U.S. Department of the Interior

U.S. Geological Survey

Survival of Bacterial Indicators and the Functional Diversity 

of Native Microbial Communities in the Floridan Aquifer 

System, South Florida

By John T. Lisle

Open-File Report 2014-1011
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Lisle, J.T., 2014, Survival of Bacterial Indicators and the Functional Diversity of Native Microbial Communities in the 

Floridan Aquifer System, South Florida: U.S. Geological Survey Open–File Report 2014-1011, 70 p.,

http://dx.doi.org/10.3133/ofr20141011

ISSN 2331-1258 (online)
iii

Contents

Acknowledgments .................................................................................................................................................... 2

Executive Summary.................................................................................................................................................. 3

Introduction............................................................................................................................................................... 4

Problem Statement................................................................................................................................................... 6

Project Objectives .................................................................................................................................................... 6

Methods.................................................................................................................................................................... 7

Site Descriptions................................................................................................................................................... 7

Field Data Collection............................................................................................................................................. 7

Geochemical and Nutrient Sample Collection and Analyses................................................................................ 7

Statistical Analyses ......................................................................................................................................... 10

Native Bacterial Abundances ............................................................................................................................. 10

Aboveground Mesocosms ................................................................................................................................. 10

Bacterial Cultures ............................................................................................................................................... 11

Inactivation Data Analyses.................................................................................................................................. 12

Quantification and Characterization of Dissolved Organic Carbon (DOC) ......................................................... 12

Volatile Fatty Acid (VFA) Data Collection............................................................................................................ 13

Bacterial Respiration and Carbon Turnover and Carbon Dioxide Production Rates .......................................... 13

Biogeochemical and Energetics Analyses .......................................................................................................... 16

Microbial Community Diversity Sample Collection and Analyses ....................................................................... 18

Statistical Analyses ........................................................................................................................................ 19

Results and Discussion ......................................................................................................................................... 20

Water Quality ...................................................................................................................................................... 20

Bacterial Abundances ......................................................................................................................................... 25

Bacterial Indicator Inactivation............................................................................................................................ 26

Escherichia coli Inactivation............................................................................................................................ 27

Pseudomonas aeruginosa Inactivation ........................................................................................................... 30

Relevance of Escherichia coli Inactivation Rates in the Upper Floridan aquifer............................................. 33

Dissolved Organic Carbon Characterization....................................................................................................... 36

Bacterial Utilization and Turnover of Native Carbon ........................................................................................... 37

Microbial Energetics and Sustainability in the Upper Floridan Aquifer ............................................................... 39

Free Energy Yields for Energetically Favorable Biogeochemical Reactions................................................... 40

Chemical Affinities for Energetically Favorable Biogeochemical Reactions.................................................... 41

Free Energy Flux Rates for Energetically Favorable Biogeochemical Reactions ........................................... 43

Functional Bacterial Diversity and the Relationship to Geochemistry in the Upper Floridan Aquifer ................. 48

Future Research Directions.................................................................................................................................... 56

References Cited.................................................................................................................................................... 56

Appendix................................................................................................................................................................. 66
iv

Figures

1. Aerial photograph showing sampling-site locations, indicated by green triangles. ........................................ 8

2.

Graph showing principal component analysis of field data from the six sampling sites ............................... 20

3. Graph showing principal component analysis of ionic species data from the six sampling sites ................. 20

4. Graph showing principal component analysis of nutrient data from the six samplng sites .......................... 21

5. Graph showing principal component analysis of carbon substrate data from the six sampling sites........... 21

6. Graph showing principal component analysis of terminal electron acceptor data from the 

six sampling sites ......................................................................................................................................... 22

7. Graph showing principal component analysis of reactant data from the six sampling sites for the 

thermodynamically favorable biogeochemical reactions ............................................................................. 22

8. Graphs showing Escherichia coli decay rates for the 42U and 42L sampling sites ..................................... 28

9. Graphs showing Escherichia coli decay rates for the 15U and 15M sampling sites .................................... 29

10. Graph showing Escherichia coli decay rates for the MZ1 and MZ3 sampling sites ..................................... 30

11. Graph showing tdecay rates for the 42U and 42L sampling sites ................................................................ 31

12. Graph showing Pseudomonas aeruginosa decay rates for the 15U and 15M sampling sites ..................... 32

13. Graph showing Pseudomonas aeruginosa decay rates from the MZ1 and MZ3 sampling sites ................. 33

14.

Graph showing chemical affinities of the thermodynamically favorable biogeochemical reactions ............. 44

15.

Comparisons between the free energy of reaction (ΔGr), free energy flux (FEF), and maximum 

acquisition rate (MAR) data from the six sampling sites .............................................................................. 47

16. Biogeochemical processes that most likely dominate in the south region of the Upper Floridan aquifer..... 48

17. Bacterial diversity distributions from the six sampling sites.......................................................................... 52

Tables

1. Sampling-site locations and well characteristics. ........................................................................................... 7

2. Water-quality data for the six sampling sites.................................................................................................. 9

3. Balanced biogeochemical reactions............................................................................................................. 16

4. Sample volumes for bacterial diversity analyses samples from the six sampling sites................................ 18

5. Principal component analyses eigenvector data for the six sampling sites.................................................. 23

6. Principal component analyses percent variance per axis data for the six sampling sites............................ 24

7. Bacterial total direct count data for the six sampling sites............................................................................ 25

8. Biphasic inactivation rate curve data for Escherichia coli and Pseudomonas aeruginosa from the 

six sampling sites. ........................................................................................................................................ 27

9. Escherichia coli inactivation rate data from the published literature............................................................. 34

10. The estimated storage time to achieve 1.0 CFU in the recovered water at the six sampling sites. ............. 35

11.

Dissolved organic carbon and carbon fluorescent index data for the six sampling sites. ............................ 36

12. 14C-acetate turnover and utilization rate data for the native bacterial community at the six sampling sites.. 38

13. 3

H-leucine-based bacterial production rates at the six sampling sites. ........................................................ 38
v

14.Bacterial growth efficiency data for the six sampling sites. .......................................................................... 39

15.

Free energy flux and maximum acquition rate data for the 42U and 42L sampling sites............................. 42

16.

Free energy flux and maximum acquition rate data for the 15U and 15M sampling sites............................ 42

17.

Free energy flux and maximum acquition rate data for the MZ1 and MZ3 sampling sites........................... 42

18.

Chemical affinities for the six sampling sites and recharge water................................................................ 43

19.

Free energy flux and maximum acquition rate data for the recharge water. ................................................ 45

20. Comparisons between the measured and predicted acetate turnover rates in the six sampling sites......... 49

21. Bacterial diversity in the six sampling sites and between sampling events.................................................. 50

22.

Bacterial diversity based on the family classifications.................................................................................. 54

23. Likely biogeochemical processes based on bacterial diversity in all of the sampling sites.......................... 55

Appendix Figures

1-1. Diffusion chamber design............................................................................................................................. 66

1-2. A completed diffusion chamber. ................................................................................................................... 66

1-3. Loading a diffusion chamber with a culture of bacteria using a syringe....................................................... 66

1-4. A diffusion chamber containing the bacterial culture suspended from a cap from the stainless steel 

flow–through chamber.................................................................................................................................. 67

1-5. The stainless steel flow–through chamber (side view)................................................................................. 67

1-6. The stainless steel flow–through chamber showing the baffles and positions of the diffusion chambers 

(top view)...................................................................................................................................................... 68

1-7. The stainless steel flow–through chamber showing the inert polymer top into which the caps that hold 

the diffusion chambers are inserted (top view)............................................................................................. 68

1-8. The stainless steel flow–through chamber showing the silicon gasket that seals the connection between the 

polymer top and the stainless steel chamber (side view)............................................................................. 69

1-9. The complete stainless steel flow–through chamber with diffusion chambers inserted (side view)............. 69

1-10. The outer flow–through chamber, flow rate controls and connections for the multiprobe 

attachment fittings. ....................................................................................................................................... 70

1-11. The outer flow–through chamber flow rate controls and multiprobe attachment fittings (rear view). ........... 70

1-12. The outer flow–through chamber discharge and multiprobe fittings............................................................. 71

1-13. An empty outer flow–through chamber showing the connections for the stainless steel flow–through 

chamber that contains the diffusion chambers............................................................................................. 71

1-14. The complete aboveground flow–through microcosm.................................................................................. 72

1-15. The above ground flow through microcosm in the field................................................................................ 72
vi

 Multiply By To obtain

Length

nanometer (nm) 0.00000003937 inch (in.)

micrometer (μm) 0.00003937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

centimeter (cm) 0.394 inch (in.)

meter (m) 3.281 foot (ft)

meter (m) 1.094 yard (yd)

kilometer (km) 0.621 mile (mi)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 2.113 pint (pt)

liter (L) 1.057 quart (qt)

liter (L) 0.264 gallon (gal)

Flow velocity

meter per day (m/d) 3.281 foot per day (ft/d)

Flow rate

milliliter per minute (mL/min) 0.0002642 gallon per minute (gal/min)

liter per minute (L/min) 0.2642 gallon per minute (gal/min) 

liter per day (L/d) 0.2642 gallon per day (gal/d)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Energy

joule (J) 0.0000002 kilowatthour (kWh)

kilojoule (kJ) 0.0002 kilowatthour (kWh)

Diffusion rate

millimeter per hour (mm/hr) 0.03937 inch per hour (in/hr)

Diffusion coefficient

centimeter squared per second (cm2/s) 0.1550 inch squared per second (in2/s) 

meter squared per second (m2

/s) 10.76 foot squared per second (ft2/s) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 

°C=(°F-32)/1.8

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms per liter (μg/L).

Conversion Factors
vii

Acronyms

ANAMMOX anaerobic ammonia oxidation

AOM anaerobic oxidation of methane

APPZ Avon Park permeable zone

ASR aquifer storage and recovery

ATP adenosine triphosphate

BGE bacterial growth efficiency

CERP Comprehensive Everglades Restoration Plan

CFU colony forming unit

DIC dissolved inorganic carbon

DNA deoxyribonucleic acid

DOC dissolved organic carbon

DPM disintegrations per minute

EC Escherichia coli

EPA Environmental Protection Agency

FDEP Florida Department of Environmental Protection

FEF free energy flux

fg femtogram

FI fluorescence index

FIB fecal indicator bacteria

HPOA hydrophobic organic acid

MAR maximum acquisition rate

λ wavelength

μg microgram

μL microliter

μM micromolar

mL milliliter

mM millimolar

mV millivolt 

mol mole

NCBI National Center for Biotechnology Information

nM nanomolar

NMR nuclear magnetic resonance

ORP oxidation/reduction potential

OTU operational taxonomic unit

PA Pseudomonas aeruginosa

PBS phosphate buffered saline

PCA principal component analyses

PCR polymerase chain reaction

PIA Pseudomonas isolation agar

PTFE polytetrafluoroethylene 

RASA Regional Aquifer-System Analysis

RMSE root mean sum of squared error

rpm revolutions per minute

rRNA ribosomal ribonucleic acid

SAGMEG South Africa Gold Mine Euarchaeotic Group

SUVA specific ultraviolet absorbance

TCA trichloroacetic acid

TDS total dissolved solids

TEAP terminal electron acceptor process

TOC total organic carbon

TPIA transphilic organic acid

UFA Upper Floridan aquifer

UIC Underground Injection Control

USDW Underground Sources of Drinking Water 

USGS U.S. Geological Survey

UV ultraviolet

VFA volatile fatty acid
viii

Acknowledgments

The author acknowledges South Florida Water Management District (Robert Verrastro) and the 

U.S. Geological Survey (USGS) Water Resources Cooperative Water Program (Robert Renken) for 

funding support; Montana State University and Dr. Gordon McFeters for permission to modify and use 

their diffusion chamber design; Dr. Christina Kellogg (USGS), Dr. Dale Griffin (USGS) and Dr. June 

Mirecki (U.S. Army Corps of Engineers) for constructive conversations and editorial comments; and 

Chris Reich (USGS) and Betsy Boynton (contractor) for assistance with report formatting and graphics.
1

Survival of Bacterial Indicators and the Functional Diversity 

of Native Microbial Communities in the Floridan Aquifer 

System, South Florida

By John T. Lisle

Executive Summary

The Upper Floridan aquifer in the southern region of Florida is a multi-use, regional scale 

aquifer that is used as a potable water source and as a repository for passively recharged untreated 

surface waters, injected treated surface water and wastewater, industrial wastes, and greenhouse gases 

(for example, carbon dioxide). The presence of confined zones within the Upper Floridan aquifer that 

range in salinity from fresh to brackish allow regulatory agencies to permit the injection of these different types of product waters into specific zones without detrimental effects to humans and terrestrial 

and aquatic ecosystems. The type of recharge that has received the most regulatory attention in south 

Florida is aquifer storage and recovery (ASR). The treated water, prior to injection and during recovery, 

must meet primary and secondary drinking water standards. The primary drinking water standard for the 

microbiological quality is total coliforms, which have been shown to be difficult to inactivate below the 

regulatory standard during the treatment process at some ASR facilities. The inefficient inactivation of 

this group of indicator bacteria permits their direct injection into the storage zones of the Upper Floridan 

aquifer. Prior to this study, the inactivation rates for any member of the total coliform group during exposure to native geochemical conditions in groundwater from any zone of the Floridan aquifer had not 

been derived. 

Aboveground flowthrough mesocosm systems that maintained native groundwater geochemical conditions, except for pressure, were used to quantify the inactivation rates of two bacterial indicators during exposure to groundwater from six wells that collect water from two ASR storage zones: the 

Upper Floridan aquifer (UFA) and Avon Park Permeable Zone (APPZ). Each mesocosm contained eight 

membrane diffusion chambers filled with Escherichia coli or Pseudomonas aeruginosa. Both bacterial 

strains followed a biphasic inactivation model. The E. coli populations had slower inactivation rates in 

the UFA (range: 0.217–0.628 per hour (h-1) during the first phase of the model than when exposed to 

groundwater from the APPZ (range: 0.540–0.684 h–1). These same populations had significantly slower 

inactivation rates during the second phase of the model, ranging from 0.006 to 0.001 h–1 and 0.013 to 

0.018 h–1 for the UFA and APPZ, respectively. Published inactivation rates of E. coli retained in membrane diffusion chambers and exposed to diverse groundwater sources range from 0.004 to 0.029 h–1. 

The inactivation rates for the first phase of the inactivation models for P. aeruginosa were not significantly different between the UFA (range: 0.144–0.770 h–1) and APPZ (range: 0.159–0.772 h–1) aquifer 

zones. The inactivation rates for the second phase of the model for this bacterial species were also 

similar between UFA (range: 0.003–0.008 h–1) and APPZ (0.004–0.005 h–1) zones, although significantly 

slower than the model’s first phase rates. There are currently no inactivation data for P. aeruginosa in 

groundwater that is geochemically similar to that in UFA and APPZ for comparison.

Geochemical data were used to determine which dissimilatory biogeochemical reactions 

were most likely to proceed under the native conditions in the UFA and APPZ aquifer zones using 
2

thermodynamics principles to calculate free energy yields and other cell-related energetics data. The 

biogeochemical processes of acetotrophic and hydrogenotrophic sulfate reduction and methanogenesis 

and anaerobic oxidation of methane dominated in all six groundwater sites. All energetically favorable 

reactions proceeded at, or slightly greater than, the minimum free energy yield (–20 kilojoules per mole, 

(kJ mol–1)), which is the threshold for maintaining cell viability. The free-energy flux rates generated 

per bacterial cell for performing these biogeochemical reactions ranged from –4.4×10–19 to –3.3×10–16

kilojoules per cell per second l (kJ cell–1 s–1). These flux rate values are similar to those recorded in deep 

subsurface microbial communities and are threefold to three orders of magnitude greater than the average cell maintenance energy requirement of –1.55×10–19 kJ cell–1 s–1). The maximum acquisition rates of 

the limiting substrate (that is, hydrogen, acetate, methane) in each of the energetically favorable reactions by the total native bacterial communities at each sample site ranged from 1.1 to 648 micromolar 

per day (μM d–1). 

A high throughput microarray platform technology, PhyloChip G3, was used to characterize the functional diversity in the native aquifer bacterial communities (bacteria and archaea). The 

diversity data were used to corroborate the most likely biogeochemical processes with the presence 

of one or more bacterial phylotypes capable of performing those processes. The bacterial diversity in 

the groundwater samples was dominated by members of the Pseudomonadaceae and to a lesser extent 

by members of Anaerolineaceae, Desulfobacteraceae, Peptostreptococcaceae, Lachnospiraceae and 

Ruminococcaceae families and the phylum Euryarchaeota. The physiological capabilities of members 

within these groups have been shown to include the biogeochemical processes of primary and secondary 

fermentation, acetogenesis, methanogenesis, anaerobic methane oxidation, syntrophy with methanogens, 

ammonification, and sulfate reduction. The functional bacterial diversity data support the likelihood of 

the energetically favorable biogeochemical reactions being present in this region of the Upper Floridan 

aquifer and provide insight into the capacity of the native bacterial communities to perform additional 

types of processes that would be required to sustain viability over geologic time scales. 

The data from this study provide the first determination of bacterial indicator survival during exposure to native geochemical conditions of the Upper Floridan aquifer in south Florida. Additionally, the 

energetics and functional bacterial diversity characterizations are the first descriptions of native bacterial 

communities in this region of the Upper Floridan aquifer and reveal how these communities persist under such extreme conditions. Collectively, these types of data can be used to develop and refine groundwater models.

Introduction

During 2005, the most recent year for which data have been compiled, a total of 3.1×1011 liters 

per day (L d–1) of groundwater were withdrawn in the United States and 1.6×1010 L d–1 were withdrawn 

in Florida (Kenny and others, 2005). The majority of the groundwater in Florida is removed from the 

Floridan aquifer system, one of the most productive aquifers in the world (Miller, 1990). Although the 

Upper Floridan aquifer is a primary source of potable water, it is also a multi-use aquifer system because 

it contains isolated zones of moderate-to-high salinity . These nonpotable zones are currently targeted 

for passive recharge of stormwater runoff (Bradner, 1991), injection for the disposal of treated sewage 

and industrial wastes (Anonymous, 2012; Anonymous, 2013c), and recharge of treated surface water 

for aquifer storage and recovery (ASR) (Anonymous, 2013c). Additionally, the Cedar Keys Formation 

in the south Florida region of the Floridan aquifer system (Meyer, 1989; Reese and Alvarez-Zarikian, 
3

2006) has been identified as a possible carbon dioxide repository (Poiencot and Brown, 2011; 

Szulczewski and others, 2012). 

The retention of any type of recharged water in the injection zone of a well is dependent upon 

the stratigraphy of the aquifer in that area, which can be highly variable in this region of the Upper 

Floridan aquifer (Renken and others, 2005). Describing the hydrogeologic framework of the Upper 

Floridan aquifer was not part of this project. The reader is directed to detailed descriptions of the Upper 

Floridan aquifer contained within the U.S. Geological Survey’s Regional Aquifer-System Analysis 

(RASA) program reports (Bush and Johnston, 1988; Johnston and Bush, 1988; Krause and Randolph, 

1989; Maslia and Hayes, 1988; Miller, 1986; Ryder, 1985; Sprinkle, 1989; Tibbals, 1990). The RASA 

report by Meyer (1989) specifically describes the hydrogeology of the Upper Floridan aquifer in south 

Florida where this study was performed. Additionally, the reader is directed to more detailed descriptions of the hydrogeologic framework of two zones within the Floridan aquifer system: the Upper 

Floridan aquifer (UFA) and Avon Park Permeable Zone (APPZ) (Reese and Richardson, 2008); these 

zones were sampled during this study.

Although there are several applications for aquifer recharge, ASR has received the most attention in south Florida because of its inclusion in the Comprehensive Everglades Restoration Project 

(CERP) (http://www.evergladesplan.org) as a source of water to augment Lake Okeechobee and maintain surface-water flow rates through the Everglades during periods of drought (U.S. Army Corps of 

Engineers and South Florida Water Management District, 1999). ASR has also been used successfully 

to supplement potable water sources (that is, surface water and groundwater) in coastal communities in 

south Florida. Recharge water injected into Florida ASR wells will reside in or above zones that contain 

potential sources of potable water (Anonymous, 2013b; Anonymous, 2013c). To protect these native 

and pristine potable sources from chemical, nutrient, and microbiological contamination, recharge 

water must meet primary and secondary drinking water standards prior to injection and during recovery 

(Anonymous, 2013a). Although significant changes in geochemistry have been shown to occur during 

storage of recharged water in this region of the Upper Floridan aquifer (Mirecki, 2006), these regulatory-related metrics provide little if any insight into the biogeochemical processes that drive these changes 

(for example, depletion of dissolved oxygen, reduction in oxidation-reduction potential, increase in 

hydrogen sulfides, reduction in phosphates, and so forth). In addition, the monitoring criterion for the 

microbiological quality of recharge water is total coliforms (Anonymous, 2013a). Although this group 

of bacteria is irrelevant to biogeochemical processes in aquifers, their fate and transport in recharge water during treatment and storage is important from a public health perspective (Anonymous, 1989). 

Native bacterial communities are viable and productive inhabitants of all subsurface biospheres, 

including the Upper Floridan aquifer. These communities are capable of aerobic, fermentative, and 

anaerobic respiration, which can significantly influence the rates of mineral dissolution and (or) precipitation and the fate and transport of metals, organic substrates, and greenhouse gases within the 

aquifer. The byproducts of these processes can greatly alter the native geochemistry along a natural flow 

path and along a similar flow path within an artificially recharged or contaminated zone of an aquifer 

(Chapelle, 2000; Fredrickson and Balkwill, 2006). The south Florida region of the UFA and APPZ 

zones in the Upper Floridan aquifer are anaerobic, extremely reduced, and oligotrophic. Few biogeochemical studies have focused on these types of groundwater, and are non-existent for this region of the 

Upper Floridan aquifer. As the interest in injecting, and possibly recovering, different types of aqueous 

products into and from one or more zones of the Upper Floridan aquifer increases, the need for modeling the effects of the injectate on the aquifer’s geologic matrix and changes in the geochemistry of the 

native and recharged water will also increase. The inclusion of the bacterial community “variable” in 
4

geochemical reactive transport models and geochemical interactions in microbial inactivation models 

will improve the predictive power of those models and their applicability to managing groundwater 

resources in the Upper Floridan aquifer as well as other aquifer systems.

Problem Statement

A major component of the Comprehensive Everglades Restoration Plan (CERP) involves the 

use of ASR wells to pump excess surface water during the summer rainy season into the Upper Floridan 

aquifer for recovery during drier months for stabilization of water flows through the Everglades ecosystem. The scale of this proposed use for ASR is unprecedented. Under current regulations, ASR 

wells are classified by the U.S. Environmental Protection Agency (EPA) as Class V Underground 

Injection Control (UIC) wells, and thus subject to regulation under the U.S. Safe Drinking Water Act. 

Development of controls on (and oversight of) Class V UIC wells, and generally other classes of UIC 

wells, falls primarily on State environmental protection agencies. As such, the Florida Department of 

Environmental Protection (FDEP) has set rules regulating ASR systems utilizing aquifer regions classified as Underground Sources of Drinking Water (USDW), which encompasses aquifers of under 

10,000 milligrams per liter (mg L-1) TDS, including the Upper Floridan aquifer. A key component of 

these regulations is that ASR wells may not inject water that violates the Total Coliform Rule of the 

U.S. Safe Drinking Water Act, which specifies that potable water must have no total coliform bacteria 

per 100 milliliters (mL) (Anonymous, 1989, 2012, 2013a). Because surface water in Florida would most 

always violate this rule, the water used for injection in ASR systems must be disinfected prior to aquifer 

recharge to reduce total coliform concentrations below detection in 100 mL and, if the water is to be 

utilized for potable water, treated again after withdrawal. The need for this is to avoid introducing harmful microbial organisms possibly present in surface water, such as bacteria, protozoa, and viruses, into 

relatively pristine groundwater.

To date, only one study has characterized the inactivation rates of members of the total coliform 

group and other microbes (bacterial pathogens, Giardia sp., Cryptosporidium sp., and viruses) in the 

Upper Floridan aquifer (John, 2003). This study used autoclaved and oxidized groundwater from the 

Upper Floridan aquifer in bench top (laboratory beaker) microcosms to investigate the influence of total 

dissolved solids (TDS) and temperature on the inactivation rates of the microbial indicators and pathogens. The geochemical conditions in these microcosms did not mimic those in the anaerobic and extremely reduced south Florida region of the Upper Floridan aquifer. Therefore, inactivation rates of total 

coliforms and other microbial indicators and bacterial, encysted parasitic, and viral pathogens during 

exposure to native geochemical conditions in the Upper Floridan aquifer are still unknown.

Project Objectives 

Determine the inactivation rates of Escherichia coli (a member of the total coliform group) and Pseudomonas aeruginosa (an opportunistic bacterial pathogen common in surface water) when exposed to 

native geochemical conditions in the Upper Floridan aquifer (UFA) and Avon Park Permeable Zone 

(APPZ) of the Floridan aquifer system. An aboveground flowthrough mesocosm system was used to 

maintain native geochemical conditions, except for pressure, during the exposure experiments.

Characterize the geochemistry in the UFA and APPZ in regard to the organic and inorganic carbon and 

inorganic substrates used by bacterial communities to drive biogeochemical reactions that generate 

energy for cell maintenance and growth while altering the geochemistry of these zones of the aquifer.
5

Identify members of the native bacterial communities that are known to perform the types of biogeochemical reactions that the geochemical data show are thermodynamically feasible. In addition, 

identify members of these same communities that perform the biogeochemical reactions that would 

be necessary to maintain the native geochemical conditions in these aquifer zones, even though supporting geochemical data are not available. 

Methods

Site Descriptions

Three well sites were selected, each being artesian and having dual production zones that accessed groundwater in the Upper Floridan aquifer (UFA) aquifer and Avon Park Permeable Zone 

(APPZ) of the Floridan aquifer system (table 1). All wells were placed and constructed as monitoring 

wells for CERP and had never been impacted by artificial or natural recharge (fig. 1). 

Field Data Collection

During each site visit, data were collected at the wellhead for temperature, specific conductance, dissolved oxygen, pH, and oxidation/reduction potential (ORP) using an YSI 556 MPS system 

(YSI Inc., Ohio) attached to a flow cell. The flow cell was attached via polytetrafluoroethylene (PTFE) 

tubing to the stainless steel fitting that had been attached to the wellhead. This same system was also 

attached to the discharge side of the stainless steel inner chamber that retained the diffusion chambers in 

the aboveground mesocosm. Regardless of which source was being monitored, the YSI 556 MPS system 

was allowed to equilibrate to in situ conditions for at least 30 minutes prior to initiating an automatic 

data collection interval of 5 minutes for at least an hour. 

Geochemical and Nutrient Sample Collection and Analyses

Samples were collected from each well, appropriately preserved and delivered to the 

TestAmerica Laboratory (Tampa, Fla.) on the day of collection. Each sample was analyzed for the 

constituents listed in table 2. Separate water samples from each well were collected for determination 

of dissolved inorganic carbon (DIC) and total alkalinity at the USGS laboratory in St. Petersburg, Fla. 

Table 1. Sampling-site locations and well characteristics.

[fbs, feet below surface; UFA, upper Floridan aquifer; APPZ, Avon Park permeable zone]

Well 

designation

Station 

name

Florida 

county

Location Aquifer 

zone

Casing 

diameter

Production 

interval Screen 

type Latitude Longitude (inches) (fbs)

MZ1 LAB-MZ1 Glades 26° 45’ 11.42” -81° 21’ 17.72” UF 18 670-837 Annular

MZ3 LAB-MZ3 APPZ 7 1645-1759 Open

42U HIF-42U Highlands 27° 13’ 11.16” -80° 57’ 21.98” UF 24 560-1040 Annular

42L HIF-42L APPZ 14 1310-1540 Open

15U PBF-15U Palm Beach 26° 44’ 16.08” -80° 21’ 48.68” UF 18 908-1144 Annular 

15M PBF-15M APPZ 12 1400-1583 Annular
6

The concentrations of bicarbonate, carbonate, and carbon dioxide were calculated using the pH, DIC, 

and total alkalinity data using the software program CO2calc (Robbins and others, 2010). Data for the 

recharge water were provided by Dr. June Mirecki (2013). Samples for quantifying the concentrations 

of dissolved hydrogen (H2) and methane (CH4) were collected from all wells using a modified “bubble 

strip” system and method (Chapelle and others, 1997) as provided by Microseeps, Inc. (Pittsburg, Pa.). 

Stabilized dissolved-gas samples were shipped to Microseeps, Inc. for analyses.

Figure 1. Aerial photograph showing sampling-site locations, indicated by green triangles.

Lake

Okeechobee

42U and 42L

MZ1 and MZ3

15U and 15M

Lake

Istokpoga

Kissimmee River

Caloosahatchee Canal

Okeechobee

Ft. Pierce

Clewiston

81°0'0"W 80°30'0"W

27°30'0"N

27°0'0"N

0 10 20

20

5

0 105

Kilometers

Miles

Scale 1:800,000

Datum WGS 1984
7

Table 2. Water-quality data for the six sampling sites.

[ORP, oxidation/reduction potential; °C, degrees centigrade; mS/cm, millisiemens per centimeter; g/L, grams per liter; ppt, parts per thousand; mV, 

millivolts; mg/L, milligrams per liter; nM, nanomolar; μM, micromolar; BDL, below detection limit; ND, not determined]

Parameter Units Well designation

MZ1 MZ3 42U 42L 15U 15M Recharge

Temperature °C 28.7 27.8 28.2 28.5 27.9 28.0 25.5

Specific conductance mS/cm 3.146 27.98 1.029 6.044 5.876 5.009 0.223

Total dissolved solids g/L 2.045 18.19 0.669 3.928 3.819 3.255 0.208

Salinity ppt 1.63 17.03 0.5 3.26 3.17 2.67 ND

pH 8.02 7.38 8.04 7.61 7.60 7.64 6.70

ORP mV -312 -309 -338 -351 -355 -365 132

Aluminum mg/L BDL BDL BDL BDL BDL BDL 0.091

Barium mg/L 0.039 0.035 0.034 0.040 0.015 0.028 0.019

Bromine mg/L 2.0 34.0 BDL 5.1 4.8 4.3 0.1

Calcium mg/L 80 550 44 200 120 110 19

Chloride mg/L 640 9700 160 1600 1600 1300 31

Chromium mg/L BDL BDL BDL BDL BDL BDL 0.001

Cobalt mg/L BDL BDL BDL 0.002 BDL BDL 0.0001

Copper mg/L BDL BDL BDL BDL BDL BDL 0.0010

Fluoride mg/L 0.78 BDL 0.57 0.29 0.97 1.10 0.10

Lead mg/L BDL BDL BDL BDL BDL BDL 0.0003

Magnesium mg/L 75.0 650.0 33.0 140.0 130.0 120.0 6.9

Nickel mg/L BDL BDL BDL BDL BDL BDL 0.0007

Potassium mg/L 24.0 230.0 5.5 40.0 36.0 29.0 4.0

Selenium mg/L BDL BDL BDL BDL BDL BDL 0.0009

Silica mg/L 9.8 9.1 14.0 12.0 13.0 13.0 1.2

Sodium mg/L 440 4700 98 800 890 740 16

Zinc mg/L BDL BDL BDL BDL BDL BDL 0.0447

Dissolved oxygen mg/L 0 0 0 0 0 0 4.4

Manganese mg/L 0.013 0.035 0.007 0.006 0.011 0.010 0.005

Ferric iron mg/L 0.14 0.19 0.10 0.17 0.28 0.38 ND

Ferrous iron mg/L 0.03 0.03 0.03 0.03 0.06 0.02 ND

Iron (total) mg/L 0.17 0.22 0.12 0.20 0.34 0.40 0.26

Ammonium mg/L 0.19 0.28 0.20 0.26 0.44 0.33 0.09

Nitrate mg/L BDL BDL BDL BDL BDL BDL 0.15

Nitrite mg/L BDL BDL BDL BDL BDL BDL 0.016

Kjeldahl Nitrogen mg/L 0.25 0.38 0.21 0.29 0.52 0.53 1.10

Sulfate mg/L 380 1800 180 510 450 370 16

Sulfide mg/L 2.1 1.6 1.4 1.6 3.7 4.2 BDL

Phosphate mg/L BDL BDL BDL BDL BDL BDL 0.067

Dissolved organic carbon mg/L 1.2 1.1 1.1 1.2 1.7 1.9 15.7

Acetic acid mg/L 0.120 0.070 0.095 0.070 0.120 0.070 ND

Lactic acid mg/L BDL 3.8 BDL BDL BDL BDL ND

Propionic acid mg/L 0.071 BDL BDL BDL BDL 0.190 ND

Pyruvic acid mg/L 0.067 BDL BDL BDL BDL BDL ND

Total inorganic carbon mg/L 160 200 210 190 310 330 ND

Bicarbonate mg/L 151.5 190.0 199.1 181.7 296.4 315.8 ND

Carbonate mg/L 6.8 4.2 8.8 3.4 5.3 6.0 ND
8

Statistical Analyses

The geochemical data from the respective wells were analyzed using the multivariate statistical procedure for principal component analyses (PCA) on a derived correlation matrix. The oxidation/

reduction potential (ORP) data for all wells were negative because of the reduced conditions. Prior 

to the data processing, these were changed to positive values to allow data transformation. All data 

were log10-transformed, normalized, and used to derive a correlation matrix on which the PCA was 

performed. These data were used to determine relative similarities between each well site and each 

depth within a well site. All statistical analyses were conducted using PRIMER 6 (version 6.1.12; 

PRIMER–E, Ltd., Plymouth, U.K.).

Native Bacterial Abundances 

Samples (50 mL) were collected from each well and immediately preserved with filter-sterilized 

formalin at a final concentration of 3.0 percent. The samples were placed in an container and kept in the 

dark during transport. Upon return to the USGS laboratory in St. Petersburg, Fla., samples were stored 

at 4 °C. Samples were filtered and stained using SYBR Gold (Molecular Probes, Inc.), as previously 

described for the enumeration of bacteria (Lisle and Priscu, 2004). All bacteria on the prepared slides 

were counted using an epifluorescent microscope equipped with a filter cube set specifically designed to 

optimize the visualization of the SYBR Gold stain.

Aboveground Mesocosms 

The diffusion chambers used in this study are an alternative design of the McFeters diffusion chamber (McFeters and others, 1974; McFeters and Stuart, 1972; McFeters and Terzieva, 1991; 

Terzieva and McFeters, 1991; Zaske and others, 1980). A series of diffusion chambers were used to 

retain the previously described bacterial suspensions, using 0.02-micrometer (μm)-pore-size membranes 

(Lisle, 2005). The membrane physically isolates the E. coli and P. aeruginosa cells from predation by 

native bacteria, while allowing the diffusion of dissolved groundwater constituents into and from the 

chambers. 

The diffusion chambers, when constructed, measure (w×l×d) 4.0×12.0×2.3 centimeters (cm) 

with an internal volume of approximately 15.0 mL (appendix 1). Because of wellhead access constraints 

and the multiple time point sampling design of the experiments, down-well deployments of the diffusion 

chambers were not practical. An aboveground mesocosm system was designed that allowed easy access 

to the diffusion chambers while insulating the chambers from the elevated surface temperatures and 

minimizing alterations in the geochemistry of the native groundwater (appendix 1). 

The mesocosm system is a two chamber system; the outermost chamber is a commercial icecooler adapted to connect directly to the wellhead via PTFE tubing on one end with a 5.0×15.0-cm discharge opening on the other to allow high flow rates through the chamber (appendix 1). The PTFE tubing leading from the wellhead has a valve system connected that allows the diversion of water through 

a flow-regulating valve and then through the side of the chamber. The second chamber is stainless 

Parameter Units Well designation

MZ1 MZ3 42U 42L 15U 15M Recharge

Carbon dioxide mg/L 1.7 5.9 2.2 4.9 8.3 8.2 ND

Hydrogen nM 120.0 1.2 95.0 6.4 62.0 35.0 ND

Methane μM 2.00 0.94 0.69 0.54 1.00 1.06 ND

Table 2. Water-quality data for the six sampling sites.— Continued.

[ORP, oxidation/reduction potential; °C, degrees centigrade; mS/cm, millisiemens per centimeter; g/L, grams per liter; ppt, parts per thousand; mV, 

millivolts; mg/L, milligrams per liter; nM, nanomolar; μM, micromolar; BDL, below detection limit; ND, not determined]
9

steel (45.1×20.3×17.8 cm), sits inside the outer chamber, and has an internal volume of approximately 

16.0 liters (L). The inner chamber is baffled with vertical stainless steel inserts that ensure laminar and 

plug flow, while allowing the placement of diffusion chambers between the baffles. The lid of the inner 

chamber is made of a nontoxic polymer that has been engineered to receive threaded plugs made of the 

same material, preventing water outside of this chamber from entering. These plugs have attachment 

points on their undersides for hanging diffusion chambers that contain the bacterial suspensions. There 

are eight plugs per stainless steel chamber. The PTFE tubing leading from the flow regulating valve is 

attached to one end of the outer chamber. A discharge PTFE tube is connected to the down-flow end 

of this chamber and run to the outside the outermost chamber (that is, the cooler). Flow through the 

outer most chamber (approximately 10.0 liters per minute (L min–1)) is maintained at high enough rates 

that the water insulates from temperature and isolates from oxygen the water and diffusion chambers 

that contain the bacterial suspensions inside the stainless steel chamber. The flow rate inside the stainless steel chamber was maintained at approximately 152 milliliters per minute (mL min–1) for all of the 

wells. This flow rate provides a linear flow velocity of approximately 6.0 meters per day (m d–1) with a 

residence time of approximately 2.0 hours. Therefore, the flow rate through the outermost chamber is 

approximately 64 times greater than that inside the stainless steel chamber. The ambient air and groundwater temperatures at the wellhead and in the outer and inner chambers were monitored throughout each 

experiment using HOBO Pro v2 temperature loggers (Onset Computer Corp., Inc., Pocasset, Mass.).

Bacterial Cultures

Treated surface water that will be injected into aquifers is regulated for chemical and microbiological quality as per the Safe Drinking Water Act; therefore, fecal indicator bacteria (FIB) in the treated 

water is a regulatory concern. However, data on the in situ inactivation and survival of FIB in the Upper 

Floridan aquifer are not available. To model the inactivation of FIB in the region of the Upper Floridan 

aquifer that underlies south Florida, two representative bacterial strains were selected from a commercial source. An E. coli (ATCC #BAA–1159) (EC) and P. aeruginosa (ATCC#29260) (PA) strain were 

selected that had been originally isolated from freshwater sources. The EC strain is the most recognizable member of the FIB group and the PA strain was selected because it is an emerging opportunistic 

pathogen of public health concern in recreational waters. 

Both bacterial strains were grown, processed, and used to load the diffusion chambers as follows. A 5.0-mL primary culture of the EC and PA strains were grown in Tryptic Soy Broth (#211824; 

BD Diagnostics, Md.) and Nutrient Broth (#233000; BD Diagnostics, Md.), respectively, at 37 °C with 

gently rotational shaking (160 rpm) overnight. A 100-microliter (μL) sample of each primary culture 

was used to inoculate a secondary 5.0-mL culture of the same media and grown under the same conditions. The next day, 1.0 mL of each culture was used to inoculate a 150-mL culture of the same media 

and again grown overnight under the same conditions. Between 20 and 25 mL of each of these overnight 

cultures were centrifuged for 5 minutes at 10,000×g and 4 °C. The resulting pellets were resuspended in 

20 mL of phosphate buffered saline (PBS) (137.0 millimolar (mM) NaCl; 2.7 mM KCl; 11.9 mM PO4

; 

pH 7.3–7.5) and centrifuged again using the same parameters. The resulting pellet from this step was 

then adjusted to a spectrophotometric absorbance value (λ=420 nanometers (nm)) using PBS that represented a cell concentration of 5×109

 cells mL–1 based on a laboratory-generated standard curve. These 

adjusted cell suspensions were placed in a cooler with coolant and transported to the well site. The 

travel time ranged from 1.5 to 3.0 hours.

At the well site, 1.0 L of the native groundwater was collected in sterile bottles and filter sterilized. Two 198-mL volumes were transferred to separate flasks, one for EC and the other for PA, and 
10

2.0 mL of the respective concentrated cell suspensions were added. These final bacterial suspensions 

were gently mixed, and 15.0 mL of each were transferred by syringe to fill a separate diffusion chamber 

for each strain, giving an estimated cell concentration of 5×108 to 1×109 cells per chamber (approximately 3–7×107

 cells mL–1). This process was repeated for a total of eight diffusion chambers per strain. 

Each loaded diffusion chamber was immediately transferred to the stainless steel inner chamber, which 

was filled with native groundwater, sealed, and submerged in the native groundwater flowing through 

the outer chamber of the aboveground mesocosm.

At each sampling time point, the groundwater flow into the outer chamber of the aboveground 

mesocosm was diverted to waste and the remaining water in the outer chamber drained until it was 

below the top of the inner chamber, which contained the diffusion chambers. One EC and one PA diffusion chamber were removed and immediately transferred to a container filled with native groundwater 

that was placed in a cooler and stored in the dark. The inner chamber was then resealed, groundwater 

flow into the aboveground mesocosm system was restarted, and the diffusion chambers were taken to 

the field laboratory. Once in the laboratory, the water in each chamber was extracted using a syringe 

and transferred to sterile tubes; the extracted volume was serially diluted with PBS. Selected dilutions 

were filtered through membrane filters (47-millimeter (mm) diameter, 0.45-μm pore size) and placed 

on modified mTEC agar (BD Diagnostics, Md.; #214884), hereafter referred to as mTEC agar, for EC 

and incubated at 35 °C for 2 hours and then transferred to a 44.5 °C incubator for an additional 22 to 

24 hours. mTEC agar was included because this medium is one of the required media listed in the U.S. 

EPA regulations for the recovery of EC from water samples (U.S. Environmental Protection Agency, 

2002).

The membrane filters inoculated with PA were placed on Pseudomonas Isolation Agar 

(BD Diagnostics, Md.; #292710) and incubated at 35.0 °C for 18 to 48 hours. At the end of the respective incubation periods, all filters were counted for the number of colony forming units (CFU) per filter 

based on the diagnostic colony characteristics (Zimbro and others, 2009). All data were normalized to 

volumes plated and expressed as CFU mL–1

. 

In addition to the strain specific media, a select set of the dilutions for both strains were also 

plated on R2A agar (BD Diagnostics, Md.; #218263) using a modified drop plate technique (Hoben and 

Somasegaran, 1982). These samples were incubated at room temperature in the dark for as many days 

as it took for the CFU values to stabilize, namely between 10 and 14 days. All data were normalized to 

volumes plated and expressed as CFU mL–1

. 

The concentrations of EC and PA in the native groundwater 

were also quantified using the same media, dilution scheme, and respective incubation conditions as 

described previously. 

Inactivation Data Analyses

The CFU mL–1 data for EC and PA on mTEC and PA agars, respectively, and R2A agar were used 

to model the inactivation rates in native groundwater from each well, as measured by the loss of culturability on the respective media over time. The best-fit inactivation model was selected by analyzing each 

dataset with a suite of six equations that have been shown to represent the most common inactivation 

data distributions for bacteria based on culturability (Crane and Moore, 1986; Xiong and others, 1999). 

Quantification and Characterization of Dissolved Organic Carbon (DOC)

Routine monitoring of groundwater systems includes quantification of total organic carbon 

(TOC). Although of general interest from an operational perspective, this parameter provides little 
11

insight into how carbon is utilized by the microbial populations in groundwater systems. The percentage of TOC that is assimilable for microbial populations is typically very small, making this parameter 

relatively insensitive when trying to assess microbial processes. The dissolved organic carbon (DOC) 

component of TOC has been shown in all ecosystems studies to be the preferred source of carbon for 

microbial populations. Not all DOC is available for microbial assimilation, however, so an understanding of which DOC constituents are bioavailable is of interest. In addition, the source of the DOC in this 

region of the Upper Floridan aquifer is of interest because it is assumed that photosynthetically fixed 

carbon has not been introduced into this groundwater since it was recharged into the subsurface over 

20,000 years before present (Meyer, 1989; Plummer and Sprinkle, 2001).

Groundwater samples (3.0 L) were collected in sterile bottles from each well, packed in coolers, and shipped overnight to the USGS National Research Program laboratory in Boulder, Colo. The 

samples were processed upon receipt by passing the water through a series of XAD resin columns as 

previously described for the separate isolation and elution of hydrophobic organic acid (HPOA) and 

transphilic organic acid (TPIA) fractions of the total DOC (Aiken and others, 1992; Aiken, 1992). The 

different eluted samples were characterized by elemental, molecular-weight titration and 13C–NMR 

analyses. The specific UV absorbance (SUVA) was also determined for each eluted fraction at a wavelength of 254 nm (Weishaar and others, 2003).

The analysis just described was used to estimate the quantity of dissolved aromatic carbon 

constituents in the water samples. In addition, the fluorescence index (FI) was determined for the total 

and eluted fractions of the DOC (McKnight and others, 2001). The FI provides information about the 

source (terrestrial versus microbial) of fluorescing organic matter in the samples. FI values are normally 

in the range of 1.0 to 2.0, with values of 1.0 to 1.3 indicating the DOC is from a terrestrial source and 

1.7 to 2.0 indicating microbial source. FI values of 1.4 to 1.6 indicate mixtures of DOC that originate 

from terrestrial and microbial sources.

Volatile Fatty Acid (VFA) Data Collection

Volatile fatty acids are short carbon-chain compounds that are byproducts of, and nutrient 

sources for, bacterial communities in groundwater systems. Samples were collected (100 mL) from 

each well, preserved and packaged per the analyzing laboratory’s instructions, and shipped overnight 

to Microseeps, Inc. (Pittsburg, Pa.). This additional analysis was necessary because one of the VFAs, 

namely acetic acid (CH3COO–) or acetate (CH3COOH), was used as the substrate to determine bacterial community respiration rates. For the derived respiration rate data to be meaningful, the concentration of acetate added to the assay must be significantly lower than the native concentration (Wright and 

Burnison, 1979). In addition, the presence and relative concentrations of VFAs provide insight into the 

microbial biogeochemical processes (for example, fermentation, acetogenesis, and sulfate reduction) 

that most likely dominate in these aquifer systems.

Bacterial Respiration and Carbon Turnover and Carbon Dioxide Production Rates

Respiratory rates of the native bacterial populations were determined using radiolabelled substrates, which are appropriate for the geochemical conditions in aquifers (Hobbie, 1973; Wright, 1978; 

Wright and Hobbie, 1966). 14C-labeled acetate was the choice for these groundwater systems because this 

volatile fatty acid is naturally present and assumed to be a carbon source the native bacterial populations 

could utilize. All sample processing in the field and laboratory was performed under a constant stream 

of nitrogen gas to ensure the in situ anaerobic conditions were maintained. Prior to the collection of the 
12

groundwater samples, the appropriate volume of 14C-acetate was added to sterile flasks to achieve a final 

concentration in the sample of 20 nanomolar (nM) and left open under continuous nitrogen gas flow until 

the ethanol carrier had evaporated. This step ensured a second and nonradiolabelled carbon substrate 

(that is, ethanol) was not available for assimilation by the microbial communities, thereby confounding the growth-rate measurements based on radiolabelled acetate. A 70-mL sample from each well was 

added to pre-dosed flasks, and gently mixed; after which 5.0 mL subsamples were transferred to separate 

25-mL serum bottles that were immediately sealed with butyl rubber stoppers. The biological activity 

in the time-zero replicate set of bottles was immediately inactivated by adding 500 microliters (μL) of 

4N H2SO4. After adding the acid, each bottle was gently shaken on a rotary shaker between 8-16 hours. 

The remaining samples were returned to the laboratory and placed in an anaerobic chamber that had been 

flushed and filled with nitrogen gas for continued incubation at room temperature in the dark.

For the time-zero samples and those for all other time points, both bottles of the replicate set 

were processed for the recovery of 14CO2 and 14C-labelled bacterial biomass. Each sample bottle was 

connected to a CO2

 scrubbing system that consisted of three airtight vials, the last two of which contained 4.0 mL of Carbo-Sorb ETM (PerkinElmer, Waltham, Mass.). The sample was inactivated, as 

described previously, and gently shaken for 5 minutes. A nitrogen gas source was then attached and used 

to gently flush the head space of the sample and scrubbing bottles for 5 minutes. The two bottles containing the Carbo-Sorb E were removed and 5.0 mL of Permafluor E+

TM (PerkinElmer, Waltham, Mass.) 

added. The acidified sample in each bottle, which contains the bacterial biomass, was filtered through 

a 25-mm-diameter 0.20-μm-pore-size filter. The filters were then rinsed three times with sterile water, 

briefly dried, and placed in a scintillation vial to which 5.0 mL of Permafluor E+

 was added. All scintillation vials were allowed to set overnight at room temperature to quench, and then read the next day in a 

scintillation counter to record disintegrations per minute (DPM). The DPM data from the two bottles of 

Carbo-Sorb E per sample were added to give a single value that was used to calculate the mean DPM at 

each time point. Each set of mean DPM per unit time data were used in all respiration and productivity 

calculations. 

The DPM per unit time data were used to calculate the acetate turnover rates using the following 

relationship (Wright and Burnison, 1979):

 

Tn = Sn + A

vn

= t

f , 1

where 

Tn is the turnover rate for, in this case, acetate; 

Sn is the natural concentration of acetate in the groundwater; 

A is the concentration of radiolabeled acetate added to each experiment; 

t is the time of each experiment; 

f is the decimal fraction of total radiolabelled acetate utilized by the microbial 

community during the experiment; and

vn is the acetate utilization rate by the native microbial communities. 

The rates of community respiration and production (vn) were the slopes of the mean DPM per unit time 

data in the linear portion of each dataset and subsequently analyzed using model I linear regression. 
13

Bacterial community biomass turnover rates can also be estimated from the incorporation 

rates of an amino acid, leucine, into cellular proteins (Bastviken and Tranvik, 2001; Buesing and 

Marxsen, 2005; Phelps and others, 1994; Simon and Azam, 1989). Accordingly, tritium-labeled leucine 

(3

H-leucine) was added to the native groundwater from the six wells following the method of Kirchman 

(2001). Briefly, 3H-leucine was added to 35 mL of the respective groundwater sources to achieve a final 

concentration of 20 nM leucine, gently mixed, then followed by the transfer of 1.7 mL of the suspension to multiple 2.0-mL screw-cap vials under a continuous flow of nitrogen gas. A set of three vials 

representing the time-zero samples were immediately inactivated with the addition of 89 μL of ice-cold 

trichloroacetic acid (TCA) and gently mixed. The vials were centrifuged at approximately 16,000×g for 

10 minutes at room temperature and the supernatant aspirated. A 1.0-mL volume of ice-cold 5.0-percent 

(v/v) TCA was added to each vial, gently mixed, and centrifuged as before. The supernatant was again 

aspirated and 1.0 mL of ice-cold 80-percent (v/v) ethanol was added, gently mixed, and centrifuged as 

described. The supernatant was aspirated and the remaining pellet air-dried to remove residual ethanol. 

To each dried pellet, 1.0 mL of Ultima GoldTM (PerkinElmer, Mass.) scintillation fluid was added, gently 

mixed, and allowed to sit at room temperature overnight. The remaining sample vials were transferred to 

an activated GasPakTM (BD Diagnostics, N.J.) for transportation back to the laboratory where the vials 

were transferred to an anaerobic chamber for incubation at room temperature in the dark. At each time 

point, three vials from each groundwater source were inactivated and processed as described previously. 

All processed samples were analyzed in a scintillation counter to record the DPM. 

The equation used for relating leucine incorporation rates to bacterial biomass production 

(Kirchman, 1993; Kirchman and Ducklow, 1993) (BPleu; grams carbon per liter per hour) is

 BPleu = Leui ⋅ MWleu ⋅ Leuf

−1 ⋅CPR⋅ Di , 2

where 

MWleu is the formula weight of leucine (131.2 grams per mole (g mol–1)); 

Leuf is the fraction of leucine in bacterial proteins (0.073); 

CPR is the cellular carbon-to-protein ratio in bacteria (0.86); 

Di is the isotope dilution factor (2); and

Leui is the leucine incorporation rate (moles leucine per liter per hour), which was determined 

from linear portion of the scintillation count (DPM) per unit time regression data as described 

for the 14C-acetate experiments. 

The acetate mineralization and leucine assimilation rates were used to calculate the quantity of 

bacterial biomass produced per unit of organic carbon substrate assimilated as expressed by bacterial 

growth efficiencies (BGE) (del Giorgio and Cole, 1998). The BGE for the respective native microbial 

communities is estimated from the following equation:

BGE = BP

(BP + BR) , 3

where 

BP is the microbial community production rate (that is, 3H-leucine data for biomass production 

expressed as micrograms carbon per hour); and 

BR is the microbial community respiration rate (that is, CO2 production from 14C-acetate expressed as micrograms carbon per hour).
14

Biogeochemical and Energetics Analyses

Data for the biogeochemical analyses were selected from those groundwater constituents in 

table 2 that were above the respective detection limits. Based on the geochemical data that provided a 

complete set of reactants and products for the respective reactions, a set of 14 biogeochemical reactions 

were selected that are commonly known to be driven by bacteria in groundwater systems (Davidson and 

others, 2011; Onstott, 2005) (table 3). 

Table 3. Balanced biogeochemical reactions.

The biogeochemical data were also used to calculate the ionic strength of the groundwater in 

each well and the respective activities coefficients of each reactant and product using Geochemist’s 

Workbench (release 7.0) (RockWare, Inc., Golden, Colo.). The activity coefficients were used to adjust 

the concentrations of the constituents to their respective activities, and these activities were used in the 

biogeochemical reaction calculations, exemplified by

aA+ bB  cC + dD , 4

where 

A, B and C, D represent the activities of the reactants and products, respectively, and 

a, b, c, d are the respective stoichiometric reaction constants from the balanced equations. 

The Gibbs free energies (in joules per mole), under standard conditions (ΔG°) (that is, 298.15 °K, 1.0 

atmosphere, pH = 0, ionic strength = 0, and the concentrations of all reactants and products are 1.0 

molar) were calculated for the balanced reactions, as follows:

ΔG = ΔGf



( products) − ΔGf



∑ ∑ (reactants)

, 5

where ΔGf

° (joules per mole) represents the standard free energy of formation values for the respective 

products and reactants in each reaction. The equilibrium constant (K

eq) for each reaction was derived 

using the ΔG° values from equation 4 and solving for K

eq

:

Reaction number Reaction equations

1 4H2 + H+ + SO4

2- → HS-

 + 4H2O

2 CH3

COO- + SO4

2- → 2HCO3-

 + HS3 4H2

 + H+ + HCO3

-

 → CH4 + 3H2O

4 CH3

COO- + H2O → CH4 + HCO3

-

5 4HCOO- + H+ + H2O → CH4 + 3HCO3

-

6 4H2 + H+ + 2HCO3

-

 → CH3COOH + 4H2O

7 2CO2

 + 4H2 → CH3COOH + 2H2O

10 HS-

 + NO3

-

 + H2O → SO4

2- + NH3

12 CH4 + SO4

2- → H2

O + HCO3

-

 + HS17 H2

S + 4NO3

-

 → SO4

2- + 4NO2-

 + 2H+

19 3H2S + 4NO2

-

 + 2H+ + 4H2O → 3SO4

2- + 4NH4+

25 NH3 + 1.5O2 → NO2

-

 + H+ + H2O

26 2NO2

-

 + O2 → 2NO3

-

27 HS- + 2O2 + H2O → SO4

2- + H+
15

K

eq = e

− ΔG

RT

⎛

⎝

⎜ ⎞

⎠

⎟

, 6

where 

R is the universal gas constant (8.3145 joules per degree Kelvin per mole (J °K–1mol–1)), and 

T is temperature (°K).

The ΔG° data were used to calculate the free energy values under in situ conditions (ΔGr) for each 

reaction using the groundwater temperatures (table 2) and activities of the reactants and products 

(table 3), denoted by square brackets, using the following relationship:

ΔGr = ΔG + RT lnQ , 7

where

Q = ⎡

⎣C⎤⎦

c

⎡A⎣ ⎤

⎦

a

⎡

⎣D⎤⎦

d

⎡B⎣ ⎤

⎦

b

. 8

Each reaction was balanced and ΔG° and ΔGr

 values calculated using the CHNOSZ software package 

(Dick, 2008). 

The quantity of energy that can be released from biogeochemical reactions can be assessed by 

comparing the energy available at equilibrium to the energy available under in situ conditions. A convenient method for doing these types of comparisons is chemical affinities (A) (kilojoules per mole):

A = RT ln Keq

Q . 9

Positive chemical affinity values indicate the reaction, as written, will proceed to the right and release 

the calculated free energy for use by the members of the microbial community that are capable of 

performing that reaction. 

The calculation of free energy yields provide insight into which of the biogeochemical reactions 

has the greatest likelihood of occurring under in situ conditions in the aquifers; however, these estimates 

do not indicate the rates at which the microorganisms acquire the necessary energy for cellular activities. Because molecular diffusion is the mechanism through which microorganisms obtain the required 

reactants for biogeochemical reactions, the limiting reactant’s rate of diffusion from the groundwater to 

the microbial cell surface will equal the overall reaction rate (Schulz and Jorgensen, 2001). 

The amount of energy the bacterial cell can potentially generate from performing the biogeochemical reaction, assuming the reaction rate is controlled by the limiting reactant, can be expressed as 

the steady state free energy flux (FEF, in kilojoules per cell per second):

FEF = 4π ⋅r⋅ Dc ⋅C ⋅ ΔGr , 10

where 

r is the radius (micrometers) of the microbial cell, 

Dc (meters squared per second) is the diffusion coefficient of the limiting reactant, 

C (moles per cubic meter) is the concentration of the limiting reactant, and 

ΔGr is the free energy of reaction (kilojoules per cell per second) for the respective biogeochemical reactions. 
16

The relationship between the FEF and the list of biogeochemical reactions in table 3 is 

predicated on the use of the calculated free energy yields for the production of adenosine triphosphate 

(ATP) (Schink, 1997; Thauer and others, 1977). In addition to the dependence on diffusion rates, this 

relationship is based on the additional assumptions that the conservation of energy occurs through 

electron transport processes for all of the biogeochemical reactions shown in table 3 and that the 

conversion of this energy to ATP proceeds with maximum efficiency, which sets a minimum free energy 

yield for ATP production. This minimum free energy yield is commonly set at –20 kilojoules per mole 

(kJ mol–1) of limiting reactant. Accordingly, only those biogeochemical reactions whose ΔGr were 

< –20 kJ mol–1 (that is, more negative) were used in the FEF calculations. The rate at which a cell can 

access the limiting reactant is expressed by the maximum acquisition rate (MAR, in micromoles per day) 

(Onstott and others, 2006):

MAR = 4π ⋅r⋅ Dc ⋅C ⋅ BA , 11

where BA is the abundance of bacterial cells (cells per liter) in the respective groundwater samples. 

Microbial Community Diversity Sample Collection and Analyses

Samples were collected from each well into sterile 20-L carboys during three sampling events 

(table 4). A cartridge filter (Sterivex GP, 0.22-μm pore size; Millipore Corp., Mass.) was connected to 

each carboy and, under gravity-induced flow, allowed to filter until flow had stopped. After removing 

the cartridge filter, its protective plastic housing was aseptically removed and the filter transferred to a 

sterile container. All filters were stored at –80 °C in separate containers. All filters were shipped frozen 

and on dry ice to Second Genome, Inc. (San Bruno, Calif.) for DNA extraction, amplification, and application on their proprietary PhyloChipTM G3 Array technology. The PhyloChip G3 microarray is capable 

of identifying approximately 60,000 operational taxonomic units (OTU) that represent approximately 

Table 4. Sample volumes for bacterial diversity analyses samples from the six 

sampling sites.

Well site Sample date Volume collected (Liters) Total volume (Liters)

MZ1 8/3/2010 7.3 18.4

9/20/2010 11.1

MZ3 8/3/2010 7.0 17.6

9/20/2010 10.6

42U 9/1/2010 6.7 16.3

9/21/2010 9.6

42L 9/1/2010 5.2 14.7

9/21/2010 9.5

15U 9/1/2010 5.9 17.9

9/27/2010 12.0

15M 9/1/2010 5.7 17.7

9/27/2010 12.0
17

840 subfamilies within the Eubacteria and Archaea kingdoms (DeSantis and others, 2007; Hazen and 

others, 2010). The coverage of total bacterial diversity by the PhyloChip G3 has been shown to be comparable to 454 pyrosequencing technology (DeAngelis and others, 2011).

DNA was isolated and polymerase chain reactions (PCR) were carried out using bacterial 

and archaeal primers. The eubacterial 16S rRNA genes were amplified using the degenerate forward 

primer, 27F.1 (5’–AGRGTTTGATCMTGGCTCAG–3’), and the nondegenerate reverse primer, 1492R 

(5’– GGTTACCTTGTTACGACTT–3’). The archaeal 16S rRNA genes were amplified using the degenerate forward primer, 4fa (5’–TCCGGTTGATCCTGCCRG–3’), and the nondegenerate reverse primer, 

1492R (5’–GGTTACCTTGTTACGACTT–3’) (Hazen and others, 2010). Twenty seven cycles of PCR 

for eubacterial 16S rRNA gene amplification and 32 cycles of PCR for archaeal 16S rRNA gene amplification was performed. For each sample, amplified products were concentrated by centrifuge filtration 

and quantified by electrophoresis using an Agilent 2100 Bioanalyzer. PhyloChip Control Mix was added 

to each amplified product. 

PCR products from each sample were pooled, 5 parts to 1 part, and fragmented, biotin labeled, and hybridized to the PhyloChip Array, version G3. PhyloChip arrays were washed, stained, 

and scanned using a GeneArray® scanner, and each scan was captured using GeneChip® Microarray 

Analysis Suite (Affymetrix, Inc., Santa Clara, Calif.). Hybridization values, the fluorescence intensity, 

for each taxon were calculated as a trimmed average, with maximum and minimum values removed before averaging. To calculate the summary intensity for each feature on each array, the central nine pixels 

of individual features were ranked by intensity and the 75th percentile was used. Probe intensities were 

background-subtracted and scaled to the PhyloChip Control Mix. The hybridization score for an OTU 

was calculated as the mean intensity of the perfectly matching probes exclusive of the maximum and 

minimum. 

The description and discussion of OTU data treat “bacteria” as an inclusive term for the eubacterial and archaeal OTUs. In this report, the term “bacteria” is used synonymously to describe 

the prokaryotes collectively, namely the members of the domains Bacteria (Eubacteria) and Archaea 

(Archaebacteria) (that is, organisms that are not eukaryotes). Although profound differences between 

Bacteria and Archaea are acknowledged, the two are quite similar with respect to basic cellular organization and general functional properties, such as biogeochemical processes in anaerobic systems. 

Because one of the objectives of this study was to characterize the functional diversity of prokaryotic 

communities and how these diversities influence groundwater geochemistry in the Upper Floridan aquifer, it was less important to, for example, describe which members of the Bacteria and Archaea reduce 

sulfate than to determine that sulfate reduction is energetically favorable, regardless of which members 

in each domain were driving the process.

Statistical Analyses 

The presence and (or) absence microbial community diversity data generated from the 

PhyloChip G3 array for each groundwater sample were inter-compared in a pairwise fashion using the 

Sorensen index to determine dissimilarity scores, which were stored in a distance matrix. The statistical 

significance between comparisons was determined using the adonis test. These statistical analyses were 

conducted by Second Genome, Inc.
18

Results and Discussion 

Water Quality

The underlying hypothesis that the geochemistry of the Upper Floridan aquifer was relatively 

consistent between and within sample sites was tested using principal component analysis (PCA). A 

visual estimation of relatedness between sample sites can be made by comparing the relative closeness 

of the well site designations along the x-axis (that is, PC1) and then the y-axis (that is, PC2) (figs 2–7). 

Figure 2. Graph showing principal component analysis of field data from the six sampling sites [PC1, 

principal component 1; PC2, principal component 2].

-4 -2 0 2 4

PC1

-2

0

2

PC2

MZ1

MZ3

42L 42U

15U

15M

Temp

Cond

TDS

Sal

pH

ORP

Figure 3. Graph showing principal component analysis of ionic species data from the six sampling sites 

[PC1, principal component 1; PC2, principal component 2].

-6 -4 -2 0 2 4

PC1

-4

-2

0

2

PC2

MZ1

MZ3

42U 42L

15U

15M

Br

F

Si

Ba

Mg

Mn

Ca

K

Na Cl
19

The proximity of well-site names along the respective axes is directly proportional to the similarity of 

their geochemical data (table 2). The interpretation of the PCA graphs for the water-quality variables is 

facilitated by the superimposition of the calculated eigenvectors (table 5), whose direction is most like 

the variable (for example, pH) represented by the vector. The length of the vector is proportional to that 

variable’s contribution to the overall variance; that is, the longer the vector, the more significant the 

influence of that variable on the overall variance. 

The PCA was applied to subsets of the data in table 2. These data subsets were grouped as follows: field data (fig. 2), ionic geochemical species (fig. 3), nutrients (fig. 4), organic and inorganic carbon (fig. 5), geochemical constituents that predominate in terminal electron acceptor processes (TEAPs) 

(fig. 6), and the geochemical constituents that were used as the reactants and products in the energetics 

Figure 4. Graph showing principal component analysis of nutrient data from the six samplng sites [PC1, 

principal component 1; PC2, principal component 2] .

Figure 5. Graph showing principal component analysis of carbon substrate data from the six sampling sites 

[PC1, principal component 1; PC2, principal component 2] .

-3 -2 -1 0 1 2 3

PC1

-1

0

1

PC2

MZ1

MZ3

42U

42L

15U

15M

NH4

N-Ttl

Fe-Ttl

DOC

-4 -2 0 2 4

PC1

-2

0

2

4

PC2

MZ1

MZ3

42U

42L

15U

15M

Methane

DIC

DOC

Acetate

HCO3

-

CO3

CO2
20

Figure 6. Graph showing principal 

component analysis of terminal electron 

acceptor data from the six sampling 

sites [PC1, principal component 1; PC2, 

principal component 2].

Figure 7. Graph showing principal 

component analysis of reactant 

data from the six sampling sites for 

the thermodynamically favorable 

biogeochemical reactions [PC1, 

principal component 1; PC2, principal 

component 2] .

-3 -2 -1 0 1 2

PC1

-3

-2

-1

0

1

2

PC2

MZ1

MZ3

42U

42L

15U

15M

SO4

Fe-Ttl HS

Mn

Hydrogen

Methane

-4 -2 0 2 4

PC1

-4

-2

0

2

PC2

MZ1

MZ3

42U

42L

15U

15M

SO4

HS

Fe-Ttl

Mn

Hydrogen

DOC Methane

Acetate

HCO3

–

CO2
21

Table 5. Principal component analyses eigenvector data for the six sampling sites.

Comparison group Variable Graph notation Principal components 

PC1 coefficients PC2 coefficients

Field data Protons pH 0.450 0.266

Temperature Temp 0.308 0.462

Oxidation/reduction Potential ORP 0.123 -0.823

Conductivity Cond -0.478 0.102

Total dissolved solids TDS -0.479 0.112

Salinity Sal -0.479 0.121

Ionic species Silica Si 0.279 -0.323

Fluorine F 0.246 -0.454

Potassium K -0.361 -0.089

Magnesium Mg -0.359 -0.114

Bromine Br -0.358 -0.137

Calcium Ca -0.356 -0.018

Sodium Na -0.352 -0.181

Chlorine Cl -0.348 -0.193

Manganese Mn -0.317 0.086

Barium Ba -0.040 0.757

Nutrients Ammonium NH4 -0.480 0.737

Dissolved organic carbon DOC -0.490 -0.610

Total iron Fe-Ttl -0.515 -0.251

Total nitrogen N-Ttl -0.515 0.145

Organic and inorganic carbon Bicarbonate HCO3- 0.512 -0.099

Dissolved inorganic carbon DIC 0.511 -0.104

Dissolved organic carbon DOC 0.461 -0.204

Carbon dioxide CO2 0.460 0.203

Carbonate CO3 0.074 -0.582

Acetate Acetate -0.109 -0.599

Methane Methane -0.185 -0.446

Terminal electron acceptor process 

substrates (TEAPs)

Di-hydrogen Hydrogen 0.554 0.115

Methane Methane 0.226 -0.066

Hydrogen sulfide HS 0.077 -0.696

Total iron Fe-Ttl -0.145 -0.689

Manganese Mn -0.508 0.147

Sulfate SO4 -0.598 0.035

Reactants Di-hydrogen Hydrogen 0.232 0.465

Acetate Acetate 0.127 0.421

Methane Methane 0.117 0.281

Manganese Mn 0.015 -0.385

Sulfate SO4 -0.077 -0.449

Hydrogen sulfide HS -0.393 0.269

Dissolved organic carbon DOC -0.418 0.242

Bicarbonate HCO3- -0.430 0.126

Carbon dioxide CO2 -0.438 -0.163

Total iron Fe-Ttl -0.453 0.072
22

calculations (fig. 7). The eigenvector loadings for the first two principal components (PC1 and PC2) for 

each of these comparisons are provided in table 5. Table 6 lists the respective contributions to the overall variance that each principal component represents. The cumulative percent variation represented by 

the first two principal components for each data comparison was relatively high, ranging from approximately 76 to 96 percent, indicating the relationships between variables are reasonably represented by 

PC1 and PC2 in the data distributions. 

The field data coefficients for PC1 (table 5) indicate the horizontal relationships are best described by specific conductance, TDS, and salinity data at one extreme (negative coefficients) and pH 

(positive coefficient) at the other. The MZ3 site is most influenced by the variables related to specificconductance, whereas 42U is most influenced by pH, relative to the other sites. The alignment of the 

other well sites between these two extremes indicates these sites are more related to each other and less 

related to the MZ3 and 42U sites (fig. 2). PC2 is dominated by ORP and, to a lesser extent, temperature 

(table 5). Figure 2 shows that the groundwater from 15U and 15M are similar and most influenced by 

ORP. Groundwater temperature separates MZ1 from the other wells, whereas 42U, 42L, and MZ3 are 

similar to each other in regard to the field data variables included in this analysis. 

The ionic geochemical species, except for silica and fluorine, were all similarly weighted 

(table 5). The groundwater from MZ3 was most impacted by these constituents, whereas 42U was least 

influenced. The vertical distribution of the remaining sites indicates these groundwaters were all similar 

in regard to these ionic species. The PC2 data indicate that barium concentrations in MZ1, MZ3, 42U, 

and 42L were similar and different from those in 15U and 15M. The groundwater in 15U and 15M had 

relatively higher concentrations of silica and fluorine (fig. 3).

The groundwater nutrients were all similarly weighted for PC1. The groundwater from 15U and 

15M were most similar to each other and dissimilar from MZ1, MZ3, 42U, and 42L. These similarities and differences are due to the relatively higher concentrations of all the nutrients in 15U and 15M 

groundwaters (table 5). PC2 was dominated by ammonium and total nitrogen at the upper end of the 

continuum and total iron and DOC at the lower end (fig. 4). This combination of nutrient factors shows 

15U, MZ3, and 42L being relatively similar in their nitrogen-based nutrients and 15M, MZ1, and 42U 

being relatively similar in the DOC and total iron concentration (fig. 4). 

The principal components for the inorganic and organic carbon data were weighted for the inorganic and dissolved organic carbon substrates that drive chemolithotrophic and heterotrophic metabolisms, respectively (table 5). In regard to PC1, MZ1 was most dissimilar to 15U and 15M, with the other 

three groundwater samples being similar to each other but slightly dissimilar to the groundwater at each 

extreme of the PC1 axis. PC2 is best described by the concentrations of methane, acetate, and carbonate (fig. 5). Samples from the APPZ from 15U, 42U, and MZ1 were most influenced by their respective 

Table 6. Principal component analyses percent variance per axis data for the six sampling sites.

[TEAPs, terminal electron acceptor process substrates]

Data sets Percent variation Cumulative percent 

PC 1 PC 2 variation

Field data 71.0 21.0 92.0

Ionic species 75.4 15.8 91.2

TEAPs 41.0 33.2 74.2

Nutrients 91.2 6.6 97.8

Organic and inorganic carbon 52.7 32.4 85.1

Reactants 45.9 32.1 78.0
23

concentrations of these organic and inorganic carbon substrates, which are known to be primary energy 

sources in anaerobic and reduced ecosystems like this region of the Upper Floridan aquifer. 

The TEAPs PCA (fig. 6) shows that PC1 is dominated by hydrogen concentrations in the 

groundwater, with all sites having higher concentrations relative to MZ3 grouped together (table 5). The 

total iron and sulfides data dominate PC2, with the higher concentrations of both in 15U and 15M driving their location as more dissimilar than the other four groundwater samples (fig. 6). 

When assessing each groundwater for the constituents used in the biogeochemical reactions, 

PC1 is dominated by the inorganic carbon variables, total iron, and hydrogen sulfide concentrations 

(table 5). Again, 15U and 15M are similar and distinctly different from the remaining sites, which are 

more similar in their hydrogen, acetate, and methane concentrations (fig. 7). The relationships derived 

from PC2 are not as definitive (table 5); 42L and MZ3 are more similar in their sulfate and manganese 

concentrations, whereas the remaining sites are more similar in their hydrogen, acetate, and methane 

concentrations (fig. 7). 

Collectively, the PCA analyses show that groundwaters within the UFA and APPZ are, as would 

be predicted, geochemically different. These zones are not homogenous on a regional basis, however, 

because there are significant differences in geochemistry between sites for a given zone. The types of 

water-quality variables (for example, methane, hydrogen, total inorganic carbon, acetate, sulfides, and 

ammonium) that show differences within and between zones in this region of the Upper Floridan aquifer indicate these differences are driven by microbial activities. The application of multivariate statistical analyses to geochemical data from groundwater samples provides (1) a quantitative platform for 

establishing native geochemical conditions and determining which variables are most influential in the 

aquifer, (2) baseline datasets to which temporally distinct data from the same site can be compared for 

determining if changes in geochemistry have occurred, and (3) a method to identify which geochemical 

variables change over time and the influence of those changes on the overall geochemistry following an 

event that disrupts the aquifer ecosystem (for example, aquifer recharge, storage and recovery, groundwater extraction, treated waste water injection, and carbon dioxide sequestration).

Bacterial Abundances

The mean bacterial abundances in all groundwater samples were relatively consistent, ranging 

from 3.92×104 to 8.01×105 cells mL–1 (table 7). These bacterial abundances are similar to those recovered from a series of samples collected from wells in the Upper Floridan aquifer located in central and 

south Florida (Lisle, 2005). 

Additionally, native groundwater from each of the well sites was collected during each sampling 

event and filtered or directly plated onto each of the media used in the inactivation experiments. None 

Table 7. Bacterial total direct count data for the 

six sampling sites.

Well site Cells per milliliter

42U 3.92×104

 ± 1.49×103

42L 4.12×105 ± 3.32×104

15U 7.21×105 ± 7.80×104

15M 7.28×105 ± 8.10×104

MZ1 8.01×105 ± 6.87×104

MZ3 6.79×105 ± 3.84×104
24

of the native water samples produced colonies on mTEC agar, an average of 0.7 CFU mL–1 (range: 

0.04–1.1 CFU mL–1) were recovered on the PIA agar and 0.7 CFU mL–1 (range: 0–1 CFU mL–1) on 

R2A agar; none of these colonies were identified. The recovery of culturable bacteria from the native 

waters on the nonselective PIA and R2A agars is not surprising because these groundwater systems contain viable bacteria and both media promote the recovery of heterotrophic bacteria, regardless of their 

identity. More importantly, the numbers of culturable bacteria on these media are not great enough to 

influence the colony counts of the E. coli or P. aeruginosa recovered from the diffusion chambers, even 

if a contamination event (such as a membrane rupture, chamber gasket leak, or contamination during 

chamber recovery) had occurred. 

Bacterial Indicator Inactivation

The colony counts from each of the experiments were first log10-transformed and analyzed using 

a suite of bacterial inactivation models to determine the best-fit model. The biphasic model (Cerf, 1977) 

provided the best fit for all datasets based on root mean sum of squared error (RMSE) values (data not 

shown). 

This biphasic model describes the inactivation of bacterial communities that can be subdivided 

into two subpopulations. One subpopulation is more susceptible to inactivation than the other, which 

generates an inactivation curve with an initial steep and negative slope that follows into a tail with a 

significantly smaller negative slope. The two subpopulations are assumed to be independently and irreversibly inactivated with the respective inactivation rates following first order reaction kinetics. The 

equation for this inactivation model is

Nt

N0

= fe−k1

t

+ (1− f )e

−k2t

, 12

where 

N0 and Nt are the log10-transformed colony counts at time zero and time t, respectively; 

t is the elapsed time (hours); 

f is the decimal fraction of the total bacterial community in the major subpopulation that 

is inactivated at a higher rate (that is, less resistant) (k1; log10 CFU per milliliter per 

hour); and 

(1–f) is the decimal fraction of the total bacterial community in the minor subpopulation that 

is inactivated at a slower rate (that is, more resistant) (k2; log10 CFU per milliliter per 

hour); the higher the k-value, the faster the inactivation rate. 

An estimation of the time required for a 1.0 log10 reduction (t

log10

) in the two bacterial subpopulations 

was derived using the following relationship:

t

log10

= 2.303

kn , 13

where kn

 is the k1 or k2 inactivation rate (table 8). The calculated inactivation curves for each experiment 

are given for E. coli in figures 8–10 and P. aeruginosa in figures 11–13. The general biphasic curve 

shape is evident in each dataset. Table 8 lists the data for each of the inactivation relevant variables 

described for the biphasic equation. 
25

Escherichia coli Inactivation

The E. coli inactivation rates (that is, loss of cultivability over time) calculated from the mTEC 

agar data for the most susceptible subpopulation (k1

) were relatively consistent for 42L (0.6842 h–1), 

15M (0.5400 h–1 ), MZ1 (0.6275 h–1), and MZ3 (0.5642 h–1 ) (table 8). The inactivation rates in 

42U (0.2950 h–1) and 15U (0.2171 h–1 ) were approximately 2 to 3 times slower. When expressing the inactivation rates as time required for a 1.0 log10 reduction in CFU mL–1 (t

log10

), the most sensitive E. coli

subpopulation required between 3.4 and 10.6 hours (table 8).

Table 8. Biphasic inactivation rate curve data for Escherichia coli and Pseudomonas aeruginosa from the six sampling sites.

[k1 and k2, inactivation rate constants; f, decimel fraction of total bacterial counts; tlog10, time to achieve a one log reduction in bacterial cell counts; hr-1, per 

hour; %, percent; hrs, hours]

Indicator bacterium Medium Variable Units Well designation

42U 42L 15U 15M MZ1 MZ3

Escherichia coli mTEC k1 hr-1 0.2950 0.6842 0.2171 0.5400 0.6275 0.5642

f % 98.3 95.9 99.8 99.0 98.3 98.8

t

log10 hrs 7.8 3.4 10.6 4.3 3.7 4.1

k2 hr-1 0.0088 0.0182 0.0064 0.0135 0.0112 0.0125

1-f % 1.7 4.1 0.2 1.0 1.5 1.2

t

log10 days 11.0 5.3 15.1 7.1 8.6 7.7

R2A k1 hr-1 0.1850 0.1721 0.2013 0.1950 0.6063 0.2550

f % 98.2 99.6 99.6 99.5 99.5 99.8

t

log10 hrs 12.5 13.4 11.4 11.8 3.8 9.0

k2 hr-1 0.0065 0.0003 < 0.0001 0.0026 0.0080 0.0035

1-f % 1.8 0.4 0.4 0.5 0.5 0.2

t

log10 days 14.7 329.0 > 959.58 36.6 12.1 27.1

Pseudomonas aeruginosa PIA k1 hr-1 0.7696 0.6917 0.1442 0.1592 0.3871 0.7717

f % 70.9 78.2 98.2 94.0 97.0 95.6

t

log10 hrs 3.0 3.3 16.0 14.5 6.0 3.0

k2 hr-1 0.0048 0.0044 0.0034 0.0054 0.0080 0.0050

1-f % 29.1 21.8 1.8 6.0 3.0 4.4

t

log10 days 20.0 21.9 28.1 17.7 12.0 19.4

R2A k1 hr-1 0.2917 0.0479 0.4696 0.6833 0.6213 0.6704

f % 83.9 86.0 96.7 92.8 97.3 96.7

t

log10 hrs 7.9 48.1 4.9 3.4 3.7 3.4

k2 hr-1 0.0041 0.0040 0.0023 0.0047 0.0094 0.0074

1-f % 16.1 14.0 3.3 7.2 2.7 3.3

t

log10 days 23.3 23.7 41.9 20.4 10.2 12.9
26

The inactivation rates for the less susceptible E. coli population (k2) followed a similar ranking as those for k1

, but the rates were between approximately 33 to 56 times slower (table 8). The times 

required for a 1.0 log10 reduction were similar for 42L, 15M, MZ1, and MZ3 (range: 5.3–8.6 d) and approximately two times slower than the other groundwater for 42U (11.0 d) and 15U (15.1 d) (table 8). 

The E. coli k1 inactivation rates calculated from the R2A agar data also were relatively consistent, ranging between 0.1721 and 0.2550 h–1, except for MZ1 (0.6063) (table 8). These rates are between 

1.0 to 4.0 times slower than the k1

 values for the same groundwater on mTEC agar. The k1 rates for the 

R2A agar data are between 1.1 to 4.0 times slower than the respective k1

 rates on mTEC agar (table 8). 

The time required for a 1.0 log10 reduction on R2A agar for this subpopulation was similar to that on 

mTEC agar, ranging from 3.8 to 13.4 hours (table 8). 

The inactivation rates for the less susceptible E. coli populations (k2

) on R2A agar for 15M, 

MZ1, and MZ3 were approximately 72 to 75 times slower than their respective k1

 rates, whereas the 

42L rate was 590 times slower (table 8). The k2 rate in the groundwater from 15U was approximately 

2,000 times slower than the k1 value because of the persistent culturability of this strain of E. coli after 

the initial inactivation event (table 8). When compared to the k2 values from the mTEC agar data, the 

Figure 8. Graphs showing Escherichia coli decay rates for the 42U and 42L sampling sites [Log10 CFU/mL, log10 colony-forming units per 

milliliter; h, hours].

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42L 

Escherichia coli 

R2A agar

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42U 

Escherichia coli 

R2A agar

3.00

4.00

5.00

6.00

7.00

8.00

9.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42L 

Escherichia coli 

mTEC agar

4.50

5.50

6.50

7.50

8.50

9.50

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42U

Escherichia coli 

mTEC agar

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
27

values from the R2A agar data are between 1.3 and 5.1 times slower for 42U, 15M, MZ1, and MZ3 and 

approximately 63 times slower for 42L and 15U. Although the 1.0 log10 reduction times for the more 

susceptible subpopulations were similar between mTEC and R2A agars, this was not the case for the 

more resistant subpopulations at all of the well sites. 42U (14.7 days) and MZ1 (12.1 days) were similar to each other and their respective mTEC agar data. It took approximately a month to achieve this 

level of inactivation at 15M (36.6 days) and MZ3 (27.1 days). The significantly lower inactivation rate 

for 42L and persistent culturability recorded in 15U provided estimated 1.0 log10 reduction times of 

329 days and > 960 days, respectively. 

The differences in inactivation rates and times between mTEC agar and R2A agar, regardless of 

the groundwater source, can be partially attributed to the different compositions of the media. mTEC 

agar is a selective and differential medium whose formulation and incubation conditions (that is, elevated temperature) were specifically designed to preferentially recover E. coli in water samples while 

inhibiting the growth of all other bacteria (Zimbro and others, 2009). Additionally, mTEC agar contains 

a chromogenic substrate that, when catabolized by E. coli, imparts a diagnostic color to each colony, 

whereas colonies formed by other bacteria remain colorless. Collectively, these conditions also apply 

Figure 9. Graphs showing Escherichia coli decay rates for the 15U and 15M sampling sites [Log10 CFU/mL, log10 colony-forming units per 

milliliter; h, hours].

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15M

Escherichia coli 

R2A agar

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15U

Escherichia coli 

R2A agar

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15M

Escherichia coli 

mTEC agar

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15U

Escherichia coli 

mTEC agar

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
28

additional physiological stresses on E. coli cells that have been shown to reduce bacterial recovery efficiencies, resulting in lower final CFU mL–1 values (Bissonnette and others, 1975; LeChevallier and others, 1983; McFeters, 1990). On the contrary, the formulation of R2A agar was designed to minimize the 

physiological stresses from incubation conditions, components of the recovery media, disinfectants, and 

environmental conditions, thereby increasing the recovery of injured and non-injured bacteria from water samples (Reasoner and Geldreich, 1985). R2A agar is a nonselective medium that contains reduced 

concentrations of nutrients that are essential for bacterial growth under environmental stress (that is, 

natural conditions) and following disinfection. Recovery rates of all heterotrophic bacteria are enhanced 

on this medium if incubated at lower temperatures, as described for this study. R2A agar has been used 

to compare recovery rates of E. coli and other bacteria of public health concern to those on selective 

media following starvation and disinfection (Keswick and others, 1982; Lisle and others, 1998). 

Pseudomonas aeruginosa Inactivation

The k1 inactivation rates calculated from the PIA agar data for P. aeruginosa were relatively 

similar for 42U (0.7696 h–1 ), 42L (0.6917 h–1 ), and MZ3 (0.7717 h–1 ), with 15U (0.1442 h–1) and 

Figure 10. Graph showing Escherichia coli decay rates for the MZ1 and MZ3 sampling sites [Log10 CFU/mL, log10 colony-forming units per 

milliliter; h, hours].

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ3 

Escherichia coli 

R2A agar

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ1

Escherichia coli 

R2A agar

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ3 

Escherichia coli 

mTEC agar

4.00

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ1 

Escherichia coli 

mTEC agar 

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
29

15M (0.1592 h–1) being approximately 5 times slower and MZ1 (0.3871 h–1 ) positioned between the 

two sets of inactivation rates (table 8). The more susceptible subpopulations in the 42U, 42L, MZ1, and 

MZ3 diffusion chambers had similar 1.0 log10 reduction times, ranging from 3.0 to 6.0 h (table 8). These 

reduction times were 2 to 5 times slower for 15U (16.0 h) and 15M (14.5 h) (table 8). 

The k2

 inactivation rates from these same experiments were similar for 42U, 42L, 15U, 15M, and 

MZ3 (range: 0.0034–0.0054 h–1), with MZ1 being moderately higher (0.0080 h–1) (table 8). The k2

 inactivation rates were 29.4 to 48.4 times slower than the respective k1

 rates for 15M, 15U, and MZ1 and 

155.6 to 160.6 times slower for MZ3, 42U, and 42L (table 8). The t

log10

values were grouped by site as 

for the k2

 rates, ranging from 17.7 to 28.1 days, with MZ1 having a moderately faster rate of 12.0 days. 

The k1 inactivation rates for P. aeruginosa on R2A agar were similar and relatively greater for 

15M (0.6833 h–1), MZ1 (0.6213 h–1), and MZ3 (0.6704 h–1). The k1

 rate for 15U (0.4696 h–1) was slightly 

slower than the other rates, whereas the 42U (0.2917 h–1) and 42L (0.0479 h–1) rates were significantly 

slower (table 8). The k2 inactivation rates were similar and relatively greater for MZ1 (0.0094 h–1) 

and MZ3 (0.0074 h–1), which were approximately two times greater than the rates from 42U (0.0041 

h–1), 42L (0.0040 h–1), and 15M (0.0047 h–1) and four times greater than 15U (0.0023 h–1) (table 8). 

Figure 11. Graph showing Pseudomonas aeruginosa decay rates for the 42U and 42L sampling sites [Log10 CFU/mL, log10 colony-forming units 

per milliliter; h, hours].

7.00

7.50

8.00

8.50

9.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42L 

Pseudomonas aeruginosa 

R2A agar

Measured Identified

6.50

7.50

8.50

9.50

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42U

Pseudomonas aeruginosa

R2A agar

Measured Identified

7.00

7.50

8.00

8.50

9.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42L 

Pseudomonas aeruginosa 

PIA agar

Measured Identified

7.00

7.50

8.00

8.50

9.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

42U

Pseudomonas aeruginosa

PIA agar

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
30

This second phase of the respective inactivation models was between approximately 12 times (42L) 

and 205 times (15U) slower than the respective k1 inactivation rates (table 8). The time periods for a 

1.0 log10 reduction of the more susceptible subpopulations on R2A agar were not consistently similar to 

those from the PIA agar data, except for MZ3 (3.4 and 3.0 h, respectively) (table 8). Unlike the systematic differences between these same rates on mTEC and R2A agars, where the R2A agar data were all 

slower (with the exception of MZ1), some of the t

log10

rates from the PIA agar were greater than those on 

R2A agar (15U, 15M, and MZ1), whereas those for 42U and 42L were significantly slower (table 8). 

PIA agar is also a selective and differential medium but the selective agents are not identical 

to those in mTEC agar in regard to their structures, activities, and degrees of inhibition (Zimbro and 

others, 2009). The higher incubation temperature for mTEC agar also increases the inhibitory effects. 

Collectively, the media formulation of and incubation conditions for mTEC agar exerts a significantly 

greater level of physiological stress on target bacteria than PIA agar. The lack of consistent differences 

between the recovery and inactivation data for PIA and R2A agars, as with the mTEC and R2A agar 

data, are due to the lower physiological stresses. 

Figure 12. Graph showing Pseudomonas aeruginosa decay rates for the 15U and 15M sampling sites [Log10 CFU/mL, log10 colony-forming 

units per milliliter; h, hours].

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15M

Pseudomonas aeruginosa 

R2A agar

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15U 

Pseudomonas aeruginosa 

R2A agar

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15M

Pseudomonas aeruginosa 

PIA agar

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

15U

Pseudomonas aeruginosa

PIA agar

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
31

Relevance of Escherichia coli Inactivation Rates in the Upper Floridan aquifer

The number of published studies on inactivation rates of E. coli in groundwater are few (John 

and Rose, 2005). Of these, a small percentage describe the use of diffusion chambers, and studies that 

have used diffusion chambers in situ or in systems that maintain in situ conditions are rare. Table 9 

lists the published studies that have specifically quantified inactivation rates of E. coli in groundwater. 

Except for the study by Sidhu and Toze (2012), these studies did not record the native groundwater 

geochemical conditions, nor make an effort to maintain those conditions during the inactivation experiments. In addition, all of the studies listed in table 9 used linear regression methods to derive the inactivation rates, except for John (2003), who selectively used a polynomial equation to model data with an 

initial increase that was followed by a decrease in culturability. A visual inspection of the graphed data 

presented in these publications revealed that most of the datasets were not linear throughout the entire 

time of the experiments, especially for the first few sampling events. Most datasets appeared to have an 

initial and rapid decrease in culturability, followed by a more gradual decrease that closely resembled 

the data from this study (figs. 8–13). 

Figure 13. Graph showing Pseudomonas aeruginosa decay rates from the MZ1 and MZ3 sampling sites [Log10 CFU/mL, log10 colony-forming 

units per milliliter; h, hours].

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ3 

Pseudomonas aeruginosa 

R2A agar

5.00

6.00

7.00

8.00

9.00

10.00

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ1 

Pseudomonas aeruginosa 

R2A agar

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ3 

Pseudomonas aeruginosa

PIA agar

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

9.50

0.00 100.00 200.00 300.00 400.00 500.00 600.00

MZ1 

Pseudomonas aeruginosa 

PIA agar

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified

Log10 CFU/mL

Time (h)

Measured Identified
32

 In the Sidhu and Toze (2012) study, diffusion chambers filled with E. coli cells were inserted 

into the production zones of groundwater wells. Although this aspect of their study is similar to this 

study, the geochemistry of their groundwater systems was significantly different from that in the region 

of the Upper Floridan aquifer investigated in this study. For example, the aquifer in their study maintained a dissolved oxygen concentration of 2.0 mg L–1 and an ORP of 222 millivolts (mV), whereas all 

of the groundwaters in this study are strictly anaerobic and extremely reduced (table 2). 

It is worth noting that the doctoral dissertation by John (2003) describes a set of experiments that 

used water from two wells whose production zones are located in the Upper Floridan aquifer (ROMP

TR7–4: lat 27.427781, long –82.489095 and PBF–3: lat 26.675833, long –80.103056). The focus of 

his experimental design was the influence of total dissolved solids (TDS) on the survival of microbial 

indicators in groundwater. The groundwater collected for these experiments was pasteurized at 70 °C 

for 30 minutes and stored at 4 °C until used for the bench-top microcosm (that is, open beaker) studies. Only temperature, pH, total organic carbon, specific conductance, and TDS data were collected. No 

geochemical data were collected on the native or pasteurized groundwater samples prior to, during, or 

after each experiment. The k1

 rates from this study are orders of magnitude greater, and the k2 rates are 

1.3 to 2.6 times greater than the fastest inactivation rate of 0.007 h–1 in John (2008) , except for rates obtained 

for 15U which were similar between these studies (tables 8 and 9). 

To place the E. coli inactivation rates in a more applied context, an example that uses data from 

the Hillsboro ASR pilot project’s treatment facility is presented. The pumping rate for recharge water 

by this facility is 5 million gallons per day (Mgal d-1). This facility has detected E. coli in the recharge 

water at a concentration ranging from below detection (<1.0 CFU 100 mL–1) to 65 CFU 100 mL–1. At 

this recharge rate and maximum E. coli concentration, there could be 1.23×1010 E. coli introduced into 

the aquifer at the completion of a 1-day recharge event. The biphasic model used to calculate the inactivation rate data in table 8 assumes that both subpopulations independently follow first order reaction 

kinetics, which permits the use of Chick’s Law for calculating the times required for total inactivation of 

both subpopulations. The most familiar form of Chick’s Law is as follows:

Nt

N0

= e−kt

, 14

where 

Nt is the concentration (CFU per milliliter) of injected bacteria at time t (hours),

N0 is the concentration (CFU per milliliter) of bacteria at the end of the recharge event, 

and 

k is the inactivation rate constant (per hour). 

Table 9. Escherichia coli inactivation rate data from the published literature.

[hr-1, per hour; mL, milliliter]

Inactivation rate

(hr-1) Experimental design Reference

0.013 Diffusion chambers in an above ground container open to air Bitton and others (1983)

0.0066 Spiked groundwater in bench top flasks Cook and Bolster (2007)

0.004-0.007 Spiked groundwater in bench top flasks Davies and Davies (2010)

0.004 Spiked groundwater in 50 mL tubes Keswick and others (1982)

0.029 Diffusion chambers that were placed in situ Sidhu and Toze (2012)
33

The variable Nt is set at 0.9 (assuming a value of <1.0 CFU represents total inactivation), N0 is adjusted 

for the respective subpopulations using the f or 1–f values, and the k1

 and k2 values (table 8) are used 

with the appropriate N0 values. Solving for t yields an estimate of the length of storage time required for 

the respective subpopulations of E. coli to be reduced to less than 1.0 CFU (table 10). The more 

sensitive subpopulation of E. coli (k1 data) was reduced to less than 1.0 CFU in all the wells at a similar 

rate when using mTEC agar, ranging from 1.4 to 4.5 days (table 10). The same subpopulation on 

R2A agar was completely inactivated at a generally slower rate, ranging from 1.6 to 5.6 d. 

The more resistant subpopulations (k2 data) were inactivated at significantly slower rates, regardless of which medium was used. Using the mTEC agar data, this E. coli subpopulation was inactivated 

after 1.5 to 3.7 months of storage in the respective aquifer zones. The R2A agar data were generally 

slower than the rates calculated from the mTEC agar data, ranging from 3.1 to 9.5 months for 42U, 

15M, MZ1, and MZ3, and 7.1 years for 42L. The outlier in the dataset is for 15U, where a predicted 

> 120.6 years of storage are required to totally inactivate the more resistant E. coli subpopulation to less 

than 1 CFU (table 10). 

Bacteria that live in natural environments are considered to be under varying degrees of physiological stress that result in significant reductions or loss of culturability (Finkel and others, 2000; 

McDougald and others, 1998; Nystrom, 2004). The type and severity of stress is dependent upon the in 

situ physical and geochemical conditions of the natural systems, disinfectants (for example, oxidants, 

UV irradiation) in engineered systems, and culture media and incubation conditions. However, the loss 

of culturability does not necessarily ensure that the noncultured cells are nonviable (that is, dead). The 

recovery or resuscitation of stressed and previously nonculturable bacteria from aquatic systems has 

been demonstrated (Barer and others, 1993; Barer and Harwood, 1999; Blatchley and others, 2007; 

Kell and others, 1998). This nonculturable physiological state in stressed bacteria (Roszak and Colwell, 

1987) has important implications when monitoring groundwater systems for the presence of bacterial 

indicators of fecal contamination. As shown in this study, for both bacterial strains, the geochemical 

conditions in the aquifer reduce the culturability of a proportion of the total bacterial populations in the 

diffusion chambers. Under treatment-plant operational conditions, the storage of recharged water in this 

region of the Upper Floridan aquifer may contribute to filtration and disinfection log reduction credits as 

defined by regulatory agencies. 

Additional inactivation of microbial indicators because of the secondary physiological stress of 

culture media and incubation conditions may reduce recovery rates even further. At low concentrations 

Table 10. The estimated storage time to achieve 1.0 CFU in the recovered water at the six sampling sites.

[f, decimel fraction of total bacterial counts]

Bacterium Media Population Time Well designation

42U 42L 15U 15M MZ1 MZ3

Escherichia coli mTEC f days 3.3 1.4 4.5 1.8 1.5 1.7

1-f months 3.1 1.5 3.7 1.9 2.4 2.1

R2A f days 5.3 5.6 4.8 5.0 1.6 3.8

1-f months 4.1 84.8 > 247.5 9.5 3.1 6.7

Pseudomonas aeruginosa PIA f days 1.2 1.4 6.7 6.1 2.5 1.3

1-f months 6.4 6.9 7.9 5.3 3.4 5.7

R2A f days 3.3 20.2 2.1 1.4 1.6 1.4

1-f months 7.2 7.3 12.1 6.1 2.9 3.7
34

of bacteria such as E. coli, these additional reductions in recovery rates may provide data below the 

regulatory maximum contaminant level, thereby promoting a false sense of confidence in the microbiological quality of the tested waters. 

Dissolved Organic Carbon Characterization

The dissolved organic carbon (DOC) concentrations in the groundwater samples were similar, 

ranging from 1.1 to 1.9 mg L–1 (table 11). These data are similar in magnitude to the DOC concentrations found in the Upper Floridan aquifer (UFA) in the Miami area (Lisle, 2005), which is also considered an isolated aquifer like that sampled during this study (table 11). The fluorescence index (FI) values 

were also similar between groundwater samples and with the UFA sample previously cited, ranging 

from 1.70 to 1.77 (table 11). When compared to FI data from the Biscayne aquifer (Lisle, 2005), which 

is a mix of terrestrial and microbially derived carbon, and Lake Okeechobee water, where the source of 

DOC is predominantly terrestrial, the DOC in the groundwater sampled during this study is classified as 

microbially derived. These data support the contention that the aquifer zones sampled during this study 

are isolated from surface-water intrusion that would carry DOC from terrestrial sources. 

Volatile fatty acids (VFA) (for example, acetate, lactate, propionate, and pyruvate) (table 2) are 

components of DOC. Most microbial cells in anaerobic and reduced aquatic ecosystems readily metabolize volatile fatty acids. The total VFA (VFAt

) concentrations in the respective groundwater samples 

ranged from 70 micrograms per liter (μg L-1) to 3.87 mg L–1 (table 2) and accounted for 7.1 to 21.5 percent of the measured DOC (table 11). The 351.8 percent value for MZ3 is due to the extremely high 

concentration of lactate (table 2). A simple explanation for such an elevated lactate concentration in one 

sample is unlikely and this could only be resolved after completing additional sampling and analyses. 

In regard to carbon concentrations in recharge water, the DOC concentration in Lake 

Okeechobee water is approximately 20 times higher than in any of the sampled groundwater (table 11). 

Any treatment process that does not remove DOC from this surface-water source, or any other source, 

prior to injection will introduce carbon substrates into these subsurface ecosystems at concentrations 

that have not been available since the initial recharge event approximately 20,000–25,000 years before 

present (Meyer, 1989; Plummer and Sprinkle, 2001). This sudden increase in carbon would initiate a 

dramatic shift of the native geochemical signature in the receiving aquifer because of biogeochemical 

responses of the native and introduced microbial communities.

Table 11. Dissolved organic carbon and carbon fluorescent index data for 

the six sampling sites.

[DOC, dissolved organic carbon; FI, flourescent index; VFA, volitile fatty acids; 

mg mL-1, milligrams per milliliter; %, percent; ND, not determined]

Well designation DOC

(mg mL-1) FI VFA

(% of DOC)

42U 1.1 1.71 8.6

42L 1.2 1.70 5.8

15U 1.7 ND 7.1

15M 1.9 ND 13.7

MZ1 1.2 1.73 21.5

MZ3 1.1 1.73 351.8

Upper Floridan Aquifer (Miami) 1.2 1.77

Biscayne Aquifer (Miami) 3.1 1.49

Lake Okeechobee 19.7 1.32
35

Bacterial Utilization and Turnover of Native Carbon

Access to, and utilization of, organic and inorganic carbon by native microbial communities that 

persist in ecosystems like this region of the Upper Floridan aquifer is required for maintaining viability. 

Because of the extended period of time since this region of the Upper Floridan aquifer has been impacted by the introduction of surface waters and photosynthetically fixed carbon, the organic carbon concentrations are very low and their source is predominantly from microbial recycling of organic carbon and 

autotrophic inorganic carbon fixation. A commonly found carbon substrate in anaerobic groundwater is 

acetate, a volatile fatty acid. Acetate was detected in all groundwater sampled during this study, making 

it well suited for assessing the total carbon utilization rates in the native microbial communities, which 

includes mineralization (production of CO2

) and assimilation (incorporation into biomass). 

Table 12 summarizes the acetate utilization rates in each of the groundwater sources sampled 

during this study. The respiration-associated rates are similar and lowest for 15M and MZ1 and moderately greater for 42U, 42L, and MZ3. The native microbial community in 15U had the highest utilization rate (5.8 nanomoles per hour (nM h–1)), being between 1.5 to 2 times greater than those for the other 

sites. The rates of incorporating acetate into the microbial biomass (range: 3.3–9.0 nM h–1) were similar 

to those for respiration at 42L and 15U, approximately 1.5 times greater at 42U and 15M and 2.5 times 

greater at MZ3. The biomass production rates being approximately equal or greater than respiration 

rates indicate the native microbial populations in these zones of the Upper Floridan aquifer are capable 

of producing low levels of new biomass (that is, cells) in the native conditions of strict anaerobiosis, 

extremely low redox potentials, and low carbon concentrations.

When taking the sum of the acetate respiration and production rates, the total acetate utilization and turnover rates can be calculated (table 12). The collective utilization rates (range: 6.9–12.2 nM 

h–1) for 42L, 15M, and MZ1 are similar and the lowest of the sampling sites, whereas the rates for 15U 

and MZ3 are highest. The acetate turnover rates, which estimate the times required to utilize all of the 

acetate in sampled groundwater assuming no replenishment of acetate during the time interval, range 

from 4.1 days (MZ3) to 12.1 days (MZ1) (table 12). 

Another method of estimating bacterial production is the use of 3H-leucine, an amino acid. The 

rates of incorporation of this amino acid into cellular proteins have been correlated with carbon content 

in bacterial cells (Kirchman, 2001). The derivation of the relationship between incorporated leucine 

rates and bacterial biomass production rates is dependent on a set of empirical factors, as described 

previously. Biomass production, in units of carbon (nanomoles carbon per hour), can then be related 

to the number of bacterial cells produced per unit time using the relationship of an average carbon 

content per bacterial cell of 20 femtograms carbon per cell (fg C cell–1) (Lee and Fuhrman, 1987). The 

3

H-leucine based biomass production rates ranged from 0.03 to 0.91 nanomoles carbon per liter per hour 

(nM C L–1 h–1), which will support the production of between 4.25×105

 to 1.31×107 cells per liter per 

day (L–1 d–1) (table 13). These cell production rates account for only 0.06 to 3.17 percent (table 13) of 

the respective native cell abundances (table 7). 

Bacterial growth efficiency (BGE) is the quantity of bacterial biomass produced per unit of substrate assimilated, which in this study was acetate. The substrate can be utilized at some ratio of energy 

production (that is, respiration) and biomass production, which varies considerably across ecosystems 

and their respective nutritional gradients. Traditionally, BGE is calculated using 3H-leucine data for productivity and 14C-labelled substrate data for respiration. 

The BGE values for the bacterial communities in this study ranged from 0.002 to 0.116 

(table 14). Published BGE values from marine and freshwater systems range from 0.02 to 0.23 for the 
36

more oligotrophic marine waters and 0.04 to 0.08 for freshwater lakes. The published BGE data provide a reference point for the data obtained in this study, but direct comparisons would be inappropriate 

because this region of the Upper Floridan aquifer is anaerobic, extremely reduced, and oligotrophic. 

Similar conditions are not represented in the published data. 

The low proportion of carbon being used for biomass production in the Upper Floridan aquifer indicates the majority of the carbon substrate is being used in respiratory functions, such as energy 

transduction, membrane energization, active transport across membranes, cellular repair, and so forth. 

These basal, or maintenance, energy requirements are critical for bacteria in all ecosystems because the 

cells die once these energetic requirements are not satisfied. Because maintenance energy requirements 

do not change with growth rates or nutritional conditions, these energy requirements (that is, carbon 

demand) account for a significant proportion of the available energy in oligotrophic and anaerobic systems like this region of the Upper Floridan aquifer. Accordingly, growth rates (that is, biomass production) would be expected to be significantly reduced (Russell and Cook, 1995). The concept of lower 

BGE values indicating lower biomass production rates is supported by the calculated BP rates in this 

study, which represent only 0.06 to 3.17 percent of the native bacterial concentrations of the respective 

groundwater sources (table 13). 

Collectively, the organic carbon cycling data show the native microbial communities are viable 

and active but have very slow rates of metabolism. The majority of the utilized organic carbon is used 

to maintain vital cellular functions, with a minor proportion diverted to producing new biomass. The 

organic carbon turnover rates, however, present a quandary in that the rates are on the order of days to 

Table 12. 14C-acetate turnover and utilization rate data for the native bacterial community at the six sampling sites.

[CO2, carbon dioxide; Vn, acetate utilization rate; Tn, acetate turnover rate; nM h-1, nanomoles per hour; d, days]

Table 13. 3H-leucine-based bacterial production rates at the six sampling sites.

[nM C L-1 h-1, nanomoles of carbon per liter per hour; cells L-1 d-1, cells per liter per day; %, percent]

Well designation Biomass production

(nM C L-1 h-1)

Bacterial cell production

(cells L-1 d-1)

Percent of native biomass

(%)

42U 0.19 2.69×106 0.65

42L 0.09 1.24×106 3.17

15U 0.03 4.25×105 0.06

15M 0.06 8.30×105 0.11

MZ1 0.91 1.31×107 1.64

MZ3 0.1 1.50×106 0.22

Well designation

Respiration (CO2

) Production (biomass) Carbon demand (respiration + production)

Acetate utilization rate

(vn

)

(nM h-1)

Acetate utilization rate

(vn

)

(nM h-1)

Acetate utilization rate

(vn

)

(nM h-1)

Acetate turnover rate

(Tn

)

(d)

42U 3.6 5.1 8.7 7.7

42L 3.7 3.3 7.0 7.1

15U 5.8 6.2 12.0 7.0

15M 2.7 4.4 7.1 6.9

MZ1 2.1 4.8 6.9 12.1

MZ3 3.2 9.0 12.2 4.1
37

weeks and additional carbon has not been transported from a source outside the Upper Floridan aquifer 

for over 20,000 years (Meyer, 1989; Plummer and Sprinkle, 2001). Possible explanations as to where 

the organic carbon originates include the following: (1) the non-acetate proportion of the DOC could be 

leaching from the geologic matrix of the karst aquifer; (2) lithoautotrophic bacteria are fixing inorganic 

carbon (for example, HCO3

–

, CO2) into cellular biomass; and (3) upon bacterial lysis (for example, bacteriophage infection, predatory bacteria) the cellular debris becomes DOC (Mathias and others, 2003; 

Middelboe and Jørgensen, 2006). This cycling of organic and inorganic carbon into and from bacterial 

biomass has been shown to be a central process in carbon cycling in surface-water ecosystems and is 

commonly referred to as the microbial loop (Jiao and others, 2011). 

Microbial Energetics and Sustainability in the Upper Floridan Aquifer

The previous section describes viable and physiologically active native microbial communities 

in all of the groundwater sampling sites. As shown by the BGE values (table 14), the majority of the 

carbon being metabolized by these communities is used in the production of energy for cellular processes. These dissimilatory reactions rely on the presence of a variety of extracellular electron donors 

and acceptors to generate this energy. As these reactions proceed and organic and inorganic substrates 

and terminal electron acceptors are respectively oxidized and reduced, the products of these reactions 

alter groundwater chemistry. The applicability of, for example, reactive transport models for predicting changes in groundwater geochemistry would therefore improve with the inclusion of variables that 

constrain these biogeochemical reactions and their rates.

The most direct method for characterizing biogeochemical reactions that alter groundwater 

chemistry is to sample the native microbial communities and determine the types and rates of reactions 

that members of these communities perform under in situ conditions. Unfortunately, there are several 

obstacles that make this approach impractical. Having culturable representative isolates of each microbial group that performs a specific biogeochemical reaction would be ideal. However, the low culturability rates of native groundwater microorganisms make this practically impossible. For example, an 

average 13 CFU mL–1 of the native microbial community were recovered from the six groundwater 

sites during this study using R2A agar (data not shown). This average value represents only 0.002 to 

0.03 percent of the total cells counted in each groundwater source (table 7). These percent recoveries 

can be increased by using recovery media and incubation conditions specifically formulated and maintained to enhance the likelihood of recovery, but expected percentage recoveries would not be predicted 

Table 14. Bacterial growth efficiency data for the six sampling sites.

[BP, bacterial production; BR, bacterial respiration; BGE, bacterial growth 

efficiency; 3H-leucine, tritium labled leucine; 14C-leucine, carbon-14 labeled leucine; 

ng C L-1 h-1, nanograms of carbon per liter per hour]

Well designation

BP 3

H-leucine

ng C L-1 h-1

BR 14C-acetate

ng C L-1 h-1

BGE

42U 1.35 86.8 0.015

42L 0.62 88.0 0.007

15U 0.21 139.6 0.002

15M 0.42 63.6 0.007

MZ1 6.59 50.4 0.116

MZ3 0.75 77.2 0.010
38

to be much greater than 1.0 percent of the total population (Amann and others, 1995). Another obstacle 

related to culturability is the difficulty of co-culturing two or more species that must be present for the 

biogeochemical reactions of interest to proceed (for example, syntrophic metabolism). In addition, the 

slow growth and metabolic rates (tables 12–14) in anaerobic and reduced groundwater systems like this 

region of the Upper Floridan aquifer would require experiments to proceed for weeks-to-decades. 

Free Energy Yields for Energetically Favorable Biogeochemical Reactions

One approach to circumvent the issues just described is to determine which biogeochemical 

reactions are most likely to proceed using thermodynamically based energetic analyses (that is, Gibbs 

free energy of reaction yields) and the geochemistry of the respective groundwaters. Regardless of the 

growth conditions (that is, high nutrients and energy in laboratory cultures versus low nutrients and extremely low energy in the Upper Floridan aquifer), the same thermodynamic principles apply. Therefore, 

free energy yields of microbially driven redox reactions can be applied to constrain the list of most probable biogeochemical reactions. This approach is especially applicable to low energy ecosystems like this 

region of the Upper Floridan aquifer, where microbial life persist close to thermodynamic equilibria. 

Amend and Shock (2001) describe free energy yields for 370 reactions that have been shown to 

be directly or indirectly involved in microbial metabolism. Approximately 200 of these reactions were 

redox reactions known to be mediated by microorganisms for the acquisition of energy for cellular metabolism. The geochemical data from this study (table 2) were used to determine which subsets of these 

biogeochemical reactions (table 3) were applicable to the groundwater in this study. A subset of five 

reactions were determined to be thermodynamically feasible in this region of the Upper Floridan aquifer, using –20 kJ mol–1 of the limiting substrate as the minimum free energy of reaction yield required 

to maintain cell viability. Detailed geochemical modeling was not part of this project; therefore, mineral 

phases are not included in the geochemical dataset. The omission of mineral phase data and incomplete 

sampling of gases (for example, N2

) prevented the use of the majority of the biogeochemical reactions 

that rely on these data as reactants and (or) products. 

Of the five favorable reactions, three reactions (reactions 1, 2, 4, and 12) were favorable in all 

samples, whereas reaction 3 was favorable only in the 42U and MZ1 groundwater (tables 15–17). Two 

of these reactions (3 and 4) are methanogenic, whereas the remaining two reactions (1 and 2) represent 

sulfate reductions that produce sulfides as hydrogen and acetate are oxidized, respectively. As shown 

in table 3, both types of reactions are dependent upon hydrogen and acetate as reactants and therefore 

compete for common reactants. The more interesting set of reactions is hydrogenotrophic sulfate reduction (reaction 1) and methanogenesis (reaction 3). It is commonly assumed that hydrogenotrophic and 

acetotrophic sulfate reduction and methanogenesis are mutually exclusive, especially in systems where 

sulfate is not a limiting reactant as in the Upper Floridan aquifer. This is due to sulfate reducers having 

a higher affinity for hydrogen (Kristjansson and others, 1982; Lovley and others, 1982; Robinson and 

Tiedje, 1984) and acetate (Ferry and Lessner, 2008; Schonheit and others, 1982) than methanogens, 

thereby maintaining hydrogen and acetate concentrations at or below a minimum threshold for methanogenesis in the presence of excess sulfate. 

Although methane was detected in each groundwater sample collected for this study. This apparent contradiction may be resolved if alternative substrates for methanogenesis are present in the aquifer. Recently, low-carbon-number methylated compounds (for example, methanol, methylated amines, 

and methylated sulfides) have been shown to be alternative reactants for methanogens while being 

unutilized by sulfate reducers (Liu and Whitman, 2008). The generation of methylated sulfides occur 

through chemical reactions between sulfides and the methyl groups within the organic matter or DOC 
39

(Mitterer, 2010). In regard to sources of methylamines, Mitterer and others (2001) identified the matrix 

of carbonate sediments as the source of methylamines because of the inclusion of biogenic proteins during formation. The region of the Upper Floridan aquifer sampled during this study is karst and contains 

sulfides and low concentrations of organic matter. Collectively, the conditions are present in this region 

of the aquifer for the noncompetitive co-occurrence of sulfate reduction and methanogenesis, and both 

types of reactions are energetically favorable, ranging from 52 to 64 kJ mol–1 and 30 to 42 kJ mol–1 for 

the sulfate reduction and methanogenic reactions, respectively (tables 15–17). 

Reaction 12 describes the anaerobic oxidation of methane (AOM), for which sulfate is the terminal electron acceptor (Knittel and Boetius, 2009). AOM is driven by cooperative or syntrophic consortia 

of methanotrophic archaea and sulfate-reducing bacteria. This reaction currently is poorly characterized 

from mechanistic and phylogenetic perspectives (Caldwell and others, 2008; Thauer and Shima, 2008). 

The efficiency of the overall process has been measured in marine sediments, however, where it was estimated to oxidize close to 90 percent of the biogenic methane. In regard to the cycling of carbon in anaerobic, reduced, and isolated regional aquifers like this region of the Upper Floridan aquifer this is an 

important process even though the free energy yields are equal to, or just above, the minimum threshold 

of 20 kJ mol–1. AOM returns the reduced carbon back to a more oxidized state (that is, HCO2

–

), which 

can then be recycled as a reactant for methanogenesis (reaction 3), acetogenesis (reaction 6), and other 

reactions listed in table 3. 

Chemical Affinities for Energetically Favorable Biogeochemical Reactions

The direct comparison of the most likely biogeochemical reactions within and between groundwater samples is somewhat simplified by using the respective chemical affinities as opposed to free 

energy yields (that is, ΔGr). Using chemical affinities allows the ranking of biogeochemical reactions, 

where the values of the most likely reactions are positive and thermodynamic equilibrium is realized at 

a value of 0.0 (table 18). Figure 14 shows the rankings of the biogeochemical reactions from lowest-tohighest chemical affinity for each groundwater sample. The most common reactions (reactions 1–4, 12) 

are similar in all samples for the reasons discussed previously, with reaction 1 (sulfate reduction) having the greatest range of values. This alignment of sample sites based on energy yields from sulfate 

reduction (reaction 1), from least-to-greatest chemical affinity, follows the increasing concentrations of 

hydrogen (table 2). Hydrogen concentrations in the subsurface have been shown to have a central role in 

determining which biogeochemical processes dominate in anaerobic ecosystems, like this region of the 

Upper Floridan aquifer, that are controlled by bacterial energetics (that is, thermodynamics) (Hoehler 

and others, 1998; Hoehler and Jorgensen, 2013).

As an example of the impact that treated surface water injected into the Upper Floridan aquifer 

can have on native microbial community energetics, a representative geochemical dataset from recharge 

water from the Hillsboro pilot plant (table 2) was analyzed as the other groundwater samples for free 

energy yields and chemical affinities (tables 18 and 19). The most significant differences between the 

native and recharge water are the presence of dissolved oxygen, dissolved organic carbon, nitrate, and 

phosphate in the recharge water. The presence of dissolved oxygen and nitrates provide concentrations 

of high energy reactants for biogeochemical reactions performed by native bacteria. As can be seen in 

table 18, not only are additional biogeochemical reactions energetically favorable (reactions 10, 17, 

19, 20, 25–27), their free energy yields are orders of magnitude greater than any of the favorable reactions in the native groundwater zones. The exclusion of reactions 1–4 and 12 from the list of favorable 

reactions in the recharge water, which were dominant in the native groundwater samples, reflects the 

general difference between the water types and the respective ecosystems: aerobic and oxidized versus 
40

Table 17. Free energy flux and maximum acquition rate data for the MZ1 and MZ3 sampling sites.

[ΔGr, free energy of the reaction; FEF, free energy flux; MAR, maximum aquitition rate; kJ mol-1, kilojoules per mole; kJ cell-1 s-1, kilojoules per cell per 

second; μM d-1, micromoles per day]

Table 15. Free energy flux and maximum acquition rate data for the 42U and 42L sampling sites.

[ΔGr, free energy of the reaction; FEF, free energy flux; MAR, maximum aquitition rate; kJ mol-1, kilojoules per mole; kJ cell-1 s-1, kilojoules per cell per 

second; μM d-1, micromoles per day]

Table 16. Free energy flux and maximum acquition rate data for the 15U and 15M sampling sites.

[ΔGr, free energy of the reaction; FEF, free energy flux; MAR, maximum aquitition rate; kJ mol-1, kilojoules per mole; kJ cell-1 s-1, kilojoules per cell per 

second; μM d-1, micromoles per day]

Equation 

number Equation

42U 42L

ΔGr

(kJ mol-1)

FEF

(kJ cell-1 s-1)

MAR

(μM d-1)

ΔGr

(kJ mol-1)

FEF

(kJ cell-1 s-1)

MAR

(μM d-1)

1 4H2 + H+ + SO4

2- → HS-

 + 4H2O -63 -8.3 4.5 -39 -3.5×10-18 3.2

2 CH3

COO- + SO4

2- → 2HCO3-

 + HS- -52 -2.7 20.0 -53 -1.8×10-16 146.9

3 4H2

 + H+ + HCO3

-

 → CH4 + 3H2O -42 -5.7 4.5

4 CH3

COO- + H2O → CH4 + HCO3

- -31 -1.6 20.0 -32 -1.0×10-16 146.9

12 CH4

 + SO4

2- → H2

O + HCO2

-

 + HS- -21 -6.6 11.2 -22 -5.5×10-17 86.4

Equation 

number Equation

15U 15M

ΔGr

(kJ mol-1)

FEF 

(kJ cell-1 s-1)

MAR

(μM d-1)

ΔGr

(kJ mol-1)

FEF 

(kJ cell-1 s-1)

MAR

(μM d-1)

1 4H2 + H+ + SO4

2- → HS-

 + 4H2O -60 -5.2×10-17 54.4 -53 -2.6×10-17 31.1

2 CH3

COO- + SO4

2- → 2HCO3-

 + HS- -50 -2.9×10-16 440.6 -48 -1.6×10-16 216.0

3 4H2

 + H+ + HCO3

-

 → CH4 + 3H2O -40 -3.5×10-17 54.4 -34 -1.7×10-17 31.1

4 CH3

COO- + H2O → CH4 + HCO3

- -30 -1.7×10-16 440.6 -28 -9.7×10-17 216.0

12 CH4

 + SO4

2- → H2

O + HCO2

-

 + HS- -20 -9.3×10-17 293.8 -20 -9.5×10-17 311.0

Equation 

number Equation

MZ1 MZ3

ΔGr

(kJ mol-1)

FEF

(kJ cell-1 s-1)

MAR

(μM d-1)

ΔGr

(kJ mol-1)

FEF 

(kJ cell-1 s-1)

MAR

(μM d-1)

1 4H2 + H+ + SO4

2- → HS-

 + 4H2O -64 -1.1×10-16 121.0 -26 -4.4×10-19 1.1

2 CH3

COO- + SO4

2- → 2HCO3-

 + HS- -54 -3.3×10-16 501.1 -56 -1.4×10-16 216.0

3 4H2

 + H+ + HCO3

-

 → CH4 + 3H2O -40 -6.7×10-17 121.0

4 CH3

COO- + H2O → CH4 + HCO3

- -30 -1.8×10-16 501.1 -30 -7.8×10-17 216.0

12 CH4

 + SO4

2- → H2

O + HCO2

-

 + HS- -24 -2.3×10-16 648.0 -26 -1.2×10-16 259.2

anaerobic and reduced. When comparing the geochemical data from the groundwater samples to those 

from the recharge water (table 2), however, the absence of data for the dissolved gases, volatile fatty 

acids and inorganic carbon species in the recharge water prohibits the calculation of free energy yields 

and chemical affinities for most of the reactions in table 3. 

Adding the chemical affinity data for the favorable biogeochemical reactions in the recharge 

water to those from the native groundwater samples, it is apparent the recharge water has a unique and 

high energy profile relative to the groundwater samples (table 18). This increase in bioavailable energy 
41

plus the organic carbon (table 2) greatly changes the overall energy landscape in the recharge zone of 

the aquifer and the rates of biogeochemical reactions that alter geochemistry. The products of the aerobic mineralization of the carbon and nutrients in the recharge water produce preferred reactants for the 

anaerobic native microbial community that, in addition to energy production, promote biomass production with the concomitant reduction in the ORP to native levels. 

Although the recharge water may meet regulatory criteria prior to injection and after recovery, 

those criteria do not provide data about the biogeochemical processes that alter the geochemistry of 

the native and recharged water during storage. However, the thermodynamics of biogeochemical reactions does provide a tool for developing predictive models of changes in groundwater chemistry that are 

driven by microbial activities. 

Free Energy Flux Rates for Energetically Favorable Biogeochemical Reactions

The previous section described the most likely or favorable biogeochemical reactions based 

strictly on their respective energy yields within the context of equaling or exceeding a minimum threshold for bacterial viability. This approach provides a framework for developing a first-order assessment 

of which reactants and products present in the native or recharged water may be utilized by the microbial communities, but it does not provide any insight into the rates at which these favorable reactions 

proceed. The free energy flux (FEF) and maximum acquisition (MAR) rates for bacterial cells constrain 

the energy production and reactant acquisition rates for the respective biogeochemical reactions based 

on the diffusion coefficient or rate (centimeters squared per second) of the limiting substrates in those 

reactions. These data can then be used to estimate energy acquisition, reactant utilization, and product 

production rates for biogeochemical reactions.

The direct relationship between diffusion and FEF and MAR rates in bacteria is due to molecular diffusion being the dominant transport mechanism for solutes and gases at distances relevant 

to microbial physiology (Koch, 1990). Using oxygen as an example, which is commonly reported to 

Table 18. Chemical affinities for the six sampling sites and recharge water.

[kJ mol-1, kilojoules per mole]

Equation 

number Equation

Well designation

42U 

(kJ mol-1)

42L

(kJ mol-1)

MZ1

(kJ mol-1)

MZ3

(kJ mol-1)

15U

(kJ mol-1)

15M

(kJ mol-1)

Recharge 

water

(kJ mol-1)

1 4H2 + H+ + SO4

2- → HS-

 + 4H2O 47.8 24.8 49.4 11.7 45.2 38.5

2 CH3

COO- + SO4

2- → 2HCO3-

 + HS- 34.8 36.2 37.1 38.7 32.9 30.5

3 4H2

 + H+ + HCO3

-

 → CH4 + 3H2O 27.4 25.0 25.1 19.3

4 CH3

COO- + H2O → CH4 + HCO3

- 14.1 14.2 12.8 12.8 12.8 11.2

10 HS-

 + NO3

-

 + H2O → SO4

2- + NH3 406.1

12 CH4

 + SO4

2- → H2

O + HCO2

-

 + HS- 4.1 4.9 7.5 8.9 3.0 3.0

17 H2

S + 4NO3

-

 → SO4

2- + 4NO2-

 + 2H+ 470.3

19 3H2

S + 4NO2

-

 + 2H+ + 4H2O → 3SO4

2- + 4NH4+ 1239.7

20 3H2

 + NO2

-

 + 2H+ → NH4

+

 + 2H2O 350.0

25 NH3

 + 1.5O2 → NO2

-

 + H+ + H2O 287.9

26 2NO2

-

 + O2 → 2NO3

- 142.3

27 HS-

 + 2O2 + H2O → SO4

2- + H+ 790.8
42

diffuse at 1.0 millimeters per hour (mm h–1), it would require approximately 1 day to diffuse 2.0 cm 

and 1,000 years to reach 10 meters (m) from its source. At the micrometer scale, however, which is 

relevant for bacterial processes at the intra- and extracellular levels, this same source of oxygen requires 

only 10–3 seconds to diffuse 1.0 μm. Gases and dissolved organic and inorganic substrates that serve as 

reactants in this study are predominantly transported across bacterial membranes by diffusion. Assuming 

that (1) each of the limiting reactants in the energetically favorable biogeochemical reactions are dissolved and present at the cell surfaces at the detected concentrations in the groundwater samples, and 

that (2) diffusion is the mechanism by which the reactants are transported into the cell, the overall FEF 

and MAR rates are controlled by the respective diffusion rates. 

Figure 14. Graph showing chemical affinities of the thermodynamically favorable 

biogeochemical reactions [kJ mol-1, kilojoules per mole].

0 10 20 30 40 50 60

Reaction number

12

4

3

2

1

42U 

42L 

15U 

15M 

MZ1 

MZ3 

Chemical affinity (kJ mol–1)

Sampling Site
43

The FEF rates for the energetically favorable biogeochemical reactions in the groundwater 

samples from this study are extremely low, ranging from –4.4×10–19 to –3.3×10–16 kilojoules per second per cell (kJ s–1 cell–1)(tables 15–17). This range of FEF rates are similar to those in deep subsurface 

ecosystems that are strictly chemolithotrophic and supported by hydrogeothermal processes (Lin and 

others, 2006). For the FEF rates to be relevant to the survival of the bacterial cell, the acquired energy 

must at least satisfy the basal or minimal energy requirements for performing bacterial cell maintenance 

and repair processes. An average maintenance energy per cell rate of –1.55×10–19 kilojoules per cell per 

second (kJ cell–1 s–1) has been estimated from chemostat studies of mesophilic nitrifying and methanogenic bacteria (Onstott, 2005). All of the FEF values for the energetically favorable biogeochemical 

reactions in this study exceed this maintenance energy threshold by approximately threefold to several 

orders of magnitude. 

The FEF data are estimates of rates for energy acquisition per bacterial cell. The MAR data 

provide estimates of the acquisition rates of the limiting reactant per bacterial abundance and, therefore, 

can also provide an estimate of product production rates from stoichiometrically balanced reactions. The 

MAR data for all of the energetically favorable reactions in this study range from 1.1 to 648 micromoles 

per day (μM d–1) for the respective limiting reactants (tables 15–17). As an example of using MAR data 

for determining reaction rates, acetate in reaction 2 for 42U is the limiting reactant with an MAR of 

20.0 μM d–1; therefore, a production rate of 40.0 μM day–1 for HCO3

–

 and 20.0 μM d–1 for HS– would be 

predicted. 

The relationships between free energy yields, FEF, and MAR for the groundwater zones are 

shown in figure 15. The horizontal axis indicates the free energy yield, from the least-to-greatest absolute 

values, for all of the favorable biogeochemical reactions. Anaerobic methane oxidation (reaction 12) and 

acetotrophic methanogenesis (reaction 4) group reasonably well at the lower free energy yields, whereas 

the other types of sulfate reduction (reactions 1 and 2) and methanogenesis (reaction 3) are more evenly 

distributed along the remaining range of free energy yield data. Although the there is a continual increase 

in the free energy yield data, the rates at which the bacterial cells generate energy for cellular processes 

(that is, FEF) follow an undulating pattern (fig. 15). The variability in the FEF data is due to the differences in concentrations of the limiting reactants, diffusion rates of those reactants, and the calculated free 

energy yields at the respective sampling sites. The differences between the free energy yield and FEF data 

indicate biogeochemical processes are most likely under the control of physical (for example, utilization 

rates of biogeochemical reactants and products) rather than strictly thermodynamic processes. 

Table 19. Free energy flux and maximum acquition rate data for the recharge water.

[ΔGr, free energy of the reaction; kJ mol-1, kilojoules per mole]

Equation 

number Equation

Recharge water

ΔGr

(kJ mol-1)

10 HS- + NO3

-

 + H2O → SO4

2- + NH3 -423

17 H2

S + 4NO3

-

 → SO4

2- + 4NO2-

 + 2H+ -488

19 3H2

S + 4NO2

-

 + 2H+ + 4H2O → 3SO4

2- + 4NH4+ -1257

25 NH3

 + 1.5O2 → NO2

-

 + H+ + H2O -305

26 2NO2

-

 + O2 → 2NO3

- -159

27 HS-

 + 2O2 + H2O → SO4

2- + H+ -808
44

The MAR data, which are energy acquisition per cell data (kJ s–1 cell–1) normalized to the total 

cell abundances in the respective groundwater samples, follow a trend similar to that of the FEF data 

(fig. 16). MAR data can be used to constrain the rate at which the limiting substrate in a biogeochemical 

reaction is utilized by the bacterial community in a volume of groundwater. To place the MAR data in 

a more relevant context to native conditions in this region of the Upper Floridan aquifer, the combined 

(that is, respiration+biomass production) 14C-acetate utilization rate data (table 12) can be compared to 

the MAR data (table 15–17). The calculated MAR rates are between two and four orders of magnitude 

greater than those measured using the 14C-acetate method (table 20). This significant discrepancy between methods for quantifying the utilization rates for acetate, and therefore product production rates, 

under native conditions can be partially resolved by acknowledging that 100 percent of the enumerated 

bacterial cells (table 7) are not acetotrophic sulfate reducers or methanogens. To estimate the number of 

bacterial cells in each of the groundwater samples that were potentially acetotrophic sulfate reducers and 

methanogens (Bacetotrophic), the following relationship was used:

Bacetotrophic = (TDC)( 14C − acetate)

MAR , 15

where data for TDC (cells per liter) and 14C-acetate (moles per day) and MAR (moles per day) were 

taken from tables 7, 12, and 15–17, respectively. Based on this relationship, between 1.33×105 and 

4.59×105 cells L–1 (or <0.5 percent of the total bacterial cell abundances in table 7) would be required to 

be active acetotrophic sulfate reducers and methanogens in the native groundwater samples for the 

calculated MAR data to model the measured 14C-acetate uptake rates. 

Although acetate was detected in all of the groundwater samples and this carbon source was 

used in the example above for estimating reactant removal and product production for a specific set of 

biogeochemical reactions, the native source of the acetate could not be determined. There are several 

acetotrophic reactions that would be expected to occur in this type of ecosystem, but none of these reactions were energetically favorable and, therefore, were not included in the data analyses. The inability 

to identify a thermodynamically favorable acetogenic reaction is most likely the result of an inadequate 

geochemistry dataset for the respective groundwater samples. The collection of a more extensive set 

of geochemical variables (for example, gases, carbon substrates, sulfur-based compounds, and mineral 

phases) that includes reactants and products for a wider selection of biogeochemical reactions, such as 

acetogenesis, would be very beneficial. 

Another example to support the collection of a more extensive geochemical data set for native, 

recharged, and recovered waters is that in anaerobic, extremely reduced, and chemolithotrophic systems 

like this region of the Upper Floridan aquifer, fermentation reactions are very important. Fermentation 

of various substrates is carried out by a wide variety of bacterial groups and is most probably the driver 

of carbon and nutrient cycling in this type of ecosystem. Figure 16 shows how fermentation supplies 

the reactants for chemoautotrophic and chemoheterotrophic reactions (Megonigal and others, 2003). 

The source of the reactants and products for these reactions are derived from the initial degradation of 

polymers of biological origin to monomers, which are then fermented, producing the reactants for the 

biogeochemical reactions like those described in this study. 

The source of the polymers that the exoenzymes degrade to monomers is also unknown for this 

region of the Upper Floridan aquifer. The source must be autochthonous because this region of the aquifer has been isolated from terrestrial input of water containing these types of substrates for thousands 

of years. A possible source could be leachate from the karst matrix, as described for the noncompetitive 

substrates for methanogens. Another source could be the native bacterial biomass, which is lyzed by 
45

bacteriophage, releasing complex polymers as dissolved organic carbon that can be readily assimilated 

by the remaining bacterial cells (Anderson and others, 2013; Noble and Fuhrman, 1999). Either or 

both of these sources would provide the substrates for fermentation that subsequently produces acetate 

directly or the reactants for acetogenesis. This simple carbon and energetic cycling model describes a 

closed system that exists at the boundary of thermodynamic equilibrium but could be sustainable on 

geologic time scales. 

Although this region of the Upper Floridan aquifer is an extreme ecosystem and isolated from 

terrestrial carbon and nutrient inputs, the native bacteria have sources of energy that support their survival and persistence. The use of thermodynamics to describe and quantify the energetics of this ecosystem provide a framework to constrain the effects that microbial activities have on the geochemical 

quality of the native groundwater, the recharged and mixed water, and the rates at which those changes 

proceed. The inclusion of physical principles that directly influence these activities (that is, diffusion 

rates of limiting reactants) allows biogeochemical data like MAR to be used in the modeling of changes 

to groundwater chemistry over time and distance. Using biogeochemical data derived from the same 

groundwater system being modeled is a preferable alternative to the common practice of using laboratory-generated data that may not be truly relevant to the native groundwater conditions being modeled. 

Figure 15. Comparisons between the free energy of reaction (ΔGr), free energy flux (FEF), and maximum acquisition rate (MAR) data from 

the six sampling sites [kJ mol-1, kilojoules per mole; kJ d-1 cell-1, kilojoules per day per cell; mM d-1, millimoles per day].

Reaction Number

12 (15U)

12 (15M)

12 (42U)

12 (42L)

12 (MZ1)

1 (MZ3)

12 (MZ3)

4 (15M)

4 (MZ3)

4 (15U)

4 (MZ1)

4 (42U)

4 (42L)

3 (15M)

1 (42L)

3 (15U)

3 (MZ1)

3 (42U)

2 (15M)

2 (15U)

2 (42U)

1 (15M)

2 (42L)

2 (MZ1)

2 (MZ3)

1 (15U)

1 (42U)

1 (MZ1)

0

10

20

30

40

50

60

70

0. 0

5.0e-12

1.0e-11

1.5e-11

2.0e-11

2.5e-11

3.0e-11

0

100

200

300

400

500

600

700

FEF

MAR 

FEF (kJ d–1 cell –1

)

ΔGr

Δ

Gr (kJ mol –1

)

MAR (mM d–1

)
46

Functional Bacterial Diversity and the Relationship to Geochemistry in the Upper Floridan Aquifer 

Using thermodynamics to constrain which biogeochemical reactions, and their respective rates, 

are most likely to occur in the native waters of the Upper Floridan aquifer and recharge water provides 

a framework that can be used to supplement groundwater chemistry and reactive transport models. This 

approach can be applied to native groundwater prior to recharge events, recharged water prior to and 

during storage, and both types of water following extraction. One of the limitations to this approach is 

the lack of a comprehensive geochemical dataset from which the energetic calculations are performed. 

As previously discussed, a proportion of the complete list of biogeochemical reactions that may have 

been likely to proceed under the conditions of this region of the Upper Floridan aquifer could not be 

included in the energetics analyses because of the absence of concentration data for specific reactants 

and (or) products. 

Figure 16. Biogeochemical processes that most likely dominate in the south region of the Upper 

Floridan aquifer.

Acetogenesis

Secondary fermentation

acetogenesis

Other organic acids & alcohols

Polymers

(e.g., polysaccharides, proteins, bacterial cell lysate)

Monomers

(e.g., monosaccharides, amino acids)

Acetate

Primary fermentation

Methanogenesis

Anaerobic oxidation of methane

CH4 CH4

 + CO2

SO4

2 – SO42 –

HCO3

–

 + HS –

CO2 + H2

Exo-enzymes 
47

Another approach to characterizing the native and altered geochemistry in aquifers is to characterize the native microbial diversities and changes in those diversities, respectively. Although this phylogenetic approach will not contribute to the microbial energetics and reaction rates data, knowing which 

types of bacteria inhabit the aquifers can provide insight into the likelihood of specific biogeochemical 

processes being present in the absence of geochemical data. These types of data provide insight into the 

physiological capacity of microbial communities to perform these biogeochemical processes.

Some biogeochemical processes are performed by a relatively limited number of bacterial species, which if detected, allow the prediction of those reactions being present in the sampled ecosystem. 

Examples of these types of reactions that could proceed in anaerobic conditions like those in this region 

of the Upper Floridan aquifer include, but are not limited to, sulfate reduction, methanogenesis, anaerobic methane oxidation (AOM), acetogenesis, anaerobic ammonium oxidation (ANAMMOX) and other 

pathways in the nitrogen cycle (that is, denitrification and ammonification). 

The PhyloChip G3 microarray platform was used to identify the bacterial inhabitants of this 

region of the Upper Floridan aquifer that have been shown to perform one or more of the previously described biogeochemical processes. This technology uses a proprietary DNA sequence binding and detection approach to identify approximately 60,000 unique operational taxonomic units (OTU), representing 

approximately 840 subfamilies of bacteria within the Eubacteria and Archaea kingdoms. Approximately 

76 percent of the OTUs from the native bacteria in this region of the Upper Floridan aquifer were 

categorized as “unclassified,” however, meaning the sequence could not be definitively classified to the 

family, genus, or species level.

A total of 3,634 unique OTUs were detected in the groundwater samples from the six well sites 

(table 21), representing approximately 6.1 percent of the total OTUs on this version of the PhyloChip 

microarray. The bacterial diversity, used here and henceforth to collectively refer to eubacterial and 

archaeal OTUs, was similar between the two sampled depths for the 42 and 15 wells but significantly 

different at the MZ well site, with MZ3 having a more diverse community structure than that in MZ1 

(table 21). The MZ3 zone of the Upper Floridan aquifer has been shown to be the more different 

groundwater source of the six that were sampled, based on previously discussed field, nutrient, geochemical, and energetics data. 

The number of OTUs for the second sampling event increased significantly relative to the first 

sampling event, except for MZ3, which decreased (table 21). For all the groundwater samples, the number of OTUs that were common in the two samples was less than the number unique to the individual 

samples. For example, the 42U samples had a total of 647 OTUs, of which 86 were unique to the first 

sample,424 were unique to the second sample, and 132 were found in both samples; 2,987 OTUs were 

Table 20. Comparisons between the measured and predicted acetate turnover rates in the six sampling sites.

[14C-acetate, carbon-14 labeled acetate; MAR, maximum acquistion rate; mol d-1, moles per day]

Well designation Combined 14C-acetate utilization rate

(mol d-1)

MAR

(mol d-1) MAR: 14C-acetate ratio

42U 2.08x10-7 4.00x10-5 192.3

42L 1.68x10-7 2.94x10-4 1750.0

15U 2.89x10-7 8.81x10-4 3048.4

15M 1.70x10-7 4.32x10-4 2541.2

MZ1 1.66x10-7 1.00x10-3 6024.1

MZ3 2.92x10-7 4.32x10-4 1479.5
48

not detected in 42U but were detected in one or more of the other groundwater samples. These same 

relationships are similar for the other five sites as well. The Sorensen Index values in table 21 provide a 

direct method for comparing the similarities in bacterial diversity between groundwater sampling sites. 

The proximity of index values are positively related to the similarity of their diversities. Results of the 

adonis test for statistically significant differences between pairwise comparisons of the OTU presence/

absence data indicated a significant difference in diversity between the six groundwater collection zones. 

Using OTUs to characterize bacterial diversities provides an overall perspective of how many 

unique bacterial species are present; however, knowing which bacteria are represented by those OTUs 

allows a more detailed assessment of not only the overall diversity of the respective microbial communities but also the possible physiological capabilities. The PhyloChip data also include a single, most 

likely bacterial “species” associated with each OTU. “Species” is used as a general term to describe 

the closest (that is, 90-percent similar) identified bacterial DNA sequence in the National Center for 

Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov). These OTU and NCBI 

sequence relationships can range from the family-to-species level of specificity. Figure 17 shows the 

OTUs from each groundwater sample organized per their proportion of the total OTUs per sample using 

family classifications. All of the groups in figure 17 are phylogenetic families except for Thermoprotei 

(class designation) and SAGMEG (South Africa Gold Mine Euarchaeotic Group) which is a nonphylogenetic grouping. Family-level classifications are used because using OTU designations from higher 

levels of phylogenetic classification (that is, genus and species) would significantly increase the number 

of graphed groups per sample, making figure 17 uninterpretable, whereas lower-level classifications do 

not provide adequate resolution between phylogenetic classes. 

The second samples from each sampling site (denoted by “(2)”) all show a significant increase 

in the proportion of Pseudomonadaceae (Garrity and others, 2005) (fig. 17). The other eight OTU classifications show a general decrease in their proportional contribution to each sample’s diversity for the 

second sample. Table 22 lists the proportion that each of the nine families contributed to the groundwater samples, as shown in figure 17. The proportions of Pseudomonadaceae increased by between approximately 14 percent (42U) and 94 percent (MZ1) in the second samples. Table 22 also shows the proportion of the other families decreased between 1 and 20 percent for all sampling sites between the first and 

second samples, with the proportions of Peptostreptococcaceae (Schleifer, 2009) and Lachnospiraceae/

Runinococcaceae (Schleifer, 2009) in MZ1 decreasing approximately 30 percent. Unlike the other 

sampling sites, the proportions of OTUs in 42U representing six of the nine families increased, although 

marginally. The Micrococcinea (Goodfellow, 2012) in 42U were not detected in the first sample but 

Table 21. Bacterial diversity in the six sampling sites and between sampling events.

[OTUs, operational taxonomic units]

Well designation

OTUs

Unique to Sorensen Index

Sample 1

Unique to 

Sample 2

 Both 

Samples

Neither 

Sample

42U 86 429 132 2987 0.51

42L 86 1047 118 2383 0.21

15U 232 837 242 2323 0.45

15M 215 1252 111 2056 0.15

MZ1 478 1001 75 2080 0.10

MZ3 607 187 309 2531 0.78
49

became a minor contributor to the community diversity in the second sample, whereas this family was 

never detected in 42L. Members of the Prevotellaceae (Krieg and others, 2010) followed a similar trend 

in 15U, 15L, MZ1, and MZ3 by becoming nondetectable in the second sample. 

The significant change in the bacterial diversity between the first and second sampling events 

cannot be explained by the introduction of new biomass and nutrients from a surface or near-surface 

source into this hydrologically isolated region of the Upper Floridan aquifer. The only perturbation to 

this ecosystem was the relatively rapid movement of groundwater in the production zones during the 

flushing of each well prior to sample collection. The suspended bacterial cells would be removed from 

the aquifer, whereas the biofilm-associated cells would either remain or be sloughed off because of the 

increased hydrodynamic shear forces at the biofilm surfaces. 

As discussed previously, the groundwater flow rates are low enough in this region of the Upper 

Floridan aquifer to make molecular diffusion the dominant transport process for dissolved constituents. 

The increased groundwater flow rates during the first sampling event would transport reactants for, 

and remove inhibitory products of, the biogeochemical reactions previously described at rates orders 

of magnitude higher than those under native conditions. This sudden increase in the availability of 

substrates for dissimilatory and biomass production reactions would favor those bacterial species best 

adapted to respond quickly and efficiently. The selective increase in bacterial groups that can successfully compete for the reactants and (or) products of the biogeochemical reactions will numerically dominate until the ecosystem returns to its native state. During this period, new biogeochemical processes do 

not necessarily have to occur, because existing processes could just proceed at different rates. The latter 

is most likely to have occurred in this region of the Upper Floridan aquifer because of its geochemistry and hydrologic isolation. The response of the bacterial community to ecosystem perturbations like 

that described here has been described for freshwater and marine ecosystems and can be divided into 

three phases: bacterial community resistance, resilience, and recovery (Allison and Martiny, 2008; Ho 

and others, 2011; Shade and others, 2011; Sjostrom and others, 2012). This may explain the significant 

increases in the abundances of OTUs for Pseudomonas species (family Pseudomonadaceae; fig. 17) 

(table 22), which have been shown to be the most physiologically diverse and adaptable group of bacteria in terrestrial and aquatic ecosystems (Spiers and others, 2000). 

The microbial diversity data also provide a framework from which a general, but sometimes specific, assignment of physiological capabilities to specific groups within the total microbial community. 

Figure 16 outlines a basic energy-flow carbon-cycle model that most likely describes how microbial life 

is supported, and figure 17 lists the dominant bacterial groups in this region of the Upper Floridan aquifer. The biogeochemical processes and microbial diversity data can be coordinated by aligning the biogeochemical processes (fig. 16) with the bacterial groups identified in the groundwater samples (fig. 17). 

Table 23 lists the biogeochemical processes in figure 16, plus several biogeochemical processes for 

which OTUs were detected but not represented in figure 17. The additional OTU classifications include Clostridiaceae (family) (Schleifer, 2009), Planctomycetes (phylum) (Ward, 2010), Euryarchaeota 

(phylum) (Garrity and Holt, 2001a) and Geobacteraceae (family) (Kuever and others, 2005). The additional biogeochemical processes or physiological classes include syntrophy with methanogens and 

ammonification. These OTU classifications and biogeochemical processes were added because OTUs 

were identified whose corresponding bacterial species or group had been shown to perform one or more 

biogeochemical processes that are not included in figure 16 but would be important to the overall carbon 

cycling, energetics, and geochemistry of this ecosystem. 

Because of the limited geochemical analyses, determining the presence of fermentative processes in this aquifer system could not be supported, nor refuted, using the energetics approach. However, 
50

Figure 17. Bacterial diversity distributions from the six sampling sites.

Proportion

Sampling site and sample number

 MZ1 (1)

 MZ1 (2)

 MZ3 (1)

 MZ3 (2)

 42U (1)

 42U (2)

 42L (1)

 42L (2)

 15U (1)

 15U (2)

 15M (1)

 15M (2)

0.0 0.2 0.4 0.6 0.8 1.0
51

the presence of bacteria capable of performing primary and secondary fermentations being in this region 

of the Upper Floridan aquifer is critical to explaining the concentrations of acetate in all of the groundwater samples. Acetogenesis was not energetically favorable and homoacetogens were not detected in 

the microbial diversity analyses. However, all but one of the OTU classifications represent one or more 

bacterial species that produce extracellular enzymes that degrade complex polymers into monomers that 

primary fermenters can utilize to produce acetate, CO2

, H2, and simple organic acids and alcohols that 

can be fermented to produce acetate (table 23). Additionally, members of the Peptostreptococcaceae 

(Schleifer, 2009), Lachnospiraceae/Ruminococcaceae (Schleifer, 2009), Clostridiaceae (Schleifer, 

2009), Desulfobacteraceae (Kuever and others, 2005), and Thermoprotei (Garrity and Holt, 2001b) 

produce and release acetate as a byproduct of fermentative or heterotrophic metabolism. Collectively, 

these groups of bacteria provide the reactants that drive dissimilatory biogeochemical reactions (energy production) and substrates for biomass production. Members of the Euryarchaeota are efficient 

methanogens (table 3, reactions 3, 4 and 5) (Methanopyri, Methanobacteria, Methanococci and 

Thermoplasmata) and anaerobic methane oxidizers (table 3, reaction 12) (Methanomicrobia: ANME–1 

and ANME–3) (Garrity and Holt, 2001a). However, OTUs were also identified that represent methanogenic syntrophic bacteria, which enable methanogenesis to proceed in the presence of sulfate reducing 

bacteria by removing H2 from the system. Members of the SAGMEG group have not been characterized 

or cultured to date. OTUs similar to sequences in this group have been recovered from the sulfate-methane transition zone in anaerobic and stratified ecosystems (Teske and Sørensen, 2007).

Nitrogen is a critical nutrient for bacterial cell maintenance and production because of its being an integral component of amino acids and proteins (for example, enzymes and membranes). 

Accordingly, nitrogen cycling is extremely important in this type of ecosystem, because allochthonous introductions of nitrogen (that is, nitrates and nitrites) and aerobic nitrogen fixation do not occur. 

Additionally, the absence of nitrates and nitrites in this region of the Upper Floridan aquifer prevents 

denitrification and anaerobic ammonia oxidation (ANAMMOX) processes from proceeding. Ammonia 

was present in all of the groundwater samples analyzed during this study. This form of nitrogen is 

required by all bacterial species and is the form of nitrogen most easily metabolized by bacterial cells. 

The source of ammonia cannot be explained by terrestrial inputs or in situ chemolithotrophic reactions 

that rely on nitrates or nitrites being present. The most likely source is the ammonification of bacterial 

cell lysates that originate from cell death or viral lysis. Representatives in four of the classifications in 

table 23 are capable of ammonification.

Figure 17. Bacterial diversity distributions from the six sampling sites.—Continued.

Domain Phylum Class Order Family

Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales Pseudomonadaceae

Bacteria Firmicutes Clostridia Clostridiales Peptostreptococcaceae

Bacteria Firmicutes Clostridia Clostridiales Lachnospiraceae / Ruminococcaceae

Bacteria Chloroflexi Anaerolineae Anaerolineales Anaerolineaceae

Bacteria Proteobacteria Deltaproteobacteria Desulfobacterales Desulfobacteraceae

Bacteria Actinobacteria Actinobacteria Actinomycetales Micrococcineae

Bacteria Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae

Archaea Crenarchaeota Thermoprotei unclassified unclassified

Archaea Euryarchaeota Thermoplasmata SAGMEG unclassified
52

Sulfate reduction (reactions 1 and 2), which is the most energetically favorable biogeochemical process in this region of the Upper Floridan aquifer (fig. 14), is well represented by members of the 

Desulfobacteraceae, Thermoprotei, and Euryarchaeota (fig. 17, table 23). The sulfate concentrations 

measured in the groundwater sampled during this study are not limiting and sulfate reduction proceeds 

in the presence of methanogenesis. Sulfate reduction and methanogenesis are commonly considered 

mutually exclusive, with methane being absent or in very low concentrations in ecosystems until sulfate 

has been reduced to sulfide (Mitterer, 2010). Recent studies have shown, however, that both processes 

can co-occur in carbonate sediment systems, similar to the Upper Floridan aquifer, if the methanogens 

Table 22. Bacterial diversity based on the family classifications.

[%, percent; ND, not determined; NA, not applicable]

Bacterial family classification Sample 1 Sample 2 Percent change Sample 1 Sample 2 Percent change

42U 42L

Pseudomonadaceae 39.71% 53.85% 14.14% 28.17% 70.76% 42.59%

Peptostreptococcaceae 2.94% 4.07% 1.13% 9.86% 4.87% -4.99%

Lachnospiraceae/Runinococcaceae 2.94% 5.88% 2.94% 14.08% 3.81% -10.27%

Anaerolineaceae 20.59% 6.33% -14.25% 12.68% 5.72% -6.96%

Desulfobacteraceae 7.35% 8.14% 0.79% 4.23% 5.72% 1.49%

Micrococcinea ND 0.90% 0.90% ND ND NA

Prevotellaceae 5.88% 10.41% 4.52% 7.04% 3.60% -3.44%

Thermoprotei 8.82% 5.43% -3.39% 12.68% 3.39% -9.29%

SAGMEG 11.76% 4.98% -6.79% 11.27% 2.12% -9.15%

15U 15M

Pseudomonadaceae 11.97% 85.69% 73.73% 37.80% 92.50% 54.70%

Peptostreptococcaceae 14.53% 0.94% -13.59% 18.29% 0.60% -17.70%

Lachnospiraceae/Runinococcaceae 7.69% 0.47% -7.22% 3.66% 0.36% -3.30%

Anaerolineaceae 16.24% 3.30% -12.94% 17.07% 0.71% -16.36%

Desulfobacteraceae 22.22% 4.25% -17.98% 10.98% 2.14% -8.83%

Micrococcinea 10.26% 0.47% -9.78% 1.22% 0.36% -0.86%

Prevotellaceae 0.85% ND -0.85% 3.66% ND -3.66%

Thermoprotei 9.40% 3.14% -6.26% 6.10% 2.14% -3.95%

SAGMEG 6.84% 1.73% -5.11% 1.22% 1.19% -0.03%

MZ1 MZ3

Pseudomonadaceae 0.35% 94.76% 94.41% 26.08% 46.72% 20.65%

Peptostreptococcaceae 27.82% 0.54% -27.28% 16.55% 3.49% -13.06%

Lachnospiraceae/Runinococcaceae 30.63% 0.40% -30.23% 19.27% 0.44% -18.84%

Anaerolineaceae 2.46% 1.08% -1.39% 7.48% 7.86% 0.38%

Desulfobacteraceae 0.70% 0.27% -0.44% 6.58% 6.55% -0.03%

Micrococcinea 15.14% 0.13% -15.01% 9.75% 25.33% 15.58%

Prevotellaceae 17.96% ND -17.96% 9.07% ND -9.07%

Thermoprotei 2.11% 1.48% -0.63% 2.72% 5.24% 2.52%

SAGMEG 2.82% 1.34% -1.47% 2.49% 4.37% 1.87%
53

utilize noncompetitive substrates. Additionally, geochemical conditions similar to those in this region 

of the Upper Floridan aquifer have been modeled and a new concept for the co-occurrence of sulfate 

reduction and methanogenesis has been put forth that relies on the physical isolation of sulfate reducers from methanogens in the fractures and pore spaces of the rock matrix (Jakobsen, 2007). Sulfide and 

methane were detected in all of the groundwater samples; the energetics analyses found both type of reactions were energetically favorable, and the bacterial diversity analyses identified sulfate reducers and 

methanogens in all of the samples. These two biogeochemical reactions are co-occurring in the Upper 

Floridan aquifer and are critical for energy production and carbon cycling in this ecosystem. 

Many groups of bacteria have physiological capabilities for performing biogeochemical reactions, even though the environmental conditions (such as the absence of chemical reactants) are not 

appropriate for those reactions to be initiated. The bacterial diversity analyses detected OTUs in this 

region of the Upper Floridan aquifer that have been shown to perform the following biogeochemical 

reactions or processes under nonphototrophic and anaerobic conditions: denitrification, ANAMMOX, 

nitrogen fixation, Fe(III) reduction, Fe(II) oxidation (nitrate dependent), elemental sulfur respiration, 

arsenite oxidation, arsenate reduction, chlorinated hydrocarbon degradation, and hydrocarbon (crude 

oil) degradation. Although this is not an exhaustive list from the PhyloChip data, those reactions and 

processes listed may of interest to groundwater resource managers responsible for (1) designing and 

monitoring managed recharge/recovery facilities, (2) remediating accidental contamination events, and 

(3) geochemical modeling.

Table 23. Likely biogeochemical processes based on bacterial diversity in all of the sampling sites.

[OTU, operational taxonomic unit]

Physiological classification

OTU Classification

 Extracellular Enzymes Primary Fermentation Secondary Fermentation Acetate Production Methanogenesis Methane Oxidation Syntrophy with Methanogens Ammonification Sulfate Reduction

Pseudomonadaceae x x x

Peptostreptococcaceae x x x

Lachnospiraceae/Ruminococcaceae x x x x x

Clostridiaceae x x x x x

Anaerolineaceae x x x

Desulfobacteraceae x x x x x x

Micrococcineae x x x x

Prevotellaceae x x

Thermoprotei x x x x x

SAGMEG (Bacteria belonging to this group are currently uncharacterized)

Geobacteraceae x

Planctomycetes x

Euryarchaeota x x x x x
54

Future Research Directions

1. Although a representative of the total coliform group was chosen as the model microbial indicator 

for this study, pathogenic bacteria, protozoan parasites, and viruses are of equal or greater public 

health concern. The aboveground, flowthrough mesocosms used during this study can also be 

adapted to retain and study these microorganisms. Because the native conditions in the UFA and 

APPZ were shown to have a negative effect on the survival of E. coli, relative to the conditions 

analyzed in other groundwater studies, characterizing the inactivation rates of these other microbial indicators and pathogens in these zones would be of interest to public health, regulatory, and 

resource-management officials. 

2. This study focused on the suspended bacterial communities in the Upper Floridan aquifer; however, it is generally accepted that the majority of the microbial biomass in any subsurface ecosystem is associated with the attached or biofilm communities (Pedersen, 2012; Whitman and others, 

1998). Using the same approaches described in this study to characterize the functional diversity 

and rates of biogeochemical reactions associated with biofilms in the UFA and APPZ would provide a significant contribution to the understanding of how these zones respond to native conditions and recharge events.

1. The inactivation of microbial indicators to satisfy primary drinking-water standards is required 

for some groundwater recharge methods. Although oxidizing disinfectants (such as chlorine and 

chloramines) are routinely used in potable-water treatment facilities prior to distribution, their use 

for treating recharge water prior to injection is discouraged because of the formation of disinfection byproducts. Alternative types of disinfectants, such as UV irradiation, are commonly used for 

treating these types of recharge water. The efficacy of UV irradiation can be compromised, however, by dissolved constituents, leading to injured microbial indicators that appear to be inactivated when using required water-quality monitoring methods (Blatchley and others, 2007; Lisle and 

others, 1998, 1999). Because UV irradiation is a commonly used disinfectant for recharge water 

in Florida, assessing the inactivation and recovery rates of bacterial, protozoan, and viral contaminants during and following treatment and during storage would assist public health and regulatory 

officials in determining appropriate disinfectants and dosages to maintain a product that satisfies 

regulatory standards.

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Figure 1-1. Diffusion chamber design: Each diffusion chamber was constructed by placing a membrane filter (d) on the central chamber (e), on 

which a silicon gasket (c) was placed. A polycarbonate screen (b) was placed on the gasket and the top chamber plate (a) placed on the screen. 

The same process was repeated for the other side of the central chamber and the completed chamber fastened and sealed with the six stainless 

steel bolts (f). The nylon syringe fittings on either end of the central chambers were closed using threaded polycarbonate caps (g). 

a b c d e

f

g

Appendix

Figure 1-2. A completed diffusion chamber. Figure 1-3. Loading a diffusion chamber with a culture of 

bacteria using a syringe.
65

Figure 1-4. A diffusion chamber containing the bacterial culture suspended from a cap from the stainless steel 

flow–through chamber (See Figure 1-9).

1

2

3

1. Off gas release fitting that allows gases 

released within the stainless steel (SS) flow 

cell due to depressurization to vent into the 

water jacket 

2. Threaded polyvinyl chloride (PVC) cap that 

fits the PVC inserts in the SS flow cell’s top

3. Diffusion chamber filled with bacterial 

suspension

Figure 1-5. The stainless steel flow–through chamber (side view).

Ground water flow path

1

2

1. Discharge connection

2. Inflow connection
66

Figure 1-6. The stainless steel flow–through chamber showing the baffles and positions of the diffusion chambers (top view).

Ground water flow pathDiffusion chamber Diffusion chamber

Diffusion chamber

Diffusion chamber

Diffusion chamber

Diffusion chamber

Diffusion chamber Diffusion chamber

1

1. Flow baffles to prevent short 

circuiting flow paths

Figure 1-7. The stainless steel flow–through chamber showing the inert polymer top into which the caps that hold the diffusion chambers are 

inserted (top view).

1

2

3 

1. Inert polymer top

2. Threaded PVC insert for cap 

with diffusion chamber (see 

figure 1-4)

3. SS bolts to secure polymer top 

to SS flow cell
67

Figure 1-8. The stainless steel flow–through chamber showing the silicon gasket that seals the connection between the polymer top and the 

stainless steel chamber (side view). This gasket prevents the source water in the outer flow–through chamber from leaking into this chamber.

1

2

3 

4

1. Inert polymer top

2. Threaded PVC inserts

3. Silicone gasket to ensure water 

tight seal between the SS flow 

cell and polymer top 

4. SS flow cell (side view)

Figure 1-9. The complete stainless steel flow–through chamber with diffusion chambers inserted (side view).

2

1

3

1. Threaded PVC cap that fits the PVC 

inserts in the SS flow cell’s top

2. Off gas release fitting with tubing 

attached for directing gas release

3. SS flow cell (side view)
68

Figure 1-10. The outer flow–through chamber, flow rate controls and connections for the multiprobe attachment fittings.

1 2

3

5

4

9

8 

6 7

1. Multiprobe flow cell for monitoring pH, 

temp, oxidation/reduction potential 

(ORP) of ground water (GW) leaving 

the SS flow–through chamber

2. Outer flow–through chamber

3. Flow meter for regulating flow through 

the SS flow chamber

4. Multiprobe flow cell for monitoring pH, 

temp, ORP of GW entering the SS flow 

chamber

5. Flow control valve for outer flow–

through chamber

6. GW source to SS flow–through 

chamber

7. Flow control valve for multiprobe flow 

cell

8. Inflow connection to GW source

9. Discharge outlet for the outer flow–

through chamber

Figure 1-11. The outer flow–through chamber flow rate controls and multiprobe attachment fittings (rear view).

1

8

5

6

3

2

4

7

9

1. Inflow connection to GW source

2. Flow control valve for multiprobe 

flow cell

3. Multiprobe flow cell for 

monitoring pH, temp, ORP of GW 

entering the SS flow chamber

4. GW source to SS flow–through 

chamber via flow meter

5. Flow meter (backside) for 

regulating flow through the SS 

flow–through chamber

6. Outer flow–through chamber

7. GW source line for SS flow–

through chamber

8. Flow control valve for outer flow–

through chamber

9. Discharge to waste outlet for 

multiprobe flow cell
69

Figure 1-12. The outer flow–through chamber discharge and multiprobe fittings.

2

1

3

4

1. Multiprobe flow cell for monitoring 

pH, temp, ORP of GW leaving 

the SS flow–through chamber

2. High volume collection box for 

the outer flow–through chamber

3. Discharge outlet for the flow–

through chamber

4. GW discharge line from the SS 

flow–through chamber

Figure 1-13. An empty outer flow–through chamber showing the connections for the stainless steel flow–through chamber that contains the 

diffusion chambers.

3

1

4

2

5

1. High volume discharge outlet 

for outer flow–through chamber 

over flow

2. GW discharge line from the SS 

flow–through chamber 

3. Outer flow–through chamber

4. GW source line for SS flow–

through chamber 

5. GW inflow line for the outer flow–

through chamber
70

Figure 1-14. The complete aboveground flow–through microcosm.

5

1

7

2

3

4

6

1. High volume discharge outlet for 

outer flow–through chamber over 

flow (not shown)

2. GW discharge line from the SS 

flow–through chamber

3. Off gas fitting with tubing

4. Threaded PVC cap with 

diffusion chamber attached 

and suspended in the SS flow–

through chamber

5. GW source line for SS flow–

through chamber 

6. GW inflow line for the outer flow–

through chamber

7. Outer flow–through chamber

Figure 1-15. The above ground flow through microcosm in the field.