The sinks of hydrocarbons in the atmosphere are usually described by
oxidation reactions in the gas and aqueous (cloud) phases. Previous lab
studies suggest that in addition to chemical processes, biodegradation by
bacteria might also contribute to the loss of organics in clouds; however,
due to the lack of comprehensive data sets on such biodegradation processes,
they are not commonly included in atmospheric models. In the current study,
we measured the biodegradation rates of phenol and catechol, which are known
pollutants, by one of the most active strains selected during our previous
screening in clouds (Rhodococcus enclensis). For catechol, biodegradation is about
10 times faster than for phenol. The experimentally derived biodegradation
rates are included in a multiphase box model to compare the chemical loss
rates of phenol and catechol in both the gas and aqueous phases to their
biodegradation rate in the aqueous phase under atmospheric conditions. Model
results show that the degradation rates in the aqueous phase by chemical and
biological processes for both compounds are similar to each other. During
day time, biodegradation of catechol is even predicted to exceed the chemical
activity in the aqueous phase and to represent a significant sink (17 %)
of total catechol in the atmospheric multiphase system. In general, our
results suggest that atmospheric multiphase models may be incomplete for
highly soluble organics as biodegradation may represent an unrecognized
efficient loss of such organics in cloud water.
Introduction
Monocyclic aromatic compounds in the atmosphere are of great interest due to
their influence on ozone formation (Hsieh
et al., 1999) and their potential to form secondary organic aerosol (Ng et al., 2007). Their main sources include
combustion processes of coal, oil and gasoline. Substituted monocyclic
aromatics are semivolatile and partition between the atmospheric gas and
particulate phases. Among those, phenol is of particular interest for air
quality as it is considered one of the main pollutants listed by the US
Environmental Protection Agency (US EPA list) since it represents a risk for
both humans and the environmental biota (TOXNET Toxicology
Data Network, 2019). Measurements of gas-phase mixing ratios of phenol in
the atmosphere are sparse. The few available measurements show rather low
values, with 4–40 ppt at the Great Dun Fell continental site
(Lüttke and Levsen, 1997) and 0.4,
2.6 and 2.7 ppt at suburban, rural and urban locations
(Delhomme et al., 2010),
respectively. However, phenol's much higher water solubility (KH=647 M atm-1) as compared to benzene (KH∼0.2 M atm-1) leads to nanomolar levels in cloud water: 5.5–7.7 nM at the Puy de Dôme (France) (Lebedev et al., 2018), 30–95 nM at Great Dun Fell (Lüttke et al., 1997), and 37 nM in the
Vosges Mountains (Levsen et al.,
1993). The further hydroxylated catechol is even less volatile and more
water soluble and, based on its Henry's law constant of KH=8.3×105 M atm-1, expected to be nearly fully
dissolved (>80 %) in cloud water, which might explain the lack
of its detection in the gas phase. Phenolic compounds have been shown to
comprise 2 %–4 % of the total organic particulate matter at several
locations in the northeastern US
(Bahadur
et al., 2010). In the same study, a strong correlation between
seawater-derived organics and phenolic compounds was found, which suggests
direct sources in addition to hydroxylation of the unsubstituted aromatics.
The oxidation of phenol by ⚫OH radicals leads to catechol in the gas
(Xu and Wang, 2013) and aqueous
(Hoffmann et al., 2018) phases and at the
gas–aqueous interface (Pillar et al.,
2014); further ⚫OH oxidation of catechol leads to ring-opening
products. A recent multiphase model study suggests that the main aqueous-phase loss processes of aromatics with two hydroxyl groups include not only
⚫OH and NO3⚫ reactions in clouds, but also reactions with
O3 and HO2⚫ (Hoffmann et al.,
2018). The nitration of phenols represents the major atmospheric source of
nitrophenols in the gas phase (Yuan et al., 2016) and aqueous phase (Harrison et al., 2005; Vione et al.,
2003). Nitrophenols can be phytotoxic (Harrison et al.,
2005) and also contribute to light absorption of atmospheric particles
(“brown carbon”; Xie et al.,
2017). They have been found in atmospheric particles (Chow et al., 2016)
and in the aqueous phases of clouds, fog and lakes (Lebedev
et al., 2018). In addition, phenols add to secondary organic aerosol
formation in the aqueous phase by oligomerization reactions (Yu et al., 2014).
Not only chemical reactions, but also microbial processes in the aqueous
phase of clouds, act as sinks for organic compounds
(Delort et al., 2010). Biodegradation rates for
several bacteria strains and aliphatic mono- and di-carboxylic
acids/carboxylates as well as for formaldehyde and methanol (Ariya
et al., 2002; Fankhauser et al., 2019; Husárová et al., 2011;
Vaïtilingom et al., 2010, 2011, 2013) have been measured in laboratory
experiments. Comparison of such rates to those of chemical radical (⚫OH
or NO3⚫) reactions in the aqueous phase show comparable rates of
chemical and microbial processes under atmospherically relevant conditions.
Such a comparison has not been performed yet for phenolic compounds in the
aqueous phase due to the lack of data on their biodegradation rates.
Our previous metagenomic and metatranscriptomic study, directly performed on
cloud water samples collected at the Puy de Dôme station in France,
showed convincing evidence of the in-cloud expression of gene coding for
enzymes involved in phenol biodegradation (Lallement et al., 2018b). We found transcripts
for phenol monooxygenases and phenol hydroxylases responsible for the
hydroxylation of phenol into catechol and transcripts for catechol
1,2-dioxygenases leading to the opening of the aromatic ring. These genes
originated from the genera Acinetobacter and Pseudomonas belonging to Gamma-proteobacteria, a major
class of bacteria in clouds (Lallement et al.,
2018b). In the same study, a large screening of bacteria in parallel
isolated from cloud water samples (Pseudomonas spp., Rhodococcus spp. and strains from the
Moraxellaceae family) showed that 93 % of the strains could biodegrade
phenol. Altogether, these results indicate a high potential of cloud
microorganisms to biotransform phenol and catechol in cloud water.
In the current study, we designed lab experiments in microcosms mimicking
cloud water conditions in terms of light, bacteria and temperature. Under
these conditions, we measured the biodegradation rates of phenol and
catechol by Rhodococcus enclensis PDD-23b-28, isolated from cloud water and one of the most
efficient strains able to degrade phenol during our previous screening
(Lallement et al., 2018b). The derived
biodegradation rates for Rhodococcus, together with literature data on phenol and
catechol biodegradation by Pseudomonas, were implemented in a box model to compare
chemical and microbial degradation rates in the atmospheric multiphase
system.
Materials and methodsExperiments in microcosms
The transformation rates of phenol and catechol were measured in microcosms
mimicking cloud water conditions at the Puy de Dôme station (1465 m).
Solar light was fitted to that measured directly under cloudy conditions
(Fig. S1 in the Supplement); 17 ∘C is the average temperature in the summer at this
location. Rhodococcus bacterial strains belong to the most abundant bacteria in cloud
waters and are very active phenol biodegraders (Lallement et al., 2018b;
Vaïtilingom et al., 2012). Fe(EDDS) was used to mimic organic ligands
of Fe(III), in particular siderophores (Vinatier et al., 2016). In addition,
this complex is stable at the working pH of 6.0
(Li et al., 2010).
Cell preparation for further incubations
Rhodococcus enclensis PDD-23b-28 was grown in 25 mL of R2A medium for 48 h at 17 ∘C, 130 rpm
(Reasoner and Geldreich, 1985). Then
cultures were centrifuged at 4000 rpm for 15 min at 4 ∘C. Bacteria
pellets were rinsed first with 5 mL of NaCl 0.8 % and after with
Volvic® mineral water (pH =7.0), previously sterilized by
filtration under sterile conditions using a 0.22 µm PES filter. The
bacterial cell concentration was estimated by optical density at 600 nm
using a UV3100 spectrophotometer to obtain a concentration close to 109 cell mL-1. Finally, the concentration of cells was precisely
determined by counting the colonies on R2A Petri dishes.
Phenol transformation
Biotransformation.Rhodococcus enclensis PDD-23b-28 cells were re-suspended in 5 mL of 0.1 mM phenol (Fluka
>99 %) solution, prepared in Volvic® mineral
water, and incubated at 17 ∘C, 130 rpm agitation for 48 h in
the dark; 0.5 mL of this culture was incubated in 25 mL of the same medium
and under the same conditions. In order to determine the concentration, the
optical density for each strain was measured at 600 nm during the
experiment. The strain concentration was ∼109 cells mL-1. The concentration ratio of bacterial cells to phenol was kept
similar to that as measured in cloud water (Lallement et al., 2018b). We showed in the past
that in repeated experiments identical cell / substrate ratios lead to the
same biodegradation rates (Vaïtilingom
et al., 2010).
A control experiment was performed by incubating phenol without bacteria;
phenol concentration remained stable over time (0.1 mM of phenol was
obtained at the end of the experiment). For phenol quantification over time
in the incubation experiments, 600 µL samples were centrifuged at
12 500 rpm for 3 min and the supernatants were kept frozen until HPLC
analysis. Complementary experiments were also performed consisting of
incubation of the cells and 0.1 mM phenol in the presence of light without
Fe(EDDS).
Phototransformation. A 0.1 mM phenol solution (Fluka >99 %), prepared in
Volvic® mineral water, was incubated at 17 ∘C, 130 rpm agitation for 48 h in photo-bioreactors designed by
Vaïtilingom et al. (2011). OH radicals were generated by
photolysis adding 0.5 mM Fe(EDDS) complex solution. The Fe(EDDS) solution
(iron complex with 1 : 1 stoichiometry) was prepared from iron(III) chloride
hexahydrate (FeCl3, 6H2O; Sigma-Aldrich) and
(S,S)-ethylenediamine-N,N'-disuccinic acid trisodium salt (EDDS, 35 % in
water). A complementary experiment was also performed consisting of
incubation of a 0.1 mM phenol solution in the presence of light without
an Fe(EDDS) complex.
The experimental conditions of the irradiation experiments (Sylvania
Reptistar lamps; 15 W; 6500 K) are described by Wirgot et al. (2017). They
mimic the solar light measured under cloudy conditions at the Puy de
Dôme station (Fig. S1). The mechanism of the ⚫OH radical production under
light irradiation is as follows (Brigante and Mailhot, 2015).
R1Fe(III)-EDDS⟶hν[Fe(III)-EDDS]∗⟶Fe(II)+EDDS⚫R2EDDS⚫+O2⟶O2⚫-+EDDSoxR3HO2⚫⇆O2⚫-+H+R4HO2⚫+O2⚫-⟶H+H2O2+O2+-OHR5HO2⚫+HO2⚫⟶H+H2O2+O2R6Fe(III)+O2⚫-⟶Fe(II)+O2R7Fe(III)+HO2⚫⟶Fe(II)+O2+H+R8Fe(II)+H2O2⟶Fe(III)+⚫OH+-OH
Using the specifications of the lamp, an overall rate constant of the
photolysis of the Fe(III)-EDDS complex jR9=1.4×10-3 s-1 was calculated (Sect. S2).
Fe(III)-EDDS⟶hν⚫OH+products
Assuming steady-state conditions for ⚫OH at the beginning of the
experiments (i.e., equal ⚫OH production and loss rates), an
⚫OH concentration of 8.3×10-13 M can be
calculated. This concentration is at the upper limit of ⚫OH
concentrations as derived from various measurements and model studies
(Arakaki et al.,
2013; Lallement et al., 2018a).
Photo-biotransformation. The protocols for biotransformation and photo-transformation of phenol in
the presence of Fe(EDDS) as described above were combined.
Catechol transformation
Biotransformation. As for phenol, Rhodococcus enclensis PDD-23b-28 cells were re-suspended in 5 mL of 0.1 mM catechol (Fluka
>99 %) solution, prepared in Volvic® mineral
water, and incubated at 17 ∘C, 130 rpm agitation for 48 h in
the dark. Four experiments were carried out with different cell
concentrations (109, 108, 107 and 106 cell mL-1). For catechol
quantification over time in the incubation experiments, 600 µL
samples were centrifuged at 12500 rpm for 3 min and the supernatants were
kept frozen until LC-HRMS analysis.
Analytical methodsPhenol HPLC analysis
Before analysis, all samples were filtered on an H-PTFE filter (pore size at
0.2 µm and diameter of 13 mm from Macherey-Nagel, Germany). Phenol
detection was done on an HPLC VWR Hitachi Chromaster apparatus fitted with a
DAD detector and driven by Chromaster software. Isocratic mode was used with
a reverse-phase end-capped column (LiChrospher® RP-18, 150 mm × 4.6 mm, 5 µm, 100 Å). The mobile phase was composed of
acetonitrile and filtered water (Durapore® membrane filters,
0.45 µm HVLP type, Ireland) in 25/75 ratio with a flow rate at 1.2 mL min-1. Sample injection volume was 50 µL, spectra were recorded
at 272 nm and the runtime was 10 min.
Catechol LC-HRMS analyses
LC-HRMS analyses of catechol were performed using an RSLCnano
UltiMate™ 3000 (Thermo Scientific™) UHPLC
equipped with an Q-Exactive™ Plus Hybrid
Quadrupole-Orbitrap™ Mass Spectrometer (Thermo
Scientific™) ionization chamber. The same conditions were used
for analyzing EDDS. Chromatographic separation of the analytes was performed
on a Kinetex® EVO C18 (1.7 µm, 100 mm × 2.1 mm, Phenomenex) column with a column temperature of
30 ∘C. The mobile phases consisted of 0.1 % formic acid and
water (A) and 0.1 % formic acid and acetonitrile (B). A three-step linear
gradient of 95 % A and 5 % B in 7.5 min, 1 % A and 99 % B in 1 min,
and 95 % A and 5 % B in 2.5 min was used throughout the analysis. This
device was associated with a Thermo Scientific™
Dionex™ UltiMate™ DAD 3000 detector (200–400 nm).
The Q-Exactive ion source was equipped with an electrospray ionization (ESI)
and the Q-Orbitrap™. The Q-Exactive was operated in either
full MS-SIM; the full MS scan range was set from m/z 80 to 1200. The mass
resolution was set to 70 000 fwhm, and the instrument was tuned for maximum
ion throughput. The AGC (automatic gain control) target or the number of ions to
fill the C-Trap was set to 106 with a maximum injection time (IT) of 50 ms.
The C-Trap is used to store ions and then transfer them to the Orbitrap mass
analyzer. Other Q-Exactive generic parameters were gas (N2) flow rate
set at 10 a.u. (arbitrary units), sheath gas (N2) flow rate set at 50 a.u., sweep gas
flow rate set at 60 a.u., spray voltage at 3.2 kV in positive mode and 3 kV
in negative mode, capillary temperature at 320 ∘C, and heater
temperature at 400 ∘C. Analysis and visualization of the data set
were performed using Xcalibur™ 2.2 software from Thermo
Scientific™.
Derivation of phenol and catechol degradation rates
The degradation rates of phenol and catechol were calculated after
normalization based on the ratio of the concentration at time t (C) and the
concentration at time t=0 (C0). The pseudo-first-order rate
constants (kphenol and kcatechol) were determined using Eq. (1):
lnC/C0=f(t)=-kphenol(or kcatechol)t.
Description of the multiphase box modelChemical and biological processes
We use a multiphase box model to compare the loss rates of phenol and
catechol in the gas and aqueous phases by radicals (⚫OH,
NO3⚫) in both phases and bacteria only in the aqueous phase over a
processing time of 15 min to simulate chemical and biological processing in
a single cloud cycle. For each set of processes
(⚫OH/NO3⚫, phenol/catechol), the three terms in the
following equation are calculated and the relative importance of each
process is determined:
dAromaticdtmoleccmgas3s=-kchem,gasRadical(gas)Aromatic(gas)︸loss by gas-phase chemistry-kchem,aqRadical(aq)Aromatic(aq)︸loss by aqueous-phase chemistry+kbact,aqCellAromatic(aq)︸loss by microbial processesin the aqueous phaseLWCNA0.001,
where [Aromatic] denotes the phenol or catechol concentration, [Radical]
the ⚫OH or NO3⚫ concentration in the gas or aqueous
phase, respectively, and kchem,gas, kchem,aq and kbact are
the rate constants as listed in Table S1 in the Supplement. The
units of the aqueous-phase processes are converted into the same units as
the gas-phase processes (molec cm-3 s-1), with LWC (liquid
water content =9.7×10-7 L(aq) L(gas)-1), NA=6.022×1023 molecules mol-1 (Avogadro constant) and 0.001 to
convert from L to cm3.
The pH value of cloud water is assumed to be constant (pH =4) to
represent conditions of a continental, moderately polluted cloud. It should
be pointed out that the choice of the pH value in the simulations does not
affect the results for a wide range of pH values (3<pH<6), as this is typical of clouds influenced by marine and
continental air masses (Deguillaume et
al., 2014). None of the parameters in Eq. (2) is pH dependent within the range
relevant for cloud water (cf. Sect. S3.3). In addition to the data for
Rhodococcus obtained in the current study, we also include literature data on the
biodegradation of phenol and catechol by Pseudonomas putida and Pseudonomas aeruginosa (Sect. 3.2), which are
usually more abundant in the atmosphere than Rhodococcus.
The processes considered in the gas and aqueous phases are summarized in
Table S1 and Fig. 1. In both phases, the reaction of phenol with ⚫OH is
assumed to yield 50 % catechol; other products of these reactions are not
further tracked in the model. The reaction of phenol with NO3⚫
results in nitrophenols (Bolzacchini et
al., 2001; Harrison et al., 2005); the loss of these products is not
explicitly included in the model either as we solely focus on the comparison
of the degradation rates. Recently, it was suggested that the reactions with
ozone and HO2⚫/O2⚫- might represent major sinks
(∼50 % and ∼20 %, respectively) of
catechol in the aqueous phase (Hoffmann et
al., 2018). However, the only available rate constant for the ozone reaction
was derived at pH =1.5 by Gurol and Nekouinaini (1984), who
postulate that at higher pH (∼5–6), the reaction with ⚫OH
likely dominates the overall loss. Therefore, in our base case simulations,
we limit the reactions of phenol and catechol to the reactions with
⚫OH and NO3⚫ radicals. Sensitivity studies including
the HO2⚫/O2⚫- and O3 reactions are discussed in the
Supplement (Sect. S4).
Schematic of the multiphase system in the box model.
Microbial activity in the aqueous phase by Rhodococcus and Pseudonomas is usually expressed as
rates (mol per cell per hour)
(Vaïtilingom et al., 2013). We converted
these experimentally derived rates into “rate constants” (liters per cell per hour) in order to adjust them to the substrate and cell concentrations
as assumed in the aqueous phase in the model (Sect. S3.2), equivalent to the treatment
of chemical processes. In order to account for the numerous additional loss
processes of ⚫OH(aq) and NO3⚫(aq) in clouds, sinks for
both radicals have been added: a general rate constant of ⚫OH with total
water-soluble organic carbon (WSOC) (kOH,WSOC=3.8×108 M-1 s-1) lumps the main loss processes of ⚫OH in cloud
water (Arakaki et al., 2013); assuming an average
WSOC concentration of 5 mM results in a first-order loss process of
kOH=2×106 s-1. The main losses of
NO3⚫(aq) are likely reactions with halides
(Herrmann et al., 2000); as a proxy, we assume here a
first-order loss process (kNO3=105 s-1), reflecting the
sum of the major NO3⚫(aq) sinks. These lumped sink processes lead
to aqueous-phase radical concentrations of
[⚫OH(aq)]day∼10-15 M and
[NO3⚫(aq)]night∼10-14 M, respectively,
in agreement with predictions from previous model studies
(Ervens et al., 2003). Kinetic-phase transfer
processes between the two phases are described for the radicals and
aromatics based on the resistance model by Schwartz (1986); all
phase transfer parameters (Henry's law constants KH, mass accommodation
coefficients α and gas-phase diffusion coefficients Dg) are
summarized in Table S1.
Initial concentrations
Initial concentrations of 4 ppt catechol and phenol are assumed in the gas
phase that partition between both phases and are chemically consumed over
the course of the simulation (15 min). These initial mixing ratios
correspond to equivalent aerosol mass concentrations on the order of several
10s ng m-3, in agreement with measurements of phenol compounds in
aerosol samples (Bahadur
et al., 2010; Delhomme et al., 2010) and nanomolar concentrations in cloud
water (Lebedev
et al., 2018). It should be noted that the assumption about the initial
aromatic concentrations does not affect any conclusions of our model
studies, as we compare the loss fluxes of all processes in a relative sense.
Two simulations are performed for each set of conditions to simulate day- or
night-time conditions, respectively, that only differ by the radical
concentrations ([⚫OH]day=5×106 cm-3; [NO3⚫]night=5×108 cm-3)
that are constant throughout the simulations. Two types of bacteria are
assumed (Rhodococcus and Pseudomonas). They have been found to contribute 3.6 % and 19.5 %
to the total number concentration of bacteria cells isolated from cloud
waters and present in our lab collection. Using a typical cell concentration
in cloud water of 6.8×107 cell L-1 (Amato et al., 2017), the assumed bacteria
cell concentrations in the model are 2.7×106 cell L-1
and 1.3×107 cell L-1 for Rhodococcus and Pseudomonas, respectively. The
simulations are performed for the conditions for monodisperse droplets with
a diameter of 20 µm. The drop number concentration of 220 cm-3
results in a total liquid water content of 0.9 g m-3. These parameters
do not change over the course of the simulation.
ResultsIncubations in microcosms
The transformation rates described in this work were measured at pH =7.0
as observed at the Puy de Dome (3.8<pH<7.6, Deguillaume
et al., 2014), but we expect that our results can be extrapolated to the
full range of pH values as encountered in clouds. In our previous studies,
we have demonstrated that pH variation has a low impact on microbial
biodegradation ability, as was shown in the case of carboxylic acids by 17
strains isolated from clouds (Vaïtilingom et
al., 2011) or phenol by Pseudomonas aeruginosa (Razika et al., 2010). This insensitivity to the solution pH can be explained by the
fact that the biodegradation experiments are performed with bacteria and not
purified enzymes. The enzymatic activities take place inside the cell and
are not impacted by the external pH. It is well known that bacteria are able
to regulate their internal pH (which is usually in the range of
∼6.5<pH<∼7 when exposed to
external pHs between 4 and 8). Yeasts, molds or acidophilic and
alcalinophilic bacteria are even active in a range of pH of 2<pH<11 (Beales, 2004). The mechanisms involved in the
intracellular pH regulation of microorganisms facing acid stress are very
complex and have been reviewed recently (Guan and Liu, 2020).
Transformation of phenol
Abiotic degradation. In the presence of light and Fe(EDDS), phenol concentration decreases with
time in the first 2 h of the experiments and then remains rather
stable (Fig. 2). In parallel, catechol, the first intermediate of phenol
transformation, is formed (Fig. S2a) and accumulates over time. Catechol
concentration is quite low because it is further oxidized over time to yield
CO2. Phenol degradation slows down after 2 h due to the lack of
OH radical production resulting from the destruction of the EDDS ligand with
time (Fig. S2b). Phenol is not directly photolyzed in the presence of light, while it
is oxidized in the presence of Fe(EDDS) complex (Figs. 2 and S2).
Transformation of phenol with time under different conditions. Phenol + light
+ Fe(EDDS) (red squares), phenol + R. enclensis + dark (blue circles), phenol + R. enclensis + light (purple triangles), and phenol + R. enclensis + light + Fe(EDDS) (green line). Rhodococcus enclensis cell concentration was 109 cells mL-1.
Biotic degradation. In the dark, phenol is biotransformed by Rhodococcus enclensis cells (Fig. 2) and completely degraded
after 5.5 h. A lag time of about 2.5 h is observed, during which
phenol is degraded extremely slowly. This is a well-known phenomenon under
lab conditions corresponding to the induction period of the gene expression
(Al-Khalid and El-Naas, 2012). Catechol is slowly formed
in parallel until t=3.5 h and is further biodegraded when bacteria
have started to be more active (Fig. S2a).
Abiotic and biotic combined transformation. When light (in the absence of Fe(EDDS)) is present, no major change is
observed for the biodegradation of phenol by Rhodococcus enclensis (Fig. 2); the lag time is still
observed. When light and Fe(EDDS) are present, the lag time is no longer
observed and the degradation of phenol is completed within 2.5 h instead
of 5.5 h when the bacteria are in the dark. The microbial activity
compensates for the limitation of radical processes due to the destruction of
the Fe(EDDS) complex (after 2 h). In parallel, the production of
catechol is increased compared to biotic or abiotic conditions alone (Fig. S2a). Catechol accumulates over approximately 3 h, after which it
decreases. As observed previously, this decrease is likely a result of the
bacterial activity.
Transformation rates (10-5 mol L-1 h-1) of catechol
and phenol under abiotic and biotic conditions. The rates were measured from
three biological or chemical replicates (independent experiments),
respectively. They were derived based on the steepest slopes in Fig. 2.
Comparison of the rates of phenol transformation under the different conditions. If we consider the numerous uncertainties, the rates of transformation under
abiotic, biotic and combined conditions are within the same order of
magnitude, namely ∼10-5 mol L-1 h-1 (Table 1). Biotic and combined conditions can be further compared in more detail
by normalizing the transformation rates with the exact number of cells
present in the different incubations (three biological replicates for each
condition). Note that the number of cells varied from 4×108 to 8×109 cell mL-1. After normalisation to the cell concentration used
in the individual experiments, it is evident that the rates of phenol
transformation are very close to each other and in the range of 10-16 mol per cell per hour (Table 2).
Biodegradation rates (mol per cell per hour) of catechol and
phenol of Rhodococcus and Pseudomonas strains normalized to the exact number
of cells present in the incubations. The calculations of biodegradation rates
for the Pseudomonas strains are detailed in Sect. S1.
Bacterial strainBiodegradation rate of phenolBiodegradation rate of catecholReferences(experimental condition)(10-16 mol per cell per hour)(10-16 mol per cell per hour)Rhodococcus enclensis PDD-23b-28 (dark)1.8±0.515.0±0.5This workRhodococcus enclensis PDD-23b-28 (light)1.2±0.5NDaThis workRhodococcus enclensis PDD-23b-281.0±0.3NDaThis work(light + Fe(EDDS))Pseudomonas putida EKII (dark)0.22.4Hinteregger et al. (1992)Pseudomonas aeruginosa (dark)5.970.7bPhenol experiments(Razika et al., 2010)Pseudomonas (average)Average: 3.0Average: 36.6
a Not determined. b This rate was estimated based on the value for phenol (Razika et al., 2010) and the ratio (∼12) for phenol/catechol biodegradation rates as determined for Pseudomonas putida by Hinteregger et al. (1992) (cf. also Sect. S1 in the Supplement).
Biotransformation of catechol
As catechol is an intermediate of phenol transformation, we measured its
biotransformation rate by Rhodococcus enclensis under dark conditions. When the cell
concentration was 108 or 109 cell mL-1, the catechol
biodegradation was too fast to be detected within the time resolution of the
experiments (Fig. 3). We performed various experiments with reduced cell
concentrations, from 107 to 106 cell mL-1 (Fig. 3). Finally, we used the results corresponding to 107 cell mL-1 to derive the initial rate of catechol biotransformation.
It was estimated as (15±0.5)×10-16 mol per cell per hour. This value is 8.5 times higher than the biodegradation rate of
phenol and was used in the model (Sect. 3.2).
Biotransformation of catechol with time by different concentrations of Rhodococcus enclensis: 109 cell mL-1 (brown stars), 108 cell mL-1 (brown squares), 107 cell mL-1 (blue triangles), and 106 cell mL-1 (black circles). C: phenol concentration at time t; C0: initial phenol concentration. C/C0 was extrapolated from the ratio of the integrals of the catechol signal m/z=110.03678 detected in mass spectra at time t=0 and t, respectively. Initial catechol concentration was 0.1 mM.
Straube (1987) showed that the activity of the
catechol-1, 2-dioxygenase of Rhodococcus sp. P1 was higher than that of its phenol
hydroxylase. This trend is in agreement with our results, as we know from the
genome sequencing of our Rhodococcus enclensis strain that a catechol-1,2-dioxygenase is involved
(and not a catechol-2,3-dioxygenase) (Lallement et al., 2017).
As opposed to the results for phenol in Fig. 2, it can be seen in Fig. 3
that no lag time is observed for catechol biodegradation. This suggests that
the first step of oxidation of phenol to catechol by a phenol hydroxylase
might be a limiting step as it needs to be induced, while the second step –
corresponding to the opening of the ring cycle by a catechol-dioxygenase –
is not induced and, thus, is faster.
Comparison of biodegradation rates by Rhodococcus to literature data for Pseudomonas strains
As we previously have shown that Pseudomonas is one of the most dominant and active
genera in cloud waters (Amato et al., 2019) and that
these strains are very active for phenol biodegradation (Lallement et al., 2018b, and references
therein), we compare in the following biodegradation rates of Pseudomonas from the
literature (Table 2) to the data for Rhodococcus derived in the current study (Sect. 4). These rates differ among Pseudomonas strains: for Pseudomonas putida EKII a value of
0.199×10-16 mol per cell per hour was found (Hinteregger et al., 1992), while it was 5.89×10-16 mol per cell per hour for Pseudomonas aeruginosa (Razika et al.,
2010). These values are both on the same order of magnitude as the one
measured here for Rhodococcus enclensis PDD-23b-28. Finally, we used an average value (3.044×10-16 mol per cell per hour) for Pseudomonas strains to derive the rates used in the model
(Sect. S3.2).
As in the case of phenol, we also calculated catechol biodegradation rates
with Pseudomonas strains based on literature data (Table 2). Values are only available for Pseudomonas putida EKII (Hinteregger et al., 1992) and show a biodegradation
rate that is 12 times higher compared to that of phenol biodegradation.
This confirms that catechol dioxygenases are much more active than phenol
hydroxylases, as observed for Rhodococcus enclensis. Similarly to phenol, catechol biodegradation
rates for Pseudomonas are within the same order of magnitude as those for Rhodococcus. The same
ratio (∼12) as for the Pseudomonas putida was applied to estimate the
biodegradation rate of catechol by Pseudomonas aeruginosa, for which only the rate for phenol was
experimentally determined by Razika et al. (2010).
Model results
Model results are expressed as the relative contributions of each loss
pathway in the gas and aqueous phases; they are summarized in Table S4. Both during
day and night, the gas-phase reactions of ⚫OH and NO3⚫
dominate the loss of phenol by >99 % (light red and blue bars
in Fig. 4a and b, respectively). The contributions of Pseudomonas to the phenol loss are
approximately a factor of 3 higher than those of Rhodococcus, in accordance with
their higher cell concentration and comparable microbial activity (Table S3).
However, during day time, the contribution of bacteria to the total loss in
the aqueous phase is about 1 order of magnitude smaller than that of the
chemical (⚫OH(aq)) reactions; during night time, this difference
is even larger and the NO3⚫(aq) reactions dominate by far (factor
>100) the loss in the aqueous phase (Fig. 4b).
Relative contributions of multiphase processes to total loss of phenol (a, b) and catechol (c, d) during day (a, c) and night (b, d) time. Loss by bacteria processes only occurs in the aqueous phase. Note that the ordinate is shown as a logarithmic scale which might falsely lead to the impression of larger contributions of Rhodococcus compared to Pseudomonas.
While the microbial activity is the same during day and night time (i.e.,
there were no significant differences in experiments with and without light,
respectively; Fig. 2), the night-time NO3⚫(aq) concentration is about
10 times higher (∼10-14 M) than that of
⚫OH(aq) (∼10-15 M) during the day, and
the chemical rate constants also differ by a factor of 4
(kOH,phenol=1.9×109 M-1 s-1;
kNO3,Phenol=8.4×109 M-1 s-1, Table S1).
These differences in radical concentrations and rate constants lead to much
higher radical reaction rates during night than during the day and, thus, to
a relatively lower importance of microbial activity during night time.
Overall, the loss in the aqueous phase by both chemical and microbial
processes contributes ∼0.1 % to the total loss of phenol
during night time.
The catechol fraction dissolved in the aqueous phase is much greater (≥85 %) as its Henry's law constant is about 1000 times larger than that of
phenol (Table S1), of which only ∼2 % partitions to the aqueous
phase. Its enhanced solubility leads to a more important role of aqueous-phase processes. During day time, the loss by aqueous-phase processes
(chemical and microbial) is >30 % for catechol (Fig. 4c), with
contributions by ⚫OH(aq), Pseudomonas and Rhodococcus of 14 %, 10 % and 7 %,
respectively, when ⚫OH as the only oxidant for the phenols in the aqueous
phase is considered. Thus, for this case, the total microbial activity in
the aqueous phase exceeds that of the chemical reactions (Fig. 4c) and contributes up
to 17 % to the total loss of catechol in the multiphase system. The
relatively higher gas-phase rate constants and NO3⚫ concentrations
as compared to the corresponding values for ⚫OH during day time
are reflected in the much higher contributions by the gas-phase reactions to
catechol loss during night (>97 %) than during day time
(Fig. 4d).
The model results in Fig. 4 imply that the only chemical loss reactions of phenol
and catechol are the reactions with the ⚫OH and NO3⚫
radicals. In agreement with findings from a recent multiphase modeling study
that discussed possible contributions of aqueous-phase reactions with
additional oxidants (O3 and HO2⚫/O2⚫-)
(Hoffmann et al., 2018), we show that
including these reactions might add significant sinks for catechol (Sect. S4).
However, we caution that these results of the model sensitivity study
including the ozone and HO2⚫/O2⚫- reactions likely represent an
upper estimate. The rate constant used in the model was determined at pH =1.5. In the original study, a decreasing trend with increasing pH was
suggested; however, the exact pH dependence was not given. Thus, the
prediction shown in Fig. S3 might not correspond to the moderate pH
values as encountered in clouds and thus might be an overestimate of the
role of the ozone reaction.
Atmospheric implications
Both experimental and modeling approaches show that, in the water phase of
clouds, phenol and catechol degradation by microbial and
chemical ⚫OH(aq) processes may be within 1 order of magnitude. When the
complete multiphase system is taken into account, phenol chemical
transformation is largely dominant in the gas phase, whereas the more
water-soluble catechol is efficiently biodegraded in the aqueous phase.
Our estimates are only based on a limited number of cloud microorganisms
(Pseudomonas and Rhodococcus). These microorganisms represent strains which are very efficient, and
previous works showed that these genera are active in clouds (Amato et al., 2017; Lallement
et al., 2018b). However, they only comprise a fraction of the total
microfora, i.e., about 22 % of all prokaryotes in clouds. Even if other
bacterial genera are less metabolically active, their combined metabolic
activity might contribute substantially to the total biodegradation of
phenols (and likely other water-soluble organics) in clouds. In addition,
other microorganisms could be active as well, such as fungi and yeasts. The
relative importance of radical chemistry compared to biodegradation will
also depend on the radical concentrations in both phases which, in turn, are
a function of numerous factors such as air mass characteristics, pollution
levels that affect OH concentrations and of microphysical cloud properties
(e.g., drop diameters, liquid water content) (Ervens
et al., 2014). In general, the importance of aqueous-phase processes
increases with increasing solubility (Henry's law constants). Our recent
FT-ICR-MS analyses of cloud water samples have shown that about 50 %
of ∼2100 identified compounds were utilized by cloud
microorganisms (Bianco et
al., 2019). Thus, microbial processes in cloud water may represent efficient
sinks for numerous organics and might even result in products different from
those of chemical reactions (Husárová et al.,
2011). Thus, atmospheric models may be incomplete in describing the loss of
some organic compounds and should be complemented by microbial processes in
order to give a complete representation of the atmospheric multiphase
system. While it has been recognized for a long time that microbial
remediation in the environment is a common process (Kumar et al., 2011; Watanabe, 2001),
we suggest that the atmosphere represents an additional medium for such
processes.
Summary and conclusions
The newly derived biodegradation data for Rhodococcus with phenol and catechol were
implemented in a multiphase box model, together with additional literature
data for Pseudomonas degradation of the two aromatics and their chemical radical
processes in the gas and aqueous phases. Model results reveal that for the chosen
model conditions ([⚫OH]gas=5×106 cm-3; [NO3⚫]gas=5×108 cm-3;
[⚫OH]aq∼10-15 M;
[NO3⚫]aq∼10-14 M; [Bacteria cell] =1.7×107 cell mL-1) the chemical and microbial
activities in the aqueous phase are comparable. However, for catechol the
loss processes in the aqueous phase are relatively more important
(∼30 % of total loss) than for phenol (0.1 % of total
loss) due to its much greater water solubility (KH,Phenol=647 M atm-1; KH,catechol=8.3×105 M atm-1). It
can be concluded that under some atmospheric conditions, the loss of highly
soluble organics may be underestimated by chemical reactions only as the
biodegradation of these organics by bacteria (and possibly other
microorganisms) could represent additional sinks resulting in different
products. Our model approach is highly simplified and limited in terms of
biological, chemical and cloud microphysical conditions. More comprehensive
experimental and model studies are needed to explore parameter spaces for
relevant cloud water constituents (highly water soluble, relatively low
chemical reactivity) in order to better quantify the role of bacteria and
other microorganisms in clouds as active entities that take part in the
conversion of organics in the atmospheric multiphase system.
Data availability
All experimental and additional model data can be
obtained from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-4987-2020-supplement.
Author contributions
AMD and GM designed the experiments in microcosms. SJ,
AL, MS and ML performed the experiments. BE performed the model simulations.
BE and AMD wrote the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by a school grant to the first author from the Walid Joumblatt Foundation for University Studies (WJF), Beirut, Lebanon, and the French National Research Agency (ANR) (grant nos. ANR-17-MPGA-0013 and ANR-13-BS06-004-01).
Review statement
This paper was edited by Ryan Sullivan and reviewed by two anonymous referees.
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