Introduction

Sulfonamide antibiotics (SAs) are synthetic antimicrobials derived from sulfanilic acid. They inhibit the multiplication of bacteria by acting as competitive inhibitors of p-aminobenzoic acid in the folic acid metabolism cycle (Sukul and Spiteller 2006). SAs have been detected in environmental media such as soils, sediment, and sludge (Kolpin et al. 2002; Accinelli et al. 2007; Nieto et al. 2010). Three SAs, sulfamethoxazole (SMX), sulfadimethoxine (SDM), and sulfamethazine (SMZ), were detected in the sludge of wastewater treatment plants (WWTP) in Taiwan (Yang et al. 2011). SAs are characterized with high water solubility, low chelating ability, and low binding constants; they can potentially enter soils (Accinelli et al. 2007). The risk of enhancing microbial resistance by prolonged exposure of microorganisms to SAs is also of great concern (Allen et al. 2010).

Microbial degradation is believed to be one of the major processes involved in cleaning up SAs-contaminated soil. Some studies have investigated the biodegradation of SAs such as SMZ, sulfadiazine (SDZ), and sulfachloropyridazine (SCP) in soils (Wang et al. 2006; Yang et al. 2009), but little is known about the degradation of SAs by the addition of sludge to soil. The addition of sludge can facilitate the degradation of organic pollutants because sludge adds supplemental nutrients and carbon into the contaminated soil (Semple et al. 2001). However, sludge tends to concentrate potential pollutants from wastewater such as organic toxic chemical residues (Clarke and Smith 2011).

Pleurotus eryngii is one of the most widespread mushrooms in the world (Lee et al. 2004). Spent mushroom compost (SMC), a waste product of the mushroom industry, contains many residual enzymes and can be used to degrade many organic toxic chemicals (Lau et al. 2003; Li et al. 2010). SMC contains bacteria that can degrade many organic toxic chemicals such as nonylphenol, tetrabromobisphenol-A, and tetracycline (Hsu et al. 2013; Chang and Ren 2015).

The diversity of the microbial community in soil is an important issue in modern soil microbiology. Metagenomic studies with next-generation sequencing allow for studying microbial diversity in environmental samples (Xu et al. 2012; Yang et al. 2015a, b). However, little is known about the microbial communities involved in SAs removal in sludge-amended soil. Here, we aimed to evaluate the degradation of SAs and bacterial community change in sludge-amended soil. With batch experiments, we evaluated SAs degradation with SMC, SMC extract (SE), and extract-containing microcapsule (MC) in soil-sludge mixtures. In bioreactor experiments, we compared SAs removal rates in soil-sludge mixtures with SMC and revealed the bacterial community composition associated with SAs removal in soil-sludge mixtures with SMC in a bioreactor. The target SAs compounds were SMX, SDM, and SMZ.

Materials and methods

Chemicals

SMX, SDM, and SMZ of 99.0 % purity were from Sigma Chemical Co. (St. Louis, MO). Solvents were from Mallinckrodt (Paris, KY); all other chemicals were from Sigma Chemical Co. Individual stock solutions of SMX, SDM, and SMZ were dissolved in 0.03 M NaOH, established at a concentration of 5000 mg/L, then diluted with water before use.

Sampling and medium

Sludge is a semi-solid slurry and was produced as sewage sludge from the Dihus wastewater treatment plant in Taipei, Taiwan. The soil was collected from Taoyuan areas in northern Taiwan. SMX, SDM, and SMZ concentrations in sludge were 0.059, 0.094, and 0.052 mg/kg, respectively, and the three SAs in soil were below the detection limit (ND). The basal medium contained the following (in mg/L): K2HPO4, 65.3; KH2PO4, 25.5; Na2HPO4‧12H2O, 133.8; NH4Cl, 5.1; CaCl2, 82.5; MgSO4‧7H2O, 67.5; and FeCl3‧6H2O, 0.75. The pH of the basal medium was adjusted to 7.2 before autoclaving at 121 °C for 20 min.

Preparation of SMC, SMC extract, and SMC extract-containing microcapsule

The SMC of P. eryngii was produced at a mushroom cultivation factory in Chiayi, Taiwan. The SMC extract (SE) was extracted from 120 g SMC by using 600 mL sodium acetate buffer (pH 5.0) for 3 h at 4 °C. The samples were centrifuged (10,000g × 10 min), and the supernatants were partially purified by precipitation with ammonium sulfate and dialysis. SMC is a potential source of ligninolytic enzymes (Li et al. 2010). Most ligninolytic fungal species constitutively produce at least one laccase isoenzyme and laccases are also dominant among ligninolytic enzymes in the environment (Baldrian 2006). We measured laccase activity in soil-sludge mixtures with SMC, SE, and MC. Laccase analysis was performed as we previously described (Liao et al. 2012). The material was stored at −20 °C.

Alginate solution was made by dissolving sodium alginate (4 wt%) in 0.9 wt% sodium chloride with stirring for 1 h at room temperature. SE was then added to the alginate solution. The final concentration of alginate was 2.0 wt% in the mixture solution. An electrostatic droplet generator was used to fabricate the capsules (Hsu et al. 2013). The mixture solution was drawn into a 10-mL syringe fitted with a 23 G needle and attached to a syringe pump that provided a steady solution flow rate. The solution was pumped through the needle at a fixed feed rate (25.2 mL/h) and fixed voltage (12 kV) into a gently agitated aqueous solution of calcium chloride (1.5 wt%) to form an MC of 250 μm in diameter for the experiments.

Experimental design

The batch experiments involved 125-mL serum bottles containing 35 mL medium, 5 g sludge, 5 g soil, 5 g SMC, and 200 mg/kg (20 mg/L) SMX. Inoculated controls were incubated with soil-sludge mixtures with shaking at 30 °C in the dark. Sterile controls were prepared by autoclaving soil-sludge mixtures with SMC, MC, or SE at 121 °C for 20 min on three consecutive days. Each treatment was performed in triplicate. Samples were periodically collected to measure residual SMX.

The bioreactor was previously described (Chang and Ren 2015). It was aerated by an air diffuser, and materials were agitated by use of a stirrer. Bioreactor A was filled with 1800 mL medium and 200 g soil; bioreactor B was filled with 1800 mL medium and 200 g sludge; bioreactor C was filled with 1600 mL medium, 200 g sludge, and 200 g SMC. Bioreactor A added three SAs one time, and bioreactors B and C added three SAs three times. When SMX decreased to below ND, then the three SAs were re-added. The SAs concentrations were 200 mg/kg (20 mg/L) (each of SMX, SDM, and SMZ) for each SA addition. After 45 days incubations, 200 g soil was added to bioreactors B and C. The three SAs were re-added when SMX decreased to below ND. The SAs concentrations were 500 mg/kg (50 mg/L) (each of SMX, SDM, and SMZ) for each SA addition. The soil-sludge mixtures experiments lasted 38 days. Samples were taken periodically to measure residual SAs, total organic content (TOC), and the bacterial community.

Chemical analysis

Residual SAs determination involved use of an Agilent 1260 HPLC equipped with a 4.6 × 250 mm column (Zorbax eclipse plus C18, Agilent) with photodiode array detector monitoring at 270 nm. The mobile phase was acetonitrile and water (with 0.1 % formic acid), 70 %:30 %. Flow rate was 1 mL/min. The 0.5-mL aqueous phase samples were collected, and an equal volume of extraction solution was added with water (with 0.1 % formic acid): acetonitrile/methanol (10:3:1), vortexed well, and centrifuged at 10,000 rpm for 5 min. The supernatant was filtrated with a 0.22-μm filter into a vial for high-performance liquid chromatography. The recovery percentages for SMX, SDM, and SMZ were 97.5, 97.4, and 96.4 %, respectively. The detection limits for SMX, SDM, and SMZ were all 0.1 mg/L. TOC was measured by using the TOC Analyzer (OI Analytical 1030 W, USA).

DNA extraction, PCR, and pyrosequencing

Total DNA was extracted by using the PowerSoil DNA Isolation Kit (MO BIO Laboratories) for each experimental sample. Partial sequences containing V5–V8 variable regions of the 16S rRNA gene were amplified from the extracted DNA. The sequence for the 5′ primer included a 454 pyrosequencing adaptor, a unique 4-mer tag for each sample, and 787F (5′-ATTAGATACCCNGGTAG-3′) for 16S rRNA genes. The sequence for the 3′ primer included a 454 pyrosequencing adaptor and 1391R (5′-ACGGGCGGTGWGTRC-3′) for 16S rRNA genes. PCR reactions were performed as we previously described (Yang et al. 2015a). Pyrosequencing was performed at the Genome Center, National Yang-Ming University, Taiwan, ROC, with the GS Junior System (Roche Diagnostics Corp., CT, USA).

Data analysis

Calculations were for SAs or TOC remaining percentage (%) = (residue substrate concentration/initial substrate concentration) × 100. SAs removal rate (%) = [1 − (residue substrate concentration/initial substrate concentration)] × 100. The degradation data for SAs were calculated with first-order kinetics: S = S 0exp (−k 1  t), t 1/2  = ln 2/k 1 , where S 0 is the initial concentration, S is the substrate concentration, t is the time, and k 1 is the degradation rate constant.

The 16S rRNA gene sequence data were analyzed by using Chimera Check to remove the chimeric sequences. The RPD classifier in the Ribosomal Database Project (RDP) web site RDPipeline (http://pyro.cme.msu.edu/) was used to analyze 16S rRNA gene sequences passing the Chimera Check for phylogenetic classification. The highest similarity (95 %) provided by the classifier software was used for grouping operational taxonomic units (OTUs) of bacteria. Comparison of bacterial community compositions between the samples was performed by the heatmap3 package of R. Spearman correlation coefficients were computed using the cor.test function of R. Differences in the distribution of the bacterial genera were identified with Mann-Whitney U tests using the wilcox.test function of R. Non-parametric ANOVA (the Kruskal-Wallis test) was computed by the posthoc.kruskal.nemenyi.test function of R. Non-parametric pairwise comparisons using Tukey and Kramer (Nemenyi) test were computed by the posthoc.kruskal.nemenyi.test function in the PMCMR package of R. p < 0.05 was considered as statistically significant.

Results and discussion

SMX degradation in batch experiments

The SMX remaining percentage in sterile controls was examined to be ranged from 94.5 to 98.1 % in soil-sludge mixtures during 8 days incubation. Therefore, the SMX degradation in the following experiments was due to microbial actions. A comparison of the effects of SMC, MC, and SE addition in soil-sludge mixtures on SMX degradation was shown in Fig. 1. At 8 days, SMX in soil-sludge mixtures with SMC was completely degraded, but remained at 3.1, 14.4, and 55.2 % for soil-sludge mixtures with MC, SE, and the inoculated control (soil-sludge mixtures without SMC, MC, and SE), respectively (Fig. 1). The degradation data for the SMX fit well with the first-order kinetics. The degradation half-life (t 1/2 ) in soil-sludge mixtures were 0.69, 1.8, and 3.0 days for SMC, MC, and SE, respectively. The order of SMX degradation efficiency in soil-sludge mixtures with the three additives was SMC > MC > SE.

Fig. 1
figure 1

Remaining percentages of SMC by the addition of SMX for SMC, MC, and SE in soil-sludge mixtures. Black down-pointing triangle non-sterile SMC, white circle non-sterile MC, black circle non-sterile SE, white triangle non-sterile inoculated control, black square sterile SMC, white square sterile MC, black diamond sterile SE, white diamond sterile inoculated control

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SMC contains ligninolytic enzymes that can enhance SMX degradation (Lau et al. 2003). The enzyme activity in the soil-sludge mixtures with SMC, MC, and SE were initially 856.4 ± 18.1, 850.1 ± 13.8, and 840.1 ± 19.2 U/L, respectively, and 850.4 ± 19.6, 808.4 ± 17.9, and 611.9 ± 16.2 U/L, after 8 days of incubation, respectively. The order of enzyme activity of the three additives was SMC > MC > SE. SMC contains enzymes, bacteria, and fungi that all might play roles in the degradation of SMX. Additionally, the SMX degradation rate was higher with MC than with SE in the soil-sludge mixtures. SMX removal was better with MC than SE in terms of improving tolerance to the environmental loadings. Similar trends were revealed in our previous study, which demonstrated more efficient removal of nonylphenol with immobilized SE than the suspended form (Hsu et al. 2013).

SAs removal in bioreactor experiments

The removal rates of three SAs in sludge with or without SMC in bioreactors are in Table 1. SA removal rates were significantly higher (p < 0.05) with than without SMC for SDM during the first and second SA additions in sludge. It is also true (p < 0.05) also for SMZ in the second and third addition (Table 1). The removal of the three SAs in soil-sludge mixture with or without SMC in bioreactors is in Fig. 2. The removal rates of SMX, SDM, and SMZ were 15.1, 5.1, and 12.9 % in soil alone, respectively, on day 38 (Fig. 2a). SA-degrading bacteria may be present in soil, and only 5~15 % SAs were removed. In soil-sludge mixtures without SMC, the removal rates of SMX, SDM, and SMZ were 95.7, 87.9, and 92.0 % on day 8 after the first addition, and 99.9, 95.8, and 97.2 % on day 38 after the fourth addition (Fig. 2b). However, with SMC, the removal rates of SMX, SDM, and SMZ were 97.8, 93.4, and 96.4 % on day 8 after the first addition, and three SAs removal rates were all 99.9 % on day 38 after the fourth addition (Fig. 2c). The order of removal rates in sludge was SMX > SDM > SMZ. The SA removal rates were not significantly enhanced by the addition of SMC. As a conclusion, the SA removal rates were not always significantly enhanced by the addition of SMC. The addition of organic supplements may increase the rate of contaminant degradation (Semple et al. 2001).

Table 1 The removal of the three sulfonamide antibiotics (SAs) in sludge with or without spent mushroom compost (SMC) in bioreactors with three times additions of SAs after 45 days of incubation
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Fig. 2
figure 2

Sulfonamide antibiotics (SAs) remaining percentages during four times additions of three SAs in the bioreactor experiments of soil alone (a), soil-sludge mixtures without spent mushroom compost (SMC) (b), and soil-sludge mixture with SMC (c). Black circle sulfamethoxazole (SMX), white circle sulfamethazine (SMZ), black down-pointing triangle sulfadimethoxine (SDM)

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Functional groups may contribute electronegativity effects that hinder degradation by affecting the interaction between the contaminant and microbes. With increasing electronegativity of the substituents, SA degradation rates decrease (Pepper et al. 2015). SA-degrading bacteria may be present in sludge and SMC, which could enhance the SAs removal. The three re-additions of SAs might increase the SAs-degrading activity of the microbes. The solutions in the bioreactor experiments were aerated with an air diffuser and agitated with a stirrer, which enhanced the removal of SAs. These results are similar to our previous study of the effects of acclimation on degradation of three tetracyclines in river sediment (Chang and Ren 2015).

The studies were further extended with TOC measurements to demonstrate the apparent mineralization of these chemicals in the bioreactors. The TOC remaining percentages during four times re-addition of the three SAs are in Fig. 3. In the soil-sludge mixtures alone, the remaining TOC percentages were 42.1, 32.2, 22.1, and 18.5 % on days 8, 18, 28, and 38, respectively. In contrast, the remaining TOC percentages were 20.6, 15.2, 12.1, and 8.5 % on days 8, 18, 28, and 38, respectively, in the soil-sludge mixtures with SMC. The correlations between the SA removal (Fig. 2) and the TOC remaining proportions (Fig. 3) were examined. In the soil-sludge mixtures alone, the Spearman correlation coefficients between the SMX, SDM, and SMZ removal and the TOC remaining proportions were 0.958 (p = 8.741e-07), 0.947 (p = 2.91e-06), and 0.911 (p = 9.559e-05), respectively. In the soil-sludge mixtures with SMC, the Spearman correlation coefficients between the SMX, SDM, and SMZ removal and the TOC remaining proportions were 0.965 (p = 3.623e-07), 0.971 (p = 1.535e-07), and 0.951 (p = 1.894e-06), respectively. These results reveal strong correlations between the TOC remaining percentages and SAs removal in the soil-sludge mixtures. Higher proportion of SAs was mineralized in the soil-sludge mixtures with SMC than in the soil-sludge mixtures alone. The addition of SMC affected bacterial communities and thus slightly enhanced the SA removal in soil-sludge mixtures.

Fig. 3
figure 3

Total organic content (TOC) remaining percentages in the soil-sludge mixtures during four times re-addition of three SAs in the bioreactor experiments. Black circle without spent mushroom compost (SMC), white circle with SMC

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Bacteria associated with SA degradation in the bioreactor experiments

A total of 160,167 16S rRNA gene sequences were produced from 36 bioreactor experimental samples. Analysis with the RDP classifier revealed 22 phyla, 39 classes, 50 orders, 103 families, and 212 genera of known bacteria in the samples. The number of sequences and biological classification groups of bacteria as well as the diversity index for each experimental sample are in Table 2. The Shannon diversity indexes (mean ± SE) are 1.79 ± 0.14, 2.18 ± 0.14, and 3.00 ± 0.16 for samples from A, B, and C bioreactors, respectively. The Chao1 diversity indexes (mean ± SE) are 148.5 ± 10.5, 192.3 ± 13.7, and 254.5 ± 33.5 for samples from A, B, and C bioreactors, respectively. The p values of non-parametric ANOVA (the Kruskal-Wallis test) for Shannon and Chao1 diversity indexes are 1.05E-4 and 0.016, respectively. The Tukey-type non-parametric multiple comparison analysis indicates the Shannon diversity indexes are significantly different between samples of bioreactors A and C (p = 7.4E-5) and between samples of bioreactors A and C (p = 0.016). The p value of the Tukey-type non-parametric multiple comparison for the Chao1 diversity indexes between samples of bioreactors A and C is 0.012. Overall, the order of diversity of bacterial community composition for samples from the three bioreactors was C > B > A.

Table 2 Number of sequences, operational taxonomic units (OTUs), classification information, and diversity indexes for each sample
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The differences of bacterial community compositions between bioreactor samples were analyzed by cluster analysis (Fig. 4). Three clusters were identified according to experimental samples. The first cluster included early stages of bioreactors A and B samples (A1–A5, B2–B5). The second cluster included samples from bioreactor C samples. The third cluster included late stages of bioreactors A and B samples (A6–A12, B6–B12). On the other hand, four clusters of bacterial genera were identified. The first cluster included bacterial genera with high proportions in bioreactors B and C samples (B2–B12, C6–C12). The second cluster included bacterial genera with high proportions in early stages of bioreactor A samples (A1–A5). The third cluster included bacterial genera with high proportions in early stages of bioreactors A and B samples (A1–A5, B1–B5). The fourth cluster included bacterial genera with high proportions in early stages of bioreactor C samples (C1–C5). The fifth cluster included bacterial genera with high proportions in all stages of bioreactor C samples (C1–C12) and late stages of bioreactor B samples (B8–B12).

Fig. 4
figure 4

Cluster analysis of bacterial community compositions in bioreactor experiments. A, B, and C represent soil, soil-sludge mixtures, and soil-sludge-SMC mixtures, respectively. 1–12 represent days 0, 4, 8, 10, 14, 18, 20, 24, 28, 30, 34, and 38, respectively

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These results suggest that the bacterial community composition substantially differed between the experiments of soil-sludge mixtures alone and soil-sludge mixtures with SMC. Moreover, the major bacterial genera composition differed greatly between the early and late stages of the experiments. Two major factors may have affected the bacterial composition during the experimental periods. First, the nutrient or carbon sources differed between the experiments of soil-sludge mixtures alone and soil-sludge mixtures with SMC. The second factor was the addition of SAs. The bacterial genera identified in the late stage of the experiments might represent bacteria that adapted or resistant to SAs.

Differentially distributed bacterial genera in different bioreactor experiments were identified by Mann-Whitney U tests (Fig. 5). Twelve bacterial genera with high proportion in bioreactor B were found (Fig. 5a). Five of the 12 bacterial genera (i.e., Brevundimonas, Diaphorobacter, Mycobacterium, Paracoccus, and Sphingopyxis) have been reported to be associated with the degradation of aromatic hydrocarbons (Teng et al. 2010; Xiao et al. 2010; Gallego et al. 2014; Kweon et al. 2015; Muangchinda et al. 2015). On the other hand, ten bacterial genera with high proportion in bioreactor C were found (Fig. 5b). Five of the 10 bacterial genera (i.e., Chryseobacterium, Hydrogenophaga, Nitrosomonas, Sphingobium, and Terrimonas) have been reported to be associated with the degradation of aromatic hydrocarbons (Chang et al. 2002; Hou et al. 2015; Oberoi et al. 2015; Song et al. 2015; Chen et al. 2016).

Fig. 5
figure 5

Differentially distributed bacterial genera in bioreactor experiments. a Bacterial genera with higher proportions in both B and C than A bioreactor experiments. b Bacterial genera with higher proportions in C than in A and B bioreactor experiments. A, B, and C represent soil, soil-sludge mixtures, and soil-sludge-SMC mixtures, respectively. 1–12 represent days 0, 4, 8, 10, 14, 18, 20, 24, 28, 30, 34, and 38, respectively. The red at sign indicate bacterial genera that have been reported to be involved in SAs degradation. The red asterisk indicate bacterial genera that have been reported to be involved in the degradation of aromatic hydrocarbons

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Results in Fig. 6 indicate the SAs degradation efficiency and bacterial community composition differed between early (B1–B5, C1–C5) and late (B6–B12, C6–C12) stages of bioreactor samples. Differentially distributed bacterial genera in different stages of bioreactor experiments were identified by Mann-Whitney U tests (Fig. 6). Eight bacterial genera with high proportion in the late stage (B6–B12) of bioreactor B were found (Fig. 6a). Five of the eight bacterial genera (i.e., Brevundimonas, Hydrogenophaga, Nitrosomonas, Paracoccus, and Sphingopyxis) have been reported to be associated with the degradation of aromatic hydrocarbons (Chang et al. 2002; Teng et al. 2010; Xiao et al. 2010; Gallego et al. 2014; Hou et al. 2015). Five bacterial genera with high proportion in early stage (B1–B5) of bioreactor B were found (Fig. 6b). Three of the five bacterial genera (i.e., Acinetobacter, Arthrobacter, and Dokdonella) have been reported to be associated with the degradation of aromatic hydrocarbons (Ferreira et al. 2013; Yuan et al. 2014; Bacosa and Inoue 2015). On the other hand, 11 bacterial genera with high proportion in the late stage (C6–C12) of bioreactor C were found (Fig. 6c). Five of the 11 bacterial genera (i.e., Brevundimonas, Chryseobacterium, Diaphorobacter, Paenibacillus, and Paracoccus) have been reported to be associated with the degradation of aromatic hydrocarbons (Haritash and Kaushik 2009; Teng et al. 2010; Xiao et al. 2010; Muangchinda et al. 2015; Oberoi et al. 2015). Thirteen bacterial genera with high proportion in the early stage (C1–C5) of bioreactor C were found (Fig. 6d). Seven of the 13 bacterial genera (i.e., Acinetobacter, Altererythrobacter, Pandoraea, Planctomyces, Pseudomonas, Rhodanobacter, and Rhodobacter) have been reported to be associated with the degradation of aromatic hydrocarbons (Tang et al. 2010; Yuan et al. 2014; Guermouche M’rassi et al., 2015; Hou et al. 2015; Huang et al. 2015; Kaci et al. 2015; Song et al. 2016). Overall, 44 differentially distributed bacterial genera were identified from different experimental settings and stages. Twenty of the 44 bacterial genera were previously found involved in aromatic hydrocarbon degradation. Four bacterial genera (Acinetobacter, Alcaligenes, Brevundimonas, and Pseudomonas) have been reported as SA-degrading bacteria (Yan et al. 2011; Islas-Espinoza et al. 2012; Zhang et al. 2012; Herzog et al. 2013). We suggest that the 20 bacterial genera previously found in aromatic hydrocarbon or SA degradation might have high potential to involve in SA degradation in this study.

Fig. 6
figure 6

Differentially distributed bacterial genera in different stages of bioreactor experiments. a Bacterial genera with high proportions in late stage of bioreactor B experiments. b Bacterial genera with high proportions in early stage of bioreactor B experiments. c Bacterial genera with high proportions in late stage of bioreactor C experiments. d Bacterial genera with high proportions in early stage of bioreactor C experiments. A, B, and C represent soil, soil-sludge mixtures, and soil-sludge-SMC mixtures, respectively. 1–12 represent days 0, 4, 8, 10, 14, 18, 20, 24, 28, 30, 34, and 38, respectively. The red at sign indicate bacterial genera that have been reported to be involved in SAs degradation. The red asterisk indicate bacterial genera that have been reported to be involved in the degradation of aromatic hydrocarbons

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Differentially distributed bacterial genera identified among different experimental settings and stages in Fig. 6 imply multiple possibilities of bacterial community compositions for SA removal. Changes in bacterial community composition are the combined result of many factors such as symbiosis/competition among bacteria for carbon sources and adaptation of bacteria for environmental conditions.

Conclusion

Microbial degradation is a major process of SA removal in soil-sludge mixtures. The presence of sludge may provide an important base for SA removal. In batch experiments, addition of SMC in soil-sludge mixtures exhibits highest SMX degradation efficiency than other additives. However, in bioreactor experiments, SA removal rates were not always significantly enhanced by the addition of SMC. With the high correlations between TOC and the proportions of SAs remaining, SAs may be mineralized to a greater extent with than without SMC in the soil-sludge mixtures. Forty-four bacterial genera were identified in different settings and stages of bioreactor experiments. Four bacterial genera Acinetobacter, Alcaligenes, Brevundimonas, and Pseudomonas were previously found involved in SA degradation, and 20 of the 44 bacterial genera were previously found involved in aromatic hydrocarbon degradation. Therefore, these bacteria have high potential to be SA degradation bacteria in this study.