Chem icals: Is aSolid-Phase
M icroextractionFiberaG ood
H E A T H E R A . L E S L I E , *, ² , ³
T H O M A S L . T E R L A A K ,²
F R A N S J . M . B U S S E R ,²
M I C H I E LH . S . K R A A K ,³
J O O P L . M . H E R M E N S²
Institute for Risk Assessment Sciences, Utrecht University,
P.O. Box 80176, 3508 TD Utrecht, The Netherlands, and
Department Aquatic Ecology & Ecotoxicology,
University of Amsterdam, P.O. Box 94084,
1090 GB Amsterdam, The Netherlands
A N D
When organic chemicals are extracted froma water
sample with solid-phase microextraction (SPME) fibers,
to the hydrophobicity of the compounds. This fiber
accumulation is analogous to the bioconcentration of
chemicals observed in aquatic organisms. The objective
of this study was to investigate the prospect of measuring
the total concentration in SPME fibers to estimate the
Using larvae of the midge, Chironomus riparius and
disposable 15-µmpoly(dimethylsiloxane) fibers, we studied
the accumulation and accumulation kinetics of a number
of narcotic compounds with a range of log Kowbetween 3
and 6. The fibers, which have a larger surface area-to-
rate constants (k1and k2, respectively) than midge
larvae and accumulated the chemicals 5 times faster.
Comparisonof the relationships of the partitioncoefficients
KPDMS-waterand Kmidge-water(lipid-normalized) to log Kow
forall compounds yieldeda factorof 28fortranslating fiber
concentrations to biota concentrations. This factor can
be used to estimate internal concentrations in biota for
compounds structurally similar to the compounds in this
study. The exact chemical domain to which this factor can
be applied needs to be defined in future research.
The hydrophobicity of many industrial chemicals is an
important factor in determining their potential to bio-
accumulate and reach target sites, which may result in
ecotoxicological risk. It is mainly this property that drives
organic chemicals in the aquatic environment to partition
to and bioconcentrate in the hydrophobic phases of organ-
isms, such as lipid bilayer membranes of cells. If certain
critical concentrations of xenobiotics are reached in
membranes, the membrane disturbance results in baseline
toxicity, a concept that has been widely applied in eco-
Because concentration addition applies to the narcotic
mode of action (1, 6-8), the total body residue (TBR)
represents a useful parameter to estimate the total contribu-
tion of baseline toxicity to the overall toxicity of a sample
(9-13). Direct measurement of xenobiotic body residues in
organisms themselves is complicated, first, by other bodily
extra cleanup steps before analysis necessary. Moreover, in
research has been focused on chemical extraction methods
as an alternative to measuring contaminants directly in
organisms. These methods can be employed even under
adverse conditions (e.g., toxic, anaerobic, turbid, dark) in
which no surviving organisms may be found for residue
The first step in estimating TBR is to simulate the
accumulation process in a surrogate phase. Methods that
and rely on physicochemical partitioningofthecompounds
over aqueous and hydrophobic phases, such as the semi-
(9, 10, 16), or the recently introduced thin films (17). Solid-
phase microextraction with negligible depletion, nd-SPME,
(11-13, 18, 19) is also based on the partitioning principle.
ratio of aqueous to hydrophobic phases required to avoid
depletion (cf., refs 9 and 18). Due to the small size of SPME
fibers, the method is characterized by faster accumulation
kinetics than SPMDs or Empore disks. When simulating
accumulation, not only the end result of the partitioning
process but also the kinetics of the accumulation in fibers
should be characterized to give information about the time
needed to reach equilibrium (often the sampling point of
SPME fibers were used in the present study to perform
biomimetic extractions of hydrophobic organic chemicals
in static test systems for comparison with the accumulation
by test organisms. These SPME fibers were coated with a
is a viscous liquid at room temperature through which
theory, when fibers are exposed to test water, the test
coating. This is a passive process driven by the differences
in fugacities of the chemical on both sides of the phase
interface. As shown by Mayer et al. (19), the organic
compounds are not adsorbed, but absorbed into the PDMS
layer so that neither saturation nor competition between
compounds will occur in the PDMS coating in aqueous
In earlier work, measured body residues of individual
compounds were compared with body residues estimated
with the biomimetic SPME technique (21). In the present
study, we set out to collect experimental data for chemicals
with various Kowvalues in order to investigate the accumula-
tion kinetics and the relationship between the KPDMS-water
and BCF. The overall aim was to establish whether the sum
concentration of different chemicals in PDMS fiber can be
used to predict TBRs in biota.
fourth instar midge larvae to a set of nonpolar and polar
narcotic compounds with log Kowvalues ranging from 3 to
*Corresponding author. Tel: +31-30-2535018. Fax: +31-30-
2535077. E-mail: H.Leslie@iras.uu.nl.
³University of Amsterdam.
Environ. Sci. Technol. 2002, 36, 5399-5404
10.1021/es0257016 CCC: $22.00
Published on Web 10/29/2002
2002 American Chemical SocietyVOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY95399
6 for different time periods to make uptake curves. An
used for analyzing pore water concentrations (22), is that
with organisms in a toxicity test. The measured KPDMS-water
values for the chemicals were compared to the Kmidge-water
measured in the same water. Comparisons of kinetic rate
constants were made between fibers and midges. On the
basis of the data, we discuss the possibilities for application
of chemical concentrations in PDMS fibers to predict the
concentrations in exposed organisms.
M aterials andM ethods
Test Chemicals. Compoundstested, with abbreviations, in-
1,2,3-trichlorobenzene (123TCB) (all Pestanal, Riedel-de
Hae Èn, 99%); 2,3,5,6-tetrachloroaniline (2356TeCA), hexachlo-
robenzene (HCB) (both Pestanal, Riedel-de Hae Èn, 98%);
4-chloro-3-methylphenol (4C3MP) (Aldrich, 99%); 2,4-
trichloronitrobenzene (234TCNB) (Aldrich, 97%).
Aqueous solutions of thetest compounds wereprepared
using 2-propanol (Baker Resi-analyzed) as carrier solvent in
copper-freeUtrechttapwater(hardness: DH 7,pH 7.6)giving
a 2-propanol concentration of 0.01% (v/v). The test con-
centration for each compound was chosen to be under the
LC10 for baseline toxicity in order to preclude toxic effects
leading to test organism mortality. (See Table 1 for log Kow
values and exposure concentrations.) Still, because of
Therefore, to avoid lethality and still have sample concen-
to test the compounds either alone or in smaller groups.
(The following compounds were tested with fibers twice:
24DCNB, 245TCA, and 1234TeCB).
The volume of the test solution was chosen to be
to satisfy the conditions for biomimetic nd-SPME (9). Tests
were carried out in half-filled, airtight 250-mL glass Erlen-
meyerflasks. Themost hydrophobic compounds, PeCB and
The temperature during the exposures was 24 °C, and no
food or substrate was added.
test) and three 2-cm-long 15-µm poly(dimethylsiloxane),
per fiber was 202 nL. Midge larvae were ∼5 mg, wet weight.
123TCB, and 2356TeCA, which were tested with fibers only.
At the end of each time interval, a different test vessel was
opened and sampled. The PDMS fibers were collected, and
each was extracted separately in 0.4 mL of hexane (Baker
Resi-analyzed) with an internal standard from the test set,
but which was not present in the exposure in question. Live
larvae from the Erlenmeyer were blotted dry, weighed
together,andstoredfrozen (-20°C) until extraction.Forthe
from the test set, as for the fibers. Also, different volumes of
hexane (3-4 mL) were used, depending on the chemical. At
the time of hexane addition, 1 mL of 2 M KOHaqwas added
and the samples were placed in an ultrasonic bath for 1 h
TheKOH andhexanephaseswereseparatedby centrifuging
the midge extracts (3000g, 10 min).
Lipid analysiscould not beperformed in thesamelarvae
wasmeasured in fourth instarsthat had been kept for4days
in similarly closed, half-headspace test systems with four
into groups (n ) 14) with g30 mg of biomass for analysis.
Lipid content was measured using a procedure developed
by Verweij (23). In short, the lipids were extracted with a
TABLE1. O verview of KPDM S-water, Wet Weight Km idge-water, andk2(h-1) Values for Fibers andM idges withStandardErrors and
CorrelationCoefficients (r2) CalculatedfromM odel Fita
ak1values calculated as K/k2, Log KPDMS, log Kmidge(lipid weight), and average aqueous exposure concentrations, Caq(µM) also shown. Log Kowvalues
from De Bruijn et al. (31).
54009ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 36, NO. 24, 2002
hexane-2-propanol mixture (v/v 3:2), hydrolyzed to fatty
Merck, in H3PO4, 85% Riedel-de Hae Èn), and measured
as standard. No correlation was found between exposure
for the midge samples was 0.96% of wet weight (SD 0.37).
Therefore, to express body residue results in terms of lipid
weight, 1% of wet weight was assumed for all larvae.
Chemical concentrations in the midge extracts were
measured by GC-ECD using a Carlo-Erba 5360 gas chro-
matograph, equipped with an electron capturedetectorand
a 15-m capillary column, J&W type DB5.625, i.d. 0.32 mm,
film thickness 0.25 µm. Injections of the hexane solutions
(1 µL) were carried out in split mode with an isothermal
a Fisons Chromcard data system. The lowest measured
concentrations in hexane were 10 pg/µL for all compounds
except 4C3MP, which was detected at 40 pg/µL. Thehexane
extracts of PDMS fibers and water samples, taken at t ) 0
and at the end of each exposure in duplicate, were similarly
analyzed by GC-ECD. Standards of all test compounds in
extraction recoveries were between 97 and 102%, so no
corrections were made, with the exception of extractions of
aqueous solution (water and midgeextracts only) of 4C3MP
exposure periods was plotted. These data were fitted using
) CaqK(1- e-tk2), whereChpistheconcentration ofchemical
in the hydrophobic phase, Caq is the concentration in the
aqueous phase, K is the equilibrium partition coefficient
between the hydrophobic phase and water (and which is
equal to k1/k2), k2is the elimination rate constant, k1is the
uptake rate constant, and t is time. The difference in K for
fibersand midgesdeterminesthecalibration factorforbody
residue estimates. A comparison of k2 values () k1/K)
indicates whether the fibers or the larvae reach equilibrium
concentrations faster (k2indicates the time to equilibrium
since from the model, t1/2) ln 2/k2). Fiber and midge data
were summarized in the plots of log K versus log Kow, log k1
versus log Kow, and log k2versus log Kow.
The decrease in water concentrations from the start to the
end of the exposures was small enough (<6%) to satisfy the
requirements for nd-SPME. The tests with larger vessels for
the two most hydrophobic compounds HCB and PeCB
showed greater decreases (up to 20% less than start con-
to the glass walls of the vessels, and partly to evaporation to
The uptake curves for the different chemicals in fibers
and midge larvae (Figure 1) showed that equilibrium
concentrations were reached, or nearly reached, so that K
took longer the more hydrophobic the compound was; e.g.,
was reached within 2 days, while for HCB (log Kow5.73) this
took ∼2 weeks.
K andk2(i.e.,k1/K) valueswithstandarderrorscalculated
from the model fits are given in Table 1. Rate constants for
the lowest precision due to difficulties fitting the start of the
curve. The precision of rate constants of compounds with
exposure tests. The uptake curve data for the very volatile
compound 12DCB (Figure 1a) showed the most variation;
therefore, its rate constants were plotted but not included
in the linear regression of log k2values (see below).
Values for Kmidge-water(based on lipid weight of midges)
for the test compounds were consistently higher than
KPDMS-water values. The slopes of the log K versus log Kow
relationships were not statistically different (p ) 0.647) so it
was possible to calculate one slope (1.17) for Kmidge-waterand
were -1.769 (SE 0.002) for fibers and -0.321 (SE 0.002) for
midge lipids and were significantly different (p <0.0001).
as calculated by the difference in y-intercepts of the two
log K versus log Kowrelationships.
Figure 3 illustrates that the k1values for the thin fibers
were greater (p < 0.0001) than for the midge (wet weight).
These uptake rate constants were similar for compounds of
not significantly deviate from zero (p ) 0.0918 and 0.0570,
Because we are particularly interested in equilibration
times, logk2datawereplotted againstlogKow(Figure4).The
fibers than for larvae, meaning that equilibrium concentra-
log k2versus log Kowrelationships for midge and fiber were
(slope, -0.991). The y-intercepts were statistically different
from each other(p)0.00939) with valuesof3.960(SE 0.002)
for fibers and 3.244 (SE 0.002) for midge lipid.
The idea of investigating the use of SPME fibers to estimate
residues in biota stems from the observation of the relation-
ships between BCF and Kow (e.g., ref 24) and between
for lipids, so lipid-based BCF or membrane partition coef-
ficientsarequitewell approximated by Kow(24). Mayeret al.
(19) have shown that the KSPME for the 15-µm PDMS fibers
used in the present study is related to Kowby the equation
log KPDMS-water) 1.00 × log Kow- 0.91 (r2) 0.99, n ) 17).
Considering BCF and KPDMS both show strong log-linear
relationships with Kow, the reasoning was that it should be
possible to predict the concentrations of hydrophobic
Based on this relationship and assuming BCF (lipid-normal-
ized) ≈ Kow, the concentration in biotic lipid would be
expected to be ∼8CPDMS (at steady state). We sought to
experimentally determine this factor to convert fiber con-
centration to body residue for a test set including both
nonpolar and polar compounds.
Therelativeaccumulation ofthedifferent compoundsin
PDMS fibers mimicked the accumulation pattern in biota;
the average factor determined in this study to translate the
total concentration of all chemicals measured in a fiber to
the total body residue of a midge larva is based on data for
which we saw a clear log-linear relationship between
concentrationsin biotaand fibersexposed totest chemicals
comprising a wide range of Kow(Figure 2). The factor, 28, is
however higher than the outcome expected based on the
above-mentioned KPDMS-waterequation (19).
A closer look at the data revealed that the factor is
somewhat influenced by the presence of polar compounds
in our test set. While the affinity of each test compound for
midge lipids was clearly greater than its affinity for PDMS,
this affinity difference is slightly more pronounced for
polar compounds (Table 2). The original relationship of
Mayer et al. (19) was computed using data for a set of 17
VOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 95401
of which were nonpolar. This can explain why KPDMS-water
measured in our study were in accordance with QSAR-
predicted valuesfor nonpolar compounds, but not for polar
compounds (which were consistently lower than predicted
based on their log Kow).
to KPDMS-waterfor polar compounds is likely due to the fact
that membrane lipids (or proteins) in the midge larvae are
FIGURE 1. Uptake curves of test chemicals in PDMS fibers (a, b); in midges based on wet weight (c, d); HCB in both midges and fibers
FIGURE 2. Log KPDMSandlog Kmidge(lipid-normalized) inrelationto
fiber, assuming lipidweightoflarvae of1%ofwetweight.Pooled
slope ofthe lines is 1.17(see text). Values fory-intercepts: -1.769
(SE 0.002) for fibers; -0.321 (SE 0.002) for midge lipid.
FIGURE 3. Uptake rate constants, k1, for midges (L/kg wet weight
54029ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 36, NO. 24, 2002
able to participate in interactions (e.g., H-bonds) with polar
groups. PDMS is nonpolar and does not engage in such
biota and fiber concentrations is clearly a good overall
conversion factor for estimating with PDMS the total body
residues of mixtures with both nonpolar and polar com-
of 0.5% of wet weight changes the calibration by a factor of
2. Still, the determination of an average calibration factor
opens up the possibility of estimating TBR even in complex
mixtures in which not all compounds can be detected and
quantified separately. With this approach, described earlier
in refs 10, 13, and 25, the total area of a full-scan chromato-
gram may be integrated and quantified using the relative
molar response of a suitable standard. The resulting total
fiber concentration is then available to estimate the TBR
Concerning the precision with which one can estimate
body residues of mixtures, if polar groups also play a subtle
role in directing partitioning to surrogate and lipid phases,
then there are two worst-case extremes possible: a mixture
is madeup of either all polar or all nonpolar compounds. In
either worst case, however, the estimate would be about a
factor 2-3 away from the actual body residue, which is for
many purposes an acceptable uncertainty. The purely
may also be adjusted for specific mixtures. For example, if
as PCBs, chlorobenzenes, PAHs, or petrochemicals, a con-
version factor of 10 gives more accurate body residue
estimates; a higher factor could be applied for very polar
mixtures. We want to emphasize that the discussion about
the factors is still based on a relatively small data set and
extrapolation to other chemicals should be performed with
greatcare.On theotherhand, thehigh numberofpublished
lend a high degree confidence to the extrapolation of such
a factor to other nonpolar chemicals. Extrapolation to other
classes of chemicals, in particular chemicals with polar
groups, is more uncertain and can only be addressed by
collecting extensive data sets for bioconcentration and
using PDMS-coated SPME fibers as a tool in environmental
of the test chemicals for the hydrophobic phases relative to
their affinities for water, but the accumulation kinetics
the accumulation in biota. Figure 3 shows that k1data for
both fibers and midge larvae showed no significant trends
with increasing Kow, indicating that the chemicals were
sufficiently hydrophobic tohaveaccumulation rateslimited
the larger k1for uptake into fibers compared to midges was
Becausek1was constant and K increased with Kow, thek2
values followed the expected trend and decreased with
increasing hydrophobicity (Figure 4): it took longer for
chemicalswith alargeK toreach equilibrium becauseofthe
larger amount of molecules that had to be transported
through the boundary layer of water, i.e., the rate-limiting
step (18, 26, 27). In fact, the best fit for the k2data would be
found if they were plotted against the K of the hydrophobic
phase in question. However, for purposes of comparing the
k2values of different compounds in the two phases, these
data were plotted against a common parameter, Kow.
Thus,thevery thin fibersusedin thisstudy showedfaster
accumulation kinetics than the test organisms in the test
test organisms in regular toxicity tests and conveniently
predict body residueswhileconcomitantly examining other
toxic effect parameters. With the exception of very hydro-
phobic compounds or short-term tests, equilibrium is
reachable in many test organisms, and thus in thin SPME
fibers, by the end of a toxicity test. Should the test exposure
run shorter than necessary for equilibrium concentrations
in the organisms, the fibers may be exposed for a period 5
chemicals that have not yet had time to reach equilibrium,
timesoffiberscan also mean timeand cost savingsforbody
residue estimation analysis.
The research described in this paper is focused on
developing a sampling method that mimics accumulation
extraction tool such as an SPME fiber. Biotransformation of
certain compounds may occur in some test organisms,
leading to body residues not only of parent compounds but
possibly also of residues of their metabolites, which both
contribute to membrane burden and narcotic effects.
residue of a species that has a large k2due to metabolism.
The TBR estimate is therefore considered as a worst-case
estimate for the slowly metabolizing or nonmetabolizing
organisms in an ecosystem. The possibility of biota being
able to biotransform certain chemicals may also lead to an
increase or decrease in bioconcentration with increasing
exposure concentrations. Although these phenomena may
affect the ultimate bioconcentration factor of certain chemi-
is still regulated by simple partitioning phenomena.
Organisms in this study were not fed, but ingestion is
often suggested as an extra route of uptake that does not
FIGURE 4. Elimination rate constants, k2(h-1), of test compounds
(12DCB point at log Kow 3.43 not included in the regression; see
text). Values for y-intercepts: 3.960(SE 0.002) for fibers; 3.244(SE
0.002) for midges.
TABLE2. Ratios of M idge (LipidWeight)-Water and
PDM S-Water PartitionCoefficients for Polar andN onpolar
1234TeCB4C3MP 245TCAPeCB HCB
23 299017 12 43
VOL. 36, NO. 24, 2002 / ENVIRONMENTAL SCIENCE & TECHNOLOGY 95403
applytofibers.Thefooduptakerouteisgenerallyconsidered Download full-text
sediment, it is often chiefly the pore water concentration
from the water phase still remains an important route of
accumulating hydrophobic chemicals (e.g., ref 29). The
simultaneous exposure of SPME fibers and organisms
represents a method for characterizing the uptake from the
waterphase, which isan essential step in understanding the
accumulation process for any hydrophobic chemical.
nd-SPME, used for sensing dissolved concentrations for
various purposes (e.g., refs 11-13, 19, 22, and 30), has the
added advantage of not measuring the total amount of
chemicals but only the fraction of a sample that is free and
available for uptake. Because of this and the significance of
SPME has potential as a tool for rapidly screening effluent
or surface water samples for accumulation potential and
This research is funded by Stichting Technische Weten-
schappen oftheDutchResearchCouncil(NWO) ProjectUBI
(1) Ko Ènemann, H. Toxicology 1981, 19, 229-238.
(3) McCarty, L. S. Environ. Toxicol. Chem. 1986, 5, 1071-1080.
(5) Van Wezel, A. P.; Opperhuizen, A. Crit. Rev. Toxicol. 1995, 25,
(6) Broderius, S. J.; Kahl, M. D. Aquat. Toxicol 1985, 6, 307-322.
Saf. 1985, 9, 321-326.
Toxicol. 1988, 12, 33-38.
(9) Verhaar, H. J. M.; Busser, F. J. M.; Hermens, J. L. M. Environ.
Sci. Technol. 1995, 29, 726-734.
(10) Van Loon, W. M. G. M.; Verwoerd, M. E.; Wijnker, F. G.; Van
Leeuwen, C. J.; Van Duyn, P.; Van de Guchte, C.; Hermens, J.
L. M. Environ. Toxicol. Chem. 1997, 16, 1358-1365.
(11) Parkerton,T.F.;Stone,M.A.AbstractBook,SETAC 17th Annual
Meeting; SETAC Press: Washington, DC, 1996; p 150.
(12) Parkerton, T. F.; Stone, M. A.; Letinski, D. J. Toxicol. Lett. 2000,
(13) Verbruggen, E. M. J.; Vaes, W. H. J.; Parkerton, T. F.; Hermens,
J. L. M. Environ. Sci. Technol. 2000, 34, 324-331.
(14) So Èdergren, A. Environ. Sci. Technol. 1987, 21, 855-859.
1990, 20, 533-552.
(16) Verbruggen, E. M. J.; Van Loon, W. M. G. M.; Tonkes, M. Van
Duin, P.; Seinen, W.; Hermens, J. L. M. Environ. Sci. Technol.
1999, 33, 801-806.
(17) Wilcockson, J. B.; Gobas, F. A. P. C. Environ. Sci. Technol. 2001,
(18) Vaes, W. H. J.; Hamwijk, C.; Urrestarazu Ramos, E. U.; Verhaar,
H. J. M.; Hermens, J. L. M. Anal. Chem. 1996, 68, 4458-4462.
(19) Mayer P.; Vaes, W. H. J.; Hermens, J. L. M. Anal. Chem. 2000,
(20) Go Ârecki, T.; Yu, X. M.; Pawliszyn, J. Analyst 1999, 124, 643-649.
(21) Leslie, H. A.; Oosthoek, A. J. P.; Busser, F. J. M.; Kraak, M. H. S.;
Hermens, J. L. M. Environ. Toxicol. Chem. 2002, 21, 229-234.
(22) Mayer, P.; Vaes, W. H. J.; Wijnker, F.; Legierse, K. C. H. M.;
(23) Verweij R. Lipid (vet) bepaling, Standaard werkvoorschrift No.
W0009, Department of Animal Ecology, Vrije Universiteit,
Amsterdam, The Netherlands, 1999.
(24) Mackay, D. Environ. Sci. Technol. 1982, 16, 274-276.
J. L. M. Anal. Chem. 1996, 68, 2916-2926.
(26) Gobas, F. A. P. C.; Mackay, D. Environ. Toxicol. Chem. 1987, 6,
(27) Flynn, G. L.; Yalkowsky S. H. J. Pharm. Sci. 1972, 61, 838-852.
(28) Kraaij, R. Ph.D. Dissertation. Utrecht University, Utrecht, The
(29) Belfroid, A. C.; Sijm, D. T. H. M.; Van Gestel, C. A. M. Environ.
Rev. 1996, 4, 276-299.
(30) Urrestarazu Ramos, E.; Meijer, S. N.; Vaes, W. H. J.; Verhaar, H.
J. M.; Hermens, J. L. M. Environ. Sci. Technol. 1998, 32, 3430-
(31) De Bruijn, J.; Busser, F.; Seinen, W.; Hermens, J. L. M. Environ.
Toxicol. Chem. 1989, 8, 499-512.
Received for review April 3, 2002. Revised manuscript re-
ceived September 18, 2002. Accepted September 25, 2002.
54049ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 36, NO. 24, 2002