Soil microbial community composition and nitrogen enrichment responses to the operation of electric power substation

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However, the ecological consequences of electric power substation operation on soil microbial communities and nitrogen enrichment have not been addressed. In this study, we collected soil samples from seven distinct sites at varying distances from an electric power substation in Xintang village, southeastern China, and investigated the microbial diversity and community structures employing metagenomic sequencing technique. Key environmental determinants shaping soil microbial communities at both the phylum and genus levels were identified as soil moisture content, pH and electric conductivity. Prominent taxa identified across all sampled soils included Acidobacteria, Proteobacteria, Actinobacteria, Chloroflexi, Basidiomycota, Ascomycota, and Mucoromycota. While the bacterial community exhibited statistically significant differences across the seven distinct sites, fungal communities did not show such variations. Correlation analysis revealed a diminished nitrogen fixation capacity at the site nearest to the substation, characterized by low moisture content, elevated pH, and robust soil electric conductivity. In contrast, heightened nitrification processes were observed at this site compared to others. These findings were substantiated by the relative abundance of key genes associated with ammonium nitrogen and nitrate nitrogen production. This study provides insights into the relationships between soil microbial communities and the enduring operation of electric power substations, thereby contributing fundamental information essential for the rigorous environmental impact assessments of electric power substations. soil microbial community electric power substation nitrogen processing metagenomic sequencing technique Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The relentless advance of modernization has ushered in a burgeoning demand for electric energy. This demand necessitates the efficient transportation of electricity from power generation facilities, often located at considerable distances from primary consumption centers, to consumers. This intricate process relies upon an expansive network encompassing power substations, transmission lines, and distribution lines. Power substations serve as pivotal assemblages of equipment designed for voltage control and adjustment. Notably, transmission lines are discernible from distribution lines due to their capacity to support higher voltages and their proclivity to span greater distances (Biasotto and Kindel, 2018 ). The Chinese landscape bears witness to a proliferation of power substations and the extensive deployment of electrical power lines, which traverse diverse terrains, including farmland and highways. This remarkable infrastructural landscape is nevertheless compelled to evolve in response to the escalating electricity demand, necessitating the establishment of additional electric power substations. However, it is imperative to acknowledge that electric power substations, throughout their construction and operational phases, harbor the potential for substantial environmental ramifications (Bagli et al., 2011 ). In numerous nations, the vicinity beneath power cables necessitates rigorous vegetation management to avert interference with line structural integrity and energy transmission. Furthermore, the operation of power lines and electrical equipment engenders the generation of electromagnetic fields. As the pivotal constituents of terrestrial ecosystems, namely soil, inevitably bear the imprint of power lines and substations. Among the custodians of soil health are soil microorganisms, integral to biogeochemical cycles and habitat sustenance across the earth (Gadd, 2010 ). These microorganisms are enmeshed in complex ecological relationships encompassing competition, predation, parasitism, reciprocity, and neutrality (Faust and Raes, 2012 ). Of paramount significance, soil microorganisms orchestrate the regulation of soil nutrient cycles, including the vital nitrogen cycle, and exert a profound influence on plant productivity. Consequently, they stand as widely acknowledged indicators of soil health (Whalen et al., 2022 ). In response to environmental stressors, soil microorganisms exhibit distinctive adaptive responses, exemplified by the production of diverse enzymes such as superoxide dismutase (SOD), which confers effective resistance against the deleterious effects of reactive oxygen species (Roubalová et al., 2018 ) and safeguards plant well-being (Huseynova et al., 2014 ). Notwithstanding substantial progress in the realm of environmental impact assessment pertaining to transmission lines and electric power substations, significant lacunae persist with regard to the representation of distinct biological strata, such as genes, and the comprehensive incorporation of diverse biodiversity metrics, spanning composition, structure, and function, into impact prognostication (Biasotto and Kindel, 2018 ). Particularly noteworthy is the paucity of scholarly inquiry into the ramifications of electric power substations on soil microorganisms. In accordance with the foregoing explanation, the primary objective of this investigation was to document the alterations in soil physicochemical properties, alongside the structural and functional aspects of microbial communities across seven distinct sites at varying distances from an electric power substation, utilizing metagenomic sequencing techniques. A special emphasis was placed on elucidating correlations between soil physicochemical properties and microbial communities, in addition to genes associated with the nitrogen cycle in the proximity of the substation. Through comprehensive analyses of these factors, we aimed to delineate the impact of the electric power substation on soil nutrition, the dynamics shifts within microbial community, and changes in functional genes related to nitrogen cycling. Materials and Methods Site description and soil sample collection Soil specimens were systematically obtained from the environs of an electric power substation located at coordinates 119°08′N latitude and 26°34′E longitude, situated within Xintang village, Minhou county, Fujian province, southeastern China (as delineated in Table S1 ). Xintang village occupies a locale characterized by a subtropical oceanic monsoon climate, typified by annual mean temperatures ranging between 17 and 21°C, and annual average precipitation levels spanning 1400 to 2000 mm. The topography of the study site comprises a modest, low-lying basin with predominant rainfall occurring between May and September. Soil samples were diligently collected from distinct designated regions within the 0–20 cm stratum of the topsoil, employing the plumblossom five-point methodology (Fig. 1 ). It is noteworthy that the selected areas exhibited minimal vegetation cover. For each of the sampling locales (denoted as S1 through S7), an array of triplicate sampling plots, each measuring 5 meters by 5 meters, was judiciously established in a randomized fashion. Within the confines of each sampling plot, five discrete soil samples were haphazardly extracted, subject to meticulous amalgamation to yield a comprehensive composite soil specimen (Guo et al., 2020 ; Chen et al., 2023 ). Each of these composite soil samples was sealed within aseptic ziplock bags and expeditiously transported, under cryogenic conditions facilitated by dry ice, to the laboratory facility. Each composite soil sample underwent a meticulous sieving process, utilizing a 2 mm mesh screen, with the dual objectives of homogenizing the soil matrix and eliminating larger particles, soil-dwelling organisms, and vegetative debris. The entirety of the composite soil samples underwent a subdivision into three discrete portions. The first portion was allocated for the comprehensive determination of the physicochemical attributes of the soil matrix, while the second portion was earmarked for the quantification of soil oxidative kinase content and soil enzyme activity. The remaining fraction was meticulously preserved at -80°C, primed for subsequent genomic DNA extraction and sequencing endeavors. Soil physicochemical properties measurement Fresh soil (100 g) was collected in each designated sampling plot using ring knives to determine soil bulk density, a parameter assessed through weight method (Zhang et al., 2023 ). The determination of soil dry matter and moisture content was undertaken employing the classical gravimetric method, while soil particle density was ascertained through the volume replacement technique. The quantification of soil organic matter (SOM) content was executed following the classical potassium dichromate method. The low-frequency mass magnetic susceptibility (LFMMS) and electric conductivity (EC) of soil samples were measured using magnetic susceptibility meter (MS2, Bartington, UK) and conductivity meter (HQ4300, HACH, USA), respectively. Moreover, the pH value of each soil sample, prepared at a soil-water ratio: 1:2.5, was diligently measured utilizing a pH meter (Sartorius PB-10, German). Specifically, for the determination of nitrite nitrogen (NO 2 − -N) and ammonium nitrogen (NH 4 + -N) concentrations, the soil samples (soil-water ratio: 1:5) were intimately mixed with KCl solution at a final concentration of 1 M. Subsequently, this mixture underwent agitation at 200 rpm for a duration of 1 h and was then subjected to centrifugation (3000 rpm, 10 minutes) to facilitate the retrieval of the supernatant. The contents of NO 2 − -N and NH 4 + -N within the supernatant were subsequently quantified utilizing a UV-vis spectrophotometer (TU-1810, Bejing Purkinje General Instrument Co. Ltd., China)(see details in SI Section 1), in strict accordance with their respective standard curves. The content of nitrate nitrogen (NO 3 − -N) within the filtered supernatant which was determined using a flow injection auto-analyzer (Skalar Analytical, AACE, Germany)(Chen et al., 2023 ). Furthermore, the overall content of total nitrogen (TN), soil organic nitrogen (SON), total carton (TC) and total organic carbon (TOC) was comprehensively determined (see SI Section 1 and Section 2). Determination of enzyme content in soil The content of soil enzymes including SOD, malondialdehyde (MDA), glutathione (GSH), lactate dehydrogenase (LDH), acid protease, acid phosphatase and soil sucrase were determined using enzyme test kit (Gene Hunter, HongKong, China) (see the details in SI Section 3). Soil DNA extraction and sequencing Microbial genomic DNA were extracted from individual soil samples (0.5 g) using the Omega E.Z.N.A Stool DNA Kit for Soil (Omega Bio-tek, Inc., USA), following the manufacturer's instructions. The purity and quality of the genomic DNA were assessed via 1% agarose gels electrophoresis (Zhang et al., 2023 ), and their concentration were accurately quantified using the Qubit 4.0 Fluorometer (Thermo Fisher Scientific Inc., USA). Subsequently, the DNA was fragmented to 300 bp employing the Covaris ultrasonic crusher, and the resulting fragments underwent further processing, including end repair, A tailing, and ligation of Illumina compatible adapters. Finally, sequencing were conducted on an Illumina NovaSeq PE150 platform at Allwegene Company (Beijing, China) (Table S2). The Illumina NovaSeq sequencing data were deposited in the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI), under accession number PRJNA1037611 (SUB13936859). The abundance of functional genes relevant to nitrogen metabolism were examined based on sequencing data combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Statistical analyses Significant differences were assessed through one-way analysis of variance (SPSS 19) with ρ < 0.05 considered as statistically significant. Non-metric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity were conducted using the R (Version 4.0.2) package ggplot2. Vegan ggplot2 package was employed for redundancy analyses (RDA) to analyze the correlation between environmental factors and the abundance of microbial community phyla. A Pearson correlation test was executed to examine the relationships between soil physicochemical properties and the relative abundance of microbial community, utilizing the R (Version 3.6) packages “psych” and “pheatmap”. Results Soil physicochemical properties of 7 different collection sites (Table 1 ) Table 1 Soil physicochemical properties from each collection site Soil pH SOM (g·Kg − 1 ) Soil bulk density (g·cm − 3 ) Soil density (g·cm − 3 ) Fresh soil LFMMS (10 − 8 ·m 3 ·Kg − 1 ) Low-frequency susceptibility factor (%) EC (µS·cm − 1 ) TC (g·Kg − 1 ) Solid TOC (g·Kg − 1 ) NO 2 − (µg·Kg − 1 ) NH 4 + -N (mg·Kg − 1 ) NO 3 − -N (mg·Kg − 1 ) SON (mg·Kg − 1 ) TN (mg·Kg − 1 ) Dry matter content (%) Moisture content (%) S1 4.98 8.84 ± 0.12 1.13 ± 0.027 1.25 ± 0.0093 75.29% 32.82% 5.85 ± 0.01 9.09% 20.31 ± 0.06 7.85 ± 0.80 6.43 ± 0.53 2.55 ± 0.040 3.84 ± 0.27 3.88 ± 0.07 785.93 ± 25.41 793.66 ± 26.21 S2 5.10 27.25 ± 0.54 1.01 ± 0.003 1.76 ± 0.033 75.70% 32.09% 35.01 ± 0.04 9.60% 30.23 ± 0.06 15.49 ± 1.01 12.31 ± 0.21 0.75 ± 0.039 40.21 ± 0.46 6.48 ± 0.12 4281.37 ± 78.34 4328.08 ± 81.24 S3 6.69 17.80 ± 0.47 1.17 ± 0.029 1.85 ± 0.0088 78.12% 28.01% 43.55 ± 0.09 5.71% 27.30 ± 0.01 12.26 ± 2.16 10.22 ± 0.19 2.36 ± 0.067 4.24 ± 0.05 1.96 ± 0.11 1599.15 ± 19.56 1605.36 ± 18.49 S4 5.10 13.46 ± 0.33 1.19 ± 0.009 2.61 ± 0.0069 76.23% 31.18% 29.55 ± 0.10 9.43% 15.46 ± 0.11 8.45 ± 0.41 6.61 ± 0.07 0.68 ± 0.039 28.16 ± 0.31 6.69 ± 0.06 1153.11 ± 61.31 1187.97 ± 60.40 S5 4.85 13.77 ± 0.33 1.26 ± 0.006 1.50 ± 0.029 76.00% 31.58% 22.26 ± 0.03 9.25% 27.40 ± 0.01 9.85 ± 003 7.52 ± 0.31 1.56 ± 0.038 16.22 ± 2.79 8.77 ± 0.13 5162.99 ± 121.46 5188.19 ± 117.95 S6 7.09 10.69 ± 0.035 1.51 ± 0.020 1.66 ± 0.019 78.06% 26.91% 24.33 ± 0.02 2.19% 115.07 ± 0.25 7.51 ± 0.25 5.60 ± 0.23 2.86 ± 0.040 3.49 ± 0.10 8.87 ± 0.12 5486.26 ± 132.51 5498.63 ± 122.41 S7 5.91 12.65 ± 0.28 1.30 ± 0.014 1.57 ± 0.048 77.73% 28.65% 14.79 ± 0.08 10.94% 25.4 ± 0.01 9.12 ± 0.79 6.55 ± 0.19 2.11 ± 0.002 15.17 ± 0.10 8.64 ± 0.22 2985.51 ± 45.54 3009.32 ± 51.94 The data were expressed as the mean of individual observations with standard deviation (mean ± SD) The physicochemical characteristics of soil samples from each collection site were comprehensively detailed in Table 1 . Variations in soil pH were observed across locations, ranging from 4.85 to 7.09, while SOM and bulk density spanned from 8.84 to 27.25 g·Kg − 1 and 1.01 to 1.51 g·cm − 3 , respectively. LFMMS and EC were employed as indicators of magnetization and salinity levels within the soil. Consequently, these properties were measured for soil samples collected in proximity to the electric power substation. As indicated in Table 1 , all soil samples exhibited relative low LFMMS (< 45×10 − 8 ·m 3 ·Kg − 1 ) and EC (< 200 µS·cm − 1 ). Notably, the EC for soil collected site S6 (115.07 ± 0.25 µS·cm − 1 ), the closest to the power substation, was significantly higher than those at other points. Furthermore, the TC and TOC content in the soil samples fell within the range of 7.51 to 15.49 g·Kg − 1 and 5.60 to 10.22 g·Kg − 1 , respectively. Nevertheless, the TC and TOC content in the soil samples from S6 were lower than those at other collection sites. It was noteworthy that the TN content (5.50 g·Kg − 1 ) at the S6 site surpassed that observed in other collection sites (see Table 1 ). Considering that TN predominantly comprises SON, fixed nitrogen was primarily utilized for biomolecule synthesis by plants and microorganisms. Inorganic nitrogen contents were considerably lower in comparison to SON content. The NO 3 − -N content in the S5 and S6 sites exceeded that in the remaining collection sites (Table 1 ). Conversely, for NH 4 + -N, the lowest content was observed at the S6 site compared to other collection points, with its content being lower than that of NO 3 − -N. (Table 2 ) Table 2 Content of soil enzymes from each collection site Soil Sample MDA (nmol·L − 1 ) SOD (U·mL − 1 ) GSH (ng·L − 1 ) LDH (IU·L − 1 ) Acid protease (U·L − 1 ) Acid phosphatase (IU·L − 1 ) Soil Sucrase (U·L − 1 ) S1 8.19 ± 0.52 18.96 ± 0.47 45.40 ± 1.16 47.70 ± 0.86 209.46 ± 16.75 46.36 ± 1.27 857.25 ± 50.14 S2 8.41 ± 0.32 17.67 ± 0.38 51.08 ± 2.02 44.25 ± 3.71 218.77 ± 15.88 47.26 ± 1.19 942.78 ± 53.68 S3 9.18 ± 0.26 18.85 ± 0.85 52.09 ± 1.19 58.06 ± 2.86 260.03 ± 2.54 50.01 ± 1.27 864.37 ± 41.92 S4 9.40 ± 0.31 16.54 ± 0.12 55.70 ± 2.73 46.81 ± 2.67 234.43 ± 9.18 51.43 ± 2.78 853.59 ± 58.70 S5 9.17 ± 0.33 20.47 ± 0.21 58.10 ± 2.26 56.78 ± 2.00 227.70 ± 9.93 40.94 ± 2.30 994.84 ± 43.49 S6 9.46 ± 0.16 21.08 ± 0.97 58.14 ± 1.51 62.09 ± 1.50 267.04 ± 12.80 53.04 ± 2.66 1066.92 ± 45.93 S7 8.05 ± 0.07 16.90 ± 1.04 49.79 ± 2.21 53.15 ± 1.85 224.85 ± 3.00 48.00 ± 2.25 904.36 ± 72.36 The data were expressed as the mean of individual observations with standard deviation (mean ± SD) We also assessed several types of soil enzyme content as key indicators for microbial functioning controlling the decomposition rate of soil organic matter and nutrient cycling processes, evaluating the influence of the operation of the electric power substation on the soil. In this study, the content of MDA, SOD, GSH and LDH in the soil samples from S6 was marginally higher than those at other collection sites, although not statistically significant (Table 2 ). Similar trends were observed for the content of acid protease, acid phosphatase and soil sucrase. Soil microbial characteristics of 7 different collection sites Metagenomic sequencing was conducted on the 7 distinct soil sample surrounding an electric power substation, yielding an average of 7.21 million reads per sample (Table S2). From these metagenomes, 5,694,546 non-redundant catalog genes were identified, characterized by an average length of 637.2bp. Among them, there were 1,648,487 genes annotated by KEEG with an annotation rate of 0.289, suggesting that the existence of numerous genes with functions yet to be elucidated. Representative sequences of the non-redundant gene catalog were annotated using the NCBI NR database (Version: 2021.11) through BLASTP implemented in Diamond (Buchfink et al., 2015 ) ( http://www.diamondsearch.org/index.php , version 0.8.35), with an e-value cutoff of 1e -5 for taxonomic annotations. In the Initial phase of analysis, an alpha diversity assessment was undertaken to evaluate species richness within the soil samples. As illustrated in Fig. 2ab, a substantial difference in the richness and alpha diversity of soil microbes was evident among the 7 sites, as indicated by the Shannon index ( ρ < 0.05) and Simpson index ( ρ < 0.05). This observation suggested that the uniformity and richness of microbial diversity were influenced by the distance of the sampling point from the electric power substation. Notably, soil samples from S6 and S7, representing the sites nearest and furthest from the power substation, respectively, exhibited the highest values of Simpson index. Furthermore, the analysis of beta diversity in soil microbial communities, employing NMDS and PCoA to elucidate differences in species composition, unveiled significant variation among the collection sites. Utilizing Bray–Curtis distance measurements, both NMDS (Fig. 2 c-e) and PCoA (Figure S1 a-c) highlighted noteworthy dissimilarities in the community composition of bacteria (stress < 0.05; PERMANOVA: ρ = 0.001) across the 7 sites. The microbial communities of sites (S1–S2) and (S4–S5) exhibited tight clustering, indicating a relatively consistent microbial community composition at the S1 and S2 sites, as well as the S4 and S5 sites. However, sites S3, S6 and S7 were distinctly separated, signifying inconsistency in the microbial community composition at these locations. In contrast, the fungal community structure did not demonstrate representativeness (stress > 0.05) across the 7 sites (Fig. 2 f-h), but exhibited significant differences at the phylum, genus and species levels (PERMANOVA: ρ = 0.001) (Figure S1 d-f). Soil microbial communities of 7 different collection sites Bacterial phyla within soil samples from each collection site were analyzed at the phylum level (Fig. 3 a). Legends presenting relative abundance below the top 30 were excluded and categorized into other groups. The predominant bacterial populations, encompassing Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi, collectively constituted over 65% of the total microbial population across all soil samples. Acidobacteria, the most abundant bacterial phylum, exhibited reduced abundance at the S6 (13.03%) and S7 (11.31%) sites relative to other collection points. The average relative abundance of Proteobacteria at the S6 site (42.64%) surpassed that of other sites. The phylum Actinobacteria attained its highest relative abundance at the S5 site (29.26%) and its lowest at the S3 site (6.15%). Chloroflexi exhibited notably lower relative abundance at the S6 site (1.55%) compared to the S1 (11.66%), S2 (5.89%), S3 (2.56%), S4 (10.94%), S5 (8.77%) and S7 (9.86%) sites (Fig. 3 a). The relative notable enrichment of Gemmatimonadetes was observed at the S3 (8.31%) and S6 (10.35%) sites (Fig. 3 a), exceeding that at the S1 (1.34%), S2 (1.15%), S4 (1.14%), S5 (1.60%) and S7 (6.34%). Bacteroidetes displayed its highest abundance at the S6 site (6.21%), followed by S7 (5.46%), S3 (1.92%), S4 (1.69%), S5 (0.77%), S1 (0.35%) and S2 (0.33%). The dissimilarities in soil microbial community composition across different sites were further elucidated through a detailed analysis employing a heatmap of bacteria genera, showcasing the top 50 species in overall abundances. As illustrated in Fig. 3 b, the relative abundances of these bacteria, including Lysobacte r (Proteobacteria), Sphingomonas (Proteobacteria), Unclassified_Comamonadaceae (Proteobacteria), Nocardioides (Actinobacteria), Knoellia (Actinobacteria), Solirubrobacter (Actinobacteria), Terrabacter (Actinobacteria), Phycicoccus (Actinobacteria), Gaiella (Actinobacteria), Luteitalea (Acidobacteria), Flavisolibacter (Bacteroidetes), Gemmatirosa (Gemmatimonadetes), exhibited significantly higher abundances at the S6 site compared to other sites. The genus Bradyrhizobium , however, demonstrated similar relative abundances across all 7 sites, averaging 3.21%. Additionally, dominant bacterial abundances at the species level were presented in Fig. 3 c. Sphingomonas mesophila and Sphingomonas edaphi , individually constituting 2.72% and 4.85% of the total bacterial community, manifested a manifold increase in abundance at the S6 site relative to other sites. Notably, there was a heightened abundance of Luteitalea_pratensis at the S6 site. Metagenomic sequence taxonomic analysis revealed nine phyla, with Basidiomycota, Ascomycota and Mucoromycota constituting the most abundant, collectively averaging over 95% of all sequences in the fungi community (Fig. 3 d). Ascomycota emerged as the most abundant phylum, with an average relative abundance exceeding 50% at the (S2, S4) sites and surpassing 60% at the S6 site, while its relative abundance at the S5 site was comparatively lower. The relative abundance of Basidiomycota at the (S1, S3 and S5) exceeded that at the other points, whereas Mucoromycota exhibited the highest relative abundance at the (S2, S3 and S7) sites. Chytridiomycota and Zoopagomycota were also present, with average relative abundances of 1.15% and 1.18%, respectively. The Heatmap of fungal genera with the top 50 species in overall abundances were presented in Fig. 3 e. The genera of detected fungal species, showing greater richness, displayed distinct distribution characteristics at the 7 sites. It was observed that a relative lower abundant was noted for most all species at the S6 site compared to other points. The most representative fungal genus was Talaromyces (Ascomycota) and Aspergillus (Ascomycota), individually constituting 4.63% and 12.75% of the total fungal community. Furthermore, Fig. 3 f delineates the distinct community composition at the species level for different collection sites. Notably, for the S6 site, the most representative species with greater abundance was Aspergillus_cristatus , which accounted for 8.16% of the total fungal community. Nitrogen processing of soil microbial communities at different collection site The functions annotated under KEGG level 1 for the 7 types of soil surrounding the electric power substation encompassed diverse categories: metabolism (ave. 51.49%), genetic information processing (ave. 14.53%), environmental information processing (ave. 13.25%), cellular processes (ave. 11.00%), human diseases (ave. 5.34%), and organic systems (ave. 4.40%) (Figure S2a and Table S3). Significantly enriched functional pathways at level 2 (> 5%) included carbohydrate metabolism, amino acid metabolism, energy metabolism, metabolism of cofactors and vitamins, cellular community-prokaryotes, signal transduction, and membrane transport (Figure S2b). Among these, the S6 site exhibited a higher proportion of amino acid metabolism, and a lower proportion of carbohydrate metabolism and cellular community-prokaryotes compared to all other sites (Table S4). Relatively more abundant functional pathways at level 3 (> 5%), such as two-component system, quorum sensing, ABC transporters, Oxidative phosphorylation, pyruvate metabolism, and ribosome, were presented in Figure S2c. No significant difference was observed among these points. Noteworthy were the nitrogen metabolism pathways, including nitrogen fixation, nitrification and denitrification. The relative abundance of the genes such as nifA , nifD , nifH , nifK , and nifV (Kuypers et al., 2018 ; Bellés-Sancho et al., 2021 ) was lower at the S6 site than at other sites (Fig. 4 ), suggesting a lower capacity for soil microbial nitrogen fixation (N 2 →NH 3 ). In contrast, the relative abundance of two genes, amoA and amoB (Wang et al., 2022 ), was higher at the S6 site than at other sites, indicating increased nitrification (NH 3 →NO 3 − ). This observation aligns with the finding of low NH 4 + -N and high NO 3 − -N at the S6 site (Table 1 ). The relative abundance of genes, encoding functions related to the production of NO 2 − , nitric oxide and nitrous oxide (Li et al., 2023 ), was also displayed in Fig. 4 . Correlation between soil microbial communities and environmental factors Environmental factors exerted a substantial influence on microbial communities and their functions. RDA served as a valuable tool for expressing the correlation between environmental factors and microbial communities. As illustrated in Fig. 5 a, the RDA1 and RDA 2 axis accounted for 46.05% and 33.74%, respectively, of the total variation in microbial community composition and soil properties at the phylum level. The RDA model, based on soil microbial phylum-level data, effectively differentiated soils from various collection sites, corroborating our NMDS results. Specifically, the S6 site, situated nearest to the electric power substation, exhibited discernible associations with NO 3 − -N, SOM, EC, pH and soil enzymes, including SOD, GSH, soil sucrase, LDH, acid phosphatase, acid protease, MDA, but was negatively correlated with moisture content and NH 4 + -N. However, the S1 and S2 sites were opposite. The S3 site followed the same trend as LFMMS, TOC, SOM, pH, EC, SOD, LDH, MDA, acid phosphatase and acid protease, while S4 and S5 sites had no strong significant correlation with these environmental factor. Cluster analysis revealed notable correlations between Acidobacteria and TOC, LFMMS, moisture content, SOD, NH 4 + -N. Actinobacteria was positively correlations with NH 4 + -N, NO 3 − -N, moisture content, GSH and soil sucrase. Proteobacteria and Bacteroidetes demonstrated positive correlations with pH, NO 3 − -N, SOM, EC, and the aforementioned soil enzymes. Chloroflexi and Candidatus_Dormibacteraeota exhibited positive correlations with NO 3 − -N, NH 4 + -N and moisture content. Other microorganisms, such as Actinobacteria and Candidatus_Eremiobacteraeota, Firmicutes and Cyanobacteria, displayed positive correlations with various environmental factors, including NO 3 − -N, NH 4 + -N, moisture content, GSH and soil sucrase. Armatimonadetes were positively correlated with NO 3 − -N, NH 4 + -N, moisture content, TOC and LFMMS, while Gemmatimonadetes demonstrated positive correlations with SOM, pH, EC and soil enzymes. Additionally, RDA of the correlation between environmental factors and microbial communities at the genus was provided in Figure S3. Pearson’s correlation analyses were further conducted to elucidate the effects of the 15 environmental factors on the microbial communities at the genus level (see the phylum level in Figure S4), which exhibited distinct abundances at the S6 site compared to other sites. As depicted in Fig. 5 b, among the 18 bacteria genera, there were 6 Actinobacteria genera, 3 Proteobacteria genera, and one unidentified Candidaus_Eremiobacteraeota genus that were significantly positively correlated with NO 3 − -N ( ρ < 0.05), while there were 3 unidentified Actinobacteria genera and one unidentified Candidaus_Eremiobacteraeota genus that were significantly positively correlated with NH 4 + -N ( ρ < 0.05). Notably, bacteria that were significantly negatively correlated with moisture content included five Actinobacteria genera, four Proteobacteria genera, two unidentified Gemmatimonadetes genera, one Acidobacteria genus, one unidentified Candidaus_Eremiobacteraeota genus and one bacteroidetes genus. Bacteria that were significantly positively related to pH included three Proteobacteria genera, two Actinobacteria genera, two unidentified Gemmatimonadetes genera, one bacteroidetes genus, while three unidentified Actinobacteria genera and one unidentified Candidaus_Eremiobacteraeota genus were opposite. Five Actinobacteria genera, one bacteroidetes genus, two Proteobacteria genera and one Gemmatimonadetes genus ( Gemmatirosa ) exhibited negative correlation with TOC ( ρ < 0.05). The only significantly negative correlation with LFMS ( ρ < 0.05) was Actinobacteria genus ( phycicoccus ). Most of bacteria presented in Fig. 5 b was positively correlated with EC and only Acidobacteria genus ( Luteitalea ) showed significance ( ρ < 0.05). For the soil fungi (Fig. 5 c), those with a positive correlation with NH 4 + -N and moisture content included three Ascomycota genera, two Mucoromycota ( Bifiguratus and Linnemannia ), one Basidiomycota genus ( Amanita ), one Eumycota genus ( Absidia ), one Zygomycota genus ( Mucor ), while those with a negative correlation with NO 3 − -N included one Ascomycota genus ( Fusarium ) and one Eumycota genus ( Absidia ). However, no significant correlation was observed for TOC. Discussion Influence of soil collection site on soil properties Soil, a vital component of basinal ecosystems, serves as the material foundation for plant and microbial survival. Its physicochemical attributes dictate the structural compositions of plant and microbial communities (Yang and Hu, 2021 ). This study focused on soil samples collected in the vicinity of an electric power substation with limited vegetation. The S6 site, being the closest to the substation, was chosen as the primary research focus. As indicated by Wu et al. ( 2019 ), sites with lower moisture content and higher pH could experience upward movement of soluble salts from the deeper soil layers, exemplified by the conditions at the S6 site. The lowest soil TOC at S6 (Table 1 ) contributed to higher soil pH through reduced H + release by roots and organic matter (Hong et al., 2018 ). The diminished TOC at S6, relative to other sites, was also associated with reduced SOM. Additionally, soil moisture content, known to enhance soil microbial activities influencing SOM mineralization and decomposition (Huang et al., 2013 ), was lower at S6 and likely contributed to lower TOC and TN levels (Cao et al., 2021 ). Notably, TN levels at the S6 site exceeded those at other sites (Table 1 ), possible attributed to the presence of strong EC, a significant factor influencing microbial community assembly (Zhang et al., 2019 ). Despite the absence of waste discharge typical of coal-fired power plants (Sun et al., 2024 ), the operation of electric power substation impacted soil physicochemical properties. Influence of collection site on the composition of soil microbial communities In this investigation, alpha diversity indexes for microbial communities displayed significant variations among soil samples from distinct collection sites (Fig. 2 a). Anthropogenic disturbances caused by the substation, such as EC, were implicated in statistically altering the alpha-diversity of microbial communities around the electric power substation. Bacterial functional prediction through PCoA analysis revealed clustering of bacterial communities from the 7 soil sites into five groups and significant differences (PERMANOVA: ρ = 0.001) among them (Figure S1 ), consistent with soil microbial alpha diversity. Across all soil samples, Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi emerged as dominant phyla (Fig. 3 a), mirroring findings in soils adjacent to mining and smelting areas (Liu et al., 2021 ) and coal-fired power plants (Sun et al., 2024 ). However, NMDS and PCoA analysis indicated distinct distribution patterns for soil bacterial community around the electric power substation. Notably, the S6 site exhibited significantly higher abundances of Proteobacteria and Acidobacteria and lower abundances of Chloroflexi compared to other sites (Table S1 ). This contradicts observations for coal-fired power plants (Sun et al., 2024 ). Acidobacteria, known for its significant role in soil carbon biogeochemical cycling (Zhou et al., 2019 ), constituted a substantial proportion in the bacterial community, with its growth inhibited by increasing pH value, particularly evident at the S6 site (pH = 7.09). Proteobacteria, recognized for nitrogen fixation and reducing nitrogen loss (Wang et al., 2023 ), demonstrated greater abundance at the S6 site (42.60%) compared to other collection points. Actinobacteriota, actively involved in the carbon and nitrogen cycle (Tao et al., 2022 ; Wei et al., 2023 ), exhibited sensible enrichment in all soil samples. Chloroflexi, with the function of autotrophic denitrification (Ge et al., 2020 ), showed significantly lower relative abundance at the S6 site (1.55%) than at the S1 (11.66%), S2 (5.89%), S3 (2.56%), S4 (10.94%), S5 (8.77%) and S7 (9.86%) sites (Fig. 3 a), thereby influencing nitrogen conversion capacity. Gemmatimonadetes, adapted to low-moisture environments (DeBruyn et al., 2011 ), displayed notable enrichment at the S6 sites (10.35%) relative to other sites (Fig. 3 a). This enrichment was attributed to the low moisture content, as it was negatively correlated with Gemmatimonadetes (also in this study)(Fig. 5 a). This higher number of Gemmatimonadetes at the S6 site, negatively relating to NO 3 − -N, NH 4 + -N, TOC and LFMMS, potentially influenced the soil due to its strong photosynthetic-fueled ability to oxidize organic and inorganic compounds (Huang et al., 2016 ). Bacteroidetes, possessing a robust ability to metabolize complex organic matter, protein and lipid, exhibited higher abundance (6.21%) at the S6 site, followed by S7 (5.46%) and S3 (1.92%). Therefore, we inferred that the substation significantly increased the abundances of Proteobacteria and Acidobacteria but inhibited Chloroflexi in soils. At the bacterial genus level, soil properties explained 85.5% of the variation in soil microorganism composition (Figure S3), and discernible differences were evident among the 7 sites (Fig. 3 b). The genus Unclassified_Comamonadaceae (Sotres et al., 2016 ), Lysobacte r (Iwata et al., 2010 ), Sphingomonas (Yang et al., 2014 ), and Solirubrobacter (Wei et al., 2023 ), renowned for their proficiency in facilitating superior nitrogen conversion, were notably enriched at the S6 site, playing a pivotal role in this observed enrichment. The former two types of bacteria exhibited positive correlations with NO 3 − -N, EC, pH, and soil enzymes, the middle type showed positive correlations with NO 3 − -N, NH 4 + -N, EC, pH, and soil enzymes, and the latter was positively correlated to NH 4 + -N, TOC, SOM, and soil moisture content (Fig. 5 b). Other microbial entities, including Flavisolibacter (Hernandez-Guzman et al. , 2022), Gemmatirosa (Liu et al., 2021 ; Sun et al., 2023 ), Terrabacter (Kruglova et al., 2017 ), and Phycicoccus (Yang et al., 2023 ), associated with nitrogen metabolism, along with the genera like Nocardioides , the functions of which were yet to be elucidated in the biogeochemical cycle, were notably abundant at the S6 site. Despite their prevalence, the precise roles of these microorganisms remained ambiguous. Notably, Bradyrhizobium , acknowledged for its pivotal role in nitrogen fixation (Sun et al., 2023 ), did not exhibit enrichment at the S6 site. Regarding the heightened enrichment of the species Sphingomonas mesophila (Li et al., 2019 ) and Sphingomonas edaphi (Kim et al., 2020 ) at the S6 site relative to other sites (Fig. 3 c), their precise contributions to the overall microbial community remained elusive. The heightened abundance of Luteitalea_pratensis at the S6 site was due to the reason that it was an organism thriving within a narrow pH tolerance range (5.3–8.3) (Vieira et al., 2017 ). These finding suggested that the operation of the electric power substation significantly increased the abundances of bacteria with nitrogen-processing function. Reports regarding the composition and structure of soil fungal communities in the vicinity of electric power substation are notably limited. In this investigation, the fungal communities at the phylum level were predominantly constituted by Basidiomycota and Ascomycota in all soil samples (Fig. 3 d), a pattern consistent with their ubiquity in most soils (Gqozo et al., 2020 ). The prevalence of Ascomycota might be associated with its capacity to degrade cellulose and hemicellulose (Shary et al. , 2007; Yang et al., 2023 ), while the phyla Basidiomycota and Mucoromycota had been reported to be linked to the degradation of complex lignocelluloses (Lundell et al., 2010 ; Huhe et al. , 2017). Beta-diversity analysis through NMDS for fungal communities across the 7 sites (stress > 0.05) did not reveal representativeness, regardless of the level of analysis. No significant differences were observed between any two sites, although significantly differences were noted across all 7 sites (PERMANOVA: ρ = 0.001) (Figure S1 d-f). Therefore, it could be inferred that fungal communities had adapted into cohesive assemblages under the prolonged operation of the substation. Influence of soil collection site on the nitrogen processing Nitrogen, a limiting nutrient in basinal ecosystems, plays a vital role in ecosystem productivity. Nitrogen fixation, the primary mechanism through which microorganisms acquire nitrogen resources, is crucial for sustaining ecosystem function. The genera Sphingomonas (15.31%), Phycicoccus (0.97%) and Solirubrobacter (0.71%) exhibited significant positive correlations with NH 4 + -N, displaying higher abundance at the S6 site characterized by lower soil moisture content (26.91%) (Fig. 3 ). Given that nitrogen fixation predominantly occurs in anaerobic environments, the relative lower moisture content at the S6 site may impede soil nitrogen fixation (Zhang et al., 2020 ), which could explain the observed lower relative abundance of nitrogen fixation genes (Fig. 4 ) and the reduction in NH 4 + -N content at this site. Nitrification, an important microbial nitrogen-loss pathway in soils, displayed a positive correlation with NO 3 − -N. In comparison to other sites, genera at the S6 site, such as Sphingomonas , Phycicoccus , Terrabacter (1.66%), Unclassified_Solirubrobacterales (0.19%), Unclassified_Comamonadaceae (15.45%), and Lysobacte r (2.56%) exhibited higher abundance. Additionally, the genes associated with the function of nitrification also exhibited an increased abundance at the S6 site. These factors collectively might have contributed to the observed escalation in NO 3 − -N levels following the operation of the electric power substation. Conclusion This study employed metagenomic sequencing to investigate the relationships between microbial communities and environmental variables in soils surrounding an electric power substation. The operation of the substation induced alterations in soil physicochemical properties, with significant variations observed across the seven sites. Notably, differences in soil physicochemical properties, diversity, composition and structure of soil bacterial communities were evident under the influence of the electric power substation, whereas such distinctions were not observed in soil fungal communities. Moisture content and pH, key environmental variables, and anthropogenic disturbance from EC influenced soil bacterial communities, while the influence of LFMMS was negligible. Genera positively associated with NO 3 − -N production, such as Gemmatimonadetes and Sphingomonas , exhibited pronounced enrichment at the S6 site, situated closest to the substation, representing a important component of the soil bacterial community. The soil bacterial communities displayed greater sensitivity to the electric power substation operation compared to eukaryotes, and their dynamics had a direct and significant impact on microbial community diversity within the substation ecosystem. Declarations Author contributions ZX Xu, B Zeng, S Chen: Conceptualization, Methodology, Investigation. S Xiao, LG Jiang: Methodology; Formal analysis. X Li, YF Wu: Validation, Formal analysis. MX Zhao, SR Chen: Data curation, original draft preparation. LX You: Conceptualization, Writing-review and editing; Funding acquisition, Project administration. Funding This work was supported by the National Natural Science Foundation of China (Grant No. 22072017). Conflict interests The authors declare no competing interests. References Bagli S, Geneletti D, Orsi F (2011) Routeing of power lines through least-cost path analysis and multicriteria evaluation to minimise environmental impacts. Environ. Impact Assess. 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J Soils Sediments 19: 356–365. https://doi.org/10.1007/s11368-018-2035-y Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3779548","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":263849822,"identity":"5eedc332-3c94-4f8e-90ff-e70945283ebb","order_by":0,"name":"Zhi-Xin Xu","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Xin","middleName":"","lastName":"Xu","suffix":""},{"id":263849823,"identity":"0037d411-6371-4490-8a08-c17d56b40050","order_by":1,"name":"Bo Zeng","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zeng","suffix":""},{"id":263849824,"identity":"d1ac9ef4-1b67-4584-a73b-cf0ecf3c313a","order_by":2,"name":"Sheng Chen","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Chen","suffix":""},{"id":263849825,"identity":"d72bee15-4b43-49da-a192-736441f5948f","order_by":3,"name":"Sa Xiao","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Sa","middleName":"","lastName":"Xiao","suffix":""},{"id":263849826,"identity":"ad671126-085f-4e3d-b470-19a5f4d773dc","order_by":4,"name":"Lin-Gao Jiang","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Lin-Gao","middleName":"","lastName":"Jiang","suffix":""},{"id":263849829,"identity":"0c279dd8-a91b-475a-a3a4-196316f2be71","order_by":5,"name":"Xiang Li","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":263849831,"identity":"09d30594-af28-489d-8de1-3256bf3e1ea5","order_by":6,"name":"Yun-Fang Wu","email":"","orcid":"","institution":"Super High Voltage Branch of State Grid Fujian Electric Power Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Yun-Fang","middleName":"","lastName":"Wu","suffix":""},{"id":263849833,"identity":"4b4a1a7c-5ade-4764-b59f-4db9f29ae7a7","order_by":7,"name":"Meng-Xin Zhao","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Meng-Xin","middleName":"","lastName":"Zhao","suffix":""},{"id":263849834,"identity":"71cabc4e-51d5-4fda-91b2-acd5ba22634e","order_by":8,"name":"Si-Ru Chen","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Si-Ru","middleName":"","lastName":"Chen","suffix":""},{"id":263849835,"identity":"e5ee15e5-cff2-48df-b70c-87139470be2e","order_by":9,"name":"Le-Xing X. You","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3RMQuCQBjG8dcCXd5qPYjyKyhCBH4ZRfAWbXZoOBFcWx2CvopwcNPZ3N7q0NgUpQVtl21B9x/uWe7HDQeg0/1g09cuAepux5+J+VrvexKy5w4hViHmmHF6yBtBIPNDZjW1mqCIPJQ8zdkxJiBpyHATqAlJ3POk5GkBckWMkoeMoKMmduvwB6FmT25DCMH+lQB7woYQjCN3L6lbgfTWgaBeiYmazCwuSJv5tl1J93TZ+oudJdWka4TdSWqAAN4/pcy49u+xIXd1Op3uH7sDZ8k+dcdW2ZEAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":true,"prefix":"","firstName":"Le-Xing","middleName":"X.","lastName":"You","suffix":""}],"badges":[],"createdAt":"2023-12-20 03:14:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3779548/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3779548/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49020899,"identity":"1ff251be-2534-4d68-af62-de59fcdffbd6","added_by":"auto","created_at":"2024-01-01 09:07:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":729509,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of soil collection sites surrounding an electric power substation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/9c3644c401d894bb9d4e306a.png"},{"id":49020900,"identity":"5e33836a-4969-487f-aedc-5c90dafdb3f1","added_by":"auto","created_at":"2024-01-01 09:07:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":809335,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity Shannon (a) and Simpson (b) index of soil microbial communities collected from 7 points surrounding an electric power substation; NMDS analysis of the differences in bacterial and fungus communities structures at phylum (c, f), genus (d, g) and species (e, h) based on Bray-Curtis distance\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/7a91394fedfd99c1d334cb4d.png"},{"id":49021340,"identity":"6fdb6516-ba64-4533-a0f4-100db877a4b9","added_by":"auto","created_at":"2024-01-01 09:15:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1764069,"visible":true,"origin":"","legend":"\u003cp\u003eComposition of soil microbial communities in seven collection sites. Composition of (a-c) bacteria and (d-f) fungal at the (a, d) phylum, (b, e) genus and (c, f) species level.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/8b68d01af236ea4e158377b6.png"},{"id":49020903,"identity":"ee4d3214-2298-4dd7-bf76-d5a277125745","added_by":"auto","created_at":"2024-01-01 09:07:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":198739,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap representing the relative abundance of nitrogen functional genes in soil community in the S1–S7 samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/eb98f3925900601b5ed015e5.png"},{"id":49021422,"identity":"53aec8c1-2361-43f5-9039-72bea0e70f38","added_by":"auto","created_at":"2024-01-01 09:23:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1035083,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis between microorganisms and soil physicochemical properties across sites S1–S7. (a) Redundancy analysis of microbial communities with environmental factors. (b, c) Pearson correlation analyses between microbial communities and environmental factors. The distance between soil sample points signifies the similarities and differences in functional composition among samples. The projected distance from the sample point to the environmental factor indicated the extent to which the sample was influenced by the environmental factor. The closer the projection line, the more similar the impact of the environmental factor on the two samples. The angle between environmental factors/species denotes the positive and negative correlations between environmental factors/species. NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, ammonium nitrogen; LFMMS: low-frequency mass magnetic susceptibility; SOM, soil organic matter; TOC, total organic carbon; NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e−\u003c/sup\u003e-N, nitrate nitrogen; EC, electric conductivity; MDA, malondialdehyde; GSH, glutathione; SOD, superoxide dismutase; LDH, lactate dehydrogenase. Significance levels: *\u003cem\u003eρ\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eρ\u003c/em\u003e \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/fb2813e08a1657944c51eb9c.png"},{"id":50122796,"identity":"e894bc0a-b6f2-468e-a268-481acb3a8ac6","added_by":"auto","created_at":"2024-01-24 20:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2467008,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/3f9c72ed-801f-4a44-a831-399c63e09a37.pdf"},{"id":49020904,"identity":"63174a2d-5fda-46ba-8ac0-2410e35d5cb8","added_by":"auto","created_at":"2024-01-01 09:07:06","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4413765,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3779548/v1/86bc16ec18b7535a5f652666.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Soil microbial community composition and nitrogen enrichment responses to the operation of electric power substation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe relentless advance of modernization has ushered in a burgeoning demand for electric energy. This demand necessitates the efficient transportation of electricity from power generation facilities, often located at considerable distances from primary consumption centers, to consumers. This intricate process relies upon an expansive network encompassing power substations, transmission lines, and distribution lines. Power substations serve as pivotal assemblages of equipment designed for voltage control and adjustment. Notably, transmission lines are discernible from distribution lines due to their capacity to support higher voltages and their proclivity to span greater distances (Biasotto and Kindel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Chinese landscape bears witness to a proliferation of power substations and the extensive deployment of electrical power lines, which traverse diverse terrains, including farmland and highways. This remarkable infrastructural landscape is nevertheless compelled to evolve in response to the escalating electricity demand, necessitating the establishment of additional electric power substations.\u003c/p\u003e \u003cp\u003eHowever, it is imperative to acknowledge that electric power substations, throughout their construction and operational phases, harbor the potential for substantial environmental ramifications (Bagli et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In numerous nations, the vicinity beneath power cables necessitates rigorous vegetation management to avert interference with line structural integrity and energy transmission. Furthermore, the operation of power lines and electrical equipment engenders the generation of electromagnetic fields. As the pivotal constituents of terrestrial ecosystems, namely soil, inevitably bear the imprint of power lines and substations. Among the custodians of soil health are soil microorganisms, integral to biogeochemical cycles and habitat sustenance across the earth (Gadd, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These microorganisms are enmeshed in complex ecological relationships encompassing competition, predation, parasitism, reciprocity, and neutrality (Faust and Raes, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Of paramount significance, soil microorganisms orchestrate the regulation of soil nutrient cycles, including the vital nitrogen cycle, and exert a profound influence on plant productivity. Consequently, they stand as widely acknowledged indicators of soil health (Whalen et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In response to environmental stressors, soil microorganisms exhibit distinctive adaptive responses, exemplified by the production of diverse enzymes such as superoxide dismutase (SOD), which confers effective resistance against the deleterious effects of reactive oxygen species (Roubalov\u0026aacute; et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and safeguards plant well-being (Huseynova et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Notwithstanding substantial progress in the realm of environmental impact assessment pertaining to transmission lines and electric power substations, significant lacunae persist with regard to the representation of distinct biological strata, such as genes, and the comprehensive incorporation of diverse biodiversity metrics, spanning composition, structure, and function, into impact prognostication (Biasotto and Kindel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Particularly noteworthy is the paucity of scholarly inquiry into the ramifications of electric power substations on soil microorganisms.\u003c/p\u003e \u003cp\u003eIn accordance with the foregoing explanation, the primary objective of this investigation was to document the alterations in soil physicochemical properties, alongside the structural and functional aspects of microbial communities across seven distinct sites at varying distances from an electric power substation, utilizing metagenomic sequencing techniques. A special emphasis was placed on elucidating correlations between soil physicochemical properties and microbial communities, in addition to genes associated with the nitrogen cycle in the proximity of the substation. Through comprehensive analyses of these factors, we aimed to delineate the impact of the electric power substation on soil nutrition, the dynamics shifts within microbial community, and changes in functional genes related to nitrogen cycling.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSite description and soil sample collection\u003c/h2\u003e \u003cp\u003eSoil specimens were systematically obtained from the environs of an electric power substation located at coordinates 119\u0026deg;08\u0026prime;N latitude and 26\u0026deg;34\u0026prime;E longitude, situated within Xintang village, Minhou county, Fujian province, southeastern China (as delineated in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Xintang village occupies a locale characterized by a subtropical oceanic monsoon climate, typified by annual mean temperatures ranging between 17 and 21\u0026deg;C, and annual average precipitation levels spanning 1400 to 2000 mm. The topography of the study site comprises a modest, low-lying basin with predominant rainfall occurring between May and September. Soil samples were diligently collected from distinct designated regions within the 0\u0026ndash;20 cm stratum of the topsoil, employing the plumblossom five-point methodology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is noteworthy that the selected areas exhibited minimal vegetation cover. For each of the sampling locales (denoted as S1 through S7), an array of triplicate sampling plots, each measuring 5 meters by 5 meters, was judiciously established in a randomized fashion. Within the confines of each sampling plot, five discrete soil samples were haphazardly extracted, subject to meticulous amalgamation to yield a comprehensive composite soil specimen (Guo et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Each of these composite soil samples was sealed within aseptic ziplock bags and expeditiously transported, under cryogenic conditions facilitated by dry ice, to the laboratory facility. Each composite soil sample underwent a meticulous sieving process, utilizing a 2 mm mesh screen, with the dual objectives of homogenizing the soil matrix and eliminating larger particles, soil-dwelling organisms, and vegetative debris. The entirety of the composite soil samples underwent a subdivision into three discrete portions. The first portion was allocated for the comprehensive determination of the physicochemical attributes of the soil matrix, while the second portion was earmarked for the quantification of soil oxidative kinase content and soil enzyme activity. The remaining fraction was meticulously preserved at -80\u0026deg;C, primed for subsequent genomic DNA extraction and sequencing endeavors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSoil physicochemical properties measurement\u003c/h2\u003e \u003cp\u003eFresh soil (100 g) was collected in each designated sampling plot using ring knives to determine soil bulk density, a parameter assessed through weight method (Zhang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The determination of soil dry matter and moisture content was undertaken employing the classical gravimetric method, while soil particle density was ascertained through the volume replacement technique. The quantification of soil organic matter (SOM) content was executed following the classical potassium dichromate method. The low-frequency mass magnetic susceptibility (LFMMS) and electric conductivity (EC) of soil samples were measured using magnetic susceptibility meter (MS2, Bartington, UK) and conductivity meter (HQ4300, HACH, USA), respectively. Moreover, the pH value of each soil sample, prepared at a soil-water ratio: 1:2.5, was diligently measured utilizing a pH meter (Sartorius PB-10, German). Specifically, for the determination of nitrite nitrogen (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N) and ammonium nitrogen (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N) concentrations, the soil samples (soil-water ratio: 1:5) were intimately mixed with KCl solution at a final concentration of 1 M. Subsequently, this mixture underwent agitation at 200 rpm for a duration of 1 h and was then subjected to centrifugation (3000 rpm, 10 minutes) to facilitate the retrieval of the supernatant. The contents of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N within the supernatant were subsequently quantified utilizing a UV-vis spectrophotometer (TU-1810, Bejing Purkinje General Instrument Co. Ltd., China)(see details in SI Section 1), in strict accordance with their respective standard curves. The content of nitrate nitrogen (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N) within the filtered supernatant which was determined using a flow injection auto-analyzer (Skalar Analytical, AACE, Germany)(Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the overall content of total nitrogen (TN), soil organic nitrogen (SON), total carton (TC) and total organic carbon (TOC) was comprehensively determined (see SI Section 1 and Section 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of enzyme content in soil\u003c/h2\u003e \u003cp\u003eThe content of soil enzymes including SOD, malondialdehyde (MDA), glutathione (GSH), lactate dehydrogenase (LDH), acid protease, acid phosphatase and soil sucrase were determined using enzyme test kit (Gene Hunter, HongKong, China) (see the details in SI Section 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSoil DNA extraction and sequencing\u003c/h2\u003e \u003cp\u003eMicrobial genomic DNA were extracted from individual soil samples (0.5 g) using the Omega E.Z.N.A Stool DNA Kit for Soil (Omega Bio-tek, Inc., USA), following the manufacturer's instructions. The purity and quality of the genomic DNA were assessed via 1% agarose gels electrophoresis (Zhang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and their concentration were accurately quantified using the Qubit 4.0 Fluorometer (Thermo Fisher Scientific Inc., USA). Subsequently, the DNA was fragmented to 300 bp employing the Covaris ultrasonic crusher, and the resulting fragments underwent further processing, including end repair, A tailing, and ligation of Illumina compatible adapters. Finally, sequencing were conducted on an Illumina NovaSeq PE150 platform at Allwegene Company (Beijing, China) (Table S2). The Illumina NovaSeq sequencing data were deposited in the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (NCBI), under accession number PRJNA1037611 (SUB13936859). The abundance of functional genes relevant to nitrogen metabolism were examined based on sequencing data combined with Kyoto Encyclopedia of Genes and Genomes (KEGG) database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eSignificant differences were assessed through one-way analysis of variance (SPSS 19) with \u003cem\u003eρ\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 considered as statistically significant. Non-metric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity were conducted using the R (Version 4.0.2) package ggplot2. Vegan ggplot2 package was employed for redundancy analyses (RDA) to analyze the correlation between environmental factors and the abundance of microbial community phyla. A Pearson correlation test was executed to examine the relationships between soil physicochemical properties and the relative abundance of microbial community, utilizing the R (Version 3.6) packages \u0026ldquo;psych\u0026rdquo; and \u0026ldquo;pheatmap\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSoil physicochemical properties of 7 different collection sites\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil physicochemical properties from each collection site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSOM\u003c/p\u003e \u003cp\u003e(g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil bulk density (g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil density (g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFresh soil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLFMMS\u003c/p\u003e \u003cp\u003e(10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u0026middot;m\u003csup\u003e3\u003c/sup\u003e\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLow-frequency susceptibility factor (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003cp\u003e(\u0026micro;S\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003cp\u003e(g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSolid TOC\u003c/p\u003e \u003cp\u003e(g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(\u0026micro;g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N\u003c/p\u003e \u003cp\u003e(mg\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N (mg\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSON\u003c/p\u003e \u003cp\u003e(mg\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003cp\u003e(mg\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDry matter content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMoisture content (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e20.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e7.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e785.93\u0026thinsp;\u0026plusmn;\u0026thinsp;25.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e793.66\u0026thinsp;\u0026plusmn;\u0026thinsp;26.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e35.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e30.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e15.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e12.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e40.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e4281.37\u0026thinsp;\u0026plusmn;\u0026thinsp;78.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e4328.08\u0026thinsp;\u0026plusmn;\u0026thinsp;81.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e43.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e27.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e12.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e10.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e1599.15\u0026thinsp;\u0026plusmn;\u0026thinsp;19.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e1605.36\u0026thinsp;\u0026plusmn;\u0026thinsp;18.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e29.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e15.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e8.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e28.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e6.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e1153.11\u0026thinsp;\u0026plusmn;\u0026thinsp;61.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e1187.97\u0026thinsp;\u0026plusmn;\u0026thinsp;60.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e22.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e27.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e16.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e5162.99\u0026thinsp;\u0026plusmn;\u0026thinsp;121.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e5188.19\u0026thinsp;\u0026plusmn;\u0026thinsp;117.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e78.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e115.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e7.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e5.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e8.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e5486.26\u0026thinsp;\u0026plusmn;\u0026thinsp;132.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e5498.63\u0026thinsp;\u0026plusmn;\u0026thinsp;122.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e14.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e9.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c12\"\u003e \u003cp\u003e6.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c13\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c14\"\u003e \u003cp\u003e15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c15\"\u003e \u003cp\u003e8.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c16\"\u003e \u003cp\u003e2985.51\u0026thinsp;\u0026plusmn;\u0026thinsp;45.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c17\"\u003e \u003cp\u003e3009.32\u0026thinsp;\u0026plusmn;\u0026thinsp;51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003eThe data were expressed as the mean of individual observations with standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe physicochemical characteristics of soil samples from each collection site were comprehensively detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Variations in soil pH were observed across locations, ranging from 4.85 to 7.09, while SOM and bulk density spanned from 8.84 to 27.25 g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1.01 to 1.51 g\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively. LFMMS and EC were employed as indicators of magnetization and salinity levels within the soil. Consequently, these properties were measured for soil samples collected in proximity to the electric power substation. As indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, all soil samples exhibited relative low LFMMS (\u0026lt;\u0026thinsp;45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u0026middot;m\u003csup\u003e3\u003c/sup\u003e\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and EC (\u0026lt;\u0026thinsp;200 \u0026micro;S\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Notably, the EC for soil collected site S6\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(115.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 \u0026micro;S\u0026middot;cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the closest to the power substation, was significantly higher than those at other points. Furthermore, the TC and TOC content in the soil samples fell within the range of 7.51 to 15.49 g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 5.60 to 10.22 g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Nevertheless, the TC and TOC content in the soil samples from S6 were lower than those at other collection sites. It was noteworthy that the TN content (5.50 g\u0026middot;Kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at the S6 site surpassed that observed in other collection sites (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Considering that TN predominantly comprises SON, fixed nitrogen was primarily utilized for biomolecule synthesis by plants and microorganisms. Inorganic nitrogen contents were considerably lower in comparison to SON content. The NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N content in the S5 and S6 sites exceeded that in the remaining collection sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, for NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, the lowest content was observed at the S6 site compared to other collection points, with its content being lower than that of NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContent of soil enzymes from each collection site\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil Sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMDA\u003c/p\u003e \u003cp\u003e(nmol\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOD\u003c/p\u003e \u003cp\u003e(U\u0026middot;mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGSH\u003c/p\u003e \u003cp\u003e(ng\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003cp\u003e(IU\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAcid protease\u003c/p\u003e \u003cp\u003e(U\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAcid phosphatase (IU\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSoil Sucrase\u003c/p\u003e \u003cp\u003e(U\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e45.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e47.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e209.46\u0026thinsp;\u0026plusmn;\u0026thinsp;16.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e46.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e857.25\u0026thinsp;\u0026plusmn;\u0026thinsp;50.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e17.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e51.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e44.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e218.77\u0026thinsp;\u0026plusmn;\u0026thinsp;15.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e47.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e942.78\u0026thinsp;\u0026plusmn;\u0026thinsp;53.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e52.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e58.06\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e260.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e50.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e864.37\u0026thinsp;\u0026plusmn;\u0026thinsp;41.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e55.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e46.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e234.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e51.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e853.59\u0026thinsp;\u0026plusmn;\u0026thinsp;58.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e58.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e56.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e227.70\u0026thinsp;\u0026plusmn;\u0026thinsp;9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e40.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e994.84\u0026thinsp;\u0026plusmn;\u0026thinsp;43.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e58.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e62.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e267.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e53.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e1066.92\u0026thinsp;\u0026plusmn;\u0026thinsp;45.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eS7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e49.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e53.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e224.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e48.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e904.36\u0026thinsp;\u0026plusmn;\u0026thinsp;72.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe data were expressed as the mean of individual observations with standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also assessed several types of soil enzyme content as key indicators for microbial functioning controlling the decomposition rate of soil organic matter and nutrient cycling processes, evaluating the influence of the operation of the electric power substation on the soil. In this study, the content of MDA, SOD, GSH and LDH in the soil samples from S6 was marginally higher than those at other collection sites, although not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar trends were observed for the content of acid protease, acid phosphatase and soil sucrase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSoil microbial characteristics of 7 different collection sites\u003c/h2\u003e \u003cp\u003eMetagenomic sequencing was conducted on the 7 distinct soil sample surrounding an electric power substation, yielding an average of 7.21\u0026nbsp;million reads per sample (Table S2). From these metagenomes, 5,694,546 non-redundant catalog genes were identified, characterized by an average length of 637.2bp. Among them, there were 1,648,487 genes annotated by KEEG with an annotation rate of 0.289, suggesting that the existence of numerous genes with functions yet to be elucidated. Representative sequences of the non-redundant gene catalog were annotated using the NCBI NR database (Version: 2021.11) through BLASTP implemented in Diamond (Buchfink et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.diamondsearch.org/index.php\u003c/span\u003e\u003cspan address=\"http://www.diamondsearch.org/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, version 0.8.35), with an e-value cutoff of 1e\u003csup\u003e-5\u003c/sup\u003e for taxonomic annotations. In the Initial phase of analysis, an alpha diversity assessment was undertaken to evaluate species richness within the soil samples. As illustrated in Fig.\u0026nbsp;2ab, a substantial difference in the richness and alpha diversity of soil microbes was evident among the 7 sites, as indicated by the Shannon index (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Simpson index (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This observation suggested that the uniformity and richness of microbial diversity were influenced by the distance of the sampling point from the electric power substation. Notably, soil samples from S6\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eand S7, representing the sites nearest and furthest from the power substation, respectively, exhibited the highest values of Simpson index.\u003c/p\u003e \u003cp\u003eFurthermore, the analysis of beta diversity in soil microbial communities, employing NMDS and PCoA to elucidate differences in species composition, unveiled significant variation among the collection sites. Utilizing Bray\u0026ndash;Curtis distance measurements, both NMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-e) and PCoA (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-c) highlighted noteworthy dissimilarities in the community composition of bacteria (stress\u0026thinsp;\u0026lt;\u0026thinsp;0.05; PERMANOVA: \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) across the 7 sites. The microbial communities of sites (S1\u0026ndash;S2) and (S4\u0026ndash;S5) exhibited tight clustering, indicating a relatively consistent microbial community composition at the S1 and S2 sites, as well as the S4 and S5 sites. However, sites S3, S6 and S7 were distinctly separated, signifying inconsistency in the microbial community composition at these locations. In contrast, the fungal community structure did not demonstrate representativeness (stress\u0026thinsp;\u0026gt;\u0026thinsp;0.05) across the 7 sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-h), but exhibited significant differences at the phylum, genus and species levels (PERMANOVA: \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed-f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSoil microbial communities of 7 different collection sites\u003c/h2\u003e \u003cp\u003eBacterial phyla within soil samples from each collection site were analyzed at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Legends presenting relative abundance below the top 30 were excluded and categorized into other groups. The predominant bacterial populations, encompassing Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi, collectively constituted over 65% of the total microbial population across all soil samples. Acidobacteria, the most abundant bacterial phylum, exhibited reduced abundance at the S6 (13.03%) and S7 (11.31%) sites relative to other collection points. The average relative abundance of Proteobacteria at the S6 site (42.64%) surpassed that of other sites. The phylum Actinobacteria attained its highest relative abundance at the S5 site (29.26%) and its lowest at the S3 site (6.15%). Chloroflexi exhibited notably lower relative abundance at the S6 site (1.55%) compared to the S1 (11.66%), S2 (5.89%), S3 (2.56%), S4 (10.94%), S5 (8.77%) and S7 (9.86%) sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The relative notable enrichment of Gemmatimonadetes was observed at the S3 (8.31%) and S6 (10.35%) sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), exceeding that at the S1 (1.34%), S2 (1.15%), S4 (1.14%), S5 (1.60%) and S7 (6.34%). Bacteroidetes displayed its highest abundance at the S6 site (6.21%), followed by S7 (5.46%), S3 (1.92%), S4 (1.69%), S5 (0.77%), S1 (0.35%) and S2 (0.33%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe dissimilarities in soil microbial community composition across different sites were further elucidated through a detailed analysis employing a heatmap of bacteria genera, showcasing the top 50 species in overall abundances. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, the relative abundances of these bacteria, including \u003cem\u003eLysobacte\u003c/em\u003er (Proteobacteria), \u003cem\u003eSphingomonas\u003c/em\u003e (Proteobacteria), \u003cem\u003eUnclassified_Comamonadaceae\u003c/em\u003e (Proteobacteria), \u003cem\u003eNocardioides\u003c/em\u003e (Actinobacteria), \u003cem\u003eKnoellia\u003c/em\u003e (Actinobacteria), Solirubrobacter (Actinobacteria), \u003cem\u003eTerrabacter\u003c/em\u003e (Actinobacteria), \u003cem\u003ePhycicoccus\u003c/em\u003e (Actinobacteria), \u003cem\u003eGaiella\u003c/em\u003e (Actinobacteria), \u003cem\u003eLuteitalea\u003c/em\u003e (Acidobacteria), \u003cem\u003eFlavisolibacter\u003c/em\u003e (Bacteroidetes), \u003cem\u003eGemmatirosa\u003c/em\u003e (Gemmatimonadetes), exhibited significantly higher abundances at the S6 site compared to other sites. The genus \u003cem\u003eBradyrhizobium\u003c/em\u003e, however, demonstrated similar relative abundances across all 7 sites, averaging 3.21%. Additionally, dominant bacterial abundances at the species level were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. \u003cem\u003eSphingomonas mesophila\u003c/em\u003e and \u003cem\u003eSphingomonas edaphi\u003c/em\u003e, individually constituting 2.72% and 4.85% of the total bacterial community, manifested a manifold increase in abundance at the S6 site relative to other sites. Notably, there was a heightened abundance of \u003cem\u003eLuteitalea_pratensis\u003c/em\u003e at the S6 site.\u003c/p\u003e \u003cp\u003eMetagenomic sequence taxonomic analysis revealed nine phyla, with Basidiomycota, Ascomycota and Mucoromycota constituting the most abundant, collectively averaging over 95% of all sequences in the fungi community (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Ascomycota emerged as the most abundant phylum, with an average relative abundance exceeding 50% at the (S2, S4) sites and surpassing 60% at the S6 site, while its relative abundance at the S5 site was comparatively lower. The relative abundance of Basidiomycota at the (S1, S3 and S5) exceeded that at the other points, whereas Mucoromycota exhibited the highest relative abundance at the (S2, S3 and S7) sites. Chytridiomycota and Zoopagomycota were also present, with average relative abundances of 1.15% and 1.18%, respectively. The Heatmap of fungal genera with the top 50 species in overall abundances were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee. The genera of detected fungal species, showing greater richness, displayed distinct distribution characteristics at the 7 sites. It was observed that a relative lower abundant was noted for most all species at the S6 site compared to other points. The most representative fungal genus was \u003cem\u003eTalaromyces\u003c/em\u003e (Ascomycota) and \u003cem\u003eAspergillus\u003c/em\u003e (Ascomycota), individually constituting 4.63% and 12.75% of the total fungal community. Furthermore, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef delineates the distinct community composition at the species level for different collection sites. Notably, for the S6 site, the most representative species with greater abundance was \u003cem\u003eAspergillus_cristatus\u003c/em\u003e, which accounted for 8.16% of the total fungal community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNitrogen processing of soil microbial communities at different collection site\u003c/h2\u003e \u003cp\u003eThe functions annotated under KEGG level 1 for the 7 types of soil surrounding the electric power substation encompassed diverse categories: metabolism (ave. 51.49%), genetic information processing (ave. 14.53%), environmental information processing (ave. 13.25%), cellular processes (ave. 11.00%), human diseases (ave. 5.34%), and organic systems (ave. 4.40%) (Figure S2a and Table S3). Significantly enriched functional pathways at level 2 (\u0026gt;\u0026thinsp;5%) included carbohydrate metabolism, amino acid metabolism, energy metabolism, metabolism of cofactors and vitamins, cellular\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ecommunity-prokaryotes, signal transduction, and membrane transport (Figure S2b). Among these, the S6 site exhibited a higher proportion of amino acid metabolism, and a lower proportion of carbohydrate metabolism and cellular community-prokaryotes compared to all other sites (Table S4). Relatively more abundant functional pathways at level 3 (\u0026gt;\u0026thinsp;5%), such as two-component system, quorum sensing, ABC transporters, Oxidative phosphorylation, pyruvate metabolism, and ribosome, were presented in Figure S2c. No significant difference was observed among these points. Noteworthy were the nitrogen metabolism pathways, including nitrogen fixation, nitrification and denitrification. The relative abundance of the genes such as \u003cem\u003enifA\u003c/em\u003e, \u003cem\u003enifD\u003c/em\u003e, \u003cem\u003enifH\u003c/em\u003e, \u003cem\u003enifK\u003c/em\u003e, and \u003cem\u003enifV\u003c/em\u003e (Kuypers et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bell\u0026eacute;s-Sancho et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was lower at the S6 site than at other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), suggesting a lower capacity for soil microbial nitrogen fixation (N\u003csub\u003e2\u003c/sub\u003e\u0026rarr;NH\u003csub\u003e3\u003c/sub\u003e). In contrast, the relative abundance of two genes, \u003cem\u003eamoA\u003c/em\u003e and \u003cem\u003eamoB\u003c/em\u003e (Wang et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), was higher at the S6 site than at other sites, indicating increased nitrification (NH\u003csub\u003e3\u003c/sub\u003e\u0026rarr;NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e). This observation aligns with the finding of low NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and high NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N at the S6 site (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The relative abundance of genes, encoding functions related to the production of NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, nitric oxide and nitrous oxide (Li et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), was also displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between soil microbial communities and environmental factors\u003c/h2\u003e \u003cp\u003eEnvironmental factors exerted a substantial influence on microbial communities and their functions. RDA served as a valuable tool for expressing the correlation between environmental factors and microbial communities. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, the RDA1 and RDA 2 axis accounted for 46.05% and 33.74%, respectively, of the total variation in microbial community composition and soil properties at the phylum level. The RDA model, based on soil microbial phylum-level data, effectively differentiated soils from various collection sites, corroborating our NMDS results. Specifically, the S6 site, situated nearest to the electric power substation, exhibited discernible associations with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, SOM, EC, pH and soil enzymes, including SOD, GSH, soil sucrase, LDH, acid phosphatase, acid protease, MDA, but was negatively correlated with moisture content and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N. However, the S1 and S2 sites were opposite. The S3 site followed the same trend as LFMMS, TOC, SOM, pH, EC, SOD, LDH, MDA, acid phosphatase and acid protease, while S4 and S5 sites had no strong significant correlation with these environmental factor. Cluster analysis revealed notable correlations between Acidobacteria and TOC, LFMMS, moisture content, SOD, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N. Actinobacteria was positively correlations with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, moisture content, GSH and soil sucrase. Proteobacteria and Bacteroidetes demonstrated\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003epositive correlations with pH, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, SOM, EC, and the aforementioned soil enzymes. Chloroflexi and Candidatus_Dormibacteraeota exhibited positive correlations with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and moisture content. Other microorganisms, such as Actinobacteria and Candidatus_Eremiobacteraeota, Firmicutes and Cyanobacteria, displayed positive correlations with various environmental factors, including NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, moisture content, GSH and soil sucrase. Armatimonadetes were positively correlated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, moisture content, TOC and LFMMS, while Gemmatimonadetes demonstrated positive correlations with SOM, pH, EC and soil enzymes. Additionally, RDA of the correlation between environmental factors and microbial communities at the genus was provided in Figure S3.\u003c/p\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analyses were further conducted to elucidate the effects of the 15 environmental factors on the microbial communities at the genus level (see the phylum level in Figure S4), which exhibited distinct abundances at the S6 site compared to other sites. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, among the 18 bacteria genera, there were 6 Actinobacteria genera, 3 Proteobacteria genera, and one unidentified Candidaus_Eremiobacteraeota genus that were significantly positively correlated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there were 3 unidentified Actinobacteria genera and one unidentified Candidaus_Eremiobacteraeota genus that were significantly positively correlated with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, bacteria that were significantly negatively correlated with moisture content included five Actinobacteria genera, four Proteobacteria genera, two unidentified Gemmatimonadetes genera, one Acidobacteria genus, one unidentified Candidaus_Eremiobacteraeota genus and one bacteroidetes genus. Bacteria that were significantly positively related to pH included three Proteobacteria genera, two Actinobacteria genera, two unidentified Gemmatimonadetes genera, one bacteroidetes genus, while three unidentified Actinobacteria genera and one unidentified Candidaus_Eremiobacteraeota genus were opposite. Five Actinobacteria genera, one bacteroidetes genus, two Proteobacteria genera and one Gemmatimonadetes genus (\u003cem\u003eGemmatirosa\u003c/em\u003e) exhibited negative correlation with TOC (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The only significantly negative correlation with LFMS (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was Actinobacteria genus (\u003cem\u003ephycicoccus\u003c/em\u003e). Most of bacteria presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb was positively correlated with EC and only Acidobacteria genus (\u003cem\u003eLuteitalea\u003c/em\u003e) showed significance (\u003cem\u003eρ\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For the soil fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), those with a positive correlation with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N and moisture content included three Ascomycota genera, two Mucoromycota (\u003cem\u003eBifiguratus\u003c/em\u003e and \u003cem\u003eLinnemannia\u003c/em\u003e), one Basidiomycota genus (\u003cem\u003eAmanita\u003c/em\u003e), one Eumycota genus (\u003cem\u003eAbsidia\u003c/em\u003e), one Zygomycota genus (\u003cem\u003eMucor\u003c/em\u003e), while those with a negative correlation with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N included one Ascomycota genus (\u003cem\u003eFusarium\u003c/em\u003e) and one Eumycota genus (\u003cem\u003eAbsidia\u003c/em\u003e). However, no significant correlation was observed for TOC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of soil collection site on soil properties\u003c/h2\u003e \u003cp\u003eSoil, a vital component of basinal ecosystems, serves as the material foundation for plant and microbial survival. Its physicochemical attributes dictate the structural compositions of plant and microbial communities (Yang and Hu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study focused on soil samples collected in the vicinity of an electric power substation with limited vegetation. The S6 site, being the closest to the substation, was chosen as the primary research focus. As indicated by Wu et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), sites with lower moisture content and higher pH could experience upward movement of soluble salts from the deeper soil layers, exemplified by the conditions at the S6 site. The lowest soil TOC at S6 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) contributed to higher soil pH through reduced H\u003csup\u003e+\u003c/sup\u003e release by roots and organic matter (Hong et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The diminished TOC at S6, relative to other sites, was also associated with reduced SOM. Additionally, soil moisture content, known to enhance soil microbial activities influencing SOM mineralization and decomposition (Huang et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), was lower at S6 and likely contributed to lower TOC and TN levels (Cao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, TN levels at the S6 site exceeded those at other sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), possible attributed to the presence of strong EC, a significant factor influencing microbial community assembly (Zhang et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite the absence of waste discharge typical of coal-fired power plants (Sun et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the operation of electric power substation impacted soil physicochemical properties.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of collection site on the composition of soil microbial communities\u003c/h2\u003e \u003cp\u003eIn this investigation, alpha diversity indexes for microbial communities displayed significant variations among soil samples from distinct collection sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Anthropogenic disturbances caused by the substation, such as EC, were implicated in statistically altering the alpha-diversity of microbial communities around the electric power substation. Bacterial functional prediction through PCoA analysis revealed clustering of bacterial communities from the 7 soil sites into five groups and significant differences (PERMANOVA: \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) among them (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), consistent with soil microbial alpha diversity.\u003c/p\u003e \u003cp\u003eAcross all soil samples, Acidobacteria, Proteobacteria, Actinobacteria and Chloroflexi emerged as dominant phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), mirroring findings in soils adjacent to mining and smelting areas (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and coal-fired power plants (Sun et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, NMDS and PCoA analysis indicated distinct distribution patterns for soil bacterial community around the electric power substation. Notably, the S6 site exhibited significantly higher abundances of Proteobacteria and Acidobacteria and lower abundances of Chloroflexi compared to other sites (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This contradicts observations for coal-fired power plants (Sun et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Acidobacteria, known for its significant role in soil carbon biogeochemical cycling (Zhou et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), constituted a substantial proportion in the bacterial community, with its growth inhibited by increasing pH value, particularly evident at the S6 site (pH\u0026thinsp;=\u0026thinsp;7.09). Proteobacteria, recognized for nitrogen fixation and reducing nitrogen loss (Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), demonstrated greater abundance at the S6 site (42.60%) compared to other collection points. Actinobacteriota, actively involved in the carbon and nitrogen cycle (Tao et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wei et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), exhibited sensible enrichment in all soil samples. Chloroflexi, with the function of autotrophic denitrification (Ge et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), showed significantly lower relative abundance at the S6 site (1.55%) than at the S1 (11.66%), S2 (5.89%), S3 (2.56%), S4 (10.94%), S5 (8.77%) and S7 (9.86%) sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), thereby influencing nitrogen conversion capacity. Gemmatimonadetes, adapted to low-moisture environments (DeBruyn et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), displayed notable enrichment at the S6 sites (10.35%) relative to other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This enrichment was attributed to the low moisture content, as it was negatively correlated with Gemmatimonadetes (also in this study)(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). This higher number of Gemmatimonadetes at the S6 site, negatively relating to NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, TOC and LFMMS, potentially influenced the soil due to its strong photosynthetic-fueled ability to oxidize organic and inorganic compounds (Huang et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bacteroidetes, possessing a robust ability to metabolize complex organic matter, protein and lipid, exhibited higher abundance (6.21%) at the S6 site, followed by S7 (5.46%) and S3 (1.92%). Therefore, we inferred that the substation significantly increased the abundances of Proteobacteria and Acidobacteria but inhibited Chloroflexi in soils.\u003c/p\u003e \u003cp\u003eAt the bacterial genus level, soil properties explained 85.5% of the variation in soil microorganism composition (Figure S3), and discernible differences were evident among the 7 sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The genus \u003cem\u003eUnclassified_Comamonadaceae\u003c/em\u003e (Sotres et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), \u003cem\u003eLysobacte\u003c/em\u003er (Iwata et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), \u003cem\u003eSphingomonas\u003c/em\u003e (Yang et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and \u003cem\u003eSolirubrobacter\u003c/em\u003e (Wei et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), renowned for their proficiency in facilitating superior nitrogen conversion, were notably enriched at the S6 site, playing a pivotal role in this observed enrichment. The former two types of bacteria exhibited positive correlations with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, EC, pH, and soil enzymes, the middle type showed positive correlations with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, EC, pH, and soil enzymes, and the latter was positively correlated to NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, TOC, SOM, and soil moisture content (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Other microbial entities, including \u003cem\u003eFlavisolibacter\u003c/em\u003e (Hernandez-Guzman \u003cem\u003eet al.\u003c/em\u003e, 2022), \u003cem\u003eGemmatirosa\u003c/em\u003e (Liu et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), \u003cem\u003eTerrabacter\u003c/em\u003e (Kruglova et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and \u003cem\u003ePhycicoccus\u003c/em\u003e (Yang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), associated with nitrogen metabolism, along with the genera like \u003cem\u003eNocardioides\u003c/em\u003e, the functions of which were yet to be elucidated in the biogeochemical cycle, were notably abundant at the S6 site. Despite their prevalence, the precise roles of these microorganisms remained ambiguous. Notably, \u003cem\u003eBradyrhizobium\u003c/em\u003e, acknowledged for its pivotal role in nitrogen fixation (Sun et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), did not exhibit enrichment at the S6 site. Regarding the heightened enrichment of the species \u003cem\u003eSphingomonas mesophila\u003c/em\u003e (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and \u003cem\u003eSphingomonas edaphi\u003c/em\u003e (Kim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) at the S6 site relative to other sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), their precise contributions to the overall microbial community remained elusive. The heightened abundance of \u003cem\u003eLuteitalea_pratensis\u003c/em\u003e at the S6 site was due to the reason that it was an organism thriving within a narrow pH tolerance range (5.3\u0026ndash;8.3) (Vieira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These finding suggested that the operation of the electric power substation significantly increased the abundances of bacteria with nitrogen-processing function.\u003c/p\u003e \u003cp\u003eReports regarding the composition and structure of soil fungal communities in the vicinity of electric power substation are notably limited. In this investigation, the fungal communities at the phylum level were predominantly constituted by Basidiomycota and Ascomycota in all soil samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), a pattern consistent with their ubiquity in most soils (Gqozo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The prevalence of Ascomycota might be associated with its capacity to degrade cellulose and hemicellulose (Shary \u003cem\u003eet al.\u003c/em\u003e, 2007; Yang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while the phyla Basidiomycota and Mucoromycota had been reported to be linked to the degradation of complex lignocelluloses (Lundell et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Huhe \u003cem\u003eet al.\u003c/em\u003e, 2017). Beta-diversity analysis through NMDS for fungal communities across the 7 sites (stress\u0026thinsp;\u0026gt;\u0026thinsp;0.05) did not reveal representativeness, regardless of the level of analysis. No significant differences were observed between any two sites, although significantly differences were noted across all 7 sites (PERMANOVA: \u003cem\u003eρ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed-f). Therefore, it could be inferred that fungal communities had adapted into cohesive assemblages under the prolonged operation of the substation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInfluence of soil collection site on the nitrogen processing\u003c/h2\u003e \u003cp\u003eNitrogen, a limiting nutrient in basinal ecosystems, plays a vital role in ecosystem productivity. Nitrogen fixation, the primary mechanism through which microorganisms acquire nitrogen resources, is crucial for sustaining ecosystem function. The genera \u003cem\u003eSphingomonas\u003c/em\u003e (15.31%), \u003cem\u003ePhycicoccus\u003c/em\u003e (0.97%) and \u003cem\u003eSolirubrobacter\u003c/em\u003e (0.71%) exhibited significant positive correlations with NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N, displaying higher abundance at the S6 site characterized by lower soil moisture content (26.91%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Given that nitrogen fixation predominantly occurs in anaerobic environments, the relative lower moisture content at the S6 site may impede soil nitrogen fixation (Zhang et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which could explain the observed lower relative abundance of nitrogen fixation genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and the reduction in NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N content at this site. Nitrification, an important microbial nitrogen-loss pathway in soils, displayed a positive correlation with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N. In comparison to other sites, genera at the S6 site, such as \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003ePhycicoccus\u003c/em\u003e, \u003cem\u003eTerrabacter\u003c/em\u003e (1.66%), \u003cem\u003eUnclassified_Solirubrobacterales\u003c/em\u003e (0.19%), \u003cem\u003eUnclassified_Comamonadaceae\u003c/em\u003e (15.45%), and \u003cem\u003eLysobacte\u003c/em\u003er (2.56%) exhibited higher abundance. Additionally, the genes associated with the function of nitrification also exhibited an increased abundance at the S6 site. These factors collectively might have contributed to the observed escalation in NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N levels following the operation of the electric power substation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study employed metagenomic sequencing to investigate the relationships between microbial communities and environmental variables in soils surrounding an electric power substation. The operation of the substation induced alterations in soil physicochemical properties, with significant variations observed across the seven sites. Notably, differences in soil physicochemical properties, diversity, composition and structure of soil bacterial communities were evident under the influence of the electric power substation, whereas such distinctions were not observed in soil fungal communities. Moisture content and pH, key environmental variables, and anthropogenic disturbance from EC influenced soil bacterial communities, while the influence of LFMMS was negligible. Genera positively associated with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N production, such as \u003cem\u003eGemmatimonadetes\u003c/em\u003e and \u003cem\u003eSphingomonas\u003c/em\u003e, exhibited pronounced enrichment at the S6 site, situated closest to the substation, representing a important component of the soil bacterial community. The soil bacterial communities displayed greater sensitivity to the electric power substation operation compared to eukaryotes, and their dynamics had a direct and significant impact on microbial community diversity within the substation ecosystem.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eZX Xu,\u0026nbsp;B\u0026nbsp;Zeng, S Chen:\u0026nbsp;Conceptualization,\u0026nbsp;Methodology, Investigation.\u0026nbsp;S Xiao,\u0026nbsp;LG Jiang: Methodology;\u0026nbsp;Formal analysis.\u0026nbsp;X Li, YF Wu:\u0026nbsp;Validation, Formal analysis.\u0026nbsp;MX Zhao, SR Chen: Data curation,\u0026nbsp;original draft preparation. LX You:\u0026nbsp;Conceptualization, Writing-review and editing;\u0026nbsp;Funding acquisition, Project administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Natural Science Foundation of China (Grant No.\u0026nbsp;22072017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBagli S, Geneletti D, Orsi F (2011) Routeing of power lines through least-cost path analysis and multicriteria evaluation to minimise environmental impacts. Environ. Impact Assess. 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J Soils Sediments 19: 356\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11368-018-2035-y\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"soil microbial community, electric power substation, nitrogen processing, metagenomic sequencing technique","lastPublishedDoi":"10.21203/rs.3.rs-3779548/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3779548/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe surge in global energy demand mandates a significant expansion of electric power substations. However, the ecological consequences of electric power substation operation on soil microbial communities and nitrogen enrichment have not been addressed. In this study, we collected soil samples from seven distinct sites at varying distances from an electric power substation in Xintang village, southeastern China, and investigated the microbial diversity and community structures employing metagenomic sequencing technique. Key environmental determinants shaping soil microbial communities at both the phylum and genus levels were identified as soil moisture content, pH and electric conductivity. Prominent taxa identified across all sampled soils included Acidobacteria, Proteobacteria, Actinobacteria, Chloroflexi, Basidiomycota, Ascomycota, and Mucoromycota. While the bacterial community exhibited statistically significant differences across the seven distinct sites, fungal communities did not show such variations. Correlation analysis revealed a diminished nitrogen fixation capacity at the site nearest to the substation, characterized by low moisture content, elevated pH, and robust soil electric conductivity. In contrast, heightened nitrification processes were observed at this site compared to others. These findings were substantiated by the relative abundance of key genes associated with ammonium nitrogen and nitrate nitrogen production. This study provides insights into the relationships between soil microbial communities and the enduring operation of electric power substations, thereby contributing fundamental information essential for the rigorous environmental impact assessments of electric power substations.\u003c/p\u003e","manuscriptTitle":"Soil microbial community composition and nitrogen enrichment responses to the operation of electric power substation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-01 09:07:01","doi":"10.21203/rs.3.rs-3779548/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c6a61660-7584-4bb9-ab6e-912d9b96d42c","owner":[],"postedDate":"January 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-24T19:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-01 09:07:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3779548","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3779548","identity":"rs-3779548","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}