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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (9): 1314-1340    DOI: 10.1007/s40333-025-0108-5     CSTR: 32276.14.JAL.02501085
Research article     
Structural and functional responses of soil microbial communities to petroleum pollution in the eastern Gansu Province on the Loess Plateau, China
WANG Jincheng1,2,*(), JING Mingbo1,2, GUO Xiaopeng3, CHANG Sijing4, DUAN Chunyan1,2, SONG Xi1,2, QIAN Li1,2, QIN Xuexue1,2, SHI Shengli1,2
1School of Agricultural and Biological Engineering, Longdong University, Qingyang 745000, China
2Key Laboratory of Protection and Utilization for Biological Resources and Ecological Restoration of Gansu Province, Longdong University, Qingyang 745000, China
3School of Life Science and Engineering, Lanzhou University of Technology, Lanzhou 730050, China
4School of Biological and Pharmaceutical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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Abstract  

Crude oil pollution is a significant global environmental challenge. The eastern Gansu Province on the Loess Plateau, an important agricultural region containing the Changqing Oilfield, is facing increasing crude oil contamination. Understanding how microbial communities respond to varying pollution levels is critical for developing effective bioremediation strategies. This study examined how different concentrations of crude oil affect soil properties and microbial communities in Qingyang City, eastern Gansu Province, China by comparing lightly polluted (1895.84-2696.54 mg/kg total petroleum hydrocarbons (TPH)), heavily polluted (4964.25-7153.61 mg/kg TPH), and uncontaminated (CK) soils. Results revealed that petroleum contamination significantly increased total organic carbon (TOC), pH, C:N:P ratio, and the activities of dehydrogenase (DHA) and polyphenol oxidase (PPO), while reducing total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), available phosphorus (AP), available potassium (AK), soil organic matter (SOM), soil water content (SWC), the activities of urease (URE) and alkaline phosphatase (APA), and microbial alpha diversity (P<0.050). Light pollution (LP) soils demonstrated an increase in culturable microorganisms, whereas heavy pollution (HP) soils exhibited increased hydrocarbon-degrading microbes and higher expression of key functional genes, such as alkane monooxygenase (AlkB), cytochrome P450 alkane hydroxylases (P450), catechol 2,3-dioxygenase (C23O), and naphthalene dioxygenase (Nah) (P<0.050). Non-metric multidimensional scaling (NMDS) and redundancy analysis (RDA) indicated evident variations in microbial community structure across different oil contamination levels. LP soils were dominated by bacterial genera Pseudoxanthomonas and Solimonadaceae, whereas Pseudomonas, Nocardioides, and hydrocarbon-degrading genera (Marinobacter, Idiomarina, and Halomonas) were predominant in HP soils. The fungal genus Pseudallescheria exhibited the most pronounced abundance shift between LP and HP soils (P<0.050). Environmental factor analysis identified AN, SWC, TN, SOM, and alpha diversity indices (Shannon index and Chao1 index) as the key differentiators of CK soils, whereas the pollutant levels and metal content were characterized in HP soils. Hydrocarbon-degrading microbial abundance was a defining trait of HP soils. Metabolic pathway analysis revealed enhanced aromatic hydrocarbon degradation in HP soils, indicating microbial adaptation to severe contamination. These findings demonstrated that crude oil pollution suppressed soil nutrients while reshaping the structure and function of microbial communities. Pollution intensity directly affected microbial composition and degradation potential. This study offers valuable insights into microbial responses across contamination gradients and supports the development of targeted bioremediation strategies for oil-contaminated loess soils.

Key words： crude oil pollution      microbial community      bacterial community function      soil physical-chemical properties      Loess Plateau     
Received: 28 February 2025      Published: 30 September 2025
Corresponding Authors: *WANG Jincheng (E-mail: ldxywjc@163.com)
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WANG Jincheng
JING Mingbo
GUO Xiaopeng
CHANG Sijing
DUAN Chunyan
SONG Xi
QIAN Li
QIN Xuexue
SHI Shengli
Cite this article:

WANG Jincheng, JING Mingbo, GUO Xiaopeng, CHANG Sijing, DUAN Chunyan, SONG Xi, QIAN Li, QIN Xuexue, SHI Shengli. Structural and functional responses of soil microbial communities to petroleum pollution in the eastern Gansu Province on the Loess Plateau, China. Journal of Arid Land, 2025, 17(9): 1314-1340.

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http://jal.xjegi.com/10.1007/s40333-025-0108-5     OR     http://jal.xjegi.com/Y2025/V17/I9/1314

Fig. 1 Location of three sampling sites in Nanzhuang Village, Xifeng District, Qingyang City, eastern Gansu Province, China. CK, control; LP, light pollution; HP, heavy pollution.
Pollutant CK LP HP F-value P-value
TPH (mg/kg) 1.45±0.29a 1899.02±172.46b 5890.20±238.06c 313.78 P<0.010
TSH (mg/kg) 1.02±0.17a 1330.46±57.82b 4066.22±193.87c 315.01 P<0.010
TAH (mg/kg) 0.06±0.03a 529.85±24.67b 1648.86±64.91c 410.90 P<0.010
Cr (mg/kg) ND 31.78±1.68b 46.80±2.19c 228.29 P<0.010
Pb (mg/kg) 8.673±0.75a 34.96±2.01b 47.68±1.21c 196.73 P<0.010
Cd (mg/kg) - 0.22±0.04b 0.47±0.03c 66.95 P<0.010
As (mg/kg) - 14.93±0.98b 25.94±2.16c 80.74 P<0.010
Mn (mg/kg) 49.93±4.05a 235.53±9.84b 439.89±10.89c 492.09 P<0.010
Ni (mg/kg) 7.53±1.05a 19.75±1.71b 25.27±1.53c 59.01 P<0.010
Bicyclic AHs (μg/kg) - 7.77±0.37b 11.74±1.11c 77.71 P<0.010
Tricyclic AHs (μg/kg) - 55.88±2.83b 90.43±3.17c 346.21 P<0.010
Tetracyclic AHs (μg/kg) - 90.54±2.97b 361.26±13.88c 526.65 P<0.010
Pentacyclic AHs (μg/kg) - 201.30±11.11b 579.49±18.82c 543.63 P<0.010
Hexacyclic AHs (μg/kg) - 142.79±7.74b 445.95±27.68c 188.41 P<0.010
Sum of 16 US EPA PAHs (μg/kg) - 407.74±20.41b 1127.60±41.79c 452.20 P<0.010
Table 1 Concentration of soil pollutants across different oil pollution levels
Soil physical-chemical property CK LP HP F-value P-value
pH 7.96±0.03a 8.25±0.02b 8.37±0.03c 78.65 P<0.010
SWC (%) 21.60±0.89a 6.07±0.35b 4.99±0.13b 527.76 P<0.010
TN (g/kg) 0.70±0.04a 0.31±0.03b 0.16±0.01c 97.25 P<0.010
TP (g/kg) 4.39±0.35a 2.09±0.27b 1.71±0.12b 30.02 P<0.010
AN (mg/kg) 65.92±2.61a 27.25±1.84b 18.31±1.01c 171.85 P<0.010
AP (mg/kg) 37.70±3.08a 25.09±1.15b 13.71±0.54c 38.90 P<0.010
AK (mg/kg) 62.37±3.22a 30.21±2.70b 22.36±1.91c 63.29 P<0.010
SOM (g/kg) 17.91±1.56a 4.62±0.22b 3.79±0.24b 74.03 P<0.010
TOC (g/kg) 11.82±0.26a 45.80±1.86b 94.64±3.24c 369.76 P<0.010
C:N:P 100.00/5.91/36.74 100.00/0.65/4.46 100.00/0.17/1.79
Table 2 Change in soil physical-chemical properties across different oil pollution levels
Fig. 2 Change in soil enzyme activities across different oil pollution levels. (a), dehydrogenase (DHA) activity; (b), polyphenol oxidase (PPO) activity; (c), urease (URE) activity; (d), alkaline phosphatase (APA) activity. Means compared using one-way analysis of variance (ANOVA). Different lowercase letters indicate significant differences across different groups at P<0.050 level, according to Duncan's multiple range test. Box boundaries indicate the 25th and 75th percentiles, respectively; bar is the standard deviation (SD); and the horizontal line within each box indicates the median value.
Fig. 3 Number of culturable microorganisms and microbial biomass carbon (MBC) content across different oil pollution levels. (a), number of bacteria (NB); (b), number of fungi (NF); (c), number of actinomycetes (NA); (d), number of alkane-degrading bacteria (ADB); (e), number of PAH-degrading bacteria (PDB); (f), number of alkane-degrading fungi (ADF); (g), number of PAH-degrading fungi (PDF); (h), MBC content. Means compared using one-way ANOVA. Different lowercase letters indicate significant differences across different groups at P<0.050 level, according to Duncan's multiple range test. Box boundaries indicate the 25th and 75th percentiles, respectively; bar is the SD; and white point within each box indicates the median value. In the violin plots, the width represents the density of data points; the wider the plot is, the higher the density is.
Fig. 4 Alpha diversity indices of soil microbial community across different oil pollution levels. (a), bacterial Shannon index; (b), bacterial Chao1 index; (c), fungal Shannon index; (d), fungal Chao1 index; (e), bacterial operational taxonomic unit (OTU) distribution; (f), fungal OTU distribution. Means compared using one-way ANOVA. Different lowercase letters indicate significant differences across different groups at P<0.050 level, according to Duncan's multiple range test. Box boundaries indicate the 25th and 75th percentiles, respectively; bar is the SD; and the horizontal line within each box indicates the median value.
Fig. 5 Soil microbial community composition across different oil pollution levels. (a), most abundant bacterial phyla; (b), most abundant fungal phyla; (c), most abundant bacterial genera; (d), most abundant fungal genera; (e), principal component analysis (PCA) plot of the bacterial community at the genus level based on Bray-Curtis dissimilarity; (f), PCA plot of the fungal community at the genus level based on Bray-Curtis dissimilarity. PC, principal component. 'Others' category refers to the collective grouping of taxa (at either the phylum or genus level) that do not fall within the most abundant taxa in the community.
Fig. 6 Linear discriminant analysis effect size (LEfSe) cladogram showing microbial taxonomic differences across different oil pollution levels. (a), bacterial community phylogenetic distribution; (b), fungal community phylogenetic distribution. The five layer rings from the inner to the outer represent taxa from phylum, class, order, family, to genus level. Red, blue, and green circles represent taxa enriched in the CK, LP, and HP groups, respectively; whereas yellow circle represents taxa with no significant difference or shared across groups. Circle size represents the taxon relative abundance; the larger the circle is, the higher the abundance is. The red, blue, and green colors of fan sector indicate the CK, LP, and HP groups, respectively; and the area of fan sector reflects inter-group taxonomic difference magnitude, the larger the area is, the more significant the difference is. The prefix "p__", "c__", "o__", "f__", and "g__" denote the taxon at the phylum, class, order, family, and genus level, respectively. Only taxa with linear discriminant analysis (LDA) scores>4.0 and P<0.050 are displayed.
Community Soil sample Edge Node Modularity Average path length Graph density Clustering coefficient
Bacterial community CK 835 99 0.406 2.436 0.172 0.551
LP 1440 100 0.488 1.870 0.291 0.659
HP 2018 100 0.419 1.738 0.408 0.748
Fungal community CK 297 98 0.620 3.535 0.062 0.386
LP 459 99 0.492 2.771 0.095 0.454
HP 623 99 0.428 2.701 0.128 0.661
Table 3 Topological property of co-occurrence network
Fig. 7 Genus-level microbial co-occurrence pattern across different oil contaminated soil groups. (a-c), bacterial co-occurrence networks for the CK, LP, and HP groups, respectively; (d-f), fungal co-occurrence networks for the CK, LP, and HP groups, respectively. A module is defined as a tightly connected subcluster formed by highly interconnected microbial taxa (e.g., genera). Nodes are connected by lines only when significantly correlated (Spearman's |ρ|>0.70, P<0.010). Node size reflects the number of connections; colors indicate modularity classes. Bar graphs display the proportions of positive and negative associations within the networks.
Fig. 8 Keystone taxon relative abundance (a and d), niche breadth (b and e), and niche overlap (c and f) of bacterial and fungal communities across different oil contaminated soil groups
Fig. 9 Distinct microbial metabolic pathway across different oil contaminated soil groups, predicted by FAPROTAX (16S rRNA dataset; a-c) and FunGuild (ITS dataset; d-f). The P-value was corrected using the Benjamini-Hochberg method to control the false discovery rate.
Fig. 10 Relative expression of functional genes related to crude oil degradation across different oil contaminated soil groups. (a), alkane monooxygenases (AlkB); (b), cytochrome P450 alkane hydroxylase (P450); (c), catechol 2,3-dioxygenase (C23O); (d), naphthalene dioxygenase (Nah). Means compared using one-way ANOVA. Different lowercase letters indicate significant differences across different groups at P<0.050 level, according to Duncan's multiple range test. Box boundaries indicate the 25th and 75th percentiles, respectively; bar is the SD; and the horizontal line within each box indicates the median value.
Fig. 11 Non-metric multidimensional scaling (NMDS; a and b) and redundancy analysis (RDA; c and d) showing relationships between the structure of soil bacterial and fungal communities and environmental across oil contaminated soil groups, based on Bray-Curtis dissimilarity. Environmental factors significantly correlated with microbial communities (Mantel test, P<0.050) were included. Arrow length indicates environmental factor influence strength; smaller angles with axes reflect higher correlation; and sample proximity to arrows indicates factor effect strength. Samples in the same or opposite direction of arrows show positive or negative correlations with community changes. In Figure 11c and d, gray circles represent microbial relative abundance, with size proportional to abundance magnitude.
Gene Primer Primer sequence Reference
Alkane monooxygenase (AlkB) AlkB-F 5′-AACTACMTCGARCAYTACGG-3′ Long et al. (2017)
AlkB-R 5′-TGAMGATGTGGTYRCTGTTCC-3′
Cytochrome P450 alkane hydroxylase (P450) P450-F 5′-GATGAAGAAGGGCGATTGGA-3′ van Beilen et al. (2006)
P450-R 5′-CCTTGATGTTGGCAGGTAGGA-3′
Catechol 2,3-dioxygenase (C23O) C23O-F 5′-ATTTAGGTGCTCGGTTTCTATCTGTTTA-3′ Xie et al. (2014)
C23O-R 5′-ATTTATGGTCTTGCCGTGAGTGTTTA-3′
Naphthalene dioxygenase (Nah) Nah-F 5′-TGAMGATGTGGTYRCTGTTCC-3′ Park et al. (2006)
Nah-R 5′-CAGGTCAGCATGCTGTTGTT-3′
Table S1 Sequence information of gene primer pair
Fig. S1 Indicator bacterial (a) and fungal (b) groups within the three groups with LDA scores higher than 4.0. Denote microbial taxon at the phylum, class, order, family, and genus level with the prefix "p__", "c__", "o__", "f__", and "g__".
Fig. S2 Co-occurrence network of bacterial (a-c) and fungal (d-f) genera based on positive and negative correlation analysis across different oil contaminated soil groups. Nodes are connected by lines only when significantly correlated (Spearman's |ρ|>0.70, P<0.010). Node size reflects the number of connections; colors indicate modularity classes.
Fig. S3 Variance in bacterial (a) and fungal (b) communities explained by different soil environmental factors. R2 reflects the proportion of community variation explained by each factor. *, significance at P<0.050 level; **, significance at P<0.010 level.
Fig. S4 Heatmap of Spearman's correlation coefficient between the relative abundance of bacterial (a) and fungal (b) communities at genus level and environmental factors. Dendrograms (top) illustrate hierarchical clustering of taxa (left) and environmental factors (bottom) based on similarity in correlation patterns. *, significance at P<0.050 level; **, significance at P<0.010 level; ***, significance at P<0.001 level. To control the family-wise error rate, this study adopted Bonferroni correction.
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