Co-occurring PAHs and Heavy Metals Drive Bacterial Community Shifts in China’s Beiluo River Riparian Soils

Co-occurring PAHs and Heavy Metals Drive Bacterial Community Shifts in China’s Beiluo River Riparian Soils | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Co-occurring PAHs and Heavy Metals Drive Bacterial Community Shifts in China’s Beiluo River Riparian Soils Xibo Pu, Yingchuan Yang, Jiahua Guo, Baoxuan Zhuo, Tamao Kasahara, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7543329/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To comprehend the response of bacterial communities to environmental variables, we examined the dispersion patterns and soil attributes associated with polycyclic aromatic hydrocarbons (PAHs) and heavy metals within the soils neighboring the Beiluo River. The structure of bacterial assemblages present along the riverbank was determined through environmental DNA metabarcoding analysis, subsequently conducting an analysis of the relationships between these microbial populations and various environmental factors. The total concentrations of 16 US EPA-listed PAHs in the Beiluo River riparian soils ranged from 3.00 to 131.76 ng/g. Heavy metal concentrations varied by element: chromium (Cr) and zinc (Zn) exhibited the highest levels (123.75–153.46 mg/kg), while cadmium (Cd) and mercury (Hg) were detected at significantly lower concentrations (0.03–0.15 mg/kg). Proteobacteria, Actinobacteriota, and Bacteroidota were found to be predominant, as these phyla are capable of degrading PAHs and exhibit high adaptability to the environment, resulting in their higher abundance compared to other phyla. Several phyla exhibited significant associations with PAHs and heavy metals, which might be explained by the increased toxicity of heavy metals in settings where both PAHs and heavy metals are present together. Moreover, Pielou’s evenness and Simpson’s diversity index exhibited notable variations at varying distances from the riparian zone, likely influenced by the fluctuations in soil moisture content as distance changes. Higher water content correlated with increased bacterial activity and diversity. This study elucidates the complex interplay between bacterial communities and environmental factors in the Beiluo River riparian zone, offering valuable perspectives for the assessment and remediation of contaminated river ecosystems. PAHs Heavy Metals Riparian Soils Soil Physicochemical Properties Bacterial Community Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The river ecosystem serves as a crucial element of the natural environment and holds significant importance in maintaining biodiversity and ecological balance (Chen et al., 2022 ). As an integral part of the river ecosystem, the riparian zone serves as a bridge between terrestrial and aquatic environments, acting as a central exchange point for nutrients, energy, and species (Lind et al., 2019 ). However, accelerating industrialization and urbanization have rendered these zones vulnerable to contamination by polycyclic aromatic hydrocarbons (PAHs) and heavy metals—two pervasive pollutants originating from shared anthropogenic sources such as fossil fuel combustion, industrial effluents, and vehicular emission (Gárfias et al., 2018 ). As an essential element of the soil ecosystem, bacterial communities are crucial for nutrient cycling, energy transfer, and the decomposition of contaminants (Chen et al., 2025 ). Understanding the response of bacterial communities to environmental changes is crucial for assessing and restoring polluted river ecosystems. Riparian ecosystems face significant threats from various chemical pollutants, such as PAHs, which largely originate from human activities like fossil fuel combustion, industrial discharges, and transportation-related emissions (Han et al., 2019 ). These contaminants can infiltrate riparian ecosystems through multiple pathways, such as atmospheric deposition, surface runoff, and soil infiltration, ultimately leading to groundwater contamination (Ehigbor et al., 2020 ). The impacts of PAHs on riparian ecosystems are far-reaching and multifaceted. Heavy metal pollution represents another significant challenge confronting riparian ecosystems. Heavy metals, such as cadmium (Cd) and zinc (Zn), frequently originate from the same pollution sources as PAHs, including industrial activities, power generation and heating systems, waste incineration, and transportation-related emissions (Maliszewska-Kordybach et al.,2003). Heavy metal contamination has the potential to deteriorate the physicochemical characteristics of riparian soils, reduce soil productivity, alter the structure and functionality of soil microbial communities, and ultimately negatively impact the health of the entire riverine ecosystem (Gran-Scheuch et al., 2020 ). There is a correlation between heavy metal and PAH pollution, and they interact with each other, collectively leading to enhanced ecotoxicity (Shang et al., 2024 ). After extended periods of exposure to pollutants, factors like the availability of carbon in the environment may reshape the composition of microbial communities (Annala et al., 2022 ). When subjected to environmental stress caused by pollutants, specific microbial groups may gain dominance, thereby causing changes in community structure (Bernhard et al., 2005 ) and altering community functions (Lors et al., 2010 ; Machado et al., 2012 ). For instance, Cao et al. ( 2008 ) showed that the combined contamination of cadmium (Cd) and PAHs had a stronger impact on soil microbial communities than individual pollutants, leading to decreased bacterial diversity and the rise of unique bacterial groups. The complex interplay between heavy metals and PAH pollution, compounded by their synergistic effects, results in heightened ecotoxicity. This not only affects biodiversity but also results in the contamination of the food chain, ultimately presenting substantial risks to human health. While the individual impacts of PAHs or heavy metals on soil microbial communities have been extensively documented, their synergistic effects remain poorly characterized, particularly in ecologically sensitive riparian zones of fragile ecosystems like China's Loess Plateau. The Loess Plateau in northwestern China has been selected as the research area. It is the largest loess sedimentary region in the world, characterized by a fragile ecological environment and severe soil erosion. The river ecosystem is a vital component of the Loess Plateau's ecological environment, playing a vital role in preserving regional ecological balance and biodiversity. The Beiluo River, a significant tributary of the Yellow River, is located in the northwestern part of Shaanxi Province, China. The Beiluo River basin is a vital agricultural and industrial center for Shaanxi Province and plays a crucial role in the region's economic growth (Li et al., 2024 a). The Loess Plateau in northwestern China, home to the Beiluo River, features a semi-arid climate marked by significant seasonal changes. The Beiluo River, an important tributary of the Yellow River, experiences significant hydrological fluctuations, particularly marked by seasonal flooding. During the wet season, typically from July to September, the river's water level rises due to the combination of heavy rainfall and melting snow from the nearby mountains, leading to frequent episodes of flooding. These seasonal floods can cause extensive inundation of the riverbanks, resulting in the temporary submersion of the riparian zone (Fu et al., 2011). As economic growth accelerates, the Beiluo River basin is facing increasingly severe environmental pollution challenges, particularly those involving PAH and heavy metal contamination. Both PAHs and heavy metals are known for their carcinogenic, teratogenic, mutagenic, persistent, bioaccumulative, and toxic characteristics (Jia et al., 2021 ). The periodic flooding of the Beiluo River significantly influences local hydrological conditions and the ecological processes of riparian soil. The alternating cycles of flooding and drying create unique environmental conditions that influence the composition and activity of bacterial communities in riparian soil. During the flood season, rising water levels can cause PAHs and heavy metals to leach from the soil into the river, potentially increasing the pollution burden. Conversely, during the dry season, the reduced water levels may concentrate these pollutants in the soil, affecting the structure and functionality of bacterial communities (du Laing et al., 2009 ). PAHs and heavy metals frequently co-occur in riparian soils due to their common emission pathways, yet their interactions may exacerbate ecotoxicity beyond additive effects. For instance, hydrophobic PAHs can enhance the bioavailability of heavy metals by altering soil redox conditions (Li et al., 2024 b), while metals such as Zn and Cd can inhibit enzymatic pathways critical for PAH degradation (Shen et al., 2006 ). These interactions likely drive nonlinear shifts in microbial community structure and function, but mechanistic insights remain scarce in dynamic riparian environments. Previous studies have predominantly focused on single-pollutant scenarios or simplified laboratory systems, neglecting the complexity of field conditions where hydrological fluctuations, soil heterogeneity, and multi-pollutant interactions coexist. Therefore, this study aims to investigate the bacterial community structure in the riparian soil of the Beiluo River using environmental DNA barcoding techniques. By examining the relationships between bacterial communities and environmental factors, such as soil properties, PAHs, and heavy metal concentrations, we seek to provide valuable insights for assessing and restoring polluted river ecosystems. In this study, the Beiluo River region in Shaanxi Province was chosen as the main focus area for this investigation. Utilizing environmental DNA barcoding technology, we characterized the bacterial community structure within the riparian soil. By analyzing soil physicochemical properties, the content of PAHs, heavy metals, and other environmental variables, we elucidated the interaction mechanism between the bacterial communities and these environmental factors. The study was structured around three primary objectives: (I) to delineate the distribution characteristics of PAHs and heavy metals in riparian soils and their impacts on bacterial communities; (II) to examine the variation in soil physicochemical properties along different distances within the riparian zone and their influence on bacterial communities; and (III) to comprehend the response mechanisms of bacterial communities to environmental alterations. 2. Materials and methods 2.1 Study Area Overview and Sampling Methodology Situated in northwestern China (34.95°–37.28°N, 107.57°–110.02°E), the Beiluo River spans a basin area of 2.69 × 10⁴ km², with its main channel extending 171 km in length. The Beiluo River flows northwest to southeast across Shaanxi Province, traversing the distinct landscapes of the Loess Plateau and Guanzhong Plain. Its course intersects sixteen counties within four prefecture-level cities: Yulin, Yan’an, Tongchuan, and Weinan. The Beiluo River merges with the Yellow River at Tongguan County, serving as the principal tributary of the Wei River. The upper reaches of the basin are dominated by industries such as oil and coal mining, which have had a significant impact on the environment. In contrast, the lower reaches are mainly agricultural land (Li et al., 2025 ). This river is an essential resource for both agricultural activities and the progress of industrial development in the surrounding area. In this study, we set up 16 sampling points in different upstream and downstream sections of the main stream of the Beiluo River, covering the area from the upper reaches to the lower reaches. These sampling points are located near four hydrological stations. Near each hydrological station, we conducted sampling at positions 1 meter, 5 meters, 10 meters and 15 meters away from the riverbank zone (Fig. 1 a). Differences in river discharge rates among hydrological stations (A, B, C, D) and variations in bacterial diversity indices (Simpson's diversity index, Pielou's evenness) across riparian distances (1m, 5m, 10m, 15m) were statistically evaluated using one-way analysis of variance (ANOVA). Post-hoc pairwise comparisons were performed with Tukey's honestly significant difference (HSD) test when ANOVA indicated significant main effects (p < 0.05). 2.2 Chemical analysis and quality control A total of 16 sampling points were collected, and the collected soil was stored in aluminum bags. During soil sampling, the gathered soil at each site was thoroughly mixed after removing dead leaves and stones. The collected samples were divided into four aliquots, with one aliquot stored at -20°C for subsequent microbial sequencing and the remaining three portions kept at 4°C for PAH concentration, heavy metals, and physicochemical properties analysis in the soil, including key parameters such as moisture content (MC), total nitrogen (TN), total phosphorus (TP), organic carbon (OC), pH, and a range of heavy metals, among others. For ecological risk assessment, the 16 priority PAHs were categorized into three classes by molecular weight and ring number, exhibiting distinct environmental behaviors: Low molecular weight PAHs (LMW, 2–3 rings): High mobility and bioavailability (e.g., Nap, Ace, Acy), indicating recent pollution inputs; Medium molecular weight PAHs (MMW, 4 rings): Moderate persistence (e.g., Fla, Pyr, BaA), serving as markers of mixed sources; High molecular weight PAHs (HMW, 5–6 rings): Pronounced adsorption capacity and carcinogenicity (e.g., BaP, DaA, InP), reflecting cumulative ecological risks. Complete compound profiles are provided in Supplementary Table S2 (including physicochemical parameters and risk thresholds). Quantification of PAHs was performed using gas chromatography-mass spectrometry (GC-MS; Agilent 6890N/5975 MSD) with a DB-5 capillary column (30 m × 0.25 mm i.d., 0.25 µm film thickness). Physicochemical parameters were analyzed employing a portable water quality analyzer (MI-200B). Heavy metals (including Cr, Zn, Cd, Hg, Ni, Cu, As, and Pb) in soil were quantified using inductively coupled plasma mass spectrometry (ICP-MS). Samples were digested with nitric acid-hydrofluoric acid in a microwave-assisted system, followed by analysis with indium (In) and rhenium (Re) as internal standards to correct for matrix effects and instrumental drift. To ensure data accuracy and reliability, stringent quality control protocols were implemented throughout the analytical procedures for PAHs and heavy metals. For each batch of samples, procedural blanks, laboratory duplicates, and field duplicates were analyzed to eliminate potential interference and cross-contamination. We established method detection limits (MDLs) by multiplying the standard deviation of target analyte concentrations in procedural blanks by a factor of three. The MDLs for PAHs ranged from 0.05 to 1.2 ng/g, while those for heavy metals (Cr, Zn, Cd, Hg, Ni, Cu, As, Pb) ranged from 0.03 to 0.15 mg/kg. 2.3 DNA isolation, PCR amplification, and next-generation sequencing Genomic DNA was extracted from soil specimens employing the OMEGA Soil DNA Kit (M5635-02, Omega Bio-Tek), per the manufacturer’s protocol. Purified extracts were stored at − 20°C for subsequent analysis. DNA concentration and purity were assessed using a NanoDrop NC2000 spectrophotometer, with integrity verified by agarose gel electrophoresis. The V3–V4 hypervariable region of bacterial 16S rRNA genes was amplified employing polymerase chain reaction (PCR) with primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Each primer contained a unique 7-bp barcode to enable multiplexed sequencing. Each 25-µl PCR master mix comprised:5 µl 5× reaction buffer, 0.25 µl Fast pfu DNA Polymerase (5 U/µl), 2 µl 2.5 mM dNTPs, 1 µl each of 10 µM forward and reverse primers, 1 µl DNA template, and molecular-grade water (ddH₂O) to 25 µl final volume. PCR amplification proceeded through: Primary denaturation: 98°C for 5 min. 25 cycles of: Denaturation: 98°C for 30 s, Annealing: 53°C for 30 s, Extension: 72°C for 45 s. Final extension: 72°C for 5 min. Amplified products were purified using Vazyme VAHTSTM DNA Clean Beads, with concentrations determined via Quant-iT PicoGreen dsDNA Assay Kit (Sun et al.,2022). After quantification, the purified amplicons were pooled in equimolar ratios and prepared for pair-end sequencing. Sequencing was performed using either the Illumina NovaSeq platform with the NovaSeq 6000 SP Reagent Kit (500 cycles) to generate 2250 bp reads or the Illumina MiSeq platform with the MiSeq Reagent Kit v3 to produce 2300 bp reads. Both sequencing processes were carried out by Shanghai Personal Biotechnology Co., Ltd, located in Shanghai, China. 2.4 Bioinformatics analyses Bioinformatics analysis was conducted utilizing QIIME2 (version 2022.11). Raw sequencing reads were demultiplexed using QIIME 2's demux plugin and subsequently trimmed of primer sequences with cutadapt (v2.1). Quality control, noise reduction, and chimera removal were carried out using the DADA2 plugin. The Vsearch plugin was utilized for sequence concatenation, quality filtering, and deduplication. Unique sequences were grouped at a 98% similarity threshold for chimera identification using uchime-denovo. The non-chimeric sequences obtained were subsequently grouped at a 97% similarity threshold to produce Amplicon Sequence Variants (ASVs) or Operational Taxonomic Units (OTUs), accompanied by a corresponding ASV/OTU table. Non-singleton ASVs were phylogenetically aligned using MAFFT (v7.490), and evolutionary relationships were inferred via FastTree 2 (v2.1.11) under the GTR + CAT model. Taxonomic classification was performed with QIIME 2's feature-classifier plugin employing the naive Bayes classifier (classify-sklearn), referenced against the SILVA 132 database and a customized NT database. Heatmaps utilizing Spearman correlation were constructed to analyze the associations among microbial taxa relative abundance, PAH concentrations, and diverse physicochemical properties. Environmental factors and pollutants exhibiting absolute correlation coefficients exceeding 0.7 were categorized to assess the microbial community's reaction to pollutants, applying a significance threshold of p 0.7, p < 0.05) were incorporated into an interaction network constructed with Cytoscape 3.9.1. We applied the Markov Cluster Algorithm (MCL) to partition the co-occurrence network into modules, subsequently identifying hub taxa per module through maximal values of both degree and betweenness centrality. We employed Partial Least Squares Path Modeling (PLS-PM) via the plspm package in R v4.3.3 to identify key drivers of biological responses. Prior to modeling, pairwise correlations between parameters were evaluated, and redundant variables (|r| 0.05) were excluded to mitigate multicollinearity (Tian et al.,2024). The relationships linking PAHs to microbial community compositions were further investigated through RDA, employing unweighted UniFrac distance matrices.To assess the impact of environmental factors, including PAHs and heavy metals, on microbial community diversity, Redundancy Analysis (RDA) was performed with Canoco 5.0. Alpha diversity metrics, such as species richness, evenness, Shannon-Wiener, and Simpson's indices, were computed for dominant phyla using PC-ORD 5.0. 2.5 Risk assessment The risk quotient (RQ) method serves as a valuable tool for assessing the ecological risks posed by polycyclic aromatic hydrocarbons (PAHs) to surrounding organisms and ecosystems. This study employs the following formula to calculate soil PAH RQs: $$\:\begin{array}{c}RQ=\frac{{C}_{PAHs}}{{C}_{QV}} （1）\end{array}$$ $$\:\begin{array}{c}R{Q}_{NCs}=\frac{{C}_{PAHs}}{{C}_{QV\left(NCs\right)}} （2）\end{array}$$ $$\:\begin{array}{c}R{\text{Q}}_{\text{M}\text{P}\text{C}\text{s}}=\frac{{C}_{PAHs}}{{C}_{QV\left(MPCs\right)}} （3）\end{array}$$ Among them, C PAHs represent the measured values of each type of PAHs in the soil, while C QV represents the reference values corresponding to each type of polycyclic aromatic hydrocarbon in the soil. Based on previous studies, the risk reference values of PAHs are classified into two types (Lin et al., 2020 ). C QV(NCs) indicates negligible concentration, and C QV(MPCs) represents the maximum allowable concentration, thereby obtaining the RQ NCs and RQ MPCs values. When the RQ NCs of individual PAHs is 0, it indicates no risk; when RQ NCs ≥ 1 and RQ MPCs < 1, it represents moderate risk; when RQ MPCs ≥ 1, it indicates high risk. For the whole, when the RQ NCs of ∑16 PAHs is 0, it represents no risk; when ∑16 PAHs' RQ NCs ≥ 1 and < 800, RQ MPCs = 0, it represents low risk; when RQ NCs ≥ 800 and RQ MPCs = 0, it represents low risk as moderate risk 1; when RQ NCs < 800 and RQ MPCs ≥ 1, it is moderate risk 2; when RQ NCs ≥ 800 and RQMPCs ≥ 1, it is high risk. The single-factor index method can quantify the pollution degree of a single heavy metal by comparing the measured concentration of the heavy metal with the reference standard value. The calculation formula is as follows: $$\:\begin{array}{c}{P}_{i}=\frac{{C}_{i}}{{C}_{t}} （4）\end{array}$$ Among them, C i represents the actual measured value of heavy metal i in the soil, C t represents the corresponding standard value of heavy metal i, and in this study, the reference standard value is the heavy metal content in the soil of Shaanxi Province. P i is the single-factor index pollution index of metal i. When P i ≤ 1, there is no pollution; when 1 < P i ≤ 2, it is slightly polluted; when 2 < P i ≤ 3, it is moderately polluted; when 3 5, it is severely polluted. 3. Results 3.1 Spatial Heterogeneity of Bacterial Community Diversity Significant differences in microbial community structures were observed across all the soil samples examined. The bacterial communities comprised 50 phyla across all samples, with Proteobacteria (22.6–52.18%) and Actinobacteria (3.18–35.37%) being predominantly represented (Fig. 1 a). Compared to other sampling sites, the abundance of Cyanobacteria at site D2 was notably greater compared to the other locations. Additionally, the abundance of Desulfobacterota at site B2 and Actinobacteriota at site A4 was significantly greater than at the other sampling sites. Notably, both the Simpson's diversity index (Fig. 2 b) and the Pielou's evenness index (Fig. 2 c) for bacterial communities were significantly reduced at 5 meters from the riparian zone when compared to the values observed at 10 meters and 15 meters from the riparian zone. The average flow rates at different hydrographic stations also exhibited significant differences (Fig. 1 c). 3.2 Patterns of PAHs and Heavy Metal Distribution in Riparian Soils All 16 PAH compounds designated by the United States Environmental Protection Agency (EPA) were detected in the soil samples collected from the Beiluo River (Fig. 3 a). Across sampling sites, total PAH concentration (∑PAHs) exhibited the following descending gradient: D4 > A4 > B4 > C1 > A3 > C4 > B3 > C3 > A2 > A1 > B2 > C2 > B1 > D3 > D1 > D2. The concentrations of low molecular weight (LMW) PAHs ranged from 0.91 to 60.92 ng/g. For medium molecular weight (MMW) PAHs, the concentrations varied between 1.44 and 47.27 ng/g. Meanwhile, high molecular weight (HMW) PAHs exhibited concentrations spanning from 0.14 to 46.12 ng/g. Across all sampling locations, the distribution of heavy metal concentrations remained largely consistent (Fig. 3 b), with chromium (Cr) and zinc (Zn) being the predominant heavy metals detected, while the levels of cadmium (Cd) and mercury (Hg) were comparatively lower. 3.3 Correlations Between Bacterial Taxa and Environmental Variables The correlation heatmap systematically elucidated the intricate interaction network between riparian microbial taxa and environmental variable (Fig. 4 a). Vicinamibacteraceae demonstrated a pronounced negative correlation with pH ( r = -0.721, p = 0.0016**). Geobacteraceae demonstrated robust negative associations with both total phosphorus (TP, r = -0.515, p = 0.003**) and organic carbon (OC, r = -0.706, p = 0.04*). The Azoarcus displayed significant antagonism toward medium-molecular-weight PAHs (MMW, r = -0.355, p = 0.062). TRA3-20 was positively correlated with total nitrogen (TN, r = 0.674, p = 0.004**). 3.4 Redundancy Analysis Identifying Key Environmental Drivers of Community Structure Redundancy analysis (RDA) elucidated differential driving mechanisms of heavy metals and physicochemical factors on the spatial divergence of core bacterial phyla (Fig. 5 ). The structure of diverse bacterial communities exhibited a notable correlation with the levels of the heavy metal cadmium at the phylum taxonomic level. The richness of Gemmatimonadota exhibits a positive correlation with mercury concentration. The richness of Proteobacteria, Acidobacteriota, Desulfobacterota, Myxococcota, and Verrucomicrobiota exhibits a notable inverse relationship with mercury concentration ( p < 0.05). In addition, the concentration of PAHs also affects the richness of each phylum. For instance, the abundance of Proteobacteria and Bacteroidota demonstrates a significant positive relationship with BaA, whereas the richness of Actinobacteriota displays a significant positive relationship with Ace ( p < 0.05). The influence of soil properties on bacterial communities must also be considered. For example, the abundance of Acidobacteriota and Myxococcota shows a significant positive relationship with soil water content, while the abundance of Bacteroidota is significantly positively correlated with total nitrogen (TN). 3.5 Co-occurrence Network Analysis of Bacterial Communities The multi-community interaction network of soils along the banks of the Northern Luo River is categorized into four distinct modules. Each module is influenced to varying degrees by different communities and environmental factors. Overall, the positive correlations among the communities surpass the negative correlations in strength (Fig. 4 b). Using co-occurrence network analysis to examine polytrophic community interactions, we detected four primary modules with correlation coefficients (R-values) greater than 0.7, each harboring distinct core species. In Module 1, the central species include JG30-KF-CM45, Subgroup22, and MBNT15. In module 2, Latescibacterota and NGB1-J are the core species, while the core species of module 3 and module 4 are Gaiella and Gitt-GS-136 respectively. 3.6 Integrated Effects of Environmental Variables on Bacterial Community Composition Environmental factors influencing the relative abundance of bacterial communities can be categorized into soil moisture, PAHs, heavy metals, soil characteristics, and nutrient content. Among them, soil moisture refers to soil water content, PAHs include the concentration data of 16 pollutants, heavy metals include the pollution data of eight heavy metals detected this time, soil properties include electrical conductivity, pH, nitrate nitrogen, and ammonia nitrogen, nutrient substance mainly includes organic carbon, organic matter, total nitrogen, total phosphorus, and available phosphorus. Partial Least Squares Path Modeling (PLS-PM) analysis has further clarified the combined effects of these variables on bacterial abundance, revealing both direct and indirect pathways of influence (Fig. 6 ). Soil water content exerted the most pronounced direct effect on the relative abundance of bacteria, with a path coefficient of 0.768. Moreover, soil properties demonstrated a notable negative impact on bacterial communities, as indicated by a path coefficient of -0.599. Nutrients also exhibited a significant negative effect on the relative abundance of bacterial species, evidenced by a path coefficient of -0.296. Most significantly, PAHs and heavy metals displayed marked effects on bacterial community structure, with corresponding path coefficients of -0.497 and − 0.343. 3.7 Risk assessment The risk entropy evaluation results are shown in Appendix Table S1 and Table S3. From the perspective of the overall PAHs, all sampling points have a low risk for ∑16PAHs. Among the individual PAHs, except for the D1 and D2 sampling points, 87.5% of the sampling points have a medium risk for Nap; Pyr is of medium risk in 50% of the sampling points (A1, A2, A3, A4, B4, C3, C4, D4), and compared with other points, both pollutants posed greater risks at upstream site A. Flu is of medium risk in 62.5% of the sampling points (A2, A3, A4, B2, B3, B4, C1, C3, C4, D4). Secondly, Acy, Ace, Phe, Ant, BbF, and BaP have medium risks in 6.25%, 31.25%, 43.7%, 12.5%, 31.25%, and 18.75% of the sampling points respectively. The results of the single-factor index evaluation are shown in Table S4. At point A1, Hg is slightly polluted; at points A1, A2, A3, and A4, As is slightly polluted; at points A4 and B4, Cd is slightly polluted; and no pollution is observed at the remaining points. 4. Discussion 4.1 Correlation and responses between bacterial communities and environmental factors The study examined the distribution of PAHs and heavy metals, along with the impact of soil physicochemical properties on bacterial communities within the riparian zone of the Beiluo River. The findings reveal that bacterial community richness and the Shannon diversity index were elevated in soils with higher water content. Bacterial communities exhibit heightened sensitivity to changes in soil moisture levels (de Vries et al., 2018 ), The average discharge in the study area exhibited seasonal variations. The riparian soil bacterial community was mainly composed of Proteobacteria, Actinobacteriota, Acidobacteriota, Gemmatimonadota, and Chloroflexi (Fig. 2 a). This result is consistent with prior studies indicating that soil moisture is a critical factor affecting bacterial communities, especially in environments with seasonal fluctuations in water levels, such as the Poyang Lake region (Tian et al., 2025 ). The seasonal soil bacterial community in Poyang Lake, which, like this study, experiences seasonal fluctuations in water levels, was predominantly composed of Proteobacteria, Acidobacteria, Chloroflexi, and Actinobacteria (Tian et al., 2025 ). In the PLS-PM model, soil moisture content was also significantly correlated with bacterial abundance (Fig. 6 a). Soil moisture regulates oxygen availability and REDOX conditions, which directly shapes microbial metabolic pathways (Liu et al., 2018 ). In soaked soil, anaerobic conditions may favor fermentative taxa (such as desulphurizing bacteria), while fluctuating humidity levels may promote facultative anaerobic bacteria, such as Proteobacteria (Nie et al., 2019 ). This is consistent with the advantages of Proteobacteria observed in the study. Bacterial activity and diversity in soil were greater with higher soil water content (Li et al., 2021 ). This may explain why Pielou’s evenness index (Fig. 2 c) and Simpson’s diversity index (Fig. 2 b) also vary at different locations from the riparian zone. The relative abundance of Bacteroidota showed a positive relationship with pH (Fig. 5 ); previous studies have demonstrated that pH is an effective predictor of bacterial communities and has a direct effect on them (Kaiser et al., 2016 ), whereas the impact of nutrients might be mediated indirectly through alterations in pH (Zeng et al., 2016 ). Bacteroidota populations are significantly affected by pH levels. This relationship is explained by the fact that higher pH levels improve the bioavailability of key nutrients like nitrogen and phosphorus, which are vital for the proliferation of Bacteroidota. The heavy metal index significantly correlated with several phyla (Fig. 5 ), suggesting that heavy metals may play a role in shaping the structure of bacterial communities. Current research suggests that an acidic pH could affect the bioavailability of metals by increasing their solubility, potentially impacting microbial activity (Qiao et al., 2021 ). A reduction of one unit in soil solution pH has been found to lead to a 100-fold rise in the solubility of Zn (Jeong et al., 2019 ). Consequently, high concentrations of heavy metals are anticipated in highly acidic environments. 4.2 Associations and responses of Bacterial community to PAHs Both Proteobacteria and Actinobacteria are recognized as resilient phyla in PAH-contaminated soils, playing a crucial role in PAH degradation. Numerous studies have shown that the relative abundance of Actinobacteria is directly correlated with the degradation of PAHs (Peng et al., 2013 ). These bacterial genera were also detected in the present study; most of the bacterial genera in the correlation heatmap that are positively associated with environmental variables belong to Proteobacteria (Fig. 4 a), as do the core species in the network map (Fig. 4 b). Numerous researchs has documented a notable rise in the abundance of Proteobacteria, Actinobacteriota, and Bacteroidota in PAH-contaminated soils (Haleyur et al., 2019 ; Liu et al., 2019 ; Wolf et al., 2019 ), which is consistent with the dominant strains identified in this study. The relatively high abundance of Actinobacteriota in the Beiluo River shows a significant positive relationship with PAH concentrations (Fig. 5 ). Proteobacteria and Actinobacteriota dominated PAH-impacted zones, consistent with their documented capacity for xenobiotic degradation (Guo et al., 2024 ). This phylum-level specialization implies pollutant-mediated selection. The higher relative abundance of the phylum Bacteroidetes is probably because Bacteroidetes can decompose high-molecular-weight organic matter (Pan et al., 2023 ). Bacteroidetes are more likely to become the dominant bacterial group in environments with elevated levels of organic matter (Chen et al., 2016 ), indicating more active degradation of organic matter in these samples. In addition to the direct effects of bacteria on the degradation and release of PAHs, there are also indirect effects. For example, bacteria can generate biosurfactants (Bastiaens et al., 2000 ; Ho et al., 2000 ) and form biofilms (Johnsen and Karlson, 2004 ) to enhance the bioavailability of PAHs. The vegetative and metabolic pathways of Chloroflexi are abundant, and they participate in the biogeochemical cycle of a series of important biogenic elements such as C, N, and S (Xian et al., 2020 ). The variations in the relative abundance of Chloroflexi across different environmental samples may be linked to specific environmental factors, particularly nutrient concentrations. An increase in nitrogen-rich nutrients has been demonstrated to improve the bioavailability of PAHs (Pelaez et al., 2013 ), which could influence microbial community dynamics and the richness of Chloroflexi. 4.3 Combined effects of PAHs and heavy metals on bacterial communities The PAH concentrations in the soil of the Beiluo River varied between 3.00 and 131.76 ng/g, with the overall distribution characteristics indicating that PAH concentrations increased with the distance from the riverbank (Fig. 3 a). The concentration of low-ring PAHs was notably greater than that of middle-ring and high-ring PAHs. Low-cyclic PAHs are often associated with petrogenic sources, such as petroleum products, and their prevalence in the region may be due to spills from industrial activities, storage facilities, or transportation networks. The distribution characteristics of heavy metal concentrations at each sampling point were essentially consistent, ranging from 123.75 to 153.46 mg/kg (Fig. 3 b). The dominance of chromium (Cr) and zinc (Zn) among heavy metals is consistent with their widespread use in industrial coatings and agricultural fertilizer. Their persistence in riparian soils may impair microbial enzymatic functions, especially those related to nitrogen cycling, this might because heavy metals can change the structure, composition and characteristics of biofilms, thus affecting the enzyme function of microorganisms (Qu et al., 2024 ), as indicated by the decreased abundance of nitrifying bacteria observed in our study. This observation corroborates findings by Campillo-Cora et al. ( 2025 ), who demonstrated that heavy metals like Zn inhibit ammonia monooxygenase, a key enzyme in nitrification. In this study of the Beiluo River, both heavy metals and PAHs exhibited a negative correlation with Shannon's diversity index and the richness of most phyla (Fig. 5 ). Among these, the heavy metal mercury showed a negative correlation with the diversity and abundance indices of numerous bacterial groups. Studies have shown that the coexistence of heavy metals at concentrations toxic to microbes could potentially impede microbial processes (Ali et al., 2022 ). In many cases, soil co-contaminated with both pollutants exhibited greater inhibition of soil microbial parameters compared to soil contaminated with either heavy metals or PAHs alone (Barbara et al., 2003). In the PLS-PM model, heavy metals and PAHs exhibited significant negative correlations with bacterial communities (Fig. 6 a). The enhanced toxicity of PAH + HMs contaminated soil may be due to the anesthetic-type toxic effects of hydrophobic compounds like PAHs, which may interact with the lipophilic components of bacterial cell membranes, potentially altering their permeability and structure (Li et al., 2024 b). As a result, in PAH-contaminated soils, heavy metals may more easily enter microbial cells and disrupt their functions. The relatively low abundance of the nitrobacteria community detected in this study may suggest that the riparian soil has experienced long-term exposure to PAHs and heavy metals. This extended contamination could have disrupted the nitrifying flora, potentially leading to a decrease in their abundance. This result aligns with the findings of Thavamani et al. ( 2012 ), who observed a reduction in nitrifying bacteria in soils exposed to long-term pollutant contamination. Additionally, higher water content may increase the solubility of LMW PAHs and ionic heavy metals (e.g., Cd²⁺), thereby exacerbating their toxicity to sensitive groups. Conversely, saturated soil may reduce the oxidative degradation of PAHs due to limited oxygen, prolonging their persistence and indirectly affecting microbial activity (Li et al., 2024 c). This could also be a reason for the reduction in nitrifying bacterial communities, as nitrification is highly oxygen-dependent. Laboratory studies have demonstrated that zinc (Zn) exhibits a stronger tendency to interact with PAHs, such as benzo(a)pyrene and phenanthrene, compared to lead (Pb) and cadmium (Cd), thereby significantly reducing soil urease activity, an enzyme vital in the nitrogen cycle (Shen et al., 2006 ). 5. Conclusion In this study, the bacterial community composition in the riparian soil of the Beiluo River was examined using environmental DNA barcoding technology, and the impacts of PAHs and heavy metal pollution, as well as soil physicochemical properties, on the bacterial community were explored. The results indicated that the levels of PAHs and heavy metals were inversely correlated with bacterial community diversity, while soil water content showed a positive correlation with the richness and diversity of bacterial communities. PAH contamination led to a rise in the relative abundance of Proteobacteria and Actinobacteria, while heavy metal pollution reduced the diversity of bacterial communities. These findings indicate that PAHs and heavy metal pollution have distinct impacts on the soil bacterial community structure in the riparian zone of the Beiluo River. Soil water content is a pivotal factor affecting bacterial communities; therefore, attention should be given to soil water regulation and management in the context of river ecosystem management and restoration. This study provides critical insights into the interactions between riparian bacterial communities and environmental factors; however, several limitations warrant consideration. First, the single-season sampling strategy failed to capture dynamic microbial responses to seasonal hydrological fluctuations. Alternating wet-season flooding and dry-season drought significantly alter soil redox conditions and pollutant mobility, driving microbial succession—a dynamic mechanism potentially obscured by the static community in this study (Tian et al., 2025 ). Furthermore, the study did not integrate land-use gradients (e.g., agricultural and industrial zones) that regulate pollutant inputs and microbial functionality. Higher synergistic toxicity of PAHs and heavy metals in industrial soils compared to agricultural areas, suggesting land-use types may indirectly shape microbial communities by modifying pollution intensity and soil physicochemical properties (Chen et al. 2022 ). Finally, the reliance on field observations and statistical correlations lacks validation through controlled laboratory experiments. For example, mechanistic interactions between PAHs and heavy metals (e.g., Zn inhibition of PAH-degrading enzymes) require clarification via microcosm experiments (Campillo-Cora et al., 2025 ). Therefore, future research should integrate multi-season sampling, land-use zoning models, and laboratory simulations to comprehensively unravel microbial response mechanisms under complex environmental stressors and inform precise pollution remediation strategies. Declarations Author Contribution Xibo Pu: Writing – original draft, Resources, Investigation, Conceptualization, Data curation, Visualization. Yingchuan Yang: Writing – original draft, Resources, Investigation, Conceptualization, Data curation, Visualization. Jiahua Guo：Writing – review & editing, Funding acquisition. Baoxuan Zhuo: Writing – review & editing. Tamao Kasahara：Writing – review & editing. Yulu Tian: Writing – review & editing, Funding acquisition. Chenghao Li: Writing – review & editing. Jipu Guo: Writing – review & editing. Haotian Sun：Writing – review & editing, Funding acquisition, Project administration, Supervision, Conceptualization, Data curation. Acknowledgment This work was supported by China Postdoctoral Science Foundation (2023M742822), Shaanxi Provincial Science and Technology Department, China, under the Youth Project (2023-JC-QN-0349), Key Research and Development Program of Shaanxi (2024GH-YBXM-14) and Shaanxi International Science and Technology Cooperation Program (2023-GHZD-30). References Ali M, Song X, Ding D, Wang Q, Zhang Z, Tang Z (2022) Bioremediation of PAHs and heavy metals co-contaminated soils: Challenges and enhancement strategies. Environmental Pollution 295. 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Soil Biol Biochem 92:41–49. https://doi.org/10.1016/j.soilbio.2015.09.018 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.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-7543329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515462768,"identity":"77824542-2ebd-4d87-b196-eff82f4e3589","order_by":0,"name":"Xibo Pu","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Xibo","middleName":"","lastName":"Pu","suffix":""},{"id":515462769,"identity":"0a7f9a74-d374-4436-84a8-93f0be93f3c8","order_by":1,"name":"Yingchuan Yang","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Yingchuan","middleName":"","lastName":"Yang","suffix":""},{"id":515462770,"identity":"5f612965-af34-49da-b76d-d3f99570e87f","order_by":2,"name":"Jiahua Guo","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Jiahua","middleName":"","lastName":"Guo","suffix":""},{"id":515462771,"identity":"b5713b02-fefe-4579-ae3a-5689d2e6a27b","order_by":3,"name":"Baoxuan Zhuo","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Baoxuan","middleName":"","lastName":"Zhuo","suffix":""},{"id":515462772,"identity":"29fef2b1-480d-4316-83f4-91e1c9fbdf56","order_by":4,"name":"Tamao Kasahara","email":"","orcid":"","institution":"Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Tamao","middleName":"","lastName":"Kasahara","suffix":""},{"id":515462773,"identity":"7563e44f-b66d-476b-8911-4f40b23c2b49","order_by":5,"name":"Yulu Tian","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Tian","suffix":""},{"id":515462774,"identity":"d261a771-3bc5-466a-979b-22c9c1afa9fa","order_by":6,"name":"Chenghao Li","email":"","orcid":"","institution":"Northwest University","correspondingAuthor":false,"prefix":"","firstName":"Chenghao","middleName":"","lastName":"Li","suffix":""},{"id":515462775,"identity":"d17b797e-9ee7-4828-bf4b-33d19f13d77e","order_by":7,"name":"Jipu Guo","email":"","orcid":"","institution":"State Grid (Xi'an) Environmental Protection Technologies Center Co., Ltd., 710199","correspondingAuthor":false,"prefix":"","firstName":"Jipu","middleName":"","lastName":"Guo","suffix":""},{"id":515462776,"identity":"043fe940-4285-41e8-8a94-71005001f65c","order_by":8,"name":"Haotian Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYHACNhDBw8DAfADCP0CcFgOgFrYE0rSALDIgTov8jNxjD35U/JHhn93z7TFvG4Mc340Exs8FeLQY3MhLN+w5Y8AjcefsdmOgFmPJGwnM0jPwaZHIMZPgbQP65UbuNuncNobEDTcS2Jh58Dosx0zyL1CL/I2cZyAt9QS1MNzIMZMG2WJwI4cNpCXBgJAWgzNvzKRlzhjzGN5IM5P+c07CcOaZh83SeB3WDnTYmwo5e7kbyc8kZ5TZyPMdTz74Ga/D0IAEEDM2kKBhFIyCUTAKRgE2AADnSUSiDxLo2AAAAABJRU5ErkJggg==","orcid":"","institution":"Northwest University","correspondingAuthor":true,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-09-05 10:23:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7543329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7543329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91456006,"identity":"6c50edf8-20e0-4e1c-9cb2-c57f70c18f61","added_by":"auto","created_at":"2025-09-16 16:22:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1255542,"visible":true,"origin":"","legend":"\u003cp\u003eThe monthly average discharge (b) and significant difference (c) between the sampling point (a) and the four hydrological stations in Beiluo River during 2018-2022.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/abe56b207e765aacdc21f000.png"},{"id":91456007,"identity":"232cd46a-3244-4cb8-8ef5-d38c310e7aa2","added_by":"auto","created_at":"2025-09-16 16:22:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":721016,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance (a) and Pielou's evenness (b) and Simpson's diversity index(c) of bacterial communities in soils along the Beiluo River. (* indicates p ≤ 0.05, ** indicates p ≤ 0.01).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/abc4321cc0b13d2c2533362d.png"},{"id":91456008,"identity":"e8cea519-fdd1-4463-81e9-3b36aac40e90","added_by":"auto","created_at":"2025-09-16 16:22:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1276944,"visible":true,"origin":"","legend":"\u003cp\u003eProportion and concentration of PAHs (a) and heavy metals (b) at each sampling site in the Beiluo River.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/8e978c7bf3575262bdd437eb.png"},{"id":91457762,"identity":"dcc8b618-a27d-49c4-bc5d-fc903068c42e","added_by":"auto","created_at":"2025-09-16 16:38:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1889229,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between the relative abundance of bacterial communities and PAHs concentration and soil physicochemical properties (a) and correlation network analysis of bacterial communities (b).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/f464a85784635c4d4f448abd.png"},{"id":91456023,"identity":"f48e5fe6-62aa-496a-97a1-b67f4d1f3c06","added_by":"auto","created_at":"2025-09-16 16:22:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1483849,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis of alpha diversity indices of bacterial phylum-level classification and soil physical and chemical properties and PAHs. (* indicates p ≤ 0.05, ** indicates p ≤ 0.01). Notes: S: Richness index; E: Evenness index; H: Shannon-Weiner diversity index; D: Simpson diversity index\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/f766b184bb499c6894153a82.png"},{"id":91457759,"identity":"d03033c3-0231-4fb5-9236-6033a28ff6e6","added_by":"auto","created_at":"2025-09-16 16:38:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":485921,"visible":true,"origin":"","legend":"\u003cp\u003eDirect and indirect effects (b) of soil moisture, PAHs, heavy metals, soil physical and chemical properties and their effects on soil bacterial richness (a) based on PLS-PM,0.01 \u0026lt; P ≤ 0.05 *, 0.001 \u0026lt; P ≤ 0.01 **, P ≤ 0.001 ***. Notes: Blue and red represent negative and positive effects, respectively. Solid and dashed lines indicate significant and non-significant effects, respectively.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/224c972bde9f7d4d88f0f577.png"},{"id":97899463,"identity":"1ca75b95-9c64-4485-b7ba-509f0f29d59f","added_by":"auto","created_at":"2025-12-10 15:44:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7856580,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/ce27c005-1760-4a49-ae8a-c25b28a0f712.pdf"},{"id":91457763,"identity":"d2af0c68-b4ce-4d23-97b7-3585cba5cd71","added_by":"auto","created_at":"2025-09-16 16:38:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":209603,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7543329/v1/52d7e98dc364df16a462f15a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Co-occurring PAHs and Heavy Metals Drive Bacterial Community Shifts in China’s Beiluo River Riparian Soils","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe river ecosystem serves as a crucial element of the natural environment and holds significant importance in maintaining biodiversity and ecological balance (Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As an integral part of the river ecosystem, the riparian zone serves as a bridge between terrestrial and aquatic environments, acting as a central exchange point for nutrients, energy, and species (Lind et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, accelerating industrialization and urbanization have rendered these zones vulnerable to contamination by polycyclic aromatic hydrocarbons (PAHs) and heavy metals\u0026mdash;two pervasive pollutants originating from shared anthropogenic sources such as fossil fuel combustion, industrial effluents, and vehicular emission (G\u0026aacute;rfias et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As an essential element of the soil ecosystem, bacterial communities are crucial for nutrient cycling, energy transfer, and the decomposition of contaminants (Chen et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding the response of bacterial communities to environmental changes is crucial for assessing and restoring polluted river ecosystems.\u003c/p\u003e\u003cp\u003eRiparian ecosystems face significant threats from various chemical pollutants, such as PAHs, which largely originate from human activities like fossil fuel combustion, industrial discharges, and transportation-related emissions (Han et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These contaminants can infiltrate riparian ecosystems through multiple pathways, such as atmospheric deposition, surface runoff, and soil infiltration, ultimately leading to groundwater contamination (Ehigbor et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The impacts of PAHs on riparian ecosystems are far-reaching and multifaceted. Heavy metal pollution represents another significant challenge confronting riparian ecosystems. Heavy metals, such as cadmium (Cd) and zinc (Zn), frequently originate from the same pollution sources as PAHs, including industrial activities, power generation and heating systems, waste incineration, and transportation-related emissions (Maliszewska-Kordybach et al.,2003). Heavy metal contamination has the potential to deteriorate the physicochemical characteristics of riparian soils, reduce soil productivity, alter the structure and functionality of soil microbial communities, and ultimately negatively impact the health of the entire riverine ecosystem (Gran-Scheuch et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There is a correlation between heavy metal and PAH pollution, and they interact with each other, collectively leading to enhanced ecotoxicity (Shang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). After extended periods of exposure to pollutants, factors like the availability of carbon in the environment may reshape the composition of microbial communities (Annala et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When subjected to environmental stress caused by pollutants, specific microbial groups may gain dominance, thereby causing changes in community structure (Bernhard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and altering community functions (Lors et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Machado et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For instance, Cao et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) showed that the combined contamination of cadmium (Cd) and PAHs had a stronger impact on soil microbial communities than individual pollutants, leading to decreased bacterial diversity and the rise of unique bacterial groups. The complex interplay between heavy metals and PAH pollution, compounded by their synergistic effects, results in heightened ecotoxicity. This not only affects biodiversity but also results in the contamination of the food chain, ultimately presenting substantial risks to human health. While the individual impacts of PAHs or heavy metals on soil microbial communities have been extensively documented, their synergistic effects remain poorly characterized, particularly in ecologically sensitive riparian zones of fragile ecosystems like China's Loess Plateau.\u003c/p\u003e\u003cp\u003eThe Loess Plateau in northwestern China has been selected as the research area. It is the largest loess sedimentary region in the world, characterized by a fragile ecological environment and severe soil erosion. The river ecosystem is a vital component of the Loess Plateau's ecological environment, playing a vital role in preserving regional ecological balance and biodiversity. The Beiluo River, a significant tributary of the Yellow River, is located in the northwestern part of Shaanxi Province, China. The Beiluo River basin is a vital agricultural and industrial center for Shaanxi Province and plays a crucial role in the region's economic growth (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003ea). The Loess Plateau in northwestern China, home to the Beiluo River, features a semi-arid climate marked by significant seasonal changes. The Beiluo River, an important tributary of the Yellow River, experiences significant hydrological fluctuations, particularly marked by seasonal flooding. During the wet season, typically from July to September, the river's water level rises due to the combination of heavy rainfall and melting snow from the nearby mountains, leading to frequent episodes of flooding. These seasonal floods can cause extensive inundation of the riverbanks, resulting in the temporary submersion of the riparian zone (Fu et al., 2011). As economic growth accelerates, the Beiluo River basin is facing increasingly severe environmental pollution challenges, particularly those involving PAH and heavy metal contamination. Both PAHs and heavy metals are known for their carcinogenic, teratogenic, mutagenic, persistent, bioaccumulative, and toxic characteristics (Jia et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The periodic flooding of the Beiluo River significantly influences local hydrological conditions and the ecological processes of riparian soil. The alternating cycles of flooding and drying create unique environmental conditions that influence the composition and activity of bacterial communities in riparian soil. During the flood season, rising water levels can cause PAHs and heavy metals to leach from the soil into the river, potentially increasing the pollution burden. Conversely, during the dry season, the reduced water levels may concentrate these pollutants in the soil, affecting the structure and functionality of bacterial communities (du Laing et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). PAHs and heavy metals frequently co-occur in riparian soils due to their common emission pathways, yet their interactions may exacerbate ecotoxicity beyond additive effects. For instance, hydrophobic PAHs can enhance the bioavailability of heavy metals by altering soil redox conditions (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb), while metals such as Zn and Cd can inhibit enzymatic pathways critical for PAH degradation (Shen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). These interactions likely drive nonlinear shifts in microbial community structure and function, but mechanistic insights remain scarce in dynamic riparian environments. Previous studies have predominantly focused on single-pollutant scenarios or simplified laboratory systems, neglecting the complexity of field conditions where hydrological fluctuations, soil heterogeneity, and multi-pollutant interactions coexist. Therefore, this study aims to investigate the bacterial community structure in the riparian soil of the Beiluo River using environmental DNA barcoding techniques. By examining the relationships between bacterial communities and environmental factors, such as soil properties, PAHs, and heavy metal concentrations, we seek to provide valuable insights for assessing and restoring polluted river ecosystems.\u003c/p\u003e\u003cp\u003eIn this study, the Beiluo River region in Shaanxi Province was chosen as the main focus area for this investigation. Utilizing environmental DNA barcoding technology, we characterized the bacterial community structure within the riparian soil. By analyzing soil physicochemical properties, the content of PAHs, heavy metals, and other environmental variables, we elucidated the interaction mechanism between the bacterial communities and these environmental factors. The study was structured around three primary objectives: (I) to delineate the distribution characteristics of PAHs and heavy metals in riparian soils and their impacts on bacterial communities; (II) to examine the variation in soil physicochemical properties along different distances within the riparian zone and their influence on bacterial communities; and (III) to comprehend the response mechanisms of bacterial communities to environmental alterations.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1 Study Area Overview and Sampling Methodology\u003c/h2\u003e\n\u003cp\u003eSituated in northwestern China (34.95\u0026deg;\u0026ndash;37.28\u0026deg;N, 107.57\u0026deg;\u0026ndash;110.02\u0026deg;E), the Beiluo River spans a basin area of 2.69 \u0026times; 10⁴ km\u0026sup2;, with its main channel extending 171 km in length. The Beiluo River flows northwest to southeast across Shaanxi Province, traversing the distinct landscapes of the Loess Plateau and Guanzhong Plain. Its course intersects sixteen counties within four prefecture-level cities: Yulin, Yan\u0026rsquo;an, Tongchuan, and Weinan. The Beiluo River merges with the Yellow River at Tongguan County, serving as the principal tributary of the Wei River. The upper reaches of the basin are dominated by industries such as oil and coal mining, which have had a significant impact on the environment. In contrast, the lower reaches are mainly agricultural land (Li et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This river is an essential resource for both agricultural activities and the progress of industrial development in the surrounding area. In this study, we set up 16 sampling points in different upstream and downstream sections of the main stream of the Beiluo River, covering the area from the upper reaches to the lower reaches. These sampling points are located near four hydrological stations. Near each hydrological station, we conducted sampling at positions 1 meter, 5 meters, 10 meters and 15 meters away from the riverbank zone (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Differences in river discharge rates among hydrological stations (A, B, C, D) and variations in bacterial diversity indices (Simpson's diversity index, Pielou's evenness) across riparian distances (1m, 5m, 10m, 15m) were statistically evaluated using one-way analysis of variance (ANOVA). Post-hoc pairwise comparisons were performed with Tukey's honestly significant difference (HSD) test when ANOVA indicated significant main effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2 Chemical analysis and quality control\u003c/h2\u003e\n\u003cp\u003eA total of 16 sampling points were collected, and the collected soil was stored in aluminum bags. During soil sampling, the gathered soil at each site was thoroughly mixed after removing dead leaves and stones. The collected samples were divided into four aliquots, with one aliquot stored at -20\u0026deg;C for subsequent microbial sequencing and the remaining three portions kept at 4\u0026deg;C for PAH concentration, heavy metals, and physicochemical properties analysis in the soil, including key parameters such as moisture content (MC), total nitrogen (TN), total phosphorus (TP), organic carbon (OC), pH, and a range of heavy metals, among others. For ecological risk assessment, the 16 priority PAHs were categorized into three classes by molecular weight and ring number, exhibiting distinct environmental behaviors: Low molecular weight PAHs (LMW, 2\u0026ndash;3 rings): High mobility and bioavailability (e.g., Nap, Ace, Acy), indicating recent pollution inputs; Medium molecular weight PAHs (MMW, 4 rings): Moderate persistence (e.g., Fla, Pyr, BaA), serving as markers of mixed sources; High molecular weight PAHs (HMW, 5\u0026ndash;6 rings): Pronounced adsorption capacity and carcinogenicity (e.g., BaP, DaA, InP), reflecting cumulative ecological risks. Complete compound profiles are provided in Supplementary Table S2 (including physicochemical parameters and risk thresholds). Quantification of PAHs was performed using gas chromatography-mass spectrometry (GC-MS; Agilent 6890N/5975 MSD) with a DB-5 capillary column (30 m \u0026times; 0.25 mm i.d., 0.25 \u0026micro;m film thickness). Physicochemical parameters were analyzed employing a portable water quality analyzer (MI-200B). Heavy metals (including Cr, Zn, Cd, Hg, Ni, Cu, As, and Pb) in soil were quantified using inductively coupled plasma mass spectrometry (ICP-MS). Samples were digested with nitric acid-hydrofluoric acid in a microwave-assisted system, followed by analysis with indium (In) and rhenium (Re) as internal standards to correct for matrix effects and instrumental drift.\u003c/p\u003e\n\u003cp\u003eTo ensure data accuracy and reliability, stringent quality control protocols were implemented throughout the analytical procedures for PAHs and heavy metals. For each batch of samples, procedural blanks, laboratory duplicates, and field duplicates were analyzed to eliminate potential interference and cross-contamination. We established method detection limits (MDLs) by multiplying the standard deviation of target analyte concentrations in procedural blanks by a factor of three. The MDLs for PAHs ranged from 0.05 to 1.2 ng/g, while those for heavy metals (Cr, Zn, Cd, Hg, Ni, Cu, As, Pb) ranged from 0.03 to 0.15 mg/kg.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3 DNA isolation, PCR amplification, and next-generation sequencing\u003c/h2\u003e\n\u003cp\u003eGenomic DNA was extracted from soil specimens employing the OMEGA Soil DNA Kit (M5635-02, Omega Bio-Tek), per the manufacturer\u0026rsquo;s protocol. Purified extracts were stored at \u0026minus;\u0026thinsp;20\u0026deg;C for subsequent analysis. DNA concentration and purity were assessed using a NanoDrop NC2000 spectrophotometer, with integrity verified by agarose gel electrophoresis.\u003c/p\u003e\n\u003cp\u003eThe V3\u0026ndash;V4 hypervariable region of bacterial 16S rRNA genes was amplified employing polymerase chain reaction (PCR) with primers 338F (5\u0026prime;-ACTCCTACGGGAGGCAGCA-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;). Each primer contained a unique 7-bp barcode to enable multiplexed sequencing. Each 25-\u0026micro;l PCR master mix comprised:5 \u0026micro;l 5\u0026times; reaction buffer, 0.25 \u0026micro;l Fast pfu DNA Polymerase (5 U/\u0026micro;l), 2 \u0026micro;l 2.5 mM dNTPs, 1 \u0026micro;l each of 10 \u0026micro;M forward and reverse primers, 1 \u0026micro;l DNA template, and molecular-grade water (ddH₂O) to 25 \u0026micro;l final volume. PCR amplification proceeded through: Primary denaturation: 98\u0026deg;C for 5 min. 25 cycles of: Denaturation: 98\u0026deg;C for 30 s, Annealing: 53\u0026deg;C for 30 s, Extension: 72\u0026deg;C for 45 s. Final extension: 72\u0026deg;C for 5 min. Amplified products were purified using Vazyme VAHTSTM DNA Clean Beads, with concentrations determined via Quant-iT PicoGreen dsDNA Assay Kit (Sun et al.,2022).\u003c/p\u003e\n\u003cp\u003eAfter quantification, the purified amplicons were pooled in equimolar ratios and prepared for pair-end sequencing. Sequencing was performed using either the Illumina NovaSeq platform with the NovaSeq 6000 SP Reagent Kit (500 cycles) to generate 2250 bp reads or the Illumina MiSeq platform with the MiSeq Reagent Kit v3 to produce 2300 bp reads. Both sequencing processes were carried out by Shanghai Personal Biotechnology Co., Ltd, located in Shanghai, China.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4 Bioinformatics analyses\u003c/h2\u003e\n\u003cp\u003eBioinformatics analysis was conducted utilizing QIIME2 (version 2022.11). Raw sequencing reads were demultiplexed using QIIME 2's demux plugin and subsequently trimmed of primer sequences with cutadapt (v2.1). Quality control, noise reduction, and chimera removal were carried out using the DADA2 plugin. The Vsearch plugin was utilized for sequence concatenation, quality filtering, and deduplication. Unique sequences were grouped at a 98% similarity threshold for chimera identification using uchime-denovo. The non-chimeric sequences obtained were subsequently grouped at a 97% similarity threshold to produce Amplicon Sequence Variants (ASVs) or Operational Taxonomic Units (OTUs), accompanied by a corresponding ASV/OTU table. Non-singleton ASVs were phylogenetically aligned using MAFFT (v7.490), and evolutionary relationships were inferred via FastTree 2 (v2.1.11) under the GTR\u0026thinsp;+\u0026thinsp;CAT model. Taxonomic classification was performed with QIIME 2's feature-classifier plugin employing the naive Bayes classifier (classify-sklearn), referenced against the SILVA 132 database and a customized NT database.\u003c/p\u003e\n\u003cp\u003eHeatmaps utilizing Spearman correlation were constructed to analyze the associations among microbial taxa relative abundance, PAH concentrations, and diverse physicochemical properties. Environmental factors and pollutants exhibiting absolute correlation coefficients exceeding 0.7 were categorized to assess the microbial community's reaction to pollutants, applying a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Species exhibiting significant correlations (∣r∣\u0026gt;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were incorporated into an interaction network constructed with Cytoscape 3.9.1. We applied the Markov Cluster Algorithm (MCL) to partition the co-occurrence network into modules, subsequently identifying hub taxa per module through maximal values of both degree and betweenness centrality. We employed Partial Least Squares Path Modeling (PLS-PM) via the plspm package in R v4.3.3 to identify key drivers of biological responses. Prior to modeling, pairwise correlations between parameters were evaluated, and redundant variables (|r| \u0026lt; 0.7 or p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were excluded to mitigate multicollinearity (Tian et al.,2024).\u003c/p\u003e\n\u003cp\u003eThe relationships linking PAHs to microbial community compositions were further investigated through RDA, employing unweighted UniFrac distance matrices.To assess the impact of environmental factors, including PAHs and heavy metals, on microbial community diversity, Redundancy Analysis (RDA) was performed with Canoco 5.0. Alpha diversity metrics, such as species richness, evenness, Shannon-Wiener, and Simpson's indices, were computed for dominant phyla using PC-ORD 5.0.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003e\u003cstrong\u003e2.5 Risk assessment\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe risk quotient (RQ) method serves as a valuable tool for assessing the ecological risks posed by polycyclic aromatic hydrocarbons (PAHs) to surrounding organisms and ecosystems. This study employs the following formula to calculate soil PAH RQs:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}RQ=\\frac{{C}_{PAHs}}{{C}_{QV}} （1）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}R{Q}_{NCs}=\\frac{{C}_{PAHs}}{{C}_{QV\\left(NCs\\right)}} （2）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}R{\\text{Q}}_{\\text{M}\\text{P}\\text{C}\\text{s}}=\\frac{{C}_{PAHs}}{{C}_{QV\\left(MPCs\\right)}} （3）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong them, C\u003csub\u003ePAHs\u003c/sub\u003e represent the measured values of each type of PAHs in the soil, while C\u003csub\u003eQV\u003c/sub\u003e represents the reference values corresponding to each type of polycyclic aromatic hydrocarbon in the soil. Based on previous studies, the risk reference values of PAHs are classified into two types (Lin et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). C\u003csub\u003eQV(NCs)\u003c/sub\u003e indicates negligible concentration, and C\u003csub\u003eQV(MPCs)\u003c/sub\u003e represents the maximum allowable concentration, thereby obtaining the RQ\u003csub\u003eNCs\u003c/sub\u003e and RQ\u003csub\u003eMPCs\u003c/sub\u003e values.\u003c/p\u003e\n\u003cp\u003eWhen the RQ\u003csub\u003eNCs\u003c/sub\u003e of individual PAHs is 0, it indicates no risk; when RQ\u003csub\u003eNCs\u003c/sub\u003e \u0026ge; 1 and RQ\u003csub\u003eMPCs\u003c/sub\u003e \u0026lt; 1, it represents moderate risk; when RQ\u003csub\u003eMPCs\u003c/sub\u003e \u0026ge; 1, it indicates high risk. For the whole, when the RQ\u003csub\u003eNCs\u003c/sub\u003e of \u0026sum;16 PAHs is 0, it represents no risk; when \u0026sum;16 PAHs' RQ\u003csub\u003eNCs\u003c/sub\u003e \u0026ge; 1 and \u0026lt;\u0026thinsp;800, RQ\u003csub\u003eMPCs\u003c/sub\u003e = 0, it represents low risk; when RQ\u003csub\u003eNCs\u003c/sub\u003e \u0026ge; 800 and RQ\u003csub\u003eMPCs\u003c/sub\u003e = 0, it represents low risk as moderate risk 1; when RQ\u003csub\u003eNCs\u003c/sub\u003e \u0026lt; 800 and RQ\u003csub\u003eMPCs\u003c/sub\u003e \u0026ge; 1, it is moderate risk 2; when RQ\u003csub\u003eNCs\u003c/sub\u003e \u0026ge; 800 and RQMPCs\u0026thinsp;\u0026ge;\u0026thinsp;1, it is high risk.\u003c/p\u003e\n\u003cp\u003eThe single-factor index method can quantify the pollution degree of a single heavy metal by comparing the measured concentration of the heavy metal with the reference standard value. The calculation formula is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:\\begin{array}{c}{P}_{i}=\\frac{{C}_{i}}{{C}_{t}} （4）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAmong them, C\u003csub\u003ei\u003c/sub\u003e represents the actual measured value of heavy metal i in the soil, C\u003csub\u003et\u003c/sub\u003e represents the corresponding standard value of heavy metal i, and in this study, the reference standard value is the heavy metal content in the soil of Shaanxi Province. P\u003csub\u003ei\u003c/sub\u003e is the single-factor index pollution index of metal i. When P\u003csub\u003ei\u003c/sub\u003e \u0026le; 1, there is no pollution; when 1\u0026thinsp;\u0026lt;\u0026thinsp;P\u003csub\u003ei\u003c/sub\u003e \u0026le; 2, it is slightly polluted; when 2\u0026thinsp;\u0026lt;\u0026thinsp;P\u003csub\u003ei\u003c/sub\u003e \u0026le; 3, it is moderately polluted; when 3\u0026thinsp;\u0026lt;\u0026thinsp;P\u003csub\u003ei\u003c/sub\u003e \u0026le; 5, it is moderately severely polluted; when P\u003csub\u003ei\u003c/sub\u003e \u0026gt;5, it is severely polluted.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Spatial Heterogeneity of Bacterial Community Diversity\u003c/h2\u003e\u003cp\u003eSignificant differences in microbial community structures were observed across all the soil samples examined. The bacterial communities comprised 50 phyla across all samples, with Proteobacteria (22.6\u0026ndash;52.18%) and Actinobacteria (3.18\u0026ndash;35.37%) being predominantly represented (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Compared to other sampling sites, the abundance of Cyanobacteria at site D2 was notably greater compared to the other locations. Additionally, the abundance of Desulfobacterota at site B2 and Actinobacteriota at site A4 was significantly greater than at the other sampling sites. Notably, both the Simpson's diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) and the Pielou's evenness index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) for bacterial communities were significantly reduced at 5 meters from the riparian zone when compared to the values observed at 10 meters and 15 meters from the riparian zone. The average flow rates at different hydrographic stations also exhibited significant differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Patterns of PAHs and Heavy Metal Distribution in Riparian Soils\u003c/h2\u003e\u003cp\u003eAll 16 PAH compounds designated by the United States Environmental Protection Agency (EPA) were detected in the soil samples collected from the Beiluo River (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Across sampling sites, total PAH concentration (\u0026sum;PAHs) exhibited the following descending gradient: D4\u0026thinsp;\u0026gt;\u0026thinsp;A4\u0026thinsp;\u0026gt;\u0026thinsp;B4\u0026thinsp;\u0026gt;\u0026thinsp;C1\u0026thinsp;\u0026gt;\u0026thinsp;A3\u0026thinsp;\u0026gt;\u0026thinsp;C4\u0026thinsp;\u0026gt;\u0026thinsp;B3\u0026thinsp;\u0026gt;\u0026thinsp;C3\u0026thinsp;\u0026gt;\u0026thinsp;A2\u0026thinsp;\u0026gt;\u0026thinsp;A1\u0026thinsp;\u0026gt;\u0026thinsp;B2\u0026thinsp;\u0026gt;\u0026thinsp;C2\u0026thinsp;\u0026gt;\u0026thinsp;B1\u0026thinsp;\u0026gt;\u0026thinsp;D3\u0026thinsp;\u0026gt;\u0026thinsp;D1\u0026thinsp;\u0026gt;\u0026thinsp;D2. The concentrations of low molecular weight (LMW) PAHs ranged from 0.91 to 60.92 ng/g. For medium molecular weight (MMW) PAHs, the concentrations varied between 1.44 and 47.27 ng/g. Meanwhile, high molecular weight (HMW) PAHs exhibited concentrations spanning from 0.14 to 46.12 ng/g. Across all sampling locations, the distribution of heavy metal concentrations remained largely consistent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), with chromium (Cr) and zinc (Zn) being the predominant heavy metals detected, while the levels of cadmium (Cd) and mercury (Hg) were comparatively lower.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Correlations Between Bacterial Taxa and Environmental Variables\u003c/h2\u003e\u003cp\u003eThe correlation heatmap systematically elucidated the intricate interaction network between riparian microbial taxa and environmental variable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Vicinamibacteraceae demonstrated a pronounced negative correlation with pH (\u003cem\u003er\u003c/em\u003e = -0.721, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0016**). Geobacteraceae demonstrated robust negative associations with both total phosphorus (TP, \u003cem\u003er\u003c/em\u003e = -0.515, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003**) and organic carbon (OC, \u003cem\u003er\u003c/em\u003e = -0.706, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04*). The Azoarcus displayed significant antagonism toward medium-molecular-weight PAHs (MMW, \u003cem\u003er\u003c/em\u003e = -0.355, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.062). TRA3-20 was positively correlated with total nitrogen (TN, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.674, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004**).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Redundancy Analysis Identifying Key Environmental Drivers of Community Structure\u003c/h2\u003e\u003cp\u003eRedundancy analysis (RDA) elucidated differential driving mechanisms of heavy metals and physicochemical factors on the spatial divergence of core bacterial phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The structure of diverse bacterial communities exhibited a notable correlation with the levels of the heavy metal cadmium at the phylum taxonomic level. The richness of Gemmatimonadota exhibits a positive correlation with mercury concentration. The richness of Proteobacteria, Acidobacteriota, Desulfobacterota, Myxococcota, and Verrucomicrobiota exhibits a notable inverse relationship with mercury concentration (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, the concentration of PAHs also affects the richness of each phylum. For instance, the abundance of Proteobacteria and Bacteroidota demonstrates a significant positive relationship with BaA, whereas the richness of Actinobacteriota displays a significant positive relationship with Ace (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The influence of soil properties on bacterial communities must also be considered. For example, the abundance of Acidobacteriota and Myxococcota shows a significant positive relationship with soil water content, while the abundance of Bacteroidota is significantly positively correlated with total nitrogen (TN).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Co-occurrence Network Analysis of Bacterial Communities\u003c/h2\u003e\u003cp\u003eThe multi-community interaction network of soils along the banks of the Northern Luo River is categorized into four distinct modules. Each module is influenced to varying degrees by different communities and environmental factors. Overall, the positive correlations among the communities surpass the negative correlations in strength (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eUsing co-occurrence network analysis to examine polytrophic community interactions, we detected four primary modules with correlation coefficients (R-values) greater than 0.7, each harboring distinct core species. In Module 1, the central species include JG30-KF-CM45, Subgroup22, and MBNT15. In module 2, Latescibacterota and NGB1-J are the core species, while the core species of module 3 and module 4 are Gaiella and Gitt-GS-136 respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Integrated Effects of Environmental Variables on Bacterial Community Composition\u003c/h2\u003e\u003cp\u003eEnvironmental factors influencing the relative abundance of bacterial communities can be categorized into soil moisture, PAHs, heavy metals, soil characteristics, and nutrient content. Among them, soil moisture refers to soil water content, PAHs include the concentration data of 16 pollutants, heavy metals include the pollution data of eight heavy metals detected this time, soil properties include electrical conductivity, pH, nitrate nitrogen, and ammonia nitrogen, nutrient substance mainly includes organic carbon, organic matter, total nitrogen, total phosphorus, and available phosphorus. Partial Least Squares Path Modeling (PLS-PM) analysis has further clarified the combined effects of these variables on bacterial abundance, revealing both direct and indirect pathways of influence (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Soil water content exerted the most pronounced direct effect on the relative abundance of bacteria, with a path coefficient of 0.768. Moreover, soil properties demonstrated a notable negative impact on bacterial communities, as indicated by a path coefficient of -0.599. Nutrients also exhibited a significant negative effect on the relative abundance of bacterial species, evidenced by a path coefficient of -0.296. Most significantly, PAHs and heavy metals displayed marked effects on bacterial community structure, with corresponding path coefficients of -0.497 and \u0026minus;\u0026thinsp;0.343.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Risk assessment\u003c/h2\u003e\u003cp\u003eThe risk entropy evaluation results are shown in Appendix Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Table S3. From the perspective of the overall PAHs, all sampling points have a low risk for \u0026sum;16PAHs. Among the individual PAHs, except for the D1 and D2 sampling points, 87.5% of the sampling points have a medium risk for Nap; Pyr is of medium risk in 50% of the sampling points (A1, A2, A3, A4, B4, C3, C4, D4), and compared with other points, both pollutants posed greater risks at upstream site A. Flu is of medium risk in 62.5% of the sampling points (A2, A3, A4, B2, B3, B4, C1, C3, C4, D4). Secondly, Acy, Ace, Phe, Ant, BbF, and BaP have medium risks in 6.25%, 31.25%, 43.7%, 12.5%, 31.25%, and 18.75% of the sampling points respectively.\u003c/p\u003e\u003cp\u003eThe results of the single-factor index evaluation are shown in Table S4. At point A1, Hg is slightly polluted; at points A1, A2, A3, and A4, As is slightly polluted; at points A4 and B4, Cd is slightly polluted; and no pollution is observed at the remaining points.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Correlation and responses between bacterial communities and environmental factors\u003c/h2\u003e\u003cp\u003eThe study examined the distribution of PAHs and heavy metals, along with the impact of soil physicochemical properties on bacterial communities within the riparian zone of the Beiluo River. The findings reveal that bacterial community richness and the Shannon diversity index were elevated in soils with higher water content. Bacterial communities exhibit heightened sensitivity to changes in soil moisture levels (de Vries et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), The average discharge in the study area exhibited seasonal variations. The riparian soil bacterial community was mainly composed of Proteobacteria, Actinobacteriota, Acidobacteriota, Gemmatimonadota, and Chloroflexi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This result is consistent with prior studies indicating that soil moisture is a critical factor affecting bacterial communities, especially in environments with seasonal fluctuations in water levels, such as the Poyang Lake region (Tian et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The seasonal soil bacterial community in Poyang Lake, which, like this study, experiences seasonal fluctuations in water levels, was predominantly composed of Proteobacteria, Acidobacteria, Chloroflexi, and Actinobacteria (Tian et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the PLS-PM model, soil moisture content was also significantly correlated with bacterial abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Soil moisture regulates oxygen availability and REDOX conditions, which directly shapes microbial metabolic pathways (Liu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In soaked soil, anaerobic conditions may favor fermentative taxa (such as desulphurizing bacteria), while fluctuating humidity levels may promote facultative anaerobic bacteria, such as Proteobacteria (Nie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This is consistent with the advantages of Proteobacteria observed in the study. Bacterial activity and diversity in soil were greater with higher soil water content (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This may explain why Pielou\u0026rsquo;s evenness index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) and Simpson\u0026rsquo;s diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) also vary at different locations from the riparian zone.\u003c/p\u003e\u003cp\u003eThe relative abundance of Bacteroidota showed a positive relationship with pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e); previous studies have demonstrated that pH is an effective predictor of bacterial communities and has a direct effect on them (Kaiser et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), whereas the impact of nutrients might be mediated indirectly through alterations in pH (Zeng et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Bacteroidota populations are significantly affected by pH levels. This relationship is explained by the fact that higher pH levels improve the bioavailability of key nutrients like nitrogen and phosphorus, which are vital for the proliferation of Bacteroidota. The heavy metal index significantly correlated with several phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting that heavy metals may play a role in shaping the structure of bacterial communities. Current research suggests that an acidic pH could affect the bioavailability of metals by increasing their solubility, potentially impacting microbial activity (Qiao et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A reduction of one unit in soil solution pH has been found to lead to a 100-fold rise in the solubility of Zn (Jeong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, high concentrations of heavy metals are anticipated in highly acidic environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Associations and responses of Bacterial community to PAHs\u003c/h2\u003e\u003cp\u003eBoth Proteobacteria and Actinobacteria are recognized as resilient phyla in PAH-contaminated soils, playing a crucial role in PAH degradation. Numerous studies have shown that the relative abundance of Actinobacteria is directly correlated with the degradation of PAHs (Peng et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These bacterial genera were also detected in the present study; most of the bacterial genera in the correlation heatmap that are positively associated with environmental variables belong to Proteobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), as do the core species in the network map (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Numerous researchs has documented a notable rise in the abundance of Proteobacteria, Actinobacteriota, and Bacteroidota in PAH-contaminated soils (Haleyur et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wolf et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is consistent with the dominant strains identified in this study. The relatively high abundance of Actinobacteriota in the Beiluo River shows a significant positive relationship with PAH concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Proteobacteria and Actinobacteriota dominated PAH-impacted zones, consistent with their documented capacity for xenobiotic degradation (Guo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This phylum-level specialization implies pollutant-mediated selection. The higher relative abundance of the phylum Bacteroidetes is probably because Bacteroidetes can decompose high-molecular-weight organic matter (Pan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Bacteroidetes are more likely to become the dominant bacterial group in environments with elevated levels of organic matter (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), indicating more active degradation of organic matter in these samples.\u003c/p\u003e\u003cp\u003eIn addition to the direct effects of bacteria on the degradation and release of PAHs, there are also indirect effects. For example, bacteria can generate biosurfactants (Bastiaens et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Ho et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and form biofilms (Johnsen and Karlson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) to enhance the bioavailability of PAHs. The vegetative and metabolic pathways of Chloroflexi are abundant, and they participate in the biogeochemical cycle of a series of important biogenic elements such as C, N, and S (Xian et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The variations in the relative abundance of Chloroflexi across different environmental samples may be linked to specific environmental factors, particularly nutrient concentrations. An increase in nitrogen-rich nutrients has been demonstrated to improve the bioavailability of PAHs (Pelaez et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which could influence microbial community dynamics and the richness of Chloroflexi.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Combined effects of PAHs and heavy metals on bacterial communities\u003c/h2\u003e\u003cp\u003eThe PAH concentrations in the soil of the Beiluo River varied between 3.00 and 131.76 ng/g, with the overall distribution characteristics indicating that PAH concentrations increased with the distance from the riverbank (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The concentration of low-ring PAHs was notably greater than that of middle-ring and high-ring PAHs. Low-cyclic PAHs are often associated with petrogenic sources, such as petroleum products, and their prevalence in the region may be due to spills from industrial activities, storage facilities, or transportation networks. The distribution characteristics of heavy metal concentrations at each sampling point were essentially consistent, ranging from 123.75 to 153.46 mg/kg (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The dominance of chromium (Cr) and zinc (Zn) among heavy metals is consistent with their widespread use in industrial coatings and agricultural fertilizer. Their persistence in riparian soils may impair microbial enzymatic functions, especially those related to nitrogen cycling, this might because heavy metals can change the structure, composition and characteristics of biofilms, thus affecting the enzyme function of microorganisms (Qu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), as indicated by the decreased abundance of nitrifying bacteria observed in our study. This observation corroborates findings by Campillo-Cora et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who demonstrated that heavy metals like Zn inhibit ammonia monooxygenase, a key enzyme in nitrification.\u003c/p\u003e\u003cp\u003eIn this study of the Beiluo River, both heavy metals and PAHs exhibited a negative correlation with Shannon's diversity index and the richness of most phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Among these, the heavy metal mercury showed a negative correlation with the diversity and abundance indices of numerous bacterial groups. Studies have shown that the coexistence of heavy metals at concentrations toxic to microbes could potentially impede microbial processes (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In many cases, soil co-contaminated with both pollutants exhibited greater inhibition of soil microbial parameters compared to soil contaminated with either heavy metals or PAHs alone (Barbara et al., 2003). In the PLS-PM model, heavy metals and PAHs exhibited significant negative correlations with bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The enhanced toxicity of PAH\u0026thinsp;+\u0026thinsp;HMs contaminated soil may be due to the anesthetic-type toxic effects of hydrophobic compounds like PAHs, which may interact with the lipophilic components of bacterial cell membranes, potentially altering their permeability and structure (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003eb). As a result, in PAH-contaminated soils, heavy metals may more easily enter microbial cells and disrupt their functions. The relatively low abundance of the nitrobacteria community detected in this study may suggest that the riparian soil has experienced long-term exposure to PAHs and heavy metals. This extended contamination could have disrupted the nitrifying flora, potentially leading to a decrease in their abundance. This result aligns with the findings of Thavamani et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who observed a reduction in nitrifying bacteria in soils exposed to long-term pollutant contamination. Additionally, higher water content may increase the solubility of LMW PAHs and ionic heavy metals (e.g., Cd\u0026sup2;⁺), thereby exacerbating their toxicity to sensitive groups. Conversely, saturated soil may reduce the oxidative degradation of PAHs due to limited oxygen, prolonging their persistence and indirectly affecting microbial activity (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003ec). This could also be a reason for the reduction in nitrifying bacterial communities, as nitrification is highly oxygen-dependent. Laboratory studies have demonstrated that zinc (Zn) exhibits a stronger tendency to interact with PAHs, such as benzo(a)pyrene and phenanthrene, compared to lead (Pb) and cadmium (Cd), thereby significantly reducing soil urease activity, an enzyme vital in the nitrogen cycle (Shen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the bacterial community composition in the riparian soil of the Beiluo River was examined using environmental DNA barcoding technology, and the impacts of PAHs and heavy metal pollution, as well as soil physicochemical properties, on the bacterial community were explored. The results indicated that the levels of PAHs and heavy metals were inversely correlated with bacterial community diversity, while soil water content showed a positive correlation with the richness and diversity of bacterial communities. PAH contamination led to a rise in the relative abundance of Proteobacteria and Actinobacteria, while heavy metal pollution reduced the diversity of bacterial communities. These findings indicate that PAHs and heavy metal pollution have distinct impacts on the soil bacterial community structure in the riparian zone of the Beiluo River. Soil water content is a pivotal factor affecting bacterial communities; therefore, attention should be given to soil water regulation and management in the context of river ecosystem management and restoration.\u003c/p\u003e\u003cp\u003eThis study provides critical insights into the interactions between riparian bacterial communities and environmental factors; however, several limitations warrant consideration. First, the single-season sampling strategy failed to capture dynamic microbial responses to seasonal hydrological fluctuations. Alternating wet-season flooding and dry-season drought significantly alter soil redox conditions and pollutant mobility, driving microbial succession\u0026mdash;a dynamic mechanism potentially obscured by the static community in this study (Tian et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the study did not integrate land-use gradients (e.g., agricultural and industrial zones) that regulate pollutant inputs and microbial functionality. Higher synergistic toxicity of PAHs and heavy metals in industrial soils compared to agricultural areas, suggesting land-use types may indirectly shape microbial communities by modifying pollution intensity and soil physicochemical properties (Chen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Finally, the reliance on field observations and statistical correlations lacks validation through controlled laboratory experiments. For example, mechanistic interactions between PAHs and heavy metals (e.g., Zn inhibition of PAH-degrading enzymes) require clarification via microcosm experiments (Campillo-Cora et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, future research should integrate multi-season sampling, land-use zoning models, and laboratory simulations to comprehensively unravel microbial response mechanisms under complex environmental stressors and inform precise pollution remediation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXibo Pu: Writing \u0026ndash; original draft, Resources, Investigation, Conceptualization, Data curation, Visualization. Yingchuan Yang: Writing \u0026ndash; original draft, Resources, Investigation, Conceptualization, Data curation, Visualization. Jiahua Guo：Writing \u0026ndash; review \u0026amp; editing, Funding acquisition. Baoxuan Zhuo: Writing \u0026ndash; review \u0026amp; editing. Tamao Kasahara：Writing \u0026ndash; review \u0026amp; editing. Yulu Tian: Writing \u0026ndash; review \u0026amp; editing, Funding acquisition. Chenghao Li: Writing \u0026ndash; review \u0026amp; editing. Jipu Guo: Writing \u0026ndash; review \u0026amp; editing. Haotian Sun：Writing \u0026ndash; review \u0026amp; editing, Funding acquisition, Project administration, Supervision, Conceptualization, Data curation.\u003c/p\u003e\u003ch2\u003eAcknowledgment\u003c/h2\u003e\u003cp\u003eThis work was supported by China Postdoctoral Science Foundation (2023M742822), Shaanxi Provincial Science and Technology Department, China, under the Youth Project (2023-JC-QN-0349), Key Research and Development Program of Shaanxi (2024GH-YBXM-14) and Shaanxi International Science and Technology Cooperation Program (2023-GHZD-30).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli M, Song X, Ding D, Wang Q, Zhang Z, Tang Z (2022) Bioremediation of PAHs and heavy metals co-contaminated soils: Challenges and enhancement strategies. Environmental Pollution 295. 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Soil Biol Biochem 92:41\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.soilbio.2015.09.018\u003c/span\u003e\u003cspan address=\"10.1016/j.soilbio.2015.09.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\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":"PAHs, Heavy Metals, Riparian Soils, Soil Physicochemical Properties, Bacterial Community","lastPublishedDoi":"10.21203/rs.3.rs-7543329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7543329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo comprehend the response of bacterial communities to environmental variables, we examined the dispersion patterns and soil attributes associated with polycyclic aromatic hydrocarbons (PAHs) and heavy metals within the soils neighboring the Beiluo River. The structure of bacterial assemblages present along the riverbank was determined through environmental DNA metabarcoding analysis, subsequently conducting an analysis of the relationships between these microbial populations and various environmental factors. The total concentrations of 16 US EPA-listed PAHs in the Beiluo River riparian soils ranged from 3.00 to 131.76 ng/g. Heavy metal concentrations varied by element: chromium (Cr) and zinc (Zn) exhibited the highest levels (123.75\u0026ndash;153.46 mg/kg), while cadmium (Cd) and mercury (Hg) were detected at significantly lower concentrations (0.03\u0026ndash;0.15 mg/kg). Proteobacteria, Actinobacteriota, and Bacteroidota were found to be predominant, as these phyla are capable of degrading PAHs and exhibit high adaptability to the environment, resulting in their higher abundance compared to other phyla. Several phyla exhibited significant associations with PAHs and heavy metals, which might be explained by the increased toxicity of heavy metals in settings where both PAHs and heavy metals are present together. Moreover, Pielou\u0026rsquo;s evenness and Simpson\u0026rsquo;s diversity index exhibited notable variations at varying distances from the riparian zone, likely influenced by the fluctuations in soil moisture content as distance changes. Higher water content correlated with increased bacterial activity and diversity. This study elucidates the complex interplay between bacterial communities and environmental factors in the Beiluo River riparian zone, offering valuable perspectives for the assessment and remediation of contaminated river ecosystems.\u003c/p\u003e","manuscriptTitle":"Co-occurring PAHs and Heavy Metals Drive Bacterial Community Shifts in China’s Beiluo River Riparian Soils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 16:22:25","doi":"10.21203/rs.3.rs-7543329/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":"4412686d-edc3-4736-a44e-b91c2e5bef1f","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-21T19:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 16:22:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7543329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7543329","identity":"rs-7543329","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}