Next Article in Journal
Ecofriendly Application of Synthetic Zinc Oxide Nanoparticles as Stress Regulator Bio-Fertilizer for Zea mays
Next Article in Special Issue
Assessing the Impact of Pontederia crassipes Extracts on the Saprophytic Soil Fungus Trametes versicolor: Implications for Agricultural Use
Previous Article in Journal
Multi-Stress Alleviation and Rapeseed Yield Enhancement by Combined Application of Organic–Inorganic Fertilizers with Super Absorbent Polymer in Arid Sandy Soils
Previous Article in Special Issue
Erosion Control Effects of a Polymer-Based Soil Conditioner on Red Soil in Okinawa, Japan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 15
Issue 12
10.3390/agronomy15122874
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil

by
Yanjie Wang
1,2,
Qiong Wang
1,
Meijuan Wang
3,
Haiqing Lei
2 and
Jiabo Chen
4,5,*
1
National & Local United Engineering Laboratory of Petroleum Chemical Process Operation Optimization and Energy Conservation Technology, Liaoning Petrochemical University, Fushun 113001, China
2
College of Forestry Science and Technology, Lishui Vocational and Technical College, Lishui 323000, China
3
Qiandao Lake Ecological Protection Bureau of Chun’an County, Hangzhou 311700, China
4
School of Hydraulic Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China
5
Zhejiang Key Laboratory of River-Lake Water Network Health Restoration, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(12), 2874; https://doi.org/10.3390/agronomy15122874
Submission received: 28 October 2025 / Revised: 11 December 2025 / Accepted: 12 December 2025 / Published: 14 December 2025
(This article belongs to the Special Issue Soil Degradation and Restoration: Challenges and Prospects for Agroecological Development)
Browse Figures
Versions Notes

Abstract

Petroleum hydrocarbons are pervasive soil pollutants that detrimentally affect the soil structure, nutrients, and microbial ecosystems. However, the effect of biochar particle size on the remediation effectiveness remains a critical, unresolved parameter. Here, a soil remediation experiment was conducted to evaluate the synergy between biochars of different particle sizes and nutrient addition. Total petroleum hydrocarbons (TPHs) were quantified gravimetrically, and specific hydrocarbon fractions were analysed via gas chromatography mass spectroscopy (GC-MS) while the microbial community composition was analysed via high-throughput sequencing. The results revealed that granular bulrush straw biochar (0.85 mm) with nutrients achieved the greatest TPH degradation (73.35%), significantly outperforming both powder biochar and soybean straw biochar. This enhanced remediation was associated with a significant shift in the microbial community (p < 0.05), characterized by substantial increases in hydrocarbon-degrading bacteria, particularly Actinobacteria and the genus Mycobacterium. This study revealed that the synergistic application of granular biochar and nutrients is a highly effective, nature-based strategy for petroleum-contaminated soil, which functions by resolving a critical biochar parameter to enhance key microbial degraders.

Graphical Abstract

1. Introduction

During oil development, transportation and related product preparation, crude oil and its products inevitably enter soils. Petroleum hydrocarbons are persistent organic pollutants with high toxicity, that can greatly affect soil ecosystem functions and human health when their concentration exceeds the self-purification capacity of soil [1,2].
These compounds exhibit low release rates and, over long periods, are bound tightly to the soil matrix, rendering their microbial degradation challenging [3,4]. After entering soil, petroleum pollutants mostly remain in the surface layer, where they can block soil pores, reduce porosity, and decrease the soil moisture content and permeability because of their high adhesion and hydrophobicity [5]. Furthermore, petroleum substances can alter soil organic matter composition and nutrient ratios, thereby reducing the availability of nitrogen and phosphorus and affecting soil fertility and microbial processes [6,7,8]. Consequently, in nutrient-deficient or imbalanced soils, the external supplementation of nutrients is critical to correct these limitations and create a more favourable environment for the microbial degradation of persistent hydrocarbons [9,10,11].
Biochar is a porous material produced via biomass pyrolysis under oxygen-limited conditions, and is derived primarily from agricultural and forestry waste materials [12]. Its application can improve soil properties, increase microbial activity and create favourable conditions for microbial survival [13,14,15]. The porous structure of biochar provides a habitat for microorganisms and sequesters contaminants, which in turn ameliorates hydrocarbon toxicity [12,15]. With respect to the remediation of hydrocarbon-contaminated soil, biochar has been verified to enhance the microbial degradation of pollutants [16,17]. For instance, Kong et al. reported that biochar combined with nitrogen could reduce the content of petroleum hydrocarbons by 78.6% [18]. Additionally, biochar can improve the availability and retention of soil nutrients, further supporting microbial metabolism and plant growth in remediation contexts [19,20]. Given these benefits, biochar-assisted biostimulation or bioaugmentation present a compelling advantage for soil remediation, and therefore warrant further systematic investigation to optimize their application and mechanistic understanding [21,22].
However, despite the recognized potential of biochar, a critical scientific gap remains in terms of optimizing its application. Much of the existing research treats biochar as a generic amendment, without systematically decoupling the influence of its key intrinsic properties. Notably, it remains unclear how key properties, specifically the particle size (powder or granular biochar) and feedstock type, individually and interactively affect its effectiveness in promoting microbial hydrocarbon degradation. For instance, powdered biochar offers a larger number of relatively uniform micro-habitats within the soil, while granular biochar may better preserve soil structure and heterogeneous microenvironments with distinct diffusion pathways for oxygen and nutrients [23,24]. Similarly, biochar derived from different feedstocks varies substantially in pore structure and surface chemistry, which could differentially shape the microbial community and degradation processes [12,25]. This lack of mechanistic clarity makes it difficult to select or design the most appropriate biochar for a given contamination scenario and soil type. Systematically decoupling these factors is essential for formulating targeted remediation strategies rather than empirical application.
The low temperature and degree of salinization of oil fields in Northeast China affect the physicochemical and biological properties of the soil, limiting the effectiveness of bioremediation methods in the region. Therefore, this study aimed to fill this knowledge gap by systematically investigating the effects of biochar with different particle sizes (0.25 mm powder vs. 0.85 mm granules) and feedstocks (bulrush straw vs. soybean straw) combined with nutrients, on the bioremediation of petroleum-contaminated soil. We focused on total petroleum hydrocarbon (TPH) degradation, changes in soil physicochemical properties, and the response of the microbial community structure, to identify the main factors influencing biodegradation performance. The findings of this study can provide a sound basis for the optimized application of biochar in petroleum-contaminated soils, particularly under regional environmental constraints.

2. Materials and Methods

2.1. Preparation and Characterization of Biochar

In this study, different types of biochar were produced from dried bulrush straw and soybean straw via slow pyrolysis in a laboratory-scale fixed-bed reactor (KBF11Q; Nanda Instrument, Nanjing, China) under an oxygen-limited atmosphere maintained by a continuous flow of high-purity nitrogen (99.99%) at a rate of 0.5 L/min. A heating rate of 10 °C/min from ambient temperature to 500 °C was employed, followed by a 3 h holding period. The approximate biochar yield, calculated as (mass of biochar/mass of dry biomass) × 100%, was 35.28% for bulrush straw and 32.75% for soybean straw. These conditions were selected on the basis of our systematic optimization studies [26]. A temperature of 500 °C was established as the optimal compromise for developing a well-developed porous structure, and a residence time of 3 h was sufficient to ensure complete carbonization without unnecessary energy consumption.
The bulrush straw and soybean straw were dried at 60 °C for 24 h and subsequently crushed. The prepared biomass was then pyrolyzed, and the biochar was sieved to obtain the two target particle sizes of 0.25 mm (powder biochar, PBC) and 0.85 mm (granular biochar, GBC). The surface morphological structure and elemental composition of the biochar were determined via scanning electron microscopy (SEM–EDS, SU8010; Hitachi, Tokyo, Japan) [27]. The specific surface area, pore volume, and pore size distribution of the biochars were determined by N2 adsorption–desorption measurements at 77 K using an automatic surface area and pore size analyzer [27]. The specific surface area was calculated from the adsorption data using the Brunauer–Emmett–Teller (BET) method. The total pore volume was estimated from the amount adsorbed at a relative pressure (P/P0) of 0.99 [28]. The pore size distribution was analyzed based on the adsorption data using a theoretical pore model. Additionally, Fourier transform infrared spectroscopy (FTIR, FTIR-660 + 610; Agilent, Santa Clara, CA, USA) was conducted out to analyse the functional groups on the surface of the biochar within the wavenumber range of 4000–500 cm−1 [29].

2.2. Soil Sampling and Applied Amendments

Petroleum-contaminated soil was collected from the top 20 cm layer in the Liaohe Oilfield (41.21° N, 121.92° E), northeastern China, where the implementation of pollution prevention and control is crucial for ensuring the safety of adjacent farmland soil and agricultural products. The collected soil was preprocessed, dried naturally and sieved (2 mm). The initial physicochemical properties and TPH in the soil used for bioremediation are detailed in Table S1. The soil was a loam soil with a clay content of 20%, an initial TPH concentration of 19.55 g/kg, and a soil organic carbon (SOC) content of 87.46 g·kg−1. The following six treatments were established (Table S2): CK, control treatment; N, nutrient addition treatment ((NH4)2SO4 and K2HPO4); PBC/bs, 5% (w/w) powder biochar (0.25 mm) from bulrush straw and nutrient addition treatment; PBC/ss, 5% (w/w) powder biochar (0.25 mm) from soybean straw and nutrient addition treatment; GBC/bs, 5% (w/w) granular biochar (0.85 mm) from bulrush straw and nutrient addition treatment; and GBC/ss, 5% (w/w) granular biochar (0.85 mm) from soybean straw and nutrient addition treatment. The biochar application rate was set to 5% (w/w), which was determined as the optimal dosage on the basis of preliminary experiments [26]. Nitrogen and phosphorus were added to achieve a C׃N׃P molar ratio of 100׃10׃1 in all the treatments except the control treatment. The experimental soil was thoroughly mixed manually with the respective biochar and nutrients to achieve homogeneity. Each plastic tray (21 cm × 21 cm × 15 cm) was filled with 3.0 kg of this homogenized soil mixture. Each treatment was assigned three replicates. The experiment was conducted for 80 days. All plastic trays were incubated in a climate-controlled chamber at 25 ± 2 °C under a 12 h light/dark cycle. To ensure aerobic conditions (crucial for microbial hydrocarbon degradation) and homogeneity, the soil was manually turned over every 10 days after TPH sampling, and the trays were left uncovered to facilitate air exchange. The content of soil moisture was maintained at approximately 60% of the field water holding capacity by periodically adding sterile water to compensate for weight loss. The TPH content was measured every 10 days. Soil cores were collected across the entire soil profile at five predetermined locations (the four corners and the centre) from each tray with a stainless steel corer (2 cm diameter). All five cores from a single tray were combined and thoroughly mixed, and a subsample of this composite material was obtained for TPH extraction and analysis. This protocol ensured that the measured TPH concentration was representative of the entire treatment. The different hydrocarbon fractions were measured after 30 and 80 days. The soil physicochemical factors and microbial characteristics were measured after remediation.

2.3. Determination of Petroleum Hydrocarbons and Physicochemical Factors in Soil

After 10 g of soil was weighed, methylene chloride was added, and the mixture was subjected to ultrasonic extraction three times. The extracted solution was then mixed and concentrated using a rotary evaporator. The residual TPH content was then determined through the gravimetric method [30]. The concentrated extract was transferred to a preweighed beaker, the solvent was evaporated under a gentle stream of high-purity N2, and the beaker was dried to a constant weight in a desiccator. The TPH mass was calculated from the net weight increase in the beaker after the average value of the procedural blanks that were run with each sample batch was subtracted. The method was performed in accordance with the core procedures of the ASTM D8193-18 standard [31]. The results of parallel sample experiments revealed a method detection limit of 10 mg/kg and an analytical precision, expressed as the relative standard deviation (RSD), less than 5%. The main fractions and degradation products of petroleum hydrocarbons were analysed by gas chromatography–mass spectrometry (GC–MS, 7890A-5975C; Agilent, Santa Clara, CA, USA) [32]. Separation was achieved using an HP-5MS capillary column (30 m × 0.25 mm × 0.25 μm; Agilent J&W, Santa Clara, CA, USA). The oven temperature program was as follows: initial hold at 40 °C for 2 min, increase to 300 °C at 6 °C/min, and hold for 15 min. High-purity helium was employed as the carrier gas at a constant flow rate of 1.0 mL/min. Compounds were identified by comparing their mass spectra with those in the NIST8.0 library, supplemented by the use of characteristic fragment ions for confirmation. The pH was determined in a soil/distilled water suspension at a ratio of 1:2.5. The organic carbon content was determined according to the potassium dichromate oxidation spectrophotometric method. The total nitrogen and phosphorus contents were determined according to the Kjeldahl procedure and the Mo–Sb anticolorimetric method, respectively [33].

2.4. Sequencing and Analysis of Soil Microorganisms

The microbial composition in the 6 treatments was analysed. Genomic DNA was extracted from samples of the six soil treatments, and the amount and quality of extracted genomic DNA were analysed via 1% agarose gel electrophoresis. Polymerase chain reaction (PCR) amplification, product recovery and elution were subsequently performed [34]. The V4–V5 hypervariable region of the bacterial 16S rRNA gene was amplified using the primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 926R (5′-CCGTCAATTCMTTTGAGTTT-3′). A two-step PCR protocol was adopted for library construction. First, specific primers were used to amplify the target fragment and perform gel recovery. The recovered product was subsequently employed as a template for secondary PCR amplification, with the aim of adding the adapter sequencing primer barcode needed for Illumina platform sequencing to both ends of the target fragment. Three replicates for each treatment were used for PCR analysis. PCR products were analysed and quantified with an FTC-3000TM real-time PCR instrument (Fengling, Shanghai, China). Sequencing was subsequently performed on an Illumina MiSeq 2 × 300 bp platform. The raw sequencing data were processed and analysed using the mothur software pipeline (version 1.48.0). The processed sequences were aligned and deposited in the National Center for Biotechnology Information database with the accession number PRJNA1056853.

2.5. Data Analysis

The experimental data were plotted using OriginPro 2023. Alpha diversity index values (Shannon, Simpson, Chao, and Ace) were calculated directly using the standard algorithms within the mothur software pipeline from the rarefied operational taxonomic unit (OTU) table (97% similarity). Additionally, rarefaction curves were generated using the ‘rarefaction.single’ command within mothur to validate the adequacy of the sequencing depth. Nonmetric multidimensional scaling (NMDS) was used to verify the relative similarity of the bacterial communities in the different treatments on the basis of the Bray–Curtis coefficient. All the statistical analyses and calculations of differences were conducted in SPSS 19.0. Prior to analysis of variance (ANOVA), the normality of the data distribution was verified using the Shapiro–Wilk test, and the homogeneity of variance was confirmed using Levene’s test. For data that did not satisfy the assumption of homogeneity of variance, an appropriate data transformation (e.g., logarithmic or square root) was applied before analysis. Significant differences among treatments were assessed using ANOVA, followed by Tukey’s honestly significant difference (HSD) post hoc test for multiple comparisons. The significance level for all tests was set to p < 0.05. The results are presented as the mean ± standard deviation (SD) of three replicates.

3. Results and Discussion

3.1. Characterization and Analysis of Materials

3.1.1. Microstructural and Textural Characterization of Biochars

The distinct porosities of the bulrush straw biochar and soybean straw biochar are highlighted in Figure 1. The morphological characteristics of the two types of biochar materials notably differed. Compared with those of the other samples, the layered structure of the bulrush straw biochar was denser, with more tubular pores and thinner pore walls. The pore structures of the bulrush straw biochar at different levels were clear and relatively uniform. The pore characteristics of the powder biochar (Figure 1A) and granular biochar (Figure 1B) were relatively similar. After the bulrush straw biomass was carbonized, components such as cellulose and hemicellulose decomposed to form loose and porous structures, with microporous structures emerging in the pore walls. The porosity of the bulrush straw biochar was high. Compared with the bulrush straw biochar, the soybean straw biochar (Figure 1C,D) contained fewer pore structures. The outer wall of the soybean straw did not form penetrating pores during carbonization, mainly because of the thick fibre wall and hardness of its biomass materials. The BET surface area, pore volume, and pore size distribution for the four samples were summarized in Table 1, Figure 1E and Figure S1. All materials exhibited high specific surface areas (>1250 m2/g) and hierarchical pore structures. The GBC series showed a dominant peak in the micropore region (~1.3 nm) while maintaining a continuous distribution in the mesopore range (10–30 nm), demonstrating superior pore synergy. GBC/bs possessed the highest specific surface area (1463.1 m2/g) and total pore volume (1.449 cm3/g) among the four samples. Its micro-meso hierarchical pore structure contributed to a well-balanced porosity, ensuring substantial internal surface area alongside continuous pore channels conducive to molecular diffusion. The surface element compositions of the bulrush straw biochar and soybean straw biochar samples were analysed via energy dispersive spectroscopy (EDS). The distributions of elemental C, O, K, Cl, Na, Mg, P, and Ca were observed from the corresponding SEM–EDS mapping images of the bulrush straw biochar (Figure S2 GBC/bs and PBC/bs), with higher Mg and P levels measured than those in the soybean straw biochar (Figure S2 GBC/ss and PBC/ss). Elemental C and O were the main component elements of the four types of biochar, accounting for a relatively large proportion of all the elements. The permeability (water and air), porosity and nutrient retention of polluted soil increase after the addition of porous biochar [13,21].

3.1.2. FT-IR Analysis

The functional groups on the surface of the biochar were analysed via FTIR spectroscopy, and the results revealed that the absorption peaks of the same biomass were essentially consistent. The typical peaks and intensities of the different types of biochar differed. The peaks at ~2920 and ~2850 cm−1 could be attributed to the stretching vibrations of aliphatic groups (-CH2, -CH3), with a higher intensity observed for the bulrush straw biochars [14]. A pair of characteristic broad bands at ~1630 cm−1 and ~1400 cm−1 were assigned to the asymmetric and symmetric stretching vibrations of carboxylate (O-C=O) groups, respectively [35,36]. The broad absorption band in the region of ~1050–1150 cm−1 for the two biomass materials was attributed primarily to C-O-C stretching vibrations (e.g., esters and ethers) [37]. The absorption peaks in the fingerprint regions at 782 cm−1 (PBC/bs and GBC/bs) and 799 cm−1 (PBC/ss and GBC/ss) could be attributed to C-H bending vibrations [37]. Studies have revealed that absorption peaks at this wavenumber can be attributed to monocyclic and polycyclic aromatic groups [29,35]. As shown in Figure 2 and Table 2, there were differences in the types of functional groups on the surface of the biochars prepared from bulrush straw and soybean straw, which in turn affected the nutrient utilization efficiency of these biochars in the soil. The surface groups of biochar bind pollutants through π-π covalent bonds, hydrogen bonds, coordination and other forces, thereby affecting the adsorption performance for pollutants [14,17].

3.2. Degradation of TPH and the Main Fractions in Crude Petroleum-Contaminated Soil

3.2.1. Total Petroleum Hydrocarbons

By adding nutrients and different types of biochar, the soil properties and remediation performance were enhanced. The TPH degradation rate in each treatment increased with increasing remediation time, and the degree of degradation decreased at the later stage (Figure 3). The initial content of TPH in the contaminated soil was 19.55 g kg−1. After 80 days of remediation, the TPH degradation efficiency ranged from 52.19% to 73.35% in the biochar-amended treatments, all of which were significantly greater than those in the nutrient-only (35.91%) and control (24.36%) treatments (p < 0.05, Table 3). The results for the degradation efficiency of petroleum hydrocarbons among the different treatments, as detailed in Table 3, revealed that compared with powder biochar (0.25 mm), granular biochar (0.85 mm) provided a significantly better treatment effect and that the effect of bulrush straw biochar was superior to that of soybean straw biochar. At 10–20 days of remediation, the CK and N treatments differed significantly (p < 0.05) from the biochar-amended groups in TPH degradation, while showing no significant differences (p > 0.05) among them. After 30–50 days, the TPH degradation rates among the different treatments gradually showed differed, and the remediation effects of the granular biochar (GBC/bs, GBC/ss) and powdered biochar (PBC/bs, PBC/ss) significantly differed (p < 0.05). The TPH degradation rates significantly differed among all the treatments from 60–80 days. Compared with the addition of nutrients alone, the addition of different types of biochar with nutrients provided better remediation effects. This synergistic effect is critical, as biochar alone primarily functions as a sorbent and an amendment for soil physical properties, thus providing limited direct enhancement in biodegradation in the absence of a nutrient supply [38]. Similar studies have indicated that nutrient stimulation can promote the degradation of petroleum hydrocarbons in deep soil layers [21]. Compared with nutrient-only treatments, the addition of porous materials such as cotton stalks significantly improved (p < 0.05) the removal rate of petroleum hydrocarbons in soils [5,18]. The addition of biomass materials to soil improved the soil properties, mainly because the humic acids produced by microbial degradation and decomposition of biomass combined with cations in the soil to induce soil aggregate formation [39].

3.2.2. Degradation of n-alkanes, Steranes and Terpanes

The content of n-alkanes, a type of biomarker compound related to petroleum genesis, is relatively high in crude oil [40]. The degradation of n-alkanes after 30 and 80 days in the different treatments is shown in Figure S3. The contents of n-alkanes (low- and high-molecular-weight) are listed in Table 4. After remediation, the contents of n-alkanes (C15–C24 and C25–C36) in each treatment decreased compared with those at 30 days. The contents of C15–C24 and C25–C36 in the GBC/bs samples decreased to 0.52–2.39%, respectively. During the same period, compared with those in the control treatment (CK30 and CK80), all the other treatments resulted in decreases in n-alkane levels. At 30 days, the main peaks of n-alkanes in CK were those of C17, C18, C25, and C31 and adjacent peaks. The C17, C18, and C25 peaks in PBC/bs, PBC/ss, GBC/bs and GBC/ss decreased noticeably, whereas the decreases in C31 and adjacent peaks were visibly smaller than those in the low-carbon-number peaks. After 80 days, the C17 and C18 peaks had markedly decreased, whereas the degradation amplitude of C31 and adjacent peaks had become more pronounced, resulting in a slight increase in the contents of C20–C26 hydrocarbons. These results also revealed the increased degradation efficiency for high-carbon alkanes under prevailing environmental conditions and under the influence of microorganisms [2,6]. From the perspective of material type, the bulrush straw biochar treatments (PBC/bs and GBC/bs) resulted in a higher degradation efficiency for n-alkanes. A comparison of particle sizes revealed that the effect of the granular biochar (0.85 mm) was greater than that of the powder biochar (0.25 mm). Compared with other components of petroleum hydrocarbons, microorganisms degrade n-alkanes to a greater extent and at higher rates. The degradation products of n-alkanes also provide sufficient available carbon sources for microbial growth, which is beneficial for increasing the quantity and community diversity of microorganisms in the soil [4].
Steroids and terpenoids form cycloalkanes through the removal of oxygen-containing functional groups and the disproportionation of hydrogen [40]. In this process, the original molecular structure is preserved, thereby forming biomarker compounds, such as the sterane series with 4 rings and the hopane series with 5 rings [40]. The bioavailabilities of steranes and terpenes are lower than those of normal alkanes, and their degradation rates and extents are relatively low [41]. The contents of the main steranes and terpanes (C21–22 low molecular steranes, C27–29 regular steranes, Ts/Tm, and C29–34 hopanes) are given in Table 4. The degradation of steranes (m/z 218) in petroleum-contaminated soil after 30 and 80 days was also analysed (Figure 4A). At 30 days, the extent of degradation of C27–29 regular steranes and C21–22 low molecular steranes in all the treatments except the GBC/bs was relatively low. The degradation of steranes in the N treatment was similar to that in the CK treatment. After 80 days of remediation, compared with that in the CK treatment, the degree of sterane degradation in N was very small. The amounts of C21–22 low molecular steranes and C27–29 regular steranes in the treatments with the two kinds of biochar combined with nutrients increased. The degradation of C27 sterane was greater than that of other regular steranes, and the GBC/bs treatment provided the greatest degradation effect on various steranes. The quantitative data for the recalcitrant hydrocarbon fractions (C27 sterane, C33–C34 terpane) were determined by integrating the peak areas of their characteristic ion chromatograms. The abundances reported represent the summed peak areas of all characteristic epimers for each homologue. The complete dataset, including the peak areas at 30 and 80 days and the calculated removal efficiencies, is provided in Table S3. The granular biochar derived from bulrush straw (GBC/bs) consistently outperformed all other treatments, achieving the highest removal rates for C27 steranes (55.6%), C33 terpane (46.3%), and C34 terpane (32.5%) between 30 and 80 days. This quantitative data directly verifies the superior efficacy of GBC/bs in mitigating persistent petroleum hydrocarbons. This difference was mainly due to the differences in the types and particle sizes of the biochar, which resulted in different effects on nutrient retention, soil property improvement, and microbial community characteristics, leading to differences in the degradation of steranes in the soil [42].
Most of the pentacyclic triterpenes in crude oil are hopanes and their derivatives [43]. When petroleum hydrocarbons are degraded by microorganisms, their structures are affected, and the contents of various types of hopanes change with the degree of microbial degradation [43]. A study revealed that when crude oil was strongly degraded, it formed a demethylated series, namely, the 10-demethylated series [3]. The distribution characteristics of the different compounds in the series help to determine the degree of biodegradation of crude oil. With increasing molecular complexity of the hopane series increased, the water solubility of the hopanes decreased, reducing their availability. After 30 and 80 days of remediation, the terpanes in each treatment were measured, and the results are shown in Figure 4B and Table 1. Compared with that at 30 days, the ratio of 17α-22,29,30-trisnorhopane relative to 18α-22,29,30-trisnorhopane (Ts/Tm) in each treatment increased at 80 days. The increase in the Ts/Tm values of GBC/bs was the greatest, followed by that of GBC/ss. These results indicated that as biodegradation increased, Tm gradually transformed into Ts with a more stable configuration. The contents of C29–34 hopanes were relatively high in the residues of the different treatments. At 30 days, the contents of hopanes in the CK and N treatments were considerably greater than those in the other combined biochar and nutrient treatments. Owing to the relatively complex structure of hopanes and their recalcitrance to degradation, the degree of degradation was limited in the first stage of remediation, and their degradation amplitude was considerably smaller than that of n-alkanes. At 80 days, there was little change in CK or N compared with that at 30 days. However, compared with that at 30 days, the degree of degradation of the combined biochar and nutrient treatments increased. The effects of the bulrush straw biochar treatments were greater than those of the soybean biochar treatments during the same period. The GBC/bs had the greatest effect on the degradation of pentacyclic terpenes. After remediation, there were fewer residuals of C33–C34 hopanes, which was related to selective utilization by microorganisms. The addition of granular biochar in this study effectively relieved compaction, increased the porosity of the polluted soil and promoted fixation and the slow release of the added nutrients. With the regulation of soil properties and sufficient recovery time, the diversity of soil microorganisms effectively improved. Microorganisms that can survive under these environmental conditions can utilize petroleum hydrocarbons. After easily degradable substances are depleted, dominant microbial groups that can exist over longer periods of remediation can use resistant substances such as terpenes in crude oil, effectively enhancing the overall biological utilization of petroleum hydrocarbons [41,44].

3.3. Influence of Different Methods on Microbial Characteristics

3.3.1. Analysis of the Soil Microbial Community Structure

At the bacterial phylum level, the obtained OTU sequences were divided into 12 main bacterial phyla (with a relative abundance > 0.002). The bacterial phyla with relative abundances < 0.002 were merged into others, as shown in Figure 5A. Actinobacteria was the dominant phylum in all the treatments except for the CK treatment, accounting for 42.93–49.58% of the bacteria. Compared with that in the control treatment, the abundance of Actinobacteria in the other treatments increased by 21.52–28.08% after remediation. Proteobacteria was the dominant phylum in CK, accounting for 46.16% of the bacteria. Compared with those in CK, the proportions of Proteobacteria in the other treatments decreased by 17.16–31.12% at 80 days. The third most abundant phylum was Chloroflexi, which accounted for 11.38–19.99% of the bacteria in each treatment group, and its abundance increased by 2.84–8.62% compared with that in the control treatment group. In other studies, Actinobacteria were found to degrade and utilize petroleum hydrocarbons, promoting their biological utilization by producing biosurfactants or corresponding reaction enzymes [45]. Studies have also indicated that Actinobacteria and Chloroflexi have certain tolerances to hydrocarbon substances and account for a relatively large proportion of the total bacteria in oil-contaminated soil [8,45]. The phyla Actinobacteria, Chloroflexi and Acidobacteria have been detected in soils contaminated with aliphatic or aromatic compounds [6]. Owing to their superior pollutant degradation characteristics, these microorganisms have been applied in several biotechnology fields [4]. As remediation progresses, microorganisms that can utilize hydrocarbons gradually become the dominant groups, mainly because of changes in soil physicochemical factors, which provide favourable conditions for microbial survival and metabolism [1,9]. In this study, this phenomenon was evidenced by the direction of changes in the soil properties and the type of microbes that survived.
The distribution of bacterial genera varied among the different treatments, as shown in Figure 5B. The composition of the bacterial genera in the CK treatment differed from that in the other treatments, with the dominant genus being unclassified_c_Proteobacteria (33.54%), followed by unclassified_c_Chloroflexi (11.82%) and Mycobacterium (8.50%). The relative abundance levels of Mycobacterium, unclassified_c_Chloroflexi and unclassified_c_Actinobacteria varied slightly, accounting for 18.94%, 17.61%, and 16.87%, respectively. The dominant bacterial genus in PBC/bs was Mycobacterium, with a relative abundance of 24.35%, followed by unclassified_c_Chloroflexi (21.97%) and unclassified_c_Actinobacteria (9.64%). The relative abundance of Mycobacterium in PBC/ss reached 23.04%, followed by unclassified_c_Chloroflexi (13.74%) and unclassified_c_Proteobacteria (11.84%). Mycobacterium remained the dominant genus (28.07%) in GBC/bs, followed by unclassified_c_Chloroflexi and unclassified_c_Actinobacteria, accounting for 17.90% and 10.56%, respectively. The genus with the highest relative abundance in GBC/ss was Mycobacterium (25.41%), followed by unclassified_c_Chloroflexi (16.52%), unclassified_c_Proteobacteria (13.64%) and unclassified_c_Actinobacteria (12.02%). After remediation, Mycobacterium was the dominant genus in the different treatments. Compared with that in the CK, the abundance of Mycobacterium in the other treatments increased by 10.43–19.56%. Compared with those in the CK, the abundances of unclassified_c_Actinobacteria and unclassified_c_Chloroflexi increased by 8.06–16.87% and 1.92–10.15%, respectively. The impact of petroleum pollutants on soil microorganisms is not simply related to the increase or decrease in microbial diversity; rather, in-depth analysis from more perspectives, such as analyses of the microbial community structure and function and specific microbial groups, is needed. Related studies have also indicated that Mycobacterium is another genus representative of Actinobacteria that appears in petroleum-contaminated soil [45,46,47]. Mycobacterium contains key active functional genes for the degradation of petroleum pollutants such as aromatic compounds [47,48]. The metabolic pathways of polycyclic aromatic hydrocarbons by Mycobacterium manifested as hydrogen peroxide-induced reactions at different carbon positions such as C-3 and C-4, or C-9 and C-10, whereas enzymes involved in the decomposition of phthalic acid were revealed in Mycobacterium [49,50]. The upper carbon positions are attacked by dioxygenases to form cis-dihydrodiol, and the metabolites enter the lower metabolic pathway or undergo catabolism through substituted biphenyl intermediates [51,52]. Hydrocarbons can be used by these microorganisms as carbon sources to promote the growth of functional groups, thereby increasing the biodegradation rate of petroleum hydrocarbon pollutants. Zhang et al. reported that biomass addition and mixed treatment significantly increased the abundance of Mycobacteria [53].

3.3.2. Variation in Soil Microbial Diversity

Alpha diversity of samples is usually analysed to reflect the abundance and diversity of microbial communities. The microbial diversity data are shown in Table S4. The coverage rate of the established bacterial library was >99.6%, indicating that the library established in this study truly and effectively reflected the diversity of the soil environmental bacteria. The adequacy of the sequencing depth was subsequently confirmed via the use of rarefaction curves, which revealed that the observed OTU counts for all the samples reached a plateau (Figure S4), indicating that the sequencing effort sufficiently captured the bacterial diversity.
The actual number of observed OTUs (Sobs) for the different treatments ranged from 704–858. The Chao1 and Ace indices were selected to reflect the richness of the bacterial communities in the soil. These two indices exhibited similar distributions in the different treatments, with the Chao1 index values ranging from 779.46–927.61 and the Ace index values ranging from 778.26–911.33. The Shannon and Simpson indices were chosen to express the species diversity of a community, which is influenced by both species richness and evenness. The higher the Shannon index is, the greater the microbial diversity. The Shannon index values ranged from 4.41–5.04, with the maximum and minimum values occurring in the GBC/bs and CK treatments, respectively. The Simpson index calculated in this study was high, indicating a greater concentration of species and lower biodiversity. The Simpson index values in the different treatments ranged from 0.032–0.080. Notably, changes in soil microbial diversity can reflect the degree of soil functional recovery and health status. Through comparative analysis of the various treatments, it was found that the microbial diversity in the treatment with granular bulrush straw biochar and nutrients was significantly greater than that in the other treatments, indicating that this method could provide a more suitable living environment for microorganisms. The skeleton structure of granular biochar can improve the physical habitat for microorganisms, as the addition of biochar and nutrients enables nitrogen, phosphorus and other substances to be stored in the soil to better provide energy for microbial growth and metabolism [24].
The differences in soil microbial community diversity among the different treatments were analysed using the Bray–Curtis distance, and the results are shown in Figure 6A. The difference in microbial diversity between the control treatment and the GBC/bs and GBC/ss treatments was greater than 0.5, and the difference in microbial diversity between the CK and N, PBC/bs and PBC/ss treatments was greater than 0.4, indicating that there was a significant difference (p < 0.05) in the soil bacterial community diversity between the control group and the other treatments. Among the other treatments, the Bray-Curtis distance of the samples was less than 0.4, especially for the N and PBC/bs, PBC/ss and GBC/ss treatments, for which the sample distance was less than 0.3, indicating that there was a small difference in soil bacterial community diversity among the corresponding treatments. Weighted UniFrac analysis was employed to evaluate beta diversity, which involved comparing each treatment pairwise to obtain the UniFrac distance matrix between samples. A representative OTU sequence evolutionary tree was established using samples from different treatments, and significant differences in the microbial community were compared among each sample in specific evolutionary lineages. Sequence abundance was fully considered in the analysis process. The NMDS results of weighted UniFrac analysis are shown in Figure 6B, and the results support the differences in microbial community diversity among the various treatments. Owing to the unique nature of oil-contaminated soil, microorganisms that can survive and increase in abundance in contaminated soil usually exhibit resistance or possess biological utilization functions with respect to petroleum hydrocarbons. Therefore, with continuous changes in soil properties during remediation, the composition and diversity of microorganisms in the soil also change, which in turn affects pollutants and soil properties, facilitating remediation [1,7,12].

3.4. Analysis of Petroleum Hydrocarbon Degradation and Influencing Factors

The physicochemical properties of the soils subjected to the different treatments after remediation are presented in Table 5. Biochars with different particle sizes affect soil permeability and nutrient retention efficiency. After remediation, the N and P contents in the granular biochar were greater than those in the powder biochar, and there was a significant difference (p < 0.05) in the P content between the various particle size treatments. Compared with those in the CK treatment, the soil C/N ratios in the treatments with added biochar significantly changed, decreasing by 46–57. After remediation, the soil C/N ratios in the GBC/bs and PBC/bs treatments decreased to 13/1 and 18/1, respectively, whereas the C/N ratios in the GBC/ss and PBC/ss treatments decreased to 20/1 and 24/1, respectively. These findings indicate that the treatments involving biochars with different particle sizes affected the soil physicochemical properties. The addition of biochar reduced the originally high soil carbon-to-nitrogen ratio. A suitable carbon-to-nitrogen ratio was more conducive to the growth and metabolism of microorganisms. In particular, in the GBC/bs treatment, obvious differences in the composition and diversity of the microbial communities were detected. The increase in soil microbial diversity indicated that the basic functions of the soil were being restored. Additionally, the proportion of functional microbial groups with hydrocarbon availability conferred a certain advantage, which also promoted the biodegradation of hydrocarbon substances, especially recalcitrant petroleum hydrocarbons.
Correlation analysis was conducted on the TPH concentration and the different categories of factors influencing hydrocarbon degradation, as shown in Figure 7. A significant correlation at p ≤ 0.05 is indicated by an asterisk (*). The degradation rate of TPH was significantly positively correlated with the properties of the added materials, and the biochar particle size (PS) significantly affected the degradation rate of TPH. In terms of the soil properties, the pH, C/N ratio, and C/N/P ratio were selected as indicators, and the correlation analysis results revealed that the C/N ratio significantly negatively affected the TPH degradation rate. There was a significant positive correlation between the TPH content and the Shannon index in terms of microbial diversity. In addition to the correlations between various categories of indicators and the TPH degradation rate, the relationship between the biochar particle size and the microbial Shannon index was significant. The addition of biochar materials of different particle sizes to soil significantly affected the species composition and diversity of the microbial communities. Moreover, owing to the skeleton structure of the granular biochar, the soil became less compact. The enhanced remediation performance of granular biochar over powder biochar originates from its ability to create a more favourable microenvironment for microbial colonization and activity. In the remediation of compacted petroleum-contaminated soil, powder biochar is susceptible to pore clogging, thereby reducing soil permeability and aeration. In contrast, granular biochar (0.85 mm) provides a more robust and porous scaffold within the soil matrix. This structural advantage was most pronounced in the GBC/bs material, which exhibited the highest specific surface area and total pore volume among all tested biochars. The enhanced effectiveness of granular biochar can be attributed primarily to the following three interconnected mechanisms: improved soil aeration, the formation of protected microbial niches, and sustained nutrient mobilization. Larger particles preserve interparticle spaces, thereby mitigating soil compaction and ensuring sufficient oxygen diffusion to support aerobic biodegradation [22]. Moreover, the hierarchical micro-meso pore network of GBC/bs establishes protected “micro-reactors” that shield hydrocarbon-degrading microbes from direct toxicity and environmental stress, fostering a more diverse and resilient consortium. Additionally, its substantial pore volume enhances water and nutrient retention, preventing leaching and facilitating a controlled release within these microbial hotspots, which is essential for sustaining long-term metabolic activity [24]. Consequently, the synergy between the granular morphology and the bulrush-straw feedstock yielded a biochar (GBC/bs) with a highly functional porous structure that actively promoted the degradation of petroleum hydrocarbons, including recalcitrant fractions [54].
To integrate the key findings of this study, a schematic diagram summarizing the relationships among biochar particle size, soil properties, microbial community structure, and TPH degradation performance is presented in Figure S5. The mechanism through which biochar influences organic pollutant degradation is complex and can depend on the biomass type, carbonization conditions, and physicochemical properties such as surface functionality and porosity. In this study, the implementation of biochar amendments, particularly those with relatively small particle sizes, was associated with increased microbial degradation of petroleum hydrocarbons, which may be attributed to the porous structure of the biochar providing attachment sites and habitats for microorganisms [15]. Moreover, biochar properties might stimulate microbial activity and enhance electron transfer processes, thereby potentially accelerating soil remediation. The addition of biochar to soil could help enrich specific groups of functional microorganisms and improve overall biological activity [22]. This approach may also increase soil permeability and facilitate the mass transfer of nutrients and electron acceptors, which are important factors influencing the microbial degradation of petroleum hydrocarbons.
The observed degradation of petroleum hydrocarbons in the biochar-amended treatments can be partly explained by the creation of a favourable physico-chemical microenvironment, in which biochar adsorbs hydrocarbons and metabolites, potentially increasing their accessibility to microorganisms. The adsorption of hydrocarbon substances onto biochar might provide more contact opportunities for microbial degradation, allowing these compounds to be gradually oxidized and decomposed.
The exogenous supplementation of nitrogen and phosphorus nutrients can support microbial growth where petroleum hydrocarbons serve as carbon sources. Biochar can further assist in retaining and redistributing nutrients such as nitrogen and phosphorus in soil [23], which is particularly relevant for sustaining ecological balance in sensitive systems like Qiandao Lake. The functional groups on biochar surfaces, along with stored nutrients and trace elements, may increase microbial activity and influence microbial growth and metabolism. Especially in soils with high pollutant concentrations or limited nutrients, the early addition of appropriate nutrient levels has been demonstrated to promote microbial activity, although excessive nutrient supplementation can inhibit bioremediation [19].
In this study, the presence of biochar, along with optimized nutrient levels, supported microbial communities that are often associated with hydrocarbon degradation. Microorganisms colonizing biochar surfaces and pores, particularly at aerobic microsites, can use hydrocarbons as carbon sources [54]. The regulation of available nutrients may have promoted increases in functional microbial groups such as Mycobacterium, which have been reported to increase the utilization of polycyclic aromatic hydrocarbons [46,52]. Previous studies have indicated that Mycobacterium species can play a significant role in driving the initial oxidation of polycyclic aromatic hydrocarbons [47,49]. Under conditions of sufficient nutrient supply, more complex petroleum fractions, including long-chain alkanes and polycyclic aromatic hydrocarbons, may be progressively degraded [10,48]. The reduction in petroleum hydrocarbons in soil is likely achieved through microbial transformation and degradation processes. Several studies have also suggested that the addition of biomass-derived amendments can generate organic acids and humic substances during decomposition, which might promote the microbial use of recalcitrant organic pollutants through cometabolism [39,50].

4. Conclusions

This study demonstrated that the coupling of biochar particle size and feedstock type significantly influenced the microbial degradation of petroleum hydrocarbons in contaminated soil. The systematic decoupling of these two factors represents a key advancement in biochar optimization, revealing that compared with powder biochar (0.25 mm) or soybean straw-derived materials, granular biochar (0.85 mm), particularly derived from bulrush straw, created more favourable conditions for microbial degradation. The integration of nutrients with biochar was essential for sustaining microbial activity, increasing TPH degradation by 16.28–42.44% compared with that in the nutrient-only treatment. The improved degradation performance was associated with shifts in the microbial community structure, including increased abundance levels of Actinobacteria and Chloroflexi, with Mycobacterium emerging as the dominant genus. The results of statistical analyses revealed significant correlations between the TPH degradation efficiency and the biochar particle size, soil C/N ratio, and microbial diversity (Shannon index). While this study revealed important associations between these parameters, future work could strengthen causal inferences through direct microbial activity assays, more detailed tracking of hydrocarbon fractions, and controlled incubation experiments focused on examining specific degradation pathways. This research provides a practical framework for selecting biochar properties to enhance bioremediation strategies in oil-contaminated soils, thereby highlighting the importance of matching the physical structure and chemical composition of biochar to specific microbial ecological conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15122874/s1. Figure S1. N2 adsorption–desorption isotherms of the biochars; Figure S2. SEM–EDS spectra of the biochar; Figure S3. Abundance of n-alkanes in the different treatments; Figure S4. Rarefaction curves of observed OTUs across all samples; Figure S5. Influence pathways of biochar particle size on soil properties, microbial community, and TPH remediation; Table S1. Initial physicochemical properties and TPH of the soil; Table S2. Description of the experimental treatments; Table S3. Peak areas and removal efficiencies of recalcitrant hydrocarbon fractions (C27 sterane and C33–C34 terpane) during the remediation process (30–80 Days); Table S4. Bacteria alpha diversity.

Author Contributions

Y.W.: Writing—Original draft, Validation, Software, Resources, Methodology, Investigation, Funding acquisition, Data curation. Q.W.: Investigation, Software, Supervision, Resources. M.W.: Methodology, Software, Visualization. H.L.: Supervision, Resources, Funding acquisition. J.C.: Writing—review and editing, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Data curation. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Foundation of Liaoning Province Education Administration (L2020034), Key Research and Development Program of Lishui Science and Technology Bureau (No. 2023zdyf08), Chun’an County Special Expert Project (CASL0240420, the corresponding author is a specially appointed expert of Chun’an County), Cultivation Fund of Lishui Vocational and Technical College (LSZX202406), National Key Technology Project of Water Contamination Controlling and Management of China (No. 2018ZX07601-003).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
TPHTotal petroleum hydrocarbons
PBC/bspowder biochar (0.25 mm) from bulrush straw and nutrient addition treatment
PBC/sspowder biochar (0.25 mm) from soybean straw and nutrient addition treatment
GBC/bsgranular biochar (0.85 mm) from bulrush straw and nutrient addition treatment
GBC/ssgranular biochar (0.85 mm) from soybean straw and nutrient addition treatment
SOCSoil organic carbon
NMDSNonmetric multidimensional scaling
OTUsOperational taxonomic units

References

  1. Galitskaya, P.; Biktasheva, L.; Kuryntseva, P.; Selivanovskaya, S. Response of soil bacterial communities to high petroleum content in the absence of remediation procedures. Environ. Sci. Pollut. Res. 2021, 28, 9610–9627. [Google Scholar] [CrossRef]
  2. Guirado, M.; Garcia-Delgado, C.; Jiménez, O.; Torre, B.; Escolano, O.; Eymar, E.; Millán, R. Bioremediation study of a hydrocarbon-contaminated soil by profiling aromatic and aliphatic chains. Appl. Soil Ecol. 2023, 190, 104983. [Google Scholar] [CrossRef]
  3. Jovančićević, B.; Antić, M.; Pavlović, I.; Vrvic, M.; Beškoski, V.; Kronimus, A.; Schwarzbauer, J. Transformation of Petroleum Saturated Hydrocarbons during Soil Bioremediation Experiments. Water Air Soil Poll. 2007, 190, 299–307. [Google Scholar] [CrossRef]
  4. Militon, C.; Boucher, D.; Vachelard, C.; Perchet, G.; Barra, V.; Troquet, J.; Peyretaillade, E.; Peyret, P. Bacterial community changes during bioremediation of aliphatic hydrocarbon-contaminated soil. Fems. Microbiol. Ecol. 2010, 74, 669–681. [Google Scholar] [CrossRef] [PubMed]
  5. Jiang, J.; Wang, S.; Wang, L.A.; Lv, Q.; Yang, Y.; Chen, K.; Liu, L.; Wang, X. The improvement of pore characteristics, remediation efficiency, and biotoxicity of petroleum-contaminated soil with the addition of bulking agent on field-scale biopile treatment. J. Soil Sediment 2021, 21, 2855–2864. [Google Scholar] [CrossRef]
  6. Abena, M.; Chen, G.; Chen, Z.; Zheng, X.; Li, S.; Li, T.; Zhong, W. Microbial diversity changes and enrichment of potential petroleum hydrocarbon degraders in crude oil-, diesel-, and gasoline-contaminated soil. 3 Biotech 2020, 10, 42. [Google Scholar] [CrossRef]
  7. Liu, H.; Wu, M.; Zhang, M.; Gao, H.; Yan, Z.; Yang, Z. New insight into the effect of nitrogen on hydrocarbon degradation in petroleum-contaminated soil revealed through 15N tracing and flow cytometry. Sci. Total Environ. 2023, 891, 164409. [Google Scholar] [CrossRef]
  8. Sutton, N.; Maphosa, F.; Morillo-Pérez, J.A.; Abu Al-Soud, W.; Langenhoff, A.; Grotenhuis, T.; Rijnaarts, H.; Smidt, H. Impact of Long-Term Diesel Contamination on Soil Microbial Community Structure. Appl. Environ. Microb. 2012, 79, 2. [Google Scholar] [CrossRef] [PubMed]
  9. Liu, Q.; He, W.; Zhang, W.; Wang, L.; Tang, J. Metagenomic analysis reveals the microbial response to petroleum contamination in oilfield soils. Sci. Total Environ. 2024, 912, 168972. [Google Scholar] [CrossRef]
  10. Wang, J.; Yang, Z.; Zhou, X.; Waigi, M.; Gudda, F.; Odinga, E.; Mosa, A.; Ling, W. Nitrogen addition enhanced the Polycyclic Aromatic Hydrocarbons dissipation through increasing the abundance of related degrading genes in the soils. J. Hazard. Mater. 2022, 435, 129034. [Google Scholar] [CrossRef]
  11. Kundu, A.; Harrisson, O.; Ghoshal, S. Evaluating the Impact of Nutrient Doses on Biostimulation of Petroleum Hydrocarbon Biodegradation in Cold Region Soils. Acs. Est. Eng. 2022, 2, 2287–2300. [Google Scholar] [CrossRef]
  12. Osadebe, A.; Uzoma, G.; Ogugbue, C.; Okpokwasili, G. Effect of Feedstock Type on Biostimulation Efficiency and Microbial Community Structure during Biochar-Facilitated Remediation of Petroleum Contaminated Soil. Bioremediat. Sci. Technol. Res. 2022, 10, 1–9. [Google Scholar] [CrossRef]
  13. Duarte, S.; Cerri, C.; Rittl, T.; Abbruzzini, T.; Pano, B. Biochar Physical and Hydrological Characterization to Improve Soil Attributes for Plant Production. J. Soil Sci. Plant Nut 2023, 23, 3051–3057. [Google Scholar] [CrossRef]
  14. Yang, Y.; Zhong, M.; Bian, X.; You, Y.; Li, F. Preparation of carbon-based material with high water absorption capacity and its effect on the water retention characteristics of sandy soil. Biochar 2023, 5, 61. [Google Scholar] [CrossRef]
  15. Kong, L.; Gao, Y.; Zhou, Q.; Zhao, X.; Sun, Z. Biochar accelerates PAHs biodegradation in petroleum-polluted soil by biostimulation strategy. J. Hazard. Mater. 2018, 343, 276–284. [Google Scholar] [CrossRef]
  16. Wang, H.; Lv, Y.; Bao, J.; Chen, Y.; Zhu, L. Petroleum-contaminated soil bioremediation and microbial community succession induced by application of co-pyrolysis biochar amendment: An investigation of performances and mechanisms. J. Hazard. Mater. 2024, 466, 133600. [Google Scholar] [CrossRef]
  17. Yan, J.; Quan, G. Sorption behavior of dimethyl phthalate in biochar-soil composites: Implications for the transport of phthalate esters in long-term biochar amended soils. Ecotox. Environ. Safe 2020, 205, 111169. [Google Scholar] [CrossRef]
  18. Kong, D.; Liu, Z.; Wu, J. Omics shed light on the mechanisms of petroleum-contaminated soil remediation: When biochar meets nitrogen. Land Degrad. Dev. 2023, 34, 5109–5121. [Google Scholar] [CrossRef]
  19. Deng, J.; He, D.; Zhu, X.; Cai, Y.; Cai, K.; Tian, J. Biochar amendment shifts bacterial keystone taxa regulating soil phosphorus dynamics. Appl. Soil Ecol. 2024, 201, 105521. [Google Scholar] [CrossRef]
  20. Vasilyeva, G.K.; Mikhedova, E.E.; Zinnatshina, L.V.; Strijakova, E.R.; Akhmetov, L.I.; Sushkova, S.N.; Ortega-Calvo, J.J. Use of natural sorbents for accelerated bioremediation of grey forest soil contaminated with crude oil. Sci. Total Environ. 2022, 850, 157952. [Google Scholar] [CrossRef] [PubMed]
  21. Nong, J.; Peng, P.; Pan, J.; Shen, T.; Xie, Q. Effect of Bioaugmentation and Biostimulation on Hydrocarbon Degradation and Bacterial Community Composition in Different Petroleum-Contaminated. Water Air Soil Poll. 2023, 234, 189. [Google Scholar] [CrossRef]
  22. Saeed, M.; Ilyas, N.; Jayachandran, K.; Gaffar, S.; Arshad, M.; Ahmad, M.S.; Bibi, F.; Jeddi, K.; Hessini, K. Biostimulation Potential of Biochar for remediating the crude oil contaminated soil and Plant Growth. Saudi J. Biol. Sci. 2021, 28, 2667–2676. [Google Scholar] [CrossRef]
  23. Singh, H.; Northup, B.K.; Rice, C.W.; Prasad, P. Biochar applications influence soil physical and chemical properties, microbial diversity, and crop productivity: A meta-analysis. Biochar 2022, 4, 8. [Google Scholar] [CrossRef]
  24. Xia, H.; Riaz, M.; Zhang, M.; Liu, B.; Jiang, C. Biochar increases nitrogen use efficiency of maize by relieving aluminum toxicity and improving soil quality in acidic soil. Ecotox. Environ. Safet 2020, 196, 110531. [Google Scholar] [CrossRef]
  25. Dai, Z.; Xiong, X.; Zhu, H.; Xu, H.; Leng, P.; Li, J.; Tang, C.; Xu, J. Association of biochar properties with changes in soil bacterial, fungal and fauna communities and nutrient cycling processes. Biochar 2021, 3, 239–254. [Google Scholar] [CrossRef]
  26. Wang, Y.J. Mechanism and Effect of Bioremediation of Petroleum Contaminated Soil by Combining Biochar Immobilized Microorganisms with Plant; Hunan Agricultural University: Changsha, China, 2017. [Google Scholar]
  27. Schnee, L.S.; Knauth, S.; Hapca, S.; Otten, W.; Eickhorst, T. Analysis of physical pore space characteristics of two pyrolytic biochars and potential as microhabitat. Plant Soil 2016, 408, 357–368. [Google Scholar] [CrossRef]
  28. Yavari, S.; Malakahmad, A.; Sapari, N.B.; Yavari, S. Sorption properties optimization of agricultural wastes-derived biochars using response surface methodology. Process Saf. Environ. Prot. 2017, 109, 509–519. [Google Scholar] [CrossRef]
  29. Wei, D.; Li, B.; Luo, L.; Zheng, Y.; Huang, L.; Yang, Y.; Huang, H. Simultaneous adsorption and oxidation of antimonite onto nano zero-valent iron sludge-based biochar: Indispensable role of reactive oxygen species and redox-active moieties. J. Hazard. Mater. 2020, 391, 122057. [Google Scholar] [CrossRef]
  30. Mukherjee, A.; Bordoloi, N. Bioremediation and reclamation of soil contaminated with petroleum oil hydrocarbons by exogenously seeded bacterial consortium: A pilot-scale study. Environ. Sci. Pollut. Res. 2011, 18, 471–478. [Google Scholar] [CrossRef]
  31. ASTM D8193-18; Standard Test Method for Total Oil and Grease (TOG) and Total Petroleum Hydrocarbon (TPH) in Water and Wastewater with Solvent Extraction Using Non-Dispersive Mid-IR Transmission Spectroscopy. ASTM International: West Conshohocken, PA, USA, 2018.
  32. Suganthi, S.H.; Murshid, S.; Sriram, S.; Ramani, K. Enhanced biodegradation of hydrocarbons in petroleum tank bottom oil sludge and characterization of biocatalysts and biosurfactants. J. Environ. Manag. 2018, 220, 87–95. [Google Scholar] [CrossRef]
  33. Lu, R.K. Soil Agricultural Chemical Analysis Methods; Agricultural Science and Technology Press: Beijing, China, 2000. [Google Scholar]
  34. Wu, M.; Ye, X.; Chen, K.; Li, W.; Yuan, J.; Jiang, X. Bacterial community shift and hydrocarbon transformation during bioremediation of short-term petroleum-contaminated soil. Environ. Pollut. 2017, 223, 657–664. [Google Scholar] [CrossRef] [PubMed]
  35. Tan, G.; Liu, Y.; Xiao, D. Preparation of manganese oxides coated porous carbon and its application for lead ion removal. Carbohydr. Polym. 2019, 219, 306–315. [Google Scholar] [CrossRef]
  36. Zhu, G.; Lin, J.; Yuan, Q.; Wang, X.; Zhao, Z.; Hursthouse, A.; Wang, Z.; Li, Q. A biochar supported magnetic metal organic framework for the removal of trivalent antimony. Chemosphere 2021, 282, 131068. [Google Scholar] [CrossRef]
  37. Ray, A. Characterization of Biochars from Various Agricultural By-Products Using FTIR Spectroscopy, SEM focused with image Processing. Int. J. Agric. Environ. Biotechnol. 2020, 13, 423–430. [Google Scholar] [CrossRef]
  38. Wang, Y.; Li, F.; Rong, X.; Song, H.; Chen, J. Remediation of Petroleum-contaminated Soil Using Bulrush Straw Powder, Biochar and Nutrients. Bull. Environ. Contam. Toxicol. 2017, 98, 690–697. [Google Scholar] [CrossRef]
  39. Cui, T.; Li, Z.; Wang, S. Effects of in-situ straw decomposition on composition of humus and structure of humic acid at different soil depths. J. Soils Sediments 2017, 17, 2391–2399. [Google Scholar] [CrossRef]
  40. Cheng, B.; Chen, Z.; Chen, T.; Yang, C.; Wang, T.G. Biomarker signatures of the Ediacaran–Early Cambrian origin petroleum from the central Sichuan Basin, South China: Implications for source rock characteristics. Mar. Petrol. Geol. 2018, 96, 577–590. [Google Scholar] [CrossRef]
  41. Gojgic-Cvijovic, G.; Milic, J.; Solevic Knudsen, T.; Beškoski, V.; Ilic, M.; Djokic, L.; Narancic, T.; Vrvic, M. Biodegradation of petroleum sludge and petroleum polluted soil by a bacterial consortium: A laboratory study. Biodegradation 2011, 23, 1–14. [Google Scholar] [CrossRef]
  42. Aboglila, S.; Elkhalgi, M. Organic Geochemical Evaluation of Cretaceous Potential Source Rocks, East Sirte Basin, Libya. Int. J. Geosci. 2013, 4, 700–710. [Google Scholar] [CrossRef]
  43. Bost, F.D.; Frontera-Suau, R.; McDonald, T.; Peters, K.; Morris, P. Aerobic biodegradation of hopanes and norhopanes in Venezuelan crude oils. Org. Geochem 2001, 32, 105–114. [Google Scholar] [CrossRef]
  44. López, L.; Romero, J.; Lo Mónaco, S. Severe biodegradation of crude oils from the Quiriquire Field, Eastern Venezuelan Basin. J. Petrol. Geol. 2022, 45, 79–94. [Google Scholar] [CrossRef]
  45. Björklöf, K.; Karlsson, S.; Frostegard, A.; Jørgensen, K. Presence of Actinobacterial and Fungal Communities in Clean and Petroleum Hydrocarbon Contaminated Subsurface Soil. Open Microbiol. J. 2009, 3, 75–86. [Google Scholar] [CrossRef]
  46. Cheung, P.; Kinkle, B. Mycobacterium Diversity and Pyrene Mineralization in Petroleum-Contaminated Soils. Appl. Environ. Microb. 2001, 67, 2222–2229. [Google Scholar] [CrossRef]
  47. Hennessee, C.T.; Li, Q.X. Effects of Polycyclic Aromatic Hydrocarbon Mixtures on Degradation, Gene Expression, and Metabolite Production in Four Mycobacterium Species. Appl. Environ. Microb. 2016, 82, 3357–3369. [Google Scholar] [CrossRef]
  48. Han, X.M.; Liu, Y.R.; Zheng, Y.M.; Zhang, X.X.; He, J.Z. Response of bacterial pdo1, nah, and C12O genes to aged soil PAH pollution in a coke factory area. Environ. Sci. Pollut. Res. 2014, 21, 9754–9763. [Google Scholar] [CrossRef]
  49. Stingley, R.L.; Khan, A.A.; Cerniglia, C.E. Molecular characterization of a phenanthrene degradation pathway in Mycobacterium vanbaalenii PYR-1. Biochem. Biophys. Res. Commun. 2004, 322, 133–146. [Google Scholar] [CrossRef]
  50. Krivobok, S.; Kuony, S.; Meyer, C.; Louwagie, M.; Willison, J.C.; Jouanneau, Y. Identification of pyrene-induced proteins in Mycobacterium sp strain 6PY1: Evidence for two ring-hydroxylating dioxygenases. J. Bacteriol. 2003, 185, 3828–3841. [Google Scholar] [CrossRef]
  51. Kim, Y.H.; Freeman, J.P.; Moody, J.D.; Engesser, K.H.; Cerniglia, C.E. Effects of pH on the degradation of phenanthrene and pyrene by Mycobacterium vanbaalenii PYR-1. Appl. Microbiol. Biot. 2005, 67, 275–285. [Google Scholar] [CrossRef]
  52. Kim, S.J.; Song, J.; Kweon, O.; Holland, R.D.; Kim, D.W.; Kim, J.; Yu, L.R.; Cerniglia, C.E. Functional robustness of a polycyclic aromatic hydrocarbon metabolic network examined in a nidA aromatic ring-hydroxylating oxygenase mutant of Mycobacterium vanbaalenii PYR-1. Appl. Environ. Microbiol. 2012, 78, 3715–3723. [Google Scholar] [CrossRef]
  53. Zhang, X.; Wang, L.; Zhou, W.; Feng, L.; Hu, M.; Hu, J.; Liu, Z. Mixing of plant litters strengthens their remediation effects on crude oil-contaminated soil. Environ. Sci. Pollut. Res. 2021, 28, 12753–12765. [Google Scholar] [CrossRef] [PubMed]
  54. Zhang, B.; Zhang, L.; Zhang, X. Bioremediation of petroleum hydrocarbon-contaminated soil by petroleum-degrading bacteria immobilized on biochar. RSC Adv. 2019, 9, 35304–35311. [Google Scholar] [CrossRef]
Agronomy 15 02874 g001
Figure 1. SEM images (A–D) and pore size distributions of the biochars. (A): PBC/bs, powder biochar (0.25 mm) from bulrush straw; (B): GBC/bs, granular biochar (0.85 mm) from bulrush straw; (C): PBC/ss, powder biochar (0.25 mm) from soybean straw, (D): GBC/ss, granular biochar (0.85 mm) from soybean straw; (E): Pore size distribution curves of the four biochars (vertically offset for clarity).
Figure 1. SEM images (A–D) and pore size distributions of the biochars. (A): PBC/bs, powder biochar (0.25 mm) from bulrush straw; (B): GBC/bs, granular biochar (0.85 mm) from bulrush straw; (C): PBC/ss, powder biochar (0.25 mm) from soybean straw, (D): GBC/ss, granular biochar (0.85 mm) from soybean straw; (E): Pore size distribution curves of the four biochars (vertically offset for clarity).
Agronomy 15 02874 g001
Agronomy 15 02874 g002
Figure 2. FTIR spectra of the different types of biochar. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Figure 2. FTIR spectra of the different types of biochar. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Agronomy 15 02874 g002
Agronomy 15 02874 g003
Figure 3. Degradation efficiency of TPH (%) over time (days) under different treatments. The different lowercase letters indicate statistically significant differences among treatments according to Tukey’s HSD test (p < 0.05) following one-way ANOVA. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Figure 3. Degradation efficiency of TPH (%) over time (days) under different treatments. The different lowercase letters indicate statistically significant differences among treatments according to Tukey’s HSD test (p < 0.05) following one-way ANOVA. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Agronomy 15 02874 g003
Agronomy 15 02874 g004
Figure 4. Distribution of sterane (A) and terpane (B) biomarkers at m/z 218 and m/z 191. CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Figure 4. Distribution of sterane (A) and terpane (B) biomarkers at m/z 218 and m/z 191. CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Agronomy 15 02874 g004
Agronomy 15 02874 g005
Figure 5. Bacterial community composition and relative abundance. ((A): phylum level, (B): genus level). CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Figure 5. Bacterial community composition and relative abundance. ((A): phylum level, (B): genus level). CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Agronomy 15 02874 g005
Agronomy 15 02874 g006
Figure 6. Heatmap of the difference in diversity (A) and NMDS results of weighted UniFrac analysis (B) of the soil bacterial communities. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Figure 6. Heatmap of the difference in diversity (A) and NMDS results of weighted UniFrac analysis (B) of the soil bacterial communities. GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Agronomy 15 02874 g006
Agronomy 15 02874 g007
Figure 7. Relationships between the TPH degradation rate and key driving factors. The heatmap shows pairwise Pearson correlations among all measured parameters (n = 18), with the relationships between the TPH degradation rate and microbial/physicochemical factors being of primary interest. TPH: total petroleum hydrocarbons; PS: particle size.
Figure 7. Relationships between the TPH degradation rate and key driving factors. The heatmap shows pairwise Pearson correlations among all measured parameters (n = 18), with the relationships between the TPH degradation rate and microbial/physicochemical factors being of primary interest. TPH: total petroleum hydrocarbons; PS: particle size.
Agronomy 15 02874 g007
Table 1. BET surface area, total pore volume, and average pore width of the samples.
Table 1. BET surface area, total pore volume, and average pore width of the samples.
SampleBET Surface Area (m2/g)Total Pore Volume (cm3/g)Average Pore Width (nm)
GBC/bs1463.11.4493.96
GBC/ss1462.61.3873.79
PBC/bs1416.31.3343.77
PBC/ss1257.21.2163.87
GBC/bs, granular biochar (0.85 mm) from bulrush straw; PBC/bs, powder biochar (0.25 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw; PBC/ss, powder biochar (0.25 mm) from soybean straw.
Table 2. Main FTIR absorption bands and their assignments for the prepared biochars.
Table 2. Main FTIR absorption bands and their assignments for the prepared biochars.
Wavenumber (cm−1)Functional Group/AssignmentVibration ModeRemarksThe
References
~2920 & ~2850Aliphatic-CH2, -CH3StretchingStronger in GBC/bs and PBC/bs[14]
~1630Carboxylate (O-C=O)Asymmetric StretchingPresent in all samples; broad band[35,36]
~1400Carboxylate (O-C=O)Symmetric StretchingPresent in all samples; broad band[35,36]
~1050–1150C-O-C (e.g., esters, ethers)StretchingBroad band in all samples[37]
~780–800Aromatic C-HBending (out-of-plane)Present in all samples; intensity varies[37]
Table 3. Final TPH removal efficiencies and statistical comparisons across treatments.
Table 3. Final TPH removal efficiencies and statistical comparisons across treatments.
TreatmentInitial TPH (g · kg−1)Final TPH (g · kg−1)Degradation Efficiency (%)
GBC/bs19.55 ± 0.345.21 ± 0.18 a73.35 ± 3.96 a
GBC/ss19.55 ± 0.347.10 ± 0.28 b63.68 ± 3.22 b
PBC/bs19.55 ± 0.348.57 ± 0.19 c56.18 ± 2.84 c
PBC/ss19.55 ± 0.349.35 ± 0.19 d52.19 ± 2.64 d
N19.55 ± 0.3412.53 ± 0.05 e35.91 ± 1.82 e
CK19.55 ± 0.3414.79 ± 0.52 f24.36 ± 1.34 f
All values are recorded as the mean ± standard deviation for three replicate samples. Within each column, the different lowercase letters indicate statistically significant differences among treatments according to Tukey’s HSD test (p < 0.05) following one-way ANOVA. CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Table 4. Contents of n-alkanes, steranes and terpanes after 30 and 80 days.
Table 4. Contents of n-alkanes, steranes and terpanes after 30 and 80 days.
CKNPBC/bsPBC/ssGBC/bsGBC/ss
N-alkanes
Low-molecular-weight (C15–C24) (%)30 days8.566.893.874.582.494.22
80 days6.484.162.583.170.522.73
High-molecular-weight (C25–C36) (%)30 days18.7116.5110.8112.667.289.66
80 days14.3212.284.846.382.394.95
Steranes
C21–22 low molecular steranes (%)30 days4.664.234.263.982.363.87
80 days3.673.211.892.140.951.96
C27–29 regular steranes (%)30 days33.4131.5427.8326.5721.9726.79
80 days31.2530.1621.2424.3915.4120.85
Terpanes
Ts/Tm30 days1.741.71.681.671.661.66
80 days1.751.721.741.731.821.78
C29–34 hopanes (%)30 days14.8214.1312.5812.9311.0212.77
80 days13.2611.679.8610.346.259.28
Ts: 17α-22,29,30-trisnorhopane, Tm: 18α-22,29,30 trisnorhopane, Ts/Tm: ratio of 17α-22,29,30-trisnorhopane relative to 18α-22,29,30 trisnorhopane. CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Table 5. Physicochemical properties of the soil in the different treatments after the bioremediation.
Table 5. Physicochemical properties of the soil in the different treatments after the bioremediation.
TreatmentpHSOC (g · kg−1)N Total (g · kg−1)P Total (g · kg−1)
CK8.25 ± 0.01 a58.23 ± 1.84 a0.83 ± 0.032 d0.072 ± 0.003 f
N8.18 ± 0.03 bc51.72 ± 1.37 b1.15 ± 0.066 c0.122 ± 0.005 e
PBC/bs8.17 ± 0.02 bcd32.93 ± 0.92 d1.85 ± 0.045 a0.522 ± 0.009 c
PBC/ss8.13 ± 0.03 d39.62 ± 0.66 c1.66 ± 0.055 b0.426 ± 0.009 d
GBC/bs8.15 ± 0.02 cd24.56 ± 0.83 e1.89 ± 0.067 a0.768 ± 0.014 a
GBC/ss8.21 ± 0.04 ab34.45 ± 1.01 d1.68 ± 0.025 b0.578 ± 0.008 b
All values are recorded as the mean ± standard deviation for three replicate samples. Within each column, the different lowercase letters indicate statistically significant differences among treatments according to Tukey’s HSD test (p < 0.05) following one-way ANOVA. CK, control; N, nutrient addition; PBC/bs, powder biochar (0.25 mm) from bulrush straw; PBC/ss, powder biochar (0.25 mm) from soybean straw; GBC/bs, granular biochar (0.85 mm) from bulrush straw; GBC/ss, granular biochar (0.85 mm) from soybean straw.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Wang, Q.; Wang, M.; Lei, H.; Chen, J. Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil. Agronomy 2025, 15, 2874. https://doi.org/10.3390/agronomy15122874

AMA Style

Wang Y, Wang Q, Wang M, Lei H, Chen J. Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil. Agronomy. 2025; 15(12):2874. https://doi.org/10.3390/agronomy15122874

Chicago/Turabian Style

Wang, Yanjie, Qiong Wang, Meijuan Wang, Haiqing Lei, and Jiabo Chen. 2025. "Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil" Agronomy 15, no. 12: 2874. https://doi.org/10.3390/agronomy15122874

APA Style

Wang, Y., Wang, Q., Wang, M., Lei, H., & Chen, J. (2025). Biochar Particle Size Modulates the Microbial Degradation of Petroleum Hydrocarbons in Contaminated Soil. Agronomy, 15(12), 2874. https://doi.org/10.3390/agronomy15122874

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop