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2-1-2017
Beyond the Obvious: Emerging Contaminants and
Biogeochemistry as a Cause and Solution for
Nitrogen Pollution
Stephanie L. DeVries
The Graduate Center, City University of New York
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BEYOND THE OBVIOUS: EMERGING CONTAMINANTS AND
BIOGEOCHEMISTRY AS A CAUSE AND SOLUTION FOR NITROGEN POLLUTION
by
STEPHANIE L. DEVRIES
A dissertation submitted to the Graduate Faculty in Earth and Environmental Sciences in partial
fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of
New York
2017
© 2017
STEPHANIE L. DEVRIES
All Rights Reserved
ii
Beyond the Obvious: Emerging Contaminants and Biogeochemistry as a
Cause and Solution for Nitrogen Pollution
by
Stephanie L. DeVries
This manuscript has been read and accepted for the Graduate Faculty in Earth
and Environmental Sciences in satisfaction of the dissertation requirement for
the degree of Doctor of Philosophy.
Date
Pengfei Zhang
Chair of Examining Committee
Date
James Biles
Executive Officer
Exe
Supervisory Committee:
Karin Block (City College)
Zhongqi Cheng (Brooklyn College)
Kevin Kroeger (United States Geological Survey
THE CITY UNIVERSITY OF NEW YORK
iii
ABSTRACT
Beyond the Obvious: Emerging Contaminants and Biogeochemistry as a
Cause and Solution for Nitrogen Pollution
by
Stephanie L. DeVries
Advisor: Pengfei Zhang
Following a comprehensive review of the occurrence and impacts of antibiotics and related
pharmaceutical compounds on the terrestrial N-cycle, three experiments were performed to
explore the topic of biogeochemistry as a source or a sink for N-pollution. The first of these
experiments addresses the question of whether environmentally relevant concentrations of
antibiotics (<1 µg·kg-1) have a significant effect on denitrification or N2O production, a question
that has not been well addressed in previous studies. Having determined that there is a significant
shift, the second study aims to comprehensively follow changes to soil N pools and N2O flux
alongside biogeochemical reaction rates under different soil moisture conditions. The final chapter
of this research, Chapter 5, looks to biogeochemistry as a solution for some of the water quality
issues associated with excess N by quantifying the rate at which sand columns inoculated with
lake sediment biodegrade undesirable taste/odor compounds and toxins produced by
cyanobacterial algae that proliferate in nitrogen-rich waters.
iv
The results of this work show that the balance between soil as a source or a sink of N
pollutants can be significantly disturbed by sources beyond the obvious, i.e. antibiotics. It further
shows that biogeochemical activity can serve as an effective treatment for secondary N-pollution.
Additional research is encouraged to test the effects of additional antibiotics and by extending the
incubation period to longer time periods. In particular, there also exists a need to examine the short
and long-term effects of antibiotics on soil microbial community structure. While the present work
shows that endemic bacteria can degrade nuisance compounds in N-polluted waters, the efficacy
of this activity may also be affected by long-term antibiotic exposure in sediment. Genetic tools
including GeoChip, will help to better constrain changes that are relevant to all aspects of these
findings.
v
AKNOWLEDGEMENTS
For my parents, Stephen and Pauline DeVries
In memory of my grandmother, Mary Lou DeVries, who would have been so proud
I would like to express my deepest gratitude and appreciation to those individuals who
have accompanied me on this lengthy journey. First and foremost, I thank my advisor, Dr. Pengfei
Zhang, for his continued support and mentorship. From its conception to its completion, Dr. Zhang
has been a patient and meticulous mentor, while also giving me the intellectual freedom to pursue
a topic for which there was no guarantee of success.
I would also like to thank my committee members, Drs. Karin Block, Zhongqi Cheng, and
Kevin Kroeger for their thoughtful input and friendly guidance over the years. In particular, I’d
like to thank Dr. Block for her unapologetic editorial eye during the drafting of this final
dissertation. In similar vein, I’d like to thank Drs. Yehuda Klein and James Biles for their
contributions to my intellectual growth and curiosity during my years at the CUNY Graduate
Center. I would be remiss if I didn’t also acknowledge Drs. Brett Branco, Jeffrey Bird, and Lijia
Yang for generously allowing me to complete a portion of this work in their laboratories and
providing technical assistance along the way. Additional gratitude is owed to Madeline Loving
and Laura Logozzo who devoted precious time as undergraduate research assistants to assist with
some of the more laborious aspects of this research.
Finally, I’d like to thank my friends and family for their quiet but essential role in helping
me to complete this final degree. Mom, Dad, Aaron DeVries, Waylon Smith, Trisha Peckosh,
David Bloom, Josh Rosenberg, and Alaethia Doctor – I couldn’t have done this without your
enduring support, love, and encouragement.
vi
The following chapters of this dissertation are reprinted with permissions from the
publishers:

Chapter 2 - Current Pollution Reports, 2(1), 51-67, doi:10.1007/s40726-016-0027-3

Chapter 3 - Scientific reports, 5:16818, doi: 10.1038/srep16818

Chapter 5 – Water Science and Technology: Water Supply, 12(5), 691-698, doi:
10.2166/ws.2012.043
vii
TABLE OF CONTENTS
Table of Tables ............................................................................................................................. xii
Table of Tables ..............................................................................................................................xv
Chapter 1: Introduction ............................................................................................................... 1
1.1. Background ......................................................................................................................... 1
1.2. The Global Nitrogen Imbalance.......................................................................................... 4
1.3. Problems and Objectives..................................................................................................... 6
1.3.1. Objective 1: Assessing the Effects of Antibiotics on the Biogeochemistry of the
Nitrogen Cycle in Soil ............................................................................................ 6
1.3.2. Objective 2: Evaluating the Efficacy of Endemic Microbial Communities to
Improve Water Quality ........................................................................................... 9
Chapter 2: Antibiotics and the Terrestrial Nitrogen Cycle: A Review.................................. 10
2.1. Introduction ....................................................................................................................... 10
2.2. Occurrence of Antibiotics in Soil and Sediment............................................................... 12
2.2.1. Antibiotics in Soil ................................................................................................. 13
2.2.2. Antibiotics in Sediment......................................................................................... 14
2.2.3. Fate of Antibiotics in Soil and Sediment .............................................................. 15
2.3. Effects of Antibiotics on the Terrestrial Nitrogen Cycle .................................................. 17
2.3.1. The Nitrogen Cycle ............................................................................................... 17
2.3.2. Nitrification ........................................................................................................... 17
2.3.3. Effects of Antibiotics on Nitrification .................................................................. 18
viii
2.3.4. Denitrification ....................................................................................................... 21
2.3.5. Effects of Antibiotics on Denitrification............................................................... 21
2.3.6. Effects of Antibiotics on NOx Emissions.............................................................. 23
2.3.7. Overview of Current Measurement Methodology ................................................ 24
2.4. Conclusions and Prospects................................................................................................ 28
2.5. Acknowledgements ........................................................................................................... 30
Chapter 3: The Effect of Ultralow-Dose Antibiotics Exposure on Soil Nitrate and N2O
Flux ............................................................................................................................................... 40
The effect of ultralow-dose antibiotics exposure on soil nitrate and N2O flux ............................ 40
3.1. Introduction ....................................................................................................................... 40
3.2. Materials and Methods ...................................................................................................... 41
3.2.1. Statistical Analyses ............................................................................................... 41
3.2.2. Soil Sampling ........................................................................................................ 42
3.2.3. Anaerobic Incubation Experiment ........................................................................ 42
3.2.4. N2O Flux Experiment ........................................................................................... 43
3.2.5. Column Experiment .............................................................................................. 43
3.3. Results and Discussion ..................................................................................................... 44
3.3.1. Anaerobic Nitrate Reduction ................................................................................ 44
3.3.2. Denitrification in Saturated Sediment Columns ................................................... 48
3.3.3. N2O Flux ............................................................................................................... 51
ix
3.4. Conclusions ....................................................................................................................... 53
3.5. Acknowledgements ........................................................................................................... 54
Chapter 4: The Effects of Trace Narasin on the Biogeochemical N-Cycle in a Cultivated
Sandy Loam ................................................................................................................................. 55
4.1. Introduction ....................................................................................................................... 55
4.2. Materials & Methods ........................................................................................................ 57
4.2.1. Antibiotic Selection .............................................................................................. 57
4.2.2. Soil Sampling and Preparation.............................................................................. 57
4.2.3. Experimental Setup ............................................................................................... 58
4.2.4. Extraction and Analysis of Soil Headspace .......................................................... 58
4.2.5. Extraction and Analysis of Soil Nitrogen. ............................................................ 59
4.2.6. Calculating Rates of Mineralization, Nitrification, and Denitrification ............... 60
4.2.7. Calculating Mass Balance ..................................................................................... 61
4.2.8. Calculating 15N-N2O enrichment .......................................................................... 62
4.2.9. Calculating relative contributions of nitrification and denitrification to N2O flux62
4.3. Results and Discussion ..................................................................................................... 62
4.3.1. Influence of NAR on NH4+ ................................................................................... 62
4.3.2. Influence of NAR on NO3-.................................................................................... 70
4.3.3. Influence of NAR on N2O..................................................................................... 73
4.4. Conclusions ....................................................................................................................... 80
x
4.5. Acknowledgements ........................................................................................................... 82
Chapter 5: Biodegradation of MIB, Geosmin, and Microcystin-LR in Sand Columns
Containing Lake Taihu Sediment.............................................................................................. 83
5.1. Introduction ....................................................................................................................... 83
5.2. Methods............................................................................................................................. 85
5.2.1. Column Preparation .............................................................................................. 85
5.2.2. Influent .................................................................................................................. 85
5.2.3. Column Experiments ............................................................................................ 86
5.2.4. Effluent Analysis .................................................................................................. 86
5.2.5. Transport Modeling .............................................................................................. 88
5.3. Results and Discussion ..................................................................................................... 89
5.3.1. Removal of MIB ................................................................................................... 89
5.3.2. Removal of Geosmin ............................................................................................ 93
5.3.3. Removal of MC-LR .............................................................................................. 94
5.4. Conclusions ....................................................................................................................... 97
Appendix .......................................................................................................................................99
Appendix A ...............................................................................................................................99
Appendix B..............................................................................................................................106
Bibliography ...............................................................................................................................109
xi
TABLE OF TABLES
Table 1-1. The nine oxidation states of nitrogen and their common forms. .................................. 1
Table 2-1. Median and Maximum Concentrations of Antibiotics Detected in Soil (μg·kg-1)*. The
mean reported frequency of detection is calculated from studies that explicitly report rate of
detection. Antibiotic sources are abbreviated as: SM = Swine Manure, CM = Cattle Manure,
PL = Poultry Litter, MM = Mixed Manure, WW = Wastewater Discharge, MD =
Manufacturing Discharge, U = Unspecified Manure. Individual studies may list more than one
potential antibiotic source. ...................................................................................................... 31
Table 2-2. Minimum and Maximum Concentrations of Antibiotics Detected in Sediment (μg·
kg-1). Antibiotics whose concentration were below the limits of quantification (LOQ) are
indicated as <LOQ*. ............................................................................................................... 33
Table 2-3. Usage and Physiochemical Properties of Select Antibiotics*. ................................... 35
Table 2-4. List of observed effects of antibiotic on nitrification rate and nitrification potential in
soil and wastewater sludge.*................................................................................................... 36
Table 2-5. List of observed effects of antibiotic on denitrification rate or potential. .................. 38
Table 3-1. Percentage of extractable nitrate lost relative to the control in soils treated with
sulfamethoxazole, sulfadiazine, narasin, and gentamicin. Results shown are the average of
three replicates collected at each sampling period with standard error shown in parentheses.
Values above 100% (shown in bold) indicate that nitrate losses are stimulated relative to the
control whereas values less than 100% point to nitrate losses inhibited relative to the control.
Individual treatments deemed by a student t-test to be statistically different from the control
are denoted with an asterisk. ................................................................................................... 46
xii
Table 3-2. Results of One-Way ANOVA for soils treated with 1, 10, 100, or 1000 ng·kg-1 Narasin,
Gentamicin, Sulfamethoxazole, and Sulfadiazine over a five-day sampling period. The Fstatistic was calculated for concentration of nitrate measured in triplicate samples grouped by
dose. Dose-response relationships are deemed statistically significant where Fstat>Fcrit. Pvalues less than 0.05 are shown in bold. ................................................................................. 46
Table 3-3. Results of One-Way ANOVA for N2O flux from Narsin-treated soils. The F-statistic
was calculated for the N2O flux measured in six replicate samples grouped by dose. Doseresponse relationships are deemed statistically significant where Fstat>Fcrit. P-values less than
0.05 are shown in bold. ........................................................................................................... 53
Table 4-1. Results of One-Way ANOVA for NH4+ pool in soils treated with 0, 1, 10, 100, or 1000
ngkg-1 narasin. The F-statistic was calculated for the NH4+ concentration determined from 6
replicates and grouped by antibiotic dose. Dose-response relationships are deemed statistically
significant where Fstat>Fcrit. P-values less than 0.05 are shown in bold. ................................. 63
Table 4-2. Overall mineralization (M) and Nitrification (N) rates (μg Ng-1d-1) over 3-day
incubation period calculated by solving for m (Eq. 1) and by using regression analysis to find
a best-fit curve for Eq. 2 and solving for m or n. .................................................................... 63
Table 4-3. Mineralization, Nitrification, and Denitrification rates (μg Ng-1d-1) calculated 24-hour
incubation periods on Days 1, 2, and 3 using Eq. 4-1 and solving for m or n and using
Eqns. 3-4. ................................................................................................................................ 68
Table 5-1. Percentage removal of MIB and geosmin and transport parameter values determined
by inverse modeling of experimental breakthrough data. ....................................................... 90
xiii
Table A-1. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3-N/gsoil) in anaerobic soils treated with Narasin. ....................................................................99
Table A-2. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3-N/gsoil) in anaerobic soils treated with Gentamicin. ............................................................100
Table A-3. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3-N/gsoil) in anaerobic soils treated with Sulfamethoxazole. ..................................................101
Table A-4. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3-N/gsoil) in anaerobic soils treated with Sulfadiazine. ..........................................................102
Table A-5. Results of Student t-test for effluent nitrate in SMX-treated soils vs. untreated soils. Pvalues less than 0.05 for paired data are shown in bold.........................................................103
Table A-6. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for N2O flux from in aerobic soils
treated with 1-1000 ng·kg-1 Narasin. ...................................................................................104
Table B-1. Pearson Correlation Coefficient (R2) calculated between measured NH4+/NO3- mass
balances calculated from initial NH4+/NO3- and the measured rates of mineralization,
nitrification, and denitrification. ............................................................................................107
xiv
TABLE OF FIGURES
Figure 2-1. The Biogeochemical Nitrogen Cycle. ....................................................................16
Figure 3-1. Time-Dose Response curves illustrating the percentage of extractable nitrate lost
relative to the control in soils treated with sulfamethoxazole. Results shown are the average
of three replicates collected at each sampling period. Values above 100% (dashed line)
indicate that nitrate losses are stimulated relative to the control whereas values less than
100% point to nitrate losses inhibited relative to the control.............................................45
Figure 3-2. Percent influent nitrate removed from control (o) and experimental (□) columns
during transport through saturated soil columns receiving a continuous flow of nitrate
nitrogen and glucose. Experimental columns were spiked with 1 ng·L-1 SMX from t = 24
to t=108. Triplicate columns were run for the spiked as well as the control tests. Statistically
different nitrate reduction (p < 0.05) was observed from t = 30 to t = 108 and is indicated
with solid markers. .............................................................................................................50
Figure 3-3. Box-whisker plot of daily N2O flux (ppm·day-1) in moist soil (40% water filled
pore space) treated with 0-1000 ng·kg-1 Narasin. For each dose, 6 replicate samples were
analyzed; statistical outliers are shown as asterisks and data that differ significantly from
the control are indicated with arrows. ................................................................................52
Figure 4-1. Box-whisker plots illustrating the NH4+-N pool over a three-day incubation period
in moist soils (40%, 60%, and 80% WFPS) treated with NAR. Statistically significant dose
responses are indicated with a star symbol. .......................................................................64
Figure 4-2. Box-whisker plots illustrating the NO3--N pool over a three-day incubation period
in moist soils (40%, 60%, and 80% WFPS) treated with NAR. Statistically significant dose
responses are indicated with a star symbol. .......................................................................72
xv
Figure 4-3. Box-whisker plots illustrating the N2O-N flux over a three-day incubation period
in moist soils (40%, 60%, and 80% WFPS) treated with NAR. Statistically significant dose
responses are indicated with a star symbol. .......................................................................75
Figure 4-4. %15N atom excess of N2O from soils receiving
15
N-NO3- (black) and
15
N-NH4+
(white) enrichment, corrected for atmospheric enrichment of headspace. ........................78
Figure 4-5. Relative contribution of denitrification (black) and nitrifier denitrification (white)
to total N2O flux. ................................................................................................................79
Figure 5-1. Breakthrough curves for MIB (left) and geosmin (right) in control column (top),
5/95 sediment/sand column (middle), and 10/90 sediment/sand column (bottom).
Experimental data and the best-fit simulations using HYDRUS 1D for the duplicate
experiments are displayed as diamonds and smooth lines, respectively. ..........................91
Figure 5-2. Forward model results for removal of MIB (solid line) and geosmin (dashed line)
in a 2 m column of lake bed sediment and quartz sand (10/90). .......................................92
Figure 5-3. Breakthrough curves for MC-LR. Data for columns E1 (5/95) and E2 (10/90) are
based on average values. Data for the control column, C1, are shown for both the first (A)
and second (B) column experiments without averaging. ...................................................96
Figure A-1. Dose-Time-Response characteristics of direct stimulation hormesis. Time 1, Time
2, and Time 3 do not reference a specific unit of time but indicate a time-based progression
during which low doses of a toxin or inhibitor may initially lead to stimulated activity,
followed by a gradual, time-dependent shift toward inhibited activity at the same dosages.
Adapted from Calabrese and Baldwin. ..............................................................................98
xvi
Figure B-1. Comparison between measured NH4+ pool (dotted line) and a mass balance (solid
line) calculated from the using the initial NH4+ concentration and rates of mineralization
and nitrification on Days 1-3. ..........................................................................................105
Figure B-2. Comparison between measured NO3- pool (dotted line) and a mass balance (solid
line) calculated from the using the initial NO3- concentration and rates of mineralization
and nitrification on Days 1-3. ..........................................................................................106
xvii
Chapter 1
Introduction
1.1.
Background
The Nitrogen Cycle is a global biogeochemical cycle in which nitrogen (N) flows between
atmospheric, aqueous, terrestrial, and biologic reservoirs. The cycle begins when N2 in the
atmosphere is “fixed”, i.e. is oxidized or reduced to more chemically reactive forms that are
common to terrestrial and aquatic environments (see Table 1-1). After fixation, reactive N (Nr)
may remain in the atmosphere as a gas or aerosolized particle or it may be deposited into terrestrial
and aquatic environments where the cycle continues. Ammonium (NH4+) and nitrate (NO3-) may
be taken up by plants and converted to organic N that is subsequently consumed by other organisms
or returned to the environment by decomposition. Alternatively, organic N, NH4+, and NO3- may
undergo oxidation-reduction reactions in soil or sediment that eventually lead to the formation of
N2, N2O, or NOx gases that are diffused back to the atmosphere and closing the cycle.
Table 1-1. The nine oxidation states of nitrogen and their common forms.
Oxidation State
+5
+4
+3
+2
+1
0
-1
-2
-3
Examples
HNO3, NO3NO2
HNO2, NO2NO
N2O
N2
NH2OH
N2H4
NH3, NH4+
Names
Nitric Acid, Nitrate Ion
Nitrogen Dioxide
Nitrous Acid, Nitrite Ion
Nitric Oxide
Nitrous Oxide
Nitrogen Gas
Hydroxylamine
Hydrazine
Ammonia, Ammonium Ion
1
When the rate of Nr input to the environment is equal to the rate at which N2 is exported
back to the atmosphere, the N cycle is said to be in balance; prior to the industrial revolution, this
was more or less the case. More recently, anthropogenic N sources have disturbed this balance,
leading to unprecedented increases in the availability of Nr in terrestrial and aquatic environments.
Biogeochemical activity in soil and sediment have historically been viewed as a negative feedback
against rising Nr and efforts have been made to maintain and/or rebuild natural riparian buffer
zones to take advantage of this natural sink. Although advantageous for terrestrial Nr (NH4+ and
NO3-), one or more of the biogeochemical reactions that deplete terrestrial and/or aquatic NH4+
and NO3- also produce N2O as an intermediate or terminal product, thus a process that reduces one
form of Nr pollution may inadvertently increase another. The balance between source and sink is
controlled by microbial activity in soils and has been extensively studied for the purpose of
understanding how obvious drivers of microbial activity such as climate variables (heat, moisture)
and soil conditions (texture, soil organic material, etc.) shift the balance between mineralization,
nitrification, and denitrification and their associated end products. Until recently, very little
consideration has been given to less obvious drivers. The inadvertent introduction of antibiotics to
agricultural soils is an example of a variable that lies beyond the obvious. Antibiotics, which have
emerged as a terrestrial and aquatic contaminant as a result of their frequent use in commercial
feed animal productions, are designed to inhibit microbial growth or enzymatic activity. Many act
on specific groups of organisms and the resulting effects on the structure and function of complex
microbial communities such as those found in soil and sediment has not been well characterized.
If environmentally relevant concentrations promote sufficiently large shifts within the microbial
community, the result may negatively impact existing models of the biogeochemical N-cycle and
2
undermine efforts to understand and mitigate anthropogenic contributions to increasing Npollution.
In the last two decades, abundant research has emerged that points to an accumulation of
human and antibiotic compounds in soil and sediment, yet little is currently known about their
effects on N-transformation rates in soils. In particular, there is a paucity of research detailing their
effects at environmentally relevant concentrations (<1 µg·kg-1). The literature presently shows that
a number of antibiotics cause structural and functional shifts to the soil microbial community, i.e.
an increase in the ratio of ammonia oxidizing archaea (AOA) to ammonia oxidizing bacteria
(AOB), when added to soils at concentrations ranging from low µg·kg-1 to mg·kg-1 [1, 2]. In many
cases, these shifts have also been correlated to modified rates of soil respiration, nitrification, and
denitrification. This dissertation research aims to provide direct evidence that similar impacts are
likely to occur when soils are exposed to sub-therapeutic and environmentally relevant
concentrations of antibiotics (<1 µg·kg-1), a hypothesis that has not previously been addressed. It
is further differentiated from prior study because it follows the changes in both process rate and
N-pool size over the incubation period in order to constrain the mechanism by which changes to
NH4+, NO3-, or N2O have occurred.
Following a review of the occurrence and effects of antibiotics in terrestrial environments,
(Chapter 2), the results of these studies will be presented in Chapters 3 and 4. Because it was
hypothesized that antibiotic exposure will lead to an increase in leachable NO3-, which is a
significant contributor to high rates of primary production in surface waters (lakes, estuaries), a
complementary study was included to determine whether biogeochemical activity could be
employed as a sink for secondary pollutants associated with excessive algae growth. The results
of this study are presented in Chapter 5.
3
1.2.
The Global Nitrogen Imbalance
Prior to the industrial revolution, N-fixation was limited to a few natural processes able to
supply the energy required to crack the strong triple bond of the N2 molecule via heat or enzymatic
catalysts. Lightning, for example, heats the surrounding atmosphere to temperatures exceeding
30,000°K. At these temperatures, N2 is oxidized to NO, NO2, and HNO3 [3] at an estimated rate
of 5±3 Tg N·yr-1 [4]. A considerably more significant source of Nr is biological nitrogen fixation
(BNF), a process by which microorganisms found in the root nodules of legumes and some trees
enzymatically reduce N2 to NH3 [5]. Historically, BNF has accounted for the majority of N2
fixation, occurring at an estimated rate of 44 Tg N·yr-1 [6].
In the last century, however, anthropogenic N-fixation has become the dominant source of
Nr in the environment. The Haber-Bosch process, an industrial reaction developed to reduce N2
to NH3, contributes the bulk of this input (120±12 Tg N·yr-1), while combustion of fossil fuels
adds an additional 30±3 Tg N·yr-1 [7]. Cultivation of N-fixing crops also makes a significant
contribution, adding another 50-70 Tg N·yr-1 [8]. The sum of these inputs, largely driven by
demand for N-fertilizers to sustain agricultural productivity goals, is nearly four times the rate of
natural N-fixation and has increased the global abundance of NH4+, NO3- and N2O to the point that
they have become environmental pollutants.
Where N-pollution is severe, it has been linked to a number of human health risks and
degradation of aquatic ecosystems and air quality. For example, consumption of NO3-- enriched
water has been correlated to increased rates of methemoglobinemia (blue-baby syndrome), colon
cancer, and reproductive disorders [9]. According to 2002 estimates, 19% of applied fertilizer-N
is leached into groundwater and streams [10], predominantly in the form of NO3- [11]. Large
agricultural producers are most affected by these pollutants. In the United States, the NO3-
4
concentration in samples from 4 regional aquifers exceeded the recommended limit of 10 mg/L
[12]. In China, where fertilization often greatly exceeds crop requirements [13] , groundwater NO3often exceeds 50 mg/L [14]. NO3- leaching is also detrimental to aquatic ecosystems. In nonlimiting quantities, reactive N species including NH4+ and NO3-, increase primary productivity in
aquatic environments [15], which contributes to eutrophication of rivers, lakes, estuaries, and
coastal areas [16, 17], biodiversity loss [18], fishery collapse [19], and increased production of
paralytic shellfish toxins [20]. Increasing NO3- loads in estuarine environments have also been
demonstrated to reduce coastal buffering that protects adjacent oceans from eutrophication [11].
More recently, extreme primary productivity has resulted in the formation of large algae
blooms, sometimes referred to as “green tides” that are unsightly, may be malodorous, and can
wreak havoc on the local aquatic ecology [21-24]. The composition of these blooms includes
species of cyanobacteria that produce organic compounds such as methyl isoborneol (MIB) and
geosmin, which impart a negative taste and smell to drinking water [25]. Other species excrete
organic toxins such as the cyclic peptide microcystin-LR [26]. The occurrence of these compounds
poses an immediate health risk and reduces consumer confidence in their drinking water supply,
so it follows that a number of treatment options have been developed to remove or degrade these
compounds from municipal water supplies. Activated carbon and ozonation have proven effective
to remove MIB and geosmin, but can be an expensive option [27]. As a cost-effective alternative,
research has turned toward biological solutions, including the use of bioactivated sand columns
[28] in which the offending compounds are removed via biodegradation.
Although much emphasis has been placed on terrestrial impacts of Nr, the effects are also
seen in air quality. N2O and NOx gases are produced as a by-product of biogeochemical activity in
soil and sediment and by combustion of fossil fuels. N2O is a powerful greenhouse gas [29] and
5
the leading contributor to stratospheric ozone depletion [30]. Recent estimates show that ~25-30%
of net terrestrial N2O emissions are linked to agricultural soils [31] and these emissions are
increasing at a rate of 0.9 ppb·yr-1 [32]. The majority of this increase is attributed to use of nitrogen
fertilizers in agriculture [33].
1.3.
Problems and Objectives
1.3.1. Objective 1: Assessing the Effects of Antibiotics on the Biogeochemistry of the
Nitrogen Cycle in Soil
This dissertation addresses two questions with respect to the biogeochemical N-cycle. The
first focuses on the occurrence of antibiotics in soil and other terrestrial environments and what
effect, if any, these contaminants have on the rate of production and/or loss of NH4+, NO3-, and
N2O in and from soils. The distribution of antibiotics in the environment has been extensively
reviewed [34-37] and there are an increasing number of studies in the literature that report on the
various effects of antibiotics on microbial communities in soil and sewage sludge. Although acute
toxicity tests have been deemed inadequate to assess the effect of antibiotics on environmental
bacteria [38], only a fraction of published impact studies address the effects of exposure at
environmentally relevant concentrations [37-39]. Even fewer have specifically investigated their
effects on microbial N transformations. Available literature [40-43] suggests that one or more steps
of the N-cycle may experience temporal shifts that vary in both magnitude and direction, even at
trace concentrations. Following a similar observation in 2012, Conkle & White proposed that soil
bacteria may respond hormetically (be stimulated) to very low doses of antibiotics [44]. Hormesis,
or a J-curve response, has been championed in recent years as a more accurate measure of subtherapeutic or chronic exposure toxicity [14].
6
In an effort to maximize nitrogen use efficiency (NUE) in agroecosystems, a number of
tools have been developed to help farmers optimize both the quantity and rate at which N-fertilizers
are applied for maximum crop yield and minimum loss of N-pollutants. These tools, which include
slow-release fertilizers and numerical and/or spatial models such as NLEAP-GIS [45] and
ADAPT-N [46], were developed with consideration to known drivers of biogeochemical Ntransformations in soil, i.e. soil carbon, temperature, soil moisture, soil N, etc. Structural and/or
functional changes to the soil microbial community that result in altered rates of activity would
therefore affect the efficacy of these models and may offset progress that has been made toward
sustainable use of N-fertilizers in agriculture. With this in mind, it is important to understand how
antibiotics affect both the rate of biogeochemical activity in soils and the resulting impact that
these changes have on the accumulation, persistence, and movement of NH4+, NO3-, and N2O.
Knowledge of these changes can be used to refine existing N-management tools in order prevent
unnecessary N-losses that are concomitant to increased non-point source N-pollution and to
prevent increased food production costs that result from the inadvertent application of too much
or too little N-fertilizer.
The first objective of this work is to assess the problem of antibiotics in the terrestrial
environment and to investigate the effects of trace concentrations (< µg·kg-1) on the process rate
of biogeochemical N-reactions in soil as well as the associated accumulation of NH4+, NO3-, and
N2O. This objective fulfills the dual purposes of (1) determining whether trace concentrations of
antibiotics, previously deemed below the No Observable Adverse Effect Level (NOAEL) in acute
toxicity tests, have a quantifiable impact on denitrification, a biogeochemical niche filled by a
select group of organisms within a more complex microbial community, and (2) the concomitant
impact NO3- and N2O losses from affected soils.
7
The first part of this objective, assessing the scope of the problem, is addressed in Chapter
2. In addition to summarizing the most recent data (published since 2012) pertaining to the
occurrence of antibiotics in soil and sediment, this chapter provides a comprehensive overview of
the microbiology of the N-cycle in soil and what is currently known about the effects of antibiotics
on individual reaction pathways within that cycle. The chapter concludes with recommendations
that future research include more studies that examine the effects of low (ng·kg-1) antibiotic
exposure levels and explicitly report on changes to soil N2O flux. Additionally, methods using
isotopic tracers are recommended to better constrain N2O source where denitrification and
nitrification are affected.
In response to these recommendations, two studies were designed to quantify the effects of
sub-therapeutic concentrations (<1 µg·kg-1) on the soil N-cycle. The results from the first of these
studies is presented in Chapter 3. This experiment tests the impact of four veterinary antibiotics,
applied to soil at concentrations of 1-1000 ng·kg-1, on the rate of denitrification, the accumulation
of NO3-, and the rate of N2O flux rate from the soil surface. The results of the second study,
designed as a continuation from the first, are detailed in Chapter 4. Here, one antibiotic was
selected for a more comprehensive investigation in which the concentrations of extractable NH4+
and NO3- in soil were measured alongside N2O flux over a 3-day incubation period following
application of 1-1000 ng·kg-1 Narasin, an anti-coccidial commonly administered to poultry chicks.
Stable
15
N tracers were also employed for this study, which allowed for the quantification of
mineralization, nitrification, and denitrification rates and to identify possible pathway shifts in the
production of N2O.
8
1.3.2. Objective 2: Evaluating the Efficacy of Endemic Microbial Communities to Improve
Water Quality
Whereas it is widely known that the biogeochemical activity in soil and sediment can
directly aid in the mitigation of Nr pollution, it is not immediately obvious that biogeochemical
activity can potentially be employed to reduce the concentration of organic pollutants that result
from excess Nr. Because biodegradation of organic compounds is widely recognized for its utility
in removal of petroleum compounds in soil and sediment, it is reasonable to suggest that other
contaminant compounds may be similarly susceptible to biodegradation. Therefore, the second
research objective addressed by this dissertation is whether normal biogeochemical activity of
sediment can be harnessed to effectively remove nuisance and/or toxic organic compounds that
often accompany increased primary productivity in surface waters with high N-loads. This
objective was examined by inoculating quartz sand columns with sediment collected in Lake Taihu
(Jiangsu Province, China) during a summer algal bloom and measuring the percentage of influent
methlisoborneol (MIB), geosmin, and microcystin-LR removed as water passed through the
columns.
9
Chapter 2
Antibiotics and the Terrestrial Nitrogen Cycle: A Review
S. L. DeVries and P. Zhang
The contents of this chapter also appeared in Current Pollution Reports, 2(1), 51-67,
doi:10.1007/s40726-016-0027-3.
2.1.
Introduction
Since their discovery in the early 20th century, antibiotics have been proved enormously
beneficial to human and animal health. Now used for variety of therapeutic, prophylactic and
growth promotion purposes, global antibiotic consumption has increased considerably. Antibiotic
production presently exceeds 100,000 to 200,000 tons per year [47], and a growing proportion of
these antibiotics are being administered to poultry and livestock raised in concentrated production
facilities [35, 36, 48, 49]. As antibiotic usage rises, so too does the risk of antibiotic contamination
to the environment. As much as 90% of the antibiotics being administered are excreted without
being metabolized [50] and are poorly removed by wastewater treatment [51]. Consequently,
active antibiotic compounds in wastewater, sewage sludge, and manure are conveyed to terrestrial
and aquatic ecosystems by a combination of disposal, discharge, and use as fertilizer amendments.
A large number of antibiotics have been detected in soil and sediment at concentrations ranging
from ng·kg-1 to mg·kg-1 [35]. In general, these concentrations are considered therapeutic and are
well below the minimum inhibitory concentrations (MICs) established by acute toxicity tests. Sublethal or therapeutic doses, however, can promote the development of antibiotic resistance in both
target and non-target organisms [52] and have been found to affect the structure and function of
10
ecologically important microbial communities [53]. Microbial communities in soil and sediment
play a fundamental role in nutrient recycling and in mitigating global imbalances caused by human
activity. This is particularly evident in the N cycle where inorganic fertilizer use, fossil fuel
combustion, and N-fixation cultivation have generated a significant imbalance, depositing up to
140 Tg·yr-1 of reactive N species to terrestrial and aquatic environments [54]. The increase in
reactive N species has significantly contributed to a number of environmental and human health
concerns [9, 16-19]. Mitigation strategies include isolating organisms capable of converting
reactive N to N2 as well as maximizing natural recycling potential in affected watersheds. For
example, wastewater treatment commonly includes nitrification and denitrification tanks to reduce
the concentration of organic and inorganic N waste prior to being discharged into surface waters.
The latter step of the reduction process, denitrification, reduces the eco-toxic compound nitrate
(NO3-) to N2 or nitrous oxide (N2O) gases which are lost to the atmosphere. In agroecosystems,
denitrification is a naturally occurring ecosystem service and is estimated to remove up to 22% of
applied N [10] and up to 51% at the watershed scale [55]. Though microbial N processing is often
regarded as a sink for ammonium (NH4+) and NO3-, it may also serve as a source of eco-toxic nitric
oxide (NO) or N2O. N2 gas is the most common product of denitrification and is not associated
with human health problems or environmental degradation, but up to 3.9% of denitrification results
in the production of N2O [56], a powerful greenhouse gas [29] and the leading contributor to
stratospheric ozone depletion [30]. NO is a component of smog and is a contributor to a number
of human health concerns [57]. Although NO is considered a minor product of nitrification and
denitrification, up to 0.75% of applied NH4+-N fertilizers may be lost as NO [58]. Considerable
advances have been made in nutrient management practices to promote high N use efficiency and
to minimize non-point source NO3- and N2O pollution. As antibiotics are introduced to soils,
11
however, the resulting impact on microbial activity and N speciation may reduce the efficacy of
these efforts.
Evidence that antibiotics affect the structure of microbial communities in soil, sediment,
and sewage sludge is abundant. In a 2010 review, Ding et al. [53] identified 31 studies reporting
the effects of 14 antibiotics on microbial communities in soil, sediment, and activated sludge.
Reported changes include positive shifts in the ratios of fungi to bacteria and ammonia oxidizing
bacteria (AOB) to ammonia oxidizing archaea (AOA), increased antibiotic resistance, decreased
rates of bacterial growth, and temporal shifts in microbial diversity. Despite functional
redundancies within the microbial community, structural changes resulting from exposure to
antibiotics may also affect community function (e.g., rates of mineralization, nitrification, and
denitrification) and therefore impact important ecosystem services in contaminated soil and
sediment. Roose-Amsalag et al. [1] provide an excellent overview of the mechanisms that may
contribute to these structural and functional changes. In this review, we focus on the effects of
antibiotics on the biogeochemical N cycle in soil and sediment. We first briefly describe the
occurrence and fate of antibiotics in the environment, concentrating on studies published since the
last major review in 2009 [37]. In the second part of this paper, we discuss the effects of antibiotics
on the microbial N cycle in soil, sediment, and wastewater sludge. In the final section, we discuss
methodological approaches to investigating the effects of antibiotics on the microbial N cycle.
2.2.
Occurrence of Antibiotics in Soil and Sediment
The occurrence of antibiotics in the terrestrial environment is well-documented. A number
of substantial reviews published between 1999 and 2009 summarize research that reports upon the
occurrence of antibiotic and antimicrobial compounds in soil and sediment [35, 37, 38, 50]. In
addition to their application in human medicine, antibiotics are broadly dispensed for therapeutic,
12
prophylactic, and growth promotion purposes in the livestock and poultry industries. Up to 90%
are excreted without being metabolized [36, 50] and recent studies have identified as many as 20
different antibiotic compounds in feces samples from swine, poultry and livestock production
facilities [59-62]. Hospital effluent and wastewater samples are also consistently found to contain
a broad range of antibiotic compounds at low concentrations [63, 64]. When contaminated manure,
sewage sludge, or polluted water are applied to agricultural soils, these residual antibiotic
compounds and their degradation products are introduced to the terrestrial environment where they
often persist and remain bioavailable [37]. Application of manure to agroecosystems is a common
practice, particularly in regions where concentrated animal production occurs. In 2009, for
example, over 15 million acres of U.S. cropland were fertilized with manure, often in close
proximity to livestock and poultry facilities [65]. This figure is likely to grow alongside organic
crop production, which doubled between 1997 and 2005 [66]. Although empirical data are scarce,
the proportion of cropland receiving manure fertilizers is presumed to be much larger in
developing countries where use of N fertilizers is rising dramatically [67]. While the occurrence
of antibiotics in soil and sediment has been documented throughout the world [35, 37], the most
recent studies have focused extensively on these regions where agricultural output and fertilizer
use are on the rise.
2.2.1. Antibiotics in Soil
A search of scientific databases yielded 20 studies reporting on the occurrence of
antibiotics in soil since 2009. Among these, 15 were conducted on field sites in East Asia where
animal manure, wastewater, or contaminated surface water were applied to the soil. A total of 36
different antibiotic compounds from 6 different antibiotic classes were quantified. The median and
maximum concentrations reported for each antibiotic are shown in Table 2-1 alongside the average
13
frequency of detection, region of study, and potential antimicrobial source. The most frequently
investigated compounds (≥50% of studies) include oxytetracycline (OXY), tetracycline (TET),
chlortetracycline (CTC), ciprofloxacin (CIP), norfloxacin (NOR), and enrofloxacin (ENR).
Sulfonamides were investigated in fewer studies but these and tetracycline antimicrobials were the
most frequently detected (up to 100%). Notably, no recent studies have investigated the occurrence
of the medically important β-Lactams group. Among the antibiotics tested, seven were detected at
least once at concentrations in excess of 1 mg·kg-1: CTC (12.9 mg·kg-1), OTC (1.41 mg·kg-1), TET
(1.01 mg·kg-1), flumequine (FLE, 1.33 mg·kg-1), CIP (5.6 mg·kg-1), ENR (1.35 mg·kg-1) and NOR
(2.16 mg·kg-1). Maximum concentrations for the remaining antibiotics ranged from 0.007 μg·kg-1
(anhydrotetracycline, ATC) to 898 μg·kg-1 (ofloxacin, OFL), though the median concentration for
most of the antibiotics tested rarely exceeded 100 μg·kg-1. Minimum concentrations were reported
for 20 of the 36 antibiotics investigated (not shown) and all but CTC were <5 μg·kg-1 and some as
low as 20 ng·kg-1. Several of these studies also reported detection of antibiotics below the limits
of quantification (LOQ), indicating that our knowledge about the extent to which antibiotics persist
at trace levels in soils is limited by analytical capabilities.
2.2.2. Antibiotics in Sediment
The occurrence of antibiotics in sediment is reported in 11 recent studies (Table 2-2). The
majority of these sampled sediments in high-intensity agricultural regions such as the Pearl and
Yangtze River basins in southern China where wastewater discharge and agricultural runoff are
significant sources of antibiotic contamination. Among the 35 antibiotics that were investigated, 5
were not detected in any sediment sample and the concentrations of 5 additional antibiotics were
below quantification limits. Tetracycline, sulfonamide, and select fluoroquinolone antibiotics were
the most frequently researched compounds, appearing in as many as 9 individual studies. Three
14
antibiotics whose concentrations exceed 1 mg·kg-1 in soil were also detected at concentrations
exceeding 1 mg·kg-1 in sediment. These include CTC (1.01 mg·L-1), NOR (1.14 mg·L-1), and OFL
(1.56 mg·L-1). Overall, the median concentration of antibiotics in sediments (0.2-54.6 µg·kg-1) are
lower than those in soil (0.23-157 µg·kg-1).
2.2.3. Fate of Antibiotics in Soil and Sediment
Once they have entered the terrestrial environment, the fate of antibiotics is largely
governed by their physicochemical properties (Table 2-3) and interactions with the environmental
matrix. In terrestrial environments, antibiotics with high octanol-water partitioning coefficients
(Kow) values and large sorption coefficients (Kd) tend to sorb strongly to the soil matrix and hence
are poorly mobile. The tetracycline class of antibiotics exemplifies this behavior. Their sorption
coefficients range from 400 to 1620 L·kg-1 (see Table 2-3), and they are rarely found to migrate
beyond upper 10 cm of the soil column [68]. Poor mobility and long half-lives provide opportunity
for fluoroquinolones (120-2310 days) and tetracyclines (400-1620 days) to accumulate over time,
likely accounting for the frequency at which these antibiotics are detected in soils at concentrations
in excess of 500 μg·kg-1, especially where manure applications are frequent. Since both sorb
strongly to soil and sediment particles, comparably high concentrations of fluoroquinolones and
tetracyclines are also observed in sediment. Sulfonamides are among the most frequently detected
antibiotic compounds in both soil and sediment but their low Kd values (0.6-4.9) render these
compounds highly mobile. In combination with low half-lives (max t½ = 21.3 days), sulfonamides
do not show the same tendency to accumulate and are infrequently detected at concentrations
beyond 50 µg·kg-1 in soil or 5 µg·kg-1 in sediment.
15
Animal Manure
Plant Debris
Gaseous Losses (N2)
Gaseous Losses (N2O)
Biological
N-Fixation
Atmospheric
Deposition
Gaseous Losses (NO)
Nitrogen Fertilizers
N2
N 2O
Soil Organic Matter
R-NH2
Gaseous Losses (N2O)
Mineralization
Nitrifier Nitrification (NN)
NH2OH
+O2
N 2O
Plant Uptake
Ammonium
(NH4+)
Nitrifier Denitrification (ND)
Plant Uptake
Nitrification
NO
Nitrites
(NO2-)
Immobilization
Denitrification
Nitrification
Nitrates
(NO3-)
Leaching
Figure 2-1. The Biogeochemical Nitrogen Cycle.
16
2.3.
Effects of Antibiotics on the Terrestrial Nitrogen Cycle
2.3.1. The Nitrogen Cycle
The N cycle is a global biogeochemical cycle in which N flows between atmospheric,
aqueous, and terrestrial reservoirs. Microbial activity in soil and sediment drives a significant
portion of the bulk cycle, converting organic N into plant available forms (NH4+ and NO3-), and
reducing excess inorganic N to gases (N2 and N2O) that escape to the atmosphere, completing the
cycle (Figure 2-1). NH4+ accumulates in soil as a result of mineralization, N fixation (legumes),
direct deposition from the atmosphere, or by application of inorganic fertilizers containing NH4+
salts, e.g.(NH4)2SO4. NH4+ strongly sorbs to the negatively charged surfaces of soil minerals and
soil organic matter (SOM) and is therefore resistant to leaching, but it may be lost by surface
runoff, plant uptake, biological nitrification, or annomox reactions. NO3- that is produced via
nitrification or directly added to soils via inorganic fertilizers, e.g., KNO3, is susceptible to a
number of losses. These include plant uptake, assimilation into microbial tissue, leaching, and
biological denitrification. In the following sections, we will briefly review the biology of
nitrification and denitrification, followed by an examination of the effects antibiotics and
antimicrobials have on these processes.
2.3.2. Nitrification
Nitrification is a general term used to describe naturally occurring NH4+ oxidation
reactions. The most common oxidation pathway leads to the production of NO3- via the
intermediate product, NO2-. Studies of chemoautotrophic nitrifying organisms such as
Nitrosomonas europaea describe the NH4+ → NO2- oxidation as a two-step enzymatic process (see
Eq. 2-1 and Eq. 2-2) catalyzed by ammoniamonoxygenase (AMO) and hydroxylamine
oxidoreductase (HAO), respectively [69]:
17
The resulting intermediate product, NO2-, is rapidly oxidized to NO3-. Chemoautotrophic
nitrifiers of the genus Nitrobacter and Nitrosomonas express the nitrite oxidoreductase (NOR)
enzyme, which facilitates a second oxidation reaction (NO2- → NO3-) to provide energy for cell
growth [70, 71]. Although considered secondary to autotrophic AOA and AOB in most soils, a
number of heterotrophic nitrifiers have also been isolated. These include the gram-negative
bacteria Alcaligenes faecalis and Pseudomonas aeruginosa [72, 73]. The mechanisms for
heterotrophic nitrification are poorly understood and the process yields insufficient energy to
support heterotrophic cell growth [70]. Heterotrophic nitrification has been also been reported to
include alternate redox pathways following the initial NH4+ → NH2OH oxidation step. These
include oxidation of NH2OH to NO or N2O (Nitrifier Nitrification) and reduction of NO2- to N2O
(Nitrifier Denitrification) [73, 74].
2.3.3. Effects of Antibiotics on Nitrification
A literature search identified a total of 13 studies that investigated the effects of 19 different
antibiotics, antimicrobials, and antibiotic mixtures on nitrification in soil, wastewater sludge, or
pure culture (Table 2-4). Inhibition is often deemed the most probable effect of antibiotics on
nitrification, but this hypothesis is ineffectually supported by the present studies. Among 19
antibiotics and antimicrobials investigated, fewer than half (9) show that the antibiotic or
antimicrobial tested inhibited nitrification and the minimum inhibitory concentration (MIC)
ranged from 200 μg·kg-1 (sulfadimethoxine, SDM) [75] to 200 mg·kg-1 (TET) [76]. Based on their
low sorption coefficients (Table 2-3), sulfonamide antibiotics are likely to be the most
18
bioavailable, which may account for the low inhibitory concentration of SDM relative to more
sorptive species like CTC and TET. Although this claim is poorly supported by the MIC of other
sulfonamides, a fair comparison is difficult because the lowest tested concentrations of the other
sulfonamides were 2 mg·kg-1 (sulfadiazine, SDZ) and 4 mg·kg-1 (sulfamethoxazole, SMX).
Among the remaining antibiotics, the following 5 had no observable effect on nitrification:
CTC, difloxacin (DIF), monensin (MON), invermectin (INV), and chloramphenicol (CPH). That
nitrification was not significantly affected at either low (μg·kg-1) or high (mg·kg-1) therapeutic
concentrations for some antibiotics does not conclusively show that the nitrifying community was
unaffected. For example, Luis Campos et al. [77] observed no change in net nitrification in soils
treated with either 10–250 mg·L-1 CPH or lower doses (<100 mg·L-1) of OTC but suggested that
shifts in the ratio of ammonia-oxidizing bacteria (AOB) to ammonia-oxidizing archaea (AOA)
may account for the lack of apparent response. Kotzerke et al. [78] proposed a similar explanation,
stating that the contributions of fungal and archaeal nitrification may be sufficient to regulate net
nitrification when AOB are inhibited. Although one study concludes that AOB are more important
in N-rich soils [79], others tend to support the hypothesis that AOA are able to regulate nitrification
when AOB are compromised. It has also been reported that AOA outnumber and likely outperform
AOB [80].
In addition to providing resiliency when AOB are compromised, some studies have shown
that population growth among AOA [81] or dose-related shifts in the fungi to bacteria ratio [82]
are stimulated by some antibiotics. These types of shifts may partially explain stimulated
nitrification, an outcome that was observed in soils treated with NOR (1 mg·kg-1) [83], bacitracin
(BAC, 100 mg·kg-1), and a mixture of BAC, MON, and INV (0.1–100 mg·kg-1) [84]. In the latter
treatment, a large shift in the AOA:AOB was correlated to accelerated nitrification observed in
19
short term soil mesocosms 7 and 30 days after receiving a 100 mg·kg-1 dose [84]. In an associated
field experiment where lower doses (0.1-10 mg·kg-1) were applied, stimulation did not become
evident until a year after the initial antibiotic application. AOA:AOB ratios were not shown for
the field soils, but the delayed (1 year) response at lower doses suggests that changes in the
microbial community may simply proceed more slowly when exposed to lower concentrations.
Stimulation was also observed in soil microcosms treated with CIP [85] and NOR [83]. In
these experiments, nitrification was stimulated only at the lowest doses tested (1 mg·kg-1). At
higher concentrations (>5 mg·kg-1 and >100 mg·kg-1, respectively), CIP and NOR inhibited
nitrification. The apparent disagreement at different doses is characteristic of hormesis, a J-shaped
dose response in which low doses of a toxin stimulate response and high doses are inhibitory [86],
though hormesis has never been explicitly studied for complex microbial communities such as
those occurring in soils.
Presently, changes to microbial communities are the dominant hypotheses proposed to
explain why nitrification is unchanged or even stimulated in some soils or sewage sludge following
exposure to antibiotics. Positive shifts in the AOA:AOB ratio, for example, illustrate functional
redundancy in the soils that may compensate for reduced AOB activity leading to no observed
effect. Alternately, if AOA outperform AOB, a shift in the AOA:AOB ratio may accelerate
nitrification in some soils following exposure to antibiotics. These changes do provide a potential
explanation for stimulated nitrification where antibiotic exposure occurs, but they do not
satisfactorily explain how the same dose of a single antibiotic can yield different results when
applied to different media. For example, Louvet et al. [87] evaluated the effect of 0.1-20 mg·L-1
erythromycin (ERY) on nitrification in two different activated sludge materials. In the first sludge
(Nancy), nitrification was inhibited (>10 mg·kg-1), an observation corroborated by Katipoglu-
20
Yazan et al. [76]. When Louvet et al. applied the same treatment to a different sludge (Epinal),
however, a stimulatory effect was observed. Disagreement between these results may point to the
role of the endemic microbial community in determining its response to antibiotic exposure.
Though the sludges were obtained from the same region, the Nancy sludge was prepared with a
biofilm on sand whereas the Epinal sludge was prepared in an oxidation ditch with no settling.
These two sludge-forming environments may favor different groups of nitrifying organisms whose
responses to ERY are sufficiently unique that stimulation is observed in one and inhibition in the
other.
2.3.4. Denitrification
Denitrification is a naturally occurring process in which NO3- is sequentially reduced to N2
gas (Eq. 2-3):
Denitrifying organisms include a diverse group of bacteria, fungi, and archaea [88], but the
majority of denitrification is attributed to heterotrophic anaerobes. The best studied denitrifying
bacteria include Paracoccus denitrificans and Pseudomonas stutzeri [89]. Each stage of the
denitrification process is facilitated by one or more membrane bound enzymes including: NAR
(NO3- reductase), NIR (NO2- reductase), NOR (NO reductase), and NOS (N2O) reductase [90].
Although N2 is the dominant denitrification product (>95%), some fraction is lost as free NO or
N2O.
2.3.5. Effects of Antibiotics on Denitrification
The effects of 18 different antibiotics on denitrification have been investigated and the
results vary considerably among the different solid matrices and concentrations tested (Table 2-5).
Inhibition was reported in soil, sediment, and/or groundwater treated with the following 12
21
antibiotics: BAC, amoxicillin (AMO), clarithromycin (CLA), CTC, ERY, FLE, gentamicin
(GTC), narasin (NAR), SDZ, SMZ, SMX, and vancomycin (VAN). In sediment, Costanzo [91],
Yan [92], Laverman [93], and Roose-Amsaleg [94] measured the effects of 8 different antibiotics
on denitrification rate. Inhibition relative to the control was observed for 7 of these antibiotics, but
none at a concentration <1000 μg·L-1 except where SMZ (0.05-100 μg·L-1) was applied [95].
Because SMZ has very low sorption coefficient in comparison to most of the other antibiotics
tested, what appears to be greater sensitivity to this antibiotic may simply be a reflection of
bioavailability. On the other hand, SMX is equally mobile and was only observed to inhibit
denitrification in sediment at concentrations in excess of 57.5 mg·L-1 [92]. Because the antibiotic
agencies and physiochemical properties of SDZ and SMX are comparable, a 1000-fold difference
between their reported MICs is unexpected, but there are a number of experimental dissimilarities
that may account for it. For example, Yan et al. [92] conducted a series of flow-through reactor
experiments in which the input solution containing 0.24, 2.1, 11, or 57,500 μg·L-1 SMX was
continuously supplied over a period of weeks and steady-state denitrification was measured from
the ratio of effluent to influent NO2- + NO3-. Significant inhibition was observed only at the 57,500
μg·L-1 dose, leading the authors to suggest that chronic exposure to therapeutic doses may promote
SMX resistance. Resistance is less likely to develop in short-term experiments following a single
antibiotic dose, which may explain why Hou et al. [95] were able to observe inhibition in sediments
1-8 hours after they were treated with lower doses of SDZ (0.05-100 μg·L-1). On the other hand,
the effects of SMX were not investigated for any dose between 11 and 52,500 μg·L-1 and future
studies conducted within this range may identify inhibitory concentrations of SMX that are more
consistent with the results of short-term studies. Furthermore, the resistance hypothesis does not
explain why therapeutic concentrations of SMX and SMZ inhibited denitrification in groundwater
22
studies even when the antibiotic was continuously supplied [41, 96]. A total of 7 antibiotics (3
sulfonamide, 1 β-lactam, 1 aminoglycoside, 1 ionophore, and 1 polypeptide) inhibited
denitrification in soils, while several others were reported to stimulate denitrification, particularly
at ultra-low (ng·kg-1 or ng·L-1) concentrations. For example, SMX inhibited denitrification in
groundwater at 1.2 μg·L-1 [41] but accelerated NO3- reduction in flow-through column experiments
(1 ng·L-1) and the effect was amplified over time [97].
2.3.6. Effects of Antibiotics on NOx Emissions
Eco-toxic NOx gases including NO and N2O are minor products of nitrification and
denitrification. N2O is produced by bacteria, archaea, and some fungi in soil and sediment as a
byproduct of nitrification or as free intermediates of denitrification. Under anoxic conditions, the
predominant pathway is via the sequential reduction: NO3-→NO2-→NO→N2O. Although a
portion of N2O produced in soil and sediment will be consumed by bacteria able to use it as a
terminal electron acceptor [98, 99], some will ultimately be diffused to the surface and lost to the
atmosphere. Because N2O is a potent greenhouse gas and can reduce stratospheric ozone, the flux
of N2O from soil and sediment is of significant interest. However, the impact of terrestrial
antibiotics on N2O emissions from soil and sediment has scarcely been addressed. In fact, only 2
studies were found that explicitly investigate this topic. Both observed an increase in N2O flux
from soils treated with sub-therapeutic doses of antibiotics. Hou et al. [95] tested the effects of
SMZ (0.05-100 μg·L-1) and reported an increase in N2O flux by as much as 300% (>50 μg·L-1
SMZ) within 8 hours of exposure. Because the increase in N2O flux coincided with inhibited
denitrification, the authors propose that antibiotics may more strongly inhibit N2O reduction to N2
than N2O production itself, resulting in an increased N2O: N2 production ratio [95]. DeVries et al.
[97] proposed a similar conclusion upon observing a 3-fold increase in N2O flux in soils amended
23
with 1-1000 ng·kg-1 NAR after a 3-day incubation. Alternately, increased denitrification, which
was reported for 4 antibiotics in soil and groundwater, may also increase N2O flux without an
associated shift in the N2O: N2 ratio and ought to be investigated in future studies. NO is also
produced in small quantities during nitrification and is a free intermediate of denitrification.
Though it is a major component of smog, no studies were found that have investigated the effects
of antibiotics on NO flux from soil or sediment.
2.3.7. Overview of Current Measurement Methodology
The results of these investigations allow few broad conclusions regarding the effects of
antibiotics and antimicrobials on nitrification and denitrification. Total consensus is not expected
because individual antibiotics target different types of organisms and vary in their efficacy.
Inconsistent results among antibiotics of the same class or for a single antibiotic compound,
however, likely are influenced by methodological differences. Konopka et al.’s [84] two
investigations illustrate this point well. In their short-term study, 100 mg·kg-1 of BAC/MON/ROX
stimulated nitrification in soil mesocosms but lower doses (0.1-10 mg·kg-1) had the same effect in
field soils, but it was not observed until 1 year after the initial application. Had the field study been
terminated after a few weeks, the authors would have reported that the lower doses have no effect,
which highlights the need for a higher degree of consistency in terms of antibiotic dose and
experimental duration. The results of individual experiments may also be influenced by natural
variations in soil or sediment composition or the use of nutrient amendments. For example,
Konopka et al. [84] reported increased nitrification and a positive shift in the AOA:AOB ratio in
a loamy soil 7 days after it was dosed with 100 mg·kg-1 BAC. In contrast, Banerjee et al. [100]
reported no effect within 5 days after applying a comparable dose to a silty loam. An
accompanying fatty acid methyl ester (FAME) profile analysis indicated that there was no
24
significant change in the microbial community [100]. Notable differences between the two studies
include the soil properties and the use of N fertilizer amendments. The organic carbon (OC) content
of the soil used by Banerjee et al. was higher (3.9% vs. 2.5%) and the soil was amended with
(NH4)2SO4 to help promote nitrification. Higher OC may enhance the role of heterotrophic
nitrifiers and if these organisms are less sensitive to BAC than autotrophic AOB there may be less
opportunity for AOA to take a more prominent role in nitrification. Alternately, amending the soil
with (NH4)2SO4 stimulates all nitrifying activity and the resulting growth may compensate for any
negative impacts that BAC may have on one or more individual groups of organisms.
Antibiotics that affect the structure and function of the soil or sediment microbial
community may also alter nitrification pathways or denitrification product ratios. Where this
occurs, standard methods for quantifying nitrification may not accurately measure the nitrification
rate in soils exposed to antibiotics or capture shifts in the N2O:N2 ratio. Nitrification is most
commonly measured by monitoring the size of the reactant (NH4+) or product (NO2-/NO3-) pools
over time. Under nitrifying conditions, the NH4+ pool will be reduced over time and nitrification
rate is taken as ΔNH4+/Δt. NH4+ can be extracted from soil and sediment with a concentrated salt
solution (2M KCl) and quantified colorimetrically. The indophenol blue method [101] is most
common and can be performed manually or by automated flow injection analysis. Since
autotrophic oxidation to NO3- via NO2- is the dominant nitrification pathway in soil and sediment
[102], nitrification rates determined by the product pools are measured by quantifying the
accumulation of NO2- + NO3- over time. Both are easily extracted from soil and sediment into
aqueous solution and can subsequently be quantified by a number of reliable and inexpensive
colorimetric methods, e.g., cadmium reduction [103, 104]. Under some conditions, NO3- may
undergo rapid reduction to N2O or N2 (denitrification) and preclude reliable measurements of
25
nitrification from the combined NO2- + NO3- pool. In these circumstances, an inhibitor such as
sodium chlorate can be added to the soil to prevent the oxidation of NO2- to NO3-. When inhibitors
are used, the measurement is called potential nitrification and is determined from the increase of
NO2- over time [105].
Alternate nitrification pathways that affect the concentration of NO2- and NO3- are not
captured by these methods. For example, nitrifier nitrification (NN), nitrifier denitrification (ND)
and annamox each influence the size of the NO2- pool. NN lowers NO2- production rate by
oxidizing NH2OH to N2O and the latter (ND and annamox) consume NO2-. Assuming no change
to total nitrification, an increase in the ratio of any of these pathways to complete oxidation (NH4+
→ NO3-) will cause the nitrification rates to be underestimated when the NO2- + NO3- pool is used
for quantification. If the shift is significant, the apparent reduction in nitrification rate may even
be reported as inhibition. Similarly, if antibiotics reduced the contributions of NN, ND, and
annamox to total nitrification, the NO2- + NO3- pool would increase in size and cause the
nitrification rate to be overestimated and reported as stimulation. In order to avoid
over/underestimation of nitrification rate, we recommend that determination of NO and N2O flux
be included in future studies evaluating the effects of antibiotics on nitrification rate.
The most common methods for quantifying the effects of antibiotics on denitrification rate
include monitoring the depletion of NO3- over time under anaerobic conditions and the acetylene
block method. In the latter method, acetylene gas is added to gas-tight sample vials to inhibit
reduction of N2O to N2 and denitrification rate is determined from the concentration of N2O in
headspace [106]. NO3- measurements often require destructive sampling, so the acetylene block
method is better suited to evaluate changes on shorter time-scales, i.e., hours vs. days. Since
denitrification follows a linear pathway, neither method is prone to over/underestimating
26
denitrification as a result of changes to the microbial community but they also do not provide a
coincident measure of the N2O:N2 or NO:N2 flux ratios. Where NO3- is used as a metric, N2O flux
is not considered at all. In the latter, NO is not quantified and acetylene inhibits the reduction of
N2O to N2 which will mask shifts in the N2:N2O ratio that may result from antibiotic exposure.
Furthermore, both of these methods are conducted under fully anaerobic conditions to prevent
nitrification from adding to the NO3- pool during the measurement period. This may be realistic
for sediment, but denitrification in soils is more often limited to anaerobic hotspots that develop
in micropore spaces and rarely occurs in complete isolation from nitrification. It may therefore be
more relevant in soils to use stable 15N methods to quantify nitrification and denitrification rates.
Stable 15N isotopic tracers have the advantage that they can capture process-rate changes
in nitrification and denitrification under conditions favoring coupled nitrification-denitrification.
For example, the isotope dilution method uses a 15N-KNO3 enrichment and nitrification rate (μg N
g-1 soil d-1) may be calculated from 15N-NO3- dilutions according to the equations 1-11 of Kirkham
and Bartholomew [107] or other modified versions of these equations [108]. Denitrification rate
(μg N g-1 soil d-1) can also be determined from this enrichment using the ratios of 28N2, 29N2, and
30
N2 in headspace [109]. These methods can be paired with a 15N-NH4+ enrichment to concurrently
measure organic N mineralization rates, which have not previously been measured in soils treated
with antibiotics. Because they allow for quantification of the cumulative effects of the antibiotic
on reaction rate and the resultant accumulation of N2O and NO3-, combining these measurements
may be particularly relevant under fluctuating soil moisture conditions or when changes to the
soil/sediment microbial community are probable.
27
2.4.
Conclusions and Prospects
Current data indicate that the biogeochemical N cycle may be altered by environmentally
relevant concentrations of antibiotics. Of the processes evaluated, nitrification appears less
sensitive to antibiotics than denitrification at therapeutic doses (<mg·kg-1). Although mg·kg-1
concentrations have been reported in wastewater and wastewater sludge where inhibition of either
process may reduce overall wastewater treatment efficiency, there remains inadequate information
regarding the effects at sub-therapeutic concentrations to conclusively evaluate the risk to
ecosystem function in aquatic and terrestrial environments. As limits of detection have improved,
it has become evident that a number of antibiotics are present in soils at concentrations in the low
ng·kg-1 range, and thus there is a clear need to examine a broader range of concentrations when
testing for effects on N processing. Where environmentally relevant concentrations have been
evaluated, the sulfonamide group appears to have the greatest potential to significantly affect
microbial N cycling. Although this partially is due to the fact that the sulfonamides have been the
most frequently tested antibiotics, the associated risk is enhanced by their high mobility in soil and
sediment and the apparent sensitivity of both nitrifiers and denitrifiers to concentrations as low as
1 ng·kg-1 or 1 ng·L-1.
The number of studies exploring the impacts of antibiotics on biogeochemical N cycling
has notably increased in recent years, yet there are a number of substantial weaknesses highlighted
by this review. Like Roose-Amsalag [1], we find that there is a distinct lack of consistency among
different studies in terms of antibiotic dose, substrate, method by which nitrification and/or
denitrification are measured, and the duration of the experiment. The result is that comparisons
between individual studies are difficult, if not impossible. Second, all of the research summarized
here focuses on process rates and with little or no regard to the measurable outcome of process-
28
related change such as the accumulation of eco-toxic NO3-, NO, and N2O. Furthermore, common
methodological approaches to quantify process rates may over/ underestimate the effects of
antibiotics on a given process where the size of the N-pool used for quantification is affected by
changes to the redox pathway.
Addressing these concerns will require a more systematic and comprehensive approach to
future investigations. Recommendations include establishing a standardized set of antibiotic doses
that include sub-therapeutic concentrations (<μg·kg-1) and including testing antibiotics from the
β-Lactams group. Where the effects of antibiotics on process rate are evaluated, e.g., nitrification
or denitrification, more comprehensive measurement tools should be considered to avoid either
(1) over/underestimating the effects of antibiotic exposure or (2) masking the accumulation of ecotoxic compounds. For example, nitrification measurements can be modified to include NO and
N2O flux measurements. In addition to providing relevant information about the effects of the
antibiotic on these fluxes, including these measurements may also afford a more accurate
determination of the effects of antibiotics on nitrification. Where possible, isotopic tracer studies
can be substituted for the acetylene block methods to allow simultaneous measurements of
denitrification and N2O flux. Furthermore, a combination of isotope dilution techniques can be
combined to study the effects of antibiotics on coupled nitrification-denitrification in soils, which
would allow net effect of antibiotics on the resulting accumulation of N2O and leachable NO3- in
soils to be effectively determined. Although isotopic tracer studies are more expensive and timeconsuming than the other methods discussed (e.g., mineral N diffusions for 15N analysis require a
1-3 week incubation [110]) these may be well-suited for long-term investigations. There is
evidence that the effects of antibiotic exposure may not be evident for as long as 1 year after initial
29
exposure, highlighting the need for future studies to include multi-year investigations in which
antibiotics applications are replicated over time or delivered continuously.
2.5.
Acknowledgements
This work was partially supported by a PSC-CUNY grant.
30
Table 2-1. Median and Maximum Concentrations of Antibiotics Detected in Soil (μg·kg-1)*. The mean reported frequency of detection
is calculated from studies that explicitly report rate of detection. Antibiotic sources are abbreviated as: SM = Swine Manure, CM =
Cattle Manure, PL = Poultry Litter, MM = Mixed Manure, WW = Wastewater Discharge, MD = Manufacturing Discharge, U =
Unspecified Manure. Individual studies may list more than one potential antibiotic source.
Max.
---
22.3
Mean.
Freq.
(%)
---
101.5
5600
70.5
[59-61, 112-118]
21.5
21.5
6.0
[60, 115, 118]
87
1347.6
65.9
[59-62, 113, 114, 117-119]
---
559
7.0
[118, 119]
114.5
1331
---
[62]
13.7
93.6
69.0
[61, 115, 117, 118]
21.5
2160
66.0
[59-62, 111, 112, 115-118]
93.5
898
80.3
[60, 61, 112, 113, 115, 116, 118]
---
9.06
6.0
[115]
U (1)
---
nd
---
[120]
Dk (1)
U (1)
---
0.0004
---
[120]
1
Dk (1)
U (1)
---
nd
---
[120]
Salinomycin
1
Dk (1)
---
0.0022
---
[120]
Erythromycin
5
Ch (4), My (1)
4.4
7.2
11.0
[61, 62, 111, 115, 118]
Josamycin
1
Ch (1)
---
nd
---
[118]
Roxithromycin
3
Ch (3)
49.2
96.3
42.5
[61, 115, 118]
Spriamycin
2
Ch (2)
U (1)
WW (3), SM (1),
CM (1), PL (1)
WW (1)
SM (1), CM (1), PL (1),
WW (2), U (1)
MM (1), WW (2)
---
1.12
0.8
[115, 118]
Tylosin
4
Ch (3), My (1)
PL (1), MM (1), WW (2)
23
679
29.9
[61, 62, 115, 118]
Sulfachlorpyridazine
1
Ch (1)
SM (1), CM (1), PL (1)
---
52.9
100.0
[61]
Region**
(# of studies)
Chloramphenicol
1
Ciprofloxacin
10
Ch (1)
Ch (7), Sp (1),
Bz (1), In (1)
Difloxacin
3
Ch (3)
Enrofloxacin
9
Ch (6), Bz (1),
My (1), Tk (1)
Fleroxacin
2
Ch (2)
SM (4), CM (3), PL (2),
WW (1), MD (1), U (1)
SM (2), CM (2),
PL (1), WW (1)
PL (4), SM (3),
CM (3), MM (1), WW
(1)
MM (1), WW (1)
Flumequine
1
My (1)
PL (1)
Lomefloxacin
4
Ch (4)
Norfloxacin
10
Ch (6), Sp (1),
Bz (1), In (1), My (1)
Ofloxacin
7
Ch (5), Sp (1), In (1)
Sarafloxacin
1
Ch (1)
SM (1), CM (1), PL (1),
MM (1), WW (2)
PL (3), CM (2), WW (3),
SM (1), MM (1), U (1)
SM (3), CM (2), PL (1),
WW (2), MD (1), U (1)
MM (1), WW (1)
Lasalosid
1
Dk (1)
Monensin
1
Narasin
Antibiotic
Amphenicol
Fluoroquinolone
Ionophore
Macrolide
31
Med.
#
studies
Antibiotic
Class
Sulfonamide
Potential Sources
(# of studies)
Reference
[111]
Tetracycline
*
Sulfadiazine
7
Ch (6), My (1)
Sulfadimethoxine
4
Ch (3), US (1)
Sulfadimidine
1
Ch (1)
SM (2), CM (2), PL (2),
WW (3), MM (1), U (1)
CM (2), SM (1), PL (1),
WW (1), U (1)
SM (1), CM (1), PL (1)
Sulfamerazine
2
Ch (2)
Sulfameter
1
Ch (1)
Sulfamethazine
6
Ch (4), US (1), K (1)
Sulfamethoxazole
6
Ch (4), Sp (1),
US (1)
Sulfamonomethoxine
3
Ch (3)
Sulfapyridine
2
Ch (2)
Sulfathiazole
1
Sufisoxazole
1
Anhydrotetracycline
1
Chlortetracycline
9
Doxycycline
5
Oxytetracycline
11
Tetracycline
10
21.5
85.5
22.5
[60-62, 115, 117, 118, 121]
26
40.4
82.0
[61, 117, 118, 122]
---
177.9
65.4
[61]
WW (1), U (1)
---
93.5
52
[117, 118]
---
120.4
87
[117]
3.2
74
77.8
[60, 111, 115, 117, 122, 123]
19.3
54.5
17.5
[61, 112, 115, 117, 121, 122]
2.79
5.37
0.8
[60, 115, 118]
2.91
5.11
98.2
[60, 115]
K (1)
WW (1), U (1)
SM (2), WW (2),
CM (1), MM (1), U (1)
SM (2), CM (2), WW
(2),
PL (1), MM (1)
SM (1), CM (1),
MM (1), WW (2)
SM (1), CM (1),
MM (1), WW (1)
SM (1)
0.23
0.38
100.0
[123]
Ch (1)
WW (1
---
nd
---
[118]
US (1)
Ch (6), Tk (1),
US (1), K (1)
U (1)
---
0.007
---
102.3
12900
77.5
[124]
[60, 61, 113, 117, 119, 121-123,
125]
157
728
100.0
[60-62, 115, 125]
40.6
1410
75.5
[60, 61, 112, 113, 117, 119, 121125]
105
1010
69.6
[60, 61, 111-113, 117, 122-125]
Ch (4), My (1)
Ch (6), Sp (1), Tk
(1),
US (2), K (1)
Ch (6), Sp (1),
US (2), K (1)
SM (4), CM (3), MM, (2)
PL (1), WW (1)
SM (2), CM (2), PL (2),
MM (2), WW (1)
SM (4), CM (3), MM (2),
U (2), PL (1), WW (1)
SM (4), CM (3), U (2),
PL (1), MM (1), WW (1)
None detected (nd)
China (Ch), Malaysia (My), Korea (K), Turkey (Tk), India (In), Spain (Sp), United States (US), Denmark (Dk), Brazil (Bz)
**
32
Table 2-2. Minimum and Maximum Concentrations of Antibiotics Detected in Sediment (μg·kg-1). Antibiotics whose concentration
were below the limits of quantification (LOQ) are indicated as <LOQ*.
Antibiotic
Class
Antibiotic
Amphenicol
Chloramphenicol
Fluoroquinolone
Ionophore
Macrolide
Sulfonamide
#
Studies
1
Region**
(# of studies)
Ch (1)
Florfenicol
1
Ch (1)
Thiamphenicol
1
Ciprofloxacin
Difloxacin
Med.
Max.
Reference
---
<LOQ
[126]
---
<LOQ
[126]
Ch (1)
---
<LOQ
[126]
1
Ch (1)
16.6
197
[126, 127]
1
Ch (1)
---
nd
[127]
Enrofloxacin
4
Ch (3), US (1)
4.84
137
[60, 126-128]
Fleroxacin)
1
Ch (1)
6.69
6.69
[127]
Lomefloxacin
3
Ch (3)
2.78
29
Norfloxacin
6
Ch (6)
26.6
1140
[60, 126, 127, 129-131]
Ofloxacin
8
Ch (7), Sp (1)
54.6
1560
[60, 112, 126, 127, 129-131]
Sarafloxacin
1
Ch (1)
---
nd
[127]
Lasalosid
1
Dk (1)
---
nd
[120]
Monensin
1
Dk (1)
---
nd
[120]
Salinomycin
1
Dk (1)
---
7E-04
[120]
Narasin
1
Dk (1)
---
4E-04
[120]
Erythromycin
5
Ch (5)
14.8
385
[60, 127, 129-131]
Roxithromycin
5
Ch (5)
3.42
302
[126, 127, 129-131]
Spriamycin
1
Ch (1)
61.9
61.9
[131]
Tylosin
1
Ch (1)
---
nd
[60]
Sulfachlorpyridazine
1
US (1)
---
nd
[128]
Sulfadiazine
6
Ch (6)
1.27
83.9
[60, 126, 127, 129-131]
Sulfadimethoxine
3
US (2), Ch (1)
0.2
0.2
[122, 127, 128]
Sulfamerazine
3
1.44
2.47
[126-128]
Sulfamethazine
9
2.87
248
[60, 122, 123, 126-131]
Sulfamethizole
1
Ch (2), US (1)
Ch (6), US (2),
K (1)
USA (1)
---
nd
[128]
[60, 127, 129]
33
Sulfonamide
Tetracycline
Sulfamethoxazole
9
Sulfamonomethoxine
2
Ch (5), US (2),
K (1), Sp (1)
Ch (2)
Sulfapyridine
2
Sulfaquinoxaline
0.52
7.86
[60, 112, 122, 123, 126-130]
1.55
1.86
[60, 127]
Ch (2)
3.71
9.12
[126, 127]
1
Ch (1)
0.54
0.959
[126]
Sulfathiazole
5
Ch (4), US (1)
2.06
5.94
[123, 126-128, 131]
Sulfisoxazole
1
Ch (1)
1.71
1.71
[127]
Chlorotetracycline
6
Ch (4), US (2)
10.5
1010
[60, 122, 123, 126, 128, 129]
Doxycycline
3
14.6
444
[60, 126, 129]
Oxytetracycline
9
41.5
214
[60, 112, 122, 123, 126, 128-131]
Tetracylcine
9
Ch (3)
Ch (5), US (2),
K (1), Sp (1)
Ch (5), US (2),
K (1), Sp (1)
42
94.79
[60, 112, 122, 123, 126, 128-131]
*None detected (nd)
**China (Ch), Malaysia (My), Korea (K), Spain (Sp), United States (US), Denmark (Dk)
34
Table 2-3. Usage and Physiochemical Properties of Select Antibiotics*.
Antibiotic
Class
Antibiotic
Usage
pKa
Fluoroquinolone
Ciprofloxacin
Human Health, Veterinary [132]
Enrofloxacin
Norfloxacin
Ofloxacin
Macrolide
Erythromycin
Roxithromycin
Sulfonamide
Half-life in soil (days)
6.09a, 6.82b [133]
0.28
61,000 [134]
2310 ± 1155 [135]
Veterinary [132]
6.27a,
8.3b
1.1 [134]
260-6000 [134]
n/a
Human Health [132]
6.40a,
8.68b
[136]
-1.0 [137]
n/a
1155 [135]
Human Health [132]
5.97a,
8.28b
+
Human Health , Veterinary [132]
Human
Health+
[132]
[134]
[134]
0.35 [134]
310 [134]
1386 ± 434 [135]
b
8.88 , 12.44 [138]
3.06 [138]
n/a
360 [139]
8.80a,
12.45b [140]
2.75 [141]
n/a
>>120 [142]
a
Tylosin
Veterinary [132]
[143]
3.5 [134]
129.5 (est.) [144]
8.3 [142]
Sulfachlorpyridazine
Veterinary [132]
1.87d, 5.45e [143]
0.31 [145]
09-1.8 [146]
21.3 [147]
Sulfadiazine
Human Health, Veterinary [132]
2.01d, 6.15e [148]
-0.092 [148]
2.0 [149]
12-18 [83]
Veterinary [150]
2.65d,
7.65e
[134]
0.89 [134]
0.6-3.1 [134]
18.6 [147]
Human Health [132]
1.97d,
5.70e
[151]
0.89 [151]
n/a
9-18.3 [152]
Veterinary [132]
1.98d,
5.96e
[153]
0.70 [145]
n/a
n/a
Veterinary [154]
2.01d,
7.11e [143]
0.05 [145]
4.9 [134]
n/a
Human Health, Veterinary [132]
3.3f,
-0.36 [155]
794 [156]
25.9-30.8 [157]
Doxycycline
Human Health, Veterinary [132]
3.02f,
0.02 [145]
n/a
533 ± 23 [135]
Oxytetracycline
Human Health, Veterinary [132]
3.3f,7.3g, 9.1h [158]
1.22 [158]
680-1030 [68]
30.2-41.3 [157]
Tetracycline
Human Health, Veterinary [132]
3.32f, 7.78g, 9.58h [143]
1.30 [145]
400-1620 [134]
578 [135]
Sulfamethoxazole
Sulfamonomethoxine
Sulfathiazole
*
Kd (L·kg-1)
7.50c
Sulfamethazine
Tetracycline
Log Kow
Chlortetracycline
7.44g,
9.27h
7.97g,
[155]
9.15h
[143]
Data not available (n/a)
Critically Important Antibiotic, tHighly Important Antibiotic
a
carboxl group, bprotonated amino group, cbasic dimethylamine group, dbasic amine group, eacidic amine group, ftri-carbonyl group,
g
dimethylamine group, hβ-diketone
+
35
Table 2-4. List of observed effects of antibiotic on nitrification rate and nitrification potential in soil and wastewater sludge.*
Antibiotic Class
Antibiotic
Concentration
(mg kg-1 or mg L-1)
Effect
Media**
Application Method
Experimental
Duration
Amphenicol
Avermectin
Chloramphenicol
10-250
No Effect
Mixed Culture4
Invermectin
0.1-10
Fluoroquinolone
Ciprofloxacin
1-50
Difloxacin
0.007-0.012
Norfloxacin
1-200
Ofloxacin
2-10
Ionophore
Macrolide
Monensin
Erythromycin
Virginiamycin
Mixed
Organoarsenic
Polymyxin
Polypeptide
Sulfonamide
(Bac/Mon/Inver)
Roxarsone
Colistin
Bacitracin
Sulfadiazine
Sulfadimethoxine
Sulfamethoxazole
No Effect
Inhibition (>5 mg kg-1)
Increased (1 mg kg-1)
No Effect
Stimulation (1 mg kg-1)
Inhibition (>100 mg kg-1)
Inhibition
Antibiotic Solution
Weeks
[77]
(Field)4
Antibiotic Solution
Weeks/Years
[44]
Soil Microcosm1
Antibiotic Solution
Hours
[85]
Soil Microcosm1
Contaminated Manure
Weeks
[159]
Soil Microcosm2
Antibiotic Solution
Weeks
[83]
Pure Culture4
Spiked Media
Hours
[160]
Spiked Manure
Weeks
[75]
Soil
Microcosm3
0.010-0.100
No Effect
Soil
0.1-10
No Effect
Soil (Field)4
100
Soil Microcosm4
1-267
No Effect
Inhibition (10 mg L-1)
Stimulation (10 mg L-1)
Inhibition (>20 mg L-1)
1.5-500
Inhibition (>15 mg kg-1)
0.1-20
0.1-10
Stimulation (>1 year)
[84]
Hours
[87]
WW Sludge4
Spiked Wastewater
Days
[161]
Soil Microcosm4
Antibiotic Solution
Days
[45]
Weeks/Years
[84]
Soil
(Field)4
Microcosm4
Stimulation
Soil
Inhibition (>150 mg kg-1)
Soil Microcosm4
Mixed
Culture4
(Field)4
0.1-10
No Effect
Soil
100
Increased
Soil Microcosm4
1.5-500
No Effect
Soil Microcosm4
kg-1)
Weeks
Spiked Wastewater
1.5-500
Inhibition (ammonia oxidation)
Antibiotic Solution
Weeks/Years
WW Sludge4
100
0.3-300
Reference
Weeks
Antibiotic Solution
Days
[100]
Antibiotic Solution
Hours
[162]
Antibiotic Solution
Weeks/Years
Weeks
Antibiotic Solution
Days
Microcosm1
[44]
[100]
10 and 100
Inhibition (100 mg
Spiked Manure
Weeks
[78]
4
Inhibition
Soil Microcosm1
Spiked Manure
Weeks
[163]
0.025-0.200
Inhibition (200 μg kg-1)
Soil Microcosm3
Spiked Manure
Weeks
[75]
Spiked Media
Hours
[160]
2-10
Inhibition
Soil
Antibiotic Solution
Pure
Culture4
36
Tetracycline
Chlortetracycline
Oxytetracycline
Tetracycline
0.0003-0.03
10-250
50 and 200
Soil Microcosm3
No Effect
Inhibition
Inhibition (200 mg
-
Mixed
kg-1)
-
WW
-
Culture4
Sludge4
Spiked Manure
Weeks
[43]
Antibiotic Solution
Weeks
[77]
Antibiotic Solution
Hours
[76]
*Nitrification potential measures accumulation of NO2 when NO2 to NO3 oxidation step is inhibited.
**Method used for quantification: 1Chlorate Inhibition (Nitrification Potential), 2NO3--N, 3NO2--N, 4NO2-+NO3-,
37
Table 2-5. List of observed effects of antibiotic on denitrification rate or potential.
Antibiotic Class
Antibiotic
Concentration
(mg kg-1 or mg L-1)
Effect
*Media
Application Method
Experimental
Duration
Reference
Aminoglycoside
Gentamicin
1E-6-0.001
Inhibition (1-10 ng·kg-1)
Soil1
Antibiotic Solution
Days
[97]
Inhibition
Sediment2
Antibiotic Solution
Hours
[91]
0.0001-1.0
No Effect
Sediment2
Antibiotic Solution
Hours
[91]
0.001-1.0
No Effect
Soil2
Days
[44]
0.007-0.012
No Effect
Soil2
Weeks
[159]
1-100
Inhibition (500 µg kg-1)
Soil2
Antibiotic Solution
Contaminated
Manure
Spiked Manure
Weeks
[78]
0.00014-52.5
Inhibition (52,500 µg L-1)
Antibiotic Solution
Weeks
[92]
Soil1
Antibiotic Solution
Days
[97]
Stimulation (>100 ng·kg-1)
β-Lactam
Fluoroquinolone
Amoxycillin
Ciprofloxacin
Difloxacin
Flumequine
Ionophore
Narasin
1.0
1E-6-0.001
Inhibition (>5 days)
+Sediment
FTR3
Stimulation (1-4 days)
Macrolide
Clarithromycin
1.0
Inhibition
Sediment2
Antibiotic Solution
Hours
[91]
Erythromycin
1.0
Inhibition
Sediment2
Antibiotic Solution
Hours
[91]
Polypeptide
Bacitracin
1.5-500
Inhibition
Soil1
Antibiotic Solution
Days
[100]
Sulfonamide
Sulfadiazine
10-100
Inhibition (10 µg kg-1)
Soil2
Spiked Manure
Weeks
[78]
Soil1
Antibiotic Solution
Days
[97]
1E-6-0.001
Inhibition (>100
ng·kg-1)
Stimulation (1-10 ng·kg-1)
Sulfamethazine
4.0
No Effect
Soil2
Spiked Manure
Weeks
[163]
0.00005-0.100
Inhibition
Sediment4
Antibiotic Solution
Hours
[95]
Groundwater3
Antibiotic Solution
Days
[96]
Antibiotic Solution
Weeks
[92]
0.01-1
Sulfamethoxazole
0.00024-57.5
Inhibition (>0.01 mg
Inhibition (57,500 µg L-1)
+Sediment
FTR3
0.0012-500
Inhibition (1.2 μg)
Groundwater3
Antibiotic Solution
Weeks
[41]
1E-6-0.001
Inhibition 10 ng·kg-1)
Soil1
Antibiotic Solution
Days
[97]
Stimulation (1, 1000
Tetracycline
L-1)
ng·kg-1)
38
1E-6
Stimulation
Groundwater1
Antibiotic Solution
Days
[97]
0.001-1.0
Inhibition (500 µg kg-1)
Soil2
Antibiotic Solution
Days
[44]
Antibiotic Solution
Antibiotic Solution
Days
[96]
Weeks
[94]
Antibiotic Solution
Days
[44]
Chlortetracycline
0.01-1
Tetracycline
2-128
0.001-1.0
Inhibition (1 mg
L-1)
Groundwater3
No Effect
+Sediment
No Effect
Soil2
FTR1
Other
Roxarsone
Virginiamycin
None
Soil1
Antibiotic Solution
Days
[100]
0.00029-0.187
None
+Sediment
FTR3
Antibiotic Solution
Weeks
[92]
0.001-1.0
Inhibition (1000 µg L-1)
+Sediment
FTR1
Antibiotic Solution
Days
[93]
1.5-500
None
Antibiotic Solution
Days
[100]
1.5-500
Soil1
*Method used for quantification: 1NO3-, 2Acetylene Block, 3NO2-+NO3-, 4Isotopic Enrichment 15N-NO3+Sediment Flow Through Reactor
39
Chapter 3
The effect of ultralow-dose antibiotics exposure on soil nitrate and N2O flux
S. L. DeVries, M. Loving, X. Li, and P. Zhang
The contents of this chapter also appeared in Scientific Reports, 5:16818,
doi: 10.1038/srep16818.
3.1.
Introduction
A significant portion of antibiotics administered to humans and livestock are excreted as
active, non-metabolized compounds [34]. When manure, sewage sludge, wastewater, or
contaminated surface waters are applied to soils, these are conveyed to the soils where they often
persist and remain bioavailable. The maximum concentration of antibiotics transferred to soil is
often within the μg·kg-1 to mg·kg-1 range where a number of studies have shown that delayed or
reduced rates of denitrification may result and thus have direct consequences for non-point source
N2O or NO3- pollution [41, 44, 78, 164]. Far less is known about the effects of antibiotics at lower
exposure levels. How and to what magnitude minimum exposure levels, including those that may
fall below analytical detection limits, impact the structure and function of soil microbial
communities has rarely been considered. The primary objective of this research was to evaluate
whether ultra-low (ng·kg-1) exposure to environmentally relevant antibiotics affects total nitrate
losses and/or N2O flux over time. The antibiotics selected for study include narasin (NAR), an
ionophore active against many gram-positive bacteria [165], gentamicin (GTC), an
aminoglycoside that targets gram-negative bacteria and some facultative anaerobes [166], and two
broad-spectrum [167] sulfonamides, sulfamethoxazole (SMX) and sulfadiazine (SDZ). NAR and
GTC are both approved in the United States for use in poultry production and the residual antibiotic
40
concentration prior to field application may range from 10-10,000 μg kg-1 litter [168]. Based on a
9200 kg·acre-1 litter application rate and a 15 cm plow depth [169], the quantity of antibiotic
transferred to soil may be as low as 100 ng·kg or as high as 100 μg·kg-1. Considered medically
important, both SMX and SDZ have restricted application in animal husbandry in the United States
[170] but are still in use elsewhere and are often detected in sewage sludge and wastewater. SMX
and SDZ are also among the most frequently detected antibiotics in groundwater with reported
concentrations ranging from 0.08 ng·L-1 [171] to 1.11 μg·L-1 [172]. Assuming a bulk density of
1.6 kg·m3 and an average porosity of 40%, the maximum potential concentration in saturated soils
can be estimated between 20 ng·kg-1 and 274 μg·kg-1 though this may vary depending upon the
antibiotic source (e.g., sewage sludge vs. groundwater) and is subject to rapid dissipative losses
[173]. The effect of all four selected antibiotics on gross denitrification was measured in terms of
nitrate losses from anaerobic pot incubations in which soils were exposed to ng·kg-1 doses. NAR
and SMX generated the strongest responses and were selected for additional study. SMX is among
the few veterinary antibiotics shown to leach into the saturated zone [174] and was therefore
chosen for saturated column experiments. N2O flux experiments, conducted over moist soils, were
performed using NAR, which is less mobile [175] and tends to sorb in the upper, temporally moist
soil horizons where N2O is easily lost to the atmosphere.
3.2.
Materials and Methods
3.2.1. Statistical Analyses
Student t-tests were used to evaluate the statistical significance of individual treatments
relative to the control at each sampling point (95% confidence interval) and an analysis of variance
(ANOVA) was used to determine whether dose-responses (C/C0) were statistically significant at
the 95% confidence level. Comparison of group means with multiple t-tests would lead to
41
significant Type-1 errors (e.g., 14.3% for 3 t-tests) whereas the Type-1 errors remain at 5% in one
way ANOVA analysis of multiple group means [176].
3.2.2. Soil Sampling
The soil used in this study was sampled from a coastal farm in (Bull’s Eye Farm) along the
Upper Indian River Bay, near Milford, Delaware. The history of the site is known beyond 20 years
by personal communication with the farmer who leases the land and the authors are assured that
the soils have not previously been exposed to antibiotics. Groundwater sampling conducted at this
site in 2012 corroborates this conclusion (unpublished data). Sandy loam topsoil and a sandy
subsoil Topsoil samples (sandy loam) were composited from 10 cm cores, air-dried, sieved to
2mm, and stored at 4°C. The subsoil (sandy) was collected from the saturated zone at 2 meters
depth using an auger. Following collection, the samples were air-dried and stored at 4°C.
3.2.3. Anaerobic Incubation Experiment
A set of 48 soil samples (10 grams each, air-dry basis) were treated with 10 mL of 12.5
mg/mL glucose solution and then pre-incubated at 25°C in 50 mL centrifuge tubes in order to
establish anaerobic conditions and deplete residual nitrate from the soil. Extractable nitrate was
confirmed to be zero after 9 days. The pre-incubated samples were then dosed with 125 mg
glucose, 100 mg KNO3 and 1, 10, 100, or 1000 ng·kg-1 narasin, gentamicin, sulfadiazine, or
sulfamethoxazole under N2 gas as a 1 mL solution. Each treatment was performed in triplicate,
with control samples receiving no antibiotic. Following amendment, the topsoil samples were
incubated in the dark at 25°C for an additional 1-5 days and then extracted with 10 mL of 1 M
KCl. The extractable nitrate was quantified using a SEAL AQ2 Discrete Nutrient Analyzer (Seal
Analytical, Mequon, Wisconsin, USA).
42
3.2.4. N2O Flux Experiment
75 g air-dried soil was measured into 144 polypropylene containers (4 cm x 4 cm x 6 cm)
and moistened with 10 mL Milli-Q water. The containers were capped and pre-incubated at room
temperature for 4 days. Following the pre-incubation period, the soils were treated with an
antibiotic solution (0, 1, 5, 10, 50, 100, 500, or 1000 ng·kg-1 Narasin final concentration) and a
nutrient solution (34 mg·mL-1 (NH4)2SO4 and 21 mg/mL KNO3). Additional Milli-Q was added
to raise the total moisture content to 40% Water-Filled Pore Space (WFPS) and the containers
were placed inside 500 mL Kilner Jars outfitted with two gas-tight sampling ports. Headspace
samples were collected from 6 replicates for each treatment at 24, 48, and 72 hours after the
addition of antibiotic and nutrient solutions. Samples were transferred to evacuated Exetainer vials
and analyzed by Isotope Ratio Mass Spectrometry at the University of California Davis.
3.2.5. Column Experiment
A set of six 15  2.5 cm (length  diameter) glass columns were packed with air-dried
sandy subsoil. The columns were purged with CO2 for 20 minutes and then saturated bottom to
top with degassed Milli-Q water. All six columns underwent a two week pre-treatment during
which a nutrient solution containing 0.5 mM NO3- and 0.4 mM glucose (Control) was continually
passed through the columns at an average linear velocity of 1 m/day. After 2 weeks, effluent
samples were collected in 6 hour increments. Twenty-four hours after the first fractions were
collected the influent to three columns (Experimental) was modified by the continuous addition of
1 ng∙L-1 sulfamethoxazole. The influent vessel, all tubing, and the columns were wrapped in
aluminum foil to prevent photodegradation of the antibiotic during transit and additional fractions
were collected for 3.5 days following the initial addition of antibiotic to the experimental columns.
43
The nitrate concentration of effluent samples was determined using ion chromatography with an
AS14A 5-μm column (Dionex, Waltham, Massachusetts, USA).
3.3.
Results and Discussion
3.3.1. Anaerobic Nitrate Reduction
KNO3 solutions with various low doses of selected antibiotics (sulfamethoxazole (SMX),
sulfadiazine (SDZ), narasin (NAR), and gentamicin (GTC)) were added to pre-incubated soils,
incubated, and extractable nitrate was determined (see Materials and Methods for details). All four
antibiotic treatments yielded some combination of stimulated (% Control > 100%) and inhibited
nitrate losses (% Control < 100%) and exhibited a temporal trend toward inversion, e.g., early
stimulation followed by inhibition after longer incubation periods (see Table 3-1). Analysis of
variance (ANOVA) identified statistically significant dose-responses in for 3 of the 4 antibiotics
tested (see Table 3-2); the majority of these were observed in soils treated with SMX. Figure 3-1
illustrates the time-dose response (in terms of percentage of extractable nitrate lost relative to the
control) in soils treated with SMX. Four statistically significant, U-shaped dose response curves
(p < 0.05) in which nitrate losses initially exceed that of the control at the lowest (1 ng·kg-1, 207%)
and highest (1000 ng·kg-1, 123%) doses but are inhibited relative to the control at 10 ng·kg-1 (12%)
are observed. This overall pattern is maintained for a total of 4 days, after which the magnitude of
both stimulation and inhibition decline. On Day 5, only the 1 ng·kg-1 dose corresponds to
stimulated nitrate losses. Treatment with SDZ, NAR, and GTC resulted in far less distinct timedose-response patterns, but showed an overall tendency for the rate of nitrate removal to increase
as a result of exposure (Table 3-1). Where SDZ was applied, no individual dose-response was
determined to be statistically significant (Table 3-2), but a general pattern of accelerated nitrate
losses were observed at one or more sampling points for all four doses (Table 3-1).
44
Figure 3-1. Time-Dose Response curves illustrating the percentage of extractable nitrate lost
relative to the control in soils treated with sulfamethoxazole. Results shown are the average of
three replicates collected at each sampling period. Values above 100% (dashed line) indicate that
nitrate losses are stimulated relative to the control whereas values less than 100% point to nitrate
losses inhibited relative to the control.
45
Table 3-1. Percentage of extractable nitrate lost relative to the control in soils treated with
sulfamethoxazole, sulfadiazine, narasin, and gentamicin. Results shown are the average of three
replicates collected at each sampling period with standard error shown in parentheses. Values
above 100% (shown in bold) indicate that nitrate losses are stimulated relative to the control
whereas values less than 100% point to nitrate losses inhibited relative to the control. Individual
treatments deemed by a student t-test to be statistically different from the control are denoted with
an asterisk.
SMX
SDZ
NAR
GTC
Dose (ng·kg-1)
1
10
1000
1
10
100
1000
1
10
100
1000
1
10
100
1000
Day 1
207* (58)
12 (26)
124 (28)
125 (31)
118 (43)
109 (27)
57 (64)
106 (39)
127 (61)
120 (41)
124 (28)
75 (24)
65 (67)
134* (32)
113 (41)
Day 2
149 (35)
31 (39)
124 (30)
128 (20)
105 (23)
98 (19)
86 (48)
113 (22)
112 (27)
96 (25)
124 (30)
111 (42)
90 (21)
144 (22)
107 (15)
Day 3
13 (50)
21 (42)
199 (81)
124 (43)
86 (37)
97 (40)
109 (35)
117 (46)
126 (45)
117 (38)
199* (81)
78 (39)
59 (96)
115 (38)
118 (38)
Day 4
102 (24)
55 (32)
119* (32)
106 (28)
77 (18)
76 (38)
83 (21)
105 (25)
104 (29)
68 (33)
119 (32)
90 (21)
107 (25)
122 (29)
72 (17)
Day 5
140 (70)
78 (29)
60 (31)
37* (60)
42 (39)
104 (44)
74 (37)
71 (29)
77 (31)
82 (30)
60* (31)
62 (52)
100 (36)
78 (40)
107 (37)
Table 3-2. Results of One-Way ANOVA for soils treated with 1, 10, 100, or 1000 ng·kg-1 Narasin,
Gentamicin, Sulfamethoxazole, and Sulfadiazine over a five-day sampling period. The F-statistic
was calculated for concentration of nitrate measured in triplicate samples grouped by dose. Doseresponse relationships are deemed statistically significant where Fstat>Fcrit. P-values less than 0.05
are shown in bold.
SMX
SDZ
NAR
GTC
F(3,8)
P value
F(4,10)
P value
F(4,10)
P value
F(4,10)
P value
Day 1
29.82
0.0001
1.75
0.21
0.400
0.80
1.88
0.19
Day 2
11.05
0.003
1.16
0.39
0.83
0.53
2.66
0.09
Day 3
4.15
0.047
1.21
0.367
4.81
0.02
0.88
0.51
Day 4
3.11
0.087
1.47
0.28
2.72
0.09
8.68
0.002
Day 5
5.43
0.024
1.99
0.17
1.35
0.31
1.13
0.39
46
These were most commonly observed on Days 1 and 2 and the lowest dose (1 ng·kg -1) yielded a
stimulatory effect for 4 of the 5 days tested. In soils treated with NAR, all four doses stimulated
nitrate losses on Day 1 and Day 3 and all resulted in a diminished removal rate on Day 5 (Table
3-1). Three of these doses (1, 10, and 1000 ng·kg-1) were observed to correspond with increased
nitrate removal rate on all but the 5th day of sampling. Both the maximum stimulation (1000 ng·kg1
, 199%) and a significant dose-response occurred on Day 3 (p = 0.02, Table 3-2). Higher doses of
GTC (100 ng·kg-1 and 1000 ng·kg-1) also stimulated nitrate removal for four of the five days tested
(Table 3-1). Though stimulation of the greatest magnitude occurs on Day 2 (144%, 100 ng·kg-1),
a statistically significant dose response does not emerge until Day 4 (Table 3-2), where inhibition
observed at 1 and 1000 ng·kg-1 contrasts with stimulation the two middle doses (10 and 100 ng·
kg-1).
The results of these anaerobic denitrification experiments provide evidence that
ecologically significant microbial communities in soil and sediment may have a statistically
significant dose-response when exposed to antibiotics at ultra-low concentrations (ng·kg-1). The
most frequently observed effect was an accelerated loss of soil nitrate, which stands in contrast to
expectation because antibiotics are generally employed to inhibit microbial activity. Based upon
broad temporal trends exhibited by these results (stimulation observed in 63% of samples on Days
1-4 and 75% inhibited on Day 5) and the distinctive U-shaped dose-response curve corresponding
to SMX treatment, it is tempting to draw some comparison between these outcomes and direct
stimulation hormesis (Figure A-1) in which sub-inhibitory exposure to a toxin can produce a
stimulatory effect in the target organism [86]. Though it is possible that hormetic
responses do occur in soils exposed to these antibiotics, any apparent hormetic effect is likely the
result of population-level consequences resulting from individual hormesis and not the hormetic
47
response in and of itself. An alternate and perhaps simpler hypothesis is that accelerated nitrate
reduction is the functional outcome of selective antibiotic pressure within the more complex soil
microbial community. For example, NAR is active against gram-positive bacteria and since most
denitrifying organisms are gram-negative [177], NAR is unlikely to inhibit or stimulate growth or
enzymatic activity within this functional group. On the other hand, inhibition of one or more grampositive organisms in the soil microbial community is expected and may increase the availability
of resources to competing organisms, including the gram-negative denitrifiers, allowing them to
grow at the expense of inhibited species.
Evidence that both broad-spectrum and gram-positive/gram-negative antibiotics affect the
structure and function of soil microbial communities at higher doses (mg·kg-1) is abundant [53].
Of the antibiotics tested in the present study, for example, SDZ has been reported to decrease
microbial diversity [178] and to increase the ratio of ammonia oxidizing archaea to ammonia
oxidizing bacteria [81]. At comparable doses, SDZ and SMX [179, 180] have both been observed
to increase the ratio of fungi to bacteria in soils. Differences in antibiotic agency, i.e., broadspectrum vs. gram negative/positive, between different antibiotics can be expected to impact the
microbial population differently and may account for variations in the overall dose-time-response
curves reported here but does not explain why maximum stimulation in the sulfonamides
corresponds to the lowest doses (1 ng·kg-1) but occurs in NAR and GTC-treated soils at higher
doses (1000 ng·kg-1 and 100 ng·kg-1, respectively).
3.3.2. Denitrification in Saturated Sediment Columns
Where the effects of antibiotics on soil function have been evaluated, denitrification has
consistently been shown to be inhibited where higher doses of antibiotics (>500 µg·kg-1) were
administered to soil [78], sediment [44, 91, 95] and groundwater [41, 181]. The consistency of
48
these results contrast greatly to the combined stimulation and inhibition reported here for ng·kg-1
doses in anaerobic soils and further to the results of anaerobic column experiments. Figure 3-3
illustrates effluent nitrate concentration (as a % of influent concentration) for a set of six columns
receiving a 1 mM nitrate influent solution. Starting from t = 24 hours, 1 ng·L-1 SMX was
continuously added to the influent of three of these columns. Prior to the addition of SMX,
approximately 60% of influent nitrate was reduced during transit through each of the six columns.
As the experiment continued, nitrate reduction in the three control columns showed slight diurnal
variations, possibly resulting from temperature changes in the laboratory, but the overall average
remained relatively constant at ~60%. In contrast, the columns receiving influent spiked with 1
ng·L-1 SMX showed an increase in overall nitrate reduction, with total nitrate losses increasing
from an initial 60% to nearly 90% at the end of the experiment. According to student t-tests, this
increase is statistically significant at or above the 95% confidence level from t=30 through the end
of the experiment (see Supplementary Information). Unlike the anaerobic incubation experiment
where the maximum stimulatory effect of SMX was observed on Day 1, stimulation in the column
experiments appears to steadily increase over time. The discrepancy between these results may
indicate that the stimulatory effect of SMX at the 1 ng·kg-1 and 1 ng·L-1 is reduced over time by
biodegradation. The soil used for the anaerobic incubation experiment received only a single dose
of SMX at the beginning of the experiment whereas the columns received a steady supply of SMXspiked influent that was prepared daily. The gradual increase in denitrification rate relative to the
control might indicate that any microbial shift resulting from 1 ng·L-1 SMX exposure is both
maintained and enhanced by continued antibiotic pressure at this dose.
49
Figure 3-2. Percent influent nitrate removed from control (o) and experimental (□) columns during
transport through saturated soil columns receiving a continuous flow of nitrate nitrogen and
glucose. Experimental columns were spiked with 1 ng·L-1 SMX from t = 24 to t=108. Triplicate
columns were run for the spiked as well as the control tests. Statistically different nitrate reduction
(p < 0.05) was observed from t = 30 to t = 108 and is indicated with solid markers.
50
3.3.3. N2O Flux
Where any changes in denitrification rate or potential in soil and sediment are observed,
changes in the flux rate of N2O, a powerful greenhouse gas are also likely. Though at least one
previous study has reported a decrease in N2O from mineral soils treated with 1-1000 μg·L-1 SMX
[44], the opposite effect was observed in moist soils treated with 1-1000 ng·kg-1 NAR. As seen in
Figure 3-, the average N2O flux is around 0.1 ppm·day-1 for all antibiotic treatments and the control
after only one day of incubation, but on Day 3 a statistically significant dose-response emerged
(see Table 3-3, p = 0.0067). The dose-response observed is nearly linear with N2O flux ranging
from 0.1 ppm·day-1 (Control) to approximately 0.4 ppm·day-1 (1000 ng·kg-1). Although NAR was
also shown to stimulate nitrate reduction at each of these doses on Day 3 (Error! Reference
source not found.), it is unlikely that accelerated denitrification alone accounts for the increase in
N2O flux, especially at the highest dose where nitrate losses are 200% of the control but net N 2O
flux are 300%. Surplus N2O flux may result from either a shift in the N2O:N2 ratio, a mechanism
suggested by Hou et. al (2015) whose experiments with 0.05-100 μg·L-1 sulfamethazine in
sediment showed an increase in N2O despite inhibited denitrification [95], or it may indicate that
antibiotics also affect nitrifier-denitrification rates (NH2OH→N2O or NO2-→NO→N2O) in
aerobic soils [182]. To better constrain source of increased N2O flux, future studies would benefit
from the use of isotopic tracers that can be used to distinguish between N2O sources [182].
51
Figure 3-3. Box-whisker plot of daily N2O flux (ppm·day-1) in moist soil (40% water filled pore
space) treated with 0-1000 ng·kg-1 Narasin. For each dose, 6 replicate samples were analyzed;
statistical outliers are shown as asterisks and data that differ significantly from the control are
indicated with arrows.
52
Table 3-3. Results of One-Way ANOVA for N2O flux from Narsin-treated soils. The F-statistic
was calculated for the N2O flux measured in six replicate samples grouped by dose. Dose-response
relationships are deemed statistically significant where Fstat>Fcrit. P-values less than 0.05 are
shown in bold.
Narasin
3.4.
F(7,40)
P value
Day 1
2.11
0.06
Day 2
1.73
0.12
Day 3
3.34
0.0067
Conclusions
Disturbances to the biogeochemical nitrogen cycle have been reported in soils and
sediment exposed to a wide-range of antibiotic compounds. The effects observed at both ultralow
(ng·kg-1) and moderate (μg·kg-1) antibiotic concentrations include shifts in microbial diversity and
community structure as well as overall function, which raises a number of concerns pertaining to
agriculture, nitrogen management, and climate change. In agriculture, factors controlling microbial
N-cycling are well-characterized and the resulting relationships have been used to develop a
number of different modeling tools to improve nitrogen use efficiency and reduce nitrogen loading
rates to sensitive ecosystems [45, 183]. At present, these models do not take into account potential
temporal and functional shifts in the biogeochemical nitrogen cycle that may arise when soil
microorganisms are exposed to antibiotics.
Natural mitigation of aquatic nitrate pollution, which is tied to a number of human health
risks [9] and to the degradation of aquatic ecosystems [16, 17] may also be affected. Excess nitrate
leached from soil is significantly reduced during transport through soil and sediment with
denitrification (NO3- →N2O→N2) estimated to reduce groundwater NO3- by as much as 50% on a
watershed scale [55]. Denitrification is inhibited by a number of antibiotics when the dose exceeds
500 μg·kg-1, which is distinctly negative outcome in terms of water quality and the health of aquatic
ecosystems, but may be stimulated for up to 3 or 4 days when soils are exposed to <1 μg·kg-1
SMX, SDZ, NAR, or GTC. A stimulated response at physically and biologically reduced
53
concentrations might partially counter high-dose inhibition by enhancing denitrification over
longer, low-dose exposures, but appears to have the potential to increase microbial production of
nitrous oxide (N2O), a powerful greenhouse gas and the leading modern contributor to
stratospheric ozone depletion [30]. Whether these pathways or anaerobic methane (CH4)
production may also be stimulated by exposure to ultralow doses of antibiotics is presently
unknown, but is very relevant to climate research. Based upon the growing body of evidence
suggesting that both low and high dose antibiotics in the terrestrial environment can and do affect
ecologically important aspects of the biogeochemical nitrogen cycle, additional research is
strongly encouraged to include: (1) a larger number of antibiotics tested at both low (ng·kg-1) and
high (μg·kg-1) exposure levels, (2) a wide variety of different soils and sediments (3) use of isotopic
tracers to better constrain N2O source where denitrification and nitrification are affected, and (4)
chronic and/or repeat exposure tests to determine whether single-dose effects are persistent and/or
cumulative and the role of antibiotic resistance in those changes.
3.5.
Acknowledgements
This work is partially supported by the Northeast Sustainable Agriculture Research and
Education (SARE) program (project # GNE13-057). SARE is a program of the National Institute
of Food and Agriculture, U.S. Department of Agriculture.
54
Chapter 4
The Effects of Trace Narasin on the Biogeochemical N-Cycle in a Cultivated Sandy Loam
S. L. DeVries, M. Loving, L. Logozzo, and P. Zhang
4.1.
Introduction
Antibiotics are routinely administered in animal feed to promote growth and to prevent or
treat bacterial infections. Although the average consumption by individual animals is low (~45
mg·kg-1, [184]), most antibiotics are poorly metabolized. In poultry, 44-95% of antibiotics
administered may be excreted as parent or metabolite compounds [185]. Storage and dispersal
(often as a nitrogen fertilizer) of contaminated poultry litter conveys these residual compounds to
soil where they have the potential to affect biogeochemical reaction rates and pathways [36].
Changes to the microbial nitrogen (N) cycle are of particular concern. In soils, ammonium
(NH4+) and nitrate (NO3-) nitrogen are essential to plant nutrition. Increasing use of organic and
inorganic N fertilizers to meet rising demand for crop production has, however, led to a surplus of
these reactive species in surface waters and contributes to widespread eutrophication [186], toxic
algal blooms [19], biodiversity loss [187], fishery collapse [18] and increased production of
paralytic shellfish toxins [188]. N fertilizer use is also a leading cause of elevated NO3- in drinking
water and an increase in atmospheric nitrous oxide (N2O). The former has been linked to a number
of human health risks including methemoglobinemia (blue-baby syndrome), colon cancer, and
55
reproductive disorders [9]. N2O, on the other hand, is an atmospheric pollutant that acts a powerful
greenhouse gas and is the leading modern contributor to stratospheric ozone depletion [30].
Recent literature reviews have concluded that even trace antibiotic exposure in soil can
affect the biogeochemical N cycle [1, 189]. All steps of the N-cycle are shown to be impacted by
antibiotics, but denitrification has thus far displayed the greatest sensitivity at therapeutic doses
(<mg·kg-1). More recently, one study demonstrated a link between sub-therapeutic concentrations
of NAR and modified rates of denitrification and increased N2O production [97] in the affected
soil. The N-cycle in soil is subject to natural variations resulting from differences in soil texture,
chemistry, and fluctuating soil moisture. The effects of these parameters are well-characterized
and can be factored into ecosystem and nutrient management models that are used to study
anthropogenic disturbances to the N-cycle [190] or predict NO3- leaching or N2O flux from crop
soils [191, 192]. Presently though, the effects of antibiotics on the biogeochemical N-cycle are not
well enough understood to be incorporated into these types of models. It is therefore difficult to
predict the landscape scale impacts on either short or long timescales. With this in mind, the
objective of this study was to comprehensively examine the temporal changes to N-pools and
transformation rates following application of an anti-coccidioidal drug, Narasin (NAR), to an
agricultural soil. The study was designed to measure the effects trace NAR exposure on N
transformation rates while simultaneously quantifying changes to NH4+, NO3-, and N2O. Previous
studies indicate that nitrification is less sensitive to antibiotics than denitrification, possibly as a
result of robust functional redundancy in the soil microbial community [77, 78], and most studies
reported that antibiotics inhibit denitrification. Where NAR was tested, inhibition was observed at
some sampling periods but was temporally variable [97]. Based on these studies, it was
hypothesized that the NH4+ pool would be minimally affected by NAR exposure but that a
56
combination of unchanged nitrification and inhibited denitrification would lead to an increased
accumulation of NO3-. The latter study also reported a significant increase in N2O flux from
saturated soils associated with NAR exposure. Since denitrification also proceeds in anaerobic
“hot spots” in unsaturated soils, it was further hypothesized that increased N2O emissions as a
result of NAR exposure would be observed under both the saturated and unsaturated conditions
tested here.
4.2.
Materials & Methods
4.2.1. Antibiotic Selection
NAR is an anti-coccidiodal drug approved for therapeutic, prophylactic, and growthpromotion in large-scale poultry production. Like most antibiotics, active parent and metabolite
forms of NAR conveys these compounds to soils when dispersed as a nitrogen fertilizer to
amendment. A fairly sorptive antibiotic (Kd = 38.8-98.4 L·kg-1) [175], NAR has moderate halflife in soils (t1/2 = 21-49 days) [193] and may persist for months after initial exposure. Recent
studies have shown that biogeochemical N turnover may be inhibited or stimulated at
concentrations as low as 1 ng·kg-1 [97].
4.2.2. Soil Sampling and Preparation
The Ultisol soil used in this study was sampled from a coastal farm (Bull’s Eye Farm)
along the Upper Indian River Bay near Milford, Delaware. The climate is temperate and the
agricultural fields are bordered by a narrow, steep riparian zone. Groundwater and runoff from this
site flows directly into the Indian River Bay. The history of the site is known beyond 20 years by
personal communication with the farmer who leases the land and the authors are assured that the
soils have not previously been exposed to antibiotics. Groundwater sampling conducted at this site
in 2012 (unpublished data) reveals no trace of the contamination and corroborates this claim.
57
Sandy loam topsoil samples were composited from 10 cm cores, collected along 5 transects, airdried, sieved to 2mm, and stored at 4°C.
4.2.3. Experimental Setup
At the start of the incubation experiments, 75 g soil were placed into 50 cm3 lidded
polycarbonate containers (n=18). Each soil was treated with 1 mL of aqueous NAR solution
prepared from a 1 µg·mL-1 standard in methanol (Sigma Aldrich, St. Louis, MO) and 1 mL of a
15
N-enriched nutrient solution to achieve final concentrations of 0, 1, 10, 100, or 1000 ng·kg-1
NAR, 93 μg·g-1 NH4+-N, and 43 μg g-1 NO3--N (equivalent to 72 kg N ha-1). Half of the samples
were enriched (10% atom excess) with
Laboratories), the other half with
15
15
N-NH4+ (99%
N-NO3- (99%
15
15
N-(NH4)2SO4, Cambridge Isotope
N-KNO3, Cambridge Isotope Laboratories).
Following these additions, Milli-Q water was added to raise the soil moisture content 40%, 60%,
or 80% water-filled pore space (WFPS) and each container was placed in the bottom of a 500 mL
glass jar. The jars were sealed with a stainless-steel lid outfitted with two gas-tight sampling ports
and three-way stopcocks. Once sealed, the jars were incubated in the dark at room temperature
(~23°C).
4.2.4. Extraction and Analysis of Soil Headspace
Sampling was performed after 1, 2, and 3 days. NH4+, NO3-, N2O flux, and
15
N-N2O
enrichment were quantified for all samples; 15N-N2, 15N-NH4+, and 15N-NO3- enrichment data for
process rate was collected for soils incubated at 40% and 60% WFPS only. To determine N2O flux
rate and the 15N enrichment of N2 and N2O gas at each sampling period, headspace samples were
taken from each of three jars enriched with 15N-KNO3 and three jars enriched with 15N-(NH4)2SO4.
A 25-mL sample lock syringe was attached to each of the two sampling ports on the jar lids and
purged with 5 mL headspace. Next, the headspace gas was mixed by extracting and purging 25
58
mL into each of the two syringes for three cycles. After mixing, 25 mL headspace was extracted
into one of the two syringes and flushed through a pre-evacuated Exetainer vial; the final 12 mL
was retained for 15N-N2 and N2O analysis.
4.2.5. Extraction and Analysis of Soil Nitrogen.
Using a hollowed-out 3 mL plastic syringe, two ~5 g soil “cores” were collected from each
sample. The gravimetric water content of each soil was measured by weighing one of the cores
before and after oven drying. The second “core” was extracted with 40 mL of 2M KCl and the
concentrations of NH4+ and NO3- in the soil extracts were determined by automated colorimetric
analysis (SEAL AQ2 Discrete Nutrient Analyzer, Seal Analytical, Mequon, Wisconsin, USA).
To separate NH4+ and NO3- for 15N analysis, the extracts were sequentially diffused onto
acidified glass fiber filters by following an adaptation of the methods described by Sigman et al.
(1997) and Holmes et al. (1998) [194, 195] that was designed to optimize reproducibility [196].
Once diffusion was complete, the filters were dried in a desiccator with fuming H2SO4 and then
wrapped in a tin capsule (Costech Analytical Technologies, Inc.). 15N enrichment was determined
by Isotope Ratio Mass Spectrometry (IRMS) at the University of California, Davis Stable Isotope
Facility using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20-20
isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Samples were combusted at 1000°C
in a reactor packed with chromium oxide and silvered copper oxide. Following combustion, oxides
were removed in a reduction reactor (reduced copper at 650°C). The helium carrier then flows
through a water trap (magnesium perchlorate) and a CO2 trap (for N-only analyses). N2 and CO2
are separated on a Carbosieve GC column (65°C, 65 mL/min) before entering the IRMS. Samples
are interspersed with several replicates of at least two different laboratory standards. These
laboratory standards, which are selected to be compositionally similar to the samples being
59
analyzed, have been previously calibrated against NIST Standard Reference Materials (IAEA-N1,
IAEA-N2, IAEA-N3, USGS-40, and USGS-41). Each sample’s preliminary isotope ratio was
measured relative to reference gases analyzed with each sample. These preliminary values were
finalized by correcting the values for the entire batch based on the known values of the included
laboratory standards.
4.2.6. Calculating Rates of Mineralization, Nitrification, and Denitrification
The rate of gross mineralization and nitrification (μg N·g-1 soil·d-1) were determined from
15
N enrichment data using the theoretical model described by Barraclough [108] (Eq. 1). This
approach, modified to simplify Kirkham and Bartholomew’s classic theoretical model [107], is
based on dilution of an enriched nitrogen pool such as NH4+ or NO3-. To calculate gross
mineralization rate, the total concentration of NH4+ is assigned the variable M (μg 14+15NH4+-N·g1
dry soil) and the abundance of 15N-NH4+ (% atom excess) is assigned the variable A (μg 15NH4+-
N·g-1 dry soil). The rate equation (Eq. 4-1) is based upon the initial and final values of M and A,
where the final value occurs at time t.
𝑚=
𝑀0 −𝑀𝑡
𝑡
log(𝐴 ⁄𝐴 )
× log(1+(𝑀 0−𝑀𝑡)/𝑀
0
𝑡
0)
Eq. 4-1
The same equation is used to determine nitrification rate, however the measured N pools
(M and A) are taken as μg 14+15NO3--N·g-1 dry soil and % atomic excess 15NO3--N, respectively,
and the symbol m is replaced by n. Daily mineralization and nitrification rates were calculated
from Eq. 1 by using the mean values of M0 and A0 measured at t = 0 and the mean values of Mt
and At as determined at times 1, 2, and 3 days. Overall (three-day) rates were also quantified by
rearranging Eq. 4-1 as Eq. 4-2 and using regression analysis to determine a best-fit line and solve
for m or n.
60
𝐴0
𝐴𝑡 =
(1+
𝑚
(𝑀0 −𝑀𝑡 (𝑀0 −𝑀𝑡) ⁄𝑡
)
𝑀0
Eq. 4-2
The rate of denitrification in soil samples (μg N·g-1 soil·d-1) was calculated for 1-day, 2day, and 3-day incubations using Hauck et. al.’s classic equations [109], as modified by Siegel et
al in 1982. The modified equations simplify the original by assuming the change in 28N2 in closed
systems is negligible [197]. The ion current ratio, r′, is measured from the ion currents of N2 at
masses 28, 29, and 30, where
𝑟′ =
𝑖𝑜𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑎𝑡 𝑚𝑎𝑠𝑠 30
𝑖𝑜𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑜𝑓 𝑚𝑎𝑠𝑠 28+𝑖𝑜𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑜𝑓 𝑚𝑎𝑠𝑠 29
Eq. 4-3
The fraction of N2 evolved from denitrification rate, d, is subsequently determined by:
𝑟′
𝑑 = (𝑋 15)2
Eq. 4-4
𝑁
where XN15 is the mole fraction of 15N-NO3-.
4.2.7. Calculating Mass Balance
The measured soil concentrations of NH4+ and NO3- were compared to a mass balance in
which
[𝑁𝐻4+ ]𝑡 = [𝑁𝐻4+ ]0 + 𝑡(𝑚 − 𝑛)
Eq. 4-5
[𝑁𝑂3− ]𝑡 = [𝑁𝑂3− ]0 + 𝑡(𝑛 − 𝑑)
Eq. 4-6
and
where m = net mineralization rate (μg N·g-1 soil·d-1)
n = net nitrification rate (μg N·g-1 soil·d-1)
d = denitrification rate (μg N·g-1 soil·d-1)
t = time (d)
61
4.2.8. Calculating 15N-N2O enrichment
15
N isotopic enrichment of N2O collected from headspace was used to estimate the relative
contributions of nitrification and denitrification to total N2O flux. Measured %15N-N2O enrichment
values were “corrected” using the mixing ratio of the general form and solving for % 15Nflux.
% 15𝑁 𝑜𝑓 𝑚𝑖𝑥 =
(𝑁𝑎𝑖𝑟 )(% 15𝑁 𝑎𝑖𝑟)+(𝑁𝑓𝑙𝑢𝑥 )(% 15𝑁 𝑓𝑙𝑢𝑥)
𝑁𝑎𝑖𝑟 +𝑁𝑓𝑙𝑢𝑥
Eq. 4-7
where %15N of mix = % atom excess measured
Nair = µmol of N2O-N in air, assumed to be 326 ppb
%15Nair = %atom excess 15N, assumed to be 0.3663
Nflux = µmol of N2O-N measured - Nair
4.2.9. Calculating relative contributions of nitrification and denitrification to N2O flux
The relative contributions of denitrification (D) and nitrification (NN) to total N2O flux
were calculated as follows:
𝐷 𝑁2 𝑂-𝑁 (𝜇𝑚𝑜𝑙) = 𝑁2 𝑂-𝑁 𝑓𝑟𝑜𝑚 15𝑁-𝑁𝑂3− 𝑎𝑚𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑖𝑙
Eq. 4-8
𝑁𝑁 𝑁2 𝑂-𝑁 (𝜇𝑚𝑜𝑙) = 𝑁2 𝑂-𝑁 𝑓𝑟𝑜𝑚 15𝑁-𝑁𝐻4+ 𝑎𝑚𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑖𝑙
Eq. 4-9
−(𝑁2 𝑂-𝑁 𝑓𝑟𝑜𝑚 15𝑁-𝑁𝑂3− 𝑎𝑚𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑖𝑙 ×
𝑟𝑎𝑡𝑖𝑜 𝑜𝑓
4.3.
15
𝑁-𝑁𝑂3− 𝑖𝑛 𝑁𝐻4+ 𝑎𝑚𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑖𝑙 𝑡𝑜 15𝑁-𝑁𝑂3− 𝑖𝑛 𝑁𝑂3− 𝑎𝑚𝑒𝑛𝑑𝑒𝑑 𝑠𝑜𝑖𝑙)
Results and Discussion
4.3.1. Influence of NAR on NH4+
Figure 4-1 shows the size of the NH4+ pool (μg NH4+-N·g-1) under the three moisture
regimes as a function of dose and time. In moderately moist soils (40% WFPS), significant doseresponse curves were observed on all three sampling days (see Table 4-1). The nature of the
response varied with length of incubation, but the dose-response curves indicate that NAR
62
exposure leads to an increase in extractable NH4+. On Day 1, the largest differences were observed
at the 1 ng·kg-1 and 1000 ng·kg-1 doses (+66% and +73%) and on Day 2, the most impactful doses
were 100 and 1000 ng·kg-1 (+59% and +76%). Interestingly, the 10 ng·kg-1 dose produced an
opposite effect, with a decrease in the NH4+-N pool. This is most evident on Day 3 where NH4+ in
the control soil exceeded the 10 ng·kg-1 NAR-treated soil by 30%. At higher moisture regimes, the
dose-response was not statistically significant (p < 0.05) for any of the sampling periods.
Table 4-1. Results of One-Way ANOVA for NH4+ pool in soils treated with 0, 1, 10, 100, or 1000
ngkg-1 narasin. The F-statistic was calculated for the NH4+ concentration determined from 6
replicates and grouped by antibiotic dose. Dose-response relationships are deemed statistically
significant where Fstat>Fcrit. P-values less than 0.05 are shown in bold.
40% WFPS
60% WFPS
80% WFPS
Day 1
3.6E-4
0.23
0.57
P value
P value
P value
Day 2
4.1E-6
0.99
0.32
Day 3
2.4E-6
0.55
0.30
Table 4-2. Overall mineralization (M) and Nitrification (N) rates (μg Ng-1d-1) over 3-day
incubation period calculated by solving for m (Eq. 1) and by using regression analysis to find a
best-fit curve for Eq. 2 and solving for m or n.
NAR Dose (ng·kg-1)
0
1
10
100
1000
40% WFPS
M
2.2±0.1
1.4±0.1
0.3±0.06
1.1±0.05
1.2±0.08
N
8.2±0.4
2.0±0.2
5.0±0.3
0.7±0.07
-0.3±0.1
60% WFPS
M
1.5±0.02
1.7±0.04
1.1±0.05
1.1±0.07
0.3±0.04
N
1.5±0.04
1.2±0.05
1.1±0.04
0.01±0.1
-0.7±0.05
63
Figure 4-1. Box-whisker plots illustrating the NH4+-N pool over a three-day incubation period in moist soils (40%, 60%, and 80%
WFPS) treated with NAR. Statistically significant dose responses are indicated with a star symbol.
64
Because trace NAR exposure appears to have significant effects on extractable NH4+ in
affected soil, it follows that these changes result from shifts in microbial activity caused by NAR
that affect the rates of N-mineralization and nitrification. Rates obtained for the 40% and 60%
WFPS incubations support this conclusion. Overall rates of mineralization determined from
regression analysis (Table 4-2) indicate that mineralization was inhibited for all NAR treatments
at 40% WFPS and all but the 1 ng·kg-1 dose at 60% WFPS. When m is calculated for each day of
the incubation using Eq. 4-1, the overall conclusion that NAR inhibits mineralization remains
evident, but the resulting m values indicate that the rate is not constant over the length of the
incubation, i.e., NAR-treatment results in temporal variability (Table 4-3). For example, Nmineralization rates in the control soil were observed to continuously decline from a maximum of
4.9 µg N g-1 d-1 on Day 1 to a minimum of 2.0 µg N g-1 d-1 on Day 3. In contrast, maximum
mineralization rates in NAR-amended soils occurred at later sampling periods. For instance, when
1-100 ng·g-1 NAR was applied, N-mineralization rates peaked on Day 2, at which time the rate
corresponding to the 10 ng·kg-1 dose exceeded that of the control by 55%. At the highest dose
tested (1000 ng·kg-1), peak mineralization rate (5.6 µg N g-1 d-1) occurred on Day 1 and the
minimum rate on Day 2 (1.3 µg N g-1 d-1). When soil moisture was increased to 60% WFPS, all
five treatments showed a temporal decline in mineralization rate, but dose-dependent rate effects
are again observed at all three sampling periods.
Overall nitrification rates also appear to be inhibited when NAR is introduced (Table 4-2),
an outcome that is particularly evident at 40% WFPS. Temporally, nitrification in untreated soil
(40% WFPS) steadily declined from an initial rate of 17.8 µg N g-1 d-1 to 6.9 µg N g-1 d-1 on Day
3 (Table 4-3). Under the same moisture conditions, nitrification rates in soils that received NAR
amendments yielded more variability over time and in some cases the calculated nitrification rate
65
was negative. For example, on Day 1 nitrification rates in soils treated with 100 and 1000 ng·kg-1
NAR were calculated to be -1.0 µg N g-1 d-1 and -1.8 µg N g-1 d-1. The 1000 ng·kg-1 NAR dose
also resulted in negative nitrification on Day 2 (-0.1 µg N g-1 d-1). Negative values for nitrification
rate will occur if the measured % atom excess 15N increases between two sampling points. Since
fractionation effects are considered to be negligible in highly enriched samples, an increase in 15N
cannot occur without additional 15N additions. In each case that negative values were obtained, the
error appears to be associated with unexpectedly low initial 15N-NO3- measurements, which may
reflect poor equilibration of the added
15
N with native soil N prior to sampling (<1 hour). Peak
nitrification rate was observed on Day 2 for three of the four doses (1, 100, and 1000 ng·kg-1) and
Day 3 for the remaining (10 ng·kg-1). Increasing soil moisture to 60% had the overall effect of
decreasing nitrification rates relative to 40% WFPS, but inhibition in NAR-treated soils remains
the prevalent response. Initially, the 100 and 1000 ng·kg-1 appear to have inhibited nitrification
whereas the rates measured on Day 2 exceed that of the control at all four doses. By Day 3, this
pattern is fully inverted and nitrification rates appear inhibited in all four NAR-treated soils.
The statistical significance of dose-response patterns observed in the mineralization and
nitrification rate measurements is difficult to assess. Although stable 15N methods have long been
used as a tool for deriving N-process rates, there exist a number of limitations that can preclude
precision in measurement. For example, both Kirkham and Bartholomew’s original equations
[107] and the modified version applied here [108] assume uniform distribution of
15
N and
equilibrium between N pools. Large errors can arise when these assumptions are not met [198,
199], which is a very likely outcome when introducing
15
N enrichments to inherently
heterogeneous soil matrices, or when mineralization and nitrification rates are similar in magnitude
[200]. Additionally, destructive sampling methods prevent the use of true replicate measurements
66
so wide variability between replicate soil samples and the occurrence of erratic values for some N
pools are not uncommon [201]. High variance leads to large standard errors that become greatly
magnified by multiplication factors [198, 202]. Furthermore, the apparent dose-response that is
observed in nitrification rate measurements cannot be solely attributed to NAR. Nitrification rate
is positively correlated to soil NH4+ [203], so inhibited mineralization will naturally result in a
reduced rate of nitrification. Therefore, what appears to be inhibited nitrification may actually be
an artifact from inhibited mineralization. Notably, mineralization is most inhibited at 10 ng·kg-1
(40% WFPS), which corresponds to the least inhibited rate of nitrification, suggesting that the
nitrification response at this dose reflects a real change in the nitrification community. For future
studies, tools such as GeoChip that are able to discern changes to microbial community structure
and function based on the abundance of genetic markers can be used to test this hypothesis.
67
Table 4-1. Mineralization, Nitrification, and Denitrification rates (μg Ng-1d-1) calculated 24-hour incubation periods on Days 1, 2,
and 3 using Eq. 4-1 and solving for m or n and Eqns. 3-4.
NAR Dose (ng·kg-1)
Mineralization
0
1
10
100
1000
Nitrification
0
1
10
100
1000
Denitrification
0
1
10
100
1000
Day 1
4.8±1.5
0.4±0.1
2.3±0.4
0.5±0.1
5.6±1.6
Day 1
17.8±8.7
5.8±2.4
8.7±3.2
-1.0±0.9
-1.8±1.7
Day 1
23.3±5.3
20.7±4.9
21.3±4.3
30.7±7.6
21.0±5.0
40% WFPS
Day 2
3.3±0.9
2.1±0.4
5.1±0.5
2.1±0.3
1.3±0.1
Day 2
11.6±4.4
8.9±3.1
5.2±0.8
1.0±0.6
-0.1±0.01
Day 2
18.8±8.2
12.2±0.7
14.1±1.9
14.2±2.7
14.1±2.0
Day 3
2.0±0.3
0.9±0.1
1.8±0.5
0.9±0.1
2.7±0.2
Day 3
6.9±1.3
9.2±1.1
8.9±3.8
1.0±0.2
0.5±0.08
Day 3
11.2±1.5
11.2±1.5
9.3±1.1
11.6±0.5
12.3±1.0
Day 1
2.4±0.3
3.4±0.4
0.7±0.02
1.7±0.2
0.01±0.001
Day 1
2.7±0.2
2.3±0.2
2.8±0.3
1.1±0.2
-2.5±0.2
Day 1
18.9±0.9
19.3±1.6
12.8±0.7
18.4±8.7
12.2±1.6
60% WFPS
Day 2
0.3±0.03
0.5±0.02
0.7±0.09
0.6±0.04
0.4±0.06
Day 2
1.1±0.06
2.0±0.1
2.0±0.3
1.3±0.2
-1.0±0.1
Day 2
24.3±1.5
22.9±2.6
15.7±1.7
155.2±2.3
9.4±0.6
Day 3
0.2±0.02
-0.1±0.02
0.6±0.04
0.3±0.05
0.06±0.002
Day 3
1.5±0.4
0.8±0.2
1.1±0.1
0.6±0.08
-0.5±0.03
Day 3
23.3±1.0
25.8±2.9
18.7±3.8
18.3±6.9
9.8±0.5
68
Although the statistical significance of mineralization and nitrification rate measurements
cannot be well established, differences between the measured NH4+ pool and mass balances
calculated from initial NH4+ using measured rates of mineralization and nitrification (Figure B-1)
can be used to evaluate whether competing NH4+ production or consumption processes may also
have been affected by NAR exposure. Competing processes include re-mineralization,
immobilization, and annamox (anaerobic ammonia oxidation). The former occurs when NH4+ is
incorporated into organic tissue and then re-mineralized and returned to the NH4+ pool. This
process, which is not well accounted for in
15
N dilutions methods, increases the NH4+ pool and
will therefore lead to an underestimate of mineralization rate. Immobilization and annamox act to
reduce the NH4+ pool, yielding the opposite effect – an overestimation of mineralization.
Nitrification rates that are equal to or in excess of mineralization will also lead to overestimates of
mineralization rate by diluting the %15N abundance of the NH4+ pool. Immobilization and remineralization of NO3- will have similar effects on measured nitrification rates. Although the
overall pattern of the mass-balances agrees with measured NH4+ values at most doses, Pearson
correlation coefficients calculated between the measured NH4+ pool and the corresponding mass
balance are poor to moderate on Days 1 and 2 for both the 40% WFPS and 60% WFPS experiments
but high on Day 3 (Table B-1). In the 40% WFPS experiment, most of the discrepancies correspond
to an overestimation of NH4+ relative to the measured concentration, pointing to annamox (NH4+
+ NO2- → N2) or immobilization as significant competing processes. Since annamox is an
anaerobic reaction it would be limited to anoxic “hot spots” in micropore sites under these
conditions, so the imbalance is more likely attributable to overestimated mineralization caused by
NH4+ immobilization or re-mineralization of immobilized NO3-.
69
4.3.2. Influence of NAR on NO3The NO3--N pool (Figure 4-2) was significantly affected by treatment with NAR at all three
sampling points for each of the moisture levels tested (Table 4-5). At lower moisture regimes (40%
and 60% WFPS), the overall pattern trends toward a smaller NO3- pool as a function of antibiotic
dose. The magnitude of difference was greatest in soils incubated at 40% WFPS, where the
concentration of NO3- in NAR-treated soils averaged 17%, 31%, and 39% (on Days 1-3,
respectively) less than the untreated soil. Similar reductions in NO3- pool size were observed at
60% WFPS (18%, 13%, and 29% over 3 days). At the highest water content (80% WFPS),
conditions are favorable for anaerobic denitrification, which will reduce the NO3- pool. Here, the
inverse effect was observed and NO3- concentrations increased with NAR dosage. When dose is
not considered, the NO3- pool size in NAR-treated soils averages 33%, 25%, and 31% relative to
the control on Days 1-3 less than untreated soils, respectively and is inhibited >50% at the 100 and
1000 ng·kg-1 doses. Like NH4+, the NO3- concentration in soil results from interacting production
and consumption processes. Here, NO3- is produced by nitrification and consumed by
denitrification or immobilization, i.e., uptake by plants or microorganisms. The results of
denitrification measurements (Table 4-3) suggest that NAR exposure leads to temporal and dosedependent shifts in denitrification activity. The direction of the shift includes both inhibited and
accelerated rates of denitrification. This is evident at both 40% and 60% WFPS, though the effects
are amplified when soil water is higher. Statistically significant dose responses are observed at 3
sampling periods (Table 4-5). At 40% WFPS, this occurs on Day 3 where the rate of denitrification
is retarded at low doses (-12% and -17% at 1 and 10 ng·kg-1 NAR) and accelerated at higher doses
(3% and 9% for 100 and 1000 ng·kg-1 NAR). A more distinct dose-response is observed when soil
water is increased to 60% WFPS. Under these conditions, the prevailing effect is a decreased rate
70
of denitrification. The effect is statistically significant on Days 2 and 3 and the magnitude of the
effect generally increases with dose (Table 4-6). Average inhibition is 35% on Day 2 and 22% on
Day 3, but exposure to 1000 ng·kg-1 are observed to induce inhibition as great as 62%.
Table 4-4 Results of One-Way ANOVA for NO3- pool in soils treated with 0, 1, 10, 100, or 1000
ng·kg-1 narasin. The F-statistic was calculated for NO3- concentration as determined from 6
replicates. Dose-response relationships are deemed statistically significant.
40% WFPS
60% WFPS
80% WFPS
P value
P value
P value
Day 1
8.79E-5
1.45E-5
3.32E-5
Day 2
2.3E-5
4.65E-5
3.33E-7
Day 3
2.72E-5
1.31E-6
2.82E-7
Table 4-5. Results of One-Way ANOVA for the denitrification rate in soils treated with 0, 1, 10,
100, or 1000 ng·kg-1 narasin. The F-statistic was calculated for denitrification rate as determined
from isotopic enrichments of 3 replicates. Dose-response relationships are deemed statistically
significant where Fstat>Fcrit. P-values less than 0.05 are shown in bold.
40% WFPS
60% WFPS
P value
P value
Day 1
0.23
0.13
Day 2
0.42
9.84E-6
Day 3
0.024
0.0039
71
Figure 4-2. Box-whisker plots illustrating the NO3--N pool over a three-day incubation period in moist soils (40%, 60%, and 80%
WFPS) treated with NAR. Statistically significant dose responses are indicated with a star symbol.
72
A mass balanced derived from initial NO3- and the measured nitrification and
denitrification rates (Error! Reference source not found.) is well-correlated to measured NO3values (Table B-1), indicating that competing NO3- consumption processes, i.e. immobilization,
are not significantly affected by NAR exposure. Where differences do exist at 40% WFPS, the
mass balance overestimates NO3- at low NAR doses (0-1 ng·kg-1) on Days 1 and 2 and
underestimates NO3- at higher doses on all three days. At 60% WFPS, the mass balance generally
underestimates NO3-. Where overestimates are observed, NO3- immobilization by microorganisms
may account for the discrepancy.
4.3.3. Influence of NAR on N2O
N2O flux is shown in Figure 4-. At 40% WFPS, N2O production is low (<6 ng g-1 d-1), yet
the effects of NAR exposure are significant on all 3 days (Table 4-6). Initially, N2O production is
suppressed relative to the control in all NAR-treated soils, but by Day 3 a clear pattern emerges in
which N2O flux increases with the applied dosage. When soil moisture was increased to 60%
WFPS, N2O flux increased nearly 2 orders of magnitude, ranging from 53 to161 ng N2O-N g-1·
d-1. Median daily flux rates declined over the course of the 3-day incubation period but N2O flux
from individual treatments increased as a function of antibiotic dose on all three days and the doseresponses are statistically significant (Table 4-6). At 80% WFPS, N2O flux was moderate, ranging
from 4 to 39 ng N2O-N g-1 d-1. As observed in the other experiments, daily flux decreased over
time but in this case NAR had the apparent effect of inhibiting N2O flux at all but the lowest NAR
dose. Throughout the experiment, the 1 ng kg-1 dose yielded a nearly 2-fold increase in N2O
production relative to the control whereas flux was comparable to the control at other doses on
Day 1 and inhibited on Days 2 and 3.
73
N2O production results from at least 3 known pathways: denitrification (NO3- → NO2→NO → N2O), nitrifier nitrification (NH4+ → NH2OH → N2O), and nitrifier denitrification (NH4+
→ NO2- → N2O). While denitrification has long been considered the predominant source of soil
N2O, both nitrifier nitrification (NN) and nitrifier denitrification (ND) are now recognized as
important sources, particularly under low oxygen conditions [74]. Where N2O flux is affected by
NAR exposure, one or more of these pathways must also be affected.
obtained from soils receiving separate
15
N-NH4+ and
15
15
N-N2O enrichment data
N-NO3- amendments can be used to
evaluate these changes.
Table 4-6. Results of One-Way ANOVA for N2O flux from soils treated with 0, 1, 10, 100, or
1000 ngkg-1 narasin. The F-statistic was calculated for N2O flux measured from 6 replicates and
grouped by antibiotic dose. Dose-response relationships are deemed statistically significant where
Fstat>Fcrit. P-values less than 0.05 are shown in bold.
40% WFPS
60% WFPS
80% WFPS
P value
P value
P value
Day 1
0.09
0.005
0.03
Day 2
0.2
0.003
0.0001
Day 3
0.0003
0.001
0.0007
74
Figure 4-3. Box-whisker plots illustrating the N2O-N flux over a three-day incubation period in moist soils (40%, 60%, and 80% WFPS)
treated with NAR. Statistically significant dose responses are indicated with a star symbol.
75
For instance, if NH4+ and NO3- are the sole sources of N2O the sum of corrected enrichment values from both
amendments should be equal to the initial 15N addition (10%). As seen in
Figure 4-, there are a number of cases where this requirement is not met. At 40 and 60%
WFPS, for example, the sums of 15N-N2O enrichments from the 1000 ng·kg-1 NAR treatment are
consistently low. This is particularly evident at 40% WFPS where the sum of enrichments at this
dose is 5.2% atomic excess, on Days 1-3 in soils incubated at 60% WFPS (3.7% 4.6%, and 6.5%
atomic excess, respectively), and Day 2 at 80% WFPS (6.5% atomic excess). Although high rates
of mineralization and nitrification will dilute the
15
N-NH4+ pool and
15
N-NO3- pools, the rates
determined by these experiments are not sufficiently high to fully account for these values. As
previously noted, however, a mass balance significantly underestimates the NH4+ and NO3- pools
at the 1000 ng·kg-1 dose on each of the days where low total %15N are calculated and may account
for some additional dilution. Alternately, the discrepancy may point to an additional N2O source
at natural abundance that can serve as a precursor to N2O. One possible source is NO2- produced
as an intermediate of denitrification. In samples labeled with 15N-NH4+, 15N-N2O at approximately
equal % atomic excess 15N is produced via NN and ND. If NO2- produced as an intermediate of
denitrification from the unlabeled NO3- pool is reduced to N2O via ND, total N2O flux from the
15
N-NH4+ pool will increase, but the %15N atomic excess will be diluted. If a valid pathway, this
would indicate that NAR has the effect of increasing the importance of ND as an N2O source at
≥1000 ng·kg-1 doses and may also account for the observed rise in total N2O flux. Future studies
employing dual isotope techniques (15N and 18O) or isotopomers that can be used to better constrain
N2O sources from nitrification are recommended to examine this claim.
Although ND is possibly underestimated, the relative contributions of NN, ND and
denitrification to N2O flux can be approximated using Equations 8 and 9. Eq. 8 assumes that
denitrification is the only source of
15
N-N2O in
15
N-NO3- labeled soils, thus the contribution of
76
denitrification to total N2O is directly determined from those measurements. Eq. 9 assumes that
both nitrification and nitrification-coupled-denitrification (NCD) contribute to
NH4+ labeled soils and includes a correction to account for
15
15
N-N2O in
15
N-
N-N2O resulting from NCD to
determine the contribution of nitrification. This correction becomes irrelevant when denitrification
is the dominant N2O source. At 40% WFPS, nitrification was determined to account of >48% of
N2O flux in all samples measured. On Day 1, 67% of N2O flux is attributable to nitrification in
untreated soils whereas the 1, 10, 100 and 1000 ng·kg-1 NAR amendments yielded 100%, 100%,
55%, and 86% contributions from nitrification, respectively (Figure 4-5). The increased proportion
of nitrifier N2O at three of these doses suggests that some NAR exposure levels lead to pathway
shifts that favor increased rates of NN/ND and may account for a portion of the observed rise in
N2O flux that corresponds to increasing NAR dosages. Similar results are observed on Days 2 and
3. At 60% WFPS, denitrification was determined to be the primary source of N2O for all soil
treatments, but at 80% WFPS, NN/ND again appear as important sources, with an apparent doserelated increase, i.e., the contribution of NN/ND to total N2O increases as a function of dose,
particularly on Days 2 and 3. Although NN/ND was proposed as one source of increased N2O flux,
the increased rate of N2O production resulting from NAR may also result from a shift in the
N2O:N2 production ratio resulting from denitrification. For future experiments, this hypothesis can
be tested using a number of genetic methods, e.g., by monitoring changes to N2O reductase.
77
% atom excess
15
N
% atom excess
15
N
% atom excess
15
N
40% WFPS
32
28
24
20
16
12
8
4
60% WFPS
Day 1
28
24
20
16
12
8
4
Day 2
28
24
20
16
12
8
4
0
Day 3
0
1
10
100
-1
Narasin Dose (ng kg )
1000
20
80% WFPS
Day 1
16
20
12
12
8
8
4
4
Day 2
16
12
8
8
4
4
Day 3
12
8
8
4
4
0
1
10
100
-1
Narasin Dose (ng kg )
1000
Day 3
16
12
0
Day 2
16
12
16
Day 1
16
0
0
1
10
100
-1
1000
Narasin Dose (ng kg )
Figure 4-4. %15N atom excess of N2O from soils receiving 15N-NO3- (black) and 15N-NH4+ (white) enrichment, corrected for atmospheric
enrichment of headspace.
78
.
40% WFPS
60% WFPS
80% WFPS
2.0
Relative Contribution to N2O flux
1.5
Day 1
Day 1
Day 1
Day 2
Day 2
Day 2
Day 3
Day 3
Day 3
1.0
0.5
1.5
1.0
0.5
2.0
1.5
1.0
0.5
0
0
1
10
100
Narasin Dose (ng kg-1)
1000
0
1
10
100
Narasin Dose (ng kg-1)
1000
0
1
10
100
Narasin Dose (ng kg-1)
Figure 4-5. Relative contribution of denitrification (black) and nitrifier denitrification (white) to total N2O flux
1000
79
4.4.
Conclusions
The aim of this study was to investigate the effects of 1-1000 ng·kg-1 NAR on soil N
turnover and speciation. It was hypothesized that the NH4+ pool would be minimally affected but
that the accumulation of NO3- and N2O would increase in response to increase NAR exposure.
Although significant effects were observed for all three N-pools evaluated (NH4+, NO3-, and N2O),
only the latter hypothesis, i.e., an increase in N2O is fully supported by the results of this
investigation. In field-moist soils at or around 40% WFPS, the results indicate that trace NAR
exposure will lead to an overall increase in NH4+ availability. Because accurate estimates of soil
N requirements are necessary to promote crop efficiency and minimize environmental N pollution
[204], this may lead to over-fertilization and increased potential runoff losses when using precision
nutrient management tools like ADAPT-N [183] or other ecological models [205] that do not
account for these effects or unexpected temporal changes to mineralization and/or nitrification
rates. NO3-, on the other hand, was observed to decrease in response to NAR exposure, except
when soil moisture was increased to 80% WFPS. From an environmental perspective, lower NO3at 40% and 60% WFPS would translate into a reduced risk of NO3- leaching to groundwater, but
it may concurrently result in reduced N-availability to crops. Where soils are closer to saturation
(80% WFPS), the increased concentration of NO3- resulting from NAR exposure may be beneficial
from the perspective of plant nutrition, but leaching is a significant risk for soils near saturation
and a rain event would obviate that benefit and increase NO3- input to groundwater.
In addition to potential NH4+ and NO3- losses that may result from NAR exposure,
production and losses of N2O, a strong greenhouse gas and ozone-reducing molecule was observed
to increase at all four doses in under all three moisture regimes tested. Isotopic enrichment data
suggests that all three N2O production pathways are affected by exposure to NAR, leading to an
80
overall increase in N2O flux as a result of exposure. Nitrifier N2O sources (NN and ND) were also
significant at 80% WFPS, particularly at high NAR dosages. This implies that even where
denitrification is unaffected, anaerobic sediment exposed to antibiotics may also yield higher N2O
production as a result of a pathway shift. At the field scale, even small increases are both significant
and of great concern, particularly because agriculture is already the primary contributor to
increasing atmospheric N2O [206]. Without considering the effects of antibiotics, the IPCC
estimates that 1% of applied N is lost as N2O. Based upon an approximate manure fertilizer
application rate of 128.3 Tg·y-1 [207], a two to four-fold increase in N2O resulting from antibiotic
exposure would lead to an increase from 1.23 Tg·y-1 N2O-N to as much as 4.8 Tg·y-1 N2O-N. With
a global budget of 17.5-20.1 Tg·y-1 N2O-N [208], that translates to a potential increase of up to1720% in total annual N2O flux to the atmosphere. N2O is now the 3rd largest contributor to radiative
forcing of the climate [208], therefore these numbers warrant further consideration.
NO3- was shown to decrease as a result of NAR treatment under several moisture
conditions and additional losses to leaching are possible. As a result, increased N-additions may
be required to in soils exposed to NAR in order to compensate for N-deficiencies resulting from
this shift, a requirement that would have economic impacts on large-scale crop production.
Furthermore, increased N-fertilization are likely to contribute to even higher N2O flux from
affected soils, an outcome that is decidedly detrimental at both local and global scales.
Future studies are recommended to examine the effects of other commonly used veterinary
antibiotics that have been detected in soils and to evaluate some of the hypotheses presented here.
In particular, microbial community analyses using genetic tools such as GeoChip are
recommended to evaluate both short and long-term changes to microbial community structure and
81
enzymatic function as a result of antibiotic exposure. Also recommended are additional studies
that better constrain how antibiotic exposure affects N2O source and net flux.
4.5.
Acknowledgements
This work is partially supported by the Northeast Sustainable Agriculture Research and
Education (SARE) program (project # GNE13-057). SARE is a program of the National Institute
of Food and Agriculture, U.S. Department of Agriculture.
82
Chapter 5
Biodegradation of MIB, Geosmin, and Microcystin-LR in Sand Columns
Containing Lake Taihu Sediment
S.L DeVries, W. Liu, N. Wan, P. Zhang, and X. Li
The contents of this chapter also appeared in Water Science and Technology: Water Supply,
12(5), 691-698, doi: 10.2166/ws.2012.043.
5.1.
Introduction
Lake Taihu, located on the border between Jiangsu and Zhejiang Provinces, is the third-
largest fresh water lake in China, with a surface area of approximately 2400 km 2. Surrounded by
six large cities, Taihu is an economically important fishery and a primary drinking water resource
serving over 2 million people [209, 210]. Economic development in this region has flourished in
the last few decades, driving a concomitant increase in water pollution and usage [211], thus
rendering the lake prone to eutrophication and seasonal development of large cyanobacterial algae
blooms [212] whose metabolites can be toxic and unpalatable. Microcystins (MCs), produced by
Microcystis aeruginosa and other blue-green algae [213], are a class of cyclic hepatotoxins whose
most common variant, microcystin-LR (MC-LR), has been linked to both chronic and acute health
risks, including tumor growth [214], high rates of primary liver cancer [215, 216], and possibly
death [217]. Though non-toxic, two common taste and odor compounds, 2-methylisoborneol
(MIB) and 1,10-dimethyl-trans-9-decalol (geosmin), are also produced by a range of
cyanobacterial species. Each imparts a foul earthy/ musky odor and flavor to water [218, 219], and
are detectable at concentrations as low as 2-10 ng L-1 [220]. The presence of MIB and geosmin in
83
drinking water can give the impression of poor quality to consumers, who subsequently seek
alternative water supplies, often at higher consumer and environmental cost [221]. In order to
prevent a drinking water crisis in the Taihu region, a low-cost and effective method is needed to
remove microcystin, MIB, and geosmin before it reaches the tap [221].
Traditional water treatment methods (e.g., coagulation, filtration) poorly remove dissolved
MC-LR, MIB and geosmin [222, 223]. Powdered activated carbon (PAC) has proven reasonably
effective at removing both MIB and geosmin, but the required dosage for complete removal varies
with influent concentrations, type of activated carbon used, and competition for sorption sites from
natural organic material (NOM) [224-227], so optimization can be challenging. Similarly, PAC
treatment has been observed to result in near-complete removal of microcystin compounds [228]
but the high dosage requirements generate significant cost barriers. Although a combination of
coagulation, sand filtration, ozonation, and chlorination was shown to remove up to 6.8 μg L-1 of
dissolved MCs [229], these and other more complex treatments, including advanced oxidation
processes [230], though effective, are expensive and may lead to the production of toxic or
undesirable byproducts [221]. Biological treatments, on the other hand, have shown considerable
promise as a low-cost removal of cyanobacterial metabolites. More than a dozen bacterial species
have been identified in MIB and geosmin biodegradation studies [231] and a number of additional
reports show that MC-LR undergoes significant degradation at the soil-water interface of natural
waters [232-234] and via slow-sand or bank filtration [235, 236].
Bank filtration is a process of collecting water from wells or infiltration galleries that are
recharged by a river/lake that flows through alluvial valley in which the wells or infiltration
galleries are installed. An infiltration gallery is a set of perforated pipes installed underneath the
river/lake sediment. Bank filtration has been used in Europe as a water pre-treatment technology
84
for more than a century [29]. Taihu lake sediment mainly consists of silty clay [30] and has a
relatively low hydraulic conductivity. As a result, an infiltration gallery directly installed below
lake sediment may not have a sufficient water production capacity. However, the production
capacity can be greatly enhanced if the hydraulic conductivity of the lake bed is increased, e.g., by
mixing the lake bed sediment with sand. Therefore, the primary aim of this study is to evaluate the
degradation of MIB, geosmin, and MC-LR in sand filters containing a proportion of Taihu bed
sediment by conducting bench-scale column experiments and transport modeling.
5.2.
Methods
5.2.1. Column Preparation
Sediment was collected from the lake bed in Meiliang Bay (31°32’42” N, 120°11’58”E)
using a grab sampler. The sediment, which has been previously characterized as silty clay with a
median diameter of 0.012 mm [237], was mixed with unwashed, medium to coarse (0.5-1 mm
diameter) quartz-sand (Binjiang Water Treatment Plant, Changshu, China) in ratios (w/w) of 5/95
(E1) and 10/90 (E2). The sediment/sand mixtures were wet-packed into 30 cm long glass columns
with an inner diameter of 5.0 cm. A control column (C1) was packed with 10/90 sediment/sand
mixture that was autoclaved at 120°C for 15 minutes. Once packed, the experimental and control
columns were acclimated for a period of four weeks by pumping unfiltered lake water using a
peristaltic pump (Masterflex, Cole-Parmer) at 0.57 mL min-1 (corresponding to an average linear
velocity of 1 m d-1).
5.2.2. Influent
Influent was prepared by pumping 10 L of filtered lake water (0.22 μm) into two 15-L gastight Teflon bags (Beijing Safelab Technology Co.). When the bags were half-full, 300 μL of
MIB/geosmin standard (Sigma Aldrich) and 400 μL of MC-LR standard (Express Bio-technology
85
Co., Beijing) were injected into the inlet port and pumping was resumed until the total influent
volume of each bag was 10 L, resulting in an estimated influent concentration of 15 μg L-1 for each
standard. It is noted that no headspace existed in the Teflon bags.
5.2.3. Column Experiments
The influent bags were connected to the columns using Teflon tubing, and influent was
injected through the columns using the peristaltic pump at a constant rate of 0.57 mL min-1. To
prevent volatile losses of dissolved compounds, effluent samples were collected by affixing a gastight 20-mL syringe (Hamilton Company, Reno, NV) to a three-way stop-cock on the outlet port
on the top of the column. When the syringe was full, the contents were quickly transferred to 20
mL glass vials, sealed, and stored at 4°C. On the first day of pumping, samples were collected at
approximately 0.5, 1, 1.5, 2, and 2.5 pore volumes (PV). Thereafter, samples were collected at 8
hour intervals (approximately 1 PV) until 20 PV had been exchanged. When the experiment (Expt.
A) was complete, the columns were re-acclimated for one week, fresh influent was prepared, and
the experiment was repeated in full (Expt. B). It has previously been suggested that some
degradation of MC-LR occurs in lake water itself [238], which may cause the influent MC-LR to
degrade during the course of the experiment. To eliminate this potential degradation route, a third
set of column experiments was conducted using distilled water in place of filtered lake water. Here,
a single influent source was used to conduct two consecutive column experiments.
5.2.4. Effluent Analysis
MIB and geosmin concentrations in all samples were analyzed using solid-phase
microextraction (SPME). After transferring 10 mL of effluent to a septum-capped vial containing
a clean magnetic stir-bar and 4 g NaCl, the vial was immersed in a water bath at 65°C and stirred
constantly. The SPME fiber (Supelco #57348U) was introduced into the head space of the vial and
86
equilibrated for 10 min. The fiber was then manually inserted into the inlet of an Agilent 6890 gaschromatograph coupled with an Agilent 5973I mass spectrometer. An Agilent DB-5MS capillary
column (30 m  0.25 mm, 0.25 µm) was used to separate the compounds. The initial oven
temperature was 50 °C. After a 2-min hold, the oven temperature was ramped to 190 °C at 10 °C
min-1, to 280 °C at 15 °C min-1, and held for 10 min. The concentration of MIB and geosmin was
subsequently quantified with reference to the internal standards 2-isopropyl-3-methoxypyrazine
(IPMP, Sigma Aldrich) and geosmin-D3 (MW = 114).
MC-LR samples were analyzed by liquid chromatography tandem mass spectrometry
(LC/MS/MS) with electron spray ionization (ESI) and multiple reaction monitoring (MRM). A
Shimadzu Prominence UFLC with an Agilent Eclipse Plus C18 (1.8 μm  4.6 mm  50 mm)
column was used for separation. The temperature of the column oven was kept at 40°C. The mobile
phase consisted of water (component A) and methanol (component B) buffered with 0.1% (v/v)
formic acid and 4 mM ammonium formate. The following gradient elution program was used:
55% B for 0.5 min, linear increase from 55% B to 95% B from 0.5 to 6.0 min, 95% B from 6.0 to
6.5 min, and linear decrease from 95% B to 55% B from 6.6 to 7.6 min. The flow rate was 1.0 mL
min-1.
Mass spectrometry was performed with an ABI 4000 Q-trap mass spectrometer (Applied
Biosystems, Carlsbad, CA, USA), with nitrogen as both the collision gas and nebulizing gas. The
curtain gas, collision gas, and ion source gas 1 and gas 2 were set at 25, 5, 60, and 60 psi,
respectively. The nebulizer current was set at 3 mA. The temperature of the interface heater was
maintained at 500°C. Identification of microcystin was made by four MRM transitions, and
quantification was based on the most abundant transition and external standards.
87
5.2.5. Transport Modeling
The breakthrough curves of MIB and geosmin were fitted to a one-dimensional transport
model that incorporates reversible sorption and first-order decay of solutes in porous media. The
numerical model HYDRUS-1D version 4.0 [239] was used, with the following partial differential
equation:

C b S
 2C
C

 D 2 v
 L C  b S S
t  t
x

x
Eq. 5-1
where C is the solute concentration in aqueous phase, S is the solute concentration on solid phase,
t is the time, b is the sediment bulk density,  is the porosity, D is the hydrodynamic dispersion
coefficient, v is the average linear velocity, x is travel distance (length of column), L is the
aqueous-phase first-order decay constant, and s is the solid-phase first-order decay constant. A
linear isotherm was used to describe possible sorption of the solutes by the sediment:
S  kd C
Eq. 5-2
where kd is the partitioning constant. Coupling the liner sorption isotherm with eq. 1 yields:
b
C
 2C
C
(1  kd )
 D 2 v
 LC

t
x
x
Eq. 5-3
where T is overall first order degradation constant given by
T  L 
b k d
S

Eq. 5-4
Since L and s cannot be uniquely determined by inverse modeling of the breakthrough data, the
overall first order degradation constant was evaluated by assigning s to 0 during inverse modeling.
Once the sorption coefficient (kd) and first order degradation constant (μT) were obtained from
inverse models, a set of direct models were run to estimate performance of a 2-m thick filtration
88
bed for the removal of MIB, geosmin, and MC-LR. The filtration distance (the depth of the
perforated pipes underneath the bed surface) of an infiltration gallery is typically more than 2 m.
5.3.
Results and Discussion
5.3.1. Removal of MIB
During reactive transport, sorption would only lead to retardation of the breakthrough of a
solute and would not lower the steady-state breakthrough (plateau) concentration. Only
degradation would result in steady-state C/Co of less than 1. MIB breakthrough was observed at
approximately 1 PV in each column ( Figure 5-1) and the shape of the breakthrough curves (i.e.,
no obvious retardation) is consistent with the low sorption coefficients derived from transport
models (Table 5-1). In contrast to the sterile column, where little to no MIB removal was observed
( Figure 5-1, top left panel), the removal of MIB from columns containing 5% and 10% lake bed
sediment (w/w) averaged 11% and 24%, respectively ( Figure 5-1, lower left panels). Fitted firstorder degradation rate constants were essentially zero for the control column (Table 5-1), and
increased to 0.48 d-1 and 1.08 d-1 for the 5/95 and 10/90 columns, respectively. MIB removal
appeared proportional to the amount of sediment in the column. Since the only difference between
experimental column E2 and the control column is sterilization, it is safe to state that the
degradation is biotic. Our results demonstrated that only a 10% (w/w) addition of lake sediment to
a sand column could support a healthy number of microbes to degrade MIB.
The degradation rate constants observed here was a full order of magnitude higher than those
achieved in rapid filtration through biologically active sand columns [231], even at the lower
sediment ratio. Forward models show that degradation in a 2-m thick sand bed with 10% lake
sediment could remove as much as 84% of influent MIB ( Figure 5-2). When the concentration of
MIB in Taihu is less than 5 ng·L-1, infiltration galleries incorporating lake sediment may require
89
little or no additional treatment for MIB. When productivity is high, traditional treatment such as
PAC or GAC may be required to bring the final concentration of MIB down to acceptable levels,
but pre-treatment using an infiltration gallery with a sediment mixture would significantly reduce
dosage requirements and their associated costs.
Table 5-1. Percentage removal of MIB and geosmin and transport parameter values determined
by inverse modeling of experimental breakthrough data.
90
Figure 5-1. Breakthrough curves for MIB (left) and geosmin (right) in control column (top), 5/95
sediment/sand column (middle), and 10/90 sediment/sand column (bottom). Experimental data and
the best-fit simulations using HYDRUS 1D for the duplicate experiments are displayed as
diamonds and smooth lines, respectively.
91
Figure 5-2. Forward model results for removal of MIB (solid line) and geosmin (dashed line) in a
2 m column of lake bed sediment and quartz sand (10/90).
92
5.3.2. Removal of Geosmin
The breakthrough of geosmin was slightly retarded with respect to the MIB breakthrough,
indicating that geosmin was more strongly sorbed to the column material. Fitted kd values for
geosmin were much higher than those for MIB (Table 1), consistent with the visual inspection. In
column E1 (5/95), removal is relatively low, averaging 8%, but the addition of an additional 5%
sediment in column E2 (10/90) led to a significant increase in geosmin removal, reaching an
average of 38.5% (Table 1). Again, fitted degradation rate constants for the control column were
zero, whereas the rate constants increased to an average of 0.26 d-1 and 1.68 d-1 for the 5/95 (E1)
and 10/90 (E2) columns, respectively (Table 1). Since the difference between experimental column
(E2) and the control column (C1) is sterilization, the degradation is likely biotic.
It is worth noting that sorption may enhance overall degradation if at least some
degradation occurs on the solid phase (see Eq. 4, where an increase in kd would increase T). An
increase in the amount of lake sediment would not only increase the number of microbes but also
the number of sorption sites (i.e., kd for columns E2 and C1, each with a 10% sediment ratio, was
higher than that for E1, with only 5% sediment). Often microbial mediated degradation of
contaminants in porous media occurs on the solid phase [240]. As such, increasing sorption would
be beneficial to the overall contaminant removal, as seen in geosmin degradation in columns E1
and E2.
The fitted degradation rate constant (µT) for experimental column E2 (Table 5-1),
containing 10% lake bed sediment, was significantly higher than those reported for rapid sand
filtration [231]. Thus, the addition of bed sediment appears to have the potential to degrade
geosmin at high rates if the flow is relatively slow. The results of this experiment indicated, like
previous studies, that dissolved geosmin was more readily removed from water than MIB. In batch
93
experiments, these observations may be influenced by the higher vapor pressure and volatility of
geosmin. Here though, care was taken to ensure no volatile losses occurred prior to sampling and
analysis, so the higher rate of removal was a function of degradation alone. Forward models
suggested that up to 96% of influent geosmin could be removed using a 2-m thick sand bed with
10% lake sediment ( Figure 5-2). Even when productivity of Taihu is high the dissolved
concentration of geosmin rarely exceeds 10 ng·L-1 [241]. Therefore, an infiltration gallery coupled
with additional volatile losses during the traditional treatment may satisfactorily treat influent
geosmin throughout the year, significantly reducing the cost associated with traditional treatment.
5.3.3. Removal of MC-LR
MC-LR has previously been observed to undergo rapid degradation in natural waters,
which helps to maintain lower concentrations that are predicted by biological productivity [238].
Although lake water used as influent was filtered at 0.22 microns, the apparent degradation of MCLR in influent samples (measured at 0, 3, 6, 9, 12, 15, and 18 PV) indicated that some microbial
activity was still present. A second attempt to quantify influent samples, one week after initial
quantification, showed that all of the influent had since decayed beyond detectable levels, so the
first set of column experiments was deemed inconclusive and repeated using an influent prepared
with distilled water. In the control column (sterilized, 10/90), there is a brief breakthrough (C/C 0
= 0.94) of MC-LR after 1 PV has been exchanged, followed by a relatively linear decay ( Figure
5-3). As the experiment proceeded, the effluent concentration gradually decreased to about 20%
in the first experiment. By the time 8 PVs had been exchanged in the replicate experiment, the
concentration of MC-LR had dropped below detectable levels. Attempts to model the data using
HYDRUS 1D were unsuccessful, but the lack of retardation in the initial breakthrough indicated
that MC-LR was poorly sorbed to the column material, so degradation, either abiotic or biotic,
94
accounted for the observed MC-LR losses. Since MC-LR is generally stable towards chemical
degradation routes [238], biotic degradation is more likely. The columns had previously been used
with lake water, which appeared to have been biologically active, so it is possible that small
microbial communities were established in the sterilized column during the first set of
experiments. Following an initial acclimation period (1-2 PV), microbial growth stimulated by
MC-LR would account for the observed pattern of degradation as the experiment progressed. The
results from the control column indicated that MC-LR would readily undergo biodegradation, even
at low microbial concentrations. Analytical results from experimental columns E1 and E2, where
no MC-LR breakthrough occurred, supported this conclusion. These and other studies [28, 232,
242] indicate that complete removal of MC-LR is possible under a variety of conditions that
promote biodegradation.
95
Figure 5-3. Breakthrough curves for MC-LR. Data for columns E1 (5/95) and E2 (10/90) are based
on average values. Data for the control column, C1, are shown for both the first (A) and second
(B) column experiments without averaging.
96
5.4.
Conclusions
The objective of this study was to evaluate the biodegradation rate of MIB, geosmin and
MCLR in sand filters containing a proportion of Taihu bed sediment. Breakthrough curves for
MIB and geosmin were well–fitted using the transport model (Table 5-1) and showed that the
addition of 10% lake bed sediment (w/w) to sand resulted in 23% and 38.5% removal of influent
MIB and geosmin at a flow rate of 1 m d-1. Forward models estimated that extending the column
length to 2 meter increased removal of MIB to 84% and geosmin to 96%. MC-LR was completely
degraded in columns with both 5% and 10% proportions of lake bed sediment in 30 cm columns,
indicating that an infiltration gallery with a 2-m filtration distance would also achieve desired
removal rates. Future research will determine degradation products of MIB, geosmin and MCLR,
assess degradation rates at higher flow rates, and evaluate whether local sediments can degrade
other known taste/odor compounds such as dimethyl trisulfide and related alkyl sulfide
compounds, which have been attributed to the main septic smell observed during the 2007 bloom
[243].
97
Appendix A
Supplementary Material for Chapter 3
Figure A-1. Dose-Time-Response characteristics of direct stimulation hormesis. Time 1, Time 2,
and Time 3 do not reference a specific unit of time but indicate a time-based progression during
which low doses of a toxin or inhibitor may initially lead to stimulated activity, followed by a
gradual, time-dependent shift toward inhibited activity at the same dosages. Adapted from
Calabrese and Baldwin.[86].
98
Table A-1. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3--N/gsoil) in
anaerobic soils treated with Narasin.
Dose
(ng/kg)
0
1
10
100
1000
Day 1
Day 2
Day 3
Day 4
Day 5
2.25
(0.50)
2.38
(0.69)
0.43
2.87
(1.22)
0.28
2.70
(0.69)
0.15
2.79
(0.08)
0.12
2.98
(0.37)
3.37
(0.52)
0.17
3.34
(0.70)
0.21
2.88
(0.66)
0.42
3.70
(0.77)
0.06
2.80
(0.89)
3.29
(0.73)
0.25
3.52
(0.54)
0.24
3.27
(0.16)
0.26
5.58
(1.42)
0.02
3.48
(0.80)
3.65
(0.27)
0.39
3.63
(0.57)
0.37
2.35
(1.00)
0.17
4.12
(0.57)
0.25
3.23
(1.09)
2.30
(0.52)
0.12
2.50
(0.53)
0.15
2.64
(0.40)
0.14
1.95
(0.76)
0.01
99
Table A-2. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3--N/gsoil) in
anaerobic soils treated with Gentamicin.
Dose
(ng/kg)
0
1
10
100
1000
Day 1
Day 2
Day 3
Day 4
Day 5
2.25
(0.50)
1.70
(0.40
0.15
1.46
(1.46)
0.17
3.02
(0.25)
0.09
2.54
(0.74)
0.35
2.98
(0.37)
3.32
(1.20
0.28
2.70
(0.52)
0.27
4.29
(0.41)
0.007
3.19
(0.20)
0.28
2.80
(0.89)
2.19
(0.84
0.30
1.65
(2.64)
0.28
3.23
(0.24)
0.22
3.30
(0.07)
0.21
3.48
(0.80)
3.13
(0.08
0.17
3.73
(0.20)
0.28
4.26
(0.24)
0.16
2.50
(0.02)
0.08
3.23
(1.09)
2.00
(1.54
0.06
3.21
(0.43)
0.49
2.53
(0.99)
0.19
3.44
(0.21)
0.37
100
Table A-3. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3--N/gsoil) in
anaerobic soils treated with Sulfamethoxazole.
Dose
(ng/kg)
0
1
10
1000
Day 1
Day 2
Day 3
Day 4
Day 5
2.25
(0.50)
4.67
(0.78)
0.01
0.28
(0.58)
0.03
2.18
(0.32)
0.38
2.98
(0.37)
4.44
(0.89)
0.08
0.92
(1.16)
0.06
3.65
(0.45)
0.10
2.80
(0.89)
2.89
(1.05)
0.46
0.59
(1.17)
0.07
2.18
(0.16)
0.19
3.48
(0.80)
3.56
(0.16)
0.44
1.90
(1.01)
0.06
2.32
(1.00)
0.009
3.23
(1.09)
4.52
(1.67)
0.23
2.53
(0.35)
0.15
1.25
(0.11)
0.04
101
Table A-4. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for extractable nitrate (mg NO3--N/gsoil) in
anaerobic soils treated with Sulfadiazine.
Dose
(ng/kg)
0
1
10
100
1000
Day 1
Day 2
Day 3
Day 4
Day 5
2.25
(0.50)
2.81
(0.29)
0.13
2.65
(0.77)
0.10
2.46
(0.26)
0.14
1.29
(1.43)
0.17
2.98
(0.37)
3.83
(0.36)
0.08
3.12
(0.58)
0.41
2.93
(0.45)
0.44
2.58
(1.39)
0.35
2.80
(0.89)
3.47
(0.44)
0.22
2.40
(0.70)
0.31
2.72
(0.69)
0.47
3.05
(0.11)
0.32
3.48
(0.80)
3.69
(0.48)
0.38
2.68
(0.07)
0.10
2.65
(1.16)
0.21
2.87
(0.32)
0.22
3.23
(1.09)
1.20
(1.90)
0.03
1.36
(1.16)
0.10
3.36
(0.84)
0.45
2.39
(0.90)
0.26
102
Table A-5. Results of Student t-test for effluent nitrate in SMX-treated soils vs. untreated soils.
P-values less than 0.05 for paired data are shown in bold.
Time (hours)
0
6
12
18
24
30
36
42
48
54
60
66
72
78
84
90
96
102
108
P-value
0.345
0.182
0.054
0.097
0.162
0.037
0.047
0.064
0.049
0.016
0.003
0.002
0.001
0.015
0.001
0.016
0.015
0.009
0.005
103
Table A-6. Mean, Standard Deviation (parentheses), and significance levels (italics) determined
by student’s t-test for paired means (relative to control) for N2O flux from in aerobic soils treated
with 1-1000 ng·kg-1 Narasin.
Dose (ng/kg)
1
5
10
50
100
500
1000
Day 1
0.048
(0.004)
0.060
(0.004)
0.044
(0.001)
0.096
(0.315)
0.070
(0.001)
0.056
(0.034)
0.098
(0.270)
Day 2
0.198
(0.040)
0.188
(0.099)
0.193
(0.225)
0.043
(0.035)
0.066
(0.223)
0.173
(0.207)
0.368
(0.063)
Day 3
0.141
(0.041)
0.224
(0.042)
0.142
(0.001)
0.263
(0.016)
0.145
(0.030)
0.305
(0.004)
0.388
(0.003)
104
Appendix B
Supplementary Materials for Chapter 4
60% WFPS
40% WFPS
120
NH+4 N ( g g-1)
NH+4 N ( g g-1)
160
100
120
100
80
80
60
Day 2
Day 2
140
100
120
100
80
80
60
120
160
NH+4 N ( g g-1)
Day 1
Day 1
140
Day 3
Day 3
140
100
120
100
80
80
60
40
0
1
10
1000
100
-1
Narasin Dose (ng kg )
60
0
1
10
1000
100
-1
Narasin Dose (ng kg )
Figure B-1. Comparison between measured NH4+ pool (dotted line) and a mass balance (solid
line) calculated from the using the initial NH4+ concentration and rates of mineralization and
nitrification on Days 1-3.
105
-
-1
NO3N (g g )
-
-1
NO3N (g g )
-
-1
NO3N (g g )
40% WFPS
100
90
80
70
60
50
40
30
20
10
60% WFPS
Day 1
40
Day 1
30
20
10
90
80
70
60
50
40
30
20
10
Day 2
90
80
70
60
50
40
30
20
10
0
Day 3
Day 2
30
20
10
Day 3
30
20
10
0
1
10
100
-1
Narasin Dose (ng kg )
1000
0
0
1
10
100
1000
Narasin Dose (ng kg -1)
Figure B-2. Comparison between measured NO3- pool (dotted line) and a mass balance (solid line)
calculated from the using the initial NO3- concentration and rates of mineralization and nitrification
on Days 1-3.
106
Table B-1. Pearson Correlation Coefficient (R2) calculated between measured NH4+/NO3- mass
balances calculated from initial NH4+/NO3- and the measured rates of mineralization, nitrification,
and denitrification.
40% WFPS
NH4+
NO360% WFPS
NH4+
NO3-
Day 1
Day 2
Day 3
R2
R2
0.40
0.98
0.24
0.94
0.71
0.82
R2
R2
-0.62
0.59
-0.16
0.77
0.89
0.93
107
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