Effects of different remediation treatments on crude oil contaminated ...

Chemosphere 117 (2014) 486–493

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Technical Note

Effects of different remediation treatments on crude oil contaminated saline soil Yong-chao Gao a,b, Shu-hai Guo c, Jia-ning Wang b, Dan Li b, Hui Wang d, De-Hui Zeng a,⇑ a

State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China Provincial Key Laboratory of Applied Microbiology, Institute of Biology, Shandong Academy of Sciences, 19 Keyuan Road, Jinan 250014, China c Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China d School of Resources and Environment, University of Jinan, Jinan 250022, China b

h i g h l i g h t s Different remediation treatments on crude oil contaminated saline soil were studied. Soil physicochemical and bacterial properties varied among treatments. Combined treatments were more effective than single treatments in remediation.

c-Proteobacteria, b-Proteobacteria, and Actinobacteria were the pioneer oil-degraders.

Firmicutes were dominant in decomposing the recalcitrant components of crude oil.

a r t i c l e

i n f o

Article history: Received 4 May 2014 Received in revised form 14 August 2014 Accepted 19 August 2014

Handling Editor: O. Hao Keywords: Remediation Bacterial community Crude oil contamination Denaturing gradient gel electrophoresis (DGGE) Saline soil

a b s t r a c t Remediation of the petroleum contaminated soil is essential to maintain the sustainable development of soil ecosystem. Bioremediation using microorganisms and plants is a promising method for the degradation of crude oil contaminants. The effects of different remediation treatments, including nitrogen addition, Suaeda salsa planting, and arbuscular mycorrhiza (AM) fungi inoculation individually or combined, on crude oil contaminated saline soil were assessed using a microcosm experiment. The results showed that different remediation treatments significantly affected the physicochemical properties, oil contaminant degradation and bacterial community structure of the oil contaminated saline soil. Nitrogen addition stimulated the degradation of total petroleum hydrocarbon significantly at the initial 30 d of remediation. Coupling of different remediation techniques was more effective in degrading crude oil contaminants. Applications of nitrogen, AM fungi and their combination enhanced the phytoremediation efficiency of S. salsa significantly. The main bacterial community composition in the crude oil contaminated saline soil shifted with the remediation processes. c-Proteobacteria, b-Proteobacteria, and Actinobacteria were the pioneer oil-degraders at the initial stage, and Firmicutes were considered to be able to degrade the recalcitrant components at the later stage. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction Oil hydrocarbons are one of the most prevalent soil contaminants in the world (Abioye, 2011). It has been estimated that the natural crude-oil seepage amounts to 0.6 Mt per year, with a range of uncertainty of 0.2–2 Mt per year (Kvenvolden and Cooper, 2003). Hydrocarbon components have been included in the family of carcinogens and neurotoxic organic pollutants (Das and Chandran, 2011). Remediation of the petroleum contaminated soil ⇑ Corresponding author. Tel.: +86 24 83970220; fax: +86 24 83970394. E-mail address: [email protected] (D.-H. Zeng). http://dx.doi.org/10.1016/j.chemosphere.2014.08.070 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.

is essential to maintain the sustainable development of local ecosystem. Technologies commonly used for the soil remediation include natural attenuation, land farming, biopiling or composting, slurry bioreactor, bioventing, soil vapor extraction, thermal desorption, incineration, soil washing, and land filling (US EPA, 2004). However, these technologies are usually expensive and can lead to incomplete decomposition of contaminants (Das and Chandran, 2011). Bioremediation using microorganisms and plants to detoxify or remove pollutants owing to their diverse metabolic capabilities is an evolving method for the removal and degradation of many environmental pollutants including the products of petroleum industry

Y.-c. Gao et al. / Chemosphere 117 (2014) 486–493

(Singh et al., 2009). Microorganisms play key roles in biotransformation of complex contaminant mixtures during soil bioremediation processes (Gómez et al., 2007; Gadd, 2010). There are two main approaches to oil spill bioremediation: Bioaugmentation (addition of oil-degrading bacteria) and biostimulation (stimulating the growth of indigenous oil degraders by the addition of nutrients or other growth-promoting co-substrates) (Das and Chandran, 2011). Meanwhile, studies have shown that plants have the ability to detoxify some xenobiotics in soil by direct uptake of the contaminants, followed by subsequent transformation, transport and product accumulation (Macek et al., 2008). Phytoremediation, with the associated role of rhizospheric microorganisms, is an important tool in bioremediation processes (Khan et al., 2013). However, soil bioremediation is susceptible to environmental factors (Venosa and Zhu, 2003). As for crude oil contaminated soil, soil structure and physicochemical and biological characteristics, e.g., soil organic matter content, bulk density, porosity, permeability, soil respiration and material transfer process, can be altered by the high hydrophobicity of hydrocarbons (Liang et al., 2012). Furthermore, saline and hypersaline environments are frequently accompanied with crude oil contamination as a result of industrial activities (Oren et al., 1992). Soil salinization has great inhibitory effects on the biodegradation of petroleum hydrocarbons (Mille et al., 1991). Therefore, plant-mycorrhiza bioremediation is a research hotspot as mycorrhiza can improve plant growth, resist various stresses, and enhance degradation and transfer of organic pollutants (Alarcon et al., 2008; Tang et al., 2009). The combination of two or more remediation techniques is necessary to improve the bioavailability and bioremediation efficiency considering the harsh contaminated environment. The Shengli Oilfield, located in the Yellow River Delta region, is the second-largest oilfield in China. The crude oil contamination due to oil well blowouts, leaks and spills from underground tank, pipelines and illegal disposals greatly threatens the ecosystem of the delta region (Wang et al., 2011a). Moreover, the Yellow River Delta is a newly born wetland, and the land–ocean interaction is very active (Wang et al., 2011a). Secondary salinization of surface soil due to excess evaporation from soil has led to land degradation, which affects 60% of total land area in the region (Fang et al., 2005). The soil salt content of this region ranges from 6 to 30 g kg1 (Wang et al., 2009). Multiple environmental stresses increase the bioremediation difficulty of the oil contaminated soil. Furthermore, information about the effects of different bioremediation treatments on crude oil contaminated saline soil is lacking. In this study, we evaluated the effects of different remediation treatments, including nitrogen addition, Suaeda salsa planting, and arbuscular mycorrhiza (AM) fungi inoculation individually or combined, on the crude oil contaminated saline soil. The objectives of this study were to investigate: (1) the effects of different remediation treatments on soil physicochemical properties; (2) the degradation rate of total petroleum hydrocarbon (TPH) and the temporal changes of crude oil fractions; and (3) the alterations of soil bacterial biodiversity and community structure, and the main bacterial groups participating in remediation.

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2.2. Experimental design A pot experiment was carried out to study the effect of different remediation treatments on the crude oil contaminated saline soil. Six treatments were designed: (1) CK, control; (2) S, seepweed (S. salsa) bioremediation; (3) N, nitrogen addition; (4) N + S, nitrogen addition and S. salsa combined bioremediation; (5) M + S, AM and S. salsa combined bioremediation, adding 1% (relative to soil mass) of commercially available AM fungi inocula (AMYkor, GmbH, Bitterfeld-Wolfen, Germany) before planting S. salsa; (6) M + N + S, AM, nitrogen addition and S. salsa combined bioremediation, adding 1% AM fungi inocula of the soil mass before planting S. salsa. In treatments 3, 4 and 6, urea was added with a dosage of 1‰ of the soil mass to the crude oil contaminated soil, and then mixed with the soil thoroughly. Each treatment was replicated four times. Filter paper was placed at the bottom of the pot (with a size of 13 cm high 15 cm in diameter) to cover the drainage hole, and 1500 g crude oil contaminated saline soil was added. For S. salsa planting, 0.5 g of seed were sown evenly to the soil in each pot and covered with 2–3 cm of soil on the top (1500 g soil added in total for each pot). Water was then added to maintain 60–80% of soil water-holding capacity. All of the experiment pots were placed in a greenhouse at 25 °C. Seven days after seeds germinated, 20 healthy seedlings were preserved in each pot for further phytoremediation. Soil samples were taken after 0, 30, 60 and 90 d with a hole-puncher (10 cm long and 1.5 cm in diameter). Three soil samples were collected and mixed to form a composite for each pot at each sampling time. The holes were filled with the surrounding soil in the pot immediately after sampling. Then the soil samples were divided into two subsamples: one was used to study the variations of soil physicochemical properties and crude oil fractions and the other was stored at 20 °C for denaturing gradient gel electrophoresis (DGGE) analysis. The dynamics of bacterial community of all treatments before and after remediation (90 d) were analyzed. Furthermore, the bacterial community of the treatment with best performance was monitored in the whole process to find out the main functional bacteria participating in the remediation at different stages. 2.3. Soil property, crude oil fraction and soil microbial biomass analyses The soil samples used for the test of electrical conductivity (EC) and pH were air-dried, passed through a 2-mm sieve, and measured according to Gao et al. (2013). The soil water content was measured according to Alef and Nannipieri (1995). TPH was extracted and measured according to Chaîneau et al. (2005). The dry extract was suspended in 10 mL of hexane by sonication (5 min, 40 kHz). Methods of separation and measurement for crude oil fractions (saturates, aromatics, resins, and asphaltenes) in the extract were in accordance with Oudot et al. (1998). Soil total Kjeldahl nitrogen (TKN) concentration was analyzed by the Kjeldahl method with a continuous-flow analyzer (AutoAnalyzer III, Bran + Luebbe GmbH, Germany). 2.4. PCR-DGGE analysis

2. Materials and methods 2.1. Soil used for experiment Uncontaminated saline soil was collected from the Shengli Oilfield in China. The soil was air dried and ground to 0.85 mm (20 meshes) before using. Crude oil was then added to a portion of the uncontaminated saline soil with a dosage of 2% of soil mass. The physicochemical properties of the uncontaminated saline soil (U1) and soil mixed with crude oil (U2) are shown in Table 1.

Total genomic DNA was extracted from the soil samples using the E.Z.N.A. Soil DNA Kit (Omega Biotek, USA) according to the manufacturer’s instructions. The variable V3 region of 16S rRNA was amplified by PCR using a pair of universal primers, 338F 50ACTCCTACGGGAGGCAGCAG-30 and 534R 50 -ATTACCGCGGCTGCT GG-30 , to which a GC clamp (CGCCCGCCGCGCGCGGCGGGCGGGGC GGGGGCACGGGGGG) was attached at the 50 -terminus (Muyzer et al., 1993). The PCR mixture consisted of 5 lL of DNA template, 2 lL of 338F/534R (10 lM) primers each, 25 lL of Tiangen 2 Taq

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Y.-c. Gao et al. / Chemosphere 117 (2014) 486–493 Table 1 Physicochemical properties of the experimental soils. Soil samples

EC (mS cm1)

TPH (g kg1)

TKN (g kg1)

pH

Water content (%)

U1 U2

4.73 4.47

1.4 12.6

0.37 0.44

8.14 8.30

22.3 20.7

U1: uncontaminated saline soil; U2: soil mixed with crude oil. EC, electrical conductivity; TPH, total petroleum hydrocarbon; TKN, total Kjeldahl nitrogen.

PCR Master Mix, and 16 lL ddH2O comprising a total volume of 50 lL (Tiangen Biotech, Beijing). A modified touch-down PCR procedure was used for cycling amplification in a Veriti PCR thermal cycler (Applied Biosystems, USA). Touch-down amplification was performed with an initial step of 10 min at 94 °C, followed by 10 cycles of denaturation for 1 min at 94 °C, annealing for 1 min with temperatures decreasing from 60 °C at 0.5 °C per cycle, and primer extension for 1.5 min at 72 °C. This step was similarly followed by 25 cycles of denaturation for 1 min at 94 °C, annealing for 1 min at 55 °C, and primer extension for 1.5 min at 72 °C, followed by a final extension at 72 °C for 10 min. DGGE analysis was performed with 8% (w/v) polyacrylamide gels (ratio of acrylamide to bis-acrylamide 37.5:1) in 1 TAE buffer (40 mM Tris– acetate, 1 mM Na-EDTA, pH 8.0) with a gradient ranging from 40 to 60% (where 100% denaturant was defined as 7 M urea and 40% formamide) at a constant voltage of 65 V and 60 °C for 16 h (BioRad Dcode System, USA). Gels were silver stained according to Ning et al. (2009). Finally, the DGGE gels were scanned using Gel Doc 2000 gel image analysis system (Bio-Red, USA) and analyzed by Quantity One image analysis software (version 4.1; Bio-Rad Laboratories, Hercules, CA, USA). Prominent DGGE bands were excised with a sterile razor blade, re-suspended in 50 lL sterilized ddH2O, stored at 4 °C overnight, re-amplified, cloned in the pGEMT Easy vector (Promega, Madison, WI), and sequenced by using an ABI Prism Big Dye terminator cycle sequencing reaction kit, version 3.1 (Perkin-Elmer Applied Biosystems, Foster City, CA, USA), and an ABI 3700 DNA sequencer (Perkin-Elmer Applied Biosystems, Foster City, CA, USA) following the manufacturer’s instructions. The sequences were edited and assembled using the BioEdit software, and inspected for the presence of ambiguous base assignments. 2.5. Biodiversity and phylogenetic analyses We used Shannon index (H’), Pielou’s evenness index (E) and richness (detected bands) to assess the bacterial diversity during the remediation of the oil-contaminated saline soil. The H’ value was calculated as follows:

H0 ¼

S X Pi lg Pi i¼1

where Pi is the relative peak area intensity of a DGGE band, calculated from ni/N, ni is the peak area of the band, N is the sum of all peak areas in the densitometry curve, and S is the total number of distinct bands in a lane (Kirk et al., 2005). The E value was calculated from each standardized band by using number and height of peaks in each band profile as representatives of the number and relative abundance of different phylotypes in each line. The formula for E value was calculated as follows: 0

E¼

H Hmax

where Hmax = lg S (Thavamani et al., 2012). 16S rRNA gene sequences were blasted against the GenBank database (www.ncbi.nlm.nih.gov/BLAST). All sequences with similarities greater than 97% were included in a phylogenetic analysis (Xing et al., 2010). All sequences used for phylogenetic analysis

have been deposited in the GenBank nucleotide sequence database under accession numbers from KJ179808 to KJ179831. The phylogenetic trees were constructed by the neighbor-joining method with the Molecular Evolutionary Genetics Analysis software (Tamura et al., 2007). 2.6. Statistical analyses The differences of soil EC, TPH, TKN and water content of oilcontaminated saline soil in different remediation treatments and sampling time were analyzed using one-way ANOVA analysis and repeated measures variance analysis. Dendrogram analysis of the DGGE fingerprints was constructed based on the Dice similarity coefficient using unweighted pair group method clustering with the Quantity One software. 3. Results 3.1. Physicochemical properties of the remediation soils EC is the most commonly used index for soil salinity assessment. The EC of the original soils used for the experiment was over 4 mS cm1 (Table 1), showing that the soils were saline. The repeated measures ANOVA analysis showed that EC, TPH degradation rate and TKN had significant differences at different remediation time and treatments. Furthermore, there existed significant interactive effects of time and treatment on EC, TPH degradation rate and TKN (Table 2). EC had no significant difference among the different treatments during the initial 30 d of remediation. The soil EC in the treatments of S, N + S, M + S and M + N + S reduced significantly with the extension of remediation time. The treatment of M + N + S was best in reducing soil salinization. Nitrogen addition alone had no obvious contribution to the alleviation of soil salinization. TPH degradation rate had significant differences among different treatments during remediation (Table 2). Nitrogen addition (the treatments of N, N + S and M + N + S) significantly stimulated the degradation of TPH at the initial 30 d. The TPH degradation efficiency of treatment M + N + S was more effective than others. However, nitrogen addition alone had little effect on the TPH degradation in the subsequent 60 d. The soil water contents changed significantly over the remediation process and they had no difference among treatments at each sampling time. No interactive effects of remediation time and treatments on the water content were observed. 3.2. Effects of different remediation treatments on crude oil fractions The composition of the oil significantly varied in different treatments after 90 d of remediation (Fig. 1). Saturates and aromatics in total accounted for about 65% of the oil at the initial stage of the experiment. After 90 d, the TPH degradation rate increased at different degrees under different treatments (Table 2). The treatments of M + S and M + N + S significantly improved the degradation rate compared with the control. Comparing the data of TPH degradation rate and the proportion of the four crude oil fractions, saturates and aromatics were the most easily degradable fractions of the

**

ns ns ***

***

** ***

**

**

***

***

***

P values Time (T) Treatment (M) TM

CK, control; S, S. salsa bioremediation; N, nitrogen addition; N + S, nitrogen addition and S. salsa combined bioremediation; M + S, AM and S. salsa combined bioremediation; M + N + S, AM, nitrogen addition and S. salsa combined bioremediation. Values reported are means (n = 4) and standard errors in the brackets. The different lowercase letters in each column indicate significant differences in the different treatments at same sampling time. ns, Not significant. ** P < 0.01. *** P < 0.001.

14.8 15.8 16.0 16.9 17.9 16.8 20.4 18.8 20.9 20.3 19.8 19.7

60 d

21.8 19.9 19.9 22.2 19.6 24.8

(0.6)a (0.5)a (1.0)a (0.3)a (0.4)a (0.9)a 30 d

0.26 0.42 0.69 0.66 0.42 0.59 0.30 0.43 0.74 0.78 0.39 0.71 (0.1)a (0.0)a (0.1)b (0.0)c (0.1)a (0.1)b 0.36 0.41 0.75 0.89 0.36 0.77 44.0 48.5 47.0 53.9 54.1 59.9 40.3 43.8 44.3 45.5 46.9 53.1 24.2 29.4 39.1 47.9 45.6 52.9 (0.1)c (0.1)b (0.1)c (0.1)b (0.2)b (0.1)a 4.2 3.4 4.0 3.4 3.1 2.5 (0.1)c (0.1)bc (0.1)c (0.1)b (0.1)b (0.2)a 4.3 4.0 4.3 3.6 3.7 3.2 (0.1)a (0.0)a (0.1)a (0.1)a (0.1)a (0.1)a 4.4 4.3 4.6 4.5 4.5 4.6 CK S N N+S M+S M+N+S

30 d

60 d

90 d

30 d

(2.1)c (1.6)c (4.9)b (3.2)a (1.2)a (0.1)a

60 d

(4.3)b (1.1)ab (3.3)ab (5.4)ab (2.0)ab (5.2)a

90 d

(1.3)b (3.0)ab (2.9)ab (1.5)ab (1.2)a (2.6)a

30 d

TKN (g kg1) TPH degradation rate (%) EC (mS cm1) Treatment

Table 2 Repeated measures analysis of variance for EC, TPH degradation rate, TKN and water content of oil-contaminated saline soil during remediation.

60 d

(0.0)a (0.0)b (0.1)c (0.0)c (0.0)ab (0.1)c

(0.1)a (0.0)b (0.2)c (0.1)c (0.2)b (0.1)c 90 d

Water content (%)

(0.7)a (0.8)a (0.8)a (1.2)a (0.2)a (0.4)a

90 d

(1.0)a (0.9)a (1.8)a (0.7)a (0.3)a (0.4)a


489

oil. By contrast, asphaltenes and resins were hardly degraded in all of the treatments. 3.3. Soil bacterial biodiversity in different remediation treatments Soil bacterial community diversity indices were analyzed using the DGGE data (Table 3). The H’ index increased immediately after the addition of crude oil contaminants in the saline soil (U2). Ninety days later, the H’ index in treatment of N + S was the highest among all the treatments. The H’ and richness indices of the combined treatments (N + S, M + S and M + N + S) were all higher than the treatments individually. The combined treatments led to the decrease of the E index, while the treatments individually had no effect on the E index. The long-term monitoring of the treatment M + N + S showed that the bacterial community diversity varied gradually during the remediation process. The H’ index of the whole bacterial community increased, while the E index decreased accordingly. 3.4. Phylogenetic analysis The DGGE profile showed that the richness and composition of the bacterial populations had significant differences among different treatments and remediation periods (Fig. 2). The number of distinct DNA bands ranged from 7 in the pristine saline soil to 21 in the soil after 90 d of remediation. The main sequences of the DGGE bands fell into corresponding operational taxonomic units (OTUs) based on a threshold of 97% similarity (Kocherginskaya et al., 2001). The phylogenetic distributions of the OTUs were divided into the following three groups (Fig. 3): Proteobacteria (c-Proteobacteria, b-Proteobacteria, a-Proteobacteria), Actinobacteria, and Firmicutes. Some OTUs could not be classified and were designated as ‘‘unclassified’’. The bacterial populations were sparse in the pristine saline soil without crude oil contamination (Fig. 2, U1). With the addition of crude oil to the soil, the bacterial species increased immediately (Fig. 2, U2). b-Proteobacteria (bands 11, 14) was a new class evoked by oil contamination. The main bacterial populations changed slightly after 90 d (see the DGGE patterns of U2 and CK in Fig. 2). The treatments of planting seepweed or nitrogen addition did not alter the bacterial community structure significantly. The richness of the bacterial populations was improved significantly in the combined treatments (N + S, M + S, and M + N + S). Comparing the DGGE profile and the phylogenetic tree (Figs. 2 and 3), the groups of b-Proteobacteria (bands 11, 14), c-Proteobacteria (bands 4, 5, 12, 13) and Actinobacteria (bands 3, 17) were the pioneer degraders participating in the TPH biodegradation at the early stage, while the groups of b-Proteobacteria (bands 9, 16), cProteobacteria (bands 1, 2), Actinobacteria (bands 10, 24) and Firmicutes (bands 22, 23) were the dominant groups participating in the TPH biodegradation at the later stage. Among which, b-Proteobacteria (band 16) was the bacteria emerging around the roots of the seepweed. The groups of a-Proteobacteria (bands 7, 20) and Actinobacteria (band 18) were the indigenous phyla that cannot degrade the TPH. 4. Discussion 4.1. Effect of nitrogen addition on promoting the degradation of crude oil Oil pollution could lead to significant changes in soil chemical properties, such as TPH, TOC, C/N and C/P ratios (Wang et al., 2010). Nitrogen is the most commonly limiting factor to biological degradation of hydrocarbon in soils (Mohn and Stewart, 2000). Nitrogen addition could stimulate the microbial activity, and

490


Fig. 1. Proportion of four crude oil fractions after 90 d of incubation under different treatments (%). CK, control; S, S. salsa bioremediation; N, nitrogen addition; N + S, nitrogen addition and S. salsa combined bioremediation; M + S, AM and S. salsa combined bioremediation; M + N + S, AM, nitrogen addition and S. salsa combined bioremediation.

Table 3 The bacterial biodiversity of the oil-contaminated saline soil under different remediation treatments. Diversity index

U1

U2

Shannon index (H’) Evenness index (E) Richness index (detected bands)

2.3 0.99 10

2.61 0.98 14

90 d

60 d

30 d

CK

S

N

N+S

M+S

M+N+S

M+N+S

M+N+S

2.82 0.98 17

2.62 0.98 14

2.75 0.98 16

3.09 0.95 25

2.84 0.97 18

2.90 0.97 19

2.62 0.98 14

2.76 0.99 16

U1, uncontaminated saline soil; U2, oil-contaminated saline soil; CK, control; S, S. salsa bioremediation; N, nitrogen addition; N + S, nitrogen addition and S. salsa combined bioremediation; M + S, AM and S. salsa combined bioremediation; M + N + S, AM, nitrogen addition and S. salsa combined bioremediation.

increase the degradation rate of hydrocarbon (Braddock et al., 1997; Xu and Obbard, 2003). In our study, the addition of nitrogen stimulated the degradation of TPH significantly at the initial 30 d of remediation (Table 2). However, the stimulating effect did not continue in the following 60 d. The low bioavailability of contaminant and the accumulation of recalcitrant compounds could inhibit the microbial biodegradation ability (Margesin and Schinner, 1999). Significant additive effects on the TPH degradation rates were observed in treatments N + S and M + N + S at the initial 30 d of remediation compared with the treatment of N addition alone. Nitrogen addition promoted the growth of plants, which may lead to greater transpirational water loss, allow more air to enter the soil and thus increase soil oxidization (Lin and Mendelssohn, 1998). 4.2. Effects of planting seepweed on the remediation of crude oil contaminated saline soil Phytoremediation is an emerging technology which uses various plants to extract, contain, degrade, and/or immobilize contaminants (Wang et al., 2011b). Plants support hydrocarbon-degrading microbes that assist in phytoremediation in the root zone through their ‘rhizosphere effects’ (Nie et al., 2009). In the present study, phytoremediation merely had a contribution to the reduction of soil salinity, but no significant effect on TPH degradation (Table 2). The efficiency of phytoremediation depends mostly on the establishment of robust plant–microbe interactions (Wenzel, 2009; Nie et al., 2011). Roots offer perfect attachment sites for microorganisms and supply nutrients in the form of exudates, which could enhance the microbial activity and thus the biodegradation of organic contaminants (Sijm et al., 2000; Xie et al., 2012). Combined

remediation of planting seepweed with nitrogen addition and/or AM fungus inoculation not only alleviated soil salinization, but also significantly reduced the TPH concentrations.

4.3. Effect of AM on the remediation of crude oil contaminated saline soil AM fungi can enhance plant growth by improving mineral nutrition, or increasing resistance or tolerance to biotic and abiotic stresses (Turnau and Haselwandter, 2002; Khan, 2006). In the present study, the treatments of M + S and M + N + S enhanced the degradation of TPH significantly and obviously prolonged the validity of remediation compared with the other treatments. Mycorrhizal fungal mycelia and surrounding soil provided suitable habitats for diverse community composition of microorganisms, which increased the availability of high-energy metabolic substrates and surfaces for colonization (Sen, 2003). The soil salinity was also reduced significantly in the treatments of S, N + S, M + S and M + N + S. The dilution effects of plant growth and AM fungi colonization were inferred to be an important reason in alleviating the soil salinity (Giri et al., 2007; Latef and He, 2011).

4.4. Functional oil-degraders during remediation process The degradation of complex pollutants such as petroleum requires a combination of different bacteria as a community, which can degrade a broader spectrum of hydrocarbons than any single bacterial species alone (Pelz et al., 1999; Röling et al., 2002). Several studies showed that the bacterial community was always


491

Fig. 2. DGGE of 16S rRNA amplification fragments and corresponding bands isolated for sequencing. PCR fragments were separated on a DGGE using a denaturant gradient of 40–60%. U1, uncontaminated saline soil; U2, oil-contaminated saline soil; CK, control; S, S. salsa bioremediation; N, nitrogen addition; N + S, nitrogen addition and S. salsa combined bioremediation; M + S, AM and S. salsa combined bioremediation; M + N + S, AM, nitrogen addition and S. salsa combined bioremediation.

changed dynamically (Zucchi et al., 2003; Vinas et al., 2005; Yu et al., 2011). In our study, the increase of certain bacterial populations responsible for the TPH degradation led to the shift of bacterial community according to the variation of microbial community richness, evenness and the phylogenetic tree. Previous studies showed that specific bacterial phylotypes were associated with different phases of polycyclic aromatic hydrocarbon (PAH) degradation in PAH contaminated soil (Vinas et al., 2005). At the early stages of biodegradation, the a-Proteobacteria were the dominant group in all treatments. At the later stages, c-Proteobacteria, a-Proteobacteria, and the Cytophaga-Flexibacter-Bacteroides were the dominant groups in the non-nutrient treatments, while c-Proteobacteria, b-Proteobacteria, and a-Proteobacteria were the dominant groups in the nutrient treatments. The microcosm experiment on the succession of bacterial community with the degradation of crude oil contaminants using saline soil sampled from the Yellow River Delta, China showed that, c-Proteobacteria were the dominant bacteria responsible for the biodegradation of TPH at the initial stage. Subsequently, the bacteria belonging to a-Proteobacteria became the dominant oil-degraders to degrade

the remaining recalcitrant constituents of the heavy crude oil (Yu et al., 2011). Acinetobacteria has the ability to utilize n-alkanes of chain length C10–C40 as a sole source of carbon (Throne-Holst et al., 2007). In the present study, c-Proteobacteria, b-Proteobacteria, and Actinobacteria were found to be the pioneer oil-degraders at the early stage. With the proceeding of remediation, c-Proteobacteria, b-Proteobacteria, and Actinobacteria were still the dominant groups in the soils, and Firmicutes were considered to play a key role for the decomposition of the remaining crude oil constituents. In conclusion, different remediation treatments had different effects on saline soil physicochemical properties, oil contaminant degradation and bacterial community structure. Coupling of different remediation techniques was more effective in degrading crude oil contaminants. Nitrogen addition, AM fungi inoculation or their combination significantly enhanced the remediation efficiency of seepweed. Soil bacterial community shifted with the remediation processes. c-Proteobacteria, b-Proteobacteria, and Actinobacteria were the pioneer oil-degraders, while Firmicutes were inferred to be able to degrade the recalcitrant components.

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Fig. 3. Phylogenetic tree of the16S rRNA amplification fragments separated by DGGE gel. The reference sequences used are shown with their species names and GenBank accession numbers. The scale bar corresponds to 0.02 substitutions per nucleotide position.

Acknowledgments We thank Gui-Yan Ai and Jing-Shi Li for their help in laboratory analyses. This work was funded by the National High Technology Research and Development Program (‘‘863’’ Program) of China (2013AA06A210), the Natural Science Foun-

dation of Shandong Province (No. ZR2011DQ002), the National Natural Science Foundation of China (31270586) and the Open Fund of the Laboratory of Marine Spill Oil Identification and Damage Assessment Technology, North China Sea Environmental Monitoring Center, Oceanic Administration of China (No. 201213).


References Abioye, P., 2011. Biological remediation of hydrocarbon and heavy metals contaminated soil. In: Pascucci, S. (Ed.), Soil Contamination. InTech, Rijeka, pp. 127–142. Alarcon, A., Davies, F.T., Autenrieth, R.L., Zuberer, D.A., 2008. Arbuscular mycorrhiza and petroleum-degrading microorganisms enhance phytoremediation of petroleum-contaminated soil. Int. J. Phytoremediat. 10, 251–263. Alef, K., Nannipieri, P., 1995. Methods in Applied Soil Microbiology and Biochemistry. Academic Press, London. Braddock, J.F., Ruth, M.L., Catterall, P.H., Walworth, J.L., McCarthy, K.A., 1997. Enhancement and inhibition of microbial activity in hydrocarbon-contaminated arctic soils: implications for nutrient-amended bioremediation. Environ. Sci. Technol. 31, 2078–2084. Chaîneau, C.H., Rougeux, G., Yéprémian, C., Oudot, J., 2005. Effects of nutrient concentration on the biodegradation of crude oil and associated microbial populations in the soil. Soil Biol. Biochem. 37, 1490–1497. Das, N., Chandran, P., 2011. Microbial degradation of petroleum hydrocarbon contaminants – an overview. Biotechnol. Res. Int. 2011 (11), 1–13. Fang, H., Liu, G., Kearney, M., 2005. Georelational analysis of soil type, soil salt content, landform, and land use in the Yellow River Delta, China. Environ. Manage. 35, 72–83. Gadd, G.M., 2010. Metals, minerals and microbes: geomicrobiology and bioremediation. Microbiology 156, 609–643. Gao, Y., Wang, J., Xu, J., Kong, X., Zhao, L., Zeng, D.-H., 2013. Assessing the quality of oil-contaminated saline soil using two composite indices. Ecol. Indic. 24, 105– 112. Giri, B., Kapoor, R., Mukerji, K., 2007. Improved tolerance of Acacia nilotica to salt stress by arbuscular mycorrhiza, Glomus fasciculatum may be partly related to elevated K/Na ratios in root and shoot tissues. Microb. Ecol. 54, 753–760. Gómez, M.J., Pazos, F., Guijarro, F.J., de Lorenzo, V., Valencia, A., 2007. The environmental fate of organic pollutants through the global microbial metabolism. Mol. Syst. Biol. 3, 1–11. Khan, A.G., 2006. Mycorrhizoremediation—an enhanced form of phytoremediation. J. Zhejiang Univ. Sci. B 7, 503–514. Khan, S., Afzal, M., Iqbal, S., Khan, Q.M., 2013. Plant–bacteria partnerships for the remediation of hydrocarbon contaminated soils. Chemosphere 90, 1317–1332. Kirk, J.L., Klironomos, J.N., Lee, H., Trevors, J.T., 2005. The effects of perennial ryegrass and alfalfa on microbial abundance and diversity in petroleum contaminated soil. Environ. Pollut. 133, 455–465. Kocherginskaya, S.A., Aminov, R.I., White, B.A., 2001. Analysis of the rumen bacterial diversity under two different diet conditions using denaturing gradient gel electrophoresis, random sequencing, and statistical ecology approaches. Anaerobe 7, 119–134. Kvenvolden, K., Cooper, C., 2003. Natural seepage of crude oil into the marine environment. Geo-Mar. Lett. 23, 140–146. Latef, A.A.H.A., He, C.X., 2011. Effect of arbuscular mycorrhizal fungi on growth, mineral nutrition, antioxidant enzymes activity and fruit yield of tomato grown under salinity stress. Sci. Hortic. 127, 228–233. Liang, Y., Zhang, X., Wang, J., Li, G., 2012. Spatial variations of hydrocarbon contamination and soil properties in oil exploring fields across China. J. Hazard. Mater. 241–242, 371–378. Lin, Q., Mendelssohn, I.A., 1998. The combined effects of phytoremediation and biostimulation in enhancing habitat restoration and oil degradation of petroleum contaminated wetlands. Ecol. Eng. 10, 263–274. Macek, T., Kotrba, P., Svatos, A., Novakova, M., Demnerova, K., Mackova, M., 2008. Novel roles for genetically modified plants in environmental protection. Trends Biotechnol. 26, 146–152. Margesin, R., Schinner, F., 1999. Biological decontamination of oil spills in cold environments. J. Chem. Technol. Biot. 74, 381–389. Mille, G., Almallah, M., Bianchi, M., Van Wambeke, F., Bertrand, J., 1991. Effect of salinity on petroleum biodegradation. Fresen. J. Anal. Chem. 339, 788–791. Mohn, W.W., Stewart, G.R., 2000. Limiting factors for hydrocarbon biodegradation at low temperature in Arctic soils. Soil Biol. Biochem. 32, 1161–1172. Muyzer, G., de Waal, E.C., Uitterlinden, A.G., 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microb. 59, 695–700. Nie, M., Zhang, X., Wang, J., Jiang, L., Yang, J., Quan, Z., Cui, X., Fang, C., Li, B., 2009. Rhizosphere effects on soil bacterial abundance and diversity in the Yellow River deltaic ecosystem as influenced by petroleum contamination and soil salinization. Soil Biol. Biochem. 41, 2535–2542. Nie, M., Wang, Y., Yu, J., Xiao, M., Jiang, L., Yang, J., Fang, C., Chen, J., Li, B., 2011. Understanding plant-microbe interactions for phytoremediation of petroleumpolluted soil. PLoS ONE 6, e17961. Ning, J., Liebich, J., Kästner, M., Zhou, J., Schäffer, A., Burauel, P., 2009. Different influences of DNA purity indices and quantity on PCR-based DGGE and

493

functional gene microarray in soil microbial community study. Appl. Microbiol. Biot. 82, 983–993. Oren, A., Gurevich, P., Azachi, M., Henis, Y., 1992. Microbial degradation of pollutants at high salt concentrations. Biodegradation 3, 387–398. Oudot, J., Merlin, F.X., Pinvidic, P., 1998. Weathering rates of oil components in a bioremediation experiment in estuarine sediments. Mar. Environ. Res. 45, 113– 125. Pelz, O., Tesar, M., Wittich, R.M., Moore, E.R., Timmis, K.N., Abraham, W.R., 1999. Towards elucidation of microbial community metabolic pathways: unravelling the network of carbon sharing in a pollutant-degrading bacterial consortium by immunocapture and isotopic ratio mass spectrometry. Environ. Microbiol. 1, 167–174. Röling, W.F., Milner, M.G., Jones, D.M., Lee, K., Daniel, F., Swannell, R.J., Head, I.M., 2002. Robust hydrocarbon degradation and dynamics of bacterial communities during nutrient-enhanced oil spill bioremediation. Appl. Environ. Microb. 68, 5537–5548. Sen, R., 2003. The root–microbe–soil interface: new tools for sustainable plant production. New Phytol. 157, 391–394. Sijm, D., Kraaij, R., Belfroid, A., 2000. Bioavailability in soil or sediment: exposure of different organisms and approaches to study it. Environ. Pollut. 108, 113–119. Singh, A., Kuhad, R., Ward, O., 2009. Advances in Applied Bioremediation. Springer Verlag, Berlin. Tamura, K., Dudley, J., Nei, M., Kumar, S., 2007. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol. Biol. Evol. 24, 1596–1599. Tang, M., Chen, H., Huang, J.C., Tian, Z.Q., 2009. AM fungi effects on the growth and physiology of Zea mays seedlings under diesel stress. Soil Biol. Biochem. 41, 936–940. Thavamani, P., Malik, S., Beer, M., Megharaj, M., Naidu, R., 2012. Microbial activity and diversity in long-term mixed contaminated soils with respect to polyaromatic hydrocarbons and heavy metals. J. Environ. Manage. 99, 10–17. Throne-Holst, M., Wentzel, A., Ellingsen, T.E., Kotlar, H.K., Zotchev, S.B., 2007. Identification of novel genes involved in long-chain n-alkane degradation by Acinetobacter sp. strain DSM 17874. Appl. Environ. Microb. 73, 3327–3332. Turnau, K., Haselwandter, K., 2002. Arbuscular mycorrhizal fungi, an essential component of soil microflora in ecosystem restoration. In: Gianinazzi, S., Schüepp, H., Barea, J.M., Haselwandter, K. (Eds.), Mycorrhizal Technology in Agriculture: From Genes to Bioproducts. Birkhauser Verlag AG, Besel, pp. 137– 149. US EPA, 2004. Cleaning up the nation’s waste sites: markets and technology trends. Technology Innovation and Field Services Division, Office of Solid Waste and Emergency Response, EPA 542-R-04-015. U.S. Environmental Protection Agency, Washington, DC. Venosa, A., Zhu, X., 2003. Biodegradation of crude oil contaminating marine shorelines and freshwater wetlands. Spill Sci. Technol. B 8, 163–178. Vinas, M., Sabate, J., Espuny, M.J., Solanas, A.M., 2005. Bacterial community dynamics and polycyclic aromatic hydrocarbon degradation during bioremediation of heavily creosote-contaminated soil. Appl. Environ. Microb. 71, 7008–7018. Wang, Y., Chen, H., Chen, H., Wu, J., 2009. Influences of chronic contamination of oil field exploitation on soil nematode communities at the Yellow River Delta of China. Front. Biol. China 4, 376–383. Wang, X., Feng, J., Zhao, J., 2010. Effects of crude oil residuals on soil chemical properties in oil sites, Momoge Wetland, China. Environ. Monit. Assess. 161, 271–280. Wang, C., Du, J., Gao, X., Duan, Y., Sheng, Y., 2011a. Chemical characterization of naturally weathered oil residues in the sediment from Yellow River Delta, China. Mar. Pollut. Bull. 62, 2469–2475. Wang, Z., Xu, Y., Zhao, J., Li, F., Gao, D., Xing, B., 2011b. Remediation of petroleum contaminated soils through composting and rhizosphere degradation. J. Hazard. Mater. 190, 677–685. Wenzel, W.W., 2009. Rhizosphere processes and management in plant-assisted bioremediation (phytoremediation) of soils. Plant Soil 321, 385–408. Xie, X.M., Liao, M., Yang, J., Chai, J.J., Fang, S., Wang, R.H., 2012. Influence of rootexudates concentration on pyrene degradation and soil microbial characteristics in pyrene contaminated soil. Chemosphere 88, 1190–1195. Xing, D., Cheng, S., Logan, B.E., Regan, J.M., 2010. Isolation of the exoelectrogenic denitrifying bacterium Comamonas denitrificans based on dilution to extinction. Appl. Microbiol. Biot. 85, 1575–1587. Xu, R., Obbard, J.P., 2003. Effect of nutrient amendments on indigenous hydrocarbon biodegradation in oil-contaminated beach sediments. J. Environ. Qual. 32, 1234–1243. Yu, S., Li, S., Tang, Y., Wu, X., 2011. Succession of bacterial community along with the removal of heavy crude oil pollutants by multiple biostimulation treatments in the Yellow River Delta, China. J. Environ. Sci. 23, 1533–1543. Zucchi, M., Angiolini, L., Borin, S., Brusetti, L., Dietrich, N., Gigliotti, C., Barbieri, P., Sorlini, C., Daffonchio, D., 2003. Response of bacterial community during bioremediation of an oil-polluted soil. J. Appl. Microbiol. 94, 248–257.