3.1. Identification of isolates for pyrene degradation
Figure 1 shows the percent depletion of PAHs in the silicone O-ring after 37 and 47 days for the biodegradation experiments. As can be seen in Fig. 1, under a mixture substrate system, URS-5, URS-6, URS-8, and URS-10 could degrade 27, 39, 17 and 41% of pyrene after 47 days. Alcanivorax borkumensis almost failed to degrade pyrene. Measurement of the PAH degradation rates showed that Pseudomonas URS-5, URS-6 and URS-8 showed less than 25 percent removal of pyrene in a mixture substrate system within 37 days at 15°C. The initial concentrations were 18.31, 2.98, 1.01, 0.095 mg/L of naphthalene, anthracene, phenanthrene, pyrene in the medium, respectively. A comparison of the degradation of pyrene by strains under a single substrate system and a mixture substrate system was conducted in order to determine if the presence of other substrate would affect (either suppressing or enhancing) the degradation of the primary substrate (Fig. 1 (a) and (b)). Figure 1 (b) plots the normalized concentration of pyrene in presence of pre-selected strains and synthetic multi-culture under single substrate system.
3.2. Determination of pyrene degradation rate by individual strain and multi-culture strains
In Fig. 1, the degradation effect of mixed bacteria composed of pre-selected bacteria and Alcanivorax borkumensis was better than pure cultures in the precence of 3 PAHs solution. Adding biosurfactant-producing bacteria i.e., Alcanivorax borkumensis and URS-6 increased the bioavailability of the pyrene and phenanthrene. In a more complicated system, the presence of other PAHs had no effect on the degradation of pyrene that might be attributed to the comprehensive PAH-degradation capability of URS-10. In some studies, genetically engineered microorganisms (GEMs) were proposed as a method of obtaining desired biodegradation pathways from a single strain of the organism. It is important, however, to consider the environmental risks associated with the release of GEMs into the environment. (Miri et al. 2022) The details about pattern of degradation observed in mixture substrate was discussed in the supplementary information (Section S1).
3.3. Culture conditions effects on enzyme activity
Produced biomass in NB was around 5-fold higher than MSM, and enzymes were produced 10-fold higher in NB compared to MSM (not shown here). The extract cells from the cultured bacteria in the presence of 100, 250 and 500 mg l− 1 of dilbit (containing 7,15 and 20 mg/L pyrene, respectively) and 25, 50 and 75 mg l− 1 solution of 3PAHs (naphthalene, phenanthrene, pyrene) showed a significant difference in activity of enzyme in order of 250\(>\) 100\(>\)500 mg/L (p \(<\)0.05) for dilbit and 50\(>\) 25\(>\)75 mg/L (p \(<\)0.05). Thus, 50 and 250 mg l−1 of 3PAHs and dilbit as the optimal concentration for enzyme production. (Miri et al. 2022) It is noticeable that our dilbit contained less than 1 mg/L pyrene, this spiked pyrene is needed for effective induction of targeted enzymes. The activity of target enzymes were measured for different consortia incubated on mixture of 3PAHs and dilbit to determine the optimum time for incubation and production of enzymes (Section S11).
3.4. Principle component analysis of enzyme activities
As PCA facilitates the easy representation of multi-dimensional data sets by scaling them and aids in identifying any significant patterns, we utilized it to analyze the similarities among the tested consortiums with regard to enzyme activities produced on the tested inducers. Figure 2 shows individual PCA biplots with the ordination of enzyme activities produced on PAHs mixture, dilbit, motor oil at 15 ◦C. The biplot graph of PCA for the observation variables in the form of enzyme activity and tested consortia as the variables showed that there is strong correlation among the enzyme activity variables as they are on the right side of the graph.
For 3 PAHs mixture, PC1 and PC2 are accounted for 63% and 25% of the total variation in data, respectively, for a total of 89% (Fig. 2 (a)). Upper pathways enzymes are closely aligned with PC2 while other enzymes are closely aligned with PC1. This indicated a strong correlation (> 0.5) among the upper and lower pathway enzymes. The consortia Mix1-3PAHs, and Mix7-3PAHs were the most naphthalene dioxygenase, and catechol dioxygenase whereas, consortia Mix4-3PAHs was found pyrene dioxygenase and 1-hydroxy-2-naphthoate hydroxylase active. Mix5 and 8-3PAHs showed more correlation towards the lower pathways. Mix7-3PAHS is closely spaced between upper and lower pathways showing a positive correlation with them and hence signifies better consortia leading to a high activity of corresponding enzymes. The combined load score on (PC1 + PC2) for Mix7-3PAHs was found to be 5.1. This further stated that these tested synthetic consortia worked better for enzyme production.
In the case of dilbit, PCA biplot were accounted for 66% and 25% for PC1 and PC2, respectively, for a total of 91% data variation. Akin to 3PAHs mixture case, upper pathway enzymes clustered together around PC2 while lower pathway enzymes clustered around PC1. The poor performance of Mix1-D and Mix3-D was also evident from biplot of PCA which shows their position in the far end 2nd and 4th quadrant, respectively and there is no proximity with eigenvectors of enzyme activity. Unlike the PCA biplot for 3 PAHs that PD eigenvector showed a negative correlation with P3,4D and C2,3D, strong correlation among the targeted enzyme was observed in this biplot. This further illustrate all consortia with Alcanovorax were found near the studied enzyme vectors and they can be suitable for an effective production of these enzymes using dilbit as inducer in NB media.
3.6. Analysis of microbial community
To analyze ifferent types of inducers affect the activities of target enzymes in tested consortia, it is necessary to analyze the composition of microbial community. After 8, 12, 16 hours of enzyme production, next generation sequencing of 16s rRNA is performed for the most promising consortium. Figure 3 illustrates the composition of microbial communities in optimum tested consortia included Mix1-3PAHs, Mix7-3PAHs, and Mix8-D. The results showed high relative abundance of genera URS-5 (31.4% % after 8 hours of incubation), URS-6 (44% after 8 hours of incubation)) and URS-8 (44.6% after 16 hours of incubation) in Mix1-3PAHs. The high activity of naphthalene dioxygenase, catechol dioxygenase, 1-hydroxy-2-naphthoate hydroxylase can also be attributed to the high relative abundance of these bacteria. After 8 hours of incubation with Mix7- 3 PAHs, URS-10 (18%) was relatively inactive in terms of pyrene dioxygenase activity. A high relative abundance of genera URS-10 (25.1%) after 8 hours of incubation in Mix8-D compared to relative abundance of genera URS-10 (7%) in Mix7-3PAHs showed the synergistic activity exist between URS-10 and Alcanovorax that resulted in superior ability to utilize a higher pyrene concentration. The high concentration of pyrene dioxygenase in Mix8-D can be attributed to this synergistic effect.
3.7. Identification of enzymes by LC-MS/MS
Liquid chromatography-Tandem mass spectrometry (LC-MS/MS) was used to confirm the presence of target enzymes in the crude cocktail obtained from consortium Mix8-D. This method could provide more information in identification of vital enzymes in a mixture compared to conventional biochemical assays. (Jardine et al. 2018)
Based on other literature, the proteomic analysis indicates according to the peptide sequences counting after pyrene treatment, 18 enzymes in the pathway are upregulated more than twofold. (Peng et al. 2008) Established from proteomic data, 27 enzymes were identified that were necessary for the complete breakdown of pyrene. The identified proteins in our cocktail of enzymes with more that 1% false discovery rate (FDR). Table 1 lists the false discovery rate (FDR). Following PAH-degrading enzymes were identified: pyrene dioxygenase, naphthalene dioxygenase, naphthalene 1,2-dioxygenase, catechol-2,3-dioxygenase, 1-hydroxy-2-naphthoate hydroxylase salicylaldehyde dehydrogenase, salicylate hydroxylase and protocatechuic acid dioxygenase. The sequence of the crude cocktail showed the presence of proteins such as membrane proteins, stress-response proteins, ATP-binding proteins etc.; however, there were no sequence similarities to any of the proteins in the mixture whose function is to degrade PAHs.
Several proteins that are associated with oxidative stress, which prevent unfolding of proteins were detected. High levels of reactive oxygen or chlorine species (ROS/RCS) leads to the protein unfolding and aggregation in the cytosol. To cope with this stress, bacterial cells employ ATP-independent chaperones such as the DnaK system and the GroEL system, which bind unfolded or aggregated proteins and maintain their solubility. (Voth &Jakob 2017) It is clearly reported that the oxidation of PAH trans-dihydrodiols to catechol’s generates reactive ROS. (Penning et al. 1999) Presence of chaperones in the crude cocktail obtained from multi-culture of PAH- degrading bacteria indicated that growth on these compounds induces a stress response. Similarly, Tomás-Gallardo et al., (2006) reported that Rhodococcus sp. strain TFB produced 60-kDa Chaperonins in the presence of phthalate. Using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS/MS), the authors identified phthalate-induced proteins. (Tomás‐Gallardo et al. 2006)
Table 1
LC-MS/MS analysis of partially purified enzymes and assignment to UniProtKB entries
Protein/Enzyme
|
Molecular function
|
Gene
|
Mass1 (kDa)
|
Mass2
(kDa)
|
UniProt ID
|
Coverage (%)
|
Naphthalene dioxygenase
|
Component of the naphthalene dioxygenase
|
doxB
|
49.6
|
49.1
|
P0A111
|
85
|
Naphthalene 1,2-dioxygenase
|
Ferredoxin component of the naphthalene dioxygenase
|
doxA
|
11.4
|
11.8
|
P0A170
|
81
|
Dihydrodiol dehydrogenase
|
The oxidation of naphthalene dihydrodiol
|
nahB
|
27.5
|
27.4
|
P0A169
|
79
|
Catechol-2,3-dioxygenase
|
The meta cleavage of catechol to 2-hydroxymuconic semialdehyde
|
catE
|
31.5
|
31.2
|
P54721
|
61
|
salicylate hydroxylase
|
The decarboxylative hydroxylation of salicylate into catechol
|
nahG
|
101.3
|
102.1
|
6BZ5
|
60
|
F-ATPase subunit beta
|
ATP synthesis
|
atpD
|
51.9
|
51
|
C1A1X8
|
57
|
Salicylaldehyde dehydrogenase
|
Involves in pathway naphthalene degradation
|
doxF
|
51.9
|
52
|
P0A390
|
58
|
Pyrene dioxygenase
|
Catalyzes the first step in the oxidative degradation of aromatic hydrocarbons
|
phnA1a
|
215.7
|
125.4
|
2CKF
|
58
|
Alkyl hydroperoxide reductase
|
Protects the cell against DNA damage
|
ahpF
|
55.3
|
55.8
|
P0A155
|
56
|
Chaperone ClpB
|
Protein refolding & Stress response
|
clpB
|
92.3
|
93
|
I3UXE4
|
51
|
Bacterioferritin
|
Iron storage
|
bfr
|
18
|
18.4
|
A0A4V3X737
|
50
|
F-ATPase subunit alpha
|
ATP synthesis coupled proton transport
|
atpA
|
58.4
|
57.7
|
C1AW01
|
20
|
Citrate synthase
|
Involves in the pathway tricarboxylic acid cycle
|
gltA
|
39.69
|
39.2
|
F8QR44
|
20
|
50S ribosomal protein L3
|
Ribonucleoprotein
|
bipA
|
67
|
66.9
|
A0A1G5MQC5
|
5
|
ATPase subunit beta
|
Proton-transporting ATPase activity,
|
atpD
|
49.5
|
50
|
A0A024ED61
|
5
|
Glutamine synthetase
|
Glutamate-ammonia ligase activity
|
glnA
|
51.6
|
51.6
|
A0A5D3GGF0
|
5
|
Elongation factor Tu
|
Promotes the GTP-dependent binding of aminoacyl-tRNA
|
tuf
|
43.57
|
43.2
|
A0A1H2I8D2
|
5
|
Alkyl hydroperoxide reductase C
|
Plays a role in cell protection against oxidative stress by detoxifying peroxides
|
OU5_0185
|
20.4
|
20.8
|
A0A024E2Y4
|
5
|
1Based on the UniprotKB entries (Theoretical Mass), the mol mass was calculated.
2 Based on LC-MS/MS (Experimental Mass), the molecular mass was determined.
3.8. Analysis of metabolites
In addition to study of enzymes involved in pyrene biodegradation, analysis of metabolites in the multi-culture bacteria would provide a better understanding of the enhanced biodegradation compared with each strain. URS-10 was cultivated in NB supplemented with 10 mg/L pyrene, and two characterized metabolites were presented including 5-hydroxy-5H-4-oxapyrene-5-carboxylic acid and phthalic acid. This study was similar to the result of Rhodococcus sp. UW1 strain as reported by Walter et.al.(Walter et al. 1991) The main pathway of pyrene biodegradation was also reported in other literature that is started by initial attack at the 4,5- position to produce cis- and trans- 4,5- pyrene-dihydrodiol by pyrene dioxygenase. (Heitkamp et al. 1988, Krishnan et al. 2004) Other strains can also be degraded by multiple pathways described by Peng et.al. that can oxidize pyrene via initial deoxygenation at the 1,2-positions, however, in this case, the dead end product such as 4-hydroxy-perinaphthenone was not detected. (Peng et al. 2008) As a result, deoxygenation at position 4,5 is the principal pathway of pyrene degradation (Fig S4). In this study, the results suggested that phthalic acid is a dead-end product of bacterial metabolism for URS-10 because pyrene: phthalic acid ration was at high value. This is an important observation of the incomplete mineralization of pyrene.
Regarding Mix7-3PAHs cultivated in NB, trends of simultaneous biodegradation of PAH mixtures was complex. It is possible that the observed trend of utilization of PAHs is not only the result of bioavailability, but also of metabolic interactions related to metabolism and competition. (Bouchez et al. 1999) The medium accumulated a number of acid metabolites produced by consortium MIX7 during incubation. Most of these metabolites were dicarboxylic aromatic acids as a result of the use of growth substrates (naphthalene, phenanthrene, or pyrene) by multibranched pathways including 1,2- dihydroxy naphthalene and phthalic acid pathways. In spite of this, no dead-end products were detected, suggesting that the intermediate metabolites produced by one or two species may have been utilized by other species.
3.9. Enzyme stability in soil Formulation for more operational stability
The batch stability experiments for key enzymes involved in biodegradation of 3 PAHs including NDH, PDH, and C2,3D was conducted for the purpose of determining whether enzymes are operationally stable in soil, the necessary time to re-inject a fresh enzyme solution into a column experiment in order to maximize biodegradation efficiency. A comparison of the relative activity of NDH, PDH, and C2,3D compared to the highest activity on the first day is shown in Fig. 4.
A remarkable restoration of activity has been observed after addition of Fe3SO4 to the extract cell. The minimum concentration of the Fe3SO4 required for optimal activation was found to be 1,1.5 and 2% (w/v) Fe3SO4 for NDH, PDH, and C2,3D, respectively. As can be seen in Fig, 1.5% (w/v) FeSO4 increased the activity of NDH, PDH, and C2,3D up to 145, 134 and 170% of their original activity, respectively. For evaluation of the stability of enzyme activities during the soil column test, enzyme activities were determined at 15 C for 3 weeks at one-day intervals in the presence of soil. Results indicated that NDH, PDH, and C2,3D enzymes were active for 20,17 and 22 days in presence of contaminated soil, with half-lives of 6.1, 7.4 and 7.8 days, respectively. In the presence of soil, NDH, PDH, and C2,3D lost 81, 85% and 86% of their initial activity respectively after 14 days. In the column test, NDH, PDH and C2,3D maintained 1%, 0% and 3% of initial catalytic activity after 3 weeks, which is the period to re-inject formulated enzyme solution.
There has been an increase in applications of various oxidoreductases in recent years, such as tyrosinases, laccases, dioxygenases, and peroxidases, however, low operational stability is a characteristic of these molecules. Following 10 days at room temperature, laccase from Trametes versicolor lost approximately 90% of its original activity. (Xu et al. 2013) After 24 hours of incubation at 4°C, Stenotrophomonas maltophilia KB2 catechol 2,3 dioxygenases exhibited no activity. (Wojcieszyńska et al. 2012) Miri et.al (2022) studied the production of xylene monooxygenases and catechol dioxygenases for degradation of p-xylene through oxidation, and ring cleavage. They suggested the injection of enzyme after 4 weeks for the columns considering the fact that both C2,3D and XMO enzymes. In the presence of soil, it showed activity for days of 26.
3.10. Soil and column characterization
Table S4 and S5 show the physicochemical characteristics of samples collected from cold climate zones. In general, pH values were neutral, and soil organic matter values was high. XRD crystallography (Table S5) determined that the key minerals in the soil are gibbsite, aragonite, dolomite, calcite, montmorillonite, and quartz. The most common mineral was Gibbsite (52%), and quartz was a close second (24%).
To determine the fate of contaminant in soil as well as the enzymatic degradation in the column, sorption of contaminant in soil was studied. In this regard, a variety of sorption isotherm models have been fitted to the equilibrium sorption data of pyrene on soils at 15 \(℃\). The parameters of each isotherm model are presented in Table S7. The Langmuir and Freundlich models were not found to be appropriate for explaining the sorption of pyrene on these soils. The composition and intrinsic heterogeneity of most natural soils can be observed at the microscopic level. It is noteworthy that even though neither Langmuir nor Freundlich could adequately fit the sorption data, the low values of the Freundlich parameter of n (ranging from 0.6) suggested that most of the isotherms involved in pyrene sorption tended to be nonlinear (Table S7). In some cases, the n value may be viewed as an indicator of the energy distribution on the surface of the site, (Weber Jr et al. 1992) where a small n value indicates sorption on heterogeneous surfaces. Table S5 presents the results of the fitting of the equilibrium sorption data for pyrene at 15 \(℃\). Based on the correlation coefficients (r2) of 0.979, the sorption of pyrene fitted the distributed reactivity model (DRM). In the DRM, several linear and non-linear soil isotherms are assumed to make up the final soil isotherm. As a result, these isotherms were regarded as composites of a particular sorption process's general linear and nonlinear isotherms.
The packed soil columns characterization employed in treatment experiments are presented in Table S8. It was important to mix different soil samples to obtain a uniform soil type for the experiments. Soil sample was a mixture of fine, medium and course particles corresponding to the site stratigraphy described in our previous work. (Miri et al. 2022) To estimate saturation time more accurately, a Bradford's method was used to determine the amount of dispersion in the samples. As shown in Fig S4, an analysis of the relative concentrations of the conservative tracer and the total protein was conducted during injection of enzyme. According to Fig, the low axial dispersion coefficient (Dax = 0.35 cm2/min) for packed soil columns might be regarded as a normal property of well-packed beds. In these experiments, hydraulic conductivity results indicated that through the bottom and top caps of the column, there was no pressure drop based on the range of flow rates applied. (Robert et al. 2017) In columns that have been packed improperly and dispersions are large, tracer arrived earlier, and 50% relative concentration was detected at a point prior to one pore volume (PV = ∼ 95 mL).
3.11. Pyrene removal
To simulate in-situ enzyme application, impacted soils were bioremediated in soil columns (0.140 L) by injecting enzyme cocktails. In Fig. 5, the bar chart graph illustrates (a) residual pyrene after injections 2 in the soil and (b) the removal of pyrene by means of solubilization and mobilization in free phase (c) degraded pyrene.
As can be seen in Fig. 5, adsorption capacity of dilbit contaminated soil for pyrene significantly increased. This was due to the fact that the adsorbed oil molecules served as a base layer on which further non-polar molecules such as pyrene could be adsorbed, thereby further reducing substrate bioavailability and pyrene biodegradation rates. (Davoodi et al. 2019, Miri et al. 2021)
Untreated soil columns showed a high concentration of undegraded (residual) pyrene (> 70% for dilbit contaminated site) even after water washing. This showed that biodegradation and solubilization enhanced the pyrene removal. Pyrene removal efficiency followed the order: Mix 7-PAH for pyrene contaminated soil column (84%) > Mix 8-D for dilbit contaminated soil column (79% residual) > AlcanivoraxD cell extract for dilbit contaminated soil column (56%) > AlcanivoraxD cell extract for pyrene contaminated soil column (54%). The final pyrene removal efficiency trend was similar to the degradation in batch test.
It has been demonstrated in a number of research studies that compounds with low solubility and high adsorptive properties may be improved in their bioavailability by adding solubilizers, such as surfactant flushing method. To determine which portion of removal is due to degradation and oil washing, autoclaved AlcanivoraxD was applied in addition to Mix 7-PAH and Mix 8-D. The solubility test study showed that pyrene solubility in the presence of autoclaved Mix 7-PAH, Mix 8-D, AlcanivoraxD -water system was around 3.1, 4.3 and 4.7 fold higher than solubility of pyrene in water. Thus, for example, In the case of AlcanivoraxD, the injected solutions can flow in the water channel and increase the recovery of contaminant (33%) compared to untreated column mainly from the action of washing oil (Fig. 5).
3.12. Degradation kinetics of pyrene in the column soil
Table 2 shows the pyrene concentration in soil samples from pyrene and dilbit contaminated soil columns in time 0 and after 3 weeks (3W) and 6 weeks (6W) of incubation for column and flask tests. In the present study, around 73 and 55% of pyrene removal occurred after 3 weeks for pyrene and dilbit contaminated soils in the presence of Mix7-3PAHs and Mix8-D and pyrene contaminated soil; however, the rate of corresponding removal in column test decreased to around 50 and 30%, respectively. These pyrene removal rates for flask test were around 1.3 and 1.4-fold higher than the pyrene removal rates in the column test. A possible influencing factor to our knowledge was stirring strength so that on stirring modes for batch tests resulted in the stripping of the adsorbed or crusted contaminants.
Table 3 shows the comparison between batch test and column test biodegradation study in terms of kinetic constant, overall degradation and degradation efficiency rate. Zero-order reaction was obtained for untreated soil columns and columns treated by AlcanivoraxD. However, first order reaction was observed for soils treated with enzyme solutions. In the case of pyrene contaminated soil columns, the kinetic constant for column test showed a decrease of 22%, 27% and 33% for Mix7-3PAHs, Mix8-D and AlcanivoraxD, respectively. However, in the case of dilbit contaminated 58%, 33% and 39% decrease were observed for Mix7-3PAHs, Mix8-D, respectively. In untreated soil, pyrene removal efficiency is low due to the removal of pyrene in the liquid and free phases by washing. The low viscosity of the biosurfactant solution in this study indicates that the flow resistance does not increase, indicating that the solution is still following in the water channel without expanding the swept volume.
Table 2
Pyrene concentration in soil samples from pyrene/dilbit contaminated soil columns in time 0 and after 3 weeks (3W) and 6 weeks (6W) of incubation. Each sample was analyzed in triplicate.
Sample Nr
|
Description
|
Pyrene contaminated column test
|
Dilbit contaminated column test
|
Pyrene Concentration (mg/g)
|
Biodegradation %
|
Pyrene Concentration (mg/g)
|
Biodegradation %
|
1
|
Autoclaved soil column (3W)
|
2.2 ± 1 a
|
-
|
2.4 ± 2 a
|
-
|
2.9 ± 2 b
|
2.5 ± 4 b
|
2.1 ± 2 c
|
2.3 ± 3 c
|
2
|
Autoclaved soil column (8W)
|
2.5 ± 4 a
|
-
|
2.7 ± 1 a
|
-
|
2.8 ± 2 b
|
2.5 ± 3 b
|
2.2 ± 1 c
|
2.6 ± 2 c
|
3
|
Untreated soil column (time 0)
|
2.4 ± 1 a
|
-
|
2.5 ± 1 a
|
-
|
2.2 ± 4 b
|
2.3 ± 4 b
|
2.0 ± 3 c
|
2.2 ± 3 c
|
4
|
Untreated soil column (3W)
|
2.7 ± 4 a
|
12.5
|
2.4 ± 1 a
|
3.2
|
2.1 ± 3 b
|
2.3 ± 2 b
|
1.6 ± 0 c
|
2.0 ± 3 c
|
5
|
Untreated soil column (6W)
|
2.0 ± 2 a
|
25.9
|
2.3 ± 2 a
|
13.1
|
1.8 ± 3 b
|
2.2 ± 3 b
|
1.5 ± 4 c
|
1.9 ± 4 c
|
6
|
Treated soil column with Mix 7-3PAHs (3W)
|
1.5 ± 2 a
|
49.7
|
1.7 ± 2 a
|
27.5
|
1.3 ± 1 b
|
1.6 ± 1 b
|
0.9 ± 4 c
|
0.7 ± 4 c
|
7
|
Treated soil column with Mix 7-3PAHs (6W)
|
0.6 ± 1 a
|
84.1
|
1.2 ± 1 a
|
51.1
|
0.5 ± 2 b
|
0.9 ± 1 b
|
ND c
|
ND c
|
8
|
Treated soil column with Mix 8-D (3W)
|
1.9 ± 3 a
|
28.2
|
1.1 ± 1 a
|
36.4
|
2.0 ± 4 b
|
1.3 ± 4 b
|
1.4 ± 3 c
|
0.5 ± 3 c
|
9
|
Treated soil column with Mix 8-D (6W)
|
0.8 ± 2 a
|
76.3
|
0.7 ± 2 a
|
81.9
|
0.7 ± 2 b
|
ND b
|
0.2 ± 1 c
|
ND c
|
10
|
Treated soil column AlcanivoraxD (3W)
|
2.3 ± 6 a
|
19.2
|
1.9 ± 6 a
|
3.8
|
2.0 ± 5 b
|
1.7 ± 5 b
|
1.5 ± 3 c
|
1.5 ± 3 c
|
11
|
Treated soil column with AlcanivoraxD (6W)
|
1.2 ± 5 a
|
45.5
|
1.4 ± 1 a
|
47.5
|
1.1 ± 5 b
|
1.2 ± 5 b
|
0.9 ± 2 c
|
0.9 ± 2 c
|
Samples from a: head (depth of 1-2cm), b: middle *dept of ((depth of 7-8cm) and c: bottom (depth of 12-13cm) of soil column.
Table 3
Estimated parameters of kinetic models for batch and soil column tests
Sample
|
Degradation rate (mg/gsoil/week)
|
Removal efficiency after 8 weeks
|
Kinetics constant [order]
|
Degradation rate (mg/gsoil/week)
|
Removal efficiency after 8 weeks
|
Kinetics constant [order]
|
Column test
|
Soil test
|
Pyrene contaminated soil
|
Untreated
|
0.046
|
34
|
0.045 mg/g soil/week [0]
|
0.064
|
41
|
0.064 mg/g soil/week [0]
|
Mix 7-3PAHs
|
0.105
|
84
|
0.229 L/week [1]
|
0.205
|
96**
|
0.294 1/week [1]
|
Mix 8-D
|
0.095
|
77 *
|
0.179 L/week [1]
|
0.183
|
83
|
0.246 1/week [1]
|
AlcanivoraxD
|
0.069
|
56
|
0.073 mg/gsoil/week [0]
|
0.110
|
67
|
0.105 mg/g soil/week [0]
|
Dilbit contaminated soil
|
Untreated
|
0.016
|
13
|
0.021 mg/gsoil/week [0]
|
0.031
|
29
|
0.035 mg/g soil/week [0]
|
Mix 7-3PAHs
|
0.068
|
53
|
0.095 L/week [1]
|
0.189
|
71
|
0.230 1/week [1]
|
Mix 8-D
|
0.105
|
79 *
|
0.202 L/week [1]
|
0.208
|
95 **
|
0.320 1/week [1]
|
AlcanivoraxD
|
0.063
|
44
|
0.064 mg/g soil/week [0]
|
0.109
|
57
|
0.113 1/g soil/week [0]
|
*and ** not different significantly |
According to studies, hydrocarbon bioremediation in cold regions should be carried out during the summer months (20–60 days), when temperatures are generally higher (10–15°C). (Whelan et al. 2015) Psychrophiles have low metabolic activity and growth rates even with sufficient nutrients and oxygen. Temperature may not be a competitive advantage but developing enzymes that do not need aeration and can consume hydrocarbons in short periods rather than using living microorganisms could be. To simulate in-situ enzyme application, pyrene and dilbit contaminated soil was bioremediated in soil columns and flask tests by injecting enzyme cocktails from most promising consortia (Mix7-3PAHs and Mix8-D). Around 73, 56% (pyrene contaminated soils) and 36 and 44% (dilbit contaminated soils) was removed 3 weeks after injection with Mix7-3PAHs and Mix8-D in flask test; however, the rate of corresponding removal in column test decreased to around 50 30%, 27 and 36% for pyrene and dilbit contaminated soils, respectively.