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Article

How Soil Microbial Communities from Industrial and Natural Ecosystems Respond to Contamination by Polycyclic Aromatic Hydrocarbons

by
Enrica Picariello
1,*,
Daniela Baldantoni
2 and
Flavia De Nicola
1
1
Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Italy
2
Department of Chemistry and Biology “Adolfo Zambelli”, University of Salerno, 84084 Fisciano, Italy
*
Author to whom correspondence should be addressed.
Processes 2023, 11(1), 130; https://doi.org/10.3390/pr11010130
Submission received: 28 November 2022 / Revised: 20 December 2022 / Accepted: 29 December 2022 / Published: 1 January 2023
(This article belongs to the Special Issue Role of Microorganisms in Remediating Contaminated Soils)
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Abstract

Soil microbial community plays a major role in removal of polycyclic aromatic hydrocarbons (PAHs) from soil, and bioremediation potentially offers an attractive and economic approach to the clean-up of polluted areas. To evaluate the contribution of different microbial groups in soil PAH degradation, enzymatic activity and phospholipid fatty acids (PLFAs) were analysed in a mesocosm trial in three different soils (two natural and one industrial) artificially contaminated with 3- and 5-rings PAHs. The Metabolic Activity Index (MAI) was applied to investigate the microbial community stability, in terms of resistance and resilience. Gram+ and Gram- bacteria were the predominant microbial groups in all soil types. In the first stage of incubation, fungi were predominant in the industrial soil, followed by mycorrhizae and actinomycetes, indicating their stimulation after PAH addition. In the two natural soils, several groups were predominant: actinomycetes in one, fungi and mycorrhizae in the other, indicating a different response of the two natural soils to PAH contamination. Regarding MAI calculated on the enzymatic activities, one natural soil showed a microbial community neither resistant nor resilient in respect to the other and to the industrial soil. Our results highlight that the microbial community changes its composition and then physiological functions according to the land use as a result of PAH addition.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs) negatively affect soil microbial communities [1,2], with heavy consequences on the whole matter cycling (due for example to the alterations of nitrification and denitrification) [3], and on the ecological equilibria regulating the structure and function of ecosystems.
Notwithstanding PAHs are worldwide contaminants with toxic, mutagenic, genotoxic, and carcinogenic properties [4]; several microorganisms are able to degrade them through the synthesis of extracellular enzymes [5]. The organisms mainly involved in PAH degradation are fungi, in particular the ligninolytic or white-rot fungi, and non-ligninolytic fungi [6,7,8]. Several Gram+ bacteria have been hypothesized to be PAH degraders [9] and several Gram-bacteria (Pseudomonas and Sphingomonas) are clearly involved in PAH degradation [10,11]. A deep understanding of the response of the indigenous microbial community to PAH contamination is important to improve the application of nature-based solutions in land management [12]. In addition to human management and control, most rehabilitation projects are focused on artificial strategies, which are costly due to the external inputs of energy and money to which they depend [13]. Conversely, nature-based solutions exploit natural processes and use native flows of matter and energy [14], representing sustainable and successful strategies. This assumes particular importance in the restoration of degraded soils, above all in large areas, contributing to achieve the sustainable development goals of the United Nations as well as the land degradation neutrality challenge [15,16].
Recent evidence [17] points at the pivotal role of the ecological memory, defined as ‘the capacity of past states or experiences to influence present or future responses of a community’; the increase in frequency of a disturbance can thus create a memory of that disturbance, modifying the structure and organization of ecological interactions within the ecosystem. As a consequence, we assume that the microbial community of an industrial soil better responds to PAH contamination, in comparison to that of natural soils. Indeed, resilience to disturbance is shaped by ecological memory of past ecosystem states, transmitted as legacies of species adaptations. Changes in disturbance regimes that modify key legacies can trigger rapid reorganization into new ecosystem states [18]. The information legacy of the community allows an ecosystem to recover to a similar state when the system is perturbed. In this context, the soil microbial community structure is pivotal in maintaining the stability of soil functioning under environmental constraints [19].
In this frame, the study aims to evaluate the impact of PAH contamination on the structure and function of microbial community of soils differently affected by anthropogenic pressure (industrial and natural areas). The microbial community changes over time, measured by PLFA (phospholipid fatty acids), laccase and peroxidase activities, were monitored in soils spiked with a mix of PAHs. Moreover, the changes in the PLFA profiles and activity of soil microbial community were related to PAH degradation rates. In addition, the Metabolic Activity Index (MAI, [20]) was applied to investigate the stability, in terms of resistance and resilience, of the microbial community to PAH contamination in the three different soils.

2. Material and Methods

Soils (0–10 cm depth) from one industrial (I) area, classified as Anthrosol [21], and two natural (N1 and N2) areas, classified as Andosols [21], under a holm oak and a beech cover (SM1), respectively, were contaminated in a laboratory with a mixture of two PAHs (3- and 5-rings; 5 g L−1 for each compound), and successively used to fill mesocosms (three replicates for each soil). In order to evaluate the resilience of the microbial community and the degradation rate of the added PAHs, all mesocosms were incubated in a dark room under controlled conditions (25 ± 2 °C, RH 70%) for 1 year and irrigated at regular time intervals with distilled water. At the beginning (Tb), intermediate (Ti), and final (Tf) times of incubation, soil samples were collected from all mesocosms, sieved to 2 mm, and divided into several aliquots for chemical (PAH concentrations) and biological (laccase and peroxidase activities, PLFA) analyses. All analyses described below were carried out in triplicate for each mesocosm, with a total of 81 samples analysed (two factor—soil type and sampling time—x three levels—N1, N2, I for soil types, Tb, Ti, Tf for sampling time—x three laboratory replicas).
PAH concentrations were determined after soil extraction in dichloromethane [22] by GC-MS. Laccase activity was determined by the method of Floch et al. [23], and peroxidase activity by the method of Jackson et al. [24]. PLFAs were extracted using the methods described by Bååth et al. [25] and detected by GC-FID.
To investigate the microbial community response to PAH contamination, the Metabolic Activity Index (MAI) [20] was applied to the enzyme activities and the microbial groups obtained from PLFA analysis to evaluate the soil functional and structural stability, respectively.
With the PLFA obtained, we evaluated the fungal/bacterial biomass ratio (F/B) that is associated to carbon use efficiency [26], and Gram+/Gram- ratio (G+/G−) that is an indicator of carbon availability [27]. The F/B ratio was calculated by dividing fungal PLFAs by the sum of all bacterial PLFAs. The G+/G− ratio was calculated by dividing Gram+ PLFAs by Gram− PLFAs.
The differences in PLFA amounts (as specific group markers), laccase and peroxidase activities among areas and sampling times were evaluated through two-way multivariate analysis of variance (MANOVA). In order to evaluate the multivariate separation of the microbial groups defined by the sampling areas, the non-metric multidimensional scaling (NMDS), with the superimposition of the confidence ellipses (α = 0.05) and the temporal gradient, was applied. The significance of the differences in biological parameters and MAI among sampling areas and along the time were evaluated by two-way RM ANOVAs (analyses of variance), followed by Tukey post hoc tests (for α = 0.05). Before using an ANOVA, data were tested for normality and homoscedasticity through Shapiro and Bartlett tests, respectively. The statistical and graphical elaborations were performed using the “tidyverse”, “nlme”, “multcomp”, “emmeans”, and “vegan” packages in the R 4.1.2 programming environment [28] and SigmaPlot 14.0 software (Systat Software, Inc., Germany).

3. Results

Two-way RM ANOVAs showed significant differences in enzymatic activities among soil types (Table 1), notwithstanding the similar temporal dynamics (Figure 1), with an increase of both laccase and peroxidase at Ti, in respect to Tb, in all the soils. While laccase activity decreased at the end of the incubation (Te) in N1 and N2 soils, it remained constant in I soil. At the end of the incubation, laccase activity tended to reach the same value in all the soils, with a significant difference only between N1 and I (Table 1). Peroxidase activity showed a different trend: it increased in natural soils at Ti and then decreased at Te, while it decreased in I soil at Ti and increased at Te.
The MANOVA highlighted significant differences among soils from different sampling areas (F = 165, p < 0.001) and sampling times (F = 23, p < 0.001), as well as for their interactions (F = 17, p < 0.001). The NMDS, with the superimposition of confidence ellipses for the sampling areas and the temporal gradient (Figure 2), showed a clear separation of the three soils, with the industrial one mainly affected by the bacteria group. The two natural soils, near each other, also differed in microbial communities, being those of N1 richest in actinomycetes and mycorrhizae. In terms of total microbial biomass, natural soils registered at the beginning of the incubation higher values in respect to the industrial soil, with means of about 97 mol% in N1, 148 mol% in N2, and 59 mol% in I soil (Table 2). The microbial biomass decreased with time in all the soils, mainly in relation to the decrease in actinomycetes and mycorrhizae groups observed in Figure 2.
Specifically, the structure of microbial community differed among soil types (Table 2) and over time. Gram+ and Gram− bacteria were the predominant microbial groups in all the soil types during the incubation. In the first stage of incubation (Tb), fungi were predominant in I soil, followed by mycorrhizae and actinomycetes. In I soil, at Ti mycorrhizae > actinomycetes > fungi and at Te the abundances varied in the order actinomycetes > mycorrhizae > fungi. In N1, these microbial groups followed the following order: actinomycetes > fungi > mycorrhizae at Tb, actinomycetes > mycorrhizae > fungi at Ti, mycorrhizae > actinomycetes > fungi at Te. In N2, mycorrhizae and fungi were predominant at Tb followed by actinomycetes; at Ti the order was actinomycetes > fungi = mycorrhizae and at Te actinomycetes = mycorrhizae > fungi.
MAI based on functional stability (Table 3) showed significant (p < 0.001) differences both among soil types and with time, as well as for soil type x sampling time interactions. In N2 and I soils, the microbial community was functionally resistant to contamination, since the index was equal to or even greater than 1, in particular, 1.14 for N1 and 0.98 for I. Conversely, in N1 the microbial community never recovered from contamination during the year of incubation, with MAI equal to 0.54 at Tb, 0.55 at Ti, and 0.34 at Tf. Regarding MAI based on structural stability (Table 3), no significant (for α = 0.05) differences were found among soil types and all the soil systems showed a structural resistance to PAH contamination, with MAI values equal to 1.25 in N1 and I, and 1.26 in N2.
Regarding PAH degradation (Table 4), 3-rings PAH showed almost complete degradation in all the soils at Ti, whereas 5-rings PAH showed at least ~50% of the residual content at the end of incubation.
F/B ratios (Table 2) showed significant (p < 0.01) differences both among soil types and over time, as well as for soil type x sampling time interactions (Table 5), while significant (p < 0.05) differences were found for G+/G− ratios (Table 2) only over time.

4. Discussion

Pollutants, such as polycyclic aromatic hydrocarbons, can lead to shifts in microbial communities [29,30], altering their structure and functioning. Soil PAH contamination can favour selected microbial groups, whereas others become less prevalent under this environmental stress [31,32]. The microbial groups that thrive under PAH exposure vary depending on the type of soil and environmental conditions [33,34]. In our study, the structure of microbial community differed among soil types (industrial soil and natural soils) over 1 year from PAH contamination. Gram+ and Gram− bacteria were the predominant microbial groups in all the soil types over time. In the first stage of incubation, when the PAH concentration was higher, fungi were predominant in industrial soil, followed by mycorrhizae and actinomycetes, indicating their quick stimulation after PAH addition. These soil microorganisms can use PAHs as source of matter and energy [35]. The highest F/B ratio after PAH contamination confirmed that the most active group of organisms in degrading the new added pollutants was fungi. The F/B ratio can be satisfactorily used to assess the relative dominance of fungi over bacteria in the different uses of organic matter [36]. In N1, the predominant microbial group was represented by actinomycetes, and in N2, fungi and mycorrhizae were predominant, indicating a different response of the two natural soils to PAH contamination.
The microbial biomass of natural soils was higher than that of the industrial one. Several studies also have shown that soil bacterial diversity in anthropogenic areas is significantly lower than that in natural areas [37]. On wider spatial scales, Ramirez et al. [38] found similar bacterial diversity between a soil from Central Park in New York City and the global data set (including arctic, tropical, and desert soils).
In addition, it has been suggested that preexposure of soils to concentrations greater than background levels is needed for microbial adaptation and subsequent quick degradation of contaminants [39]. The microbial diversity in natural soils, far away from the anthropogenic activities and disturbance, is expected to be higher than in contaminated soils, where it is usually suppressed by the presence of toxic organic and inorganic pollutants. Besides, microbial community in natural soils is more dynamic and sensitive to contamination than that of contaminated soils due to its low historical exposure and less tolerance to toxic pollutants [40]. Accordingly, in natural soils, the biomass at the end of the experimentation decreased by almost two times if compared to the beginning of the incubation, whereas in the industrial soil, it decreased only by 1.2 times.
The slower and lower degradation rate of 5-rings PAH in respect to 3-rings PAH, confirming that reported in Baldantoni et al. [41], can be attributable to a limited number of bacteria that use PAHs with five or more aromatic rings as an energetic source. Since PAH solubility decreases with increasing molecular weight [42,43], the high retention of these compounds by the soil solid phase results in a very low availability.
In I and N1 soils, in respect to N2 soil, the degradation proceeds with a lesser extent for the 5-rings PAH, while for 3-rings PAH, the degradation proceeds with a lesser extent in N2 and I in respect to N1. This effect is mainly related to the sequestration of organic compounds in soil rich in organic matter [44], according to the higher content and stability of organic matter found in natural soils [45] in respect to the industrial soils (SM1), that can protect PAHs from microbial degradation. It is likely that the microbial community of industrial soil (SM1) adapted to the contaminated soil over the years. These particular environmental conditions potentially enabled the microbial community to adapt, use pollutant as carbon source, or tolerate pollutant by detoxification mechanisms [46,47]. The microbial biomass of the natural soils is greater than the industrial one, and the presence of plant-soil-microorganism interactions (present to a lesser extent in the industrial area), that increase the microbial community with the plant microbiome, can contribute to the different PAH degradation rates observed among soil systems.
Even though in I soil, in respect to N1 and N2 soils, the degradation seemed to proceed with a minor extent for the two PAHs, laccase and peroxidase activities never decreased over time, which is likely thanks to the microbial community already being under stress conditions in this type of environment. Regarding MAI calculated on the enzymatic activities and giving information on the functional stability of microbial communities, N1 showed a microbial community neither resistant or resilient in respect to N2 and I soils. N2 soil had higher MAI values compared to the other two soils, highlighting that the microbial community not only changes its composition, but also its physiological functions, making it highly resistant to PAH stresses. Since functional redundancy may allow ecosystems to withstand the effects of disturbance [48], we can suppose a high functional redundancy in N2 and I soils, but not in N1 soil. In N1, the function, but not the structure, of the community was negatively affected by the addition of PAHs and was no longer able to recover. The different soil organic matter contents in the two natural soils [45] can explain the different PAH toxicity on the soil microbial community.
These results pointed out that the added PAHs can be degraded by microorganisms in the three soils, thus, the edaphic community has a key role in the biodegradation of these polluting compounds and in bioremediation of contaminated soils [49]. Since bioremediation potentially offers an attractive and economic approach to the clean-up of polluted sites [50], it is of fundamental importance to establish the composition of the microbial community to develop a proper bioremediation strategy, also in relation to the land use. In fact, ecological memory is central to how ecosystems respond to disturbance and disturbance characteristics that support or maintain this memory enhance ecological resilience [18].
Successful bioremediation strategies for environments contaminated by recalcitrant pollutants require in-depth knowledge of microbial processes involved in pollutant degradation and of the microorganism responses to soil pollutant contamination. For this reason, further research in this direction is needed.

5. Conclusions

To apply successful bioremediation strategies on contaminated soils, it is of fundamental importance to establish the composition of their microbial groups. In our study, Gram+ and Gram− bacteria were the predominant microbial groups in all the soil types (natural and industrial). In the first stage of incubation, fungi become predominant in industrial soil, followed by mycorrhizae and actinomycetes, indicating their quick involvement in PAH degradation. In the two natural soils, diverse groups were predominant: actinomycetes in one, fungi and mycorrhizae in the other, indicating a different response of the two natural soils to PAH contamination. Accordingly, natural soils showed a different functional stability of the microbial community: one natural soil (N1) showed a microbial community neither resistant nor resilient in respect to the other (N2) and to the industrial soil (I). Despite the different involvement of microbial groups among the three soils for the two PAHs (3- and 5-rings), the degradation of PAHs at the end of incubation highlighted the importance of the soil microbial community to degrade these compounds. Our results highlight that the microbial community changes its composition and then its physiological functions according to the land use as a result of the PAH contamination event.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr11010130/s1, Table S1: Physico-chemical properties (mean values ± s.e.) of the three soils.

Author Contributions

Conceptualization, D.B. and F.D.N.; methodology, D.B. and F.D.N. formal analysis, E.P.; investigation, E.P.; resources, FDN.; data curation, E.P.; writing original draft preparation, E.P.; writing review and editing, D.B. and F.D.N.; supervision, F.D.N. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by University of Sannio (FONDO DI RICERCA D’ATENEO 2021).

Data Availability Statement

Data is contained within the article or Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Processes 11 00130 g001aProcesses 11 00130 g001b
Figure 1. Temporal dynamics of laccase and peroxidase activities in natural (N1: yellow line, N2: blue line) and industrial (I: light blue line) soils during incubation. Vertical bars represent standard errors of the means (n = 3).
Figure 1. Temporal dynamics of laccase and peroxidase activities in natural (N1: yellow line, N2: blue line) and industrial (I: light blue line) soils during incubation. Vertical bars represent standard errors of the means (n = 3).
Processes 11 00130 g001aProcesses 11 00130 g001b
Processes 11 00130 g002
Figure 2. Non-metric multidimensional scaling (NMDS) biplot (stress value: 0.0177), based on PLFA groups, with the superimposition of the confidence ellipses (for α = 0.05) for the soil types (N1: yellow circles and line; N2: blue triangles and line) and industrial (I: light blue squares and line), as well as of the temporal gradient (grey lines).
Figure 2. Non-metric multidimensional scaling (NMDS) biplot (stress value: 0.0177), based on PLFA groups, with the superimposition of the confidence ellipses (for α = 0.05) for the soil types (N1: yellow circles and line; N2: blue triangles and line) and industrial (I: light blue squares and line), as well as of the temporal gradient (grey lines).
Processes 11 00130 g002
Table 1. q values of Tukey HSD tests for soil types x time interactions in pair comparisons according to laccase and peroxidase activities. Asterisks indicate significant differences among soil sampling areas (*** p < 0.001, ** p < 0.01, * p < 0.05).
Table 1. q values of Tukey HSD tests for soil types x time interactions in pair comparisons according to laccase and peroxidase activities. Asterisks indicate significant differences among soil sampling areas (*** p < 0.001, ** p < 0.01, * p < 0.05).
Comparisons for FactorLACCASEPEROXIDASE
ComparisonqComparisonq
within TbN2 vs. I 9.94 ***N1 vs. I85.4 ***
N1 vs. I6.16 **N1 vs. N216.4 ***
N2 vs. I69.0 ***
within TiN2 vs. I9.88 ***N2 vs. I90.9 ***
N2 vs. N14.51 **N1 vs. I87.7 ***
N1 vs. I5.38 **
within TfI vs. N13.87 *N1 vs. I78.1 ***
N2 vs. I77.1 ***
Table 2. Microbial groups in each soil system (N1, N2, I) at each sampling time (Tb, Ti, Tf), expressed as mean values ± standard errors in respect to the total microbial biomass (mol%).
Table 2. Microbial groups in each soil system (N1, N2, I) at each sampling time (Tb, Ti, Tf), expressed as mean values ± standard errors in respect to the total microbial biomass (mol%).
TimeSoilBacteriaFungiMycorrhizaeActinomycetesGram+Gram−TotalF/BG+/G−
N149 ± 0.603.1 ± 0.112.8 ± 0.143.5 ± 0.1023 ± 1.018 ± 1.497 ± 250.03 ± 0.011.3 ± 0.05
TbN242 ± 0.142.8 ± 0.102.8 ± 0.0432.5 ± 0.1824 ± 0.7126 ± 0.73148 ± 100.02 ± 0.010.91 ± 0.01
I29 ± 1.54.2 ± 0.834.0 ± 0.113.8 ± 0.2414 ± 0.8523 ± 0.7559 ± 4.00.07 ± 0.020.63 ± 0.05
N151± 1.42.1 ± 0.312.9 ± 0.133.1 ± 0.1019 ± 0.5522 ± 1.191 ± 280.02 ± 0.010.85 ± 0.01
TiN245 ± 0.212.1 ± 0.142.1 ± 0.0202.5 ± 0.1022 ± 0.1327 ± 0.20151 ± 130.01 ± 0.010.82 ± 0.01
I32 ± 1.02.7 ± 1.03.5 ± 0.263.3 ± 0.5517 ± 2.120 ± 1.161 ± 5.00.04 ± 0.010.86 ± 0.14
N149± 0.412.4 ± 0.133.7 ± 0.102.8± 0.3122 ± 0.7121 ± 0.4261 ± 2.00.04 ± 0.011.04 ± 0.01
TfN245 ± 0.302.1 ± 0.302.2 ± 0.102.2± 0.3421 ± 1.027 ± 1.284 ± 5.00.03 ± 0.010.78 ± 0.02
I28± 0.771.5 ± 0.132.5 ± 0.0423.6 ± 0.1415 ± 0.3418 ± 0.5346 ± 2.00.03 ± 0.010.83 ± 0.02
Table 3. MAI values based on all soil microbial groups and enzyme activities for each soil (N1, N2, I) at each sampling time (Tb, Ti, Tf). Different letters, if present, indicate significant differences (for α = 0.05) among soil types at each sampling time.
Table 3. MAI values based on all soil microbial groups and enzyme activities for each soil (N1, N2, I) at each sampling time (Tb, Ti, Tf). Different letters, if present, indicate significant differences (for α = 0.05) among soil types at each sampling time.
Functional StabilityN1N2I
Tb0.54 b1.14 a0.98 a
Ti0.55 c3.25 a1.13 b
Tf0.34 c1.58 a 1.03 b
Structural stability
Tb1.251.261.25
Ti1.181.181.24
Tf1.221.221.13
Table 4. Mean degradation (% of the Tb initial values) of 3- and 5-rings PAHs in each soil system (N1, N2, I) and at each sampling time (Ti, Tf).
Table 4. Mean degradation (% of the Tb initial values) of 3- and 5-rings PAHs in each soil system (N1, N2, I) and at each sampling time (Ti, Tf).
N1N2I
3-rings
Ti0.985.013
Tf0.754.44.3
5-rings
Ti329587
Tf334549
Table 5. q values of Tukey HSD tests for soil types x time interactions in pair comparisons according to F/B ratios. Asterisks indicate significant differences among soil sampling areas (*** p < 0.001, * p < 0.05).
Table 5. q values of Tukey HSD tests for soil types x time interactions in pair comparisons according to F/B ratios. Asterisks indicate significant differences among soil sampling areas (*** p < 0.001, * p < 0.05).
Comparisons for FactorF/B
Comparisonq
within TbN2 vs. I13.2 ***
N1 vs. I10.4 ***
within TiN2 vs. I4.29 *
N1 vs. I4.51 *
within Tf--
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Picariello, E.; Baldantoni, D.; De Nicola, F. How Soil Microbial Communities from Industrial and Natural Ecosystems Respond to Contamination by Polycyclic Aromatic Hydrocarbons. Processes 2023, 11, 130. https://doi.org/10.3390/pr11010130

AMA Style

Picariello E, Baldantoni D, De Nicola F. How Soil Microbial Communities from Industrial and Natural Ecosystems Respond to Contamination by Polycyclic Aromatic Hydrocarbons. Processes. 2023; 11(1):130. https://doi.org/10.3390/pr11010130

Chicago/Turabian Style

Picariello, Enrica, Daniela Baldantoni, and Flavia De Nicola. 2023. "How Soil Microbial Communities from Industrial and Natural Ecosystems Respond to Contamination by Polycyclic Aromatic Hydrocarbons" Processes 11, no. 1: 130. https://doi.org/10.3390/pr11010130

APA Style

Picariello, E., Baldantoni, D., & De Nicola, F. (2023). How Soil Microbial Communities from Industrial and Natural Ecosystems Respond to Contamination by Polycyclic Aromatic Hydrocarbons. Processes, 11(1), 130. https://doi.org/10.3390/pr11010130

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