Constraining the Impact of Bacteria on the Aqueous Atmospheric

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Constraining the impact of bacteria on the aqueous atmospheric chemistry of small organic compounds Alison M. Fankhauser, Dexter Dante Antonio, Asher Max Krell, Simone J. Alston, Scott Banta, and V. Faye McNeill ACS Earth Space Chem., Just Accepted Manuscript • DOI: 10.1021/ acsearthspacechem.9b00054 • Publication Date (Web): 15 Jul 2019 Downloaded from pubs.acs.org on July 17, 2019

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ACS Earth and Space Chemistry

Constraining the impact of bacteria on the aqueous atmospheric chemistry of small organic compounds Alison M. Fankhauser, Dexter D. Antonio, Asher Krell, Simone J. Alston, Scott Banta, V. Faye McNeill* Department of Chemical Engineering, Columbia University, New York, NY 10027 USA KEYWORDS: Aerosols, Clouds, Bioaerosols, Atmospheric Chemistry, Microbial Metabolism

ABSTRACT. In this study we use a modeling approach to evaluate the potential impact of microbial metabolism on the organic composition of cloud droplets and atmospheric aerosols. Microbial consumption rates for small organic molecules typically found in cloud and aerosol water were incorporated in to a 0-D multiphase photochemical atmospheric chemistry model. We then use the model to simulate the evolution of the organic content of individual cloud and aerosol particles, along with the atmospheric gas phase. We find that metabolically active microorganisms may significantly impact organic acid concentrations in the individual aerosols and cloud droplets in which they reside. However, due to the low density of metabolically active cells in the atmosphere, the impact of these processes on the chemical composition of the overall population of cloud droplets of aerosols, or on gas phase chemistry, is likely negligible.

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1. INTRODUCTION Particles of biological origin are ubiquitous in the atmosphere.1,2 Diverse live, culturable microorganisms have been isolated from clouds and atmospheric aerosol samples.3–15 Typical phyla include Proteobacteria (especially Pseudomonas syringae, which is an effective ice nucleating agent16), Bacteroidetes, Cyanobacteria, and Firmicutes. rDNA/rRNA sequencing of microbes collected in clouds11 and aerosols13 at mountaintop locations has provided evidence for metabolically active cells in these atmospheric samples. Cell loadings reported for cloud water samples range from 7.9×105 cells per liter of collected cloudwater to 4.3×108 cells L-1.11,17–23 Microbes detected in clear skies3,24–31 vary in concentration from 9.4×103 cells per cubic meter of air sampled to 6.6×106 cells m-3. Organic material and liquid water make the majority of atmospheric aerosol mass.32,33 Oxidation of water-soluble organic compounds in cloud droplets or aerosol water is an important pathway for the formation of secondary organic aerosol material.34–37 The amount of soluble organic material in atmospheric aerosols influences their physical properties which are important for climate, such as cloud condensation nucleus ability and optical properties.38 In their role as cloud condensation nuclei, aerosols take up water to form cloud droplets, as often as several times per day.39 Any cells, biomolecules, and other low-volatility material present in a cloud droplet at the time it evaporates will form an aerosol. It has been proposed that the metabolism of the atmospheric microbiome may be a sink for small organic compounds in cloud water.40 Culture-based metabolic studies of microbes isolated from clouds suggest that they actively degrade species such as small organic acids, formaldehyde, methanol, and phenol,40–44 at rates that compete with photochemical reaction with OH under some


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conditions.42,45 Microbial metabolism may contribute to phenomena that remain unexplained by atmospheric photochemistry, such as the deficit of oxalate in marine boundary layer clouds.46,47 Bacterial species such as Cupriavidus oxalaticus, which have been identified in high-altitude aerosol samples, are known to metabolize oxalate.3 In this study, we incorporated metabolic rates for microbial oxidation of small organic molecules into GAMMA, the McNeill group photochemical multiphase box model.48 We then used the model to evaluate the potential impact of microbial metabolism on aerosol, cloud, and gas-phase atmospheric chemistry. 2. METHODS 2.1 Model systems. Pseudomonas and Cupriavidus oxalaticus, representative bacterial strains which have been previously isolated from aerosol and cloud samples,3,34 are rod-shaped, 0.5-1 m in diameter and 1-5 m in length.49 Their size suggests that at most one bacterium could be accommodated in a single atmospheric aerosol particle. Cloud droplets are sufficiently large that more than one cell could exist within the volume, and in fact it has been proposed that bacteria could multiply under cloud conditions (on a timescale of days).23 However, the density of microorganisms in cloud water samples is typically low enough to suggest that the presence of metabolically active bacteria in a cloud droplet or aerosol is a rare occurrence. For a spherical cloud droplet with a diameter of 10 m, the volume is 5.2×10-13 L. Taken together with a representative concentration of metabolically active bacteria in cloudwater of 8.1×107 cells L-1,21 this suggests that only 1 in every 2.4×104 droplets contains a cell. For this reason, in our simulations we consider one metabolically active cell per aerosol particle or cloud droplet.


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2.2 Metabolic rate data. Rate data from metabolic studies in the literature are summarized in Table 1. Consumption rates for methanol, formaldehyde, formate, acetate, and succinate by different microbial species isolated from cloudwater samples have been measured and reported at 17 °C and 5 °C.41,42,50 Lifetimes for malonic acid and succinic acid for a mixture of organisms cultured from airborne samples were reported by Ariya et al. (2002).44 We also list the consumption rate of oxalate by Cupriavidus oxalaticus as measured in our laboratory (Supporting Information). For our model calculations, we use an average of the rates reported for the various Pseudomonas species for methanol, formaldehyde, formate, and acetate,41,42,50 the succinic and malonic acid degradation rates from Ariya et al. (2002), and our own measured rate for oxalate. In the absence of sufficient data demonstrating the impact of, e.g., nutrient, electrolyte, and substrate concentrations on the metabolic rates, we assume that the fundamental rates in Table 1 may also be applied to simulate aerosol conditions. The rate values used in the simulations are listed in the Supporting Information (Table S2). Our goal for this study is to establish an upper bound for the potential impact of microbial metabolism on atmospheric aerosol and cloud chemistry. Therefore, when data are available for more than one temperature condition, we use the data for the warmest temperature available (17 °C

41,42,50

). Similarly, we assume that microbial metabolism results in the complete oxidation of

the consumed organic to CO2 and H2O, and assume zero-order kinetics for metabolic processes. 2.3 GAMMA 5.1 GAMMA is a 0-D model of multiphase atmospheric photochemistry developed by the McNeill group.48 Updates to the chemical mechanism for version 5.1 are described in the Supporting


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Table 1. Summary of metabolic rate data from the literature for consumption of small organic species by microbes isolated from cloudwater Molecule

Organism

CH2O Formaldehyde

Pseudomonas graminis Pseudomonas syringae Frigoribacterium sp. Bacillus sp.

HCO2Formate

Sphingomonas sp. Pseudomonas graminis Pseudomonas sp. Pseudomonas viridiflavia Pseudomonas syringae Rhodococcus sp. Frigoribacterium sp. Bacillus sp.

CH3CO2Acetate

Sphingomonas sp. Pseudomonas graminis Pseudomonas sp. Pseudomonas viridiflavia Pseudomonas syringae Rhodococcus sp. Frigoribacterium sp. Bacillus sp.

-2

C4H4O4 Succinate

Sphingomonas sp. Pseudomonas graminis Pseudomonas sp. Pseudomonas viridiflavia

Succinic Acid Malonic Acid C2O4-2 Oxalate

Pseudomonas syringae Rhodococcus sp. Frigoribacterium sp. Bacillus sp. Various Various Sphingomonas sp., Pseudomonas graminis, Pseudomonas sp., Pseudomonas viridiflavia, Pseudomonas syringae, Rhodococcus sp.,

Loss rate, (mol h-1 cell-1) 17 °C 5 °C 6.8 ×10-17 2.9 ×10-17 5.0 ×10-16 3.1 ×10-16 -17 2.3 ×10 2.3 ×10-17 -17 7.2 ×10 1.1 ×10-17 -17 3.32×10 1.12×10-16 -16 2.4 ×10 0 3.44×10-16 4.58×10-16 1.4×10-14 2.5×10-15 -16 3.10×10-17 1.65×10 -14 3.1 ×10 9.0 ×10-15 -16 1.68×10-16 5.86×10 -15 2.5×10 3.2×10-16 -15 6.1 ×10 2.0 ×10-15 -15 3.6×10 2.9×10-15 0 0 0 0 -17 9.84×10 5.89×10-19 0 0 1.07×10-16 1.09×10-15 -15 1.8×10 1.8×10-16 -17 6.34×10-17 9.40×10 -14 1.4×10 4.0×10-15 -16 4.29×10-17 2.02×10 -16 3.2×10 0 3.5×10-15 2.4×10-16 -14 1.4×10 7.2×10-15 0 0 0 0 9.37×10-17 3.91×10-17 -16 3.6×10 0 3.54×10-16 3.77×10-16 -15 1.4×10 2.5×10-16 -17 6.67×10-17 5.08×10 -15 1.6×10 1.9 ×10-16 -16 2.20×10-16 1.05×10 -16 3.6×10 3.6×10-17 -15 1.8×10 4.0×10-16 -16 2.5×10 1.4×10-16 0 0 0 0 1.5×10-16 1.0×10-15 0 0

Ref.

42

41 50a 41 50 41 50 41 50 50 50 50 50 41 50 41 50 41 50 41 34 34 34 34 34 41 34 41 50 41 50 41 50 50 50 50 50 44b 44

50


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Frigoribacterium sp., Bacillus sp. This work (Supporting Information) a Results shown from Väitilingom et al. (2011) are for simulated continental cloudwater conditions. Average value from the cultures reported. 4.9 ×10-15

Cupriavidus oxalaticus

b Calculated from lifetimes reported by Ariya et al. (2002) assuming 8.1×107 cells L-1 concentration. Temperature not reported.

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and 3 m organic

Information. GAMMA features simultaneous gas and aqueous-phase reactive processing and mass transfer between the phases, following Schwartz (1986).51 The temporal evolution of the gas and aqueous-phase concentrations of a chemical species, i, is described by equations (1) and (2): 𝑑𝑃 𝑑𝑡 𝑑𝐶 𝑑𝑡

𝑘

,

𝑤 𝑃

𝑘 , 𝑃 𝐑𝑇

𝑘

𝑤

,

𝐻

∗

𝐶

𝑘 , 𝐶 𝐻 ∗ 𝐑𝑇

𝑟

𝑟

𝑟

,

𝐸

,

𝐷

,

1

2

Here, Pi is partial pressure of a gas-phase species i, Ci is its aqueous-phase concentration, wL is the aqueous liquid volume fraction, Hi* is the effective Henry’s Law constant, R is the universal gas constant, T is temperature, and rij,gas and rij,aq are the reaction rates in each phase. Ei and Di are the emissions and deposition rates for gas-phase species. rloss,bio represents the loss of an individual species to microbial metabolism, in units of mol l-1 s-1. The gas-aerosol (or gas-cloud droplet) mass transfer coefficient for species i, kmt,i, is given by:

k mt ,i 

1 2

4R R  3D g ,i 3 i i

(3)

where R is the radius, Dg,i is the gas-phase diffusion coefficient, i is the thermal velocity, and i is the accommodation coefficient.


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2.4 Simulation conditions Simulations were run for 12 daytime hours (sunrise to sunset) in order to study the impact of bacterial metabolism at different periods over a diurnal cycle. Initial conditions, emissions, deposition, and photolysis rates were as specified in McNeill et al. (2012)48 (see the Supporting Information for more details). Since in GAMMA the composition of the aqueous phase is uniform across the droplet population, the simulations only consider particles or droplets which contain a cell. For the aerosol simulations, ammonium bisulfate aerosols with R = 0.5 m and wL = 2.5 ×1015

cm3 cm-3 were assumed, based on typical bacteria sizes and atmospheric abundance. 65% RH

and pH 2 was assumed, and E-AIM was used to calculate initial in-particle inorganic concentrations.52 For the cloud simulations, wL = 4.6 ×10-15 cm3 cm-3, R = 5 m and pH = 4.5. Volume displaced by the bacterium was calculated assuming a rod-shaped organism, 1 m in length, with a diameter of 0.5 m.49 The displaced volume was considered in the calculation of the volume of an individual particle or cloud droplet, for calculating the values of rloss,bio listed in Table S2. Aerosol, droplet, and bacterial physical parameters were assumed to be time-invariant, and the same for the microbial metabolism simulations and the base case. The only changes made between the base case and the microbial metabolism simulations were in rloss,bio from eq. (2).

3. RESULTS AND DISCUSSION The results of the aerosol simulations are shown in Figure 1. In the base case with no microbial metabolic activity, modest amounts of organic acids are formed via photochemical reactions through the day, consistent with previous modeling studies.37,48 When microbial metabolism is introduced, all the organic acids tracked here are consumed faster than they are formed, preventing their accumulation in the aerosol phase. The consumption of formaldehyde by microbes results in


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Figure 1. Results of the aerosol simulations. In-particle concentration is shown as a function of time. A) base case (no microbial metabolic activity). B) test case, including the effects of microbial metabolism. Organic acids are shown as the sum of all ionized and unionized forms.

a slightly reduced in-particle concentration, but the production rate of formaldehyde in the gas phase was high enough to prevent the aqueous phase from becoming significantly depleted in formaldehyde. Since organic acids are primarily late-generation products of the photooxidation of other water-soluble organic compounds in the aqueous phase,34,35 the biological sink of organic acids in the aqueous phase did not result in significant changes in aqueous-phase oxidant levels or organosulfate formation. The results of the cloud simulations are shown in Figure 2. In the absence of microbial activity, formation of organic acids, especially oxalic acid/oxalate, is observed, as is typical for


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Figure 2. Results of the cloud simulations. In-droplet concentrations are shown as a function of time in the simulation. A) base case (no microbial metabolism) B) with microbial metabolic activity. Organic acids are shown as the sum of all ionized and unionized forms.

cloud conditions.37,53 The effect of metabolically active bacteria is similar in cloudwater as in the aerosol simulations. The organic acids, except for acetic acid, are effectively removed from the aqueous phase by the microbial metabolism. The accumulation of acetic acid/acetate in the aqueous phase is suppressed. The concentration of formaldehyde is reduced by less than 5% in the aqueous phase, due to efficient replenishment from the gas phase. As in the aerosol simulations, the concentration of organic acids was not high enough for their consumption to impact aqueousphase OH concentrations. For the aerosol simulations, the maximum aqueous-phase hydroxyl


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radical concentration is 1.0×10-12 M, and for the cloud simulations it is 1.5×10-13 M. While OH concentrations in atmospheric waters are uncertain,54 our calculated values are consistent with other models.55 Given the relatively low bioaerosol loading in the simulation, microbial metabolism in the aqueous phase (aerosol or cloud) wasn’t sufficient to impact the gas phase composition (Figure S3). Our simulations demonstrate that the impact of microbial metabolism on the chemical composition of individual cloud droplets and aerosol particles in which metabolically active bacteria reside may be significant. In this respect, these results are consistent with previous work.41,44,45,50 However, due to the overall low abundance of metabolically active cells in the atmosphere, we anticipate that the impact of this effect on atmospheric chemistry is minor. As discussed in Section 2, given a typical concentration of 8.1×107 metabolically active cells per liter of cloudwater,21 we estimate that one in 2.4×104 cloud droplets contains a metabolically active cell. This suggests that microbial activity will affect the average composition of the metabolized species in cloudwater by at most 0.004%, even if metabolic rates are higher than the ones used in our calculations. Similarly, we can take the microbial concentration reported by DeLeonRodriguez and coworkers for their aerosol samples, 1.5×105 cells m-3, as a representative loading. Assuming a background aerosol of 10 g m-3, with R = 0.5 m and a density of 1 g cm-3, roughly 1 in 130 aerosols contains a cell. Therefore, even though organic acid formation is effectively suppressed by metabolic activity in the aerosols which contain microorganisms, and this may be apparent in single-particle mass spectra, the impact on the loading of those species in the aerosol population as a whole would be at most 0.8%. A greater impact could be observed under conditions with higher prevalence of metabolically active microbes. For a 10% impact on the wholepopulation average aerosol composition, more than ~2×106 cells m-3 metabolically active bacteria


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would be required. The impact could also be more significant in environments with fewer or larger particles but a comparable number of metabolically active cells. A scenario in which aerosol particles containing bacteria are preferentially activated as cloud condensation nuclei or ice nuclei could theoretically result in an in-cloud enrichment of bacteria and a more significant role for bacterial metabolism. Given the scarcity of data regarding the abundance of metabolically active bacteria in atmospheric aerosols at the time of their study, Ariya and coworkers44 did not consider this factor in their estimates of the loss rates of dicarboxylic acids due to microbial metabolism in atmospheric particles. The calculations of Vaïtilingom and coworkers41,42,50,56 used the same value of 8.1×107 active cells per liter of cloudwater21 as we have used here, however they did not account for the physical separation of the droplets containing bacteria from cell-free droplets, which prevents bacterial metabolism from directly affecting the composition in cell-free droplets. Neglecting this separation leads to an overestimate of the potential impacts of bacterial metabolism. Noting that the organic acids studied here make up a minor fraction of water soluble organic carbon (WSOC) further limits the potential impact of microbial metabolism on atmospheric aerosol composition. Given that many WSOC formation processes, such as IEPOX SOA formation, are acid-catalyzed, the fraction of WSOC comprised by organic acids will vary with pH,33 but in these simulations the metabolized species comprised

Constraining the Impact of Bacteria on the Aqueous Atmospheric

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