Environ. Sci. Technol. 1995, 29, 7-18
Predictive Model for Estimating the Extent of Petroleum Hvdrocarbon Biodearadation in MICHAEL H . HUESEMANN Battelle, Pacific Northwest Laboratories, Richland, Washington 99352
A series of solid- and slurry-phase soil bioremediation experiments involving different crude oils and refined petroleum products were performed in order to investigate the factors which affect the maximum extent of total petroleum hydrocarbon (TPH) biodegradation. Utilizing a comprehensive petroleum hydrocarbon characterization procedure involving group type separation analyses, boiling point distributions, and hydrocarbon typing by field desorption mass spectroscopy, initial and final concentrations of specified hydrocarbon classes were determined in each of the seven bioremediation treatments. In this study, it was found that the degree of TPH biodegradation was mainly affected by the type of hydrocarbons in the contaminant matrixwhile the influence of experimental variables such as soil type, fertilizer concentrations, microbial counts, and treatmenttype (slurryvs landtreatment) on the overall extent of TPH biodegradation appeared to be insignificant. Based on these findings, a predictive algorithm was developed to estimate the extent of TPH biodegradation from the average reduction of 86 individual hydrocarbon classes and their respective initial concentrations. Model predictions for gravimetric TPH removals were in close agreement with analytical results from two independent laboratories.
0013-936W95/0929-0007$09.00/0
Q 1994 American
Chemical Society
Introduction The extent of hydrocarbon biodegradation in contaminated soils is critically dependent upon four factors, namely, the presence of hydrocarbon degrading bacteria (1, 21, the creation of optimal environmental conditions to stimulate biodegradative activity (3-8),the predominant petroleum hydrocarbon types in the contaminated matrix (9-15), and finally the bioavailabilityof the contaminants to degradative bacteria (16-19). In addition to these fundamental factors, the measured degree of hydrocarbon biodegradation is also dependent upon the analytical methods employed to quantify the contaminant concentrations (20-22). As a result of these numerous factors, it is not surprising that a wide range of conclusions regarding the success of soil bioremediation have been published in recent years. For example, Raymond et al. (23)conducted an extensive field bioremediation study involving six different hydrocarbon types that were applied to field plots in three locations with different climatic conditions. Within 1year of bioremediation treatment, the average reduction of gravimetric oil and grease (O&G) ranged from 48.5% to 90%, depending on the type of oil applied and the location. Huesemann et al. (24) reported a 90% removal of total petroleum hydrocarbons (TPH) in weathered Michigan crude oil-contaminated soil during 22 weeks of bioremediation treatment. Song et al. (15) demonstrated the effectiveness of soil bioremediation for a number of different petroleum distillates. These investigators reported an increasing potential of biodegradation based on boiling point distribution in the following order: bunker C diesel fuel heating oil jet fuel gasoline. Even with respect to a supposedly well-defined contaminant type such as diesel fuel, the extent of TPH removal was found to range from 55% to around 90% depending upon treatment conditions and analytical methods employed (15,22,25). Assuming that hydrocarbon degrading microbes as well as optimum environmental conditions (Le.,fertilizer amendments, aeration, moisture, and pH control) are present in most soil bioremediation projects, the extent of petroleum hydrocarbonbiodegradationis likely to be affected primarily by the molecular composition of the hydrocarbon contaminant unless bioavailability poses a limitation to the biodegradation mechanism. The availability of soil contaminants to biodegradative bacteria may be limited in the presence of clay or humus fractions in the soil matrix (1618) and may be enhanced by the addition of agents (Le., water, surfactants, solvents)which facilitatethe desorption of contaminant molecules from the soil solid phase to the surrounding aqueous medium (16, 26, 27). The work presented in this paper provides evidence that the extent of hydrocarbon biodegradation is largely affected by the molecular composition of the soil contaminant. A comprehensive analytical scheme for hydrocarbon characterization has been developed and is used to determine the fate of selected hydrocarbon types or classes during the biodegradation of petroleum compounds under a wide variety of experimental conditions. Based on the data presented here, it is possible to predict the extent of hydrocarbon removal in most cases given the initial composition of the hydrocarbon contaminant.
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VOL. 29, NO. 1, 1995 / ENVIRONMENTAL SCIENCE &TECHNOLOGY 1 7
4
Freon Extraction
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Column Chromatography
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FIGURE 1. Comprehensive analytical characterization procedure for petroleum hydrocarbons in contaminated soils or slurries.
Experimental Methods Bioremediation Treatments. Treatment A. A sample of 6 kg of a sieved (Tyler screen 16) potting soil mixture consisting of 64.5% sand, 19.4%silt, 16.1%clay, and 14.1% total organic carbon was spiked with 300 g (5%wt) of crude oil. The API gravity (at 60 "C) of the crude oil was 21". The freshly contaminated soil was amended with 32.1 g of ammonium nitrate (NH4N03) and 12.6 g of potassium phosphate (dibasic K2HP04),which is equivalent to a CINIP ratio of approximately 100:5:1 (wt). After pH and moisture adjustment, the contaminated soil was land-treated for a 52-week period in a closed land-farming mesocosm that has been described earlier (24). Treatment B. This treatment was equivalent to treatment A with the exception that a crude oil with an API gravity of 39" was used to spike the soil. Treatment C. This treatment was equivalent to treatment B with the exception that a similar API gravity (39") crude oil from a different source location was used to spike the soil. TreatmentD. A diesel-based drillingmud was amended with ammonium nitrate, phosphoric acid, and a dried bacteria preparation. This slurry was subsequently biotreated for a 41-week period as reported earlier (22). Treatment E. Highly weathered crude oil-contaminated site soil was sieved (Tyler screen 4) and amended with cow manure, refinery activated sludge solids (inoculum), ammonium nitrate, and potassium phosphate as outlined earlier (24). The site soil consisted of 97.6%sand, 1.3%silt, and 1.1%clay. After pH and moisture adjustment, the contaminated site soil was land-treated for a 52-week period in a closed land-farming mesocosm as described recently (24).
Treatment F. A sample of 10kg of a sieved (Tylerscreen 4) topsoil consisting of 78.7% sand, 11.5%silt, 9.8% clay, and 0.3% total organic carbon was spiked with 1008 g of 8
ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29. NO. 1.1995
fresh (unused) 1OW-30motor oil. After a 2-month weathering period, the gravimetric TPH and O&Gwere 7.1% (wt) and 8.4% (wt), respectively. At this point, 4.7 g of Miracle Gro fertilizer (15-30-15)was addedIkgof contaminated soil (dry wt), which is equivalent to a TPHINIP ratio of 100: 1:0.2. The fertilizer-amended soil was slurry biotreated for 34 weeks as described elsewhere (28). The solids content of the slurry was approximately 65% (wt). After 30 weeks of treatment, an additional 4.7 g of Miracle Gro fertilizer was addedlkg of slurry solids (dry wt) in order to eliminate any possible N or P limitations during biodegradation. Treatment G. A sample of 10 kg of sieved topsoil (same as in treatment F) was spiked with 2157 g of fresh diesel oil no. 2. After a 2-month weathering period, the gravimetric TPH and O&Gwere 7.5% (wt) and 14.7%(wt), respectively. At this point, 5 g of Miracle Gro fertilizer was addedIkg of contaminated soil (dry wt), which is equivalent to a TPHI NIP ratio of 100:1:0.2. As described elsewhere (28), the fertilizer- amended soil was slurry treated for 34 weeks at a solids content of approximately 50% (wt). After 30 weeks of treatment, an additional 5 g of Miracle Gro fertilizer was addedIkg of slurry solids (dry wt) in order to eliminate any potential N or P limitations during biodegradation. ComprehensiveHydrocarbon Characterization Scheme. Initial and final samples from bioremediation treatments were subjected to an extensive petroleum hydrocarbon characterization strategy as outlined in Figure 1. The soil or soil slurry sample is Soxhlet freon-extracted, and a fraction of the resulting O&G extract is dried to obtain the gravimetric O&G concentration. The remainder ofthe O&G extract is treated with silica gel to remove any polar compounds. The concentration in the resulting TPH extract is measured either gravimetrically (TPH-GR) or using infrared spectroscopy (TPH-IR). A portion of the gravimetric TPH extract is redissolved in cyclopentane and separated into saturate, aromatic, and polar fractions by
column chromatography. Both saturate and aromatic fractions are further characterized by a boiling point profile (BPP) analysis and field desorption mass spectroscopy (FDMS). The BPP gives detailed information regarding the molecular weight distribution from C13 to C44 as well as the fraction of any hydrocarbons heavier than C44. The FDMS analysis provides compositional data for any C13C44 hydrocarbon based on both carbon number (or molecular weight) and hydrogen deficiency (or z-number). Details regarding each analytical method employed are given below. Analytical Methods. Oil and Grease (O&G). O&Gwas measured gravimetricallyfollowingSoxhletfreon extraction according to Standard Method 5520 E (29). Total Petroleum Hydrocarbons, Gravimetric (TPHGR). Following Soxhlet freon extraction of the contaminated soil, the O&G extract was treated with silica gel (3 gll00 mg of O&G) to remove polar organic compounds. The resulting extract was dried under nitrogen at room temperature, and the weight of the residue was reported as gravimetric TPH according to Standard Method 5520 F (30). Total Petroleum Hydrocarbons, Infrared (TPH-IR). A portion of the above TPH extract was analyzed with an infrared analyzer (Horiba) according to EPA Method 418.1 (31). The calibration standard consisted of 25% (vlv) n-hexadecane, 37.5% (vlv) isooctane, and 37.5% (vlv) chlorobenzene. The hydrocarbon absorption was measured in the infrared spectral frequency range of 3.4-3.5 Pm* Group Type Separation (GTS). The TPH extract was separated into saturate, aromatic, and polar organic fractions using column chromatography as follows. The dried TPH extract was redissolved in cyclopentane and placed on top of a glass column packed with silica gel. The saturated hydrocarbons were eluted from the column with pentane, while the aromatic compounds were eluted using a pentane-benzene (60%-40%) solvent. The polar fraction was eluted with a benzene-isopropyl alcohol (80%-20%) solution. The total mass of each fraction was determined gravimetrically after the respective solvents had been evaporated at 60 "C. Boiling Point Profile (BPP). The saturate or aromatic fraction obtained from the group type separation analysis was boat-injected into a heating furnace kept at 330 "C in order to vaporize any compounds with a carbon number up to C44. The vaporized compounds were condensed at the inlet of a chromatographic column held at -15 "C. The column temperature was increased at a rate of 4 "Clmin to 350 "C. Helium was used as a carrier gas to elute the vaporizing hydrocarbons from the column resulting in a separation of hydrocarbon fractions according to differences in their respective boiling points (or carbon numbers). The eluted hydrocarbons were combusted at 750 "Cover copper oxide yielding carbon dioxide and water. After removal of the water by magnesium chloride, the carbon dioxide concentration in the helium carrier gas was quantified using a thermal conductivity detector. After the column temperature had reached 350 "C and hydrocarbons with carbon numbers up to C44 had been eluted, the system switched to a backflush mode, which produced one combined output for all the higher boiling point components in the sample. The combiced output consisted of a whole backflush of residue cracked at 750 "C and a subsequent oxygen burnout backflush for polar compounds.
Field Desorption Mass Spectroscopy (FDMS). The saturate fraction was dissolved in cyclohexane, and the aromatic fractionwas dissolved in toluene at approximately 5-10 mglmL. ApproximateIy 1 pL of the sample was deposited on the carbidized surface of a tungsten wire emitter. The solvent was evaporated at ambient temperature for 2-3 min, and the emitter was placed into a VG ZAB mass spectrometer via a probe. The sample was initially desorbed without heat, and as the total ion current decreased, heat was applied to the emitter to desorb the heavier components of the sample. The datawere acquired using field desorption with a scan range of 1500- 100 amu, a source temperature of 100 "C, a resolution of 2000, and a scan rate of 20 sldecade. The sample molecules at the tips of the carbidized wire were subjected to high electrical gradients on the order of 10 Vlcm between the emitter and the extraction plate 2 mm away. An electron is removed from the molecule under these conditions resulting in positively charged molecular ions that are extracted into the mass analyzer. Field desorption spectra were recorded as the sample was heated slowly and the sample molecules vaporized. Asingle field desorption spectrum was obtained by averaging all scans. A hydrocarbon type data system program was utilized to determine the z-number distribution of the sample. Data Analysis and Interpretation. The power of the comprehensive hydrocarbon characterization scheme (Figure 1)is related to the fact that all hydrocarbons with carbon numbers greater than 13 can be accounted for on a mass balance basis. Data from the group type separation analysis in conjunction with the boiling point profiles are used to differentiate the TPH compounds into four broad classes, namely, C44 saturates, C44 aromatics. Field desorption mass spectroscopy of the saturate and aromatic fractions provides more detail regarding the composition of c44
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While building a predictive model using more than 400 hydrocarbon classes (14 z-types x 31 C-numbers) seems appealing, rigorous analysis of the data revealed high variabilities between f, values calculated from different hioremediation treatments. This observed variability between f, values is most likely caused by the analytical imprecisions associated with the semiquantitative FDMS analysis. At the other extreme would be a model which incorporates only afew, for example, four compound classes such as GI4 saturates, C44 aromatics. However, based on statistical regression analysis,the four-compound model consistentlyfailed to predict finalTPH-GRvalues. Theobvious reason f o r t e poor predictive properties of this model is the fact that important compositional information is lost in the process oflumping hundreds of compound types into so fewclasses. This is the classic problem of balancing bias and variance. The general solution to the problem is to choose an intermediate number of categories for which intraclass variation is expected to be low. This should be the case for compound classes whose C-numbers do not differ substantially. Based ofi the above considerations, a predictive model was designed using an intermediate number of compound classes. Table 3 presents the "prediction matrix" which contains averagefivaluesfor86 distinct compound classes. Average f,values for any of the GI4 saturates and >C44 aromatics remaining after biodegradation were computed as the data averages from treatments A, B, C, and E (Figure 2). Usingthef,valuesgiveninTable3,thefinalgravimetric TPH concentration can be predicted from eq 2 if the concentration of the 86 compound classes in the initial gravimetric TPH extract is known. Before discussing the
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FIGURE 8. Comparison between measured (by labs 1 and 21 and model-predicted fraction of gravimetricTPH remaining atter biore-
mediation. prediction results, it is important to recognize the hasic model assumption, namely that fi values are relatively constant and independent of contaminant (oil) type and treatment conditions. If each hydrocarbon molecule were identifiable and each class (i) consisted of only one molecule, fi would he absolutely constant (i.e., 0% for biodegradable molecules and 100% for recalcitrant ones, assuming that no partially degraded intermediates are formed). Since each class, because of andyticallimitations, must necessarily consist of many different molecules, the h representing this class is only relatively constant. Specifically, because the gravimetric TPH was separated into 86 different classes with each class containing perhaps hum dreds if not thousands of hydrocarbons characterized by different molecular stmctures and biodegradation properties, variation must necessarily be observed between the fivehvalues from different bioremediation treatments (see example above and Figure 7). The presence of different hydrocarbon molecules within each compound class also weakens the model's predictive capability with respect to partially or fuUy biodegraded oils. In this case, the model would falsely predict the disappearance of recalcitrant hydrocarbons within a specific compound class. In the ideal case in which each hydrocarbon molecule could be identified,the modelwould be fully predictive forweathered oils. In order to investigate the accuracy of the prediction model, the final gravimetric TPH remaining after biodegradation is calculated using the measured initial TPH composition for all treatments A-G (using lab 1 data, see Table 2 and Figure 8). In addition, the predicted TPH remaining after biodegradation is compared to the actual fraction ofTPH remaining as measured by two independent laboratories. As shown in Figure 8, model-predicted TPH fractions remaining are in very close agreement with measured (by lab 1)TPH fractions remainingfortreatments A-E. This observation is not completely unexpected because the prediction matrix contains the data averages obtained from these five treatments, and these averages are in turn used to calculate the fraction ofTPH remaining in each of these treatments. Despite this positive bias, it is nevertheless surprising how closely the prediction algorithm estimates the TPH fraction remaining. In fact, there appears to be a stronger correlation between the prediction results and the data from laboratory 1 ( r = 0.98 VOL. 29. NO. 1.1995 I ENVIRONMENTAL SCIENCE &TECHNOLOGY m 15
for treatments A-E) than between the independent data from the first and second laboratory ( r = 0.95 for treatments A-E).
The most rigorous test for any predictive model is to apply it to an entirely independent set of petroleumcontaminated soils which were not used for the development of the original prediction matrix. The initial hydrocarbon type compositionwas determined for the petroleumcontaminated soils of treatments F and G (usinglab 11, and the prediction algorithm was applied to estimate the gravimetric fraction remaining after biodegradation. As shown in Figure 8, the model slightly overestimates the percentage of TPH remainingin treatment F (35% predicted versus 18% and 27% measured by laboratories 1 and 2, respectively) and slightly underestimates the percentage in treatment G (14% predicted versus 23% and 15% measured by laboratories 1 and 2, respectively). The reasons for the over- and underestimation are most likely the same as those outlined above for the causes of the less than perfect correlation between biodegradation extent in different treatments and the approximate molecular structure as determined by FDMS. Nevertheless, the fact that predictive results are generallyclose to measured data would demonstrate the overall correctness of the above stated hypothesis, namely, that the extent of TPH biodegradation is mainly affected by the type of molecular hydrocarbon structures present in the contaminated soil. Potential Model Applications and Limitations. The most obvious model application is the estimation of optimal achievable final TPH values given initial composition data for a specified petroleum hydrocarbon-contaminated soil. Such estimates can generallybe availablewithin a few weeks while laboratory biotreatability studies are sigdicantly more time consuming and expensive. In addition, most laboratory studies are not performed long enough to determine the maximum extent of TPH removal. By contrast, the model parameters are based on experiments which have been carried out for time periods long enough to assure that TPH concentration profdes have reached asymptotic levels. Another useful application of the prediction model and the comprehensive hydrocarbon analysis scheme (Figure 1) relates to the detailed characterization of petroleum hydrocarbon-contaminated media prior to initiating bioremediation activities. If the source of the soil contaminant is not known, this analytical strategy can be used to determine whether the petroleum contaminants consist of high or low molecular weight compounds and whether a particular refined product or a certain API gravity crude oil was the likely contaminant source. In addition to using the model to predict the probable extent of TPH removal in a bioremediation project, the model can also be employed to estimate the molecular composition of compounds which are likely to be left behind after the biodegradation process has stopped. Presently, these compounds reside in the "unresolved hump" of commonly publicized gas chromatograms. The comprehensive waste characterization scheme (Figure 1) can be utilized to obtain a general idea regarding the characteristics of these unresolved hump compounds. For example, only high molecular weight hydrocarbons are likely to be found remaining in intensely weathered or bioremediated soils. The approximate concentration of these compounds types together with information regarding their physical, chemi16
ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29, NO. 1 , 1 9 9 5
cal, or biological properties may be used in risk assessment procedures. For example, the presence of certain C35 saturates remainingin the soil may prove harmless because these compounds are expected to be virtually immobile due to their low aqueous solubility. In general, it is necessary to note that all conclusions strictly apply only to the conditions studied. There are a number of specific limitations regarding the applicability of this predictive model. First, the model can onlyestimate the biodegradation extent accurately if all extracted (freon) hydrocarbons are truly available to soil microbes for biodegradation. In cases where freonis able to extract these compounds from the soil matrix while at the same time they are not readily bioavailable under normal bioremediation conditions,the model willunrealisticallyoverpredict the extent of TPH biodegradation. This situation may occur in highly weathered soils with either high clay or organic matter content (16-18). Second, the model is unable to predict biodegradation rates because these are strongly dependent on experimental conditions such as temperature, pH, microbial numbers, degree of weathering, soil types, acclimation times, etc. Third, only the extent of gravimetric TPH biodegradation can be estimated. The loss of TPH as measured by infrared spectroscopy cannot be predicted with this model since some TPH-IR removal may be due to volatilization. Fourth, the model is unable to accurately predict the TPH biodegradation potential of partially or fullybiodegraded oils. Consequently,the model can only be applied in situations where no or very little contaminant biodegradation has occurred prior to hydrocarbon characterization. Finally, the model cannot predict the ultimate long-term fate (> 1 year) of these petroleum hydrocarbons in contaminated soils. Even though most TPH concentration profiles appeared to have leveled off after 20 weeks of bioremediation in all treatments, it is possible that hydrocarbon biodegradation will continue at imperceivable low rates for long periods of time. If this is the case, all petroleum hydrocarbons and their metabolic breakdown products may ultimately be incorporated into the soil humus fraction and be no longer detectable by standard TPH analytical methods.
Conclusions (1)Depending upon the petroleum contaminant type (crude oil or refined product) present in the soil bioremediation treatments, gravimetric O&G and TPH removals by biodegradation ranged from 50 to 89% and from 55 to 87%, respectively. TPH removals as measured by infrared spectroscopy (TPH-IR)were slightly higher, indicating the volatilization of some lighter hydrocarbons during the bioremediation treatments. (2) Biodegradation does not appear to be limited by the aqueous solubility of hydrocarbon molecules since a significant fraction of high molecular weight (> C44) saturates (>70%) and aromatics (25%)were biodegraded in all treatments. (3) The biodegradation extent of
