Modeling the inhibition of dissolved H2 on propionate fermentation and methanogenesis in wetland sediments

Ecological Modelling 322 (2016) 115–123

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Modeling the inhibition of dissolved H2 on propionate fermentation and methanogenesis in wetland sediments David S. Pal, Peter R. Jaffé ∗ Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, 08544, United States

a r t i c l e

i n f o

Article history: Received 22 June 2015 Received in revised form 10 November 2015 Accepted 13 November 2015 Available online 29 December 2015 Keywords: Wetlands Methanogenesis Hydrogen inhibition Modeling Bubble content Plant mediated venting

a b s t r a c t The complex interactions between hydrogen (H2 ) and methane (CH4 ) dynamics in wetland sediments are studied here with laboratory measurements supported by a methanogenesis model that includes interactions with H2 . Results from a series of 24-day incubation experiments producing CH4 from wetland sediment slurries with different carbon-compound amendments and under different H2 headspace partial pressures are presented. A simple model was formulated and calibrated to these experimental data, accounting for the interactions between propionate fermenters and H2 . This methanogenesis model is based on Michaelis–Menten and Monod kinetics with propionate as the only root exudate. While the biological interactions of CH4 production of the model were based on and calibrated with the results of the batch experiments, the model was expanded to simulate the effects of other well-studied processes than control methanogenesis in wetlands and rice paddies: sulfate reduction, plant-mediated gas volatilization, and the presence or absence of a gas phase in sediments into which H2 can partition. These common physical and microbial processes generally reduce the negative effects of the H2 inhibition of propionate degradation in many scenarios, but there may still be some important situations where the inhibition of propionate degradation by H2 may have significant controls over CH4 production. More important, processes that aid in reducing the H2 inhibition of propionate degradation, like plant induced venting, will result in less CH4 production by hydrogenotrophic methanogens, which should be taken into account in established model formulations. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Wetland and rice paddy sediments are characterized by large carbon pools (∼1.5 × 106 Tg C) (Whiting et al., 2001) and significant CH4 emissions to the atmosphere, 53 [wetlands] and 115–237 [rice paddies] Tg CH4 year−1 (Chmura and Gail, 2003; Fung et al., 1991; Hein et al., 1997). These carbon pools remain fairly constant on a scale of decades (Whiting et al., 2001) and a large portion of CH4 is derived from fast carbon sources (root exudates), as released by roots, while some carbon is released during plant turnover and the degradation of more recalcitrant soil carbon pools. Thus, a large fraction of these emissions are controlled through land management practices such as water control (Conrad, 2002) or plant selection and harvesting. CH4 is a substantial greenhouse gas (28 times more potent per unit mass than CO2 ) (IPCC, 2013) and as wetland restoration is encouraged (Zedler et al., 2005) and the global area of rice paddies increases (Wu et al., 2010), managing CH4 emissions

∗ Corresponding author. E-mail address: [email protected] (P.R. Jaffé). http://dx.doi.org/10.1016/j.ecolmodel.2015.11.005 0304-3800/© 2015 Elsevier B.V. All rights reserved.

from these sediments has become a rising concern. For this reason, understanding the processes controlling methanogenesis in these systems is becoming more important in CH4 production/emission managing, their modeling, and their links to climate forecasting. H2 can affect multiple steps of the methanogenesis process as an electron donor and as an inhibitor of fermentation processes. Fukuzaki et al. (1990) and Nozoe (1997) presented results examining the relationship between the inhibition of propionate fermentation and the accumulation of its end products (CO2 , H2 , Acetate) (Fukuzaki et al., 1990; Nozoe, 1997). Their study demonstrates that the accumulation of both H2 and acetate significantly slows propionate fermentation when there are no organisms to consume these fermentation end products. However, both studies ignore how complex interactions between propionate fermenters and fermentation end products affect methanogenesis. The experiments and process-based model described here provide new insights into this problem. In wetland sediments, there are many organisms that can consume H2 and/or acetate and prevent the accumulation of these products, including acetoclastic methanogenesis and hydrogenotrophic methanogenesis (Jetten et al., 1992; Zehnder

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Fig. 1. Simplified propionate degradation pathway in wetland soils.

et al., 1980; Krylova et al., 1997). The acetoclastic pathway utilizes acetate produced from the fermentation of root exudates and fatty acids while the hydrogenotrophic pathway utilizes H2 and CO2 produced during fermentation. In fact, studies have shown that acetate does not tend to accumulate in wetland sediments because it is consumed by many organisms (Krylova et al., 1997). Other studies have explained that some level of H2 production may be required for both methanogenic pathways and, furthermore, dissolved H2 does not inhibit acetoclastic methanogenesis (O’Brien et al., 1984; Krzycki et al., 1987). Studies that examine H2 dynamics in wetlands have not systematically tested how changes in dissolved H2 concentrations control methanogenesis. In wetlands and rice paddies, one conceptualization of the flow of carbon begins with root exudation, followed by a series of fermentation steps, and concludes with methanogenesis. In vegetated sediments, there are many different carbon molecules that are exuded from plant roots or produced from microbial processes during carbon turnover from plant litter and root die-off. Propionate is a significant methanogenic precursor and has been shown to be the most common root exudate in rice paddies, and can also be formed from the degradation of litter and root turnover (Yao and Conrad, 2001; Chin and Janssen, 2002). Using propionate as a root exudate allows for a model formulation that describes methanogenesis in only two steps. Fig. 1 represents the simplest methanogenic degradation pathway to capture all major processes, from root exudation and propionate fermentation to methanogenesis. Propionate has been shown to be the main fatty acid in rice paddies that leads to methanogenesis (Wu et al., 2010). While there are other pathways that produce other precursors like acetate (homoacetogenesis), they have not been included in this simplified formulation. H2 and acetate are consumed by many competing organisms using various electron acceptors and should also be accounted for when modeling CH4 production in wetland sediments. Sulfate is a readily available electron acceptor in estuarine marshes and can reduce the availability of CH4 precursors (Fig. 1). Tidal wetlands exhibit seasonal changes in carbon loading due to changes in plant activity and turnover, while sulfate loading may change diurnally due to tides, seasonally due to changes in river discharge, or over longer periods with sea-level rise. Poffenbarger et al. (2011) reported that CH4 production decreases as sulfate concentrations increase and becomes negligible at sulfate concentrations above 1200 mg/L.

Three dominant transport processes govern CH4 emissions from wetland sediments: diffusion, ebullition, and plant mediated transport. CH4 diffusion accounts for as much as 15% of total CH4 emissions (Bodegom and Stams, 1999) and is driven by concentration gradients in soils. Methanotrophic bacteria can oxidize CH4 during diffusion through upper sediments (Bodegom and Stams, 1999). Plant-mediated transport accounts for as much as 85% of CH4 emissions and has been shown to be consistently the most important emissions pathway (Bodegom and Stams, 1999; Bridgham et al., 2013; Li et al., 2010; Kraus et al., 2014). This pathway involves the diffusion of dissolved CH4 into specialized plant structures (aerenchyma) that serve as a conduit between the sediment and the atmosphere. Ebullition, or bubbling, is a small fraction of total emissions (Bridgham et al., 2013; Li et al., 2010). H2 will also be transported through these processes but in different proportions depending on diffusivity, which is inversely proportional of the square of their molecular weights (Reid and Jaffe, 2012). The volatilization coefficient of H2 through plants, kv[H2] , was approximated to be 0.15 h−1 by Reid and Jaffe (2012), and the volatilization rate depends only on the dissolved concentration of the gas in the sediments. Just as plants mediate the transport of CH4 and H2 out of sediments, wetland plants also facilitate transport of O2 into the sediments. The release of O2 into the sediments results in the oxidation of CH4 and other organic and inorganic compounds and may result in decreased emissions of total CH4 . Modeling of CH4 emissions from wetlands takes two distinct forms: empirical and process-based. Empirical models aim to relate CH4 reservoirs or CH4 emissions to temperature and sediment carbon pools with simple relationships based on field or laboratory results (Pal et al., 2014; Atkinson, 2012). These models answer specific questions, but do not allow users to explain CH4 production in terms of important microbial processes associated with CH4 production. In general, process-based models use theoretical approaches to approximate root exudation, microbial consumption rates, and predict the availability and competition for methanogenic carbon precursors (Xu et al., 2007; Li et al., 2010). For many of these models, these processes are simplified in terms of the key microorganisms or sources/sinks of various dissolved components. The focus of this work is the inclusion of H2 inhibition of propionate degradation into process-based models of methanogenesis. This paper presents experiments combined with a process-based model and examines when interactions between H2 and propionate fermentation are important in terms of CH4 production. The model formulation allows to gain insights on how sulfate reduction (consuming both acetate and H2 ), H2 volatilization via plants, and physical partitioning of H2 into sediment gas phases alter the dissolved H2 dynamics in wetland sediments and affect the inhibition effects of H2 on CH4 production while altering the potential CH4 production via hydrogenotrophic methanogenesis. Results from sediment incubation experiments, using sediments from a brackish tidal marsh in the New Jersey (NJ) Meadowlands, with different carbon amendments and H2 headspaces, show how H2 gas influences CH4 production, and are used for model calibration. 2. Materials and methods 2.1. Field sediment incubations 2.1.1. Sediment collection Sediment samples were collected at the Hawk Property (HP) site (40.77 ◦ N 74.08 ◦ W), which is a natural, tidal, brackish marsh along the Hackensack River in northern New Jersey (USA). The HP has an area of 14.73 hectares, with vegetated monoculture of Spartina patens and Phragmites australis. The P. australis

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vegetation sediments are muddy and the sediment surface is lower in elevation and has more frequent and more complete inundations. Sediment samples were collected during low tide to allow for excess pore water to be drained before extraction. Sediment samples were dug by shovel to about 13 in., and intact samples were wrapped with cellophane to slow the drying and oxidation. Sediment samples were kept wrapped in a cooler with ice packs in the field and taken immediately to the laboratory to start the incubations. The average time between collection of the sediments in the field and incubation vial preparation was 5 ± 0.5 h. Sediments were split in half and small samples were taken from the center of the core to avoid utilizing the dry, oxidized exterior sediment. Because of the dense root mass, sediment was homogenized manually. 2.1.2. Incubation experiment set-up Every effort was made to ensure that the soil used each vial was as similar to the others as possible. Sediment was weighed (between 3.0 g and 4.0 g) and added to a 35 mL glass incubation vial. Excess root material was removed when possible from sediment samples. After the sediment mixture was added to each vial, 5 mL of degassed DI water with or without 1 mM sodium propionate was added to each vial. The vials were capped with butyl rubber stoppers, crimped, and the headspace was flushed with ultra high purity N2 (Airgas East, Inc.) for 30 s. The headspace was then mixed with 20 mL of either 0 ppm, 10 ppm, 50 ppm, 100 ppm of H2 in N2 (Air Liquide, Plumsteadville, PA, USA). Samples were prepared with five replicates. During the incubations, vials were inverted to prevent gas leaks, and kept in the dark at ∼20 ◦ C between sampling. Before sampling, the headspace was over-pressurized with 20 mL of ultra high purity N2 , vials were then shaken to allow for liquid/gas phase equilibration before the gas was sampled and analyzed with gas chromatography. After sampling, vials were augmented again with H2 , as was done at the initial time point, they were then shaken, and incubation continued as described above. Samples were taken after 1, 8, 13, and 24 days. A second identical experiment was performed amending the vials with 5 mM sodium acetate and 0 ppm, 10 ppm, 25 ppm, and 50 ppm H2 in their headspace. 2.1.3. Gas analysis Headspace gases samples were split into two syringes and sampled simultaneously for H2 and CH4 . H2 was measured using a gas chromatograph with a reduction gas analyzer (Trace Analytical RGA3, Peak Labs, Mountain View, CA, USA) supplied with Ultra Zero Air as a carrier gas. CH4 was measured using a gas chromatograph (GC-2014, Shimadzu, Kyoto, Japan) equipped with an FID, 2 m Porapak Q column (Supelco, Bellefonte, PA, USA), and supplied with He carrier gas. 2.2. Microbial model formulation The model presented here aims to explain how the 6 key processes (propionate fermentation, sulfate reduction, acetoclastic methanogenesis, hydrogenotrophic methanogenesis, H2 volatilization via plants, and H2 partitioning into gas phases), shown in Fig. 1, contribute to total CH4 production with 4 different generalized representations of microbial communities (fermenters, sulfate reducers, two methanogens), and 5 different chemical species (CH4 , H2 , SO4 2− , acetate, propionate). The consumption of different compounds by microbes is modeled via traditional Monod kinetics to stay consistent with the sources of the microbial parameters. This system is described mathematically via a simultaneous system of ordinary differential equations (Appendix A). The iterative calibration process was performed to reduce the least squares error between the modeled CH4 production and the measured CH4 from the experiments at each measurement time point (Section A.2).

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The temperature remained constant at 20 ◦ C throughout all incubations. Propionate concentrations are modeled as a balance between a source of propionate and its consumption by fermentation bacteria. The propionate source term represents propionate release from roots, and production of propionate from the degradation of larger organic molecules. The propionate fermentation process produces acetate and H2 . Acetate is modeled as a balance between the production of acetate from propionate fermentation, and consumption via sulfate reduction and acetoclastic methanogenesis. Sulfate, SO4 2− , the only sulfur species tracked by the model, is calculated as a balance between an initial concentration and the consumption of sulfate during the sulfate reduction processes. Given the effect of sulfate reduction on methanogenesis discussed above, sulfate reduction was incorporated into this model framework to understand how the consumption of H2 and acetate during sulfate reduction can affect CH4 production. H2 concentrations are calculated as a balance between production during propionate fermentation, and its consumption during sulfate reduction and hydrogenotrophic methanogenesis and the loss of H2 by volatilization through plants or partitioning into gas bubbles. For the reasons described above, our model considers plant-mediated transport as the only non-consumption based loss mechanism for H2 . CH4 concentrations are calculated as a balance between production from acetoclastic methanogenesis and hydrogenotrophic methanogenesis and the loss due to volatilization via plants. As described above, plant mediated volatilization is the most significant CH4 loss mechanism (Bodegom and Stams, 1999; Bridgham et al., 2013; Li et al., 2010; Kraus et al., 2014). While some fraction of CH4 is oxidized in natural sediments, this model focuses on calculating total CH4 production and does not include this loss mechanism. This formulation assumes that CO2 is not limiting, and production/consumption is not calculated in this model. Microbial growth is modeled as a balance of growth from the consumption of substrates and first order biomass decay rates. These equations were modeled using a differential equation solver in MATLAB. The propionate consumption rate (r) was modeled as reversible, competitive inhibition (Eq. (1)) (Atkinson, 2012). r=



vmax P

Km 1 +

H2 Ki



(1) +P

concentration [mg/L]; H2 = dissolved H2 P = propionate concentration [mg/L]; Km = half saturation coefficient [mg/L], Ki = Ki  /Km = inhibition coefficient [dimensionless], vmax = maximum consumption rate [h−1 ]. Parameters for the microbial kinetics were taken from Haarstrick et al. (2004), Pareek et al. (1999), Henze et al. (1995). The parameters in these papers were used to describe organisms responsible for landfill processes and wastewater sludge degradation. These organisms are similar to the organisms found in wetlands and the biological parameters were measured for organisms growing on the same substrates (propionate, acetate, H2 ) as in our experiment. Therefore these parameters are a reasonable first-estimate to simulate these microbial communities in wetland sediments, given that there is not much knowledge about microbial activity and parameterization in estuarine wetlands. In addition to these parameters, the propionate addition rate and inhibition coefficient were calibrated to the experimental data as described in Appendix A. The vials contained a N2 headspace allowing for the partitioning of gases such as CH4 and H2 between the dissolved and gaseous phases. Headspace samples were taken throughout the incubations to directly measure gas-phase concentrations of CH4 and H2 . The experimental procedure as well as model formulation assume that the gas-phase measurement represent the instantaneous

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equilibrium between the dissolved and gaseous phase of all volatile compounds (H2 and CH4 ). Dissolved CH4 and H2 concentrations were estimated using Henry’s law. The ratio of liquid to gas in the vials was used to calculate the total gas production from headspace gas concentration measurements. The ratio of gas and liquid volumes, while constant in the batch experiments, is not constant in the field with time or location and depends on the trapped bubble volume in the wetland sediments. As seen in Reid et al. (2013), in sediments that are consistently inundated with water, CH4 bubbles can form from oversaturation and can lead to equilibrium between gas and aqueous phases. Because bubble formation in wetland sediments is not directly modeled here, this model simulates how partitioning of H2 into a set bubble volume (i.e. gas:liquid ratio) affects H2 inhibition and can affect methanogenesis. Unless explicitly stated, the gas-phase volume (Vg ) was set to be equal to the liquid-phase volume (VL ) in most model simulations to remain consistent with the laboratory experiments. 3. Results and discussion 3.1. Experimental results 3.1.1. Experiment 1: Effect of propionate addition Sediment samples were incubated with propionate and H2 to examine how the H2 inhibition of propionate degradation can reduce CH4 production. Sediments for this incubation were from sediment cores collected beneath P. australis. The headspace of every vial was sampled for CH4 and these measurements were used to calculate the CH4 concentration in each vial accounting for dilution and sampling effects. The CH4 concentrations measured during this experiment fall within the ranges (<1 ␮M–1200 ␮M) measured in field experiments with dialysis samplers (Pal et al., 2014). Table 1 represents average cumulative CH4 concentrations with 1 mM propionate added and with no propionate addition with different H2 initial conditions. Each point represents the average of 5 replicate vials. The results indicate that there is a decrease in total CH4 production as initial H2 concentration increases in vials without additional propionate. When additional propionate is added, there is no measureable difference of CH4 production between difference H2 conditions. This behavior is supported by the difference in the final CH4 concentrations under the highest (100 ppm) and lowest (0 ppm) H2 headspaces, as well as the decreasing average CH4 production with increasing H2 levels for the condition of no additional propionate. While when additional propionate was added to the vials there was no significant differentiation in the final CH4 concentrations or average CH4 production as a function of initial H2 concentration (Table 1). While Table 1 only shows the results from Day 24, these trends were present at all sampling points. 3.1.2. Experiment 2: Effect of acetate addition and vegetation type This experiment was performed to examine if the plant type, sediment depth, or carbon source had any measurable effect on the

Fig. 2. Comparison of the effects of SO4 2 and H2 on simulated CH4 production after 24 days in batch incubations (VG = VL ) with or without additional carbon.

CH4 production. Experiment 2 was performed with new sediments, segmented into three depths (3 in., 7 in, 11 in.), from sediment samples collected beneath S. patens and P. australis and incubated with acetate instead of propionate. The sampling and preparation process from Experiment 1 was used in Experiment 2. The results from a two-way ANOVA analysis between CH4 production and vegetation types or depths show no significant correlation at a 90% confidence level in Experiment 2. In both experiments, when additional acetate is added, there is no difference between the total CH4 production between the different H2 conditions. This stands in contrast to the conditions without additional carbon (propionate or acetate), which show decreasing CH4 production vs. increasing initial H2 concentration. 3.2. Simulation results 3.2.1. Influence of sulfate on batch incubations One common alternative electron acceptor in tidal wetlands is sulfate, and in order to understand if and how sulfate reduction affects the inhibition of propionate fermentation by H2 , sulfate reduction was included in the model formulation as an additional microbial process competing for acetate and H2 . The total simulated CH4 production shown in Fig. 2 represents the amount of CH4 produced after 24 days of incubation with different initial sulfate loadings and the same propionate and H2 conditions from Experiment 1. For low propionate conditions, there is a big impact from sulfate reduction on CH4 production (86% decrease). This impact is reduced to a 41% decrease under high propionate loadings. A single pulsing of sulfate, as done for the simulations presented here, may not completely account for how site salinity affects CH4 production. In this model, there are no physical transport mechanisms that would be present in saline/brackish wetlands allowing

Table 1 Average and range of final CH4 concentrations from the 5 replicate incubation vials in Experiment 1. H2 headspace

Average cumulative 24 day CH4 production (mM)

No propionate addition 0 ppm 10 ppm 50 ppm 100 ppm

0.0404 0.0337 0.0186 0.0149

0.0324–0.0522 0.0088–0.0139 0.0120–0.0284 0.0096–0.0278

N/A −17% −54% −64%

Propionate addition 0 ppm 10 ppm 50 ppm 100 ppm

0.7393 0.8886 0.7576 0.8893

0.4468–1.31 0.7765–1.04 0.3836–1.1027 0.06855–1.0923

N/A +20% +2% +20%

Range of 24 day cumulative CH4 production (mM)

% diff from no H2

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Fig. 3. Effect of the volatilization rate of H2 (kv ) through plant root systems on simulated total 24-day CH4 production in wetland sediments with no gas volume (VG = 0) in sediments with or without additional propionate. The total CH4 production does not change between initial H2 headspaces.

for sulfate replenishment (tides, advection, constant addition of sulfate, oxygen movement with tides, etc.). 3.2.2. Influence of H2 volatilization on methanogenesis In addition to sulfate loadings, this model formulation incorporated the effect of plant mediated H2 volatilization on CH4 production. Simulations accounting for H2 volatilization showed that when there is a large gas volume, as in the experimental setup, there is sufficient H2 partitioning into the gas phase and plants play a small role in further inhibition of CH4 production. The large gas volume allows for much of the H2 mass to be stored in the gas phase, resulting in lower dissolved H2 concentrations, which result in less inhibition as well as less venting via plants. The effects of including the plant-mediated H2 volatilization in sediments that do not contain a gas phase or bubbles (VG = 0), for different volatilization rates, are shown in Fig. 3. The results demonstrate that increasing the plant-mediated volatilization rate of H2 when the soil gas volume is zero increases CH4 production in wetland sediments regardless of carbon loading. This behavior is only exhibited when H2 -inhibition of propionate degradation is included in the model. These model simulations indicate that when the bubble volume is greater than 1% of the total liquid volume, there are negligible effects of volatilization on total CH4 production after 24 days (1–5% of total) (data not shown). Due to the partitioning of H2 into the gas phase, in combination with plant volatilization, additional H2 does not significantly affect the CH4 production in the presence of a gas-phase. When the bubble volume is less than 1% of the total liquid volume there is a lower H2 reservoir for a given dissolved H2 concentration and more significant H2 volatilization rate. This results in a slightly increasing 24-day total CH4 production as H2 volatilization rate increases (data not shown). In sediments with less carbon available, increasing H2 volatilization has a smaller net effect on total CH4 production than in carbon amended sediments, mostly because when the carbon source is low H2 production is low and H2 levels do not reach concentrations at which they inhibit propionate fermentation. 3.2.3. Application to open systems In order to gain insights on how methanogenesis is affected by gas volatilization, sulfate reduction, and gas partitioning over longer equilibration times (∼100 days) in an open system, which closer approximates natural wetlands, simulations considering all

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of these processes simultaneously were conducted. The simulations compare the amount of CH4 produced with or without inhibition by H2 on the propionate degradation (this will be referred to as H2 inhibition in the remainder of the text). After a 100-day simulation, the dissolved CH4 concentration estimated with the model with H2 inhibition is within CH4 concentrations that have been observed by field measurements (Conrad and Klose, 1999). The model uses the same parameters that were calibrated using the incubation experiments for these natural system models with and without H2 inhibition, and these incubation experiments represent conditions that may be found in the field (Glissmann and Conrad, 2000). The saturated wetland sediments allow for the formation of bubbles as air get trapped during tides or sufficient CH4 is produced resulting in oversaturation and the formation of a gas phase. The presence of bubbles allows for dissolved gases to partition between the liquid phase and the gas phase, which under equilibrium conditions can be described by Henry’s Law. The mass distribution of a specific gas between the dissolved and gas phase is of course depending on the ratio bubble volume to water volume in the sediment. The bar graph in Fig. 4 shows how total CH4 production changes with respect to different gas:liquid volume ratios and the inclusion or removal of H2 -inhibition of propionate degradation from the model formulation. The model predicts that sediments without a gas phase, “1-phase”, [gas volume (Vg ) = 0] will produce less CH4 than sediments with a gas phase, “2-phase”, [gas volume (VG ) = liquid volume (VL )] because in the absence of a gas phase more H2 will remain in the dissolved phase and can inhibit propionate degradation more strongly. In sediments without plantmediated gas volatilization, as the gas volume approaches 0% all of the H2 remains in the aqueous phase where small additions of H2 to the aqueous phase cause H2 /KI  KS . In addition to this phase equilibrium behavior, the model with H2 inhibition indicates that in bubble-free sediments, H2 volatilization by plants plays an important role in controlling CH4 production, leading under certain circumstances to an increase by as much as ten fold, depending on the carbon loading. In the formulation without H2 -propionate interactions, H2 is only a methanogenic precursor and is not considered to inhibit propionate production. That is why plant-mediated volatilization in the 1-phase scenario does not decrease propionate degradation inhibition, but only decreases the availability of H2 as a methanogenic precursor. As seen in Fig. 4, when Vg = 0, increasing H2 volatilization to levels seen in the field decreases CH4 production by 10–15% if H2 -inhibtion is not included in the model. In the “2-phase” system (where VG = VL ) and without H2 -inhibition, there are negligible effects of H2 volatilization on CH4 production. Fig. 5 shows the impact of plant-mediated volatilization on CH4 production with an increasing gas volume from 0 to 10% with or without H2 inhibition of propionate fermentation. Results show how sensitive CH4 production is to gas volume and H2 interactions as the gas volume approaches 0 when there is H2 volatilization via plants. Alternatively, it is important to note that as the gas volume increases, the CH4 production approaches the same concentrations as the model formulation without H2 interactions. The change in total CH4 production after 100 days with changing sulfate concentration shows that total CH4 production decreases as sulfate concentrations increase. In the model formulation with H2 -inhibition this decrease in CH4 production is dependent on the gas:liquid volume ratio. Based on these results, the it is clear that the inclusion of H2 -inhibition reactions have significant controls on total CH4 production in sediments with high sulfate concentrations and lower carbon availability, regardless of gas:liquid volume ratios. Fig. 4 shows that when there is inhibition of propionate fermentation by H2 the highest CH4 production occurs when there is H2

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Fig. 4. Effect of carbon source, phase equilibrium, and H2 volatilization rates affect 100-day CH4 production with and without H2 inhibition of propionate degradation.

Fig. 5. This figure represents the influence of gas volume on H2 inhibition of propionate fermentation and methanogenesis, under constant plant-mediated gas volatilization. The solid lines represent simulated CH4 concentrations without H2 interactions, and dashed lines included H2 interactions. The top lines represent conditions with additional propionate, and the bottom lines represent conditions without additional propionate.

venting. Whereas, when this inhibition is not considered, the highest CH4 production occurs when there is no H2 venting. In the later case, this is because the H2 that is not vented is converted to CH4 by hydrogenotrophic methanogens. 4. Conclusions Comparing simulations with and without accounting for H2 inhibition indicates that H2 -propionate degradation interactions are most important to consider in wetlands with small gas/liquid volume ratios (<2% gas). The simulations also indicate that this H2 inhibition may be more significant in sediments that do not have large sources of organic materials. For such sediments one can for certain circumstances expect models that do not consider H2 interactions to estimate as much as ten fold more CH4 production

than if H2 interactions were included in the model formulation. Based on recent measurements conducted in the NJ Meadowlands (Pal et al., in preparation), the physical and microbial process that affect H2 concentrations in these wetlands generally maintained in situ dissolved H2 concentrations between 0 and 5 nM (equivalent to a headspace of ∼6 ppm), so that KS > H2 /Ki and H2 -inhibtion interaction can be ignored. At some specific times and locations, the in situ H2 concentrations reached almost 20 nM (equivalent to a headspace of about 30 ppm), at which point, based on the simulations presented here, H2 -inhibition interactions should be considered when estimating the instantaneous CH4 production. As important as the H2 -propionate-CH4 production interactions may be under certain conditions, the results of this work have shown that they become less important when there is a gas phase present into which the produced H2 can partition and/or when there is significant venting of H2 via plants. Venting of gases via plants is well established in wetlands and important, and so is the formation of biogas bubbles, especially during warmer temperatures when biological activity is high. Hence, simulations of CH4 production without considering the inhibition by H2 might yield accurate CH4 production estimates under such conditions, but loss of H2 via plants does then have to be accounted for since it will result in less CH4 production than if that H2 had been utilized by hydrogenotrophic methanogens to produce CH4 . This model presented here was calibrated to a limited set of laboratory experimental data and the simulations are limited to within the endpoints of these experiments. The model was built as a tool to examine the interactions between sulfate levels, H2 , dynamics, propionate fermentation, and methanogenesis, and should be expanded upon by including key physical transport processes (advection, dispersion, tidal mixing, diffusion, ebullition), temperature dependency of the physical and biological processes, oxidation of CH4 , plus rigorous field-scale validation before it is used as a predictive tool for field conditions. Regardless of these limitations, this model has been used here to examine the key processes that affect the interactions between H2 dynamics and methanogenesis, which have so far not been considered in model formulations for CH4 emissions from wetlands. Results have provided new insights on when these interactions might be important, so that they can be incorporated into more complex predictive methanogenesis models.

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Acknowledgements This project is an outcome of different results supported by NSF grants CBET-1133074 and CBET-1133281 Logistic support during sampling of the sediments by staff from the Meadowlands Environmental Research Institute is gratefully acknowledged. qPCR analyses were conducted by Dr. Huang from the Department of Civil and Environmental Engineering at Princeton University.

Appendix A. Model formulation and calibration A.1. Model formulation and parameters

Variable name

Symbol

Value

Source

Conversion from propionate to acetate (mg/L) Conversion from sulfate to acetate (mg/L) Maximum acetatoclastic methanogenesis Half saturation conc. A. methanogenesis

a

60/74

Constant

b

177/48

Constant

max,A

8.3 × 10−3 h−1

KS,A

50 mg/L

XA

2 mg/L

Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004) Pareek et al. (1999) qPCR results (Section A.2) + calibration

Acetoclastic methanogen biomass

Hydrogenotrophic methane mass balance:

Propionate mass balance: d[Prop] [Prop] XF = S − A = SourceP − max,P dt (KS,P + [Prop] + [H2 ]/Ki )

dCH4,H [H2 ] XH − KV,C [CH4,H ] = d ∗ max,H dt (KS,H + [H2 ])

Variable name

Symbol

Value

Source

Variable name

Symbol

Value

Source

Maximum propionate consumption rate

max,P

125 h−1

2 4.2 × 10−3 h−1

KS,P

2500 mg/L

KS,H

650 mg/L

Ki

3.2e−8

Conversion to CH4 Maximum acetatoclastic methanogenesis Half saturation conc. A. methanogenesis

d max,H

Half saturation conc. propionate fermentation Inhibition coefficient Fermenter biomass

XF

8 mg/L

Haarstrick et al. (2004) Henze et al. (1995) Haarstrick et al. (2004) Henze et al. (1995) Calibrated (this study) qPCR results (Section A.2) + calibration

Acetoclastic methanogen biomass H2 volatilization constant CH4 volatilization constant

XH

2 mg/L

KV,H

Variable

Conversion Factor Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004) Pareek et al. (1999) qPCR results (Section A.2) + calibration This study

KV,C

Variable

This study

Sulfate mass balance: d[Sulf] [Sulf][Acet] = −max,SA dt (KS,SA + [Sulf])(KA,SA + [Acet]) × XSA −max,SH

[Sulf][H2 ] XSH (KS,SH + [Sulf])(KH,SH + [H2 ])

d[Sulf] = −B − C dt

Variable name

Symbol

Value

Source

Maximum sulfate consumption rate (by H2 ) Half saturation conc. sulfate reduction (by H2 ) Maximum sulfate consumption rate (acetate) Half saturation conc. sulfate reduction (acetate) Sulfate reducer (H2 ) biomass

max,SH

8.3 × 10−2 h−1

Haarstrick et al. (2004)

Sulfate reducer (acetate) biomass

KS,SH

120 mg/L

Haarstrick et al. (2004)

max,SA

.116 h−1

Haarstrick et al. (2004)

KS,SA

60 mg/L

Haarstrick et al. (2004)

XSH

4 mg/L

XSA

4 mg/L

qPCR results (Section A.2) + calibration qPCR results (Section A.2) + calibration

Acetate mass balance: d[Acet] [Acet] XA − bB = aA − max,A (KS,A + [Acet]) dt

Acetoclastic methane mass balance: dCH4,A [Acet] XA − KV,C [CH4,A ] = d ∗ max,A dt (KS,A + [Acet])

Variable name

Symbol

Value

Source

Conversion to CH4

e

16/60

Conversion factor

Biomass mass balance: dBiomass [I] = max X Yx − mXi dt (KS,I + [I]) i

Variable name

Symbol

Value

Source

Yield coefficient – propionate ferment

YP

1 kg biomass/kg consumed

Yield coefficient – H2 methanogenesis

YHM

0.06 kg biomass/kg consumed

Yield coefficient – H2 sulf reduction Yield coefficient – Acet methanogenesis

YHS

0.05 kg biomass/kg consumed 0.06 kg biomass/kg consumed

Haarstrick et al. (2004) Henze et al. (1995) Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004) Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004) Haarstrick et al. (2004) Henze et al. (1995)

Yield coefficient – Acet sulf reduction Decay constant – propionate ferment

YAM

YAS mP

0.05 kg biomass/kg consumed 7.34 × 10−4 h−1

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D.S. Pal, P.R. Jaffé / Ecological Modelling 322 (2016) 115–123

Variable name

Symbol

Value

Source

Decay constant – H2 methanogenesis

mHM

5 × 10−7 h−1

Decay constant – H2 sulf reduction Decay constant – Acet methanogenesis

mHS

4 × 10−6 h−1

Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004) Haarstrick et al. (2004) Pareek et al. (1999) Haarstrick et al. (2004)

Decay constant – Acet sulf reduction

−3

−1

mAM

1.1 × 10

mAS

4 × 10−6 h−1

h

Hydrogen mass balance: Gas partitioning derivation: VL



d[HL ] = VL dt

a A − max,H

H  L

VL

d[HL ] = VL dt −



VL +

VG K



K =

Caw HL = Cgas HG

[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]

VG d[HL ] K  dt

 d[H ] L



dt



a A − max,H

= VL

;



a A − max,H

d[HL ] VL   V dt VL + KG

[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]



 a A − max,H

[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]

VG  VL K d[HL ] VL  VG  dt  K



 a A − max,H



a A − max,H

[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]

In Incubation Experiments: VL ∼ = VG d[H2 ] = K ∗ dt



a A − max,H



[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]

Variable name

Symbol

Value

Source

Conversion to H2 Conversion to H2

a c

6/74 8/96

Conversion factor Conversion factor

A.2. Model calibration

− loss gas

d K d[HG ] = loss gas = VG dt dt





[H2 ] XH − cC − KV,H [H2 ] (KS,H + [H2 ])

VG

VL d[H2 ] ∗ = K ∗ VG dt

[H2 ] XH − cC − KV,H [H2 ] KS,H + [H2 ]

The model described above was built in MATLAB using the ode45 differential equation solver in order to explain the behavior measured by the experiments presented in Section 3. Of the variables presented above, there were only three variables that were unknown and needed calibration: Propionate Source, Initial Propionate Concentration, and the Inhibition Coefficient (Ki ). The microbial decay coefficients were changed slightly from those reported by Haarstrick et al. (2004) to better fit the model to the data. These variables were adjusted iteratively to reduce the least squares error between the modeled CH4 production and the measured CH4 from the experiments at each measurement time point. Fig. A1a shows how the calibrated model fits the experimental data for the conditions with no additional propionate loading. With a calibrated Ki , the model is able to capture the different CH4 profiles under increasing H2 conditions. For the conditions with additional propionate, the model accurately represents the small differences, relative to the no additional propionate condition, between CH4 produced under different H2 headspaces from the experimental data. For the high propionate loadings (Fig. A1b) the model overpredicts CH4 production for incubation times shorter than 15 days and under predicts total CH4 production after 15 days. Different propionate concentrations and source terms are used to differentiate between the conditions with or without additional propionate in order to model the addition of propionate and the degradation of remaining soil organic material to propionate. The initial propionate concentrations were set to 8 mg/L when no propionate was added and 110 mg/L when 100 mg/L of propionate was added, and the propionate source terms 0.027 and 4 mg L−1 h−1 for the

Fig. A1. Demonstration of the fit between the modeled data (solid lines) and the experimental data (error bars). (a) (left) The four H2 headspace conditions without additional propionate and (b) (right) the four H2 headspace conditions with additional propionate.

D.S. Pal, P.R. Jaffé / Ecological Modelling 322 (2016) 115–123

no additional propionate and the propionate addition experiment, respectively, were obtained by calibration. The need for a propionate source term in the model is confirmed by previous experiments that have shown that when there is excess acetate, CO2 , and H2 , more propionate can be produced (Yao and Conrad, 2001; Conrad and Klose, 1999). The addition of organic carbon can encourage growth of certain microbial populations that will initiate the decomposition of soil carbon to methanogenic precursors including more propionate (Yao and Conrad, 2001; Yao and Zhang, 2009; Hees et al., 2005) and, thus explaining the differences between model input parameters (specifically the propionate source) between experiment calibrations. A.3. Biomass Measurements DNA samples were extracted from bulk soil samples that were not used for incubation samples. DNA was extracted using the FastDNA® spin kit for soils (MP Biomedicals, USA) as described by the manufacturer and following the same procedure in Huang and Jaffé (2014). Once extracted, the DNA was analyzed via qPCR on a StepOnePlusTM Real-Time PCR System (Life Technologies, USA). The soils were sampled for total 16S counts (total bacteria), mcrA (methanogens), and ␦-proteobacteria (Sulfate reducer). Average cell mass was assumed to be 1 pg. The number of acetoclastic methanogens and hydrogenotrophic methanogens was set to be equal since the measurements did not distinguish between these methanogens. Propionate fermentation bacteria were not measured directly, but were set to 10% of the total cell mass, which is in the range of previously reported values (Hees et al., 2005). 16S sample

Count/g soil

Est. mass*/kg soil

mg biomass/L pore water

Total bacteria Methanogen (mcrA) Sulfate reducer (␦-proteobacteria)

2.8e7 3e4 1.2e5

2.8e−2 g/kg soil 3 e−4 g/kg soil 1.2e−3 g/kg soil

∼100 ∼1 ∼4

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