Arch Environ Contam Toxicol DOI 10.1007/s00244-015-0154-7

Partitioning of Aromatic Constituents into Water from Jet Fuels Chien-Jung Tien1 • Youn-Yuen Shu2 • Shih-Rong Ciou1 • Colin S. Chen1

Received: 7 October 2014 / Accepted: 14 March 2015 Ó Springer Science+Business Media New York 2015

Abstract A comprehensive study of the most commonly used jet fuels (i.e., Jet A-1 and JP-8) was performed to properly assess potential contamination of the subsurface environment from a leaking underground storage tank occurred in an airport. The objectives of this study were to evaluate the concentration ranges of the major components in the water-soluble fraction of jet fuels and to estimate the jet fuel–water partition coefficients (Kfw) for target compounds using partitioning experiments and a polyparameter linear free-energy relationship (PP-LFER) approach. The average molecular weight of Jet A-1 and JP-8 was estimated to be 161 and 147 g/mole, respectively. The density of Jet A-1 and JP-8 was measured to be 786 and 780 g/L, respectively. The distribution of nonpolar target compounds between the fuel and water phases was described using a two-phase liquid–liquid equilibrium model. Models were derived using Raoult’s law convention for the activity coefficients and the liquid solubility. The observed inverse, log–log linear dependence of the Kfw values on the aqueous solubility were well predicted by assuming jet fuel to be an ideal solvent mixture. The experimental partition coefficients were generally well reproduced by PP-LFER.

& Colin S. Chen [email protected] 1

Department of Biotechnology, National Kaohsiung Normal University, 62 Shen-Chung Road, Yanchao, Kaohsiung 824, Taiwan

2

Department of Chemistry, National Kaohsiung Normal University, Kaohsiung 824, Taiwan

Petroleum products are highly complex, and compositions can change over time after their release into the environment. Consequently, it is challenging to select the most appropriate analytical methods to perform constituentspecific analyses of these mixtures for the evaluation of environmental samples. A significant number of sites have been affected by petroleum hydrocarbons resulting from a wide range of past industrial, military, and petroleum production and distribution practices. Much more detailed data are typically needed to analyze environmental risk than are required by the petroleum industry to determine the performance characteristics of petroleum mixtures. The difficulties in evaluating and remediating these sites arise from the complexity of the regulatory, scientific, and economic issues regarding affected soil and water/groundwater. Risk-based analyses of jet fuel-contaminated sites may be hampered by the absence of readily available data on the composition of jet fuels. The potential for a large release of jet fuels into the environment and the small release from underground storage tanks has prompted increased interest in contaminant characterization and remediation research and its applications to jet fuel-contaminated sites (Rodgers et al. 1999; Xie and Barcelona 2003; Mezher et al. 2013). Jet fuels (aviation turbine fuels) are the most widely available aviation fuels and one of the primary types of fuels used for internal combustion engines worldwide. These fuels are complex mixtures of hydrocarbons whose environmental fate depends primarily on the specific chemical and physical properties of their individual components. Jet fuels consist primarily of hydrocarbon compounds (paraffins, cycloparaffins or naphthenes, aromatics, and olefins) and contain additives that are determined by the specific uses of the fuel (Chevron Corporation 2006; ExxonMobil Aviation International 2008).

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Major commercial grades of jet fuels include Jet A, Jet A-1, and Jet B. Both Jet A-1 and Jet A are kerosene-type fuels. JP-8 is the military equivalent of Jet A-1 with the addition of a corrosion inhibitor and anti-icing additives. Jet fuels also contain nonhydrocarbon compounds such as sulfur and sulfur compounds. Jet fuels contain additives such as antioxidants, metal deactivators, fuel system-icing inhibitors, corrosion inhibitors, and static dissipater additives in limited quantities to improve performance (Chevron Corporation 2006; ExxonMobil Aviation International 2008). A series of experiments and calculation was performed to expand our current knowledge of the partitioning behavior of jet fuels in the subsurface environment. Partition coefficients of target compounds, measured by partitioning experiments, were compared with estimation by the polyparameter linear free-energy relationship (PP-LFER) developed by Goss and Schwarzenbach (2001). The objectives of this study were (1) to evaluate the concentration ranges of major components in the water-soluble fraction of jet fuels (i.e., Jet A-1 and JP-8), (2) to measure the jet fuel–water partition coefficients for major aromatic components, and (3) to compare the experimental partition coefficients with those predicted using the PP-LFER approach. Thus, technical information was obtained that regulators, risk assessors, and site managers can use to make health risk-based decisions at jet fuel-contaminated sites.

Theory The process of equilibrium partitioning between nonaqueous phase liquids (NAPLs) and water governs the leaching of contaminants from NAPLs. In the liquid–liquid partitioning of organic compounds between jet fuel and water, a partition coefficient (Kfw) can be defined as the ratio of solute concentrations in the fuel and water phases, i.e., Kfw ¼ Co;e =Cw;e

ð1Þ

where Co,e and Cw,e are the molar concentrations (mol/L) of the compound of interest in the organic (jet fuel) and the aqueous phases at equilibrium, respectively. At equilibrium, the respective compound has the same chemical potential in the aqueous and organic phases: Xo c o ¼ X w c w

ð2Þ

where the subscripts o and w denote the organic and aqueous phases, respectively; Xo and Xw are the mole fractions of the solute in the organic and aqueous phases, respectively; co is the activity coefficient of the solute in the organic phase that is in equilibrium with the aqueous

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phase; and cw is the activity coefficient of the solute in the aqueous phase that is in equilibrium with the organic phase. For ideal behavior, four assumptions are made: (1) the presence of other components does not affect the activity coefficient of the chemical, i.e., cw of the chemical in the mixture is set equal to cw of the pure solute; (2) the solute behaves ideally in the organic phase; (3) the aqueous mole fraction solubility of the pure liquid solute is equal to 1/cw; and (4) the aqueous solution is sufficiently dilute. Therefore, the solute concentration in the aqueous phase (Cw) is proportional to the mole fraction of the solute in the organic phase (Banerjee 1984): Cw;e ¼ Xo Sw

ð3Þ

where Sw is the aqueous solubility (moles/L) of the pure liquid solute. Assuming that the organic phase is an ideal mixture, Eq. (1) can be expanded as follows: Kfw ¼ Co;e =Xo Sw ¼ ðqo =MWo Þ=Sw

ð4Þ

where qo is the jet fuel density (g/L), and MWo is the average molecular weight of the organic phase (jet fuel) (g/mole). Taking logarithms on both sides of Eq. (4), an inverse relationship between log Kfw and log Sw is expected. The corresponding plot has a unit-negative slope, and the intercept is a function of the density (qo) and the average molecular weight (MWo) of the organic phase: logKfw ¼ logSw  logðMWo =qo Þ

ð5Þ

Because the polynuclear aromatic hydrocarbons (PAHs) investigated in this study are solids in their pure form, the hypothetical super-cooled liquid solubilities (Sl) of the solid solutes must be employed instead of aqueous solubility (Sw) in Eq. (5). The super-cooled liquid solubility (Sl) at a given temperature can be calculated directly from the aqueous solubility (Sw), heat of fusion (DHf), and melting point (Tm [°K]) for the chemical (Yalkowsky and Valvani 1980): log Sl ¼ logSw þ DHf ðTm  T Þ=2:303RTm T

ð6Þ

where Sw is the crystal aqueous solubility (mole/L) at a given temperature T (°K), and R is the gas constant (kJ/d e.g., mole); or, alternately, Sl can be estimated by assuming a constant entropy of fusion (DSf = DHf/T) for the PAHs of interest. The partitioning behavior of multicomponent liquids, such as different types of fuel and coal tar in water, was evaluated (Cline et al. 1991; Lane and Loehr 1992; Lee et al. 1992a, b; Chen et al. 1994, 2008a, b; Peters and Luthy 1993, 1994). In these studies, the aqueous phase concentrations of aromatic hydrocarbons were estimated using Raoult’s law convention for the activity coefficient calculations in conjunction with the liquid or supercooled liquid solubilities. These studies led to the conclusion that it is

Arch Environ Contam Toxicol

acceptable to use Raoult’s law for these fuels for most field-scale applications involving nonpolar contaminants at fuel spill/disposal sites. However, estimating the fuel– water equilibria poses an additional challenge because the compositions of different types of fuels can vary widely. Fuel mixtures may contain different types of polar compounds, and less accurate estimates of Kfw can be obtained using Raoult’s law for compounds with polar functional groups. An estimation method, such as the singleparameter linear free-energy relationship (SP-LFER), can only predict the compound variability within a single compound class (Goss and Schwarzenbach 2001; Schmidt et al. 2002; Arey and Gschwend 2005; Endo and Schmidt 2006). The applicability of SP-LFER is fairly limited because no single parameter can be used to appropriately describe all of the molecular interactions that determine the equilibrium partitioning of a given compound between two phases (Goss and Schwarzenbach 2001; Schmidt et al. 2002; Endo and Schmidt 2006). Therefore, this estimation method is inaccurate for polar compounds. Hence, polar fuel constituents (e.g., phenols and anilines) may be especially sensitive to the presence of other polar additives such as oxygenates. A solvation model was developed for polar and nonpolar fuel constituents. The liquid–liquid partition coefficients in a variety of fuel– water systems were predicted for a broad range of dilute solutes by combining linear solvation energy relationships that were developed using linear solvent theory (Arey and Gschwend 2005). The PP-LFER was introduced to describe partitioning by capturing all of the relevant molecular interactions in the respective phases (Goss and Schwarzenbach 2001). In particular, it may not be possible to estimate the partitioning behavior of polar and nonpolar constituents using Raoult’s law convention. The PP-LFER is based on considering all of the interactions that are involved in partitioning using separate parameters. The PP-LFER can be applied to many organic compounds of various polarities using a single equation (Goss and Schwarzenbach 2001; Schmidt et al. 2002; Endo and Schmidt 2006). The PP-LFER has the following form (Endo and Schmidt 2006): logKsw ¼ c þ eE þ sS þ aA þ bB þ vV

ð7Þ

where log Ksw is the logarithm of the partitioning coefficient of a given solute between an organic solvent and water, and E, S, A, B, and V are the excess molar refraction, a parameter for characterizing the dipolarity/polarizability, the overall hydrogen bond acidity, the overall hydrogen bond basicity, and McGowan’s characteristic volume, respectively, of the solute (Abraham 1993; Abraham et al. 1994, 1999, 2004; Endo and Schmidt 2006). The parameters e, s, a, b, and v denote the corresponding solvent characteristics for different molecular interactions with solutes.

PP-LFER approaches have been successfully employed to reproduce experimentally obtained fuel–water partition coefficients for nonpolar and polar compounds in gasoline (Schmidt et al. 2002). PP-LFER has also been used to predict partition coefficients for various types of NAPLs such as diesel, jet fuel, synthetic fuels, coal tar, and creosote (Schmidt et al. 2002; Arey and Gschwend 2005; Endo and Schmidt 2006; Torres-Lapasio´ et al. 2008). Thus, PP-LFER can be used to predict the partition coefficients for a wide range of organic compounds with varying polarities between the fuel and water phases for which data from partitioning experiments are not available.

Materials and Methods Estimation of Average Molecular Weight Jet A-1 and JP-8 were obtained from the Chinese Petroleum Company, Kaohsiung, Taiwan. One-to-ten dilutions of Jet A-1 and JP-8 (in methylene chloride) were injected into an Agilent 6890 gas chromatograph coupled with a 5973 N mass selective detector and a ChemStation for control and data acquisition. The fused silica capillary column (5 % phenyl and 95 % methyl polysiloxane) had a 0.25-mm i.d., a 60-m length, and a 0.25-lm film thickness (DB-5MS; Agilent J&W). The temperature program consisted of an initial temperature of 32 °C with a 2-min hold time, which was ramped at 3 °C/min–52 °C, followed by a ramp to 280 °C at 15 °C/min, and a final ramp to 300 °C at 3 °C/min. Helium was used as the carrier gas at a flow rate of approximately 1.0 mL/min. The detector scan range was 50–550 amu. The electron multiplier voltage was 2082 eV. The average molecular weights of Jet A-1 and JP-8 were calculated using the simulated distillation method (Cline et al. 1991; Lee et al. 1992a, b; Chen et al. 1994, 2008a, b). This method is based on considering the elution of compounds from the column as a simulated distillation, wherein the complex compounds reach their respective boiling points at different temperature. The elution corresponds to certain molecular weights at certain temperatures in the column. In this procedure, the centroid of the chromatogram is located, and the paraffin peaks closest to the centroid, along with their fractions relative to the centroid, are used to determine the average carbon number (n). The average molecular weight of the paraffins is then calculated by the empirical expression CnH2n?2. Reference aliphatic (C5–C20) standards were injected initially to divide the total ion chromatograms for a jet fuel into a series of sections. Total area count of all peaks representing every section was performed using gas chromatography (GC)/mass spectrometry (MS) software. Based on the area distribution in

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different sections, the average molecular weight was determined by: MWj ¼ RMWi AFi

ð8Þ

where MWj is the average molecular weight for jet fuel (g/mole); MWi is the molecular weight of reference alkane or aromatic in ‘‘i’’th section; and AFi is the area fraction in ‘‘i’’th section. Partition Experiments In the laboratory, the fuels were transferred to different glass containers with Teflon-lined caps and stored in the dark at 4 °C. Two-liter reagent bottles with ground glass stoppers and Teflon stopcocks positioned approximately 5 cm from the bottom were used as reaction bottles. Distilled, deionized, organic-free water was obtained from a Millipore Milli-Q water purification system. A fuel-to-water ratio of 1:10 and a 12-h contact time was used in the experimental design. Two hundred milliliters of jet fuel were carefully introduced onto the surface of 2 L of deionized water in the reaction bottles, which were protected from light at all times by aluminum foil covers. The reaction bottles were placed in a horizontal shaker and agitated at room temperature (22 ± 2 °C). The shaking speed was set at a rate that would not promote the formation of fuel globules or emulsions in the stopcock of the reaction bottle. After shaking, all of the reaction bottles were allowed to rest for 30 min before sampling. The water phase was withdrawn from the reaction bottle with minimal turbulence. Water samples were collected in 40-mL volatile organic analyte (VOA) bottles with Teflonlined septa for volatile organic compound analysis. The VOA analyses were performed within 2 days (48 h) of sample collection. The water samples that were analyzed by United States Environmental Protection Agency (USEPA) Method 625 for base/neutral and acid extractions were stored in 1-L amber glass bottles that were fitted with screw caps lined with Teflon. The extractions were performed within 1 week of sampling, and the extracts were stored in crimp seal vials at 4 °C. The extracts were analyzed within 1 week of extraction. The equilibrium concentration in the organic phase (i.e., the fuel phase of Jet A-1 or JP-8) was determined by diluting the jet fuels in an organic solvent (1:100 dilution in methylene chloride) and analyzed by the same method as described in ‘‘estimation of average molecular weight’’ section. Analytical Methods The purgeable aromatic compounds in the water-soluble fractions of the jet fuels were analyzed using USEPA Method 624 (Code of Federal Regulations 2012). Five

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milliliters of water samples were concentrated by the purge-and-trap technique using a Tekmar Liquid Sample Concentrator 3100/Automatic Laboratory Sampler system. The samples were analyzed using an Agilent gas chromatograph/mass spectrometer. The VOC compounds were separated from the mixture using a 0.25-mm i.d., 60-m, fused silica capillary column (100 % methyl phenyl cyanopropyl polysiloxane) with a 1.4-lm film thickness (DB624; Agilent J&W). The temperature program consisted of an initial temperature of 35 °C, ramping at 2 °C/min to 70 °C, followed by a ramp to 100 °C at 4 °C/min, another ramp to 170 °C at 8 °C/min, and a final ramp to 220 °C at 6 °C/min with a 2-min hold time. Helium was used as the carrier gas at a flow rate of approximately 1.0 mL/min. The base-neutral and acid-extractable compounds in the water-soluble fractions were characterized using USEPA Method 625. A surrogate standard spiking solution of 1,4dichlorobenzene-d4, phenol-d5, naphthalene-d8, anthracened10, and perylene-d12 was added to each sample for quality control. Two 500-mL samples were extracted three times with 60 mL of methylene chloride by vigorous shaking for 2 min and settling for 10 min before withdrawal of the methylene chloride. The extracts were dried by passing through a sodium sulfate column to remove residual water, and the extract volume was concentrated to 3–5 mL using a rotary evaporator. The extract volume was further reduced by gentle nitrogen evaporation to 1 mL. The extracts were transferred to 1-mL crimp-seal vials and refrigerated (at 4 °C) until analysis. The extracts were analyzed using GC/ MS. Separation was achieved by DB-5MS (Agilent J&W) as described in ‘‘estimation of average molecular weight’’ section. The temperature program consisted of an initial temperature of 50 °C, ramping at 8 °C/min–180 °C followed by a 3-min hold time, a ramp to 220 °C at 5 °C/min, another ramp to 275 °C at 6 °C/min followed by a 5-min hold time, and a final temperature ramp at 10 °C/min– 300 °C with a 15-min hold time. Helium was used as the carrier gas at a flow rate of approximately 1.0 mL/min. The detector scan range was 50–550 amu. The electron multiplier voltage was 2294 eV. The quality-assurance/quality-control procedures for purgeable and extractable aromatic compounds included banks, spike samples, and replicated samples. Sample blanks were processed to ensure against background contamination at low concentrations during analysis. Concentrations of purgeable and extractable aromatic compounds in blanks were lower than the detection limit. Known concentration of purgeable and extractable aromatic compounds was spiked into 5 of blank water. Purgeable spiked samples were analyzed as the same as the samples. These extractable spiked samples were extracted and analyzed as the samples. The recoveries of purgeable and extractable aromatic compounds were calculated by

Arch Environ Contam Toxicol

dividing the concentrations of spike samples (after subtraction of the concentration of an unspiked (blank) sample) with the original spiked concentrations. The average recoveries of purgeable aromatic compounds were 85–97 % in water. The average recoveries of extractable aromatic compounds were 87–90 % in water. The method detection limits (MDLs) of purgeable compounds in water ranged from 0.006–0.021 mg/L for water. The MDLs of extractable aromatic compounds in water ranged from 0.009–0.024 mg/L for water.

Results and Discussion Composition of Jet Fuels A series of direct injections was used to obtain the area distributions of the total ion chromatograms for Jet A-1 and JP-8. The area fractions in the different sections were used to calculate the average molecular weight. The carbon range of the constituents fractionated by weight percent is listed in Table 1. The most abundant range of carbon numbers in Jet A-1was C10–C12 at 32.8 % followed by C8– C10 at 28.4 %, C12–C14 at 24 %, and C14–C16 at 10.4 %. The average molecular weight and density of Jet A-1 were 161 g/mole and 786 g/L, respectively. The most abundant range of carbon numbers in JP-8 was C10–C12 at 47.6 % followed by C8–C10 at 38.4 % and C12–C14 at 12 %. The average molecular weight and density of JP-8 were 147 g/mole and 780 g/L, respectively. The densities of Jet A-1 and JP-8 were greater than regular gasoline (728–740 g/L) and ethanol-blended gasoline (738–776 g/ L) (Chen et al. 2008a). The average molecular weight of the jet fuels was estimated to be smaller than those of diesel (240–242 g/mole) and biodiesel (249–253 g/mole) (Chen et al. 2008b). Table 1 Carbon range distribution, molecular weight, and density of Jet A-1 and JP-8 Carbon range

Jet A-1 (%)

JP-8 (%)

C7–C8

2.78

0.86

C8–C10

28.4

34.7

C10–C12

32.8

52.8

C12–C14

24.0

11.1

C14–C16

10.4

0.44

C16–C18

1.49

–

C18–C20 C20–C22

0.21 0.04

– –

C22–C24

0.01

–

Molecular weight (g/mole)

161

147

Density (g/L)

786

780

Jet Fuel–Water Partition Behavior The hydrocarbons that partition into the aqueous phase are predominantly aromatic compounds, which were selected as the target compounds in this study. The concentration range of the target compounds in the fuel and the aqueous phases after the partition experiments are listed in Table 2. The target compounds and the extent of concentration in the partition experiments of jet fuel differed from those for gasoline and diesel fuel. In a partition study of gasoline, the aromatic constituents and additives that partitioned into the aqueous phase were benzene, toluene, ethylbenzene, xylenes, n-propylbenzene, 3,4-ethyltoluene 1,2,3 trimethylbenzene, and methyl tert butyl ether (Cline et al. 1991). Xylenes exhibited the highest concentration in the water-soluble fractions of Jet A-1 and JP-8. The water-soluble fractions of Jet A-1 and JP-8 contained alkylbenzenes, such as 1,3,5 trimethylbenzene, and 1,2,4 trimethylbenzene. The alkylbenzene, n-butylbenzene, was only detected in the watersoluble fraction of Jet A-1; however, isopropylbenzene, npropylbenzene, and sec-butylbenzene were found in JP-8. In particular 1,2,4 trimethylbenzene exhibited a relatively greater concentration in the water-soluble fraction of Jet A-1 and JP-8 compared with the other alkylbenzenes. However, only naphthalene and 2-methylnaphthalene were detected in the water-soluble fraction of the jet fuels because of there being a lower fraction of PAHs in jet fuels. Jet A-1 and JP-8 are used worldwide, and these concentration ranges provide estimates of the upper limits of concentrations that may be present in the aqueous-phase leachate from a jet fuel-contaminated area. Equilibrium theory has been used in several studies to predict aqueous concentrations after equilibration with NAPLs. A model based on Raoult’s law was successfully applied for the NAPL-water partitioning of NAPL constituents of gasoline, diesel fuel, coal tar, and motor oil (Cline et al. 1991; Lee et al. 1992a, b; Peters and Luthy 1993, 1994; Chen et al. 1994; Mukherji et al. 1997; Reckhorn et al. 2001). This work is the first trial to employ Raoult’s law to study the partition behavior of jet fuels. A two-phase liquid–liquid equilibrium model was used to describe the distribution of nonpolar solutes between the water (the polar phase) and the NAPL phase (the nonpolar phase) in terms of equilibrium principles. The results of the partition experiments were used to calculate the jet fuel–water partition coefficients (Kfw). The average values of the jet fuel–water partition coefficients for the target compounds are listed in Table 3. The fuel– water partition coefficients of aromatic compounds that were obtained for gasoline, ethanol-blended gasoline, or biodiesel were generally compatible with the Kfw values measured in jet fuel samples. The relationship between the

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Arch Environ Contam Toxicol Table 2 Concentration range of target compounds in fuel and aqueous phases after partition experiment

Concentration (mg/L) (average [minimum–maximum]) Compound

Fuel phase

Aqueous phase

Jet A-1 Toluene

244 (183–289)

0.286 (0.266–0.371)

Ethylbenzene

2880 (2150–3300)

0.879 (0.700–1.002)

m ? p-Xylene

6720 (5120–7730)

2.16 (1.75–2.91)

o-Xylene

3840 (2930–4390)

1.27 (1.00–1.46)

1,3,5-Trimethylbenzene

2880 (2120–3680)

0.364 (0.298–0.447)

1,2,4-Trimethylbenzene

9960 (7630–11300)

p-Isopropyltoluene

1070 (977–1120)

0.0660 (0.0508–0.0807)

n-Butylbenzene

1950 (1530–2200)

0.0858 (0.0799–0.0927)

295 (275–315)

0.0346 (0.0294–0.0403)

130 (105–155)

0.00530 (0.00396–0.00672)

Naphthalene 2-Methylnaphthalene JP-8 Toluene

210 (197–236)

0.203 (0.171–0.221)

Ethylbenzene

2880 (2710–3410)

0.854 (0.840–0.864)

m ? p-Xylene

6840 (4260–5680)

1.99 (1.91–2.06)

o-Xylene

4490 (3980–5300)

1.44 (1.40–1.46)

Isopropylbenzene

1150 (1000–1370)

0.117 (0.114–0.120)

n-Propylbenzene

2500 (2210–2990)

0.183 (0.170–0.198)

1,3,5-Trimethylbenzene

2740 (2408–3285)

0.226 (0.203–0.249)

1,2,4-Trimethylbenzene

11300 (10100–13300)

1.08 (1.06–1.11)

sec-Butylbenzene

1010 (896–1200)

p-Isopropyltoluene

1320 (1170–1570)

0.0633 (0.0611–0.0653)

n-Butylbenzene

2140 (1910–2550)

0.0368 (0.0269–0.0564)

0.0206 (0.0135–0.0254)

Naphthalene

312 (292–332)

0.0282 (0.0219–0.0347)

2-Methylnaphthalene

95.3 (84.0–106)

0.00421 (0.00384–0.00444)

jet fuel–water partition coefficients (Kfw) and the aqueous phase solubility (S) for an ideal mixture is: logKfw ¼ logS þ 0:689ðJet A  1Þ

ð8Þ

logKfw ¼ logS þ 0:724ðJP  8Þ

ð9Þ

Figure 1 is a plot of the measured log Kfw values against the corresponding log S values for the target compounds: The ideal mixture line calculated from Eq. (5) for jet fuel is also shown. The results from the models that were derived using Raoult’s law convention for the activity coefficients and the liquid solubility are presented. The observed inverse, log–log linear dependence of the Kfw values on aqueous solubility were well predicted by assuming Jet A-1 and JP-8 to be ideal solvent mixtures. There may be considerable variability in the composition of a fuel mixture: The fuel composition was found to have an observable effect on the estimated partition coefficient. The distribution of constituents between the aqueous and organic phases depends on the ideality of the organic phases. Overall, the target constituents did not show significant deviations from ideality, which resulted in somewhat

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1.63 (1.36–1.95)

greater aqueous concentrations than those predicted by Raoult’s law for most of the compounds. For most of the compounds in jet fuels, the log Kfw values lay near the ideal mixture line indicating that the assumption of an ideal mixture may be adequate for describing the partitioning of target compounds from jet fuel to water. The PP-LFER approach offered the advantage of being widely applicable to organic compounds of various polarities. Solute–solute and solvent–solute interactions were accounted for by using PP-LFER to estimate the partition coefficients of the target compounds in jet fuel–water systems. The experimental partition coefficients of the target compounds were compared with the partition coefficients that were estimated by PP-LFER. The partition coefficients of the target compounds in our study were estimated using the interaction parameters given by Abraham et al. for the coupling of the solvent and solute with the jet fuel–water in a PP-LFER equation that was developed by Endo and Schmidt (Abraham 1993, 1994, 1999, 2004; Endo and Schmidt 2006). Table 3 compare the experimental jet fuel–water partition coefficients with those

Arch Environ Contam Toxicol Table 3 Jet fuel–water partition coefficients in this study for Jet A-1 and JP-8 log Kfw (experiment)

log Kfw (PP-LFER)

log Kfw previous research

851

2.93

2.87

3.03a, 3.10b, 3.34d

Ethylbenzene

3300

3.52

3.46

3.54a, 3.65b, 3.72d

m ? p-Xylene

3130

3.50

3.40

3.59a(m-xylene), 3.47a(p-xylene), 3.64b, 3.69d

o-Xylene

3040

3.48

3.36

3.39a, 3.56b, 3.58d

1,3,5-Trimethylbenzene

7930

3.90

3.88

1,2,4-Trimethylbenzene

6100

3.79

3.84

p-Isopropyltoluene

16,200

4.21

4.51

n-Butylbenzene

22,700

4.36

4.67

8510

3.93

3.42

3.68c,e, 3.78e

24,500

4.39

4.07

4.42c

Toluene

1060

3.01

2.87

3.03a, 3.10b, 3.34d

Ethylbenzene

3380

3.53

3.46

3.54a, 3.65b, 3.72d

m ? p-Xylene

3430

3.54

3.40

3.59a(m-xylene), 3.47a(p-xylene), 3.64b, 3.69d

o-Xylene

3120

3.49

3.36

3.39a, 3.56b, 3.58d

Compound

Kfw (experiment)

Jet A-1 Toluene

Naphthalene 2-Methylnaphthalene JP-8

Isopropylbenzene

a

9810

3.99

4.04

n-Propylbenzene

13,600

4.13

4.08

1,3,5-Trimethylbenzene

12,200

4.08

3.88

1,2,4-Trimethylbenzene

10,400

4.02

3.84

sec-Butylbenzene

49,100

4.69

4.67

p-Isopropyltoluene

20,800

4.32

4.51

4.27b

n-Butylbenzene

58,100

4.76

4.67

Naphthalene

11,100

4.04

3.42

3.68c,e, 3.78e

2-Methylnaphthalene

21,500

4.33

4.07

4.42c

Gasoline–water partition from American Petroleum Institute (1985)

b

Gasoline–water partition from Cline et al. (1991)

c

Diesel–water partition from Lee et al. (1992a)

d

Gasoline–water and ethanol blended gasoline-water partition from Chen et al. (2008a)

e

Diesel–water and biodiesel–water partition from Chen et al. (2008b)

calculated using the PP-LFER equation for a jet fuel–water system that was derived by Endo and Schmidt (2006) (i.e. log KJP-4/w = 0.268 ? 0.633E - 1.535S - 3.45A 4.819B ? 4.316 V). The experimental partition coefficients of most of the monoaromatics were found to be greater than the values estimated using PP-LFER except for 1,2,4-trimethylbenzene, p-isopropyltoluene, and nbutylbenzene in Jet A-1 and isopropylbenzene and p-sopropyltoluene in JP-8. The experimental partition coefficients of naphthalene and 2-methylnaphthalene were greater than those calculated using PP-LFER. A similar trend was observed for biodiesel–water partitioning because the experimental Kfw values for polyaromatics tended to be greater than those estimated by PP-LFER (Chen et al. 2008b). These deviations may originate from inaccuracies in the parameters of the PP-LFER equation,

which were derived for JP-4 and not Jet A-1 or JP-8. JP-4 is the military equivalent of Jet B with the addition of a corrosion inhibitor and anti-icing additives. The solvent parameters used to represent the fuel components in the PP-LFER equation (i.e., JP-4) may be slightly different for Jet A-1 or JP-8 mixtures because of the complex nature of jet fuel mixtures. Experimental artifacts may also have produced the observed deviations. In particular, for target compounds that were present in small quantities in the fuel phase, analytical inaccuracies may have resulted from the detection errors of low aqueous-phase concentrations. It may have caused spuriously high experimental Kfw values. It is essential to predict the concentrations of the target contaminants in a jet fuel mixture accurately to assess potential risks posed by the leaching of these contaminants. Methods based on

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Arch Environ Contam Toxicol Fig. 1 Relationship between jet fuel–water partition coefficients (Kfw) and aqueous solubility for major aromatic constituents

A

-HW$ 5.5 1. Toluene 2. Ethylbenzene 3. m+p-Xylene 4. o-Xylene 5. 1,3,5-Trimethylbenzene 6. 1,2,4-Trimethylbenzene 7. p-Isopropyltoluene 8. n-Butylbenzene 9. Naphthalene 10.2-Methylnaphthalene Ideal Line

log Kfw = -logS + 0.689

5.0

4.5

log Kfw

8

10

4.0

7 9 6

5 2

3.5

3

4

3.0

2.5 -4.5

1

-4.0

-3.5

-3.0

-2.5

-2.0

log S

B

-3



ORJ.IZ ORJ6

 13

12

log Kfw



10 11



9 8 7

6 5

3

1. Toluene 2. Ethylbenzene 3. m+p-Xylene 4. o-Xylene 5. Isopropylbenzene 6. 1,2,4-Trimethylbenzene 7. 1,3,5-Trimethylbenzene 8. n-Propylbenzene 9. p-Isopropyltoluene 10. Naphthalene 11. 2-Methylnaphthalene 12. sec-Butylbenzene 13. n-Butylbenzene

2



Ideal line

4 1



 











log S

Raoult’s law are commonly used to model the partitioning of mixtures and have been approved by regulatory agencies. However, these equations may require minor modifications in some cases. The PP-LFER approach is applicable to a wider range of organic compounds with varying polarities than existing methods. This method can be applied to sites contaminated with complex NAPLs. In addition, regulators and scientists can use this method to estimate the jet fuel–water partition coefficients of unregulated compounds and to assess the environmental transport of these compounds. In particular, this method could be used to determine whether contaminant levels in groundwater from leaching of fuel–contaminated soil are acceptable.

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The Kfw values were expected to exhibit more variation than other parameters, such as the octanol–water partition coefficients (Kow), because jet fuel is a complex mixture. With the well-established and widely used database for octanol–water partition coefficients (Kow) and the similar molar volumes (i.e., MWo/qo) of the jet fuels investigated, a general correlation between the measured log Kfw and log Kow is expected. Thus, a general correlation between the measured values for log Kfw and log Kow was expected. A regression of all of the data presented in Fig. 2 yielded the following relationship: logKfw ¼ 0:840logKow þ 0:794 logKfw ¼ 0:887 log Kow þ 0:735

R2 ¼ 0:962ðJet A  1Þ R2 ¼ 0:941ðJP  8Þ:

Arch Environ Contam Toxicol Fig. 2 The empirical relationship of log Kfw versus log Kow for jet fuel

A

Jet A-1 5.0 ORJ.IZ ORJ.RZ  5t 

4.5

10

8

7

log Kfw

4.0

9

5 6 2

3.5

1. Toluene 2. Ethylbenzene 3. m+p-Xylene 4. o-Xylene 5. 1,3,5-Trimethylbenzene 6. 1,2,4-Trimethylbenzene 7. p-Isopropyltoluene 8. n-Butylbenzene 9. Naphthalene 10.2-Methylnaphthalene

3

4

3.0

1

2.5 2.5

3.0

3.5

4.0

4.5

log Kow

B

JP-8 5.0 11

log Kfw = 0.887 log Kow + 0.735 R² = 0.941

9

4.5 10

log Kfw

7

4.0

13 1. Toluene 2. Ethylbenzene 3. m+p-Xylene 4. o-Xylene 5. Isopropylbenzene 6. 1,2,4-Trimethylbenzene 7. 1,3,5-Trimethylbenzene 8. n-Propylbenzene 9. p-Isopropyltoluene 10. sec-Butylbenzene 11. n-Butylbenzene 12. Naphthalene 13. 2-Methylnaphthalene

6 12

8 5 2

3.5

3 4

3.0

2.5 2.5

1

3.0

3.5

4.0

4.5

5.0

log Kow

The empirical relationship given previously can only be used for fuels with molar volumes (MWo/qo) that are similar to those investigated in this study.

Conclusions The process of equilibrium partitioning between NAPLs and water governs the leaching of contaminants from NAPLs. An accurate prediction of the concentrations of the target contaminants in a mixture of many chemicals is essential for assessing the potential risks posed by the leaching of these contaminants. Experimental jet fuel– water partition coefficients were compared with the

partition coefficients estimated using PP-LFER models. The experimental partition coefficients of most monoaromatics and polyaromatics were found to be greater than the values estimated using PP-LFER except for those of 1,2,4trimethylbenzene, p-isopropyltoluene, and n-butylbenzene in Jet A-1 and isopropylbenzene and p-sopropyltoluene in JP-8. Various experimental artifacts, including poor recovery of target compounds from the aqueous phase, may have contributed to the differences between the predicted and experimental values. Compared with other methods, the PP-LFER approach is applicable to a wider range of organic compounds with varying polarities; thus, the PPLFER method can be applied to sites contaminated with complex NAPLs.

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Arch Environ Contam Toxicol Acknowledgments This study was supported by funding from the Soil and Groundwater Pollution Remediation Fund Management Board of the Taiwan Environmental Protection Administration.

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