ARTICLE pubs.acs.org/est
Effect of Radial Directional Dependences and Rainwater Influence on CVOC Concentrations in Tree Core and Birch Sap Samples Taken for Phytoscreening Using HS-SPME-GC/MS Olaf Holm† and Wolfgang Rotard*,† †
Department of Environmental Engineering, Technische Universit€at Berlin, Germany, Strasse des 17. Juni 135, D-10623 Berlin, Germany
bS Supporting Information ABSTRACT: Phytoscreening for chlorinated volatile organic compounds (CVOC) in tree core samples is influenced by many factors. For instance, greater fluctuations are observed for CVOC concentrations in samples taken around the trunk at a fixed height compared to samples taken directly next to each other. To avoid false negatives and inaccurate interpretation of the results, we investigated this radial directional dependence as well as the influence of rainwater on measured concentrations. CVOC analysis was performed by gas chromatography/mass spectrometry (GC/MS) following SolidPhase-Microextraction (SPME). Phytoscreening was successfully carried out at three sites using this method. In addition, sap samples were taken from white birches during their budding period as a novel phytoscreening approach. Birch sap sampling is shown to be a suitable means of characterizing contaminant distribution within the soil subsurface. Radial directional dependence of CVOC concentrations varies by almost 80% for tree core samples and 50% for birch sap samples. Variations in concentrations measured around the trunk do not, however, provide information on the inflow direction of contaminated groundwater. The weather conditions were shown to have a greater influence so that CVOC concentrations measured from samples taken during colder, rainier weather were, on average, a factor of 100 lower than those taken during a warm and dry period. Nevertheless phytoscreening is adequate for CVOC characterization in the soil subsurface if the campaign is carried out during a dry weather period, the results then can be taken as being semiquantitative.
’ INTRODUCTION The term phytoscreening is associated with the application of contaminant detection in plants as a means for extensive characterization of contaminant distribution in the subsoil.1 Work in the mid-1990s 2 4 established that volatile organic compounds (VOC) are incorporated by plants through their root uptake of water from the aquifer, soil or soil gas,5 and are transferred throughout the stems via the transpiration stream. Therefore, the use of xylem saps or plant tissues are suitable as sample sources for detection of contaminants. Taking drill cores from tree trunks is the most common sampling technique. Sampling of branches, leaves, and fruits as well as reeds and other types of plants is also possible.3,6,7 The first phytoscreening applications based on tree cores were published as early as 1999.8 Since then, several examples of applications have been added documenting not only the diverse utilization but also the limitations of this procedure.1,6,9 For example tree core samples were used to monitor natural attenuation.9 Phytoscreening to trace subsurface contaminations of volatile chlorinated hydrocarbons (CVOC) is widely applied and also by commercial users. Vroblesky10 summarized several aspects of this procedure for VOC in a user guide in 2008, presenting advantages and drawbacks of the technique and its applicability for particular contaminants as well r 2011 American Chemical Society
as a list of factors influencing the sampling and analysis of tree cores. Various aspects which have a significant influence on both the planning of sampling and the interpretation of results are considered in depth in this paper. These influential parameters are primarily the effects of rainwater and the sampling location on the tree. High fluctuations were observed for sampling at a given height around the tree trunk.1,5,8,11 The reasons for this radial directional dependence are headed by, among others, the inflow direction of contaminated groundwater,10 which should allow undetected points of contamination to be traced. Several of our investigations give additional support to the hypothesis that the contaminant inflow direction is decisive for analyte distribution within the tree. Therefore, more detailed examinations concerning radial directionality were performed. Because of the locally uniform groundwater flow at the examined site, it is expected that maximum concentrations in the trees should be measured in direction of the defined contaminant source. Received: June 14, 2011 Accepted: October 10, 2011 Revised: October 6, 2011 Published: October 10, 2011 9604
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Environmental Science & Technology Additional objectives of this work are to present the application of birch sap samples for phytoscreening and the fluctuations of rainwater influence and radial directional dependence of both, birch sap and tree core samples. Furthermore, we anticipate increasing concentrations in tree core samples with decreasing rainwater availability. The investigations presented in this paper make use of SolidPhase-Microextraction (SPME) for enrichment of CVOC. SPME combines sampling, analyte isolation, and enrichment and has been widely applied to the sampling and analysis of e.g. environmental samples.12 On the other hand the use of carboxen/ polydimethylsiloxane (PDMS) fibers are not that common. This fiber shows very high sensitivity for TCE and cDCE but also a poorer repeatability and prolongation of equilibrium time.13 Other fibers do not show these effects14 but are not as sensitive.
’ EXPERIMENTAL SECTION Site Description. A dry cleaning plant in the northwest area of the former military base Potsdam-Krampnitz (west of Berlin, Germany) has caused considerable groundwater contamination, which distributes subsequently into the neighboring (bordering) wetlands. The geology on the base is predominantly simple with a fixed top of the aquifuge. The groundwater level in the existing wells ranged from 0.85 to 2.40 m. In the northeast of the base the geology is more complex and suggested the situation in the wetlands is complex as well. The groundwater flow is directed to the north into the wetlands. The area of investigation is dominated by two tree species: in the wetland almost exclusively by white willow (Salix alba) and on the base by white birch (Betula alba). Based on direct-push groundwater screening, the concentrations of CVOC reached a maximum of 122 mg CVOC/L.15 Main components are trichloroethene (TCE) and cis-1,2-dichloroethene (cDCE). More details on the site are given in the Supporting Information. The main focus is placed on this contaminated site in Krampnitz. However, the data of two more sites (Hamburg and Neuruppin) are referred for the statistical information only. Sampling and Sample Handling. To avoid any contamination by surface-adsorbed components, the outer layer of the bark was removed from the sampling sites using hammer and chisel before either birch sap or tree core samples were taken. For this investigation, samples were taken at a height of 50 cm above ground level. In addition to the sampling documentation (see Table S3 in the Supporting Information), the coordinates of the trees were determined by GPS. Tree core samples were taken with increment borers (Suunto, Finland; length 15 cm, internal diameter 0.5 cm). The bark was discarded, and the first five centimeters of the xylem wood was collected. A second sample was obtained about two centimeters away from the first sample location. Tree core sampling for weather dependency investigations took place in July 2007 on one willow, one poplar, and one birch tree, respectively. In the same month, the radial direction dependence was determined for three birch trees by taking samples at the same height at eight points around the tree. At this stage the investigations were not yet standardized, hence the bark was not discarded and the core lengths varied between 3.8 and 4.3 cm. Further investigations to evaluate variability due to radial direction dependence were undertaken between 22nd of May and 10th of July 2008. Two parallel samples were taken from each of the four compass points for each tree. On two white willows, five samples were taken in an
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x-shaped configuration with a maximum distance of two centimeter between them. Altogether, 19 trees were sampled (eleven white willows, five birches plus one poplar, one oak, and one apple tree). Birch sap samples were taken by drilling an eight centimeter deep hole using a cordless drill (Bosch, model: PSR 14,4 Li-2). This was followed by pressing in a brass spreading dowel (eight mm with M6 internal thread) which had initially been covered with Teflon tape. Final insertion was completed with a hammer if necessary. A specially constructed aluminum tube with external thread, also covered with Teflon tape, was screwed in. To catch the birch sap, a vial was hung on a nut screwed onto the tube. The vial and the sampling syringe were first rinsed. In general it takes only a few seconds, round about 10 s, to fill the vial. Ten milliliters of birch sap was carefully transferred from the bottom of the vial into a second vial, while the sap is flooding. The vial was immediately sealed. Therefore the potential loss of CVOC is negligible. In general, the flow allowed the filling of at least two further vials. To close the wound after completion of sampling, the aluminum tube was unscrewed, and the hole was sealed with a threaded bolt covered with Teflon tape. To determine any radial direction dependence, six birches were sampled at eight points around the trunk, and six more birches were each sampled on sides facing and opposite to the contaminant source, all at the same height. To compare the results with those from tree cores, 31 holes were made with an increment borer as described above, and the respective tree cores and birch sap samples from each resulting core hole were analyzed separately. Data from all trees depicted in the graphics are compiled in Table S3, and their locations are shown in Figure S3 in the Supporting Information. All samples were transported and stored at room temperature. Analysis of the gas space in the sampling vial took place by means of SPME followed by GC/MS determination (see below) within 24 h of sampling. The fresh tree cores were subsequently weighed. Their water content was then determined by oven drying at 105 °C for at least 24 h and reweighing. Solid-Phase-Microextraction (SPME). The SPME system used consisted of a carboxen/PDMS coated fiber (Supelco) attached to a plunger within a protective needle, which directly pierced the septum of the vial thus being exposed to the headspace within the sample vial. The fibers showed slightly differences in the sensitivity to each other and a decrease in sensitivity when used repeatedly. Assuming a linear relation between the loss of fiber sensitivity and the number of measurements, it is possible to implement a drift factor based on aqueous standards measured at the beginning and end of a sample series. Applying the calculated drift, each peak area was corrected to the value that would have been obtained had the sample been on a fresh fiber. Using aqueous standards, it was likewise possible to calculate compensation factors for the differences between individual fibers within a measurement campaign. Each sample peak area value was multiplied by a factor relating to the ratio between the peak areas of standards using a chosen reference fiber and the peak areas of the standards for the actual fiber used. Data collected in 2007 relating to weather dependency and radial direction dependence were neither corrected for drift, nor was a fiber comparison carried out. The number of measurements within the relevant series was sufficiently low so that corrections were not required. The samples were conditioned and extracted each for 30 min at 35 °C. The fiber then is desorbed in the GC-injector for 1 min 9605
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Table 1. Semiquantitative Evaluation Scale Including Peak Area Relationships and Corresponding Concentrations for TCE and cDCE in Aqueous Samplesa scale
a
peak area
TCE [ng/L]
cDCE [ng/L]
1
nd
2
uncertain
3
500 10,000
3 67
2 49
4
10,000 25,000
67 169
49 122
5 6
25,000 50,000 50,000 100,000
169 337 337 674
122 243 243 487 487 4870
7
100,000 1,000,000
674 6740
8
1,000,000 10,000,000
6740 67,400
4870 48,700
9
10,000,000 100,000,000
67,400 674,000
48,700 487,000
10
>100,000,000
>674,000
>487,000
nd: not detected.
at 250 °C (see Table S2 in the Supporting Information). The calibrations are made by external standards with aqueous standards under the same conditions, which is widely accepted for SPME.16 The conditions of the subsequent GC/MS measurements and annotations to the calibration (Table 1) are summarized in the Supporting Information. Procedures of Data Processing. Graphical representations of contaminant distribution were based on the semiquantitative results, taking the main mass peak areas from the parallel samples. Gradation of the semiquantitative evaluation scale is given in Table 1. Variations in the results from a single tree are represented by normalizing the measured values to the maximum concentration (c/cmax) or to the maximum content of the applicable series of measurements, respectively. Assuming that a tree core length correlates to its weight, the 2007 samples, which were taken before the sampling technique had been standardized, were weight corrected. The results of the radial directionality tests are presented in three forms. In the first, a simple network diagram, the relative concentrations are shown in relation to compass points. For the second representation, the tree core sample results were evaluated using the semiquantitative scale and finally interpolated using the program Surfer (Version 8.05; Golden Software, Inc.) in kriging mode. This results in a two-dimensional representation of the isolines from all four compass points as well as of the previously averaged values. The third representation uses vectors to emphasize the direction for which the highest concentrations were measured. The vector representation uses the average from multiple tree core sampling on the south side, from which the value from the north is subtracted; similarly, the west from the east. These two vectors were then added. The resulting vector was then divided by the sum of all concentration values of the four compass points. The vector length thus becomes a relative value of the extent of concentration differences between the radially distributed sampling points on the tree. These radial concentration difference (RCD) vectors were recalculated into polar coordinates and described by the directional angle and length (see Figure 1). The maximum possible vector length value equal to one indicates that contaminants were only measured in one direction. The RCD vector is represented by an arrow, beginning at the point of the respective tree’s coordinates. Additionally, the RCD vectors were used to evaluate the correlation with respect to the four compass points as well as the direction of the contamination source. This required the
Figure 1. Schematic illustration for the determination of the radial concentration difference (RCD) vectors.
measurement of northings and eastings (positioning coordinates) of the trees and the angle with respect to the point of contamination, stated by the position of the storage tanks. Thus, the angular deviations relative to the contaminant source with respect to the compass points could be calculated for each tree. The smaller the resulting angle is, the greater was the agreement between the directions of the highest measured concentration to the reference direction. By using a factor which relates to the length of the RCD vector, these angles may be normalized so that the trees with especially high internal concentration differences may be more strongly weighted when determining a mean value of the angular deviation of all trees. From the reference-related averaging of these angles for all trees, it is possible to estimate the probability that the highest concentration will be measured in the reference direction.
’ RESULTS Radial Directionality for Samples Taken at the Same Height. Tree core samples (duplicate samples) of the phyto-
screening applications showed average variation coefficients for various CVOCs ranging from 15% to 22%, with the exception of PCE at the site in Hamburg with 40% (Table 2). Radial directionality examinations also support this variation range. In contrast, radial directionality values vary by 79% for cDCE and 63% for TCE. Variations in birch sap sample results show a similar trend. Variations from multiple single core samples show a much lower mean variation coefficient of 7% compared to radially distributed samples with 50%. Although they are somewhat lower than those for the tree cores, they nevertheless clearly show dependence on the radial direction. The network diagram shown in Figure 2 also supports the argument that this effect is not caused by statistical errors. Moreover, it can be seen that an area of higher concentrations is formed with pronounced directionality, as illustrated by the relatively similar shapes of the structures depicted. Statistical errors would have resulted in much more diffuse forms. The very similar mean variation coefficients for birch sap sampling from diametrically opposite sample sites and from the eight directions 9606
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Table 2. Summary of the Mean Relative Standard Deviations for Substances and Campaignsb mean relative standard deviation number of campaign
maximum
average
in %
in %
substance trees/samples Duplicate Samples PCE
9
104
40
1,2-DCA
13
36
15
cDCE
19
57
15
TCE
23
83
22
phytoscreening
tDCE
12
40
17
Krampnitz
cDCE
35
58
20
TCE TCE
120 59
130 31
21 7
tree coring
cDCE
51
116
21
(radial samples)
TCE
60
88
16
tree coring
cDCE
8
40
17
(radial samples)
TCE
8
36
20
birch sap (total)
TCE
phytoscreening Hamburga phytoscreening Neuruppina
birch sap
Quintuplicate Samples
Radial Distributed Samples 12
132
50
birch sap (two Sides) TCE birch sap (radial) TCE
6 6
91 132
46 54
tree coring
cDCE
14
136
79
TCE
15
122
63
Samples were taken at a height of 100 cm. b The “number of trees/ samples” represent the number of samples in which the substances are detected.
a
Figure 2. Radial concentration differences from birch sap samples; above: relative concentrations related to the maximal value for the particular tree; below: semiquantitative evaluation.
also argue against statistical errors. Therefore, a systematic radial direction dependence is indicated. All three trees sampled in 2007 show highest core concentrations from the side nearest to the contamination source (see Figure S8 in the Supporting Information). The birch sap samples from early 2008 do not,
however, support this. Core samples were taken from Tree 01 in 2007 and birch sap in 2008. The TCE concentration maximum shows a clear shift from a southwest direction (contamination source) toward the north (compare Figure 2 with Figure S8 in the Supporting Information). The RCD vectors for TCE and cDCE resulting from the 2008 radial tree core sampling campaign are presented in Figure 3. The direction as well as the magnitude of the radial variations as depicted by RCD vector length appears to be somewhat random. No dependence on tree species or concentration gradient of subsurface contaminants (compare Figure 3 with Figure 4 or Figure S2 in the Supporting Information) can be observed. Completely different vectors are seen for closely spaced single species trees of similar size or age. Angular variations with respect to the compass points and the contamination source are lower for the latter. However, the differences are small (Table 3). Figure 4 shows several isolines based on the semiquantitative evaluation (Table 1) from the radial tree core sampling campaign which enable interpolation in the case of a fixed sampling direction. It should be considered that samples were taken under varying weather conditions. This has, however, no relevance for the effects on semiquantitative evaluation. The differences for fixed sampling directions (in this case the four compass points) are only small when applying semiquantitative evaluation. Comparison of Birch Sap and Tree Cores. Parallel sampling of ten milliliter birch sap and five centimeter diameter birch tree cores resulted in almost identical results for TCE. The ratio of peak areas in birch sap to tree cores for 31 samples was around 1.0 with a variation coefficient of 20%. Birch sap was, however, clearly more sensitive for cDCE, which could be detected in 29 of the 31 samples but in only 22 of the respective tree cores. The ratio for these 22 values was around 5.0, with a variation coefficient of 66%. With respect to radial directionality, the birch sap and tree core samples show similar distribution patterns (compare Figure 2 with Figure S8 in the Supporting Information). The Influence of the Weather. Figure 5 shows the influence of an extreme change in weather conditions on TCE concentrations. On the first day of sampling (11th of July, 2007), just as during the days preceding the sampling campaign, the daily maximum temperature was around 15 °C accompanied by continuous medium to heavy rainfall. On the 14th of July, 2007, the temperature climbed to a maximum of over 30 °C with no rain. The following days remained dry with similar temperatures. The values measured during this campaign on the colder, rainier days were, on average, a factor of 100 lower, single values being up to 1000 times lower. This trend was confirmed for all three trees and also for cDCE (data not shown). The increase in concentrations occurred simultaneously for two of the trees, with the third following one day later.
’ DISCUSSION Radial directional dependence in sampling was investigated to assess possible fluctuations in results obtained in relation to the context of phytoscreening and to verify whether the inflow direction of contaminated groundwater contributes to this. The results of radial directionality correspond closely to previously published values. Various studies1,5,8,11 have shown that the variations in amounts of contaminants from tree core samples taken at the same height around the trunk are significantly greater than for sampling points placed directly next to each other (duplicate samples). 9607
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Figure 3. Radial concentration difference (RCD) vectors for trichloroethene (TCE, left), for cis-1,2-dichloroethene (cDCE, right) and tree location. The lengths of the RCD vectors do not reflect concentration levels but rather the extent of variation between the radially distributed tree core abstractions for each tree.
Figure 4. Isolines from semiquantitative evaluation of values related to the four compass points and the previously averaged values; left: for trichloroethene (TCE); right: for cis-1,2-dichloroethene (cDCE).
Table 3. Average Angular Deviation between the RCD Vector and the Contaminant Source for the Four Compass Points substance
contamination
north
east
south
west
source [deg]
[deg]
[deg]
[deg]
[deg]
c-DCE
69
76
82
104
98
TCE
69
94
73
86
107
The published variations in tree cores around the trunk are presented in different ways and show maxima of around 90% for TCE and cDCE8 and a factor of 5 for TCE1 and PCE,5 respectively. A single examination of birch sap samples from Tree 05 implies that the radial concentration differences converge with increasing sampling height, as discussed previously.8 The examinations presented here show that systematic radial dependence in tree cores at the same height exists. At this site this does not, however, allow any conclusions on the inflow direction of contaminated groundwater or the gradient of the contaminants in the subsurface. Concentration variations for radially distributed samples may involve a number of influencing factors, such as sorption,17 decomposition,4 and diffusion.18 The results
Figure 5. Relative concentrations (logarithmic scale) for Tree 04 related to the maximum of a set of measurements during a change in both temperatures from 15 °C to more than 30 °C and in daily rainfall.
of the birch sap sampling show that sap transport plays a substantial role. Exposure to the sun and heterogeneous soil and root structures are regarded to cause radial differences in sap flow.1,10 Contaminated sites are often subject to anthropogenic influences, so that surface sealing or root capping can contribute to heterogeneous sap flow. Also, each tree’s individual structure is especially relevant.19 In addition, it appears that the distribution characteristics of CVOC around the trunk are not constant over time. In Tree 01, the TCE concentration maximum shows a clear shift between tree core sampling in 2007 and birch sap sampling in 2008 (compare Figure 2 with Figure S8 in the Supporting Information). Both sampling campaigns were carried out at the same sampling height on the tree, although the tree cores from 2007 were not taken at exactly the four compass points. Any influence of spiral growth20 on the different concentration distributions can thus be excluded. Compared to variations due to dependency on radial difference (Figure 2), the weather (Figure 5) has a much greater 9608
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Environmental Science & Technology influence on overall variation and leads to a clear change of the categorization when semiquantitative evaluation is applied. In four of twelve samples, cDCE could not be detected attributable to the cold, wet weather. On dry days, the sampled trees could be allocated to categories 7 and 8 on the semiquantitative evaluation scale (Table 1). For TCE, the classification ranged from categories 3 to 9. Vroblesky11 previously illustrated the impact of precipitation on VOC concentration through an irrigation experiment, which resulted in a factor of less than two. This is below variations due to radial difference dependence.1,5,8,11 A rapid response to irrigation was seen within one day during the above experiment, namely at all sampled heights up to 3.5 m above ground level. Different influencing factors such as sorption,17 decomposition,4 and diffusion18 may well lead to changes in the spectrum of contaminants within the tree. In the period of active growth, these processes apparently play a minor role for CVOC. The concentration increase presented here shows the great dependence on weather conditions. Trees only take up groundwater from the saturated zone when there is insufficient water in the unsaturated zone. At sites where, due to climatic influences, trees mainly take up groundwater, this influence is probably not as serious. Doucette21 documented factors of 10 to 100 for variations in contaminant levels at climatically different sites with comparable groundwater concentrations. Volatile contaminants such as CVOC diffuse out of the tree trunk. Due to loss to air, concentrations usually decrease with height.5,8,18,22,23 The gradients shown by Vroblesky11 and Sorek et al.1 do not match this concept. Our own height profiles range from unclear to inverse gradients (data not shown). A change in contaminant concentration of the extracted water results in a likewise change of concentration in the tree. Depending on the retention, a step gradient may occur and migrate upward along the tree. In addition, a shift in contaminant spectrum with increased height may be expected. Ma and Burken18 assume that, based on losses by diffusion, the depth horizontical profile will show higher concentrations inside the trunk. Our own examinations show no consistent depth profiles (data not shown). Migration gradients caused by slow diffusive transport processes are likely responsible for these findings. Based on the many factors influencing water uptake, deposition, and behavior of CVOC,10 only a semiquantitative evaluation of the measured concentration is meaningful. Nevertheless, these evaluations lead to meaningful contaminant images at all three sites, which coincide closely with those from direct push groundwater samples.15 Sampling at different directions raises the probability of taking a sample from the side of highest concentration. As could be shown, additional effort in taking radially distributed samples is actually not required for semiquantitative evaluations. The limitation to sampling of one point is reasonable, however critical for trees, whose analyte concentrations are close to the detection limit. Sampling from one fixed direction, e.g. the sunny side 1 of a tree, is not decisive, considering the results presented here. Rainwater has a considerable influence on concentrations of TCE and cDCE measured in the tree. Sampling in the course of a phytoscreening should therefore be undertaken during dry weather. Implications for Phytoscreening. Birch sap sampling is a suitable means of characterizing contaminant distribution within the soil subsurface. The difference in sensitivity relative to cDCE and TCE from birch sap and tree cores from the same point on the tree confirms the dependence of the kind of substance. For better correlation factors to groundwater contents, birch sap
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sampling is an interesting option. However, the method is only applicable during a very narrow annual time window. The points of measurement cannot be used over periods of several years, since the tree reacts to such injuries by building various reaction or barrier zones24 thus sealing off this area. The sampling port already seals itself within a few days, so that it is not even usable for the roughly four week long budding period. The sap flow is certainly weather dependent. Birch sap sampling could be repeated at points which had already dried up.
’ ASSOCIATED CONTENT
bS
Supporting Information. Detailed site description. Data of tree location and sampling conditions. Conditions of SPME extraction, GC/MS measurements and annotations to the calibration with aqueous standards. Radial directional concentrations of tree cores in 2007. Height profiles. Geological data and groundwater levels. Interpolated groundwater concentrations. This material is available free of charge via the Internet at http:// pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: +49 30 314-25220/-21978. Fax: +49 30 314-29319. E-mail:
[email protected].
’ ACKNOWLEDGMENT This work was funded by the German Federal Ministry of Education and Research, project SINBRA, contract no. 0330757D. The authors also thank A. Horn, F. Jaeger, S. Klemer, W. Seis, F. Zietzschmann, R. Hatton, and W. Frenzel for their assistance in preparing this manuscript. ’ REFERENCES (1) Sorek, A.; Atzmon, N.; Dahan, O.; Gerstl, Z.; Kushisin, L.; Laor, Y.; Mingelgrin, U.; Nasser, A.; Ronen, D.; Tsechansky, L.; Weisbrod, N.; Graber, E. R. ”Phytoscreening”: The Use of Trees for Discovering Subsurface Contamination by VOCs. Environ. Sci. Technol. 2008, 42 (2), 536–542. (2) Burken, J.; Dietz, A.; Jordahl, J.; Schnabel, W.; Thompson, P.; Licht, L.; Alvarez, P.; Schnoor, J., Phytoremediation at Hazardous Waste Sites. Proceedings - WEFTEC ’96, Annual Conference & Exposition, 69th, Dallas, Oct. 5 9, 1996 1996, 1, 327-332. (3) Schnabel, W. E.; Dietz, A. C.; Burken, J. G.; Schnoor, J. L.; Alvarez, P. J. Uptake and Transformation of Trichloroethylene by Edible Garden Plants. Water Res. 1997, 31 (4), 816–824. (4) Newman, L. A.; Strand, S. E.; Choe, N.; Duffy, J.; Ekuan, G.; Ruszaj, M.; Shurtleff, B. B.; Wilmoth, J.; Heilman, P.; Gordon, M. P. Uptake and Biotransformation of Trichloroethylene by Hybrid Poplars. Environ. Sci. Technol. 1997, 31 (4), 1062–1067. (5) Schumacher, J. G.; Struckhoff, G. C.; Burken, J. G. Contamination Using Tree Cores at the Front Street Site and a Former Dry Cleaning Facility at the Riverfront Superfund Site, New Haven, Missouri, 1999 2003; 2004 5049; Virginia, 2004; p 41. (6) Gopalakrishnan, G.; Negri, M. C.; Minsker, B. S.; Werth, C. J. Monitoring subsurface contamination using tree branches. Ground Water Monit. Rem. 2007, 27 (1), 65–74. (7) Chard, B. K.; Doucette, W. J.; Chard, J. K.; Bugbee, B.; Gorder, K. Trichloroethylene uptake by apple and peach trees and transfer to fruit. Environ. Sci. Technol. 2006, 40 (15), 4788–4793. 9609
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Environmental Science & Technology
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dx.doi.org/10.1021/es202014h |Environ. Sci. Technol. 2011, 45, 9604–9610
