Environ. Sci. Technol. 2005, 39, 1055-1063
Model Polymer Release System Study of PAH Bioaccessibility: The Relationship between “Rapid” Release and Bioaccessibility M O N A W E L L S , * ,† L U K A S Y . W I C K , ‡ A N D HAUKE HARMS‡ The Swiss Federal Institute of Technology, ENAC/ISTE/LPE, Baˆtiment GR, CH-1015, Lausanne, Switzerland, and Department of Microbiology, Center for Environmental Research, Permoserstrasse 15, D-04318, Leipzig, Germany
This paper examines bacterial uptake of polycyclic aromatic hydrocarbons (PAHs) entrained within model polymer release systems (MPRSs) whose release kinetics, particularly for operationally defined “slow” release, are similar to PAH release kinetics from sediments and soils. We find that biodegradation is not restricted to the fraction “rapidly” released, f1, as quantified by an empirical biphasic exponential fitting parameter. Though our results indicate that f1 does not predict bioaccessibility (defined by a recent paper calling for a standard definition of same), we analyze the causes of the reported limitation of biodegradation to rapidly released PAHs and we find that, for the MPRSs, there are very strong correlations between an ad hoc bioaccessibility and a wide range of fitting parameters from various kinetic expressions used to phenomenologically characterize release. These results indicate that fitting parameters may be used to predict ad hoc bioaccessibility; however, it is not clear if this is actually a particularly useful quantity. We also report experimental results which indicate that bacteria may influence their environment and cause biological uptake to exceed that expected from abiotic release data obtained under quasi-infinite sink conditions. When this occurs, fitting parameters from simple empirical expressions are even inadequate to predict ad hoc bioaccessibility.
Introduction Polycyclic aromatic hydrocarbons (PAHs) constitute a class of pollutants that are widespread and toxic; some particular PAHs are genotoxic, mutagenic, or carcinogenic (2, 3). Since most PAHs are biodegradable, many cases of PAH contamination are suitable for bioremediation; however, bioremediation of contaminated sites often remains incomplete (4, 5). One important reason for this is that the pollutants are not in a form available to degrading organisms (6). In recent years, the difference between total and biodegradable PAH content has been recognized, and correspondingly, a variety of chemical techniques aimed at quantifying solely the bioavailable or toxicologically relevant fraction of PAHs have been developed (7-15). * Corresponding author phone: 41 21 693 3766; fax: 41 21 693 5670; e-mail:
[email protected]. † The Swiss Federal Institute of Technology. ‡ Center for Environmental Research. 10.1021/es035067b CCC: $30.25 Published on Web 01/07/2005
2005 American Chemical Society
Many papers characterize the release kinetics of various pollutants, PAHs included, via biphasic or triphasic exponential release models (11, 14, 16-19). The operative idea is to identify the percentage of pollutant that desorbs so slowly as to be essentially biounavailable; using n-phasic exponential fitting, one quantifies the bioavailable fraction through one or more of the fitting parameters that nominally represent the fraction of pollutant “rapidly” released. In some cases, authors have found an excellent correlation between biodegradability and the amount of material rapidly desorbing (11, 16, 19). Such an approach is both mathematically and experimentally facile but suffers from being purely phenomenological. In contrast, other studies have reported biodegradation rates exceeding the rates of desorption, and in such situations, the biodegradable fractions may well exceed the rapidly desorbing fractions (20-23). These results apparently contradict conjectures about the relation between rapidly desorbing and degraded fractions, a contradiction that needs to be resolved before any useful conclusions can be derived at the regulatory level regarding the bioremediation of contaminants that may have low bioavailability. With regard to bioavailability, we note that the often used term “bioavailable fraction” is a combination of terms that is misleading; Semple et al. (1) have recently pointed out that syntactically the word bioavailable provides reference only to the amount of a substance readily at hand or available to an organism at some point in timesan instantaneous quantity. Again syntactically, Semple et al. defined the term bioaccessible fraction as the amount of a substance that is available at any point in time plus that which could potentially become availablesi.e., availability integrated over time. In the interests of clarity, forthwith, we adhere to the terminology proposed by Semple et al. unless otherwise indicated. Understanding contaminant bioaccessibility on a mechanistic level is complicated by the wealth of factors that can limit biodegradation (i.e., temperature, nutrients, presence in the natural matrix of inhibitors, low concentrations of substrate, etc.). In the present study, one objective was to use model polymer release systems (MPRSs) in wellconstrained experimental conditions to exclude these factors. During the course of manuscript preparation, some of the anonymous reviewers correctly pointed out that release from MPRSs cannot possibly involve the complex mechanistic dynamics presumed operative in release from soils and sedimentssthat they do so was never our intention. The MPRS matrix we employ produces, as quantified by exponential biphasic fitting, release kinetics that that mimic those of PAH release from natural materials such as soils and sediments (vide infra, (24)). If bioaccessibility is specifically dependent upon the release matrix, then the usefulness of simple phenomenological approaches that have no explicit capacity to represent the matrix (e.g., relating parameters from n-phasic exponential fitting of release curves to the bioaccessible fraction) is a priori invalid. Clearly, since exponential biphasic fitting cannot explicitly address anything other than kinetics, and this even in an arbitrary manner per the empirical nature of the approach, a central precept of such fitting is that bioaccessibility is predicated by release kinetics only. Hence, a second objective of this study was to use MPRSs to address the question of to what extent the biodegradation of the PAHs naphthalene, phenanthrene, and anthracene is contingent upon “rapid” release (and not extensible to “slow” release), as characterized by exponential biphasic fitting parameters and in the absence of obfuscating environmental factors. A final objective, subsidiary to the second, was to examine the relationship between biodegVOL. 39, NO. 4, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1. Fitting Expressions Used in Characterizing MPRS PAH Release empirical expressiona
fitting parametersb,c
orthogonal polynomial q(t)/q0 ) 1 - (a0,OP + a1,OPt1/2 + a2,OPt)
a0,OP, a1,OP, a2,OP
Elovich equation q(t)/q0 ) 1 - (aEl + bEl ln t)
aEl, bEl
γ model q(t)/q0 ) (β/(β + t))R
R, β
(∑ )
exponential n-phasic fit (Karickhoff) n
q(t)/q0 )
∑ fe
n
-kit
;
i
i)1
fi ) 1
(∑ ) n
n
∑
diffusive release + chemical reaction/ chemical resistance single-rate model multirate model
n ) 1:
k
n ) 2:
f1 (f2 ) 1 - f1), k1, k2
two-site model (n ) 2) or exponential biphasic fit
i
n-phasic Weibull fit q(t)/q0 )
conceptual originc
fie-t
bi,Wei/a i,Wei
i)1
;
fi ) 1
n ) 1:
aWei, bWei
n ) 2:
f1 (f2 ) 1 - f1), a1,Wei, b1,Wei, a2,Wei, b2,Wei
probabilistic
i
a q(t)/q is the fractional amount of substance present in the release matrix at time t. b Nominal representations: a 0 0,OP, loosely bound material; a1,OP, diffusive release; a2,OP, chemical reaction/resistance; aEl, initial rate of release; bEl, k, 1/aWei, characteristic rate constants of release; R, bWei, shape parameters; β, scale parameter; f1, rapid release fraction; f2, slow release fraction; k1, 1/a1,Wei, rate constants of rapid release; k2,1/a2,Wei, rate constants of slow release; b1,Wei, shape parameter of rapid release; b2,Wei, shape parameter of slow release. c Reference 24.
radation and other parameters from empirical expressions fit to release data. We find that in some cases bioaccessibility is only well correlated with fitting parameters when the former is defined on an ad hoc basis, i.e., that bioaccessibility is not actually predicted by the fitting parameters. In this manner, we reconcile the apparent contradiction between bioaccessibility being sometimes predicted by fitting parameters and sometimes not.
Experimental Section Chemicals. Protanal LF10/60 alginate was obtained from FMC Biopolymer (Drammen, Norway), and kaolinite was obtained from Ward’s Natural Science (Rochester, NY). All other chemicals were supplied by Fluka, Acros Organics, Aldrich, Merck, and Sigma. Media and Bacterial Cultures. Two organisms were used in this study. Pseudomonas putida 8909N was isolated by Volkering et al. (25) and is reported to grow on naphthalene, phenanthrene, and anthracene and to form biofilms on naphthalene (26). Since work in our laboratory indicated that P. putida 8909N does not appear to grow rapidly on solid anthracene per our usual protocols, we chose Mycobacterium frederiksbergense LB501T for the anthracene experiments instead because it is very efficient at abstracting anthracene from solution and grows well on anthracene crystals, forming biofilms (27, 28). Bacteria were grown in Schott flasks containing 100 mL of pH 7.0 buffered minimal medium (29) with trace elements solution (30) (5 mL:1 l minimal medium), the precautionary addition of 187.5 µg/ mL actidione to suppress fungal growth, and PAH crystals as the sole carbon source. Strain 8909N was grown on naphthalene and phenanthrene, respectively, while strain LB501T was grown on anthracene. Cells were harvested at predetermined times during the linear growth phase and residual PAH crystals removed by filtration through a sterile paper filter. Cells were centrifuged, rinsed, and resuspended in buffered medium. Polymer Bead Synthesis. Alginate bead synthesis is described in detail elsewhere (24). The average radius (ra, in mm)/average PAH load (q0, in mg/bead) for the beads used in experiments described below was 1.58/1.38. Biological Uptake Experiments and Analysis. All experiments were performed in a low chelator medium suitable 1056
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for bacteria. The medium was as described above save that NH4Cl was substituted for (NH4)2SO4, the trace element solution had no added chelator, and the amount of phosphate was 10 mM. A 15-mL portion of this medium was added to 100-mL Schott flasks along with sufficient alginate beads to deliver a total PAH dose of ∼6.5-8.0 mg/flask (in all cases here, five beads). Each flask was inoculated with bacterial solution sufficient to produce an initial OD578 of ∼0.035 (or ∼1.3 × 108 cells) and then placed in a shaker at 22 °C and 150 rpm. Sampling consisted of extracting the beads and analyzing them for PAHs as described below. Additionally, solution samples were periodically taken for bacterial counts using a DAPI filter method (31). The average relative percent difference (RPD) for duplicate PAH analyses in the biological uptake experiments was 16%. We periodically monitored solution concentrations of PAHs to verify that these were below or near the detection limit; this observation and that of PAH recoveries of 90-110% for separate abiotic release experiments with MPRSs confirm that the PAHs released from the beads in the biotic experiments were abstracted from solution by the bacteria present and that measurements made correspond to actual biological uptake. Blank samples, in the form of both analytical blanks and MPRS controls (with bacteria, but no PAHs), were performed to verify that no PAH contamination was present. Abiotic controls were also performed (PAH-loaded MPRSs, no bacteria). Additional methods and comments upon ancillary experiments are available in the Supporting Information accompanying this article, available online. Other Analytical Methods. PAHs were quantified by HPLC with UV detection (HPLC-UV) on a Hewlett-Packard 1100 system equipped with a variable-wavelength detector operated in single-wavelength detection mode at 254 nm and using an 80:20 methanol/water isocratic elution from a Vydac RP-C18 column. PAHs in the MPRS alginate beads were extracted for analysis by first crushing the beads thoroughly and then refluxing 24 h in 5:5:2 water/hexane/acetone. Sixhour reflux extractions of this type remove a reported 85100% of contaminants even from aged sediment reference materials (32). OD578 was determined spectrophotometrically. Data Analysis. Various empirical expressions describing release are tabulated in Table 1. The origins and history of the various applications of these expressions are discussed
in a previous paper, a work wherein fitting parameters were obtained by fitting these expressions to MPRS release data measured under quasi-infinite sink conditions in the absence of bacteria (24). In contrast with empirical fitting, a shrinking core model (SCM) of diffusion is appropriate for our MPRSs, as well as some environmental materials including sediments and soils (33-37). The SCM we employ here is a zeroparameter mechanistic model wherein release occurs through a mechanism of diffusion (Fickian), transitioning to retarded diffusion as the compound near the release matrix surface is exhausted, and as the soluble material diffuses out, the entrained solid dissolves; the amount of entrained dissolving solid is much greater than its aqueous solubility, i.e. A . Cs in the core region. As a result, there is a saturated core containing solid that gradually shrinks in radial extent (see diagram in Supporting Information). Dissolution kinetics can be neglected for the very small PAH inclusions in question (24, 33, 38), and the matrix is assumed to be completely immersed in the release medium under infinite (really quasiinfinite) sink conditions. Model predictions, as compared to experimental MPRS release data, result in high values of nonlinear least squares R2 (e.g., 0.95-0.99). An approximation to the model (37), not valid for q(t)/q∞, f 1.0 (note, q(t)/q∞ f 1.0 concomitant with A f Cs), is
q(t) ) q∞
[x
4πra2 QT
2(A - Cs)CsDefft +
{
} ]
4Cs Cs - 3 Defft (1) 9ra 2A - Cs
where q(t)/q∞ is a notation commonly used in diffusion literature to represent the amount of diffusing substance released at any time, q(t), defined with reference to the amount ultimately released at t ) ∞, q∞sthe ratio q(t)/q∞ being the cumulative fraction of diffusing species released (normalized, unitless). On the right-hand side of eq 1, QT is the total amount of PAH per bead (weight or moles), A is the amount of solid PAH per matrix volume (weight/volume or moles/volume), Cs is the amount of dissolved PAH per matrix volume (weight/volume or moles/volume), Deff is the effective diffusion coefficient (length squared/time), and ra is the average bead radius (length) (24). Modifications to these conditions, described below, as well as solutions for q(t)/q∞ < 1.0, are effected numerically using a modified version of the Crank numerical explicit finite difference approximation (EFDA) (24, 37, 39). Numerical calculations were scripted and executed in Matlab using a radial grid with g70 nodes and adapted step size control (24). During the course of preparing this article, one reviewer suggested that we might better use a “standard diffusion equation for a spherical particle”, the solution of which has the form of an infinite series of exponential terms. Entirely aside from questions of release geometry, it is a common misconception that such an expression is appropriate for many types of environmental matrices containing contaminants, and substantial errors in the effective diffusivity fitting parameter can result, as we illustrated elsewhere (24).
Results and Discussion Fitting Parameter Classification of Release Kinetics. A central goal of this paper is to examine the validity of utilizing fitting parameters from kinetic empirical release expressions to evaluate bioaccessibility. The exponential biphasic fit (i.e., n ) 2 for the n-phasic exponential fit in Table 1), often used in the literature, has three fitting parameters, two of which, k1 and k2, have units of inverse time and are typically construed as representing the rate constants associated with rapid and slow release, respectively. The third fitting parameter, f1, is a coefficient or “fraction” of the total compound
released with rapid release kinetics (the coefficient f2 that also appears in the expression is not a separate parameter since it equals 1 - f1); the coefficient f2 represents the fraction of the total compound released with slow release kinetics. We note parenthetically that there are various problems associated with using the empirical expressions in Table 1 for fitting release data. These are discussed in more detail in a separate publication (24). For release of naphthalene, phenanthrene, and anthracene from sediments, soils, and related geosorbents, a literature survey indicates that values of k1 and k2 from exponential biphasic fitting vary between 0.0019-1.51 h-1 and 0.0000125-0.00616 h-1, respectively (11, 14, 17, 18). We note that there is overlap of rapid and slow kinetics from these data. Although some authors have tried to clarify matters by adventitiously classifying rapid release as release having a rate constant of g0.5 h-1, and by extension slow release as everything slower (40), this practice has not been universally adopted, and the literature ranges above represent values for rapid and slow release via the alternate arbitrary operational classification imposed via biphasic exponential fitting. For MPRS release of naphthalene, phenanthrene, and anthracene, the fitted values of k1 and k2 are 0.16 and 0.0075 h-1, 0.0041 and 0.00017 h-1, and 0.0015 and 0.000026 h-1, respectively (nonlinear R2s > 0.99). While we note a trend of decreasing k2 with decreasing PAH solubility, most literature work has focused on the parameter f1 rather than individual values of k1 and k2. Overall, however, the MPRS release kinetics described here for PAHs are not distinguishable from the ranges for desorption from NOM-containing natural materials (note: NOM is natural organic matter and we use the terms release and desorption interchangeably throughout this paper with no implied mechanistic meaning). This is not a coincidence since we developed the particular biocompatible MPRS formulation used here solely for such kinetic mimicry. The similarity between NOM-containing natural materials and our MPRSs, however, ends with release kinetics, and in the subsequent discussion, we exploit these differences to critically illustrate problems with the validity of using an approach as simplistic as exponential biphasic fitting to address problems as sophisticated as predicting bioaccessibility from matrices as chemically complex as sediments and soils and for a purpose as important as the public health issues surrounding bioaccessibility-based remediation end points. Observed Biological Uptake. Figures 1A-3A compare the Tenax-driven (41) (abiotic) release of PAHs to bacterial uptake, and Figures 1B-3B show the attendant changes in OD578 reflecting bacterial growth (DAPI counts confirmed what the OD578s show, that there is an initial period of linear bacterial growth, followed by a period consisting primarily of maintenance). In Figures 1A-3A, the fractional amount of substance present in the release matrix as a function of time is represented by q(t)/q0 (normalized, unitless), a common notation used in diffusion literature wherein q(t) is the amount of diffusing substance in the release matrix at any time, defined with reference to the amount initially present at t ) 0, q0. For naphthalene and phenanthrene, rates of abiotic release and biodegradation were equivalent. Biodegradation of MPRS naphthalene and phenanthrene thus appears to be mass transfer limited, in which case, degradation rates will be independent of bacterial density (33). We confirmed this by repeating the naphthalene release experiment with a greater than 1 order of magnitude increase in initial biomass. In contrast to Figures 1A and 2A, biological consumption of anthracene was only initially equal to Tenax-driven release and became considerably faster after ∼230 h. Results in Figure 3A are from two separate, individual, and independent experiments performed at different times; hence, the obVOL. 39, NO. 4, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. (A) Biological uptake and corresponding abiotic release (q(t)/q0) for MPRSs (abiotic data from ref 24) with entrained naphthalene. Inset illustrates how the “geometric” exponential biphasic fitting parameter f1 is obtained via linear regression. (B) Changes in OD578 attendant upon biological uptake. See Figure 3 for legend.
FIGURE 2. (A) Biological uptake and corresponding abiotic release (q(t)/q0) for MPRSs (abiotic data from ref 24) with entrained phenanthrene. (B) Changes in OD578 attendant upon biological uptake. See Figure 3 for legend. served drop in anthracene is apparently not an analytical artifact or the result of a systematic error in a single experiment. As the explanation for why bacteria can extract more anthracene from a given matrix than Tenax resin can (Tenax ensuring quasi-infinite sink conditions in the solution) is not obvious, we performed a number of experiments to more closely elucidate the basis of the effect. Summarizing our analysis of the trend in the Figure 3A biological data, the only explanation we find that fully and cogently treats the totality of the data, mechanistically and in the context of the literature, is enhanced biological uptake caused by enhanced release resulting from LB501T bacterial exudate-induced increases in Deff and Cs across either an effective penetration depth or an effective half-life of the active agent (we note that results for 8909N are different; see open squares in Figure 3A and Supporting Information). We tested this hypothesis regarding changes in Deff and Cs by reformulating the EFDA SCM for the nodes representing the outer 40% of the bead volume beginning at 210 h. It is likely that any chemical effect leading to changes in Cs would also impact Deff, so the model projects that Cs in these outer nodes must increase by 20-fold and that simultaneously Deff ≈ DH2O. These 1058
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FIGURE 3. (A) Biological uptake and corresponding abiotic release (q(t)/q0) for MPRSs (abiotic data from ref 24) with entrained anthracene. The strain 8909N was used in the control experiment for comparison to the results with LB501T (see details in Supporting Information). (B) Changes in OD578 attendant upon biological uptake.
FIGURE 4. Observed abiotic release (q(t)/q0) and biological uptake of anthracene from MPRSs shown with a trace of the denoised experimental data and SCM predictions of release. In the case of abiotic release, eq 1 was used, and for biological uptake experiments, a modified EFDA SCM was used in which Cs and Deff in the outer layers of the MPRS increased during the early stationary phase of bacterial growth (details in text and in Supporting Information). projections are consistent with the appearance of exudates in LB501T supernatants at the onset of stationary phase (e.g., 7-10 days in the solutions harboring MPRSs described here), the reported effects of exudates on anthracene solubility, and reasonable suppositions about changes in the MPRS Deff. Figure 4 shows the model results in comparison to the experimental results, as well as denoised experimental results (4-point adjacent averaging algorithm, per expectations of inherent biological variability or noise). The denoised trace, though not superimposable with the experimental results, agrees reasonably well and provides a logical physical interpretation of the latter. For a more detailed discussion of our analysis of the anthracene biological uptake data, including SEM and ESEM images, we refer the reader to the online Supporting Information for this article. For the purpose of the present study, however, the precise cause of the enhanced biological uptake by LB501T in Figure 3A is not of primary concern, the important point being that biological uptake of anthracene is at minimum equal to abiotic release as measured by the Tenax method. Biodegradation and f1. Basis for Correlation. We now examine the relationship between biodegradation and fitting parameters obtained from abiotic desorption experiments.
The arrows in Figures 1A-3A indicate the times at which abiotic desorption curves transition from rapid into the regime of slow release; i.e. each arrow represents the abscissa for the point of intersection of separate linear regressions of ln(q(t)/q0) vs time, as shown in the inset of Figure 1A. From the point of intersection, the parameters f1 (rapid fraction) and f2 (slow fraction) are “geometrically” determined based on the corresponding ordinate value, or alternately, they may be determined by nonlinear fitting of the biphasic exponential expression. In contrast to some reports relating biodegradability to f1 (11, 16), here PAH degradation substantially exceeds f1. Though mimicking slow desorption kinetics of natural materials quite admirably, our MPRS are admittedly artificial, but nonetheless one presumes that bacteria are not subject to some veiled preference dictating that they may utilize PAHs slowly released from alginate MPRSs but not from sediment. Also, it remains to be explained why in one study remediated sediments still exhibited up to 20% rapid release, even for lower molecular weight hydrocarbons (study employed exponential biphasic fits to Tenax release experiments such as we describe in the methods section above (11))sif the rapid release f1 is the bioaccessible fraction, and in the remediated sediments mentioned, the percent biodegradation of PAHs already exceeded f1 in many cases, how is it that the values of f1 for remediated sediments were not consistently equal to zero? This leads us to seek a unified, mechanistic explanation for why biodegradability is sometimes equal to or correlated with the amount of rapidly desorbing species and, as in the case of MPRSs, sometimes not or not well correlated. We first focus on predicting release. We note above that release from MPRSs is very well described by the zeroparameter nonempirical SCM. The SCM is, unlike the biphasic exponential fitting expression, a nonempirical model and mechanistic in its formulation. The mechanistic foundation for this particular SCM is that it is a single-compartment Fickian monolithic matrix model; in other words, the mathematical formulation of this model assumes that the release matrix is a single material and is homogeneous to the first order, similarly having uniform thermodynamic affinity for anthracene (e.g., partition coefficient, partitioning being reflected via chemical resistance to diffusion such that Deff < DH2O), that the matrix harbors random domains wherein the local concentration exceeds that of the aqueous solubility, and that release occurs via a mechanism of Fickian diffusion. Thus, release from MPRSs is almost equally well described by a meaningful (i.e., mechanistic) single-compartment model (the SCM) as by a meaningless (i.e., empirical) “twocompartment” model (the biphasic exponential fit). Because the former is both meaningful, and consistent in its ability to accurately describe PAH release from MPRSs, we can use the eq 1 SCM expression (for q(t)/q0 > 0) or alternately the numerical EFDA SCM (for q(t)/q0 ∼ 0) to predict hypothetical “perfect” release data given information about the matrix (ra, Deff) and diffusing substance (Cs). In contrast, the exponential biphasic fit is not able to predict release in any way based on information about the matrix or compounds released by it. Prediction of release eliminates all experimental uncertainties such as small variations in PAH loading and MPRS bead size. For our purposes, it is necessary to have essentially perfect data because of the gross uncertainties in fitting parameters associated with the wide global or numerous local minima present when solving an overparameterized nonlinear biphasic exponential fit, implicit errors from which can well exceed standard calculated errors reported by available fitting routines (24). Figure 5 shows such SCMgenerated “perfect” data for hypothetical beads of the approximate size and loading of the MPRSs described here, as well as exponential biphasic fits to the SCM-generated data.
FIGURE 5. Abiotic release (q(t)/q0) predicted by the SCM for hypothetical MPRSs in which bead radius and loading are the same and release is affected only by Cs and Deff. Lines are exponential biphasic fits of the SCM projected release. Because the biphasic fitting expression contains two terms, a common interpretation of release data fit with this expression is that PAHs are released from a material composed of two “compartments”, each with different chemical resistance to release. Instead, we know that the biphasic exponential fits in Figure 5 result from fits to data generated by an a priori single-compartment model wherein release is controlled by the interplay of diffusivity and PAH solubility. Further, we may conjecture that notable trends between the curves would mainly arise from considerable differences in the solubility of different PAHs, as compared to less notable differences in diffusivities. In Figure 5 we see that k2 (slow) decreases by 2 orders of magnitude as does solubility from naphthalene to anthracene, while k1 (rapid) does not change much, and is almost the same for phenanthrene and anthracene, as are the effective diffusion coefficients, and that f1 decreases from naphthalene to anthracene. From a quantitative appraisal, there are R2 > 0.99 correlations between f1 and ln(Cs), between k1 and Deff, and between k2 and Cs. It is not surprising that f1 correlates to ln(Cs) instead of Cs, given that f1 represents a coefficient to a quantity that varies as a function of e (Table 1). For clarity in Figure 5, only data for the three PAHs considered here are shown; however, the relationships between exponential biphasic fitting parameters and Deff and Cs/ln(Cs) still hold when other PAHs are considered (data not shown). Thus, we may conjecture that k1 derives from initial diffusiondominated release from the outermost layer of a material. Accordingly, k2 derives from long-term release dictated by compound solubility, or lack thereof, and the compound’s having to travel substantive radial distances through the matrix prior to reaching the outer surface layer, and f1, though representing the fraction of this diffusive release, also represents the solubility-controlled fraction subject to longterm release (i.e., f2 or 1 - f1) and is hence also controlled by the aqueous solubility, Cs. Tangentially, f1 values from biphasic exponential fits of release data from Figures 1A-3A do not correlate with ln(Cs). This could be a result of all other factors not being exactly equal (i.e., small variations in loading, particle diameter). On the other hand, for the present MPRS release data, “geometric” values for f1 correlate with ln(Cs) with an R2 of 0.90. Overparameterization of the fitting expression is sufficient to explain the discrepancy between these two observations (24). It is important to keep in mind that the above rationale also applies when domains of PAHs at concentrations exceeding Cs exist within the release matrix, i.e., for PAHs partitioned into native organic matter or NAPLs (nonaqueous phase liquids) and for nanoscaled entrained inclusions. For this reason, monolithic matrix desorption models will most often be appropriate to NOM-containing samples, and in a VOL. 39, NO. 4, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 7. Relationship between R 2 for f1 and ln(Cs) and weathering index for data from ref 14.
FIGURE 6. (A) Data from ref 14 showing the relationship between f1 and ln(Cs). (B) Data from ref 11 showing the relationship between f1 and biodegradation, and (C) the relationship between biodegradation and ln(Cs) using the same data. recent work, we have illustrated the types of errors in Deff (and hence inferences about chemical resistance to release) that arise from using monolithic solution models such as that used in a recent publication on release of PAHs from sediments (42). We adduce an example of f1 correlating with ln(Cs) for PAH release from the sediments and soils studied by Hawthorne et al. using XAD instead of Tenax (14). Figure 6A presents data wherein seven of the eight sites studied showed a positive correlation between f1 and ln(Cs), the average R2 being 0.82. Hawthorne et al. also determined f1 by exponential biphasic fitting of release curves obtained using supercritical fluid extraction (SFE). Eighty percent of the data in this larger sample set exhibit positive correlations between f1 and ln(Cs). If f1 is related to solubility, and systems are not too variable, then we should consider possible correlations between biodegradation and ln(Cs). Figure 6B shows an oftcited data set wherein there is a relationship between the percent of PAH degraded and f1, with an R2 value of 0.82. It includes three subsets of data, two from Petrol Harbor and one from a landfarm, all in The Netherlands. Considering the first two separately, we see in Figure 6C that there are 1060
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also similar correlations between the percent PAH biodegraded and ln(Cs). The correlations between ln(Cs) and biodegradation are surprising because they persist despite the possible confounding interplay of adjuvant factors (e.g., polydispersity, particle size, initial PAH concentration, and other factors). We see this same trend in an earlier work by Hawthorne et al. (43). Taken together, Figures 5 and 6 suggest that the correlation between f1 and the biodegradable fraction arises from mass transport because less soluble and less diffusive compounds arrive more slowly at the bulk aqueous phase, and hence, aqueous concentrations drop more rapidly and biodegradation ends sooner, in which case biodegradation correlates with f1 but may not be limited to f1. This explains why a rapid fraction still remains after remediation as reported in ref 11. For any diffusing volume there will be a finite concentration in the outermost layer, which, subjected to an environment containing a sink or having a lower bulk-phase concentration of the diffusing species, will gradually diminish. For retentive materials having high concentration domains within the innermost portion not subject to rapid release, the overall amount of diffusing substance in the volume will still be appreciable; i.e. the rapidly released fraction will decrease but will not be zero. There is potentially an interplay between the release history, how this affects compound distribution throughout the release volume, and the value of f1. Consequently, weathering, biodegradation, and aging are factors that could disrupt correlations between f1 and ln(Cs). Hawthorne et al. (14) reported weathering indices (WI ) total concentration of two-ring PAHs/total concentration of fiveand six-ring PAHs) for PAHs in most of the soils and sediments that they studied. Figure 7 plots R2 values for f1 (obtained by SFE) vs ln(Cs) from these data against WI for 12 of the 15 sites (those 12 having positive correlations between f1 and ln(Cs)). We see that the R2 correlation between f1 and ln(Cs) increases as the WI increases; i.e., correlation between f1 and ln(Cs) is better for unweathered samples. If we discard the weatheringprone naphthalene data, the trend in Figure 7 disappears as all but three of the sites in question have R2 correlations ranging from 0.71 to 1.00, with an average of 0.87. Of the three sites not conforming to these trends, PAH data are incomplete for two. Hawthorne et al. (43) illustrated that a correlation between f1 determined by XAD and SFE for the same samples may have an R2 value as high as 0.87. Hence, correlations between f1 determined from SFE and ln(Cs) are presentative, and via the work of Hawthorne et al., we may extend this idea to f1 determined by XAD, Tenax, or other extraction techniques. One may further extend the idea to the guts of oligochaetes and consider this to be yet another type of solvation environment. Ten Hulscher et al. (19) have reported excellent correlations between f1 (Tenax) and biota to sediment accumulation factors (BSAFs) after oligochaete exposure to
contaminated soils and sediments. Their reported f1 values do not correlate well with ln(Cs), but of course, there are numerous factors including a large variation in the concentration of different PAHs in their samples that might otherwise result in lack of a correlation. The correlations found in the oligochaete study were much stronger than those observed for similar studies on bacteria (e.g., Figure 6B). This could be due to subtle fluctuations in conditions that in turn cause fluctuations in bacterial uptake (e.g., temperature, nutrients, etc.). On the other hand, correlations between f1 and BSAFs found by ten Hulscher have a slope of ∼1, whereas we see from Figure 6B that bacterial degradation can exceed f1 by up to a factor of ∼3. It could be that, per the differences in uptake that we observe between 8909N and LB501T growing in the presence of MPRSs releasing anthracene, bacterial dynamics are more varied and difficult to predict relative to oligochaetes due to bacterial adaptations and the ability of bacteria to influence their environment (per Figure 4). The preceding discussion on correlations between f1 and ln(Cs) in weathered samples and how these relate to PAH bioaccessibility is pertinent to the results of Shor et al. (16). For a suite of five PAHs in four different sample types from New Jersey, including two whole sediments, these authors reported values of f1 and percent PAH biodegraded that have R2 values of 0.82, 0.14, 0.13, and 0.54, respectively, for the four samples. They concluded that “the correlation between f1 and percent biodegraded was apparent for many PAHs in both whole sediments” and “extent of bioavailability is controlled by extent of fast domain desorption.” From our own inspection of the same results, we conclude that there is an apparent correlation between f1 and percent biodegradation in only one of four samples (i.e., for whole Newtown Creek sediment). Further, we find that in no instance does f1 correlate with ln(Cs). Hence, in this case, f1 seems to be similarly unrelated to both percent biodegradation and ln(Cs). We noted that the samples of Shor et al. appear substantially more weathered than those in Figure 6B. With this in mind, we find that if we eliminate the most weatheringprone PAH in their data set, phenanthrene (per our elimination of naphthalene from the Hawthorne et al. (14) data set containing weathered samples above), in three of the four data sets biodegradation correlates with ln(Cs) yielding R2 values between 0.94 and 0.97. Of course, correlations may beguile; WI is calculated from concentrations, and these, like particle size, etc., may affect mass transport. Our analysis of release associated with Figure 5 (wherein concentration remains constant and equal for all PAHs) implicates Deff in controlling k1 and Cs in controlling the parameter f1 associated with the rapid release term. So while, due to enhanced system complexity, we cannot unequivocally authenticate this finding from literature data on soil and sediments, it is nonetheless striking how the foregoing analysis of literature data supports the role of Cs as the main contributing factor in controlling biodegradation for PAH release from these more complicated materials. The fact that there are so many instances in the literature wherein an apparent relationship between f1 and Cs exists further reinforces the idea that monolithic matrix diffusion models are more appropriate than monolithic solution models for contaminated samples (24). Use of Empirical Fitting Parameters To Predict Bioaccessibility. Analytical error and its relative increase with decreasing concentration may skew projections of PAH bioaccessibility. Recoveries of PAHs from Tenax and XAD experiments on soils and sediments typically deviate substantially from 100%. For instance, one report cites recoveries of 70-130% (11), whereas another report cites recoveries generally around 70-110% but, in some cases, as low as 38% and as high as 146% (18) (both of these reports also used biphasic exponential fitting in the analysis of results). As
TABLE 2. R2 Values for Linear Correlations between Ad Hoc Bioaccessibility and Empirical Fitting Parametersa empirical fitting parameter
a2,OP R k f1b k1 k2 1/aWei f1(Weibull) 1/a1,Wei b1,Wei
elapsed time for definition of ad hoc bioaccessibility (h) 100 170 450 650 0.999 0.996 0.996 0.548 0.994 0.994 0.994 0.960 0.999 0.957
1.000 0.992 0.992 0.570 0.989 0.989 0.997 0.969 0.997 0.967
0.999 0.985 0.985 0.606 0.981 0.981 1.000 0.980 0.991 0.978
0.997 0.976 0.977 0.635 0.972 0.972 1.000 0.987 0.985 0.986
a Values of fitting parameters in ref 24. b Geometric, from exponential biphasic fit.
release progresses, q(t)/q0 will decrease, relative analytical error in concentrations used to calculate q(t)/q0 will increase at some point, all as the magnitude of d(q(t)/q0)/dt gradually decreases. As a result, changes in q(t)/q0 may become undetectable well before the analytical detection limit for the target compound is reached, and when this occurs biodegradation can be, and typically is, defined as being complete, ad hoc (i.e., in the standard lexical sense). Representative times for declaring bacterial biodegradation complete varyse.g., from between 5 days to 3 months (11, 16). From the foregoing, it is obvious that such ad hoc bioaccessibility underestimates bioaccessibility (per the definition of Semple et al. (1), vide supra). We arbitrarily take ∼100, 170, 450, and 650 h as representing times by which to evaluate ad hoc MPRS bioaccessibility, i.e., the amount of biodegradation that has occurred at these times. For anthracene, we assume for the moment that, at minimum, the amount bioaccessible is reflected by the abiotic release curve. Defining ad hoc bioaccessibility in this way, we find that there are generally strong correlations between naphthalene, phenanthrene, and anthracene ad hoc MPRS bioaccessibility and empirical parameters describing release. Table 2 shows the R2 values from the resulting correlations with various empirical parameters. We see that, in an idealized system with no limitations on growth, all of the empirical parameters evaluated, representing most of those given in Table 1, predict ad hoc bioaccessibility much better than f1. Our experimental design provides that the present results are hampered neither by the above-mentioned synergy between analytical error and decreasing d(q(t)/q0)/dt nor a shortfall of bacterial resources, and thereupon results in Figures 1A-3A connote that bioaccessibility for MPRSs is at least equal to the total amount released, projected as an eventual 100% in all cases. Naphthalene is entirely consumed by 600 h, but in the case of phenanthrene and anthracene, reduction by 99% should take just under 3 years and just over 17 years, respectively, as predicted by exponential biphasic fitting. Of course, the validity of extrapolating to such time periods based on experiments spanning only ∼2.5 months or less is questionablesbut the determination of f2 itself involves the ultimate extrapolation since it represents the fraction of release occurring over the interval [ttrans,∞), where ttrans is the approximate time when release transitions from fast to slow. Still, in the, relatively, short time course of these experiments it is clear that bioaccessibility well exceeds f1, and hence f1 does not represent bioaccessibility of PAHs from MPRSs even for the cases such as naphthalene and phenanthrene where uptake is identical to release. Of course, in natural systems the picture will be more complex, given soil and sediment diagenetic processes, and considerations of the role microorganisms may play in same. VOL. 39, NO. 4, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Certainly, the results for anthracene uptake by LB501T show that uptake may occur in a fashion that is difficult to predict a priori and certainly beyond the realm of simplistic empirical expressions such as those in Table 1. If such phenomena occur in simple systems, we have no reason to assume that the situation becomes more simple in complex systems such as soils and sediments. Although we find strong correlations between empirical fitting parameters and ad hoc bioaccessibility, our analysis indicates that both of these are of equally dubious utility in addressing the question of how to accurately quantify the bioaccessible fraction of contaminant in a given sample. The constitutive problem with using any of the fitting parameters in Table 1 to estimate a “bioavailable fraction” is that there are more permutations of how these can underestimate bioaccessibility than not, as even when assuming a passive role for organisms bioaccessibility is approached asymptotically, and in some cases very slowly, by ad hoc bioaccessibility. We see an example of this clearly in Figure 6B where biodegradation can exceed f1 by a factor as high as ∼3. In the absence of being able to conservatively estimate the bioaccessible fraction, a ratiocinative approach is that of Mulder et al. (33) who focus on predicted release. This approach requires delineation of the target level of acceptable PAH and then calculation of the maximum time necessary for remediation. This has the disadvantage of neglecting any influence that organisms may have, save that of consuming substrates after release. However, it has the advantage of being conservative; in the case of MPRSs, the Mulder et al. approach would overestimate bioremediation time in only one of three cases (one in four cases if 8909N degradation of anthracene is considered; see Supporting Information). Evidence from the present MPRS release systems indicates that rapid and, hence by extension, slow release may be a simple consequence of solubility; analysis of literature results is in accord with this finding, the caveat being that other complicating factors such as concentration, particle size, etc., can equally well obscure release trends in complex systems. Our not being able to identify an artifactual cause for the enhanced uptake of anthracene by LB501T over what one would expect solely from mass transport considerations suggests that organisms are able to dynamically influence bioavailability, and this in turn may perturb the position of the effective release endpoint determining bioaccessibility. Most significantly however, in well-constrained, nonlimiting conditions, using MPRSs that have slow release kinetics for PAHs mimicking those of sediments and soils, we find that empirical fitting parameters are excellent predictors of ad hoc bioaccessibility but entirely insufficient predictors of actual bioaccessibility. In well-constrained MPRS systems, biological uptake occurs well past the artificial limit of f1, strongly suggesting that f1 should not be used as a remediation end point for more complex systems. The goal of defining bioaccessibility for remediation should be to minimize the economic costs while maximizing the public health benefit; underestimating the bioaccessible fraction is a disservice to the latter goal.
Acknowledgments We gratefully acknowledge the financial support of the Swiss Federal Office for Education and Science (Grant BBW 01.0088) who funded the EC ABACUS project (EVK1-CT-2001-00101) of which this work is part. We are also grateful to Roseanne Ford and Stefan Haderlein for helpful discussions about the manuscript and to Brian Senior at the EPFL Center for Electron Microscopy for the SEM work.
Supporting Information Available Information on supplementary experiments, analysis of non mass transfer limited biological uptake of anthracene, SEM 1062
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and ESEM MPRS images, and other details relating to this work. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review September 29, 2003. Revised manuscript received October 9, 2004. Accepted October 18, 2004. ES035067B
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