Contaminant and plant-derived changes in soil chemical and microbiological indicators during fuel oil rhizoremediation with Galega orientalis

Geoderma 160 (2011) 336–346

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Geoderma j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o d e r m a

Contaminant and plant-derived changes in soil chemical and microbiological indicators during fuel oil rhizoremediation with Galega orientalis Anu Mikkonen a,⁎, Elina Kondo b, Kaisa Lappi a, Kaisa Wallenius a,c, Kristina Lindström a, Helinä Hartikainen b, Leena Suominen a a b c

Department of Food and Environmental Sciences, Division of Microbiology, Viikinkaari 9, P.O. Box 56, FIN-00014 University of Helsinki, Finland Department of Food and Environmental Sciences, Division of Environmental Soil Science, Latokartanonkaari 11, P.O. Box 27, FIN-00014 University of Helsinki, Finland Finnish Environment Institute, Hakuninmaantie 6, P.O. Box 140, FIN-00251 Helsinki, Finland

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Article history: Received 27 January 2010 Received in revised form 2 September 2010 Accepted 3 October 2010 Available online 30 October 2010 Keywords: Bioremediation Legume Biodegradation Biomonitoring Hydrocarbon

a b s t r a c t The aim of this work was to evaluate the effects of vegetation and hydrocarbon (HC) contamination on the development of soil chemical and biological status during rhizoremediation of fuel oil contamination with the legume Galega orientalis. Uncontaminated and unvegetated references monitored in parallel with the rhizoremediation treatment enabled the identification of the partial effects. A 21-week greenhouse experiment simulated one growing season with a single 3000 ppm contamination event in the beginning. For a comprehensive view of the restoration process, the following soil parameters were assessed by ten destructive samplings at increasing intervals: plant growth, HC content, pH, C/N ratio, DNA content, culturable oil-degrader numbers and six enzymatic activities. After 21 weeks, 90% and 87% of the initial HC load was depleted in the rhizoremediation treatment and the unvegetated reference, respectively. Contamination retarded the growth of Galega; a majority of the hydrocarbons were degraded by the indigenous soil microbial community by week 6, when the legume seedlings were still very small. In the end of the experiment, when HC contamination had decreased to the clean soil threshold level, Galega biomass in the rhizoremediation treatment reached that of the uncontaminated reference. In contrast, fuel oil stimulated the growth and activity of the soil microbial community and may have masked vegetation-associated changes. The amounts of total and oil-degrader micro-organisms mirrored the HC degradation curve and seem an efficient and ecologically relevant tool to monitor the biodegradation process of light HC contamination. No negative HC effects were seen in soil enzymatic activities either, but aminopeptidases were induced by contamination. Vegetation-associated upward trend was observed for aminopeptidases and phosphomonoesterase. Biological HC degradation raised soil pH. Part of the fuel oil carbon remained in the soil, perhaps assimilated by the degrader microorganisms. The results of this study support the theory of high soil resilience to moderate contamination with light hydrocarbons, demonstrate the power of microbiological methods in monitoring bioremediation, and back up the current legislative clean soil threshold level for hydrocarbon contamination. © 2010 Elsevier B.V. All rights reserved.

1. Introduction European Union Thematic Strategy for Soil Protection lists contamination as one of the main threats that human activities pose to soil (Commission of the European Communities, 2006). Amongst other factors that degrade soil condition, functionality and health, contamination may compromise not only the agricultural productivity of land but also its ability to perform numerous ecosystem services of incalculable ecologic and economic value (see e.g. Kibblewhite et al., 2008).

⁎ Corresponding author. Tel.: + 358 9191 59281; fax: + 358 9191 59322. E-mail address: anu.s.mikkonen@helsinki.fi (A. Mikkonen). 0016-7061/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2010.10.001

Currently replacement of contaminated soil with clean material is the most common method to remediate terrestrial sites. Such mass exchange is undoubtedly the most rapid clean-up strategy, but disturbs the integrity of the soil system, increasing for example erosion risk. In addition, the incorporated mass is usually sand or other relatively poor mineral material, thus likely to reduce the chemical and biological functioning of the soil on the site. Also intensive physico-chemical remediation methods may enable rapid reduction of contamination, monitored as decrease in the concentration of the original, unmodified pollutant molecules. However, the intermediate products may be even more toxic and mobile, and the remediation activities themselves may have adverse effects on the biological functions and habitat properties of the soil. Compared to physical cleaning techniques, biological methods (bioremediation) may provide a more ecological and less expensive option for soil

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remediation. For example hydrocarbons (HCs), likely the most common soil contaminant class globally, can be degraded by a wide taxonomical array of organisms (Van Hamme et al., 2003). Most bioremediation techniques are based on this ability of soil microorganisms to consume HCs as source of energy and carbon, simultaneously cleaning up the soil. Compared to the soil degrading effects of contamination, vegetation is famous for the opposite effect. Production, turnover and accumulation of soil organic matter derived from primary production are among the most important indicators of healthy soil (Kibblewhite et al., 2008). Plants, as well as heterotrophic microbes thriving in the vicinity of the organic input sources, are the key stakeholders of these processes. Plant roots increase the diversity and number of microbes in the rhizosphere soil by providing sources of carbon and nitrogen through root exudates and sloughing cells (Atlas and Bartha, 1998). This rhizosphere effect may be exploited to enhance HC breakdown in soil with an approach called rhizoremediation (Anderson et al., 1993; Radwan et al., 1995; Kuiper et al., 2004). The technique is promising both as a primary (Günther et al., 1996; Chaîneau et al., 2000) and secondary (Fiorenza et al., 2000, Parrish et al., 2005) treatment for HCcontaminated soils. Besides the rhizosphere effect, vegetation has been suggested to enhance degradation of HCs by improving soil water and air flow (Anderson et al., 1993), by increasing the bioavailability of contaminants through their desorption (Miya and Firestone, 2001), and by transporting the microbes to locations otherwise inaccessible, such as inside tight soil aggregates or into deeper soil layers (Kuiper et al., 2004). Structural homology between oil compounds and plantderived molecules may also play a role in rhizoremediation through stimulation of appropriate enzymatic pathways and enhance especially degradation of mono- and polycyclic aromatic hydrocarbons (Günther et al., 1996; Reilley et al., 1996; Kanaly and Bartha, 1999; Miya and Firestone, 2001; Renz et al., 2005; Spriggs et al., 2005). Leguminous plants have been reported to be effective in rhizoremediation (Günther et al., 1996; Palmroth et al., 2002; Rutherford et al., 2005), presumably owing to their ability to increase the nitrogen concentration in soils with high C:N ratio (Alexander, 1999). In our previous studies the deep-rooted legume Galega orientalis (fodder galega) proved to be promising for revegetation of contaminated sites. This robust perennial forage plant is suitable for cultivation in temperate northern climate and has potential also for use in landscaping and as a honey plant. Suominen et al. (2000) observed growth, nodulation and nitrogen fixation of Galega in diesel oil-contaminated soil (3000 ppm). Lindström et al. (2003) isolated several oil hydrocarbon degrading bacterial species from the Galega rhizosphere. In the study of Kaksonen et al. (2006), G. orientalis was found to increase the numbers and diversity of cultivable bacteria in oil-contaminated soil. However, these earlier studies quantified neither the effect of Galega on the HC removal nor the plant-derived changes of other soil parameters during the bioremediation process. In this study the biodegradation process in the Galega rhizosphere was monitored in a 21-week greenhouse experiment using a soil artificially contaminated with fuel oil. The decline in HC concentration and the concurrent changes in soil parameters were studied with a diverse set of quantitative chemical and microbiological monitoring methods. In addition to the rhizoremediation treatment, unvegetated, uncontaminated and sterile reference treatments were analysed in parallel to unveil the proportional effects of the contamination, vegetation and indigenous soil microflora on soil conditions and the restoration process. Our objectives were: i) to monitor the effect of Galega on the biodegradation of fuel oil hydrocarbons, ii) to study the simultaneous changes in various chemical and biological soil parameters caused by contamination and vegetation, and iii) to evaluate the power of the utilised monitoring methods for assessing the progress of the remediation process.

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2. Materials and methods 2.1. Experimental design 2.1.1. Soil material The experimental growth medium was a mixture of a loam soil low in organic matter (Partala, Juva, Finland) amended with freshly sampled soil (Viikki Experimental Farm, Helsinki, Finland, collected at 0–30 cm depth from turf). Pure sand (0.5–1.2 mm, Optiroc, Helsinki, Finland) was added to improve aeration and water infiltration properties of the loam. The mixture was prepared in proportions 73:4:23 (freshwt). Some key chemical and physical properties of the soil mixture are presented in Table 1. 2.1.2. Spiking and preparation of the experimental pots The soil mixture was prepared and fertilised with nitrogen-free fertilizer (NPK 0–5–3, Mg 6, Puutarhan ystävä, Kemira, Finland) individually for 120 2-liter pots. To contaminate the soil, 3 g (kg drywt soil)−1 of fuel oil was first mixed with the sand and then with the rest of the soil material. The fuel oil was typical European type fuel oil with a boiling point range of 191.9–362.2 °C, consisting of 30.5 % of aromatics (analysis report by Neste Oil Inc. laboratories). Pots were underdrained with a 4 cm layer of expanded clay pellets (Kekkilä Group, Parkano, Finland). A 1-cm layer of clean soil mixture was added on the top of each pot to alleviate the acute toxicity of HCs to germinating seeds. For the rhizoremediation treatment (ContVeg), 20 seeds of G. orientalis cultivar Gale (Naturcom, Ruukki, Finland) were sown in each contaminated pot four days after oil spiking, and inoculated with the symbiotic nitrogen fixing nodule bacterium Rhizobium galegae strain HAMBI 540 by spraying approximately 1 ml of yeast extractmannitol culture broth (grown for 2 days and then diluted 1:1 in water) on the soil surface. In addition, three reference treatments were prepared in parallel: i) uncontaminated vegetated soil (Uncont) ii) contaminated unvegetated soil (Unveg) and iii) sterilised soil (Ster). Three replicate pots per treatment for ten time points of destructive sampling (0, 1, 2, 3, 4, 6, 8, 12, 16 and 21 weeks after sowing) were prepared, except for the sterilised reference, for which only pots for six sampling time points (0, 1, 3, 6, 12 and 21 weeks from sowing) were reserved. Extra ContVeg and Uncont pots were prepared for testing nitrogenase activity in root nodules. After sowing, all the pots were covered with gauze for the first week. Microbial community in the sterilised reference was killed with silver nitrate water solution (AgNO3, 3 g [kg freshwt soil]−1) added to the sand–fuel oil mixture (Margesin and Schinner, 1997). Sterilisation was repeated every 5 weeks by adding the same amount of AgNO3 solution on the soil surface. Sterility of the soil was tested in weeks 1, 6 and 12 by inoculating approximately 0.1 g of fresh soil sample into tryptone–glucose–yeast extract (TGY) cultivation broth. Turbidity of the broth was visually monitored after 1 week of incubation at room temperature on a horizontal shaker. Any increase in turbidity was regarded as a sign of living heterotrophic bacteria in the sterilised soil. Table 1 Characteristics of the experimental soil material. Sand (%) Silt (%) Clay (%) Texture (USDA) Total C (%)a Total N (‰)a pHb CECpot (cmol(+) kg−1)c CECef (cmol(+) kg−1)d a b c d

47 47 6 Sandy loam 1.8 0.7 5.4 7.3 6.4

LECO CNS 1000. In 0.01 M CaCl2 at soil:solution ratio 1:2.5. As a sum of 1 M CH4COONH4 (pH 7.0) extractable Na+, K+, Ca2+, Mg2+ and acidity. As a sum 1 M KCl extractable Na+, K+, Ca2+, Mg2+ and acidity.

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2.1.3. Greenhouse conditions and vegetation management The pots were placed in three adjacent blocks on the greenhouse table, each containing one replicate of each treatment for each sampling time point. Within the blocks the position of each pot was randomized weekly. Conditions in the greenhouse were as follows: relative humidity 70% (weeks 0–4) or 50% (weeks 5–21), maximum temperature 25 °C, maximum light level 400 W m−2. A 12-h light period and 12-h dark period with minimum temperatures of 20 °C and 15 °C, respectively, was applied. Moisture level in the soil was maintained at approximately 30% of the water holding capacity and controlled gravimetrically. The pots were provided with automatic irrigation sticks (daily water dosage per one irrigation stick and the number of sticks per pot were adjusted according to the observed evaporation, which depended on greenhouse temperature and Galega biomass) and the watering was completed manually when needed. Because of low germination rate, sowing was repeated with five seeds per each vegetated pot on week 1. The vegetation was thinned to five plants per pot 5 weeks after sowing and the remaining shoots were mowed to 4 cm height on week 15. Weeds were removed weekly. 2.1.4. Sampling and plant and soil analyses At sampling the entire contents of the pots were homogenised by sieving (4 mm) and manual mixing, and sub-samples stored at −70 °C (for DNA extraction) or −20 °C (other analyses). pH of the fresh soil was measured in 0.01 M CaCl2 suspension (1:2.5 soil:solution ratio). C and N were measured from airdried and ground soil with a CN-analyser (LECO CNS 1000). Shoot biomass was collected and dried for 24 h at 70 °C, weighed and ground (Rechts mechanical grinder). On weeks 12, 16 and 21, the N-concentration of the shoots was determined with a CN-analyser (LECO CNS 1000). Nitrogenase activity in the nodules of two plants from both vegetated treatments was tested after the end of the experiment with the acetylene reduction method (Lindström, 1984). 2.2. Hydrocarbon analysis Hydrocarbons (HCs) were quantified according to the ISO 16703:2004 standard procedure. According to this method, the soil total petroleum hydrocarbon (TPH) concentration is defined as the sum of compounds forming the total area of peaks eluting between n-decane (C10) and ntetracontane (C40) in a gas chromatography–flame ionisation detector system (GC–FID). HCs were extracted from soil in duplicate in a horizontal shaker with a 1:2 mixture of HPLC-grade heptane and acetone. Acetone was removed with deionised water in a separatory funnel and polar substances removed from the extract with Florisil (60–100 mesh, activated maximum 24 h before use). The extracts were stored at −20 °C prior to measurement and analysed with a Hewlett-Packard 5890 Series II GC equipped with an automatic sampler HP-7673 (Karlsruhe, Germany), an HP-5 column (30 m, I.D. 0.32 mm, film thickness 0.25 μm) and an on-column injection system (injection volume 0.5 μl). The detector temperature was set at 300 °C and the carrier gas He (99.996 %, AGA, Espoo, Finland) was used with the constant flow rate 1.4 ml min−1 (110 kPa, 200 °C). The temperature programme was: 2 min at 60 °C, 10 °C min−1 to 300 °C, 60 min at 300 °C. The suitability of the gas chromatographic system for the resolution of n-alkanes and for the detector response was verified according to the ISO-procedure. Quantification was done with an external standard method using a mixture of diesel purchased from a local gas station and additive-free lubricating oil (HELCOM Intercomparison Lubricating Oil). Chromatograms were integrated manually and the soil TPH concentration determined using a linear regression approach. 2.3. Microbiological and biochemical assays 2.3.1. Soil DNA extraction and quantification Soil microbial DNA was extracted with FastDNA SPIN Kit for Soil (Qbiogene, USA). Extraction was performed with duplicate 0.50 g soil

samples according to the manufacturer's instructions except for the following steps: the centrifugation time of the lysing matrix tubes was extended to 5 min and the final DNA elution volume was increased to 120 μl. The extracted DNA was quantified with PicoGreen dsDNA Quantitation Reagent Kit (Molecular Probes, USA) on a 96-well plate according to the manufacturer's instructions. This fluorometric quantification method with PicoGreen dye that specifically binds to double-stranded DNA is better suited for measuring often impure soilderived DNA extracts than the conventional spectrophotometric assay (Sandaa et al., 1998). 2.3.2. MPN determination of fuel oil degrading bacteria Most Probable Number (MPN) enumeration of fuel oil degrading bacteria was modified from the methods of Wrenn and Venosa (1996) and Johnsen et al. (2002). Analysis was carried out with fresh soil from two replicate pots (blocks 1 and 2) immediately after sampling and sieving. Cells were released from 5 g soil samples by shaking in 45 ml of phosphate buffer (vertical shaker 400 rpm 10 min). Slurries were allowed to settle for 5 min (week 0, 3, 6, and 8 samples) or 20 min (week 12, 16, and 21 samples), after which a tenfold dilution series of the supernatant was prepared in Bushnell Haas mineral medium (Difco™, USA). Each dilution was added in two parallel 96-well plates (Nunc™, Denmark): one containing 2 μl of the fuel oil substrate per well and the other serving as a negative control with no added carbon source. Eight sequential dilutions, with 12 parallel wells for each, were prepared on both plates. The microtiter plates were incubated at room temperature (21– 24 °C) in the dark for 3 weeks. Growth was detected as turbidity measured with an absorbance reader (Labsystems iEMS Reader MF) at 620 nm (week 0 and 3 samples) or 450 nm (week 6, 8, 12, 16, and 21 samples). Based on the background absorbance measured from the carbon source-free negative control wells, the threshold absorbance for detection of positive wells in the sample plates was set to 0.1. The small modifications to the MPN protocol (were adopted during the experiment to ease the automated turbidity detection. Change of the absorbance reading wavelength emphasised the difference in absorbance between positive and negative wells on sample plates. Increase of the settling time eliminated sporadic false positive wells on the negative control plates, caused likely by carbon substrates derived from soil. When both old and modified conditions were tested in parallel, the same number of positive wells was observed on the sample plates. Thus, the changes were observed not to affect the final calculated MPN counts. 2.3.3. Measurement of enzymatic activities of soils Activities of six enzymes in the soil, phosphomonoesterase, phosphodiesterase, alanine aminopeptidase, leucine aminopeptidase, α-glucosidase and cellobiosidase, were measured with ZymProfiler® test, developed at the Finnish Environment Institute, using the procedure described in ISO/TS 22939:2010. In this microtiter plate application, freeze-dried artificial fluorogenic substrates labelled with either 4-methylumbelliferyl (MUF) or aminomethylcoumarin (AMC) were incubated with homogenised soil suspension, and the fluorescence released upon enzymatic degradation quantified with a multilabel reader. Quenching and autofluorescence caused by soil particles were quantified and subtracted for each sample separately using standard plates with defined amounts of the fluorophores. All assays were conducted on duplicate soil samples. Briefly, 4.0 g samples of frozen soil were homogenised with a Bamix hand mixer in 200 ml of Na-acetate buffer pH 5.5 for 3 min. Twofold dilutions of the slurries were prepared in the same buffer and added to four replicate microtiter sample plate wells to give a final soil suspension ratio of 0.01 and a substrate concentration of 500 μM. Soil suspensions were similarly added to MUF standard plates and AMC standard plates with three replicate wells per soil suspension per standard concentration. Plates were incubated on a vertical shaker at 700 rpm at 30 °C.

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Fluorescence was measured with Wallac 1420 Victor2 (PerkinElmer, Inc., USA) using excitation light of 355 nm and emission detection at 460 nm. Measurements were made immediately after adding the soil dilutions to the plates and after 3.0-h incubation. The enzymatic activities were calculated based on the sample-specific standard series as micromole of fluorophore (MUF or AMC) released during the 3-h incubation time per g drywt soil.

nitrogen fixation and positive results in the qualitative acetylene reduction test verified the nitrogenase activity in the nodules in both soils. The shoots grown in ContVeg soil had a higher N-concentration compared with the shoots collected from the Uncont soil until the clipping (2.9 ± 0.2 and 2.2 ± 0.1, respectively, at week 12). At weeks 16 and 21 the plant N-levels in both treatments were equal (3.1 ± 0.6 and 1.6 ± 0.1 in ContVeg vs. 3.3 ± 0.3 and 1.6 ± 0.3 in Uncont).

2.4. Statistical tests

3.2. Quantitative and qualitative changes in soil hydrocarbon content

Statistical analyses were carried out with SPSS Windows 13.0 and Microsoft Office Excel 2003. Average values (+ standard deviation) were calculated for the three biological replicates (separate pots located in the different blocks). Changes in the HC concentration, the amount of DNA, C/N ratio and the enzyme activities were studied with regression analysis (confidence level 95%), the first two with logarithmic transformation of the time variable. One-way ANOVA (alpha 0.05) was used to identify significant differences between ContVeg soil and the reference treatments. Total correlations between the results from different analyses were calculated with Pearson correlation for each treatment separately.

To examine the contribution of abiotic and biotic removal of HCs and to follow up HC breakdown in the presence and absence of Galega, the soil TPH concentration was determined in the rhizoremediation treatment (ContVeg) as well as in the unvegetated (Unveg) and sterilised (Ster) references at 6 sampling time points (0, 1, 3, 6, 12 and 21 weeks after sowing; Fig. 2A). The presence of biogenic HC was controlled with a TPH analysis of the Uncont soil at weeks 0 and 21, and was observed to be less than the detection limit of the assay (data not shown). In the Ster soil, 70% of the added oil could be recovered with the TPH analysis in the first sampling, week 0. The unrecovered portion was likely attributable to evaporation of the lightest HC during the 4-day stabilisation period before sowing and to irreversible sorption of some HC compounds to soil organic matter. The HC concentration remained stable for the first 6 weeks, after which it started to slightly decrease, possibly due to low extractability of the aged HCs. On week 21 the HC concentration in the Ster soil had decreased from the initial value 2.10 ± 0.08 g (kg drywt soil)−1 to 1.80 ± 0.10 g (kg drywt soil)−1. No turbidity was observed in the control cultivation in TGY broth at any time point, confirming the sterility of the soil. It was concluded that after the initial evaporation abiotic HC loss was insignificant, and that the higher HC removal in the non-sterilised soils was mainly attributable to biodegradation. The HC concentration in ContVeg and Unveg soils formed a typical depletion curve that indicated exponential first-rate degradation kinetics. Decrease in the TPH concentration of both non-sterilised treatments was very similar throughout the whole 21-week experiment (Fig. 2A). At the end of the experiment, the lowest concentration, 0.30 ± 0.04 g (kg drywt soil)−1, was achieved in the ContVeg soil, the final concentration in the Unveg soil being 0.40 ± 0.04 g (kg drywt soil)−1 (ANOVA p-value 0.023). Despite the slightly lower HC concentration in the rhizoremediation treatment on week 21, regression analysis for the entire experiment duration showed no statistically significant difference in the degradation trends between the two treatments. In all treatments the variation between the replicates was very small, presumably due to homogenous mixing of fuel oil using sand as a carrier material, and the destructive sampling strategy. Qualitative changes in the HC composition during the biological degradation were observed in the GC–FID chromatograms. In Ster soil, only the peaks of the shortest alkanes (bC14) decreased during the experiment (data not shown), reflecting the tendency of the lightest HC compounds to evaporate. In the ContVeg and Unveg soils, depletion of both the resolved alkane peak series as well as the unresolved complex mixture (UCM) hump occurred during the 21week biological HC degradation (Fig. 3). The differences in the degradation rates of straight chain n-alkanes and respective branched iso-alkanes were monitored by quantifying the C17/pristane and C18/ phytane ratios. Both ratios behaved identically in ContVeg and Unveg soils: they decreased steeply until week 3 after which they started to gradually increase (data not shown). At the end of the experiment, a similar series of iso-alkanes was detectable in the chromatograms of both treatments. Visually, the UCM hump appeared slightly but consistently smaller in the GC–FID chromatograms of ContVeg soil compared to the Unveg soil (Fig. 3C and D), but the size of the UCM could not be reliably quantified independent of the alkane peaks.

3. Results 3.1. Plant growth and nitrogen fixation The ability of G. orientalis to grow and fix nitrogen in the presence of fuel oil was evaluated by comparing germination, biomass production and N-concentration of the shoots in the rhizoremediation treatment (ContVeg) and the uncontaminated reference soil (Uncont). In all vegetated pots, the seeds germinated 1 week after sowing. The average germination rate, calculated 5 weeks after sowing, was 31% in both ContVeg and Uncont soils. HC contamination retarded the growth of Galega; by week 12 the plant biomass in Uncont soil was twice as high as in the ContVeg soil (Fig. 1). On week 15 especially the plants in Uncont soil showed retarded growth and withering symptoms, due to which all plants were cut down to 4 cm height. After this clipping, the plant biomass in ContVeg soil reached that of the Uncont soil at week 21. Except for the slower biomass production, the plants growing in the contaminated soil showed no additional signs of stress. Nodulation in the roots was observed in both ContVeg and Uncont soil from week 4 onwards. The purple colour of the nodules indicated active

6

ContVeg Uncont

Mass g dw

4

2

0 0

5

10

15

20

Week Fig. 1. Development of Galega shoot biomass ContVeg and Uncont soil. Standard deviation between the three replicate pots is indicated as error bars. Shoots in both treatments were cut down to 4 cm height on week 15 to alleviate symptoms of withering.

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A

3.4. Changes in soil C/N ratio

2.4

Soil carbon and nitrogen concentrations were measured at each sampling week to monitor changes in soil C/N ratio caused by contamination and vegetation (Table 2). Regression analysis revealed a slightly but significantly decreasing trend in the contaminated soils, probably due to mineralisation of the HC, whereas in Uncont soil the ratio was stable. Surprisingly, the C/N ratio remained significantly higher in ContVeg than in Uncont soil till week 21. No difference caused by Galega could be seen between ContVeg and Unveg soils.

TPH g (kg soil dw)-1

2.0 1.6 1.2 0.8 0.4

3.5. Development of soil microbial biomass

0.0

7

Soil DNA concentration in the three non-sterilised treatments was monitored to follow changes in total soil microbial biomass (Fig. 2C). A rapid increase in microbial biomass was observed at the beginning of the experiment in both contaminated treatments. Already on week 1 the DNA concentration in ContVeg and Unveg soils was significantly higher than in Uncont soil (p b 0.001). The rise continued more moderately after the first 3 weeks and levelled off after week 12. The changes in both contaminated treatments followed the same pattern and were not statistically different throughout the experiment. A strong negative correlation was found between TPH concentration and DNA concentration in both ContVeg and Unveg soil (Pearson correlation −0.9 and −0.8, respectively, with p b 0.001). In Uncont soil the increase in DNA concentration was slow during the first weeks and accelerated only after week 12 (Fig. 2C), when the roots started to cover a significant proportion of the soil volume in the pot. However, at the end of the experiment the microbial biomass was similar in all three treatments.

6

3.6. Changes in the numbers of fuel oil degraders

0

5

10

15

20

15

20

B 6.4

pH

6.2

6.0

ContVeg Unveg Uncont Ster

5.8

5.6 0

DNA µg (g soil dw)-1

C

5

10

5 4 3 2 1 0 0

5

10

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Week Fig. 2. Changes in soil TPH concentration (A), pH (B), and microbial biomass detected as yield of directly extracted DNA (C) in ContVeg, Unveg, Uncont and Ster soil. Symbol explanations in figure B apply to figures A and C too. Standard deviation between three replicate pots is indicated as error bars.

3.3. Changes in soil pH Soil pH increased slightly in all four treatments during the experiment (Fig. 2B). In Ster soil, pH showed a modest increase until week 12, after which it began to decrease. The highest increases, up to 0.6 units, were observed in the non-sterilised contaminated soils during the most active phase of the HC degradation. In Uncont soil, pH increased more slowly and only 0.3 units. An acidifying effect of the Galega rhizosphere was observed, as in both vegetated treatments pH slightly decreased in the last weeks of the experiment. On week 21 pH in ContVeg soil was 0.2 units (p 0.008) and in Uncont soil 0.3 units (p 0.01) lower than in the unvegetated reference.

A turbidity-based MPN application was developed to quantify changes in the numbers of aerobic fuel oil degrading bacteria induced by the fuel oil contamination and the rhizosphere effect. The cultivable oil-degrader community responded quickly to the HC addition; the numbers of oil degraders were two orders of magnitude higher in the ContVeg and Unveg soils compared to the Uncont soil already on sampling week 0 (Fig. 4). Increase in the MPN counts continued till week 6, after which they began to slowly decrease. The degrader numbers in ContVeg and Unveg soils were similar throughout the entire experiment. In Uncont soil the number of oil degraders grew slightly during the experiment. However, degrader numbers remained 1–2 orders of magnitude lower in Uncont soil than in the contaminated treatments throughout the whole experiment. 3.7. Changes in the enzymatic activities of soils The potential activities of six enzymes were measured in the three non-sterilised soils to monitor the degradation capacity related to the cycling of N, P and C (Table 3). Contamination seemed to stimulate leucine aminopeptidase; its activity was higher in ContVeg than in Unveg soil throughout the experiment. Contrastingly, the activities of alanine aminopeptidases were not significantly different between the rhizoremediation treatment and the references before the end of the experiment. The activities of the aminopeptidases showed a continuous increase towards the end of the experiment in both vegetated treatments but not in Unveg soil. Notably, at the end of the 21-week experiment, activities of both leucine and alanine aminopeptidases were significantly higher in ContVeg soil than in either of the reference treatments. Fuel oil stimulated initially also the production of phosphatases, as the activities of these enzymes were higher in the contaminated treatments than in Uncont soil on week 1. In the ContVeg and Uncont soils the activity of phosphomonoesterase rose towards the last weeks

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Fig. 3. Qualitative and quantitative changes in the hydrocarbon content in ContVeg (left column) and Unveg (right column) soil illustrated with representative TPH GC–FID grams from weeks 0 (upper row) and 21 (lower row). C10 = decane, C40 = tetracontane, UCM = unresolved complex mixture.

of the greenhouse experiment, whereas no clear trend was observed in the Unveg soil. In contrast to the other enzyme activities which generally increased towards the end of the experiment, the activity of phosphodiesterase peaked on weeks 3–6. On week 21, the activity had decreased and was close to the initial level in all treatments. Neither contamination nor vegetation affected the activities of α-glucosidase or cellobiosidase in soil. The activities of these enzymes were similar in ContVeg soil and both references throughout the experiment, and showed a slightly but significantly increasing trend in all three treatments. 3.8. Correlations between the different analyses Pearson correlations between various experimental parameters and the results from the different chemical and biological analyses were calculated to find new connections or reveal possible redundancies between the monitoring methods utilised in this experiment. Correlations were calculated separately for each non-sterilised treatment, because the underlying causative mechanisms were hypothesised to differ depending on contamination and/or vegetation. Only non-sterilised treatments and those analyses, for which all data was available from six sampling weeks, were included. Parameters that showed no significant differences between the treatments (i.e. α-glucosidase and cellobiosidase activities) were left out of the correlation calculations. All the observed significant correlations (p b 0.01) had a value of the correlation coefficient N0.6,

and the sign of the correlation for a certain parameter pair was always the same regardless of the treatment. The treatments where correlations were found between the pairs of parameters, together with the sign of the correlation, are presented in Table 4. The observed correlations differed in ContVeg, Unveg and Uncont soils, which confirmed that the underlying causes behind parameter responses indeed varied depending on the treatment. The highest number of correlating parameter pairs was found for the ContVeg soil (black squares in Table 2), suggesting that both fuel oil and Galega rhizosphere affected the dynamics of soil chemical and biological parameters. Sampling week and DNA yield correlated with the highest number of other parameters and showed also a strong positive inter-correlation in all treatments. Apparently most analysed factors changed along the course of the experiment and were attributable to the soil microbial biomass increase. For the ContVeg soil, TPH concentration was the character that best explained variation in all other variables, having strong negative correlation with 5 of the 8 other parameters. Even though most analyses separately revealed little difference between the rhizoremediation treatment and the unvegetated reference, correlations between the parameter pairs differed for these two soils. A much higher number of correlations were observed for the ContVeg soil compared to the Unveg soil, especially in the biological analyses (microbial biomass and the activities of the aminopeptidases and phosphatases). For these parameters more correlations were found in the vegetated soils than in the unvegetated

Table 2 Soil C/N ratio in ContVeg, Unveg and Uncont soils at different sampling weeks as the average value ± standard deviation between three replicate pots. Shading indicates no significant difference (pN 0.05) between ContVeg and the reference treatment at the particular week.

Treatment

0

1

2

3

4

6

8

12

16

21

Unveg ContVeg Uncont

16.0 ± 0.4 20.3 ± 5.7 14.7 ± 0.3

16.0 ± 0.0 17.2 ± 0.2 14.0 ± 0.5

15.7 ± 0.8 16.6 ± 0.3 14.3 ± 0.2

15.9 ± 0.5 16.7 ± 0.2 14.3 ± 0.7

16.0 ± 0.1 16.5 ± 0.4 14.2 ± 0.2

15.7 ± 0.1 17.9 ± 2.0 14.4 ± 0.4

15.7 ± 0.6 16.0 ± 0.4 14.5 ± 0.2

15.8 ± 0.4 16.1 ± 0.3 14.8 ± 0.6

15.2 ± 0.4 15.3 ± 0.2 14.5 ± 0.4

15.4 ± 0.5 15.5 ± 0.2 14.4 ± 0.3

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A. Mikkonen et al. / Geoderma 160 (2011) 336–346 9

10

ContVeg b1 ContVeg b2 Unveg b1 Unveg b2 Uncont b1 Uncont b2

8

MPN (g soil dw)-1

10

7

10

6

10

4. Discussion

5

10

4

10 0 10

reference. This finding, along with the observed increasing trend in the three of six enzyme activities only in the vegetated treatments, suggests that these biotic soil properties may have been either stimulated by or partly attributable to the vegetation. Also soil dry weight and block (pot location on the greenhouse table) were included as parameters in the correlation analysis. These parameters had no significant correlation with other data in any treatment, confirming that the randomisation of pot table location and the normalisation of soil moisture were successful.

0

03

06

08

12

21

16

Week Fig. 4. MPN counts of culturable fuel oil degraders in ContVeg, Unveg and Uncont soil. b1 and b2 stand for the two replicate pots (blocks 1 and 2). Error bars show the 95% confidence limits for the MPN enumeration.

The aim of this work was to evaluate the combined and individual contributions of vegetation and hydrocarbon (HC) contamination on the development of selected chemical and microbiological soil indicators during rhizoremediation with G. orientalis. We hypothesized the legume could alleviate the presumed adverse effects of fuel oil contamination on soil properties, and possibly also promote the biological degradation of HC via stimulation of degrader microbes or alteration of nutrient dynamics.

Table 3 Changes in the activities of six enzymes in ContVeg, Unveg and Uncont soils at different sampling weeks as the average value ± standard deviation between three replicate pots. Shading indicates no significant difference (p N 0.05) between ContVeg and the reference treatment at the particular week. The trend of the change is indicated, if linear regression was significant (p b 0.05).

Enzyme

Treatment

0

1

3

6

12

21

A-APa

Unveg ContVeg Uncont Unveg ContVeg Uncont Unveg ContVeg Uncont Unveg ContVeg Uncont Unveg ContVeg Uncont Unveg ContVeg Uncont

0.64 ± 0.05 0.63 ± 0.05 0.59 ± 0.05 0.39 ± 0.02 0.38 ± 0.02 0.35 ± 0.02 2.17 ± 0.34 2.11 ± 0.12 1.90 ± 0.14 0.63 ± 0.02 0.61 ± 0.02 0.54 ± 0.01 0.13 ± 0.00 0.13 ± 0.01 0.12 ± 0.01 0.14 ± 0.03 0.13 ± 0.01 0.12 ± 0.03

0.71 ± 0.03 0.65 ± 0.05 0.59 ± 0.08 0.42 ± 0.02 0.41 ± 0.02 0.37 ± 0.02 2.40 ± 0.16 2.50 ± 0.14 1.72 ± 0.15 0.94 ± 0.06 0.83 ± 0.03 0.58 ± 0.04 0.14 ± 0.01 0.14 ± 0.01 0.13 ± 0.01 0.14 ± 0.02 0.12 ± 0.01 0.12 ± 0.01

0.74 ± 0.04 0.69 ± 0.08 0.54 ± 0.08 0.42 ± 0.04 0.41 ± 0.03 0.33 ± 0.03 2.46 ± 0.30 2.39 ± 0.28 1.95 ± 0.32 1.38 ± 0.19 1.20 ± 0.30 0.98 ± 0.37 0.12 ± 0.01 0.12 ± 0.01 0.12 ± 0.00 0.13 ± 0.01 0.12 ± 0.00 0.12 ± 0.02

0.72 ± 0.06 0.70 ± 0.07 0.62 ± 0.05 0.42 ± 0.06 0.46 ± 0.08 0.36 ± 0.04 2.37 ± 0.10 2.97 ± 0.34 2.40 ± 0.04 1.19 ± 0.14 1.84 ± 0.48 1.01 ± 0.23 0.13 ± 0.00 0.14 ± 0.01 0.13 ± 0.01 0.14 ± 0.03 0.13 ± 0.00 0.15 ± 0.01

0.84 ± 0.09 0.69 ± 0.11 0.66 ± 0.02 0.44 ± 0.08 0.45 ± 0.04 0.40 ± 0.05 2.73 ± 0.23 2.43 ± 0.59 3.08 ± 0.53 1.00 ± 0.20 0.93 ± 0.16 0.72 ± 0.10 0.13 ± 0.01 0.14 ± 0.00 0.14 ± 0.01 0.23 ± 0.12 0.14 ± 0.01 0.14 ± 0.01

0.67 ± 0.12 0.92 ± 0.10 0.68 ± 0.07 0.44 ± 0.05 0.51 ± 0.03 0.46 ± 0.03 2.26 ± 0.42 2.91 ± 0.27 3.05 ± 0.72 0.73 ± 0.10 0.84 ± 0.04 0.64 ± 0.05 0.16 ± 0.01 0.17 ± 0.03 0.15 ± 0.01 0.32 ± 0.18 0.22 ± 0.05 0.16 ± 0.04

L-AP

b

PMEc

d

PDE

α-glucosidase

Cellobiosidase

Trend + + + + + +

+ + + + + +

a

Alanine aminopeptidase. Leucine aminopeptidase. Phosphomonoesterase. d Phosphodiesterase. b c

Table 4 Treatments with significant correlations between the different monitored parameters (n = 18). Symbols of the treatments are ContVeg (■), Unveg (□), and Uncont (○) soil. Only those treatments, in which Pearson correlation between the variable pair was ≥ 0.6 with p-value ≤ 0.01, are presented. The sign of the correlation (+ or −) was consistently the same for a variable pair regardless of the treatment. Variable PDEa PME A-AP L-AP DNA pH N C TPH a

Week (+) ○ (+) ■ (+) ■ ○ (+) ■□○ (+) ■□

TPH

C

(+) ■ ○ (+) ■□ (+) ■○

(–) ■□

pH (+) ■ (+) ○

(−) ■ (−) ■ (−) ■ (−) ■□ (−) ■□

Abbreviations of the enzyme names are as in Table 3.

N

DNA

L-AP

A-AP

(+) ○ (+) ○ (+) ■○

(+) ○ (+) ○

(+) ■□○

PME

A. Mikkonen et al. / Geoderma 160 (2011) 336–346

4.1. The effect of Galega on hydrocarbon degradation The feasibility of any rhizoremediation treatment relies on the ability of the selected plant to grow in the contaminated soil. The germination of Galega was unaffected by fuel oil, whereas the production of shoot biomass was slower in the contaminated soil, reaching uncontaminated reference only at week 21. The disappearance of the easily degradable straight chain alkanes, which are relatively more toxic to plants than aromatics (Chaîneau et al., 1997), might have accelerated growth in the contaminated soil during the late weeks. Nitrogen fixing nodules were observed in both soils in 4week old Galega seedlings, and acetylene reduction test confirmed the activity of the rhizobial nitrogenase enzyme also under the contamination stress. At the end of the experiment both the dry weight and the nitrogen concentration of the Galega shoots were equal in both vegetated soils. The TPH standard analysis recovers the unaltered fraction of the added HCs. Changes in the TPH curve of the sterilised reference where small compared to the non-sterilised treatments, which confirmed that fuel oil degradation was mostly biotic by nature. HC depletion in the rhizoremediation treatment and the unvegetated reference formed a typical hockey-stick shaped degradation curve: the most rapid reduction occurred by week 3 after which the degradation rate began to level out. In 21 weeks the TPH level in the rhizoremediation treatment reached the threshold concentration (C10– C40 300 mg kg−1) requiring no further assessment of risk and remediation needs according to the Finnish environmental legislation, whereas 33% higher concentration remained in the unvegetated reference treatment. This difference could be also visually observed in the GC–FID chromatograms, where the UCM hump, comprised of the more persistent unresolved HC molecules (Chaîneau et al., 1996), appeared smaller in the rhizoremediation treatment compared to the unvegetated reference. Although the difference in the end TPH concentrations was statistically significant, the relevance of such a minor positive effect by Galega (90% vs. 87% of the initial hydrocarbon load depleted in 21 weeks) may be argued. In the favourable greenhouse conditions fuel oil was rapidly degraded by the indigenous soil microorganisms without any need for stimulation by legume rhizosphere. With the light petroleum hydrocarbon product and moderate contamination level used in this experiment, a majority of the hydrocarbons in the rhizoremediation treatment were degraded before the root system of the seedlings even reached most of the soil volume. Due to the sampling strategy where the entire pot content was sieved and mixed, the possible effects in the proximity of the roots may have been masked by the much greater volume of the non-rhizosphere soil. Putative benefits of Galega could be evaluated with a longer experiment with more persistent hydrocarbons or heavier contamination level. The assessment of the true potential of the plant rhizosphere effect would also require a field experiment where the ability of the vegetation to improve the conditions in situ could be assessed (Chaîneau et al., 2000; Gerhardt et al., 2009). During this study we developed an MPN application for the enumeration of culturable oil-degrader micro-organisms. In contrast with the traditional visual MPN methods, it utilises an automated turbidity measurement as a rapid and reproducible means to detect growth. The response of the culturable HC-degrader community to fuel oil addition was immediate: 100-fold degrader numbers were recovered from the contaminated soils compared to the uncontaminated reference already on week 0, four days after the fuel oil addition. The increase in oil-degrader numbers in both contaminated treatments continued until week 6, concomitantly with the most active phase of the oil degradation, after which the degraders started to slowly decrease. These results are consistent with those of Bachoon et al. (2001), who saw a reversible increase in HC-degrader counts in marine sediment amended with crude oil. In the uncontaminated reference degrader-MPN continued to slowly

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increase throughout the experiment, but remained approximately an order of magnitude lower than in the contaminated soils at week 21. This increase, concurrent with the growth of Galega, was possibly an indication of rhizosphere effect, raising the numbers of heterotrophic bacteria. However, in the contaminated soils no Galega associated increase in the number of the cultivable oil degrading bacteria was observed. 4.2. Contamination and vegetation derived changes in soil indicators In this experiment we aimed to quantify the individual effects of contamination and vegetation on the dynamics of soil properties during Galega rhizoremediation treatment. The task of extracting the partial effects was non-trivial, since both factors, once combined, may affect one another. Identifying the vegetation effect was easier, since the reduction of HC content was nearly identical in the rhizoremediation treatment and the unvegetated reference. Quantifying the effect of the contamination was more demanding, since fuel oil significantly retarded the growth of Galega and thus delayed any plant associated changes in the contaminated soil compared to the uncontaminated reference. Plant biomasses in the rhizoremediation treatment and the uncontaminated reference were identical only at the last sampling week. Therefore the specific effect of fuel oil on most soil indicators could thus be reliably quantified only once the rhizoremediated soil could already be considered practically clean. To our knowledge, very few studies have monitored pH changes during rhizoremediation treatment. Soil pH is unlikely to remain stable since both degradation of hydrocarbons (Aislabie et al., 2006) and biological nitrogen fixation by legumes (Jensen and Hauggaard-Nielsen, 2003) may decrease soil pH. Polluted soils are often multicontaminated, containing also heavy metals most of which are more soluble in acidic pH (Wenzel, 2009). On the other hand, Finnish soils are non-calcareous and acidic by nature. Surprisingly, in this experiment soil pH rapidly increased due to the fuel oil addition in the rhizoremediation treatment and unvegetated reference and, to a lesser extent, in the sterilised reference. Development of anaerobic conditions due to high oxygen consumption during the HC oxidation would explain this observation. However, the rapid, apparently aerobic fuel oil degradation contradicts this interpretation. Galega on the other hand seemed to slightly lower soil pH during the last weeks in both vegetated soils. Most likely the protons secreted by Galega nodules during N2 assimilation were responsible for the acidifying effect (Graham and Vance, 2000). However, as changes in pH were less than one unit in all treatments, and pH on week 21 was lowest in the sterilised reference, the acidifying effect of biological nitrogen fixation was negligible. According to Wenzel (2009), competition for nitrogen can be an important factor restricting bioremediation in the rhizosphere. The total nitrogen level of the soil remained rather constant in all treatments throughout the experiment (data not shown), and a correlation between soil carbon and nitrogen content was found in the vegetated treatments but not in the unvegetated reference. These observations suggest that Galega was able to biologically fix the nitrogen needed for biomass production and unlikely restricted nitrogen availability to the contaminant-degrading soil microbes even during the intensive growth. Our results are consistent with earlier observations of HC not disturbing the biological nitrogen fixation by legumes, a finding first reported by Carr (1919). Fuel oil degradation was reflected in the slight downward trend of the soil C/N ratio observed only in the contaminated treatments. No Galega associated differences in this ratio were seen between the rhizoremediation treatment and the unvegetated reference. Contrastingly, C/N ratio in the uncontaminated reference remained slightly but significantly lower till the end of the experiment. Likely some of the fuel oil carbon remained in the soil either assimilated into microbial biomass of the degrader organisms or in the form of partly oxidised but not mineralised metabolites that are not recovered by the TPH analysis.

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We hypothesized fuel oil addition would degrade the biological properties of the soil and monitored these in the form of microbial biomass (measured as soil DNA concentration) and enzymatic activities (measured directly from soil using fluorogenic substrates). Unexpectedly, both of these microbiological indicators responded positively to fuel oil. The growth of the microbial biomass in the contaminated soils reflected the hockey-stick shaped kinetics of fuel oil degradation as its mirror image: DNA increase was steepest during the first 3 weeks and gradually levelled off towards the end of the experiment in both contaminated soils. A similar stimulative effect has been observed with heavier HC by Marin et al. (2005), who found that oil refinery sludge application increased soil microbial biomass C, basal respiration and adenosine-5-triphosphate (ATP) levels in soil. Also Bachoon et al. (2001), assessing petroleum contaminated sediment microcosms, noticed that oil enlarged both culturable and total microbial population densities, measured as heterotroph-MPN and DNA yield, respectively. A slow increase in microbial biomass in the uncontaminated reference was likely associated to the Galega rhizoexudates, but also to mowing (performed on week 15), which increases root turnover and may promote microbial activity in soil (Olson et al., 2008). Other plant-independent mechanisms behind the increase might have been identified with an additional uncontaminated-unvegetated reference treatment. However, the quadruplication of microbial biomass, which accelerated several weeks after the stabilisation of the soil, seems unlikely without any apparent carbon and energy source. Nevertheless, no vegetation-induced biomass increase was seen when comparing the contaminated soils, but any rhizosphere effects were masked by the impact of the oil addition. Due to their central role in nutrient cycling, enzymatic activities in soil have been suggested to represent a sensitive indicator of soil quality (Dawson et al., 2007; Bastida et al., 2008; Kibblewhite et al., 2008). We monitored the changes in the activities of six hydrolytic enzymes related to release of nitrogen, phosphorus and carbon along the rhizoremediation experiment. Lack of N and P often restricts degradation of hydrocarbon contaminants in bulk soil and rhizosphere (Aislabie et al., 2006; Wenzel, 2009) and biological C sources may either compete with HCs or serve as substrates for cometabolic degradation. The activities of enzymes related to degradation of cellulose (cellobiosidase) and starch (α-glucosidase) showed no variation between the rhizoremediation treatment and the two references. Thus neither fuel oil contamination nor Galega rhizosphere seemed to affect the enzymes responsible for plant material degradation during one growing season. Leucine aminopeptidase, an enzyme that hydrolyses peptide bonds selectively releasing the terminal leucine from a peptide, was clearly induced by the HCs. The response may reflect both the rapid growth of microbial biomass in the contaminated treatments as well as the relative scarcity of N in the oil-affected soils rich in utilisable carbon. Elevated alanine aminopeptidase activity due to contamination was only seen on the last sampling week. Our observation of both aminopeptidase activities increasing towards the end of the experiment in both vegetated soils is in accordance with earlier studies where the activities in agricultural soils were found to rise during the growing season (Palmroth et al., 2007; Niemi et al., 2008). At week 21 the activities of aminopeptidases were significantly higher in the rhizoremediation treatment than in either of the reference soils. It is possibly that the microbes in the contaminated rhizosphere efficiently utilised the Nsources available in the Galega rhizosphere. Phosphomonoesterase cleaves monoester bonds releasing phosphate from low molecular weight organic compounds (Turner and Haygarth, 2005). Increased levels of this enzyme in soils are usually correlated with higher microbial biomass but may also signal P limitation or a response to stressful conditions (Margesin and Schinner, 1994; Turner and Haygarth, 2005; Niemi et al., 2008). In the early weeks of the experiment the rise in the phosphomonoesterase activity reflected the rapid HC degradation; a negative

correlation with soil TPH content was observed in the rhizoremediation treatment. This finding is in accordance with the results of Marin et al. (2005), who observed that phosphatase activity peaked at the initiation of a landfarming treatment for oil refinery sludges. Also Palmroth et al. (2005) detected in a phytoremediation study that contamination of soil with diesel did not reduce soil enzyme activities, which they used as a measure of “general biocatalytic activity of soil microbiota.” However, the increasing trend continued throughout the whole experiment only in the rhizoremediation treatment and the uncontaminated reference. Also plants can exude phosphomonoesterase, especially in phosphate-limited soils (Margesin and Schinner, 1994; Graham and Vance, 2000). Thus the observed steady increase in phosphomonoesterase activity in the vegetated soils may have been partly accounted for phosphatases produced by Galega. The poor phosphate status of the soil supports this view; plant-available P concentration was below 5 mg [kg drywt soil]−1) regardless of the fertilizer added. Phosphodiesterase releases phosphate from nucleic acids and phospholipids, and is therefore especially important for P-cycling in soil (Margesin and Schinner, 1994; Turner and Haygarth, 2005). Contamination seemed to initially induce this enzyme that was more active in the early weeks in the contaminated soils compared to the uncontaminated reference. In contrast to the other five enzymes, the highest activities of phosphodiesterase were observed at weeks 3–6 when the fuel oil degradation in the contaminated soils began to level out. The phosphodiesterase activity peak might indicate that the depletion of the readily consumable HC substrates caused microbial biomass turnover, upon which P from the cell components of the recessive community members was recycled. This microbial community shift was suggested also by the oil-degrader MPN numbers, which began to decrease soon after the rapid HC degradation levelled off. 4.3. The power of biological monitoring tools Regardless of the goal to comprehensively restore the quality of contaminated soil, evaluation of bioremediation treatments has traditionally been based on chemical quantification of the remaining unmodified contaminant. The use of restricted chemical analyses seems short-sighted, as the biological soil properties may tell more about the ecological status of the soil, hydrocarbon bioavailability and potential toxicity of the degradation products. A proportion of the fuel oil derived carbon remained in the rhizoremediated soil even when the TPH content had decreased to the level below which the soil could be regarded clean. C was either assimilated by the degrader microorganisms or modified into polar but poorly degradable metabolites undetected by the TPH analysis. The first possibility suggests that petroleum hydrocarbon contamination could be utilised as a beneficial C input in the soil trophic networks, adding to soil organic carbon reserves. The second alternative is more alarming and emphasises how little the legislation-required TPH standard analysis tells about the mineralisation and fate of HCs in soil. Our observations suggest that moderate fuel oil contamination was processed by the soil microbes as a beneficial growth-supporting substrate. No toxic effects were observed either in the microbial numbers or functions. The quantity of soil DNA and oil-degrader MPN counts illustrated the responses of the microbial community to carbon inputs in the form of HC addition and root exudation. Both biomass and degrader quantification mirrored the TPH curve and seemed suitable microbiological methods to monitor the biological oil degradation process; the former stabilised and the latter decreased after the easily utilisable HC molecules, which make the majority in fuel oil and diesel-like light petroleum products, were depleted. Contamination also induced 1 or 2 of the 6 monitored enzymatic activities in soil, whereas immediate or delayed negative effects were seen in none. Though rhizosphere effect likely increased the total microbial population in the uncontaminated reference, no statistically

A. Mikkonen et al. / Geoderma 160 (2011) 336–346

significant Galega associated stimulation was seen in the rhizoremediation treatment even when the plant biomass had reached that of the uncontaminated control. The soil microbial biomass saturated at DNA concentration of ~ 5 μg g−1 possibly because some other nutrient than C and N providable by the plant became the factor limiting microbial growth. Because microbial biomasses were equal in all three treatments in the last week, the chance of toxic HC degradation metabolites restricting the growth of heterotrophic bacteria cannot be ruled out either. Anyhow, this seems unlikely since combined induction by fuel oil and Galega was seen in both aminopeptidase activities, and also because phosphodiesterase was stimulated by HC still at week 21. Overall, Galega associated effects were more visible in the soil enzymatic activities; in three of the six measured enzymes an upward trend was seen only in the vegetated treatments. Biological monitoring tools also give us a possibility to evaluate the ecological relevance of the TPH threshold concentration for clean soil, which was attained in the rhizoremediation treatment on the last week. According to the microbiological indicators even the initial fuel oil addition was not harmful but rather a beneficial substrate input event to the microbial community. Galega was much more susceptible to HC, the added moderate contamination initially seriously retarding the plant biomass build-up. Once TPH lowered to the clean soil threshold also the plant shoot size in the rhizoremediation treatment reached that of the uncontaminated reference. Obviously the initial contamination level did not exceed the resilience threshold of this soil–microbe–plant system. However, soils with other properties, more specific microbial functions, and other plants and soil fauna, might be much more sensitive to fuel oil. 4.4. Conclusions Rhizoremediation using leguminous plants holds a promise of combining biological stimulation of microbial hydrocarbon degrader communities and improvement of soil structure in a cost-efficient means of nitrogen fertilisation. The general positive influence of legumes on soil health and functions might also alleviate the adverse effects of the contaminants. The monitoring methods utilised in this experiment showed that moderate level contamination with fuel oil did not inhibit soil microbial activities but stimulated many of them. Changes in the sizes of the total and oil-degrader microbial communities mirrored microbial responses to easily consumable carbon from the added hydrocarbons and the growing legume G. orientalis. However, the influence of the moderate HC contamination on all soil properties was so pronounced that it masked the vegetation effect in the rhizoremediation treatment. Plant-derived changes in the monitored soil chemical and microbiological indicators were only seen at the end of the experiment, when the vegetated pots were full of legume roots. In 21 weeks, 3000 ppm fuel oil addition was biologically degraded by the indigenous soil microbes down to a concentration, in which soil can be regarded clean. Contamination retarded the biomass production of Galega and the majority of the hydrocarbons were depleted in the early weeks when the legume seedlings were still very little. Though a small but statistically significant beneficial rhizosphere effect was seen in the end of the experiment, it can be concluded that in the case of moderate pollution by easily degradable HC contaminants, seeding Galega into contaminated soil has little if any effect on oil degradation rate in otherwise optimal conditions. Our results support the theory of high soil resilience to moderate contamination with light hydrocarbons, demonstrate the power of microbiological methods in monitoring bioremediation, and back up the current legislative clean soil threshold level for hydrocarbon contamination. Acknowledgements We thank Laura Huikko, Martina Metzler and Ilse Heiskanen for the technical assistance, and Henrik Westerholm, Seppo Mikkonen,

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Matti Salovaara and Anders Risbjerg Johnsen for the technical consultancy. We are grateful for Maarit Niemi for providing us with the possibility to use the ZymProfiler enzyme activity assay set up at the Finnish Environment Institute, and also for the experienced advice on the interpretation of the results. Sincere thanks go to two anonymous reviewers whose comments significantly improved the manuscript. This work was funded by the Ekokem Oy, University of Helsinki, Neste Foundation and Fortum Foundation.

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