Modeling Nitrous Oxide Production and Reduction in Soil through

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Modeling Nitrous Oxide Production and Reduction in Soil Through Explicit Representation of Denitrification Enzyme Kinetics Jianqiu Zheng, and Paul V. Doskey Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es504513v • Publication Date (Web): 14 Jan 2015 Downloaded from http://pubs.acs.org on January 20, 2015

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Environmental Science & Technology

Modeling Nitrous Oxide Production and Reduction in Soil Through Explicit Representation of Denitrification Enzyme Kinetics Jianqiu Zheng† and Paul V. Doskey*†,‡,§ †

Atmospheric Sciences Program, ‡Department of Civil and Environmental Engineering, §School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, Michigan 499311295, United States

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ABSTRACT: An enzyme-explicit denitrification model with representations for pre- and de

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novo synthesized enzymes was developed to improve predictions of nitrous oxide (N2O)

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accumulations in soil and emissions from the surface. The metabolic model of denitrification is

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based on dual substrate utilization and Monod growth kinetics. Enzyme synthesis/activation was

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incorporated into each sequential reduction step of denitrification to regulate dynamics of the

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denitrifier population and the active enzyme pool, which controlled the rate function.

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Parameterizations were developed from observations of the dynamics of N2O production and

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reduction in soil incubation experiments. The model successfully reproduced the dynamics of

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N2O and N2 accumulation in the incubations and revealed an important regulatory effect of

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denitrification enzyme kinetics on the accumulation of denitrification products. Pre-synthesized

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denitrification enzymes contributed 20, 13, 43, and 62% of the N2O that accumulated in 48-hr

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incubations of soil collected from depths of 0-5, 5-10, 10-15, and 15-25 cm, respectively. An

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enzyme activity function (E) was defined to estimate the relative concentration of active

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enzymes and variation in response to environmental conditions. The value of E allows activities

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of pre-synthesized denitrification enzymes to be differentiated from de novo synthesized

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enzymes. Incorporating explicit representations of denitrification enzyme kinetics into

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biogeochemical models is a promising approach for accurately simulating dynamics of the

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production and reduction of N2O in soils.

ACS Paragon Plus Environment


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INTRODUCTION Nitrous oxide (N2O) is a long-lived greenhouse gas and the principal biogenic source of

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stratospheric N2O contributing to the destruction of the ozone layer.1,2 Global N2O emissions

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from cultivated soils have been estimated at 4.2 Tg yr-1, which accounts for 50% of global

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anthropogenic N2O sources.3 Application of synthetic nitrogen fertilizers has substantially

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stimulated emissions of N2O from agroecosystems.4-6 Sequential reduction of nitrate (NO3-),

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nitrite (NO2-), and nitric oxide (NO) during denitrification produces N2O.7 Low levels of N2O

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relative to NO3- and NO2-, which are the principal products of nitrification, were observed in

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tracer studies of nitrification and suggest denitrification is a more significant source of N2O than

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nitrification.8,9 Nitrous oxide reductase (N2OR) is the only known enzyme that mediates the

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reduction of N2O to molecular nitrogen (N2), and thus, denitrification is also an important

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biological sink for N2O.

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The sequential reduction of nitrogen oxides during denitrification is typically modeled with

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Michaelis-Menten kinetics using enzyme-specific values of the maximum rate of the reaction

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(Vmax) and the Michaelis or substrate affinity constant (Km), which is the substrate concentration

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at which the reaction rate is 0.5 Vmax.10,11 Reaction intermediates accumulate if rates of

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consumption are slower than rates of production. Dual substrate, Michaelis-Menten kinetic

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models that use Vmax and two Km values for each denitrification step to account for affinities of

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the electron donor and acceptor (i.e. reduced carbon and nitrogen oxides, respectively), have also

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been developed to explain observations.12,13 Pan et al. decoupled reduced carbon oxidation and

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nitrogen oxide reduction in a denitrification model by introducing reduced and oxidized electron

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carriers in the Michaelis-Menten kinetic expression and by using different substrate affinity

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constants to explain competition for electrons.14 Dynamics of the denitrifier population are 2


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usually simulated in denitrification models with the Monod equation that relates microbial

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growth rates to the concentration of a limiting substrate. Relative rates of the sequential

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denitrification reactions are parameterized using unique values of Vmax or are simulated by

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assuming reductases are unique to the various denitrifying populations.15 Thus, sequential

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induction of denitrification enzymes is represented by differences in growth rates of the various

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reductase populations.

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Accumulation of denitrification intermediates result from unbalanced rates of the sequential

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denitrification reactions, which can be ascribed solely to enzyme kinetics,10,12 or together with

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sequential gene expression.16 Denitrification is essentially regulated at the enzymatic level rather

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than solely at the population level. Enzyme synthesis and the dynamics of enzyme activities are

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relatively fast compared to population dynamics. Transcriptional analysis showed that expression

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of genes encoding all denitrification reductases peaked within 2-3 hours after switching from

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oxic to anoxic conditions in cultured Pseudomonas fluorescens.16 De novo syntheses of NO3-

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and NO2- reductases (NAR and NIR, respectively) were observed within 2-3 h and 4-12 h,

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respectively, following incubations under anaerobic conditions.11,17 Rapid accumulation of N2O

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in incubations of soil-extracted bacteria under oxic conditions immediately following incubations

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under anoxic conditions could not be ascribed to growth of the denitrifying communities.18 The

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primary denitrification product in incubations under anoxic conditions was N2; however, a shift

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to N2O was observed when the incubations were re-exposed to molecular oxygen (O2), which

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deactivated N2OR. The activity of N2OR recovered when O2 was depleted after the O2-exposure

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period.18 The results suggest an important role of the pre-synthesized denitrification proteome in

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the accumulation of N2O. Thus, denitrification models based solely on population dynamics

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might have difficulty in simulating transient N2O accumulations in soil under rapidly fluctuating 3



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environmental conditions that regulate O2 tensions like the infiltration of water during

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precipitation.

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Enzyme dynamics can be incorporated into a conventional denitrification model by

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representing the rate of each denitrification step as a function of both population dynamics and

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the active enzyme pool.19 Here we formulate a mathematical model for denitrification with

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explicit representation of enzyme dynamics. Enzyme synthesis and activation are regulated by

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enzyme substrate saturation and inhibitor concentrations. An enzyme activity function is defined

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to estimate the relative concentration of active enzymes and allows activities of pre-synthesized

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denitrification enzymes to be differentiated from de novo synthesized enzymes. Parameters

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describing enzyme dynamics are estimated and validated with experimental data obtained from

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soil incubations. The primary objective of the work is to improve model forecasts of N2O

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accumulations in soil and emissions by developing a model framework that incorporates

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representations of explicit enzyme dynamics into denitrification models.

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EXPERIMENTAL SECTION

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Soil collection and analysis. Soils were collected 19-26 July 2012 as part of a rainfall

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simulation study20 at the Bondville, Illinois AmeriFlux site (40°00′N, 88°18′W). No-till

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agriculture has been practiced at the site for more than 20 a, and soybeans and corn have been

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rotated annually since 2000.21 The soil type is silt loam, with an average porosity of 45%

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between 0-50 cm and an inorganic fraction composed of 25% clay, 70% silt and 5% sand.21,22

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Total precipitation was 2 mm during the previous 10 days before the field experiment.

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Soil samples were collected in-the-row of soybean down to a depth of 25 cm using a 1.27-cm o.d. stainless steel sampler (AMS, Inc. American Falls, ID) and sectioned into 4 depth 4


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increments (i.e., 0-5, 5-10, 10-15 and 15-25 cm). Soil core sections were stored in 15-mL sterile

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plastic tubes (Fisher Scientific, Pittsburgh, PA), flash-frozen in the field in liquid N2, and

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transported to the laboratory in a liquid N2 dewar (PrincetonCryo, Flemington, NJ). Details of

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the analytic techniques and the complete results can be found in Zheng.20 Briefly, subsamples

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from soil cores sections were sieved (4 mm) prior to analyses of soil pH, NO3-, extractable

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organic carbon (i.e., dissolved organic carbon; DOC), and microbial biomass carbon (SMBC).

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Soil pH was determined in a soil suspension using the 1:1 slurry method. The DOC and soil

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soluble N were extracted with potassium sulfate and analyzed with a TOC Analyzer (Sievers

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900, GE Analytical Instruments, CO) and Rapid Flow Analyzer (Perstorp Analytical Inc., Silver

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Spring, MD), respectively. The SMBC was determined through a correlation with the

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phospholipid fatty acid (PLFA) content of soil.23,24 Lipids were extracted from freeze-dried soils

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with chloroform-methanol25 and the methylated PLFAs were quantified by high-resolution gas

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chromatography with flame ionization detection (FID; HP6890; Agilent, Palo Alto, CA).

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Calculation of SMBC was based on the following correlation:20

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SMBC=4.5 PLFAT +33 (R2=0.85)

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where SMBC and total PLFAs (PLFAT) are expressed as µg C g-1 and nmol g-1, respectively. Soil Incubations. Subsamples of soil core sections (3 g) were incubated in 40 mL amber vials

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containing 5 mL of synthetic rainwater and sealed with mininert valves (Sigma Aldrich, MO).

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The average volumetric air content was between 40-50% before the incubation. Levels of

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chemical constituents in the synthetic rainwater were determined from the average

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concentrations in annual precipitation.26 Air was evacuated from the vial headspace for 30 min 5



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and replaced by helium (He) for a total of 3 times to reduce headspace O2 levels below1% (v/v).

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Treatments with chloramphenicol (CHL; 2.5 g L-1) were used to inhibit protein synthesis and

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estimate the activity of pre-synthesized denitrification enzymes.27 To inhibit the activity of

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N2OR, which reduces N2O to N2, 3.5 mL of He was removed from the headspace and replaced

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with acetylene (C2H2) to make the headspace concentration 10% v/v. The activity of pre-

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synthesized N2OR was estimated by adding both CHL and C2H2. Vials with the various

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treatments were prepared in triplicate, incubated at 25°C, and gently mixed on a rotary shaker

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(250 rpm) during the 48-h experiments.

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The headspace of each vial was sampled at 0, 3, 6, 12, 24, 36, and 48 h to match the sampling

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schedule of the rainfall simulation study. Samples of headspace were injected into a 1-mL

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stainless steel sample loop connected to a 2-position, 6-port valve (VICI, Houston, TX) upstream

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of a high-resolution gas chromatograph with electron capture detector (ECD; HP5890; Hewlett

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Packard, Palo Alto, CA). The N2O was separated from other electron capturing species with a

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30-m × 0.530-mm fused silica capillary coated with a 3.00-µm carbon film (GS-CarbonPlot;

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Agilent). The carrier and ECD makeup gases were He and N2, respectively. The C2H2

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diminished sensitivity and impeded recovery of the ECD, and thus, was removed from the

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column effluent by redirecting the column flow through a 2-position, 4-port valve (VICI) to an

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FID after N2O eluted from the column. The precision for N2O quantitation was better than 2%

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and the detection limit was less than 5 ppbv.

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Model development. The model represents the enzyme dynamics of each sequential

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denitrification reaction by including an enzyme activity function, which simulates changes in

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size of the active enzyme pool with fluctuating environmental signals, i.e. substrate level and O2

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concentration. The rate of enzyme synthesis/activation is assumed to obey Michaelis-Menten

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kinetics as follows (Table 1):

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K I ,R Ci dE R = Vmax, R ⋅ ⋅ (1 − E R ) K E ,i + Ci K I ,R +C O2 dt

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where ER is the enzyme activity function (dimensionless), Vmax,R is the maximum specific

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synthesis/activation rate of the corresponding reductase reaction (h-1), KE,i is the half-saturation

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constant for enzyme induction (M), KI,R is the O2 inhibition coefficient for the corresponding

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reductase (M), Ci is the aqueous-phase concentration of the substrate (M), R is the reductase (i.e.,

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NAR, NIR, NOR, and N2OR), and i is the corresponding substrate (i.e., NO3-, NO2-, NO, and

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N2O, respectively). Values of E range from 0-1, where 0 represents no active enzymes and 1

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represents full activation of the total enzyme pool. Estimates of KE,i and KI,R are based on

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properties of denitrification enzymes and their sensitivity to the O2 level.7,28 A low KI value was

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assigned to the O2 inhibition coefficient for N2OR to compensate for the strong inhibitory effect

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of O2.28,29 The value of E is 1 under conditions of anoxia and ideal substrate concentration.

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Under suboptimal conditions, the initial estimate of E and the temporal variation of enzyme

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synthesis are the parameters that control enzyme activity.

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Denitrification reactions are simulated based on dual substrate utilization and Monod growth

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kinetics.12,13 Microbial oxidations of C using O2, NO3-, NO2-, NO, and N2O as electron acceptors

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are considered and stoichiometric relationships are obtained through electron balance between

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the C source (electron donor) and electron acceptors. Microbial mediated transformations are

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assumed to occur in the aqueous phase with equilibrium for O2, NO, N2O and N2 between the 7



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gas and aqueous phases following Henry’s law. All chemical species follow a time-dependent

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mass balance in the gas and liquid phase. The specific reaction rate depends upon the maximum

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utilization rate of the substrate (q), microbial biomass (B), and the term describing dual substrate

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utilization. All denitrifiers are assumed to be equally saturated with different denitrification

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enzymes, and thus, a linear dependency of E is applied in the rate expressions to approximate the

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relative active enzyme concentrations. The net variation in Ci and CO2 depends on the rate of

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production and consumption by the corresponding biomass (Bi), which is calculated from the C

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utilized as substrate and the growth efficiency via microbial respiration using O2 and nitrogen

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oxides.

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Kinetics and stoichiometry of the transformations are summarized in Table 2. Respiration is

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blocked by NO through binding to cytochrome oxidase and nM levels of NO can cause

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substantial inhibition of respiration.30 Competitive inhibition from NO increased the apparent

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value of KNO, which is determined by KO2, CNO, and KI,NO,O2 in the rate expression for O2

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respiration.31,32 Two molecules of NO are bound to NOR during reduction of NO and substrate

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inhibition was observed to occur at µM levels.33 Thus, the kinetics of NO reduction follows the

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classic Haldane formula for substrate inhibition.34 However, levels of NO in the soil incubations

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are unlikely to reach µM levels due to the lower levels of initial substrate concentrations. Kinetic

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reaction parameters previously estimated and validated by laboratory studies and process-scale

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soil denitrification models35-37 are listed in Table 2S.

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Soil slurries were sufficiently buffered and remained constant at about pH 7 over 48 hr, and

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thus, inhibition of N2OR activity at suboptimal pH (6.0) is not considered in the model.38-40 In

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the absence of inhibitory effects, denitrification rates are related to availability of electron

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acceptors and donors and active enzymes mediate the reactions. The rate of volatilization of

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gaseous chemical species from the aqueous phase is calculated as follows:41 Rtr,i = K L (

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Ci ,g Hi

− Ci ,aq )

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where Rtr,i is the transfer rate of the chemical species (M h-1), Ci,g and Ci,aq are gas and liquid

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phase concentrations (M), Hi is Henry’s law constant expressed as LH2O Lair-1 and KL is the

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overall liquid-phase mass transfer coefficient (h-1). The value of KL depends on the

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physicochemical properties of the chemical species and the depth of liquid in the soil slurry.

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Estimates of KL for O2, NO, N2O and N2 were calculated based on Henry’s law constants and

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reported values of individual gas- and liquid-phase mass transfer coefficients for H2O and CO241

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and varied from 16.1-19.3 h-1. Estimates of KL for the incubation system are at the low end of

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experimentally determined values (19.4-20.2 h-1) from a robotic incubation system.42

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The system of differential equations (Tables 1 and 2) is solved numerically using Matlab (The

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Mathworks, Inc., Natick, MA, USA) with ODE solvers. The average time step is about 0.003 h.

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Initial conditions are assigned according to levels measured in the incubations,20 including

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concentrations of O2, NO3-, DOC, SMBC, and the measured activity of pre-synthesized enzymes

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prior to incubation (Table 3S). Parameters developed in the model were optimized by the

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Levenberg-Marquardt method.43 Model fitness was evaluated by calculating the coefficient of

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determination as follows: R

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2

∑ (C = 1− ∑ (C

exp

− C mod el ) 2

exp

− Cexp ) 2

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where Cexp and Cmodel are experimentally determined and model simulated concentrations (M),

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respectively.

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RESULTS

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Model Evaluation. The dynamics of N2O in the headspace of all soil incubations were similar

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(Figure 1S). Levels of N2O increased sharply within the first 12-24 h in the headspace of the soil

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slurries in synthetic rainwater (CTR) and then ceased when N2O was likely being reduced to N2.

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Production of N2O in slurries treated with C2H2 to block N2OR activity exhibited a continuous

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increase in headspace N2O and indicates the activity of N2OR is represented by the difference in

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headspace N2O between the CTR and C2H2 treatment. Accumulation of N2O from the

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CHL+C2H2 treatment represents the activity of pre-synthesized denitrification enzymes and

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differences in the accumulation of N2O between the CHL and CHL+C2H2 treatments represents

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pre-synthesized N2OR activity. Negligible pre-synthesized N2OR activity was observed;

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however, a substantial amount of N2O accumulation was attributed to the activities of pre-

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synthesized NAR, NIR and NOR.

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The initial value of E was estimated from the measured denitrification rates with and without CHL treatment as follows: E = RCHL / RCTR

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where RCHL and RCTR are derived from the initial, linear portions of the N2O production curves

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from the CHL and CTR treatments, respectively (Figure 1S). Activities of NAR, NIR, and NOR

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can not be distinguished unless products of each of the sequential reductions are measured. The

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rate of N2O production is limited by the slowest reduction step that is catalyzed by NAR, NIR, or

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NOR. A lower activity of NAR or NIR would not produce sufficiently high concentrations of

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NO2- and NO to produce the observed levels of N2O. Thus, initial values of E for NAR, NIR, and

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NOR are assumed to be equal and are estimated directly from the experimental data. The

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assumption also limits the number of unknown variables and avoids introducing unnecessary

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uncertainties into the model.

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The initial biomass of denitrifiers, which carry reductases for the various nitrogen oxides and

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switch from O2 respiration to NO3- respiration, is assumed to be a fraction of the measured total

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microbial biomass as follows:

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BDen = δ ⋅ BTotal

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where BDen is the initial biomass (on a mol C basis) of the total denitrifiers in the incubation

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solution (mol C L-1), δ is the fraction of the total microbial biomass represented by the

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denitrifiers (dimensionless), and BTotal is SMBC (on a mol C basis) in the incubation solution

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(mol C L-1). Initial biomasses of denitrifiers carrying NAR (BNO3-), NIR (BNO2-), NOR (BNO), and

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N2OR (BN2O) in the rate expressions for Monod growth kinetics (Table 2) are assumed to be

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equal to BDen. The initial value of BO2, which is the microbial biomass mediating respiratory

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metabolism using O2 as the electron acceptor, is assumed to be equal to BTotal. Values of δ are

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estimated from the soil incubations by using data derived from the CHL treatment and gradually

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increased with depth in the soil (Figure 2S). The average value of δ was 0.0461 ± 0.0013.

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Values of Vmax,R are estimated from data obtained from incubation of the 0-5 cm soil core

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section. The increased accumulation of N2O in the C2H2 treatment relative to the CHL+C2H2

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treatment is attributed to de novo activity of NAR, NIR, and NOR that produces N2O; however,

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in the CTR relative to the CHL treatment, N2O accumulation is due to the net effect of N2O

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production by the combined de novo activities of NAR, NIR, and NOR and reduction by de novo

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N2OR activity. The experiment does not differentiate rates of de novo synthesis for NAR, NIR, 11



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and NOR, and thus, values of Vmax,NAR, Vmax,NIR, and Vmax,NOR are assumed to be equal and

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designated as Vmax,1. Initial estimates of Vmax,1 and Vmax,N2OR (Vmax,2) are made by examining the

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temporal sequence in de novo enzyme synthesis. De novo synthesis of NAR, NIR, and NOR

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begins within the first 3 h with synthesis of N2OR beginning about 12 h later. Values of Vmax,1

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and Vmax,2 were optimized with the CTR and the C2H2 treatment data by minimizing the sum of

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squares of the deviations between the measured and predicted N2O and N2 mixing ratios,

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respectively. The correlation between Vmax,1 and Vmax,2 is low (Figure 4aS). The range in estimates

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of Vmax,1 are wider than the range in estimates of Vmax,2, which is likely due to the assumption that

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Vmax for NAR, NIR, and NOR are equal.

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Model Sensitivity Analysis. A sensitivity analysis was performed on several key model

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parameters (Figure 2). Variations of ±5, ±10, ±15, and ±20% were applied to parameters and

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resulting changes in the cumulative concentrations of NO3-, NO2-, NO, N2O, and N2 were

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normalized to the corresponding reference simulation (Figure 2). Activities of NAR, NIR, and

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NOR are regulated by Vmax,1, which determines the sequential flux of N substrates.

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Concentrations of NO3-, NO2-, and NO are sensitive to changes in Vmax,1, which creates an

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imbalance between production and reduction rates. Cumulative concentrations of N2O and N2 are

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slightly influenced by variations in Vmax,1. The low sensitivity of the model performance to Vmax,1

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is likely related to the wide range in estimated values of Vmax,1. Changes in Vmax,2 have a more

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direct effect on the accumulation of N2O and N2 through regulation of EN2OR. Variations in the

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parameter enlarge the imbalance between activities of NAR, NIR, NOR, and N2OR, resulting in

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a greater accumulation of N2O. The parameters KE,N2O and KI,N2OR are key regulators of N2OR

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activity and variations demonstrate a strong impact on accumulation of N2O and N2 and minimal

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impact on accumulation of NO3-, NO2-, and NO (Figure 2). Other important parameters include 12


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KO2, which regulates O2 consumption, and KL, which controls volatilization of NO, N2O and N2

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from the aqueous phase. Accumulation of N2O and N2 are both highly influenced by KO2, which

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is mainly due to the sensitivity of N2OR to O2 concentration. Model sensitivity to variations in

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KL was low. The first gaseous intermediate product of denitrification is NO, which shows the

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strongest response.

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Additional sensitivity analyses were performed with parameters derived from the literature.

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Twelve parameters were grouped into three categories: half-saturation constants for nitrogen

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oxide uptake, Michaelis constants for denitrification enzyme induction, and substrate utilization

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rates. Variations in half-saturation constants for nitrogen oxide uptake and Michaelis constants

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for enzyme induction cause limited changes in the accumulation of nitrogen oxides (< 5%).

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However, variations in the substrate utilization rates (q) show significant influence on the

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accumulation of the corresponding substrate (Figure 3S). In particular, changes in the utilization

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rate of NO3- (qNO3-) strongly influenced accumulations of N2O and N2, which were expected due

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to a forced minimization of the accumulations of NO2- and NO by the model.

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Variation in Production and Reduction of N2O with Depth. Transformation rates of N2O

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are regulated by denitrification enzyme activity, which is parameterized by E. Values of E for

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each enzyme are dependent on the initial enzyme activity (E0) and Vmax,1 and Vmax,2. Measured

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and model predicted temporal variations of N2O and N2 during the incubations are presented in

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Figure 1. Estimated values of Vmax,1 and Vmax,2 for different soil core sections are summarized in

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Table 3. The value of Vmax,1/Vmax,2 is significantly greater than 1, which implies a lag in the

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synthesis of N2OR. Measurements of reaction rates derived from the CHL and CHL+C2H2

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treatments of incubations of the 5-10 cm soil core section were substantially different, which is

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due to the activity of pre-synthesized N2OR (Table 3S). The estimated value of Vmax,1/Vmax,2 is 13



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significantly lower than the other three enzymes (Table 3). The lag in N2OR synthesis causes an

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extremely high value of N2O/(N2O+N2) during the first few hours of the incubation (Figure 3).

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Production of N2 from pre-synthesized N2OR in the 5-10 cm soil core section diminishes the

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early plateau in the magnitude of the ratio.

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Role of pre-synthesized denitrification proteome. Contributions from pre-synthesized

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denitrification enzymes are evaluated with the model by setting Vmax,1 to zero to suppress de novo

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synthesis of NAR, NIR and NOR, which ascribes N2O accumulation solely to the activity of pre-

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synthesized enzymes. Pre-synthesized enzymes contributed 63, 38, 48, and 48% of the total

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cumulative N2O flux during incubations of the 0-5, 5-10, 10-15, and 15-25 cm soil core sections,

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respectively (Figure 4). Contributions of pre-synthesized enzymes normalized to SMBC

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increased with soil depth and were 20, 13, 43, and 62%. Due to the activity of pre-synthesized

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N2OR (Figure 1S), the contribution of pre-synthesized enzymes to N2O accumulation in the 5-10

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cm soil core section is relatively low.

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DISCUSSION Representations of N2O production in soils in current denitrification models are based on

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growth of the microbial biomass.35,36 An enzyme-explicit model of the biological regulation of

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denitrification that incorporates dynamics of pre- and de novo synthesized denitrification

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enzymes was developed in the subject study to improve forecasts of the production of N2O in

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soils. Incubation studies have demonstrated persistence of denitrification enzymes in soils

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subjected to aerobic conditions.17,27,44 Denitrification activity and product gases observed 1-3 h

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after the onset of anaerobiosis during the incubations were ascribed to the activity of pre-

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synthesized enzymes.17 After anaerobiosis was imposed, de novo synthesis of NAR, NIR, and 14


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N2OR were found to occur within 2-3, 4-12, and 24-42 hr, respectively.11 A similar dynamic was

323

observed in the subject study. Rapid accumulation of N2O was attributed to the activity of pre-

324

synthesized NAR, NIR, and NOR. Pre-synthesized levels of N2OR were negligible and de novo

325

synthesis of N2OR lagged behind the other 3 denitrification enzymes, which contributed to the

326

rapid accumulation of N2O (Figure 1S). Incorporating values of E for the denitrification enzymes

327

in the model allowed quantification of pre-synthesized enzymes and parameterization of the

328

delay in N2OR synthesis.

329

The sensitivity analysis demonstrated the important role of Vmax,2 in regulating accumulation of

330

N2O. Differences between Vmax,1 and Vmax,2 lead to imbalances in E (Figure 5S) and result in

331

transient accumulation of N2O. Previous modeling work identified a critical role of qN2O when

332

estimating the accumulation of N2O.36 The model developed in the subject study also showed a

333

high sensitivity of N2O accumulation to the value of qN2O. However, in the enzyme-explicit

334

model, qN2O regulates the magnitude of the N2O maxima and Vmax,2 regulates the width of the

335

N2O peak (Figure 5).

336

Estimates of cumulative N2O emissions by conventional denitrification models are in general

337

agreement with low-temporal resolution measurements of the N2O flux; however, the dynamics

338

of the emissions are poorly represented.45,46 The temporal sequence of denitrification enzyme

339

synthesis likely explains differences between model simulations of the timing of peak N2O

340

emissions and field observations of maxima in N2O fluxes.45-47 High temporal resolution

341

measurements reveal that pulse emissions of N2O related to precipitation events are a significant

342

contribution to the annual emission inventory.48 The enzyme-explicit denitrification model with

343

representations for pre- and de novo synthesized enzymes (Figure 5S) is able to capture rapid

15



344

changes in N2O production and reduction and should improve forecasts of the timing and

345

magnitude of pulse emissions.49

346

Development of the parameter, E, in the subject study enabled parameterization of pre- and de

347

novo synthesized enzyme activity in the enzyme-explicit denitrification model. The parameter

348

allows quantification of the “denitrification population” that mediates the various sequences of

349

denitrification and integrates variations in denitrification enzyme activity in response to

350

environmental conditions. The enzyme-explicit denitrification module could be coupled with

351

other denitrification or biogeochemical models like the electron competition model of

352

denitrification14, DNDC45 or ecosys46, to improve model predictions of N2O emissions from soil.

353 354

ASSOCIATED CONTENT

355

Supporting Information

356

Additional tables with references and figures are provided.

357 358

AUTHOR INFORMATION

359

Corresponding Author

360

*E-mail: [email protected]. Phone: 906-487-2745. Fax: 906-487-2943.

361

Notes

362

The authors declare no competing financial interest.

363 364

ACKNOWLEDGEMENTS

365

The authors acknowledge start-up funding, which supported Jianqiu Zheng, and an equipment

366

loan to Paul V. Doskey through Michigan Technological University and Argonne National 16


Page 16 of 32

Page 17 of 32


367

Laboratory, respectively. Partial support for Jianqiu Zheng through the Atmospheric Sciences

368

Program is also greatly appreciated.

369 370 371 372 373 374 375

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(4) Kroeze, C.; Mosier, A.; Bouwman, L., Closing the global N2O budget: A retrospective analysis 1500–1994. Global Biogeochem. Cy. 1999, 13, 1-8. (5) Sowers, T.; Rodebaugh, A.; Yoshida, N.; Toyoda, S., Extending records of the isotopic

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extracted soil bacteria, regulation by NO2- and O2 concentrations. FEMS Microbiol. Ecol. 2008,

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65, 102-112.

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of chloroform fumigation-extraction, phospholipid fatty acid, and DNA methods to determine

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microbial biomass in forest humus. Soil Biol. Biochem. 2004, 36, 529-532.

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(25) Bligh, E. G.; Dyer, W. J., A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 1959, 37, 911-917. (26) NADP, National Atmospheric Depostion Program http://nadp.isws.illinois.edu/. 2012.

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dissolved oxygen level and respiratory substrate in continuous culture of Pseudomonas stutzeri.

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Appl. Environ. Microbiol. 1989, 55, 1670-1676.

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2002; pp 318-349. (33) Girsch, P.; de Vries, S., Purification and initial kinetic and spectroscopic characterization of NO reductase from Paracoccus denitrificans. Biochim. Biophys. Acta 1997, 16, 1-2. (34) Copeland, R. A., Kinetics of single-substrate enzyme reactions. In Enzymes, John Wiley & Sons, Inc.: 2002; pp 109-145. (35) Gu, C.; Riley, W. J., Combined effects of short term rainfall patterns and soil texture on soil nitrogen cycling — A modeling analysis. J. Contam. Hydrol. 2010, 112, 141-154.

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(36) Maggi, F.; Gu, C.; Riley, W. J.; Hornberger, G. M.; Venterea, R. T.; Xu, T.; Spycher,

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N.; Steefel, C.; Miller, N. L.; Oldenburg, C. M., A mechanistic treatment of the dominant soil

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(37) Blagodatsky, S.; Grote, R.; Kiese, R.; Werner, C.; Butterbach-Bahl, K., Modelling of

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microbial carbon and nitrogen turnover in soil with special emphasis on N-trace gases emission.

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Plant Soil 2011, 346, 297-330.

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(38) Bergaust, L.; Mao, Y.; Bakken, L. R.; Frostegård, Å., Denitrification response patterns

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during the transition to anoxic respiration and posttranscriptional effects of suboptimal pH on

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nitrogen oxide reductase in Paracoccus denitrificans. Appl. Environ. Microbiol. 2010, 76, 6387-

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(39) Bakken, L. R.; Bergaust, L.; Liu, B.; Frostegård, Å., Regulation of denitrification at the

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cellular level: A clue to the understanding of N2O emissions from soils. Philos. T. Roy. Soc. B.

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2012, 367, 1226-1234.

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(40) Liu, B.; Morkved, P. T.; Frostegård, Å.; Bakken, L. R., Denitrification gene pools,

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transcription and kinetics of NO, N2O and N2 production as affected by soil pH. FEMS

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Microbiol. Ecol. 2010, 72, 407-417.

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(41) Thibodeaux, L. J., Environmental Chemodynamics. John Wiley and Sons, New York,

1996. (42) Molstad, L.; Dorsch, P.; Bakken, L. R., Robotized incubation system for monitoring gases (O2, NO, N2O, N2) in denitrifying cultures. J. Microbio. Meth. 2007, 71, 202-211.

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(44) Firestone, M. K.; Tiedje, J. M., Temporal change in nitrous oxide and dinitrogen from denitrification following onset of anaerobiosis. Appl. Environ. Microbiol. 1979, 38, 673-679. (45) Li, C.; Frolking, S.; Frolking, T. A., A model of nitrous oxide evolution from soil driven

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(46) Metivier, K. A.; Pattey, E.; Grant, R. F., Using the ecosys mathematical model to

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simulate temporal variability of nitrous oxide emissions from a fertilized agricultural soil. Soil

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Biol. Biochem. 2009, 41, 2370-2386.

487

(47) Rabot, E.; Cousin, I.; Hénault, C., A modeling approach of the relationship between

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nitrous oxide fluxes from soils and the water-filled pore space. Biogeochemistry 2014, 1-14.

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(48) Groffman, P.; Butterbach-Bahl, K.; Fulweiler, R.; Gold, A.; Morse, J.; Stander, E.;

490

Tague, C.; Tonitto, C.; Vidon, P., Challenges to incorporating spatially and temporally explicit

491

phenomena (hotspots and hot moments) in denitrification models. Biogeochemistry 2009, 93, 49-

492

77.

493

(49) Butterbach-Bahl, K.; Baggs, E. M.; Dannenmann, M.; Kiese, R.; Zechmeister-

494

Boltenstern, S., Nitrous oxide emissions from soils: How well do we understand the processes

495

and their controls? Philos. T. Roy. Soc. B. 2013, 368, doi:10.1098/rstb.2013.0122.

496 497 498

22


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Page 23 of 32

499 500


Table 1. Enzyme synthesis/activation kineticsa Enzyme

Rate expression R E NAR = Vmax, NAR ⋅

Nitrate Reductase (NAR)

RE NIR = Vmax, NIR ⋅

⋅ (1 − E NAR )

3

C NO −

K I , NIR

⋅

2

K E , NO − + C NO − K I , NIR + CO2

RENOR = Vmax, NOR ⋅

Nitrous Oxide Reductase (N2OR) a

K E , NO − + C NO − K I , NAR + CO2

2

Nitric Oxide Reductase (NOR)

K I , NAR

⋅

3

3

Nitrite Reductase (NIR)

501 502 503

C NO −

⋅ (1 − E NIR )

2

K I , NOR C NO ⋅ ⋅ (1 − E NOR ) K E , NO + C NO K I , NOR + CO2

R E N 2OR = Vmax, N 2OR ⋅

C N 2O

⋅

K I , N 2OR

K E , N 2O + C N 2O K I , N 2OR + CO2

Parameters are listed in Table 1S.

23


⋅ (1 − E N 2OR )


504

Page 24 of 32

Table 2. Rate expressions for Monod growth kineticsa Biological Reactions

Rate Expressions

CO2 COC ⋅ C NO ) + CO2 K OC + COC K O2 (1 + K I , NO ,O2

CH 2O + O2 ( aq) → CO2 ( aq) + H 2O

RO2 = qO2 ⋅ BO2 ⋅

2 NO3− + CH 2O → 2 NO2− + CO2 ( aq) + H 2O

RNO− = qNO− ⋅ BNO− ⋅ 3

3

3

CNO− 3

KNO− + CNO− 3

− 2

R NO − = q NO − ⋅ B NO − ⋅

+

4 NO + CH 2O + 4H → 4NO(aq) + CO2 (aq) + 3H 2O

2

2

2

3

⋅

COC ⋅ ENAR KOC + COC

C NO − K NO − + C NO − 2

CNO

8 NO ( aq ) + 2CH 2 O → 4 N 2 O ( aq ) + 2CO 2 ( aq ) + 2 H 2O

RNO = qNO ⋅ BNO ⋅

4 N 2O ( aq ) + 2CH 2O → 4 N 2 ( aq ) + 2CO2 ( aq ) + 2 H 2 O

R N 2O = q N 2O ⋅ B N 2O ⋅

a

2

COC ⋅ E NIR K OC + COC

2

 CNO  ) K NO + CNO ⋅ (1 + K I ,NO  

C N 2O K N 2O + C N 2O

505 506 507

⋅

2

Variables and parameters are listed in Table 2S.

24


⋅

2

⋅

COC ⋅ ENOR KOC + COC

COC ⋅ E N 2OR K OC + COC

Page 25 of 32

508


Table 3. Model parameter estimations from soil core incubations

509 510

0-5 cm

5-10 cm

10-15 cm

15-25 cm

Vmax,1 (h)

0.4455

0.5008

3.7982

1.4011

Vmax,2 (h)

0.0512

0.1401

0.2712

0.0695

9

4

14

20

Vmax,1/Vmax,2 511 512 513 514

25



515

Figure Captions

516 517

Abstract Art

518 519

Figure 1. Measured (Open Circles) and modeled (Line) temporal variation of N2O concentrations

520

in the He headspace of soil core sections (0-5 cm, 5-10 cm, 10-15 cm and 15-25 cm from top to

521

bottom) incubated in synthetic rainwater (CHL+ C2H2, synthetic rainwater containing

522

chloramphenicol with He headspace containing acetylene; CTR, synthetic rainwater; C2H2, He

523

headspace containing acetylene).

524 525

Figure 2. Simulated variations in the accumulation of NO3-, NO2-, NO, N2O, and N2 when

526

variations of ±5, ±10, ±15, and ±20% were applied to model parameters.

527 528

Figure 3. Simulations of the temporal variation of the N2O/(N2O+N2) product ratio in the He

529

headspace of 4 soil core sections incubated in synthetic rainwater.

530 531

Figure 4. Simulations of the temporal variation of N2O in the He headspace of 4 soil core

532

sections incubated in synthetic rainwater with both pre- and de novo synthesized enzymes

533

(PE+DE) and with pre-synthesized enzymes (PE) only.

534 535

Figure 5. Simulations of the temporal variations of N2O with variations of ±10 and ±20% applied

536

to (a) qNO3- and (b) Vmax,2 .

537 538

26


Page 26 of 32

Page 27 of 32

539


Abstract Art

540 541 542

27



543

Figure 1.

544

545 546

28


Page 28 of 32

Page 29 of 32

547


Figure 2.

548

549

29



550

Figure 3.

551 552

30


Page 30 of 32

Page 31 of 32

553


Figure 4.

554 555

31



556

Figure 5.

557 558

32


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Modeling Nitrous Oxide Production and Reduction in Soil through

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