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Article

Polycyclic Aromatic Hydrocarbon Pollution Stress Impairs Soil Enzyme Activity and Microbial Community

by
Yuancheng Wang
1,2,
Donglei Wu
2,
Junxiang Liu
3,* and
Haolong Xu
1,*
1
Zhejiang Key Laboratory of Ecological Environmental Damage Control and Value Transformation, Ecological and Environmental Science and Research Institute of Zhejiang Province, Hangzhou 310007, China
2
College of Environmental and Resource Science, Zhejiang University, Hangzhou 310058, China
3
Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Authors to whom correspondence should be addressed.
Microorganisms 2026, 14(2), 494; https://doi.org/10.3390/microorganisms14020494
Submission received: 9 January 2026 / Revised: 2 February 2026 / Accepted: 16 February 2026 / Published: 18 February 2026
(This article belongs to the Section Environmental Microbiology)
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Abstract

Polycyclic aromatic hydrocarbons (PAHs) are widely prevalent harmful organic pollutants. Enzymatic activities (such as those of dehydrogenases, catalase, protease and urease), as well as the microbial community structure and assembly (through 16S and ITS amplicon sequencing), were evaluated 90 days after PAH contamination and compared to those in normal soils. Microbial activity, as indicated by soil urease, catalase, and protease activities, was inhibited under PAH stress. Furthermore, PAH stress exerted significant impacts on the soil microbial community structure. Notably, PAH stress reduced soil bacterial and fungal biomass and inhibited the abundance of microbial taxa involved in soil carbon and nitrogen cycling (e.g., Marmoricola, Pedobacter, and Streptomyces), along with the majority of predicted responsive metabolic functions, particularly those related to amino acid and carbohydrate metabolism. PAH stress enriched PAH-degrading microorganisms, including Pseudomonas, Mycobacterium, Bacillus, Cycloclasticus, and Flavobacterium. The niche breadth of bacterial and fungal communities decreased significantly under PAH stress (51.5 and 14.1, respectively) compared to that in normal soil (63.7 and 22.3), which was further supported by Beta Nearest Taxon Index and co-occurrence network analysis. PAH stress increased the contribution of heterogeneous selection to soil microbial assembly (100%) compared to that in normal soil (80%). Thus, the majority of microbial community responses to PAH stress were adversely affected. These results suggest that PAH contamination may profoundly affect the soil quality by restricting the survival space of bacteria and fungi.

1. Introduction

Polycyclic aromatic hydrocarbons (PAHs; e.g., Naphthalene (Nap), Anthracene (Ant), Phenanthrene (Phe), Fluorene (Flu), Pyrene (Pyr), Benzo[a]pyrene (B[a]p), Benzo[b]fluoranthene (B[b]p), Dibenzo[a,h]anthracene (DBA)), as persistent organic pollutants, pose a serious threat to soil ecological services. PAH contamination causes a reduction in soil quality [1] and adverse effects on ecology and human health, thereby limiting global soil utilization [2]. The effects of contamination on the soil ecosystem manifest as multifaceted changes, encompassing physicochemical properties, microbial communities, and ecological functions [3,4]. Previous research has primarily focused on the remediation of environmental elements through advanced oxidation technology, microbial remediation technology, and phytoremediation technology for the removal of PAHs [2,5]. The focus of remediation has expanded to enhance the overall health and functionality of the soil ecosystem [4,6,7]. Pollution leads to a decline in soil ecological functions. Therefore, clarifying the damaging effects of PAHs on soil environmental elements and ecological functions is of great significance for the effective remediation of PAH-contaminated soil.
Microorganisms, which are crucial for maintaining ecological functions, serve as an important indicator of soil quality [8]. In PAH-contaminated soil, microbial communities are subjected to toxic stress caused by freshly introduced PAHs, which inhibits soil amino acid metabolism and carbohydrate metabolism-related functions and reduces the diversity and richness of soil microorganisms [9]. The diversity of microorganisms and the multifunctionality of microbial communities are critical for providing stable ecological functions, as they are key drivers of microbial resistance and resilience [10] and are also important constraints on soil health [11]. Therefore, a thorough understanding of soil microbial community structure and microbial function is crucial in assessing the response of soil quality to PAH stress. Previous studies have shown that PAHs and heavy metals (HMs) can cause significant stress to microbial communities in healthy soils. For example, Cao et al. [12] found that artificial contamination with Phe and Cadmium led to a reduction in soil microbial numbers. Ling et al. [13] observed a significant decrease in microbial diversity in seagrass sediments exposed to severe PAH contamination after 7 days, with incubation time playing an important role in the shift in microbial communities. It has been reported that PAHs and HMs have complex synergistic effects in soil, which can alter the migration and bioavailability of PAHs by affecting the adsorption and desorption behavior of pollutants in soil [14] and change the transformation of PAHs by influencing their redox reactions [15]. HMs can inhibit PAH-degrading microorganisms and exhibit synergistic effects with PAHs at the biological level, thereby enhancing biological toxicity and altering the response patterns of microorganisms [16]. However, the long-term response of microorganisms to the introduction of fresh PAHs remains unknown.
It has been reported that soil microorganisms show significant transcriptional responses to newly introduced Phe, with increased transcription of genes involved in aromatic metabolism, stress response, and detoxification, and decreased transcription of genes related to carbohydrate, DNA metabolism, and phosphorus metabolism in microorganisms exposed to Phe [17]. Soil metabolomics analysis showed reduced levels of melibiose, isomaltose and p-anisic acid and increased levels of cholestan-3beta-ol, 3-hydroxybutyric acid, and 2-ketoadipate under PAH stress [18]. Various enzymes secreted by microorganisms play a crucial role in responding to PAH stress. Among them, hydrogenases initially cleave PAH intermediates to form catechol derivatives, which are subsequently metabolized through the tricarboxylic acid cycle [19]. The structural reorganization of soil microbial communities can signal corresponding changes in their functional roles and metabolic processes within terrestrial ecosystems [20]. However, the collective response of soil microbial systems, including bacteria and fungi, to freshly introduced PAH stress—encompassing changes in microbial community structure, function, and assembly processes—has rarely been reported.
This study’s objective was to provide a detailed analysis of the prolonged physiological and ecological responses of uncontaminated soil to the abrupt introduction of PAH pollutants. First, the impact of PAH stress on soil quality was assessed by measuring key soil enzyme activities, which play a critical role in microbial metabolic processes and nutrient dynamics. Subsequently, alterations in the structural composition and functional dynamics of bacterial and fungal communities were analyzed to elucidate the impact on soil microbial ecosystems under PAH-induced stress. Finally, the response of soil microorganisms to fresh PAH stress in soil was revealed through microbial community assembly processes and interactions between microbial communities. The following hypotheses were proposed: (1) PAH contamination may affect soil enzyme activity and the expression of microbial function. (2) PAH contamination may influence soil microbial assembly processes and interactions by altering selective pressure. The findings of this study elucidate the detrimental effects of PAHs on soil environmental factors and ecological functions, offering reliable and valuable insights for addressing pollution and restoring contaminated ecosystems.

2. Materials and Methods

2.1. Pot Experiment

The properties of clay loam soil are detailed in Table 1. PAH-contaminated soil containing Phe, Pyr, and B[a]p was prepared. The PAH-stressed soils containing Phe, Pyr, and B[a]p were prepared as follows. First, 0.60 g of Phe, 0.60 g of Pyr, and 0.30 g of B[a]p were dissolved in 500 mL of acetone and added to 0.30 kg of soil. Subsequently, the soil was placed in a fume cupboard until the acetone evaporated and was then mixed with another 0.30 kg of soil. Finally, a total of 60 kg of PAH-polluted soil was prepared by stepwise mixing the polluted soil into the remaining soil. A pot experiment was conducted in a greenhouse with a temperature of 25–35 °C. Each pot was filled with 0.7 kg of soil. The experiment utilized a total of 36 pots (PAH treatments × 18 replications). The following two treatments were used: soil without Phe, Pyr, and B[a]p; soil with Phe, Pyr, and B[a]p. The field capacity of the soil was maintained at 60% by regularly weighing the pots and adding distilled water. After 30, 60 and 90 days of incubation, the soil was stored in liquid nitrogen or air-dried for further analysis.

2.2. Determination of Soil Enzyme Activity

To facilitate the analysis, we combined soil samples from two pots to form a mixed sample. Soil polyphenol oxidase (PPO) activity (Cat: BC0115), soil dehydrogenase (DHA) activity (Cat: BC0395), soil laccase activity (Cat: BC1965), soil catalase (CAT) activity (Cat: BC0105), soil protease (PR) activity (Cat: BC0880) and soil urease activity (Cat: BC0125) were measured using assay kits (Solarbio, Beijing, China) following the manufacturer’s protocol (n = 3 biological replicates) [21,22]. The activities of CAT, laccase, PPO, DHA, urease, and PR were measured using a UV spectrophotometer at 240 nm, 420 nm, 430 nm, 485 nm, 630 nm, and 680 nm, respectively.

2.3. DNA Extraction, Illumina Sequencing and qPCR

The DNA extraction and Illumina sequencing were carried out with reference to previous reports [23]. Soil DNA was extracted from 0.6 g of each soil sample using the E.Z.N.A. Soil DNA Kit (Omega Biotek, Norcross, GA, USA) according to the manufacturer’s protocol. 16S rRNA and ITS genes of distinct regions (16S V3–V4, ITS1) were amplified using specific primers with the barcode. The primer pairs for the 16s and ITs qPCR genes are shown in Table 2. The detailed PCR amplification and sequencing procedures are shown in Texts S1 and S2. Sequences with ≥97% similarity were assigned to the same operational taxonomic unit (OTU). The standard curves of the 16S and ITS qPCR genes are shown in the Supporting Information (Figure S1).

2.4. Statistical Analysis

The normality of the datasets was assessed using SPSS V.20.0 software. Variables were analyzed through one-way ANOVA or independent samples T-tests, utilizing the IBM SPSS Statistics package. The co-occurrence network and PICRUSt (KEGG, http://www.kegg.jp/, accessed on 10 April 2025) were evaluated using an adapted approach [23]. The linear discriminant analysis (LDA) effect size (LEfSe) was employed to identify significant microbial responders. The Nearest Taxon Index (NTI) and Beta Nearest Taxon Index (βNTI) were computed using a null model with 999 randomizations, and deterministic processes were categorized into two ecological processes based on βNTI values. Stochastic processes were considered dominant when |βNTI| < 2. The co-occurrence network was generated to reveal the interactions among soil microbial taxa under PAH stress. The interactive platform Gephi 9.2 was used to visualize the network, and the igraph R package 4.3.1 was used to describe the topology of the network, including the numbers of nodes and edges, levels of clustering, modularity, and graph densities. The detailed network construction is shown in Text S3.

3. Results

3.1. PAHs and Soil Enzyme Activity

The soil PPO activity increased over time without the PAH treatment (Table 3). Compared with the uncontaminated soil, PAH-exposed soil showed significantly decreased PPO activity for 90 days. Compared with the uncontaminated soil, PAH-contaminated soil showed significantly increased PPO activity for 30 days. The soil laccase activity remained unchanged among the treatments after different time periods. Compared with the uncontaminated soil, PAH-exposed soil showed significantly decreased DHA activity for 30 days. The soil DHA activity remained unchanged under the PAH treatment for 60 and 90 days. Under PAH stress, the soil DHA activity for 60 and 90 days increased significantly compared with that for 30 days. Compared with the uncontaminated soil, PAH-contaminated soil showed significantly decreased soil urease, PR and CAT activity for 30, 60 and 90 days. With PAHs, the soil urease activity remained unchanged. The soil urease and PR activities remained unchanged under PAH conditions for 30, 60 and 90 days. The soil CAT and PR activities remained unaltered under uncontaminated conditions for 30, 60 and 90 days.

3.2. Microbial Community Analysis

After the soil was treated for 90 days, the recognized species were visualized in a PCA score plot (Figure 1c,d). The soil bacterial and fungal communities were clearly different between the two treatments. PAH stress caused the formation of a bacterial and fungal community structure that was different from that in the normal soil.
LEfSe analysis was conducted to identify statistically significant biomarkers in soil samples and to determine specialized communities in the soil. As shown in Figure 2a, at the genus level, a total of 83 bacteria (LDA > 2.5, p < 0.05) and 52 fungi (LDA > 2.0, p < 0.05) exhibited statistically significant differences. Among these, under non-PAH stress, 35 biomarkers predominantly belonged to Proteobacteria: Massilia, Skermanella, Brevundimonas, Methylotenera, Nibrib; Actinobacteria: Nocardioides, Streptomyces, Kribbella, Marmoricola, Iamia, Saccharopolyspora, Aeromicrobium, Actinomyces, Promicromonospora, Agromyces, Nocardia, Aurantisolimonas, Pseudonocardia, Miconospora, Amycolatopsis, Williamsia, Roseimicrobium, Cellulomonas, Streptosporangium; Bacteroidetes: Adhaeribacter, Flavobacterium, Ferruginibacter, Segetibacter; Firmicutes: Paenisporosarcina, Paenacillus, Tumebacillus; and Cyanobacteria: Hassallia. Under PAH stress, 48 biomarkers predominantly belonged to Proteobacteria: MND1, Dongia, Steroidobacter, Ramlibacter, Pedomicrobium, Polycyclovorans, Geoalibacter, Noviherbaspirillum, Bdellovibrio, Rhodoplanes, Caenimonas, Citrifermentans, Azoarcus, Phenylobacterium, Ahniella, Pajaroellobacter, Stenotrophobacter, Sumerlaea, Phaselicystis, Fontic; Myxococcota: Haliangium, Anaeromyxobacter, Sorangium; Acidobacteria: Bryobacter, Acidibacter, Candidatus Solibacter; and Bacteroidetes: Flavisolibacter, Chryseolinea, Sphosinicella. The results indicate significant differences in the dominant microbial communities among the soil samples. These changes may reveal the response of soil microbial communities to PAH stress.
Shifts in the microbial community caused changes in soil microbial function, and the functional genes were predicted by PICRUSt. The predicted functional genes were predominantly enriched in seven major KEGG pathway categories (Figure S2), with metabolic pathways accounting for the highest proportion (52%). Functional genes involved in the nutritional patterns of saprotrophs showed a significant increase after PAH exposure (p < 0.05) (Figure 2c). Genes involved in the metabolism of amino acids, carbohydrates and lipids, which were predicted to be abundant, were significantly suppressed upon PAH stress (p < 0.05) (Figure 2d). Soil bacterial and fungal biomass was reduced under PAH stress as compared to non-polluted conditions (p < 0.05) (Figure 2e).
Niche breadth functions as a metric for forecasting microbial adaptability to environmental changes by characterizing the range of resources usable by the microorganism [24,25]. An investigation was conducted to assess the niche breadth variations in bacterial and fungal assemblages exposed to PAH contamination (Figure 3a).
Irrespective of PAH exposure, the NTI values of both soil bacterial and fungal communities were positive (Figure 3b,c), indicating that their phylogenetic structures exhibited phylogenetic clustering. This suggests that habitat filtering plays a dominant role in shaping these microbial communities. According to the assertions of Figure 3d, the majority of bacterial βNTI values in the soil exceeded 2, regardless of PAH exposure, indicating that deterministic processes predominantly governed the bacterial community structure in both environments.
To analyze community dynamics caused by interactions between soil microorganisms, co-occurrence networks of microorganisms in the soil were constructed and compared based on random matrix theory (Figure 4). Figure 4e shows the topological characteristics of the network under different treatments. The numbers of nodes and edges, levels of clustering, graph densities and modularity index in the network for normal soil were higher than those for soil contaminated by PAHs. The average path length in the microbial co-occurrence network for normal soil was lower than that for PAH-contaminated soil.

4. Discussion

4.1. The Effects of PAHs on Soil Enzyme Activity

In this study, a 90-day experiment was conducted to elucidate the changes in soil microorganisms under PAH stress. Kumar et al. found that PAHs consisting of a low number of benzene rings (less than five), such as Phe and Pyr, cause acute toxicity, and PAHs consisting of a high number of benzene rings (not less than five), such as B[a]p, have stronger carcinogenic properties [26]. This section focuses on the discussion of Phe, Pyr, and B[a]p because of their ubiquitous distribution and severity in pollution [27].
Soil microbial communities are major drivers of the cycling of soil nutrients that sustain soil fertility and health. Enzyme activity is considered an indicator to evaluate the impact of different treatments on soil biological quality [28]. Studies have shown that abiotic stress has an inhibitory effect on soil enzyme activities in loam soil [29]. In this study, the activities of urease, PR, PPO, and CAT were significantly reduced in PAH-contaminated soils compared to uncontaminated soils. PAH contamination significantly increased the soil PPO activity for 30 days. This suggests that the response of soil to PAH contamination may be to promote PAH degradation by enhancing PPO activity [30]. Dehydrogenase is associated with the degradation of organic compounds in soil. Urease and PR activities are important indicators for evaluating soil quality. A decrease in soil urease and PR activities suggests that PAHs inhibit soil microbial activity. This reduction in enzymatic activity indicates a weakening of soil organic matter decomposition capacity and a decrease in nutrient cycling efficiency, which will ultimately lead to a decline in soil fertility over time. As a diagnostic indicator of soil health in contaminated environments, CAT serves as a crucial parameter for assessing the impact of pollutant stress on soil microbial activity [31]. Therefore, the microbial structure and functions of soil microbial communities may be altered after the introduction of PAHs.

4.2. Microbial Community Structure and Functions

Alterations in microbial community structure reflect ecological functions and signal changes in soil quality due to contaminants. Among the 35 biomarkers identified by the LEfSe in normal soil, some have been associated with soil carbon and nitrogen cycling and with organic matter degradation and metabolism. For example, Marmoricola and Kribbella have been shown to participate in soil nitrogen cycling [32,33], and Paenibacillus and Micromonospora have been used as soil amendment agents with potential for nitrogen fixation and bioremediation [34,35]. Pedobacter, Cellulomonas, Promicromonospora, and Nocardioides can act as organic-matter-degrading taxa, promoting carbon mobilization in early succession stages and participating in soil carbon cycling, thereby improving carbohydrate metabolism [33,34,36,37]. Streptomyces, as a microbial strain combined with nutrient application, significantly enhances soil enzyme activity and improves soil quality [38]. Brevundimonas is a microbial genus capable of promoting nitrogen and phosphorus turnover, thereby improving soil health [39,40]. Among the 48 biomarkers identified by the LEfSe in PAH-contaminated soil, several have been associated with soil nitrogen cycling and PAH degradation and metabolism. For example, Pseudomonas, Mycobacterium, Bacillus, Cycloclasticus, and Flavobacterium are related to PAH degradation [41,42,43,44,45]. It has been reported that MND1, Ellin6067, and Azoarcus are associated with soil nitrogen cycling [46,47,48]. Chiodi et al. [49] found that Candidatus Solibacter is a characteristic biomarker of contaminated soil as a harmful bacterium. Additionally, the fungal genus Mortierella has significant plant growth-promoting effects in agricultural soils and, as a decomposer, can degrade cellulose, hemicellulose, and chitin, providing the potential to eliminate soil pathogens and dominate the environment [50]. At the phylum level, Gram-positive bacteria (Actinobacteria and Firmicutes) showed a negative correlation with PAH stress, while Gram-negative bacteria (Candidatus Patescibacteria and Proteobacteria) showed a positive correlation. This is consistent with the results of other studies, indicating that PAH stress inhibits the development of oligotrophic bacteria and induces the development of polytrophic bacteria [9]. Overall, the microbial compositions of soils with different PAH contamination levels show significant differences, reflecting the impact of PAHs on soil microbial communities.
Given the significant enrichment of metabolism-associated genes, the adaptive mechanisms of soil microbial metabolism under PAH stress should be further investigated (Figure S1). PAHs significantly altered the nutritional patterns of soil fungi, leading to a substantial increase in saprotrophs. This indicates that under PAH stress, the proliferation and growth of soil fungi were more inclined toward the utilization of organic matter [51]. This is consistent with our results from soil enzyme activity and microbial composition analyses. Amino acids are important primary metabolites driving microbial metabolism and biosynthesis, and their related metabolic functions are also inhibited by PAH stress. Overall, PAH contamination has a negative impact on microbial metabolic functions.

4.3. Niche Breadth, Microbial Assembly Mechanisms and Interactions

Niche breadth represents the capacity of microorganisms to adapt to environmental stress. The bacteria exhibited a slightly wider niche breadth than the fungi under PAH stress. The observed expansion in niche breadth potentially reflects the superior metabolic versatility and remarkable adaptability to diverse environmental conditions exhibited by bacterial communities, in contrast to their fungal counterparts. This phenomenon can be attributed to the presence of an enzymatic system in bacteria, which enables the degradation of a broader spectrum of organic compounds compared to fungi, thereby facilitating the utilization of diverse substrates [52,53]. PAH stress significantly reduced the niche breadth of the bacterial and fungal communities (Figure 3a). The reductions in niche breadth and bacterial and fungal biomass indicated that environmental disturbance caused by PAH contamination greatly altered the soil microbiome structure and function at contaminated sites, thereby impairing microbial adaptability and restricting the survival space of some bacterial species.
Microbial adaptability to the environment is germane to many fundamental processes in ecology (i.e., community assembly and biotic interactions) [54]. In contrast, the partial βNTI values in soils without PAHs ranged from −2 to 2, whereas all βNTI values for bacterial communities exposed to PAHs exceeded 2. This indicated an enhanced role of heterogeneous selection in shaping soil microbial communities under PAH contamination, suggesting that PAHs serve as selective factors for microbial community assembly. The spatial heterogeneity of PAHs leads to variations in selection pressures across different environments, thereby resulting in the differentiation of species compositions among microbial communities. In the presence or absence of PAH exposure, the majority of |βNTI| values for fungi in the soil were less than 2, suggesting that stochastic processes predominantly governed fungal community assembly in both treatment conditions (Figure 3e).
PAHs exerted remarkable effects on the co-occurrence networks in terms of node and edge numbers, levels of clustering, graph densities and average path lengths (Figure 4e). This suggests that soils under PAH stress form soil microbial co-occurrence networks different from those for normal soils, consistent with the addition of abiotic factors such as carbon sources and pollutant stress that affect the co-occurrence patterns of soil microbiota [55]. The network for PAH-contaminated soils presented a smaller network size, represented by vertices, and lower network connectivity, represented by edges, compared to the network for normal soil. The thickness of the edges in the co-occurrence network was directly proportional to the correlation, allowing the magnitude of the correlation to be visually observed; with this approach, stronger correlations between microbial taxa were indicated in soils uncontaminated by PAHs (Figure 4a,b). The smaller average path distance indicated a faster transfer of energy, information, and materials between microorganisms, thereby enabling an effective response to changes in the external environment [56]. Changes in microbial community degradation activity may be affected by complex interactions between microorganisms and contribute to enhancing the dissipation of pollutants [57]. The average path length in the soil microbial co-occurrence network increased under PAH stress, indicating a less cohesive microbial interaction network, which is unfavorable for microbial coexistence in PAH-contaminated soils [23]. This observation aligns with evidence showing that PAHs can alter the microbial community structure, reduce diversity, and decrease network complexity under stress. There were more positive associations in normal soils than under PAH stress (Figure 4c,d). If the interactions of microbial organisms are indeed indicated by these associations, positive associations may primarily reflect cooperative behaviors, while negative associations may primarily represent competition for limited resources and unique environmental niches—consistent with our previous niche analysis [58,59] (Figure 3a,b).
PAH stress intensifies competition among microorganisms, leading to changes in the modes of interaction between microorganisms. This further reduces the ecological niche breadth and restricts microbial survival and activity. However, it is crucial to conduct further studies on microbial community succession, as the current experiment was carried out in pots.

5. Conclusions

In this study, we comprehensively assessed the impacts of PAH contamination on a soil ecosystem, regarding the aspects of soil enzyme activity, niche breadth, microbial community assembly mechanisms and community structure. Integrative analyses of soil enzymatic activities, microbial community structure and microbial function revealed a pronounced inhibition of microbial metabolic activity. The niche breadth and microbial community assembly collectively reflected the altered selective pressure exerted by PAH contamination on the soil environment. Furthermore, the co-occurrence network demonstrated that the interaction between soil bacteria and fungi was significantly suppressed under PAH stress.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microorganisms14020494/s1, Figure S1: Standard curves of 16S (a) and ITS (b). Figure S2: The effects of PAH stress on significantly altered functional metabolic pathways. References [60,61,62,63,64] are cited in the supplementary materials.

Author Contributions

Y.W.: Conceptualization, Data curation, Methodology, Visualization, Writing—original draft. D.W.: Writing—review and editing. J.L.: Resources, Supervision, Project administration, Writing—review and editing. H.X.: Supervision, Project administration, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Fundamental Research Funds of Chinese Academy of Forestry [grant number CAFYBB2022XA002], the Zhejiang Provincial Government Subsidy for Provincial Institutes and Research Institutes (CB0443202504070) and Zhejiang Provincial Government Subsidy for Provincial Institutes and Research Institutes (CB04-43-202602-009).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We would like to thank all colleagues and friends who have contributed to this study.

Conflicts of Interest

No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been previously published, in whole or in part, and it is not under consideration by any other journal, in whole or in part.

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Microorganisms 14 00494 g001
Figure 1. Bacterial community structure at phylum level of soil based on high-throughput 16S rRNA pyrosequencing (a). Fungus community structure at class level of soil based on high-throughput ITS rRNA pyrosequencing (b). PCA of bacterial genes (c) and fungal genes (d).
Figure 1. Bacterial community structure at phylum level of soil based on high-throughput 16S rRNA pyrosequencing (a). Fungus community structure at class level of soil based on high-throughput ITS rRNA pyrosequencing (b). PCA of bacterial genes (c) and fungal genes (d).
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Figure 2. LEfSe analysis shows differentially abundant genera of bacteria ((a), LDA > 2.5) and fungi ((b), LDA > 2.0) as biomarkers determined using Kruskal–Wallis test (p < 0.05) and Wilcoxon test (p < 0.05). Effects of PAH stress on soil fungi trophic types (c) and bacterial function (d) associated with extent of changes in metabolic function. Gene copy numbers of 16S and ITS in different treatments (e). Red indicates normal soil; blue indicates soil with PAHs. Averages ± SEs are listed (n = 3). Different letters indicate that values are significantly different (p < 0.05).
Figure 2. LEfSe analysis shows differentially abundant genera of bacteria ((a), LDA > 2.5) and fungi ((b), LDA > 2.0) as biomarkers determined using Kruskal–Wallis test (p < 0.05) and Wilcoxon test (p < 0.05). Effects of PAH stress on soil fungi trophic types (c) and bacterial function (d) associated with extent of changes in metabolic function. Gene copy numbers of 16S and ITS in different treatments (e). Red indicates normal soil; blue indicates soil with PAHs. Averages ± SEs are listed (n = 3). Different letters indicate that values are significantly different (p < 0.05).
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Microorganisms 14 00494 g003
Figure 3. Niche breadths of bacterial and fungal communities in different treatments (a). CK, normal soil; P, soil with PAHs. Different letters indicate that values are significantly different at p < 0.05. Different capital letters indicate differences between the CK and P treatments, and the lowercase letters indicate differences between the bacteria and fungi. Averages ± SEs are listed in line charts (n = 6). NTI values of bacteria (b) and fungi (c) compared for different treatments. βNTI values of bacteria (d) and fungi (e) compared for different treatments. Dotted lines were plotted for |βNTI| = 2.
Figure 3. Niche breadths of bacterial and fungal communities in different treatments (a). CK, normal soil; P, soil with PAHs. Different letters indicate that values are significantly different at p < 0.05. Different capital letters indicate differences between the CK and P treatments, and the lowercase letters indicate differences between the bacteria and fungi. Averages ± SEs are listed in line charts (n = 6). NTI values of bacteria (b) and fungi (c) compared for different treatments. βNTI values of bacteria (d) and fungi (e) compared for different treatments. Dotted lines were plotted for |βNTI| = 2.
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Figure 4. Co-occurrence networks of microbes in normal soil (a,c) and soil with PAHs (b,d). Node size is based on the relative abundance of each node. A connection stands for a strong (Pearson’s correlation |ρ| > 0.7) and significant (p < 0.01) correlation. Round nodes ●: 16S nodes; triangular nodes ▲: ITS nodes. Node size is proportional to the relative abundance of genera; node connection (edge) thickness is proportional to the value of Pearson’s correlation coefficient. Different colors represent different modules (a,b). Red indicates a positive correlation; blue indicates a negative correlation (c,d). The topological properties of the networks are given in (e).
Figure 4. Co-occurrence networks of microbes in normal soil (a,c) and soil with PAHs (b,d). Node size is based on the relative abundance of each node. A connection stands for a strong (Pearson’s correlation |ρ| > 0.7) and significant (p < 0.01) correlation. Round nodes ●: 16S nodes; triangular nodes ▲: ITS nodes. Node size is proportional to the relative abundance of genera; node connection (edge) thickness is proportional to the value of Pearson’s correlation coefficient. Different colors represent different modules (a,b). Red indicates a positive correlation; blue indicates a negative correlation (c,d). The topological properties of the networks are given in (e).
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Table 1. Properties of soil in this study.
Table 1. Properties of soil in this study.
pHSOMTNTPTKAPAKANCEC
Content8.42 ± 0.0818.86 ± 0.510.93 ± 0.010.94 ± 0.0618.03 ± 0.6335.99 ± 0.59173.78 ± 17.43101.52 ± 2.7911.53 ± 0.34
SOM: soil organic matter, g·kg−1; TN: total nitrogen, g·kg−1; TP: total phosphorus, g·kg−1; TK: total potassium, g·kg−1; AP: available phosphorus, mg·kg−1; AK: available potassium, mg·kg−1; AN: alkali-hydrolyzable nitrogen, mg·kg−1; CEC: cation exchange capacity, cmol·kg−1.
Table 2. Primers used for qPCR.
Table 2. Primers used for qPCR.
Target GroupPrimersSequences (5′ to 3′)
16s341FCCTAYGGGRBGCASCAG
806RGGACTACNNGGGTATCTAAT
ITsITS5GGAAGTAAAAGTCGTAACAAGG
ITS2GCTGCGTTCTTCATCGATGC
Table 3. Soil enzyme activities in different treatments.
Table 3. Soil enzyme activities in different treatments.
CKP
30 Day60 Day90 Day30 Day60 Day90 Day
PPO
activity		0.24 ± 0.03
Ab		0.30 ± 0.03
Ab		0.43 ± 0.03
Aa		0.47 ± 0.16
Aa		0.07 ± 0.03
Bb		0.25 ± 0.05
Bab
Laccase
activity		1.41 ± 0.21
Aa		1.73 ± 0.11
Aa		1.67 ± 0.16
Aa		1.90 ± 0.19
Aa		2.07 ± 0.42
Aa		1.58 ± 0.10
Aa
DHA
activity		1.30 ± 0.15
Aa		1.49 ± 0.13
Aa		1.28 ± 0.23
Ba		0.47 ± 0.22
Bc		1.43 ± 0.33
Ab		2.55 ± 0.54
Aa
Urease
activity		1.51 ± 0.07
Aab		1.43 ± 0.04
Ab		1.60 ± 0.07
Aa		0.77 ± 0.05
Ba		1.03 ± 0.08
Ba		0.97 ± 0.20
Ba
PR
activity		42.21 ± 1.47
Aa		39.35 ± 5.45
Aa		45.48 ± 2.81
Aa		19.23 ± 3.64
Ba		13.97 ± 0.53
Ba		18.02 ± 1.56
Ba
CAT
activity		59.63 ± 0.67
Aa		63.01 ± 3.55
Aa		66.19 ± 0.57
Aa		43.38 ± 1.32
Bb		52.63 ± 1.42
Ba		54.97 ± 4.85
Ba
Polyphenol oxidase (PPO) activity, mg·g−1·d−1; laccase activity, mmol·g−1·min−1; dehydrogenase (DHA) activity, mg·g−1·h−1; urease activity, mg·g−1·d−1; protease (PR) activity, umol−1·g−1·d−1; catalase (CAT) activity, mg·g−1·d−1. CK, normal soil; P, soil with PAHs. Different letters indicate that values are significantly different at p < 0.05. Averages ± SEs are listed in a line chart (n = 3). The different capital letters indicate differences between the CK and P treatments, and the lowercase letters indicate differences between time intervals (30, 60, and 90 days).
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Wang, Y.; Wu, D.; Liu, J.; Xu, H. Polycyclic Aromatic Hydrocarbon Pollution Stress Impairs Soil Enzyme Activity and Microbial Community. Microorganisms 2026, 14, 494. https://doi.org/10.3390/microorganisms14020494

AMA Style

Wang Y, Wu D, Liu J, Xu H. Polycyclic Aromatic Hydrocarbon Pollution Stress Impairs Soil Enzyme Activity and Microbial Community. Microorganisms. 2026; 14(2):494. https://doi.org/10.3390/microorganisms14020494

Chicago/Turabian Style

Wang, Yuancheng, Donglei Wu, Junxiang Liu, and Haolong Xu. 2026. "Polycyclic Aromatic Hydrocarbon Pollution Stress Impairs Soil Enzyme Activity and Microbial Community" Microorganisms 14, no. 2: 494. https://doi.org/10.3390/microorganisms14020494

APA Style

Wang, Y., Wu, D., Liu, J., & Xu, H. (2026). Polycyclic Aromatic Hydrocarbon Pollution Stress Impairs Soil Enzyme Activity and Microbial Community. Microorganisms, 14(2), 494. https://doi.org/10.3390/microorganisms14020494

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