely to occur in Indian River Lagoon s ediments due to lower CaCO 3 concentrations, though Fe oxide mineral precipitation could sorb remineralized P. However, previous studies suggest that most reduced Fe co precipitates with sulfide to form iron sulfide minerals, which have limited sorption cap acity for P (Roy, M. et al., 2010; Roy et al., 2013a) .. This study highlights that bi ogeochemical processing in STEs significantly impacts organic carbon (DOC and CDOM), remineralized carbon (CO 2 and CH 4 ) and nutrient concentrations in SGD, but that aquifer hydrogeology exerts an important control on the magnitude and type of reactions tha t may occur. These differences may

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135 cause systematic variations in the composition of SGD between these two end member systems. The high variability in the concentrations and ranges of carbon cycling reactions observed, as well as sensitivity to flow rate a nd end member composition may preclude upscaling of these results to estimate large scale impacts of SGD on coastal carbon cycling, although SGD may play a critical role in carbon budgets in individual study settings.

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136 APPENDIX A PARAFAC MODEL

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137 Table A 1. Complete list of samples included in PARAFAC model. Site ID codes for Indian River Lagoon sites are: BRL (Banana River Lagoon), EGN (Eau Gallie North), and RWP (Riverwal k Park). Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 1 BRL 10 2014 6 0.2 2.76 6.84 225 4.83 1.51 1.80 0.84 0.28 2 BRL 10 2014 6 0.6 1.43 6.89 144 2.86 0.96 0.98 0.69 0.13 3 BRL 10 2014 6 1.2 1.46 6.91 120 4.30 1.53 1.72 0.87 0.25 4 BRL 10 2014 6 1.5 1.47 6.92 121 6.18 1.98 2.28 1.14 0.35 5 BRL 10 2014 6 1.8 1.48 6.93 119 5.61 1.87 2.09 1.10 0.32 6 BRL 10 2014 6 2.1 1.51 6.97 111 5.72 1.82 2.06 1.11 0.31 7 BRL 10 2014 6 0 23.85 7.61 12 1.56 0.39 0.76 0.28 0.29 8 BRL 10 2014 1 0.1 5.83 6.28 272 30.73 4.32 6.61 3.36 1.51 9 BRL 10 2014 1 0.2 8.38 6.28 276 36.40 4.55 7.42 3.66 1.76 10 BRL 10 2014 1 0.3 9.87 6.30 273 13.32 6.00 4.53 4.23 0.47 11 BRL 10 2014 1 0.6 11.85 6.27 269 25.38 4.52 5.86 3.34 1.14 12 BRL 10 2014 1 1 .0 7.91 6.28 245 22.04 4.68 5.31 3.59 1.05 13 BRL 10 2014 1 1 .0 6.72 6.31 253 10.48 3.80 3.46 2.53 0.46 14 BRL 10 2014 11 0.2 1.85 6.92 134 4.22 1.69 1.71 0.93 0.19 15 BRL 10 2014 11 0.6 1.41 6.94 111 5.57 2.07 2.13 1.22 0.24 16 BRL 10 2014 11 1 .0 1.42 6.94 118 4.90 3.55 2.53 2.31 0.16 17 BRL 10 2014 11 1.5 1.40 6.94 119 6.08 3.64 3.04 2.03 0.24 18 BRL 10 2014 11 1.8 1.39 6.94 119 5.22 1.63 1.88 0.92 0.25 19 BRL 10 2014 11 2.1 1.41 7.00 163 4.56 1.45 1.66 0.83 0.21 20 BRL 10 2014 11 2.1 1.40 6.97 110 5.06 1.62 1.84 0.91 0.23 21 EGN 10 2014 0 0.15 5.45 6.63 6 0.43 0.14 0.26 0.07 0.06 22 EGN 10 2014 0 0.25 0.98 6.71 4 0.22 0.09 0.15 0.04 0.02 23 EGN 10 2014 0 0.35 0.72 6.73 29 0.22 0.09 0.15 0.04 0.02 24 EGN 10 2014 0 0.55 0.72 6.77 51 0.27 0.10 0.17 0.05 0.03 25 EGN 10 2014 0 0.75 0.72 6.75 28 0.24 0.09 0.15 0.04 0.02 26 EGN 10 2014 0 0.95 0.72 6.76 40 0.24 0.09 0.16 0.04 0.02 27 EGN 10 2014 0 1.15 0.73 6.81 39 0.22 0.08 0.14 0.04 0.02 28 EGN 10 2014 0 1.15 0.73 6.84 25 0.22 0.09 0.15 0.03 0.02 29 EGN 10 2014 17.5 0.07 18.28 7.78 216 1.61 0.37 0.81 0.24 0.23 30 EGN 10 2014 17.5 0.15 13.02 7.66 335 1.62 0.38 0.80 0.24 0.22 31 EGN 10 2014 17.5 0.25 19.10 7.55 244 1.56 0.37 0.78 0.23 0.21

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138 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 32 EGN 10 2014 17.5 0.35 19.49 7.54 271 1.49 0.36 0.75 0.21 0.20 33 EGN 10 2014 17.5 0.55 18.59 7.14 259 1.35 0.37 0.74 0.23 0.19 34 EGN 10 2014 17.5 0.75 17.78 7.02 294 1.04 0.36 0.58 0.19 0.13 35 EGN 10 2014 17.5 0.95 16.09 6.77 171 0.98 0.36 0.54 0.18 0.10 36 EGN 10 2014 17.5 1.15 16.11 6.74 84 0.94 0.35 0.52 0.17 0.10 37 EGN 10 2014 17.5 0 18.32 8.23 32 1.64 0.38 0.82 0.25 0.24 38 EGN 10 2014 20 0.07 19.03 8.09 195 1.57 0.37 0.80 0.23 0.25 39 EGN 10 2014 20 0.15 19.11 7.66 264 1.58 0.38 0.79 0.23 0.24 40 EGN 10 2014 20 0.25 19.65 7.53 298 1.50 0.37 0.76 0.22 0.22 41 EGN 10 2014 20 0.55 20.12 7.12 288 1.24 0.35 0.69 0.22 0.18 42 EGN 10 2014 20 0.95 18.52 6.10 219 1.04 0.40 0.63 0.22 0.14 43 EGN 10 2014 20 1.15 18.37 6.81 179 1.00 0.40 0.60 0.22 0.12 44 EGN 10 2014 22.5 0.36 21.88 7.35 304 1.35 0.35 0.70 0.20 0.20 45 EGN 10 2014 22.5 1.06 21.19 6.95 272 1.09 0.37 0.64 0.23 0.15 46 EGN 10 2014 22.5 1.86 22.21 7.16 287 1.25 0.41 0.65 0.26 0.13 47 EGN 10 2014 22.5 1.86 22.15 7.23 221 1.23 0.39 0.62 0.25 0.12 48 EGN 10 2014 10 0.07 13.62 7.32 212 1.76 0.43 0.79 0.27 0.21 49 EGN 10 2014 10 0.15 9.50 7.15 198 1.17 0.34 0.59 0.18 0.15 50 EGN 10 2014 10 0.25 2.77 7.11 137 0.71 0.27 0.42 0.11 0.09 51 EGN 10 2014 10 0.35 2.56 7.11 144 0.82 0.29 0.46 0.13 0.11 52 EGN 10 2014 10 0.55 1.47 7.14 95 0.64 0.25 0.38 0.10 0.08 53 EGN 10 2014 10 0.75 0.86 7.13 99 0.46 0.17 0.27 0.07 0.06 54 EGN 10 2014 10 0.95 0.79 7.16 77 0.63 0.25 0.38 0.11 0.08 55 EGN 10 2014 10 1.15 0.79 7.13 39 0.69 0.28 0.43 0.11 0.09 56 EGN 10 2014 10 1.15 0.83 7.44 57 0.45 0.17 0.27 0.07 0.06 57 EGN 10 2014 10 0 14.95 8.34 34 2.29 0.52 0.97 0.37 0.27 58 EGN 10 2014 15 0.07 15.88 7.90 150 2.01 0.48 0.91 0.32 0.26 59 EGN 10 2014 15 0.25 13.24 7.19 232 1.19 0.34 0.61 0.19 0.16 60 EGN 10 2014 15 0.35 12.64 7.14 231 1.22 0.35 0.62 0.19 0.16 61 EGN 10 2014 15 0.55 13.30 7.16 233 1.18 0.34 0.62 0.19 0.17 62 EGN 10 2014 15 0.75 10.69 7.06 222 0.81 0.29 0.46 0.13 0.12 63 EGN 10 2014 15 0.95 5.47 6.99 194 0.70 0.30 0.42 0.12 0.09 64 EGN 10 2014 15 1.15 1.29 6.94 174 0.74 0.30 0.43 0.13 0.09 65 EGN 10 2014 15 1.15 6.12 6.94 181 0.74 0.30 0.43 0.13 0.09 66 RWP 10 2014 0 0.6 22.73 7.03 284 3.19 0.70 1.09 0.61 0.34 67 RWP 10 2014 0 1.5 10.95 6.60 257 7.23 1.64 2.03 1.17 0.37

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139 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 68 RWP 10 2014 0 1.5 11.29 6.60 270 7.02 1.63 1.99 1.17 0.36 69 RWP 10 2014 5 0.1 25.00 7.16 279 1.56 0.35 0.75 0.24 0.24 70 RWP 10 2014 5 0.2 25.05 7.17 266 1.71 0.39 0.79 0.28 0.22 71 RWP 10 2014 5 0.3 22.63 6.75 239 2.57 0.62 0.95 0.46 0.22 72 RWP 10 2014 5 0.6 22.87 6.69 254 2.66 0.65 0.95 0.49 0.21 73 RWP 10 2014 5 1 .0 7.58 6.78 208 3.25 0.98 1.21 0.53 0.24 74 RWP 10 2014 5 1.5 0.53 6.89 149 1.34 0.56 0.90 0.20 0.16 75 RWP 10 2014 5 2 .0 0.36 6.95 145 2.77 0.93 1.17 0.43 0.26 76 RWP 10 2014 5 0 24.41 8.29 10 1.31 0.29 0.73 0.18 0.26 77 RWP 10 2014 10 0.6 15.43 6.62 215 2.59 0.67 1.00 0.46 0.24 78 RWP 10 2014 10 1 .0 0.77 6.89 162 1.98 0.67 0.84 0.29 0.19 79 RWP 10 2014 10 1.5 0.41 7.01 136 2.81 0.93 1.18 0.42 0.27 80 RWP 10 2014 10 2 .0 0.36 7.01 180 3.09 1.00 1.24 0.52 0.27 81 BRL 05 2015 1 0.2 5.51 6.23 268 2.11 9.13 3.51 7.91 0.16 82 BRL 05 2015 1 0.3 2.37 6.28 271 19.26 9.31 5.69 7.07 0.53 83 BRL 05 2015 1 0.6 2.14 6.30 261 17.60 8.90 4.86 8.39 0.43 84 BRL 05 2015 1 1 .0 1.71 6.54 78 12.85 5.24 4.12 3.80 0.45 85 BRL 05 2015 11 0.6 1.71 6.98 5.26 2.45 2.18 1.45 0.22 86 BRL 05 2015 11 0 22.95 8.21 48 1.06 0.29 0.65 0.17 0.26 87 BRL 05 2015 21 0.2 16.69 7.01 283 3.84 1.65 1.72 1.14 0.26 88 BRL 05 2015 21 1 .0 6.44 6.27 239 5.74 1.57 1.80 1.04 0.30 89 BRL 05 2015 21 1.5 1.67 6.92 217 5.03 2.03 2.00 1.15 0.23 90 BRL 05 2015 21 1.8 1.56 6.91 148 5.96 3.11 2.78 1.71 0.26 91 BRL 05 2015 11 0.2 2.15 6.95 106 5.64 1.86 1.95 1.16 0.29 92 BRL 05 2015 11 1.5 1.65 6.97 122 7.12 2.24 2.58 1.30 0.39 93 BRL 05 2015 11 2.1 1.63 7.02 120 5.42 2.55 2.42 1.41 0.26 94 BRL 05 2015 21 0.6 10.86 6.77 277 5.29 1.61 1.83 1.11 0.31 95 BRL 05 2015 21 2.5 1.55 6.90 189 4.77 1.63 1.81 0.89 0.25 96 EGN 05 2015 0 1.15 0.96 6.92 27 0.46 0.21 0.32 0.09 0.05 97 EGN 05 2015 17.5 0.15 22.63 7.43 249 1.11 0.27 0.63 0.15 0.19 98 EGN 05 2015 17.5 0.35 21.75 7.25 274 1.14 0.30 0.63 0.16 0.18 99 EGN 05 2015 17.5 0.55 18.19 7.05 239 1.07 0.31 0.60 0.19 0.16 100 EGN 05 2015 17.5 0.95 12.29 6.84 147 0.93 0.34 0.50 0.17 0.12 101 EGN 05 2015 17.5 1.15 12.67 6.82 118 0.92 0.34 0.50 0.16 0.12 102 EGN 05 2015 20 0.07 22.33 7.71 160 1.10 0.26 0.63 0.17 0.21 103 EGN 05 2015 20 0.25 22.29 7.35 256 1.18 0.30 0.66 0.18 0.19

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140 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 104 EGN 05 2015 20 0.55 21.68 7.11 210 1.20 0.34 0.74 0.25 0.21 105 EGN 05 2015 20 0.95 19.73 6.74 113 1.09 0.43 0.67 0.25 0.16 106 EGN 05 2015 20 1.15 19.56 6.78 166 1.14 0.46 0.70 0.27 0.15 107 EGN 05 2015 22.5 0.36 22.25 7.37 231 1.52 0.48 0.94 0.26 0.24 108 EGN 05 2015 22.5 1.06 23.33 6.93 138 1.20 0.47 0.75 0.29 0.17 109 EGN 05 2015 22.5 1.86 23.36 7.29 205 1.24 0.41 0.61 0.25 0.12 110 EGN 05 2015 22.5 1.86 23.40 7.16 256 1.94 0.62 0.96 0.36 0.18 111 EGN 05 2015 22.5 0 22.93 7.79 19 1.14 0.26 0.66 0.15 0.22 112 EGN 05 2015 20 0.15 22.42 7.43 252 1.52 0.50 1.00 0.28 0.26 113 RWP 05 2015 0 0.6 21.49 6.90 257 1.94 0.49 0.84 0.34 0.25 114 RWP 05 2015 0 1 .0 2.18 6.76 178 2.74 0.96 1.20 0.45 0.25 115 RWP 05 2015 0 1.5 0.70 6.83 137 2.74 0.95 1.20 0.41 0.25 116 RWP 05 2015 0 2 .0 0.56 6.95 92 2.33 0.82 1.03 0.35 0.23 117 RWP 05 2015 10 0.2 10.18 6.73 179 2.88 0.89 1.08 0.56 0.23 118 RWP 05 2015 10 0.6 0.90 6.91 160 2.86 0.98 1.20 0.45 0.25 119 RWP 05 2015 10 1.0 0.36 7.01 120 2.64 0.90 1.15 0.37 0.26 120 RWP 05 2015 10 1.5 0.42 7.22 103 2.45 0.87 1.12 0.33 0.25 121 RWP 05 2015 20 0.2 13.52 6.77 181 2.28 0.62 0.97 0.36 0.26 122 RWP 05 2015 20 0.6 1.11 6.84 2.49 0.87 1.14 0.33 0.26 123 RWP 05 2015 20 1 .0 0.42 6.86 2.85 0.93 1.21 0.38 0.29 124 RWP 05 2015 20 1.5 0.37 6.87 2.66 0.89 1.17 0.35 0.27 125 RWP 05 2015 20 2 .0 0.42 6.85 166 2.60 0.85 1.11 0.35 0.26 126 RWP 05 2015 20 2.5 0.68 6.82 2.65 0.91 1.16 0.40 0.26 127 RWP 05 2015 20 2.5 0.67 6.94 2.69 0.91 1.18 0.38 0.26 128 RWP 05 2015 20 0 23.77 7.88 24 1.09 0.23 0.65 0.13 0.21 129 BRL 09 2015 1 0.2 5.08 6.14 247 31.99 7.63 8.50 5.88 1.29 130 BRL 09 2015 1 0.3 3.03 6.11 237 37.76 8.64 9.80 6.68 1.47 131 BRL 09 2015 1 0.6 3.65 6.04 246 37.39 8.47 9.67 6.67 1.47 132 BRL 09 2015 1 1 .0 6.59 6.74 245 16.84 4.52 5.05 3.34 0.79 133 BRL 09 2015 1 1 .0 7.92 6.27 240 15.75 4.27 4.77 3.21 0.81 134 BRL 09 2015 11 0.2 1.78 6.88 80 5.62 1.83 2.03 1.01 0.27 135 BRL 09 2015 11 0.6 1.61 6.90 88 5.96 3.48 2.92 1.90 0.23 136 BRL 09 2015 11 1.5 1.63 6.90 87 6.53 2.06 2.25 1.19 0.30 137 BRL 09 2015 11 2.1 1.65 6.90 90 6.33 2.10 2.28 1.20 0.30 138 BRL 09 2015 11 0 20.66 7.86 40 1.14 0.26 0.63 0.17 0.26 139 BRL 09 2015 21 0.2 4.90 6.86 193 5.83 1.55 1.86 1.09 0.32

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141 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 140 BRL 09 2015 21 0.6 5.83 6.74 175 6.37 2.03 2.21 1.28 0.31 141 BRL 09 2015 21 0.9 2.92 6.76 181 6.63 1.94 2.18 1.20 0.32 142 BRL 09 2015 21 1.5 1.65 6.89 113 5.47 1.78 2.03 0.95 0.28 143 BRL 09 2015 21 2 .0 1.63 6.88 97 5.71 1.85 2.09 1.02 0.28 144 BRL 09 2015 21 2.5 1.94 7.08 104 5.47 1.85 2.09 0.97 0.28 145 BRL 09 2015 45 0.2 12.82 7.18 218 3.55 1.01 1.29 0.69 0.25 146 BRL 09 2015 45 0.6 9.81 7.01 230 5.79 1.83 1.94 1.17 0.26 147 BRL 09 2015 45 1.2 2.42 6.92 128 6.86 2.03 2.16 1.29 0.28 148 BRL 09 2015 45 1.5 2.30 60 6.40 2.05 2.08 1.29 0.25 149 BRL 09 2015 45 1.8 2.36 7.03 28 6.07 1.83 1.90 1.23 0.25 150 BRL 09 2015 45 2.1 2.64 7.02 97 5.90 1.82 1.92 1.17 0.25 151 BRL 09 2015 45 2.1 2.68 7.03 43 5.83 1.78 1.89 1.15 0.25 152 EGN 09 2015 0 0.15 0.48 6.18 108 0.21 0.07 0.14 0.03 0.03 153 EGN 09 2015 0 0.25 0.52 6.26 104 0.56 0.24 0.43 0.09 0.07 154 EGN 09 2015 0 0.55 0.65 6.72 0 0.80 0.34 0.57 0.14 0.09 155 EGN 09 2015 0 0.95 0.67 6.63 99 0.60 0.25 0.41 0.10 0.07 156 EGN 09 2015 0 1.15 0.66 6.51 100 0.37 0.15 0.27 0.06 0.05 157 EGN 09 2015 0 1.15 0.67 6.50 100 0.18 0.07 0.13 0.03 0.02 158 EGN 09 2015 17.5 0.15 19.07 7.19 349 1.09 0.30 0.62 0.18 0.16 159 EGN 09 2015 17.5 0.35 18.63 7.25 292 1.05 0.29 0.56 0.17 0.16 160 EGN 09 2015 17.5 0.55 16.65 7.33 237 1.37 0.46 0.76 0.25 0.22 161 EGN 09 2015 17.5 0.95 15.32 6.75 223 0.91 0.32 0.48 0.15 0.12 162 EGN 09 2015 17.5 1.15 15.41 6.68 74 1.11 0.43 0.61 0.20 0.13 163 EGN 09 2015 20 0.07 17.27 7.83 260 2.12 0.66 1.19 0.41 0.29 164 EGN 09 2015 20 0.15 17.98 7.55 349 2.16 0.73 1.26 0.44 0.28 165 EGN 09 2015 20 0.25 19.14 7.48 375 2.02 0.64 1.15 0.38 0.27 166 EGN 09 2015 20 0.55 20.11 7.21 337 1.20 0.35 0.66 0.22 0.21 167 EGN 09 2015 20 0.95 19.53 6.81 235 1.10 0.43 0.65 0.19 0.14 168 EGN 09 2015 20 1.15 19.41 6.80 232 2.02 0.84 1.20 0.38 0.22 169 EGN 09 2015 20 1.15 19.49 6.82 219 1.88 0.80 1.14 0.36 0.20 170 EGN 09 2015 22.5 0.66 20.77 7.43 370 2.10 0.75 1.27 0.41 0.28 171 EGN 09 2015 22.5 0 18.79 8.06 90 1.38 0.36 0.77 0.23 0.22 172 EGN 09 2015 22.5 1.06 23.46 6.97 248 0.91 0.36 0.57 0.25 0.13 173 EGN 09 2015 22.5 1.86 22.89 7.34 261 1.39 0.51 0.75 0.38 0.13 174 RWP 09 2015 0 0.6 6.65 6.63 210 14.47 2.67 3.19 2.07 0.61 175 RWP 09 2015 0 1 .0 0.73 6.83 56 2.91 1.04 1.30 0.45 0.26

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142 Table A1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 176 RWP 09 2015 0 2 .0 0.54 6.91 72 2.39 0.88 1.11 0.35 0.24 177 RWP 09 2015 20 0.2 4.58 6.67 128 3.36 1.01 1.34 0.55 0.35 178 RWP 09 2015 20 0.6 0.41 6.87 90 2.72 0.97 1.24 0.37 0.28 179 RWP 09 2015 20 1 .0 0.36 6.89 63 2.55 0.92 1.18 0.35 0.27 180 RWP 09 2015 20 1.5 0.36 6.89 91 2.71 0.97 1.23 0.38 0.28 181 RWP 09 2015 20 2 .0 0.35 6.95 93 2.71 0.95 1.21 0.38 0.27 182 RWP 09 2015 20 2.5 0.36 7.09 118 3.09 1.09 1.38 0.43 0.29 183 RWP 09 2015 35 0.1 18.11 7.16 225 1.99 0.48 0.91 0.30 0.27 184 RWP 09 2015 35 0.2 17.44 7.04 220 2.22 0.58 0.96 0.35 0.25 185 RWP 09 2015 35 0.3 13.29 6.95 225 2.83 0.79 1.12 0.47 0.26 186 RWP 09 2015 35 1 .0 1.70 7.10 186 4.07 1.33 1.57 0.67 0.30 187 RWP 09 2015 35 1.5 3.00 7.04 158 3.79 1.20 1.49 0.57 0.29 188 RWP 09 2015 35 2 .0 7.11 7.01 164 4.56 1.32 1.62 0.74 0.29 189 RWP 09 2015 35 2 .0 6.36 7.02 148 4.42 1.30 1.61 0.72 0.29 190 RWP 09 2015 35 0 21.70 8.40 61 1.28 0.26 0.76 0.17 0.30 191 BRL 05 2016 1 0.2 6.13 6.10 264 1.12 9.27 3.25 8.49 0.22 192 BRL 05 2016 1 0.3 2.68 6.17 265 1.62 9.78 2.98 10.52 0.32 193 BRL 05 2016 1 0.6 2.65 6.19 255 1.22 8.98 2.74 9.42 0.29 194 BRL 05 2016 1 1 .0 2.19 6.32 262 32.50 4.39 6.32 3.61 1.36 195 BRL 05 2016 11 0.2 1.70 6.93 101 5.56 5.42 3.70 3.22 0.16 196 BRL 05 2016 11 0.6 1.69 6.93 100 4.83 2.13 2.00 1.22 0.22 197 BRL 05 2016 11 1 .0 1.71 6.91 103 7.05 2.85 2.78 1.65 0.33 198 BRL 05 2016 11 1.5 1.69 6.92 105 5.96 2.83 2.61 1.61 0.26 199 BRL 05 2016 11 1.8 1.69 6.96 87 9.20 3.04 3.54 1.59 0.49 200 BRL 05 2016 11 2.1 1.76 6.96 92 4.74 1.89 1.90 1.08 0.23 201 BRL 05 2016 11 2.1 1.81 7.02 100 4.57 1.68 1.78 0.93 0.21 202 BRL 05 2016 21 0.2 5.38 6.82 258 3.88 1.87 1.75 1.15 0.28 203 BRL 05 2016 21 0.1 21.35 7.35 234 3.59 1.74 2.00 1.25 0.43 204 BRL 05 2016 21 0.6 3.57 6.87 233 6.18 5.14 3.66 3.19 0.19 205 BRL 05 2016 21 0.25 16.01 6.98 224 5.06 3.01 2.17 2.59 0.22 206 BRL 05 2016 21 0.9 3.48 6.81 226 4.34 6.90 3.90 4.58 0.03 207 BRL 05 2016 21 1.5 1.73 6.92 141 4.42 1.95 1.84 1.11 0.20 208 BRL 05 2016 21 0.4 12.68 6.81 225 4.36 3.06 2.17 2.12 0.13 209 BRL 05 2016 21 2 .0 1.72 6.96 145 3.64 1.62 1.51 0.90 0.16 210 BRL 05 2016 21 2.5 1.72 7.04 131 4.24 2.03 1.96 1.01 0.21 211 BRL 05 2016 45 0.2 24.56 7.06 298 1.32 0.40 0.59 0.29 0.14

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143 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 212 BRL 05 2016 45 0.6 21.48 6.87 286 2.36 0.81 0.93 0.60 0.17 213 BRL 05 2016 45 1.2 2.87 7.14 133 5.40 3.43 2.63 2.15 0.17 214 BRL 05 2016 45 1.5 2.49 7.15 74 3.28 1.25 1.26 0.72 0.14 215 BRL 05 2016 45 1.8 2.89 7.09 117 4.52 2.14 1.92 1.24 0.17 216 BRL 05 2016 45 2.1 3.07 7.17 51 5.39 2.46 2.30 1.39 0.21 217 BRL 05 2016 45 2.1 3.02 7.05 86 4.56 2.00 1.92 1.07 0.19 218 BRL 05 2016 45 0 22.33 8.04 20 1.52 0.60 1.15 0.34 0.38 219 EGN 05 2016 0 0.15 0.76 6.24 59 0.33 0.13 0.23 0.05 0.05 220 EGN 05 2016 0 0.25 0.97 6.42 34 0.27 0.11 0.18 0.04 0.03 221 EGN 05 2016 0 0.35 1.01 6.58 77 0.37 0.16 0.27 0.06 0.05 222 EGN 05 2016 0 0.75 1.11 6.93 59 0.33 0.12 0.21 0.06 0.03 223 EGN 05 2016 0 0.95 1.14 7.01 82 0.35 0.13 0.22 0.06 0.04 224 EGN 05 2016 0 1.15 1.09 6.88 36 0.37 0.15 0.24 0.06 0.04 225 EGN 05 2016 10 0.15 18.98 7.14 190 1.15 0.39 0.68 0.21 0.19 226 EGN 05 2016 10 0.15 16.20 7.00 158 0.64 0.20 0.38 0.11 0.09 227 EGN 05 2016 10 0.25 4.07 7.09 139 1.51 0.67 0.99 0.27 0.17 228 EGN 05 2016 10 0.35 1.53 7.18 23 0.93 0.39 0.60 0.15 0.11 229 EGN 05 2016 10 0.55 1.17 7.36 24 0.90 0.38 0.54 0.14 0.16 230 EGN 05 2016 10 0.75 2.59 7.15 155 1.16 0.51 0.76 0.20 0.13 231 EGN 05 2016 10 0.95 0.90 7.00 0.87 0.38 0.57 0.14 0.10 232 EGN 05 2016 10 0.75 0.97 7.23 35 0.76 0.31 0.47 0.12 0.11 233 EGN 05 2016 10 1.15 0.90 7.11 70 0.85 0.36 0.55 0.14 0.09 234 EGN 05 2016 10 1.15 0.90 73 0.95 0.41 0.62 0.16 0.11 235 EGN 05 2016 20 0.07 25.17 7.61 105 1.32 0.43 0.84 0.26 0.22 236 EGN 05 2016 20 0.15 24.47 7.34 219 1.18 0.38 0.73 0.23 0.20 237 EGN 05 2016 20 0.25 24.35 7.31 250 1.45 0.51 0.90 0.29 0.21 238 EGN 05 2016 20 0.55 22.39 7.01 247 1.64 0.64 1.08 0.36 0.23 239 EGN 05 2016 20 0.95 16.49 6.80 132 2.40 1.15 1.53 0.54 0.26 240 EGN 05 2016 20 1.15 16.01 6.82 91 2.17 0.99 1.36 0.46 0.23 241 EGN 05 2016 22.5 0.36 25.03 7.36 238 1.45 0.50 0.87 0.29 0.20 242 EGN 05 2016 22.5 0.36 24.13 7.33 261 1.18 0.29 0.60 0.17 0.18 243 EGN 05 2016 22.5 1.06 23.23 6.91 157 1.08 0.45 0.71 0.27 0.16 244 EGN 05 2016 22.5 1.86 22.52 7.37 218 1.56 0.47 0.83 0.32 0.17 245 EGN 05 2016 5 0.1 8.33 6.99 164 1.73 0.67 1.08 0.33 0.25 246 EGN 05 2016 5 0.2 1.23 7.15 52 0.56 0.20 0.33 0.08 0.10 247 EGN 05 2016 5 0.4 1.02 7.17 8 0.63 0.23 0.37 0.09 0.10

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144 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 248 EGN 05 2016 5 0.6 1.09 7.28 11 0.93 0.34 0.53 0.14 0.17 249 EGN 05 2016 5 0.8 1.63 7.31 37 0.91 0.33 0.55 0.14 0.11 250 EGN 05 2016 5 0 23.80 7.46 29 1.25 0.37 0.75 0.22 0.20 251 RWP 05 2016 0 0.2 24.93 7.31 228 2.26 0.87 1.31 0.56 0.34 252 RWP 05 2016 0 0.25 2.08 0.52 0.90 0.36 0.27 253 RWP 05 2016 0 0.6 3.87 1.66 1.77 0.80 0.32 254 RWP 05 2016 0 0.8 3.05 1.39 1.52 0.56 0.43 255 RWP 05 2016 10 0.6 9.05 6.82 228 3.52 1.18 1.39 0.64 0.31 256 RWP 05 2016 10 0.2 24.10 7.37 226 1.46 0.34 0.76 0.21 0.23 257 RWP 05 2016 10 1 .0 0.45 7.03 140 3.26 1.17 1.42 0.51 0.30 258 RWP 05 2016 10 1.5 0.40 7.10 137 3.11 1.11 1.39 0.47 0.30 259 RWP 05 2016 10 0.4 23.92 7.32 208 1.37 0.32 0.66 0.20 0.29 260 RWP 05 2016 10 2 .0 0.36 7.16 97 3.01 1.08 1.36 0.44 0.29 261 RWP 05 2016 10 0.6 24.08 7.42 216 1.46 0.35 0.69 0.23 0.25 262 RWP 05 2016 10 0.8 8.14 6.95 218 3.64 1.14 1.30 0.72 0.32 263 RWP 05 2016 20 0.2 11.29 6.81 170 2.42 0.74 1.03 0.40 0.28 264 RWP 05 2016 20 0.1 24.44 7.35 222 1.32 0.32 0.65 0.18 0.31 265 RWP 05 2016 20 0.6 0.50 7.12 119 2.27 0.84 1.07 0.30 0.25 266 RWP 05 2016 20 1 .0 0.40 7.00 120 2.49 0.89 1.13 0.34 0.26 267 RWP 05 2016 20 1.5 0.39 7.00 131 1.83 0.68 0.87 0.26 0.21 268 RWP 05 2016 20 0.4 14.67 6.99 205 2.23 0.65 0.94 0.35 0.28 269 RWP 05 2016 20 2 .0 4.00 7.12 122 2.67 0.96 1.21 0.37 0.26 270 RWP 05 2016 20 2.5 0.36 7.11 127 2.41 0.87 1.11 0.33 0.25 271 RWP 05 2016 35 0.1 24.36 7.19 257 1.32 0.32 0.70 0.20 0.20 272 RWP 05 2016 35 0.2 24.36 7.24 239 1.53 0.52 0.96 0.30 0.24 273 RWP 05 2016 35 0.3 24.26 7.22 240 1.25 0.40 0.76 0.24 0.20 274 RWP 05 2016 35 1 .0 2.23 7.06 131 3.68 1.74 1.80 0.78 0.30 275 RWP 05 2016 35 1.5 1.77 7.08 172 3.41 1.18 1.44 0.53 0.27 276 RWP 05 2016 35 2 .0 5.56 6.96 207 3.23 1.24 1.32 0.69 0.20 277 RWP 05 2016 35 0 24.37 7.53 25 1.17 0.25 0.67 0.15 0.23 278 Mangroves 04 2014 1.1 16.94 3.15 4.01 1.56 0.34 279 Mangroves 04 2014 0 7.45 1.43 2.07 0.79 0.24 280 Gorgos spring 09 2014 20.29 7.39 158 0.83 0.36 0.41 0.19 0.07 281 Gorgos spring 09 2014 20.21 7.05 130 0.98 0.41 0.47 0.22 0.09 282 Gorgos spring 09 2014 19.91 7.12 224 0.93 0.38 0.44 0.22 0.08 283 Gorgos spring 09 2014 19.97 7.15 221 0.81 0.33 0.38 0.19 0.07

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145 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 284 Gorgos spring 09 2014 19.17 7.13 227 1.20 0.50 0.57 0.28 0.10 285 Hol Kokol spring 09 2014 9.88 7.29 188 1.85 0.74 0.84 0.38 0.12 286 Hol Kokol spring 09 2014 9.77 7.21 205 1.44 0.57 0.67 0.28 0.09 287 Lagoon SW 09 2014 32.74 8.08 83 0.04 0.01 0.02 0.01 0.02 288 Laja spring 09 2014 21.33 7.14 208 0.84 0.34 0.41 0.18 0.06 289 Pargos spring 09 2014 22.07 7.26 49 0.68 0.28 0.33 0.16 0.06 290 Pargos spring 09 2014 21.89 7.16 181 0.75 0.31 0.36 0.17 0.07 291 Pargos spring 09 2014 21.84 7.16 192 0.74 0.30 0.35 0.17 0.06 292 Pargos spring 09 2014 22.38 7.21 174 0.81 0.35 0.40 0.19 0.07 293 Pargos spring 09 2014 24.58 7.36 26 0.56 0.23 0.26 0.13 0.04 294 Pargos spring 09 2014 32.68 8.05 71 0.08 0.03 0.04 0.02 0.02 295 Pargos spring 09 2014 26.64 7.38 3 0.48 0.19 0.23 0.11 0.04 296 Pargos spring 09 2014 32.87 8.01 88 0.04 0.02 0.02 0.01 0.02 297 Pargos spring 09 2014 24.62 7.43 21 0.55 0.23 0.27 0.13 0.04 298 Pargos spring 09 2014 32.77 7.87 57 0.04 0.02 0.03 0.01 0.02 299 Pargos spring 09 2014 32.81 8.08 83 0.03 0.01 0.02 0.01 0.01 300 Pargos spring 09 2014 0.06 0.03 0.04 0.02 0.01 301 UNAM well 09 2014 18 5.63 7.13 240 1.62 0.70 0.82 0.34 0.10 302 UNAM well 09 2014 26 13.38 7.14 221 1.26 0.52 0.61 0.28 0.07 303 UNAM well 09 2014 35 29.44 7.32 230 0.23 0.09 0.12 0.05 0.02 304 UNAM well 09 2014 40 31.04 7.29 198 0.16 0.07 0.10 0.04 0.02 305 Cenote C7B 09 2014 20 0.67 7.20 83 0.15 0.08 0.11 0.03 0.02 306 Cenote C7B 09 2014 28 0.68 7.10 159 0.06 0.03 0.04 0.01 0.01 307 Cenote C7B 09 2014 29 0.67 7.38 229 0.16 0.08 0.12 0.03 0.02 308 Cenote C7B 09 2014 30 1.50 7.21 302 0.20 0.10 0.14 0.04 0.05 309 Cenote C7B 09 2014 31 5.14 6.48 313 0.48 0.18 0.40 0.15 0.16 310 Cenote C7B 09 2014 32 9.42 6.36 306 1.13 0.37 0.77 0.26 0.39 311 Cenote CKH 09 2014 30 0.76 7.04 72 0.15 0.08 0.11 0.03 0.03 312 Cenote CKH 09 2014 31 0.76 7.02 99 0.11 0.06 0.08 0.02 0.03 313 Cenote CKH 09 2014 32 0.75 7.00 94 0.16 0.08 0.12 0.03 0.03 314 Cenote CKH 09 2014 33 0.75 6.98 119 0.16 0.08 0.12 0.03 0.03 315 Cenote CKH 09 2014 34 0.75 6.92 152 0.12 0.06 0.09 0.02 0.02 316 Cenote CTC 09 2014 1 0.27 7.04 101 1.59 0.57 0.64 0.34 0.09 317 Cenote CZ 09 2014 2 0.70 7.11 98 0.20 0.09 0.13 0.04 0.02 318 Cenote CZ 09 2014 35 1.34 6.97 247 0.19 0.09 0.14 0.04 0.02 319 Cenote CZ 09 2014 38 2.96 6.93 270 0.43 0.19 0.27 0.10 0.08

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146 Table A 1. Continued Site ID Sample date Distance Offshore (m) Sample Depth (m) Salinity pH ORP C1 (R.U.) C2 (R.U.) C3 (R.U.) C4 (R.U.) C5 (R.U.) 320 Cenote CZ 09 2014 40 2.97 6.90 258 0.25 0.10 0.17 0.07 0.05 321 Cenote Muj 09 2014 1 0.29 7.79 53 0.28 0.10 0.20 0.05 0.17 322 Cenote Muj 09 2014 5 0.57 7.18 26 0.29 0.12 0.21 0.06 0.10

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147 APPENDIX B YUCATAN WATER CHEMISTRY DATA

PAGE 148

148 Table B 1. Water chemistry input parameters for geochemical modeling in PHREEQc Sample type Site ID S alinity Temp. (C) HS (uM) Cl (mM) SO 4 (mM) Na (mM) K (mM) Mg (mM) Ca (mM) DIC (mM) NH 4 (M) PO 4 (uM) Cenote Cenote Siete Bocas 20 m 0.7 25.1 37.1 6.2 0.3 5.5 0.1 1.2 3.0 7.4 0.5 0.09 Cenote Cenote Kin Ha 20 m 0.7 24.7 19.4 6.6 0.4 5.9 0.1 1.4 3.2 8.0 0.6 0.10 Cenote Cenote Zapote 2m 0.7 26.3 21.4 6.6 0.4 5.8 0.1 1.4 3.1 7.8 1.3 0.10 Spring Gorgos spring 20.3 30 21.4 354.6 17.9 301.6 10.3 33.0 8.2 4.0 99.3 0.47 Spring Gorgos spring 20.2 28.8 41.0 356.7 18.0 290.4 6.5 31.7 8.1 4.1 85.2 0.32 Spring Gorgos spring 19.9 29.2 53.7 330.6 16.7 284.3 6.3 31.2 7.7 4.0 102.2 0.41 Spring Gorgos spring 20.0 29.2 37.1 361.8 18.3 321.4 6.8 35.4 8.3 4.0 85.9 0.22 Spring Gorgos spring 19.2 28.9 39.0 351.2 17.8 281.4 3.2 30.7 7.8 4.3 108.6 0.53 Spring Hol Kokol spring 9.9 28.8 24.3 164.9 8.2 143.1 0.0 15.5 5.1 4.7 43.6 0.77 Spring Hol Kokol spring 9.8 28.7 37.1 181.4 9.1 143.5 3.2 15.5 5.1 4.3 46.0 0.54 Spring Laja spring 21.3 28.7 61.6 369.4 18.7 314.2 6.2 34.2 8.5 3.6 80.4 0.48 Spring Pargos spring 26.6 30.1 27.3 438.3 22.3 373.1 7.8 41.2 9.1 3.2 58.0 0.43 Spring Pargos spring 32.9 30.1 38.0 516.4 10.9 57.8 10.9 2.0 8.4 0.07 Spring Pargos spring 24.6 29.7 40.0 432.1 22.0 366.2 10.2 40.3 8.6 2.1 36.0 0.84 Spring Pargos spring 32.8 30.1 29.2 579.7 29.5 486.7 0.0 54.7 10.3 3.2 3.7 0.01 Spring Pargos spring 22.1 29 39.0 387.8 19.7 322.5 6.9 35.4 8.5 3.7 54.1 0.49 Spring Pargos spring 21.8 29.4 29.2 377.1 19.1 316.4 6.9 34.8 8.4 3.7 72.6 1.11 Spring Pargos spring 22.4 29.8 22.4 392.5 19.9 320.0 7.0 35.3 8.3 3.6 54.2 0.73 Spring Pargos spring 24.6 30.2 21.4 435.6 22.1 372.4 7.9 41.2 9.1 3.2 42.8 0.44 Spring Pargos spring 32.8 30.1 33.1 577.6 29.4 485.4 8.6 54.3 10.3 2.0 2.9 0.13 Spring Pargos spring 24.6 30.2 43.9 406.4 20.7 405.4 7.0 44.9 9.1 3.6 54.9 0.38 Spring Pargos spring 32.7 29.3 28.2 566.3 28.8 478.7 10.4 53.5 10.3 2.1 10.2 0.75 Well UNAM 18 m 5.6 32 33.1 509.2 24.0 85.3 0.8 9.5 4.2 5.9 75.5 0.44 Lagoon Lagoon surface water 32.7 30.1 26.3 583.1 29.6 476.3 9.9 57.5 11.5 2.1 2.1 0.04

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149 A PPENDIX C INDIAN RIVER LAGOON WATER CHEMISTRY DATA

PAGE 150

150 Table C 1. Water chemistry input parameters for geochemical modeling in PHREEQc Piezometer Depth (m) SAL (PSU) T (C) pH Cl (mM) SO 4 (mM) Na (mM) K (mM) Mg (mM) Ca (mM) DIC (mM) BRL 1 0.2 6.13 26.9 6.1 118.05 5.44 96.62 1.99 10.68 7.33 8.50 BRL 1 0.3 2.68 26.3 6.17 49.34 1.75 40.50 0.85 4.55 5.93 9.82 BRL 1 0.6 2.65 25.6 6.19 38.42 1.10 37.00 1.14 4.82 3.76 10.90 BRL 1 1 2.19 25 6.32 31.30 0.96 22.84 0.73 2.69 5.38 9.56 BRL 11 0.2 1.7 27.4 6.93 64.75 3.41 56.00 1.19 5.93 6.77 4.87 BRL 11 0.6 1.69 27 6.93 23.41 1.36 17.41 0.49 2.17 4.51 5.42 BRL 11 1 1.71 25.8 6.91 24.07 1.36 17.42 0.50 2.18 4.51 5.44 BRL 11 1.5 1.69 26.3 6.92 23.52 1.35 17.14 0.49 2.15 4.51 5.39 BRL 11 1.8 1.69 25.3 6.96 23.66 1.33 17.26 0.52 2.17 4.57 5.28 BRL 11 2.1 1.76 25.6 6.96 24.27 1.27 17.39 0.51 2.13 4.65 5.43 BRL 11 2.1 1.81 25.8 7.02 24.27 1.29 17.49 0.53 2.14 4.68 5.38 BRL 21 0.2 5.38 27.5 6.82 206.34 10.56 172.34 3.62 19.27 9.07 4.12 BRL 21 0.6 3.57 27.1 6.87 125.65 6.63 108.67 2.42 11.49 9.77 4.88 BRL 21 0.9 3.48 26.2 6.81 101.02 5.26 82.34 1.84 9.38 6.51 5.08 BRL 21 1.5 1.73 26 6.92 49.70 2.43 20.20 0.70 2.36 4.84 5.56 BRL 21 2 1.72 25.4 6.96 24.39 1.24 17.64 0.64 2.13 4.52 5.47 BRL 21 2.5 1.72 25.2 7.04 23.78 1.25 17.08 0.61 2.06 4.38 5.31 BRL 45 0.2 24.56 27 7.06 359.84 18.23 305.79 6.56 34.58 7.92 3.45 BRL 45 0.6 21.48 26.7 6.87 338.32 17.38 287.22 6.07 32.50 8.17 3.80 BRL 45 1.2 2.87 26 7.14 77.95 4.07 68.10 1.98 7.73 3.13 4.37 BRL 45 0.5 2.49 25.9 7.15 34.56 1.86 28.12 0.98 4.11 2.82 4.52 BRL 45 1.8 2.89 25.1 7.09 43.30 2.28 34.54 1.19 4.63 3.55 4.59 BRL 45 2.1 3.07 25.3 7.17 45.57 2.40 36.28 1.20 4.72 3.74 4.61 BRL 45 2.1 3.02 28.4 7.05 45.80 2.40 28.96 0.62 3.45 5.51 4.62 BRL 45 0 22.33 27.7 8.04 360.45 18.25 307.48 6.71 34.61 7.05 1.90 EGN 0 0.15 0.76 26.9 6.24 9.23 0.65 4.63 0.13 2.22 1.94 3.57 EGN 0 0.25 0.97 27 6.42 11.95 0.80 5.80 0.13 2.86 2.73 1.14 EGN 0 0.35 1.01 27.1 6.58 12.63 0.83 6.19 0.13 3.08 2.98 4.88 EGN 0 0.75 1.11 27.4 6.93 13.47 0.88 6.88 0.13 3.12 3.80 5.33 EGN 0 0.95 1.14 27.6 7.01 13.45 0.88 6.86 0.13 3.14 3.79 5.42 EGN 0 1.15 1.09 27.4 6.88 13.39 0.87 6.78 0.13 3.29 3.61 5.52

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151 Table C 1. Continued Piezometer Depth (m) SAL (PSU) T (C) pH Cl (mM) SO 4 (mM) Na (mM) K (mM) Mg (mM) Ca (mM) DIC (mM) EGN 10 0.25 4.07 26.8 7.09 51.15 3.15 66.87 1.33 8.34 4.85 4.27 EGN 10 0.75 2.59 27.1 7.15 18.89 1.47 14.17 0.28 2.45 3.61 4.69 EGN 10 0.95 0.9 27 7 10.44 1.03 7.10 0.09 1.71 3.55 4.81 EGN 10 1.15 0.9 26.8 7.11 10.46 1.04 7.03 0.18 1.70 3.56 4.77 EGN 10 1.15 0.9 26.8 -10.42 1.03 7.08 0.09 1.70 3.56 4.80 EGN 10 0 23.8 27.7 7.46 386.24 19.67 328.73 7.07 36.89 7.47 1.99 EGN 20 0.07 25.17 30.2 7.61 400.62 20.42 342.40 7.22 38.63 7.75 1.99 EGN 20 0.15 24.47 29.1 7.34 397.13 20.23 340.75 7.31 38.21 7.82 2.14 EGN 20 0.25 24.35 28 7.31 392.99 20.00 335.27 7.17 37.60 7.86 2.70 EGN 20 0.55 22.39 27.6 7.01 343.21 17.46 294.24 6.18 32.71 7.49 3.40 EGN 20 0.95 16.49 27.7 6.8 248.68 12.92 214.94 4.56 22.79 6.05 4.21 EGN 20 1.15 16.01 26.9 6.82 253.16 13.38 217.74 4.60 24.42 6.40 4.27 EGN 22.5 0.36 24.13 27.4 7.33 397.49 20.08 341.75 7.18 38.64 7.97 2.56 EGN 22.5 0.36 25.03 27.9 7.36 400.21 20.32 340.37 7.33 38.24 7.96 2.64 EGN 22.5 1.06 23.23 27.2 6.91 369.72 18.36 314.18 6.72 36.28 8.35 3.60 EGN 22.5 1.56 22.52 26.3 7.37 356.80 17.69 301.53 6.42 34.22 7.62 3.85 RWP 10 0.6 9.05 27.3 6.82 76.55 3.69 75.31 1.61 8.73 4.79 5.32 RWP 10 1 0.45 26.7 7.03 3.19 0.04 2.66 0.13 0.89 2.63 6.91 RWP 10 1.5 0.4 26.4 7.1 2.15 0.01 1.56 0.08 0.62 2.77 6.82 RWP 10 2 0.36 25.7 7.16 1.60 0.00 1.07 0.04 0.21 3.11 6.76 RWP 20 0.2 11.29 27.9 6.81 203.26 10.19 172.15 3.79 18.57 7.52 4.41 RWP 20 0.6 0.5 27.1 7.12 4.48 0.13 3.50 0.10 0.31 3.44 6.73 RWP 20 1 0.4 26.6 7 1.84 0.01 1.31 0.08 0.18 3.14 6.81 RWP 20 1.5 0.39 25.6 7 1.81 0.01 1.31 0.07 0.18 3.14 6.81 RWP 20 2 4 25.4 7.12 1.87 0.02 1.58 0.04 0.15 3.07 6.84 RWP 20 2.5 0.36 24.6 7.11 1.50 0.01 1.19 0.04 0.19 3.04 6.82 RWP 35 0.1 24.36 27.2 7.19 381.87 19.19 328.36 7.09 36.91 7.71 2.70 RWP 35 0.2 24.36 27.1 7.24 382.07 19.26 327.15 7.05 36.78 7.67 2.60 RWP 35 0.3 24.26 27 7.22 383.22 19.32 326.72 7.04 36.73 7.65 2.54 RWP 35 0.6 20 26.5 7.06 274.81 13.61 236.97 5.25 26.57 6.36 3.92 RWP 35 1 2.23 25.9 7.06 31.29 1.19 22.70 0.63 3.57 2.99 6.78 RWP 35 1.5 1.77 26.5 7.08 35.88 1.76 33.36 0.56 3.75 4.58 6.93 RWP 35 2 5.56 24.9 6.96 81.40 4.35 72.72 1.29 8.64 5.00 6.69 RWP 35 2 5.53 25.2 7.02 82.02 4.38 67.34 1.20 7.96 4.85 6.68 RWP 35 0 24.37 27.6 7.53 383.39 19.42 328.77 7.03 36.84 7.56 2.03

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166 BIOGRAPHICAL SKETCH Andrea Joy Pain is from St. Paul Minnesota. She received her Bachelor of Arts from Wesleyan University in earth & environmental sciences in 2008, where she began studying the biogeochemistry of bioluminescent bays in Vieques, Puerto Rico. After her undergraduate she spent two years teaching English on the French Caribbean island of Guadeloupe. She then pursued a Master of Science and graduated from ETH Zurich 2012, with a focus in biogeochemistry and pollutant d ynamics. She began her doctoral studies at th e University of Florida in 2013.