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Disturbance, carbon physicochemical structure, and soil microenvironment codetermine soil organic carbon stability in oilfields
Environment International 135 (2020) 105390
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Disturbance, carbon physicochemical structure, and soil microenvironment codetermine soil organic carbon stability in oilfields ⁎
Juejie Yanga, Jian Wangb, Aiyang Lia, Guanghe Lia, , Fang Zhanga, a b
T
⁎
School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China Shenyang Academy of Environmental Sciences, Shenyang, Liaoning 110167, China
A R T I C LE I N FO
A B S T R A C T
Handling Editor: Da Chen
The stability of soil organic carbon (SOC) is crucial for soil quality, fertility, and natural attenuation processes of pollutants. The physicochemical structures of SOC were believed to control its stability, yet has become controversial. Here we hypothesized that disturbance intensity and variations in the soil environment can also influence the SOC stability, and conducted a case study with oil contaminated soils to quantify the contributions to SOC stability of various factors including contamination level, carbon physicochemical structure, and soil properties. Oil contamination led to increased SOC stability, as suggested by appreciably decreased soil CO2 fluxes, the enrichment of the δ13C in the oil contaminated soils, as well as analysis of soil aggregates and humic substances. Redundancy analysis indicated that overall SOC stability were highly correlated to microaggregate (M2), HA/FA, Fe, soil porosity, EC, pH, and total petroleum hydrocarbon (TPH) in oilfields. Variance partitioning analysis showed that carbon physicochemical structure (S), soil properties (P), and oil contamination (O) could explain the variance of overall SOC stability by up to 90%, while 18% of the variation was explained by S × P and 43% by S × P × O. These results show that multiple factors of the disturbance, carbon physicochemical structure, and soil properties should be essential for future studies of SOC stability.
Keywords: Soil organic carbon Stability Stable carbon isotopes Oilfields Soil aggregates Humic substances
1. Introduction Soil organic carbon (SOC) is found to have notable correlation with mechanistic representations of pollutant mobility and electron transfer reactions (Lehmann and Kleber, 2015). The growing interest in SOC stability is, thus, not only due to its immense role in soil quality and fertility but also its role in the degradation of pollutant (Wiesmeier et al., 2019). Studies have previously focused on the response of SOC stability to different types of disturbance, such as warming (Davidson and Janssens, 2006), increased atmospheric CO2 (van Groenigen et al., 2011), addition of nitrogen (Neff et al., 2002), and contamination (Zhang et al., 2016). While these studies have rapidly improved our understanding of the mechanisms of the SOC stabilization process, the key factors for SOC stability with disturbance are still disputed. SOC stability is vital for the nature based solutions (NBS) for contaminated land remediation, the new concept of which has drawn much attention in recent years and may offer environmental, social and economic benefits for many applications (Song et al., 2019). The inherent properties of soil carbon, based on its molecular structure (e.g., recalcitrant compounds such as humic substances) and physical protection by aggregates, have long been considered the decisive factors of
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SOC stability (Angst et al., 2017; Rabot et al., 2018). Waksman (1936) elaborated on the origin, chemical composition, and importance of humus, with relation to SOC stability. For the next 70 years, soil scientists continued to emphasize the physics and chemistry of soil carbon, particularly in relation to soil fertility. In a widely cited review of stability mechanisms, Sollins et al. (1996) concluded that there were three factors of SOC stability: (1) recalcitrance, a molecular characteristic influencing bioavailability; (2) interactions of molecules, surface condensation, or sorption; and (3) the location of SOC, which influences the accessibility of microbes and enzymes. Ten years later, the three classic mechanisms of SOC stability had evolved to the characteristics widely used today (von Luetzow et al., 2006): (1) chemical stability or the recalcitrance of molecules; (2) physical stability or the spatial inaccessibility and aggregate protection; and (3) interactions with mineral surfaces such as Fe-, Al-, Mn-oxides, phyllosilicates. Until now, many approaches for modeling SOC dynamics and decomposition, such as the CENTURY (Parton et al., 1987) and ROTH-C (Jenkinson, 1990) models, were based on the inherent characteristic of soil carbon (the chemical and physical structure of SOC). At approximately the same time as the publication of the review by von Luetzow et al. (2006), Jastrow et al. (2007) emphasized that the
Corresponding authors. E-mail addresses: [email protected] (G. Li), [email protected] (F. Zhang).
https://doi.org/10.1016/j.envint.2019.105390 Received 2 August 2019; Received in revised form 2 November 2019; Accepted 3 December 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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analyzed by ultrasonic-Soxhlet extraction gravimetric analysis. The components of residue oil in the soil (saturated hydrocarbons and polycyclic aromatic hydrocarbons, PAHs) were analyzed by GC/MS (Method S1). The soil porosity was estimated from soil bulk density by metal cylinders (5 cm in diameter, 5 cm in length) and measured particle density (i.e. 2.6 mg m−3) (Sasal et al., 2006). The soil redox potential (ORP), water content, and pH was measured in situ with multiple electrodes (FJA-6 Portable ORP Meter, China) and the time-domain reflectometry (TDR). The following soil properties were measured according to the recommended soil testing procedures (Lu, 1999): total carbon (TC), organic matter (OM), total nitrogen (TN), electrical conductivity (EC), soil total phosphorus (TP), Al, Fe, sulfate and NO3–-N. The range of TPH in the contaminated soil samples was from 100 mg kg−1 to 3634 mg kg−1, and TPH was not detected in the uncontaminated soils (Table S1). The contaminated soil properties were as follows: soil porosity, 44.1% ± 1.3%; soil ORP, 480 ± 40 mV; TC, 2.1% ± 0.5%; OM, 1.5% ± 0.7%; TN 401.6 ± 108.9 mg kg−1; TP, 492.4 ± 91.5 mg kg−1; EC, 3978.2 ± 1762.4 μS cm−1; pH, 8.1 ± 0.2; water content, 17.2% ± 5.6%; Al, 59.4 ± 1.8 g kg−1; Fe, 21.8 ± 2.2 g kg−1; sulfate, 203.4 ± 68.7 mg kg−1; and NO3–-N, 0.9 ± 0.6 mg kg−1 (Table S1). Among these soil properties, OM, EC, and pH were higher in the oil contaminated than in the uncontaminated soils (P < 0.05). Significantly lower concentrations of TN and TP, Al, Fe, and sulfate were detected in the oil contaminated soils compared with those in the uncontaminated soils (P < 0.05, Table S1).
soil microenvironment was also a key factor in controlling the status of SOC stabilization. This was followed a few years later by Schmidt et al. (2011), who proposed that the molecular properties of SOC could not totally explain the persistence of organic carbon in soils for over hundreds of years, and the soil environmental conditions might be more important for SOC stability. In the latest review, Lehmann and Kleber (2015) concluded that reliable predictions of SOC dynamics should focus on its spatial arrangement, soil environment, and microbial ecology, not its quality. They even suggested abandoning the use of the term “humus”, which was routinely used for recalcitrant chemical components in SOC (Lehmann and Kleber, 2015). Although there was much controversy on the nature of recalcitrant carbon in soil (Baveye and Wander, 2019), the recent consensus was that the carbon physicochemical structure was not the only factor influencing SOC stability. However, SOC dynamics under a variety of factors has not been thoroughly investigated, especially lacking a quantitative characterization. Here we hypothesized that the disturbance intensity, inherent properties of soil carbon and soil microenvironment may all be the important factors of SOC stability. We therefore took oil contamination as a case study, and conducted a comprehensive analysis considering oil contamination level, soil carbon physicochemical structures, soil properties and overall changes in SOC stability. Redundancy analysis (RDA) and variance partitioning analysis (VPA) were performed to provide a quantitative evaluation of contributions of each factor to SOC stability. This study aimed at addressing the following questions: (i) Whether variations of carbon physicochemical structures are consistent with overall changes in SOC stability? (ii) How does disturbance intensity affect SOC stability and what role the soil properties play in this processes? Increased recognition of disturbance-driven changes in SOC stability will also be crucial for improving soil quality and fertility, as well as mitigating the adverse impact of soil contamination.
2.2. SOC stability analysis In situ soil gas emissions (CH4 and CO2) and soil stable carbon isotope ratios were used as indicators for the overall changes in SOC stability. The humic substances (humus) and its subcomponents, including humic acid (HA), fulvic acid (FA), and humin (HM), and the ratios of HA/FA and HA/humus, were used as indicators for the chemical structure of SOC. We also compared the aggregate size distribution and aggregate microstructures between oil contaminated and uncontaminated soils, by scanning electron microscopy (SEM), to represent the changes of the SOC physical structure.
2. Materials and methods 2.1. Sampling sites and soil properties Surface (0–10 cm) soils from a previous study (Yang et al., 2018a) were used in the current study. Twenty-two soil samples were collected from the Shengli Oilfield in the Yellow River Delta (YRD; 36˚55′–38˚12′N, 118˚07′–119˚18′E), Northern China, in August of 2015. The YRD has a warm temperate monsoon climate, with a mean annual rainfall of 596.2 mm and a mean annual air temperature of 11.9 °C. The main soil type is sandy clay loam, and the dominant aboveground vegetation, at our sampling sites was Phragmites australis. Of the 22 samples collected, 14 were from contaminated areas near oil wells in the Shengli Oilfield. The space-for-time substitution method was applied to meet the needs of in situ study of SOC stability, which hypothesizes that samples in uncontaminated plots away from oil wells represent pre-perturbation state, while contaminated soils represent after-perturbation state. Based on the assumption that oil contamination levels would be higher closer to the oil wells, soil samples were collected with different distances away from the well (1 m, 5 m, and 10 m) for contaminated plots to obtain samples of different contamination level around. The 5 oil wells were randomly selected and 3 soil samples (1 m, 5 m, and 10 m away from the well) were collected around each oil well. The other 8 samples were considered ‘uncontaminated’ and were taken simultaneously from undisturbed pristine soils about 20 m away from the oil wells. The collected soils were sealed in sterile sampling bags without air and taken to the laboratory (please refer to Yang et al., 2018a for detailed soil and site characterization data). The plant litter carbons were not analyzed for both oil contaminated and uncontaminated soils, as soil around the oil wells had already lost their plant within a 10-meter radius in our study areas by crude oil exploration, production and maintenance. The total petroleum hydrocarbon (TPH) concentrations were
2.3. Soil CO2 and CH4 fluxes We sampled the soil and the gas at the same location (gas samples first and then soil samples collection), so that soil and gas samples had one-to-one correspondence. Soil gas samples were collected on every day that it was feasible to do so, which generally means sunny days in October 2014 and May and August 2015, for daily and seasonal dynamics analysis. Gas samples (250 mL) were collected in stainless steel square boxes (0.5 m × 0.5 m × 0.3 m), every hour, on the hour, from 8:00 a.m. to 8:00p.m. at time intervals of 0, 5, 10, 15, 20, and 25 min (Method S2). The gas samples were transported to the laboratory within 24 h of collection and their CH4 and CO2 concentrations determined using a gas chromatograph (7890A GC, Agilent, Santa Clara, CA, USA, fitted with a flame ionization detector and a thermal conductivity detector). 2.4. Soil stable carbon isotope ratios The stable carbon isotope ratios (δ13C) of all the soil samples were analyzed using an elemental analyzer (Thermo Fisher Scientific Inc., USA) and the isotope ratio mass spectrometry (IRMS) system (Finnigan MAT model delta plus, Thermoquest, Bremen, Germany) (Method S3). The δ13C values of the soil samples were calculated using the following equation (Craig, 1957):
δ 13C = 103 (Rsa Rst − 1)
(1) 13
12
where Rsa denotes the ratio of C/ C in the sample and Rst is for the 13 C standard (Pee Dee Belemnite standard (PDB)). The precision of the 2
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decreasing trend of CO2 emissions from the early growing season in May to the end of the growing season in October (Fig. 1a). The lowest CO2 flux of 3 mg m–2 h−1 was observed from the oilfield sample with the highest TPH value in October, and the highest CO2 flux (4203 mg m–2h−1) was observed from an uncontaminated site in May (Fig. 1a). Conversely, CH4 fluxes were higher from oil contaminated soils (1.27 ± 1.25 mg m–2h−1) than from the uncontaminated soils (0.18 ± 0.23 mg m–2h−1, P < 0.05) during the growing season. Gas samples in the contaminated and uncontaminated soils both showed decreasing trends for CH4 emissions between the early growing season in May and the later growing season in October (Fig. 1b). The lowest CH4 fluxes (0.02 mg m–2h−1) were observed in samples of the uncontaminated soils in October. The highest CH4 fluxes (4.63 mg m–2h−1) were observed in May, in samples of the contaminated soil with the highest TPH content (Fig. 1b). The diurnal variation of the soil carbon release (CH4 and CO2 fluxes), from the oil contaminated and uncontaminated soils, showed the same trends (Fig. S1). Increased CH4 fluxes and reduced CO2 emissions were observed from the oil contaminated soils compared to the uncontaminated soils and peaked about 11:00 am each day during the diurnal changes. The δ13C values in the soil samples were also analyzed to study the SOC stability in the oil contaminated and uncontaminated areas (Table 1). A high degree of δ13C-enrichment, which was statistically significant, was detected in the contaminated soils compared with the uncontaminated soils: the δ13C values were –25.2‰ ± 0.4‰ and –25.6‰ ± 0.1‰, respectively. Both soil carbon release data (CH4 and CO2 fluxes) and isotopic evidence showed that oil contamination increased the overall SOC stability.
repeated analysis was ± 0.1‰ (Minagawa et al., 1984). 2.5. SOC physical and chemical stability We considered two major mechanisms for SOC stability, including ‘physical stability’ through soil aggregates protecting SOC by spatial inaccessibility, and ‘chemical stability’ through the formation of recalcitrant SOC compounds against decomposition by microbes and enzymes (von Luetzow et al., 2006). Chemical extraction schemes (Mathers et al., 2000), spectroscopic methods (such as 13C-nuclear magnetic resonance spectroscopy, night-time ozone profile, diffuse reflectance infrared Fourier transform spectroscopy, and pyrolysis field ionization mass spectrometry) all could be used to determine structural chemistry (Leinweber et al., 2008; Plante et al., 2011; Saenger et al., 2013). Here we applied chemical extraction schemes using classical alkaline solutions (NaOH and Na4P2O7) to extract the humic substance components from soil samples to indicate the SOC chemical stability. Humic (HA), humin (HM), and fulvic acid (FA) was separated and labelled according to their solubility in base and acid solutions (Olk, 2006; Parsons, 1983). Soil aggregation is a key physical indicator of SOC stability (Dong, 2019). The sieve-based methods, ultrasonic treatment, X-ray computed tomography (CT) and scanning electron microscopy (SEM) could be used to analyze the aggregate microstructure aggregate dynamics (Dal Ferro et al., 2012; Khan et al., 2012; Zaccone et al., 2018). In this study, different size fractions of soil aggregates (A, macroaggregates > 250 μm; M1, microaggregates 53–250 μm; M2, microaggregates < 53 μm) were collected using different sizes of mesh and sieves and used to indicate the physical stability of the SOC (Christensen, 2001; Lützow et al., 2007). The mean weight diameter (MWD) was calculated as: n+1
MWD =
∑ 1
ri − 1 + ri × mi 2
3.2. The variations of carbon physicochemical structures The mass percentage of the macroaggregate (A, > 250 μm) size fraction in the contaminated soils was 3.8% ± 2.1%, a significant increase from that in the uncontaminated soils, where it was 1.4% ± 1.0%. For the microaggregates, the percentage of the M1 (53–250 μm) size fraction increased, while that of M2 (< 53 μm) decreased in the oil contaminated soils compared with the uncontaminated soils (Table 1). The aggregate stability values (expressed as MWD) in the contaminated soils (4.7 ± 1.1 mm) were much higher than those in the uncontaminated soils (3.1 ± 0.6 mm) (Table 1). The SEM images of the oil contaminated and uncontaminated soils are shown in Fig. 2. In the uncontaminated soil (Fig. 2a, b), large and small particles were distributed homogeneously. In the oil contaminated soil (Fig. 2c, d), the small particles appreciably decreased and low clustering degree was observed. The order of the humus content in both the oil contaminated and uncontaminated soils was HM > FA > HA (Table 1). The amount of HM in the oil contaminated soils (5.0 ± 2.6 g kg−1) was much higher than that in the uncontaminated soils (2.7 ± 1.6 g kg−1). Higher values of the HA/FA and HA/humus ratios were observed in the oil contaminated soils (0.12 ± 0.07, 0.04 ± 0.03, respectively) than in the uncontaminated soils (0.23 ± 0.09, 0.09 ± 0.06, respectively), and they positively correlated with the level of contamination (Table 1, Fig. 3). The increased MWD and higher values of HA/FA and HA/humus ratios, indicated a more stable state of the carbon physicochemical structure in the oil contaminated soils, which was consistent with the change of overall SOC stability.
(2)
where ri is the average diameter of the ith size fraction of the aggregate, mi is the mass of the ith size fraction of the aggregate and i = 1, 2, 3, representative of the aggregate size being: > 250 μm, 53–250 μm, and < 53 μm, respectively. SEM images of the soil aggregates were taken on a Zeiss SUPRA55 scanning electron microscope with accelerating voltages of 20 kV to analyze the intra-aggregate microstructures. 2.6. Statistical analysis The differences between the soil properties and SOC stability from the contaminated and uncontaminated soils were analyzed with independent sample T tests, using the SAS software (SAS Institute Inc., Cary, NC, USA). Redundancy analysis (RDA) and variance partitioning analysis (VPA) were performed to evaluate the contributions of various factors to SOC stability using the “Vegan” package within R (http:// cran.r-project.org/web/packages/vegan) (R Development Core Team, 2011). All soil data were Hellinger transformed for standardization. The significance tests were carried out using Monte Carlo permutation (999 times). 3. Results 3.1. Overall changes of SOC stability
3.3. Key factors to determine SOC stability Although CH4 emissions increased, the total carbon released from the oil contaminated soils appreciably decreased, when compared with the uncontaminated soils, during the growing season of May, August, and October) (Fig. 1). The seasonal variation of the soil CO2 emissions was obvious, and the values from the oil contaminated areas were significantly lower than those of the uncontaminated soils. There was a
We analyzed the correlation between the soil properties and carbon physicochemical structures using Pearson correlation analysis (Table 2). The A, M1, and MWD were positively related to the EC and pH (P < 0.05). The M2 was positively related to TN, TP, soil porosity, Al, Fe, and sulfate and negatively related to EC and pH (P < 0.05, 3
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Fig. 1. CH4 and CO2 fluxes in oil contaminated and uncontaminated sites (UC) in May, August, and October (box plots). Each box and whisker plot is drawn from the quartile and outlier values. The differences between the oil contaminated and uncontaminated sites are analyzed by independent samples T test. ** P < 0.01, * P < 0.05.
We further quantified the contributions of the carbon physicochemical structure (S), soil properties (P), and oil contamination (O) to the SOC stability by using VPA, including the pure effects of S, P, and O, interactions between any two components (S × P, S × O, and P × O), interactions of all three components (S × P × O), and the unexplained portion (Fig. 5a). The variance in the “SOC stability,” consisting of CO2 and CH4 fluxes and δ13C values, could be explained by up to 19% with “soil property,” up to 8% with “oil contamination,” and only up to 2% by “carbon physicochemical structure” (P < 0.001) (Fig. 5b). A total of 90% of the variation was explained by these three components, and the interactions among them seemed to have more influence than did the individual components with 18% for S × P and 43% for S × P × O.
Table 1 . Soil organic carbon (SOC) stability indicators in oil contaminated and uncontaminated areas. Soil stability indicators Stable carbon isotope ratios δ13C (‰) SOC physical stability A (%) M1 (%) M2 (%) MWD (mm) SOC chemical stability Humus (g kg−1) HA (g kg−1) FA (g kg−1) HM (g kg−1) HA/FA HA/Humus
Oil contaminated
Uncontaminated
−25.2 ± 0.4*
−25.6 ± 0.1
3.8 ± 2.1* 21.8 ± 4.5 74.3 ± 6.1* 4.7 ± 1.1**
1.4 ± 1.0 16.3 ± 6.1 82.3 ± 7.7 3.1 ± 0.6
7.8 ± 3.4 0.3 ± 0.1 2.5 ± 1.1 5.0 ± 2.6* 0.12 ± 0.07* 0.04 ± 0.03*
5.3 ± 3.6 0.5 ± 0.4 2.1 ± 2.0 2.7 ± 1.6 0.23 ± 0.09 0.09 ± 0.06
4. Discussion Our results showed that the disturbance of oil contamination led to appreciably increased SOC stability by decreased carbon emissions and enrichment of δ13C from the oil contaminated soils. Meanwhile, the increased MWD and higher values of HA/FA and HA/humus ratios with oil contamination, indicated the consistent change in the carbon physicochemical structure. The hydrophobic compounds in the crude oil might be the main reason for their increased physical stability (Dindar et al., 2015). Crude oil could enter the soil pore and coat microaggregates, thereby promoting the aggregation of microaggregates and subsequently increasing the percentage of macroaggregates in comparison to that of uncontaminated soils. Oil pollutants can also limit water and oxygen transfer in soils and act as physical barriers to prevent the access to roots and microorganisms (Adam and Duncan, 2002; Yang et al., 2018a). These results were consistent with that of Martens (2000), which demonstrated that residue biochemistry influenced soil aggregation and regulated soil carbon sequestration. Previous studies also showed that petroleum hydrocarbon components altered the soil microenvironment, such as soil porosity, carbon to nitrogen ratio, EC, and pH, which influenced the soil aggregate size distribution and aggregate stability (Hagedorn et al., 2003; Khan et al., 2012). Meanwhile, the decreased content of Fe and Al in the oil contaminated soils might be the reason for the reduced microaggregates, since the microaggregates were formed by the organic-mineral bonds through anion and inner-sphere ligand-exchange reactions (Torn et al., 1997). Consistent with the increasing physical stability, the SOC chemical
The differences between the oilfields and control sites were analyzed by independent samples T test. ** P < 0.01, * P < 0.05. Abbreviation: A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin.
Table 2). The ratios of HA/FA and HA/humus were positively related to soil porosity and Fe, and HA/FA were negatively related to EC (P < 0.05, Table 2). RDA and VPA were conducted to investigate the key factors that determine SOC stability (Figs. 4 and 5). The RDA showed that the explanatory variables (indices of carbon physicochemical structure, soil properties, and oil contamination) accounted for 98.1% of the total variation (Fig. 4). The longer arrow projected length meant more importance of the variable, and the same arrow orientation between parameters meant positive correlation and vice versa. The soil CO2 fluxes were positively correlated with Fe, M2, soil porosity, and HA/FA ratio, and negatively correlated with EC, TPH, and pH in the oilfields. On the contrary, the soil CH4 fluxes and δ13C values were positively correlated with EC, TPH, pH, and MWD, and negatively correlated with Fe, M2, soil porosity, and HA/FA ratio with oil contamination (Fig. 4). The EC, M2, Fe, TPH, soil porosity, HA/FA, and pH played important roles in the dispersion of the sites along the first axis (Table S2). 4
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Fig. 3. Relationships between soil carbon chemical stability indicators and soil total petroleum hydrocarbon (TPH): humic (HA)/humus and HA/fulvic acid (FA).
trisnorhopane (Tm), 17α-diahopane (Diahopane), 17α,21β-hopance (30ab) and 17α,21β-30-homohopane (C31S, C31R) ], compared with nalkanes (Fig. 6a and b). Meanwhile, numerous compounds of PAHs were detected in contaminated soil, which were difficult to biodegrade (Fig. 6c). Consistently, Wang, Zhang, and Li (2011) found that aromatic or multi-aliphatic ring compounds, including saturated alkyl, fatty, and naphthenic acids, were abundant in the residual oils found in contaminated soils. These compounds had very complex carbon structures and some had biological toxicity, leading to a low bioavailability (Dindar et al., 2015). As the primary microbial mediated processes, soil humification was mainly controlled by the microbial habitat variables like soil water content and porosity, pH, and nutrients (Zech et al., 1997). Oil contamination altered the soil microenvironment, directly influencing the microbial community structure, activity, functional genes, and even the interaction network, which might influence the formation and decomposition of humic substances (Yang et al., 2018a, 2018b). Here we found that only the carbon physicochemical structure could not completely explain the stability of the SOC in oilfields; disturbance and the carbon physicochemical structure and microenvironment codetermine the persistence of organic compounds in soils. The molecular attributes indeterminable from the long-term decomposition rates of organic carbon in mineral soils (Amelung et al., 2008; Marschner et al., 2008) were also verified in the present study by compound-specific isotopic analysis. The individual molecular compounds previously considered to have “slow” turnover (such as lignins or plant lipids) surprisingly had rapid turnover times, while some labile compounds (such as proteins and saccharides) had decades of turnover time (Amelung et al., 2008). Without denying the role of the chemical composition and spatial distribution in the stabilization of soil carbon, especially those of plant residue quality, chemical reactions, and aggregate protection, it should be recognized that perhaps microenvironmental conditions restricted the access of microorganism to decompose organic carbon or controlled the chemical process (for example, oxidation and reduction, hydrolysis and polymerization) (Schmidt et al., 2011). The mechanism of SOC stability is important for the natural attenuation processes of pollutants and sustainable management of contaminated land (Hou et al., 2018). According to Waksman (1936), the key to understanding the problem lies in the “invisible ties” connecting SOC and living microorganisms, which are primary agents in the formation and transformation of humus. Numerous researchers have recognized the microbial contributions to SOC storage and have tried to integrate genomic and metabolic knowledge to predict SOC turnover (such as individual-based models, heterogeneous network models, trait-
Fig. 2. Low- and high-magnification (inset) scanning electron microscopy (SEM) images of uncontaminated soil (a), (b) and oil contaminated soil (c), (d).
structure was more persistent in the oil contaminated soils, owing to the larger amounts of recalcitrant compounds from the oil pollutants. We detected 95 compounds of residual oils in contaminated soil samples by GC/MS, including alkanes (nC12 to nC40), gonane, terpane and PAHs (Fig. 6, Table S3). Crude oil mainly consisted of carbonaceous compounds with heavy molecular weights, including aliphatics, aromatics, resins, and asphaltenes (Ramadass et al., 2015). The results showed that the soil residual oils contained higher content of resistant branched acyclic and monocyclic hydrocarbons [such as 17α-22,29,20-
5
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Table 2 Correlations between soil properties and soil organic carbon (SOC) physical and chemical stability. Soil properties
TC OM TN TP EC Water PH Soil porosity Al Fe Sulfate NO3–-N
Physical stability
Chemical stability
A
M1
M2
MWD
Humus
HA
FA
HM
HA/FA
HA/Humus
0.241 0.052 −0.408 −0.434* 0.757** −0.037 0.462* −0.792** −0.606** −0.639** −0.395 0.025
0.283 0.193 −0.575** −0.539** 0.845** −0.229 0.648** −0.664* −0.438* −0.532* −0.478* −0.045
−0.242 −0.176 0.545** 0.586** −0.901** 0.187 −0.651** 0.782** 0.531* 0.652** 0.463* 0.034
0.155 0.239 −0.455* −0.483* 0.523* −0.277 0.489* −0.625* −0.636** −0.513* −0.603** −0.268
0.172 0.037 −0.188 −0.281 0.225 −0.059 0.311 −0.066 −0.107 −0.105 −0.088 −0.137
0.016 0.226 −0.006 0.208 −0.322 −0.27 −0.069 0.393 0.323 0.533* 0.275 −0.33
0.022 0.041 −0.07 −0.236 0.016 −0.114 0.285 0.031 0.134 0.128 −0.02 −0.211
0.23 0.004 −0.225 −0.286 0.345 0.011 0.285 −0.166 −0.264 −0.281 −0.143 −0.037
−0.175 0.102 0.021 0.422 −0.453* −0.282 −0.388 0.626* 0.167 0.498* 0.243 −0.304
−0.187 0.132 0.031 0.367 −0.413 −0.244 −0.086 0.655* 0.412 0.557** 0.226 −0.237
Significance: ** P < 0.01, * P < 0.05. Abbreviation: A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin.
Fig. 5. Variation partitioning analysis of soil organic carbon (SOC) stability explained by carbon physicochemical structure (S), soil properties (P), and oil contamination (O). (a) General outline; (b) explanatory variation. Hollow circle shows the variation explained by each factor alone and were proportional to the variation. Black circles represent a combination of factors. Fig. 4. Tri-plot diagram of the redundancy analysis (RDA) of “SOC release and turnover data.” Descriptors (blue arrows) are the δ13C, CH4, and CO2 fluxes. TPH represented soil contamination levels (orange arrows). MWD, M2, HA/FA, and HA/humus represent SOC physicochemical structure (red arrows). TC, OM, TN, EC, PH, Nit, porosity, Al, Fe, TP, Sul, and Water represent soil properties (yellow arrows). These indicators are all used as quantitative explanatory variables. Axes 1 (90.4%, P < 0.001) and 2 (7.7%, P < 0.001) are represented. The total of explained variance in the correlative model is 98.1%. Samples are labeled (circle) as oil contaminated (O1-O14) and uncontaminated soils (C1-C8). Abbreviations: RDA, Redundancy analysis; A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin; TPH, Total petroleum hydrocarbons; Water, Soil water content; TC, Soil total carbon; OM, Soil organic matter; TN, Soil total nitrogen; TP, Soil total phosphorus; EC, Electrical conductivity; Porosity, Soil porosity; Nit, NO3–-N; Sul, Sulfate. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
link soil structure and plant–microbe–soil carbon transfer in the soil around the roots. In a recent study, Schlueter et al. (2019) found that bacteria prefer to forage near macropore surfaces and fresh particulate carbon in fine textured-soil by 3D microstructure analysis and biogeochemical microscopic data. For future studies, understanding processes of soil carbon stabilization to multiple disturbance and well-designed experiments to investigate uncertainty and vulnerability of SOC are needed. To accurately predict natural attenuation processes of pollutants, the combination of chemical, physical, and biological data at the soil microscale will also be important for exploring the mechanisms and better managing the contaminated sites. Acknowledgements We thank Dr. Cong Wang from the Research Center for EcoEnvironmental Sciences, Chinese Academy of Sciences (RCEES-CAS) for his comments and suggestions that have helped to improve our paper. The research was supported by the National Natural Science Foundation of China (41672238) and the Thousand Talents Plan for Young Professionals and Yong Elite Scientist Sponsorship Program by CAST (2015QNRC001).
based microbial models, and bioclimatic models) (Creamer et al., 2015; Kallenbach et al., 2016; Trivedi et al., 2013). However, over 80 years later, it remains a topic of debate, and the existence of interdisciplinary barriers has made progress in identifying the mechanisms of SOC accumulation and decomposition, which calls for comprehensive and advanced technologies (Baveye and Wander, 2019). Hopefully, progress in spectroscopic analysis techniques (e.g., nuclear magnetic resonance methods) and X-ray computed tomography equipment (e.g., Xray absorption near-edge structure) will make it possible to quantify the chemical composition of organic carbon and the geometry and topology of soil pores at nanometric and micrometric scales (Dal Ferro et al., 2012; Khan et al., 2012; Sutton and Sposito, 2005). Vidal et al. (2018) combined in situ 13C-labeling and different spectroscopic techniques to
Declaration of Competing Interest All authors declare no conflicts of interest. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// 6
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Fig. 6. Content of residue oil in the soil (saturated hydrocarbons and polycyclic aromatic hydrocarbons, PAHs) by GC/MS. Data was presented as the mean ± standard error.
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Disturbance, carbon physicochemical structure, and soil microenvironment codetermine soil organic carbon stability in oilfields ⁎
Juejie Yanga, Jian Wangb, Aiyang Lia, Guanghe Lia, , Fang Zhanga, a b
T
⁎
School of Environment and State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China Shenyang Academy of Environmental Sciences, Shenyang, Liaoning 110167, China
A R T I C LE I N FO
A B S T R A C T
Handling Editor: Da Chen
The stability of soil organic carbon (SOC) is crucial for soil quality, fertility, and natural attenuation processes of pollutants. The physicochemical structures of SOC were believed to control its stability, yet has become controversial. Here we hypothesized that disturbance intensity and variations in the soil environment can also influence the SOC stability, and conducted a case study with oil contaminated soils to quantify the contributions to SOC stability of various factors including contamination level, carbon physicochemical structure, and soil properties. Oil contamination led to increased SOC stability, as suggested by appreciably decreased soil CO2 fluxes, the enrichment of the δ13C in the oil contaminated soils, as well as analysis of soil aggregates and humic substances. Redundancy analysis indicated that overall SOC stability were highly correlated to microaggregate (M2), HA/FA, Fe, soil porosity, EC, pH, and total petroleum hydrocarbon (TPH) in oilfields. Variance partitioning analysis showed that carbon physicochemical structure (S), soil properties (P), and oil contamination (O) could explain the variance of overall SOC stability by up to 90%, while 18% of the variation was explained by S × P and 43% by S × P × O. These results show that multiple factors of the disturbance, carbon physicochemical structure, and soil properties should be essential for future studies of SOC stability.
Keywords: Soil organic carbon Stability Stable carbon isotopes Oilfields Soil aggregates Humic substances
1. Introduction Soil organic carbon (SOC) is found to have notable correlation with mechanistic representations of pollutant mobility and electron transfer reactions (Lehmann and Kleber, 2015). The growing interest in SOC stability is, thus, not only due to its immense role in soil quality and fertility but also its role in the degradation of pollutant (Wiesmeier et al., 2019). Studies have previously focused on the response of SOC stability to different types of disturbance, such as warming (Davidson and Janssens, 2006), increased atmospheric CO2 (van Groenigen et al., 2011), addition of nitrogen (Neff et al., 2002), and contamination (Zhang et al., 2016). While these studies have rapidly improved our understanding of the mechanisms of the SOC stabilization process, the key factors for SOC stability with disturbance are still disputed. SOC stability is vital for the nature based solutions (NBS) for contaminated land remediation, the new concept of which has drawn much attention in recent years and may offer environmental, social and economic benefits for many applications (Song et al., 2019). The inherent properties of soil carbon, based on its molecular structure (e.g., recalcitrant compounds such as humic substances) and physical protection by aggregates, have long been considered the decisive factors of
⁎
SOC stability (Angst et al., 2017; Rabot et al., 2018). Waksman (1936) elaborated on the origin, chemical composition, and importance of humus, with relation to SOC stability. For the next 70 years, soil scientists continued to emphasize the physics and chemistry of soil carbon, particularly in relation to soil fertility. In a widely cited review of stability mechanisms, Sollins et al. (1996) concluded that there were three factors of SOC stability: (1) recalcitrance, a molecular characteristic influencing bioavailability; (2) interactions of molecules, surface condensation, or sorption; and (3) the location of SOC, which influences the accessibility of microbes and enzymes. Ten years later, the three classic mechanisms of SOC stability had evolved to the characteristics widely used today (von Luetzow et al., 2006): (1) chemical stability or the recalcitrance of molecules; (2) physical stability or the spatial inaccessibility and aggregate protection; and (3) interactions with mineral surfaces such as Fe-, Al-, Mn-oxides, phyllosilicates. Until now, many approaches for modeling SOC dynamics and decomposition, such as the CENTURY (Parton et al., 1987) and ROTH-C (Jenkinson, 1990) models, were based on the inherent characteristic of soil carbon (the chemical and physical structure of SOC). At approximately the same time as the publication of the review by von Luetzow et al. (2006), Jastrow et al. (2007) emphasized that the
Corresponding authors. E-mail addresses: [email protected] (G. Li), [email protected] (F. Zhang).
https://doi.org/10.1016/j.envint.2019.105390 Received 2 August 2019; Received in revised form 2 November 2019; Accepted 3 December 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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analyzed by ultrasonic-Soxhlet extraction gravimetric analysis. The components of residue oil in the soil (saturated hydrocarbons and polycyclic aromatic hydrocarbons, PAHs) were analyzed by GC/MS (Method S1). The soil porosity was estimated from soil bulk density by metal cylinders (5 cm in diameter, 5 cm in length) and measured particle density (i.e. 2.6 mg m−3) (Sasal et al., 2006). The soil redox potential (ORP), water content, and pH was measured in situ with multiple electrodes (FJA-6 Portable ORP Meter, China) and the time-domain reflectometry (TDR). The following soil properties were measured according to the recommended soil testing procedures (Lu, 1999): total carbon (TC), organic matter (OM), total nitrogen (TN), electrical conductivity (EC), soil total phosphorus (TP), Al, Fe, sulfate and NO3–-N. The range of TPH in the contaminated soil samples was from 100 mg kg−1 to 3634 mg kg−1, and TPH was not detected in the uncontaminated soils (Table S1). The contaminated soil properties were as follows: soil porosity, 44.1% ± 1.3%; soil ORP, 480 ± 40 mV; TC, 2.1% ± 0.5%; OM, 1.5% ± 0.7%; TN 401.6 ± 108.9 mg kg−1; TP, 492.4 ± 91.5 mg kg−1; EC, 3978.2 ± 1762.4 μS cm−1; pH, 8.1 ± 0.2; water content, 17.2% ± 5.6%; Al, 59.4 ± 1.8 g kg−1; Fe, 21.8 ± 2.2 g kg−1; sulfate, 203.4 ± 68.7 mg kg−1; and NO3–-N, 0.9 ± 0.6 mg kg−1 (Table S1). Among these soil properties, OM, EC, and pH were higher in the oil contaminated than in the uncontaminated soils (P < 0.05). Significantly lower concentrations of TN and TP, Al, Fe, and sulfate were detected in the oil contaminated soils compared with those in the uncontaminated soils (P < 0.05, Table S1).
soil microenvironment was also a key factor in controlling the status of SOC stabilization. This was followed a few years later by Schmidt et al. (2011), who proposed that the molecular properties of SOC could not totally explain the persistence of organic carbon in soils for over hundreds of years, and the soil environmental conditions might be more important for SOC stability. In the latest review, Lehmann and Kleber (2015) concluded that reliable predictions of SOC dynamics should focus on its spatial arrangement, soil environment, and microbial ecology, not its quality. They even suggested abandoning the use of the term “humus”, which was routinely used for recalcitrant chemical components in SOC (Lehmann and Kleber, 2015). Although there was much controversy on the nature of recalcitrant carbon in soil (Baveye and Wander, 2019), the recent consensus was that the carbon physicochemical structure was not the only factor influencing SOC stability. However, SOC dynamics under a variety of factors has not been thoroughly investigated, especially lacking a quantitative characterization. Here we hypothesized that the disturbance intensity, inherent properties of soil carbon and soil microenvironment may all be the important factors of SOC stability. We therefore took oil contamination as a case study, and conducted a comprehensive analysis considering oil contamination level, soil carbon physicochemical structures, soil properties and overall changes in SOC stability. Redundancy analysis (RDA) and variance partitioning analysis (VPA) were performed to provide a quantitative evaluation of contributions of each factor to SOC stability. This study aimed at addressing the following questions: (i) Whether variations of carbon physicochemical structures are consistent with overall changes in SOC stability? (ii) How does disturbance intensity affect SOC stability and what role the soil properties play in this processes? Increased recognition of disturbance-driven changes in SOC stability will also be crucial for improving soil quality and fertility, as well as mitigating the adverse impact of soil contamination.
2.2. SOC stability analysis In situ soil gas emissions (CH4 and CO2) and soil stable carbon isotope ratios were used as indicators for the overall changes in SOC stability. The humic substances (humus) and its subcomponents, including humic acid (HA), fulvic acid (FA), and humin (HM), and the ratios of HA/FA and HA/humus, were used as indicators for the chemical structure of SOC. We also compared the aggregate size distribution and aggregate microstructures between oil contaminated and uncontaminated soils, by scanning electron microscopy (SEM), to represent the changes of the SOC physical structure.
2. Materials and methods 2.1. Sampling sites and soil properties Surface (0–10 cm) soils from a previous study (Yang et al., 2018a) were used in the current study. Twenty-two soil samples were collected from the Shengli Oilfield in the Yellow River Delta (YRD; 36˚55′–38˚12′N, 118˚07′–119˚18′E), Northern China, in August of 2015. The YRD has a warm temperate monsoon climate, with a mean annual rainfall of 596.2 mm and a mean annual air temperature of 11.9 °C. The main soil type is sandy clay loam, and the dominant aboveground vegetation, at our sampling sites was Phragmites australis. Of the 22 samples collected, 14 were from contaminated areas near oil wells in the Shengli Oilfield. The space-for-time substitution method was applied to meet the needs of in situ study of SOC stability, which hypothesizes that samples in uncontaminated plots away from oil wells represent pre-perturbation state, while contaminated soils represent after-perturbation state. Based on the assumption that oil contamination levels would be higher closer to the oil wells, soil samples were collected with different distances away from the well (1 m, 5 m, and 10 m) for contaminated plots to obtain samples of different contamination level around. The 5 oil wells were randomly selected and 3 soil samples (1 m, 5 m, and 10 m away from the well) were collected around each oil well. The other 8 samples were considered ‘uncontaminated’ and were taken simultaneously from undisturbed pristine soils about 20 m away from the oil wells. The collected soils were sealed in sterile sampling bags without air and taken to the laboratory (please refer to Yang et al., 2018a for detailed soil and site characterization data). The plant litter carbons were not analyzed for both oil contaminated and uncontaminated soils, as soil around the oil wells had already lost their plant within a 10-meter radius in our study areas by crude oil exploration, production and maintenance. The total petroleum hydrocarbon (TPH) concentrations were
2.3. Soil CO2 and CH4 fluxes We sampled the soil and the gas at the same location (gas samples first and then soil samples collection), so that soil and gas samples had one-to-one correspondence. Soil gas samples were collected on every day that it was feasible to do so, which generally means sunny days in October 2014 and May and August 2015, for daily and seasonal dynamics analysis. Gas samples (250 mL) were collected in stainless steel square boxes (0.5 m × 0.5 m × 0.3 m), every hour, on the hour, from 8:00 a.m. to 8:00p.m. at time intervals of 0, 5, 10, 15, 20, and 25 min (Method S2). The gas samples were transported to the laboratory within 24 h of collection and their CH4 and CO2 concentrations determined using a gas chromatograph (7890A GC, Agilent, Santa Clara, CA, USA, fitted with a flame ionization detector and a thermal conductivity detector). 2.4. Soil stable carbon isotope ratios The stable carbon isotope ratios (δ13C) of all the soil samples were analyzed using an elemental analyzer (Thermo Fisher Scientific Inc., USA) and the isotope ratio mass spectrometry (IRMS) system (Finnigan MAT model delta plus, Thermoquest, Bremen, Germany) (Method S3). The δ13C values of the soil samples were calculated using the following equation (Craig, 1957):
δ 13C = 103 (Rsa Rst − 1)
(1) 13
12
where Rsa denotes the ratio of C/ C in the sample and Rst is for the 13 C standard (Pee Dee Belemnite standard (PDB)). The precision of the 2
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decreasing trend of CO2 emissions from the early growing season in May to the end of the growing season in October (Fig. 1a). The lowest CO2 flux of 3 mg m–2 h−1 was observed from the oilfield sample with the highest TPH value in October, and the highest CO2 flux (4203 mg m–2h−1) was observed from an uncontaminated site in May (Fig. 1a). Conversely, CH4 fluxes were higher from oil contaminated soils (1.27 ± 1.25 mg m–2h−1) than from the uncontaminated soils (0.18 ± 0.23 mg m–2h−1, P < 0.05) during the growing season. Gas samples in the contaminated and uncontaminated soils both showed decreasing trends for CH4 emissions between the early growing season in May and the later growing season in October (Fig. 1b). The lowest CH4 fluxes (0.02 mg m–2h−1) were observed in samples of the uncontaminated soils in October. The highest CH4 fluxes (4.63 mg m–2h−1) were observed in May, in samples of the contaminated soil with the highest TPH content (Fig. 1b). The diurnal variation of the soil carbon release (CH4 and CO2 fluxes), from the oil contaminated and uncontaminated soils, showed the same trends (Fig. S1). Increased CH4 fluxes and reduced CO2 emissions were observed from the oil contaminated soils compared to the uncontaminated soils and peaked about 11:00 am each day during the diurnal changes. The δ13C values in the soil samples were also analyzed to study the SOC stability in the oil contaminated and uncontaminated areas (Table 1). A high degree of δ13C-enrichment, which was statistically significant, was detected in the contaminated soils compared with the uncontaminated soils: the δ13C values were –25.2‰ ± 0.4‰ and –25.6‰ ± 0.1‰, respectively. Both soil carbon release data (CH4 and CO2 fluxes) and isotopic evidence showed that oil contamination increased the overall SOC stability.
repeated analysis was ± 0.1‰ (Minagawa et al., 1984). 2.5. SOC physical and chemical stability We considered two major mechanisms for SOC stability, including ‘physical stability’ through soil aggregates protecting SOC by spatial inaccessibility, and ‘chemical stability’ through the formation of recalcitrant SOC compounds against decomposition by microbes and enzymes (von Luetzow et al., 2006). Chemical extraction schemes (Mathers et al., 2000), spectroscopic methods (such as 13C-nuclear magnetic resonance spectroscopy, night-time ozone profile, diffuse reflectance infrared Fourier transform spectroscopy, and pyrolysis field ionization mass spectrometry) all could be used to determine structural chemistry (Leinweber et al., 2008; Plante et al., 2011; Saenger et al., 2013). Here we applied chemical extraction schemes using classical alkaline solutions (NaOH and Na4P2O7) to extract the humic substance components from soil samples to indicate the SOC chemical stability. Humic (HA), humin (HM), and fulvic acid (FA) was separated and labelled according to their solubility in base and acid solutions (Olk, 2006; Parsons, 1983). Soil aggregation is a key physical indicator of SOC stability (Dong, 2019). The sieve-based methods, ultrasonic treatment, X-ray computed tomography (CT) and scanning electron microscopy (SEM) could be used to analyze the aggregate microstructure aggregate dynamics (Dal Ferro et al., 2012; Khan et al., 2012; Zaccone et al., 2018). In this study, different size fractions of soil aggregates (A, macroaggregates > 250 μm; M1, microaggregates 53–250 μm; M2, microaggregates < 53 μm) were collected using different sizes of mesh and sieves and used to indicate the physical stability of the SOC (Christensen, 2001; Lützow et al., 2007). The mean weight diameter (MWD) was calculated as: n+1
MWD =
∑ 1
ri − 1 + ri × mi 2
3.2. The variations of carbon physicochemical structures The mass percentage of the macroaggregate (A, > 250 μm) size fraction in the contaminated soils was 3.8% ± 2.1%, a significant increase from that in the uncontaminated soils, where it was 1.4% ± 1.0%. For the microaggregates, the percentage of the M1 (53–250 μm) size fraction increased, while that of M2 (< 53 μm) decreased in the oil contaminated soils compared with the uncontaminated soils (Table 1). The aggregate stability values (expressed as MWD) in the contaminated soils (4.7 ± 1.1 mm) were much higher than those in the uncontaminated soils (3.1 ± 0.6 mm) (Table 1). The SEM images of the oil contaminated and uncontaminated soils are shown in Fig. 2. In the uncontaminated soil (Fig. 2a, b), large and small particles were distributed homogeneously. In the oil contaminated soil (Fig. 2c, d), the small particles appreciably decreased and low clustering degree was observed. The order of the humus content in both the oil contaminated and uncontaminated soils was HM > FA > HA (Table 1). The amount of HM in the oil contaminated soils (5.0 ± 2.6 g kg−1) was much higher than that in the uncontaminated soils (2.7 ± 1.6 g kg−1). Higher values of the HA/FA and HA/humus ratios were observed in the oil contaminated soils (0.12 ± 0.07, 0.04 ± 0.03, respectively) than in the uncontaminated soils (0.23 ± 0.09, 0.09 ± 0.06, respectively), and they positively correlated with the level of contamination (Table 1, Fig. 3). The increased MWD and higher values of HA/FA and HA/humus ratios, indicated a more stable state of the carbon physicochemical structure in the oil contaminated soils, which was consistent with the change of overall SOC stability.
(2)
where ri is the average diameter of the ith size fraction of the aggregate, mi is the mass of the ith size fraction of the aggregate and i = 1, 2, 3, representative of the aggregate size being: > 250 μm, 53–250 μm, and < 53 μm, respectively. SEM images of the soil aggregates were taken on a Zeiss SUPRA55 scanning electron microscope with accelerating voltages of 20 kV to analyze the intra-aggregate microstructures. 2.6. Statistical analysis The differences between the soil properties and SOC stability from the contaminated and uncontaminated soils were analyzed with independent sample T tests, using the SAS software (SAS Institute Inc., Cary, NC, USA). Redundancy analysis (RDA) and variance partitioning analysis (VPA) were performed to evaluate the contributions of various factors to SOC stability using the “Vegan” package within R (http:// cran.r-project.org/web/packages/vegan) (R Development Core Team, 2011). All soil data were Hellinger transformed for standardization. The significance tests were carried out using Monte Carlo permutation (999 times). 3. Results 3.1. Overall changes of SOC stability
3.3. Key factors to determine SOC stability Although CH4 emissions increased, the total carbon released from the oil contaminated soils appreciably decreased, when compared with the uncontaminated soils, during the growing season of May, August, and October) (Fig. 1). The seasonal variation of the soil CO2 emissions was obvious, and the values from the oil contaminated areas were significantly lower than those of the uncontaminated soils. There was a
We analyzed the correlation between the soil properties and carbon physicochemical structures using Pearson correlation analysis (Table 2). The A, M1, and MWD were positively related to the EC and pH (P < 0.05). The M2 was positively related to TN, TP, soil porosity, Al, Fe, and sulfate and negatively related to EC and pH (P < 0.05, 3
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Fig. 1. CH4 and CO2 fluxes in oil contaminated and uncontaminated sites (UC) in May, August, and October (box plots). Each box and whisker plot is drawn from the quartile and outlier values. The differences between the oil contaminated and uncontaminated sites are analyzed by independent samples T test. ** P < 0.01, * P < 0.05.
We further quantified the contributions of the carbon physicochemical structure (S), soil properties (P), and oil contamination (O) to the SOC stability by using VPA, including the pure effects of S, P, and O, interactions between any two components (S × P, S × O, and P × O), interactions of all three components (S × P × O), and the unexplained portion (Fig. 5a). The variance in the “SOC stability,” consisting of CO2 and CH4 fluxes and δ13C values, could be explained by up to 19% with “soil property,” up to 8% with “oil contamination,” and only up to 2% by “carbon physicochemical structure” (P < 0.001) (Fig. 5b). A total of 90% of the variation was explained by these three components, and the interactions among them seemed to have more influence than did the individual components with 18% for S × P and 43% for S × P × O.
Table 1 . Soil organic carbon (SOC) stability indicators in oil contaminated and uncontaminated areas. Soil stability indicators Stable carbon isotope ratios δ13C (‰) SOC physical stability A (%) M1 (%) M2 (%) MWD (mm) SOC chemical stability Humus (g kg−1) HA (g kg−1) FA (g kg−1) HM (g kg−1) HA/FA HA/Humus
Oil contaminated
Uncontaminated
−25.2 ± 0.4*
−25.6 ± 0.1
3.8 ± 2.1* 21.8 ± 4.5 74.3 ± 6.1* 4.7 ± 1.1**
1.4 ± 1.0 16.3 ± 6.1 82.3 ± 7.7 3.1 ± 0.6
7.8 ± 3.4 0.3 ± 0.1 2.5 ± 1.1 5.0 ± 2.6* 0.12 ± 0.07* 0.04 ± 0.03*
5.3 ± 3.6 0.5 ± 0.4 2.1 ± 2.0 2.7 ± 1.6 0.23 ± 0.09 0.09 ± 0.06
4. Discussion Our results showed that the disturbance of oil contamination led to appreciably increased SOC stability by decreased carbon emissions and enrichment of δ13C from the oil contaminated soils. Meanwhile, the increased MWD and higher values of HA/FA and HA/humus ratios with oil contamination, indicated the consistent change in the carbon physicochemical structure. The hydrophobic compounds in the crude oil might be the main reason for their increased physical stability (Dindar et al., 2015). Crude oil could enter the soil pore and coat microaggregates, thereby promoting the aggregation of microaggregates and subsequently increasing the percentage of macroaggregates in comparison to that of uncontaminated soils. Oil pollutants can also limit water and oxygen transfer in soils and act as physical barriers to prevent the access to roots and microorganisms (Adam and Duncan, 2002; Yang et al., 2018a). These results were consistent with that of Martens (2000), which demonstrated that residue biochemistry influenced soil aggregation and regulated soil carbon sequestration. Previous studies also showed that petroleum hydrocarbon components altered the soil microenvironment, such as soil porosity, carbon to nitrogen ratio, EC, and pH, which influenced the soil aggregate size distribution and aggregate stability (Hagedorn et al., 2003; Khan et al., 2012). Meanwhile, the decreased content of Fe and Al in the oil contaminated soils might be the reason for the reduced microaggregates, since the microaggregates were formed by the organic-mineral bonds through anion and inner-sphere ligand-exchange reactions (Torn et al., 1997). Consistent with the increasing physical stability, the SOC chemical
The differences between the oilfields and control sites were analyzed by independent samples T test. ** P < 0.01, * P < 0.05. Abbreviation: A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin.
Table 2). The ratios of HA/FA and HA/humus were positively related to soil porosity and Fe, and HA/FA were negatively related to EC (P < 0.05, Table 2). RDA and VPA were conducted to investigate the key factors that determine SOC stability (Figs. 4 and 5). The RDA showed that the explanatory variables (indices of carbon physicochemical structure, soil properties, and oil contamination) accounted for 98.1% of the total variation (Fig. 4). The longer arrow projected length meant more importance of the variable, and the same arrow orientation between parameters meant positive correlation and vice versa. The soil CO2 fluxes were positively correlated with Fe, M2, soil porosity, and HA/FA ratio, and negatively correlated with EC, TPH, and pH in the oilfields. On the contrary, the soil CH4 fluxes and δ13C values were positively correlated with EC, TPH, pH, and MWD, and negatively correlated with Fe, M2, soil porosity, and HA/FA ratio with oil contamination (Fig. 4). The EC, M2, Fe, TPH, soil porosity, HA/FA, and pH played important roles in the dispersion of the sites along the first axis (Table S2). 4
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Fig. 3. Relationships between soil carbon chemical stability indicators and soil total petroleum hydrocarbon (TPH): humic (HA)/humus and HA/fulvic acid (FA).
trisnorhopane (Tm), 17α-diahopane (Diahopane), 17α,21β-hopance (30ab) and 17α,21β-30-homohopane (C31S, C31R) ], compared with nalkanes (Fig. 6a and b). Meanwhile, numerous compounds of PAHs were detected in contaminated soil, which were difficult to biodegrade (Fig. 6c). Consistently, Wang, Zhang, and Li (2011) found that aromatic or multi-aliphatic ring compounds, including saturated alkyl, fatty, and naphthenic acids, were abundant in the residual oils found in contaminated soils. These compounds had very complex carbon structures and some had biological toxicity, leading to a low bioavailability (Dindar et al., 2015). As the primary microbial mediated processes, soil humification was mainly controlled by the microbial habitat variables like soil water content and porosity, pH, and nutrients (Zech et al., 1997). Oil contamination altered the soil microenvironment, directly influencing the microbial community structure, activity, functional genes, and even the interaction network, which might influence the formation and decomposition of humic substances (Yang et al., 2018a, 2018b). Here we found that only the carbon physicochemical structure could not completely explain the stability of the SOC in oilfields; disturbance and the carbon physicochemical structure and microenvironment codetermine the persistence of organic compounds in soils. The molecular attributes indeterminable from the long-term decomposition rates of organic carbon in mineral soils (Amelung et al., 2008; Marschner et al., 2008) were also verified in the present study by compound-specific isotopic analysis. The individual molecular compounds previously considered to have “slow” turnover (such as lignins or plant lipids) surprisingly had rapid turnover times, while some labile compounds (such as proteins and saccharides) had decades of turnover time (Amelung et al., 2008). Without denying the role of the chemical composition and spatial distribution in the stabilization of soil carbon, especially those of plant residue quality, chemical reactions, and aggregate protection, it should be recognized that perhaps microenvironmental conditions restricted the access of microorganism to decompose organic carbon or controlled the chemical process (for example, oxidation and reduction, hydrolysis and polymerization) (Schmidt et al., 2011). The mechanism of SOC stability is important for the natural attenuation processes of pollutants and sustainable management of contaminated land (Hou et al., 2018). According to Waksman (1936), the key to understanding the problem lies in the “invisible ties” connecting SOC and living microorganisms, which are primary agents in the formation and transformation of humus. Numerous researchers have recognized the microbial contributions to SOC storage and have tried to integrate genomic and metabolic knowledge to predict SOC turnover (such as individual-based models, heterogeneous network models, trait-
Fig. 2. Low- and high-magnification (inset) scanning electron microscopy (SEM) images of uncontaminated soil (a), (b) and oil contaminated soil (c), (d).
structure was more persistent in the oil contaminated soils, owing to the larger amounts of recalcitrant compounds from the oil pollutants. We detected 95 compounds of residual oils in contaminated soil samples by GC/MS, including alkanes (nC12 to nC40), gonane, terpane and PAHs (Fig. 6, Table S3). Crude oil mainly consisted of carbonaceous compounds with heavy molecular weights, including aliphatics, aromatics, resins, and asphaltenes (Ramadass et al., 2015). The results showed that the soil residual oils contained higher content of resistant branched acyclic and monocyclic hydrocarbons [such as 17α-22,29,20-
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Table 2 Correlations between soil properties and soil organic carbon (SOC) physical and chemical stability. Soil properties
TC OM TN TP EC Water PH Soil porosity Al Fe Sulfate NO3–-N
Physical stability
Chemical stability
A
M1
M2
MWD
Humus
HA
FA
HM
HA/FA
HA/Humus
0.241 0.052 −0.408 −0.434* 0.757** −0.037 0.462* −0.792** −0.606** −0.639** −0.395 0.025
0.283 0.193 −0.575** −0.539** 0.845** −0.229 0.648** −0.664* −0.438* −0.532* −0.478* −0.045
−0.242 −0.176 0.545** 0.586** −0.901** 0.187 −0.651** 0.782** 0.531* 0.652** 0.463* 0.034
0.155 0.239 −0.455* −0.483* 0.523* −0.277 0.489* −0.625* −0.636** −0.513* −0.603** −0.268
0.172 0.037 −0.188 −0.281 0.225 −0.059 0.311 −0.066 −0.107 −0.105 −0.088 −0.137
0.016 0.226 −0.006 0.208 −0.322 −0.27 −0.069 0.393 0.323 0.533* 0.275 −0.33
0.022 0.041 −0.07 −0.236 0.016 −0.114 0.285 0.031 0.134 0.128 −0.02 −0.211
0.23 0.004 −0.225 −0.286 0.345 0.011 0.285 −0.166 −0.264 −0.281 −0.143 −0.037
−0.175 0.102 0.021 0.422 −0.453* −0.282 −0.388 0.626* 0.167 0.498* 0.243 −0.304
−0.187 0.132 0.031 0.367 −0.413 −0.244 −0.086 0.655* 0.412 0.557** 0.226 −0.237
Significance: ** P < 0.01, * P < 0.05. Abbreviation: A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin.
Fig. 5. Variation partitioning analysis of soil organic carbon (SOC) stability explained by carbon physicochemical structure (S), soil properties (P), and oil contamination (O). (a) General outline; (b) explanatory variation. Hollow circle shows the variation explained by each factor alone and were proportional to the variation. Black circles represent a combination of factors. Fig. 4. Tri-plot diagram of the redundancy analysis (RDA) of “SOC release and turnover data.” Descriptors (blue arrows) are the δ13C, CH4, and CO2 fluxes. TPH represented soil contamination levels (orange arrows). MWD, M2, HA/FA, and HA/humus represent SOC physicochemical structure (red arrows). TC, OM, TN, EC, PH, Nit, porosity, Al, Fe, TP, Sul, and Water represent soil properties (yellow arrows). These indicators are all used as quantitative explanatory variables. Axes 1 (90.4%, P < 0.001) and 2 (7.7%, P < 0.001) are represented. The total of explained variance in the correlative model is 98.1%. Samples are labeled (circle) as oil contaminated (O1-O14) and uncontaminated soils (C1-C8). Abbreviations: RDA, Redundancy analysis; A, Macroaggregates > 250 μm; M1, Microaggregates 53–250 μm; M2, Microaggregates < 53 μm; MWD, Mean weight diameter; HA, Humic acid; FA, Fulvic acid; HM, Humin; TPH, Total petroleum hydrocarbons; Water, Soil water content; TC, Soil total carbon; OM, Soil organic matter; TN, Soil total nitrogen; TP, Soil total phosphorus; EC, Electrical conductivity; Porosity, Soil porosity; Nit, NO3–-N; Sul, Sulfate. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
link soil structure and plant–microbe–soil carbon transfer in the soil around the roots. In a recent study, Schlueter et al. (2019) found that bacteria prefer to forage near macropore surfaces and fresh particulate carbon in fine textured-soil by 3D microstructure analysis and biogeochemical microscopic data. For future studies, understanding processes of soil carbon stabilization to multiple disturbance and well-designed experiments to investigate uncertainty and vulnerability of SOC are needed. To accurately predict natural attenuation processes of pollutants, the combination of chemical, physical, and biological data at the soil microscale will also be important for exploring the mechanisms and better managing the contaminated sites. Acknowledgements We thank Dr. Cong Wang from the Research Center for EcoEnvironmental Sciences, Chinese Academy of Sciences (RCEES-CAS) for his comments and suggestions that have helped to improve our paper. The research was supported by the National Natural Science Foundation of China (41672238) and the Thousand Talents Plan for Young Professionals and Yong Elite Scientist Sponsorship Program by CAST (2015QNRC001).
based microbial models, and bioclimatic models) (Creamer et al., 2015; Kallenbach et al., 2016; Trivedi et al., 2013). However, over 80 years later, it remains a topic of debate, and the existence of interdisciplinary barriers has made progress in identifying the mechanisms of SOC accumulation and decomposition, which calls for comprehensive and advanced technologies (Baveye and Wander, 2019). Hopefully, progress in spectroscopic analysis techniques (e.g., nuclear magnetic resonance methods) and X-ray computed tomography equipment (e.g., Xray absorption near-edge structure) will make it possible to quantify the chemical composition of organic carbon and the geometry and topology of soil pores at nanometric and micrometric scales (Dal Ferro et al., 2012; Khan et al., 2012; Sutton and Sposito, 2005). Vidal et al. (2018) combined in situ 13C-labeling and different spectroscopic techniques to
Declaration of Competing Interest All authors declare no conflicts of interest. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// 6
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Fig. 6. Content of residue oil in the soil (saturated hydrocarbons and polycyclic aromatic hydrocarbons, PAHs) by GC/MS. Data was presented as the mean ± standard error.
doi.org/10.1016/j.envint.2019.105390.
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