Environ. Sci. Technol. 1994, 28, 429-434
Chlorinated Hydrocarbons in Pine Needles in Europe: Fingerprint for the Past and Recent Use Davlde Calamari,. Paolo Tremolada, Antonlo Di Guardo, and Marco Vighi Group of
Ecotoxicology, Institute of Agricultural Entomology, University of Milan, Via Celoria 2, 1-20133, Milan, Italy
Pine needles have been demonstrated as a useful monitoring matrix for the evaluation of the tropospheric contamination levels of persistent chlorinated hydrocarbons, such as DDTs, HCHs, and HCB. Global chlorinated hydrocarbon distribution has been investigated with major attention to remote areas, while the factors affecting the distribution trends in regions of major use are less known. Six countries in Europe were analyzed by the transect sampling mode. Homogeneous contamination intensities were present within each transect, and correspondence factor analysis was used for the characterization of the typical distribution patterns. The relative composition of the contaminants is revealed as characteristic for each area (“fingerprint”), and the history of the use and the socioeconomic conditions appear as the most important factor determining the distribution patterns.
Introduction The large use of persistent chlorinated hydrocarbons, such as DDT, HCH, and HCB has caused world-wide contamination ( I , 2). Several works can be found on the description of the contamination level of chlorinated hydrocarbons in various countries and on the use of plants and lichens as bioindicators of the tropospheric contamination level (3-6). In recent times, much attention has been devoted to understanding the global cycle of these xenobiotics by means of the use of plant foliage as a sampling tool (7). The most relevant result of these works is that physicochemical properties combined with environmental characteristics, e.g., cold temperatures, seem to play the most important role in the distribution patterns of remote areas, while an unpredictable and very scattered regional pattern is present in proximity of the source areas. The present work is an attempt to describe the distribution pattern of the areas characterized by an intense and prolonged use of these pesticides.
Materials and Methods Sample Collection. Thirty-seven pine needle samples were collected along seven transects in sixstates of Europe, as reported in Figure 1, during the years 1991 and 1992. The modality of sampling is called “transect sampling” and consists of the collection of avariable number (between 3 and 10) of samples at a fixed distance (in tens of kilometers) across an entire area. Four more samples, one from Val Grande (northern Italy), two from the Northern Po Valley (northern Italy), and one from Napoli (southern Italy), were also collected. They can not be assumed as transects because the sampling points were very distant from each other and scattered; therefore, they cannot be representative of the area. Pine needle samples (about 10 g each) were collected from the ground a t the end of their life cycle (2-3 years) 0013-938X/94/0928-0429$04.50/0
0 1994 American Chemical Society
Figure 1. Map of Europe with the locations of the seven transects of the present work, indicated by an *, and the locations of the sampling areas of the data from the literature (3,5,6, 7, 9), indicated by a H. The length of the present work transects ranges from some tens of kilometers to about 100.
and wrapped in aluminium foil, kept cold (4 “C) whenever possible, and then stored at -20 “C until analysis was performed. Chemicals. HCB, a-HCH, y H C H , o,p’-DDE, p,p‘DDE, o,p’-DDT, and p,p’-DDT were purchased from Supelco Inc. (Bellefonte, PA). All compounds were analytical standards of >99 % purity. n-Hexane for residue analysis was purchased from Riedel-de Haen (Seelze, Germany);florisil for residue analysis 0.150-0.250 mm (60100mesh ASTM), acetone for residue analysis, and sulfuric acid 95-97 % p.a. were purchased from Merck (Darmstadt, Germany); cellulose extraction thimbles were from Schleicher & Schuell (Dassel, Germany). Chemical Analysis. Sample Preparation. After a partial oven drying (30 “C; 12 h), the samples were minced and homogenized. Residual water was measured on subsamples (5 h a t 105 “C). Extraction and Cleanup. Cellulose extraction thimbles were oven-dried (130 “C; 4 h) and washed successively with acetone and n-hexane. Samples were weighed in the extraction thimbles, and the extraction was performed with a Soxhlet apparatus using n-hexane (8 h). For each Environ. Sci. Technol., Vol. 28, No. 3, 1994
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Table 1. Geometric Means of Transect Concentrations a n d Minimum-Maximum Interval of a-HCH, HCB, r-HCH, o,p'-DDE, p,p'-DDE, o,p'-DDT, a n d p,p'-DDT i n P i n e Needles in Europea transect Finland Lappeeranta-Imatra Holland Island of Texel Czechoslovakia Bratislava-Nitra
n
wHCH
HCB
T-HCH
o,p'-DDE
5
7.0 (5.6-7.6)
2.2 (2.0-2.8)
2.9 (2.2-4 .O)
NDb
3.9 (3.0-9.2)
3.8 (3.2-4.3)
13 (9.5-26)
ND
14 (6.5-31)
13 (4.6-30)
27 (15-87)
6 6
p,p'-DDT 0.6 (0.5-0.6)
ND
1.3 (1.1-1.8)
0.7 (0.3-1.6)
1.5 (1.1-2.2)
0.4c (ND-2.4)
20 (7.9-79)
2.lC (ND-9.8)
8.2
0.3
(0.2-0.4)
(4.2-23)
Austria Villach-Gratz
3
7.7 (4.7-20)
7.7 (4.0-17)
19 (11-51)
0.4c (ND-0.9)
ND
Italy Lake Garda
2.4 ( 1.4-5.3)
1.5 (0.9-2.0)
5
3.0 (1.5-4.4) 4.3 (2.4-7.0)
2.7 (1.8-3.4) 7.1 (5.5-9.8)
3.7 (2.5-4.7) 4.5 (2.4-10)
0.2c (ND-0.8) 0.2c (ND-0.9)
3.5 (2.0-5.6) 7.9 (4.9-39)
1.4 (1.0-2.2) 3.4 (1.8-14)
5.7 (3.3-10) 14 (7.5-79)
Milan
9
Greece Ithaca-Gerolimena
3
6.9 (3.6-13)
5.9 (5.0-7.2)
6.6 (2.8-16)
ND
5.4 (2.6-9.4)
1.9 (1.8-2.0)
single samples Napoli Northern Po Valley 1 Northern Po Valley 2 Val Grande
3.9 (2.8-6)
1 1 1 1
1.6 2.2 1.4 2.6
2.2 4.4 5.0 1.4
3.1 4.3
0.3 ND ND ND
3.2 5.2 1.6
1.0 1.9 0.3 0.6
7.1
0.6
2.5
2.0
11
2.6 4.0
Concentration values are expressed in ng/g dry wt. n indicates the number of samples in the transect. The last group is composed of four single samples from Italy. ND = below detection limits. The detection limit was 0.1 ng/g dry wt for all the compounds analyzed. The geometric mean was calculated assuming 0.1 ng/g dry wt for the ND concentrations, and so it is an overestimation of the mean contamination level.
set of the extraction, a blank sample was done in order to detect possible contaminations; however, no positive detections were found. Sulfuric acid cleanup (10mL added to the extracts; 12 h) was done. Florisil column chromatography (1.5 g of florisil in 8 mm i.d. glass columns) was performed after reduction of the volume by a rotary evaporator operated at 45 "C. Florisil was previously dried (130 "C; 4 h) and washed with acetone and n-hexane. Samples were then adsorbed on the florisil phase and eluted with 45 mL of n-hexane. The volume of the samples was reduced by a rotary evaporator to 1or 0.5 mL for the gas chromatographic analysis. High-Resolution GLC-ECD. Samples were analyzed by a Perkin-Elmer 8500 gas chromatograph with a splitsplitless injector and an electron capture detector (ECD). GC separation was obtained with a capillary column, Hewlett-Packard HP-5 (25 m, 0.32-mm i.d. 0.52 pm film thickness). The carrier gas was argon-methane, 95/5% ; the head pressure was 60 kPa, and the makeup was pressure 270 kPa. Injections were in the splitless mode. The injector and detector temperatures were respectively 280 and 300 "C, and the oven temperature was programmed from 100 "C with an increase of 10 "C/min to 210 "C for 6 min, then with an increase of 15 "C/min to 250 "C for 10 min, finally with an increase of 20 "C/min to 280 "C, and maintained for 3 min a t 280 "C. The analyses were performed with external standard calibration curve from 1 to 100 pg. The detection limit was 0.1 ng/g dry wt for all the compounds analyzed, assuming 10 g of sample. The procedure described above has been checked for recoveries and reproducibility. Recovery was investigated by spiking pine needle subsamples with four increasing amounts of the standards. For all the compounds analyzed, recovery results were in the 80 and 100% range. 430
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Reproducibility was calculated on replicate analyses, giving an error of between 10 and 20% . Data presented here are a t least the means of duplicate analyses. Concentration data are reported on the nanogram per gram dry wt basis, not considering the specific lipid content of each sample. This was done in accordance with most of the papers on the same argument (3, 5 , 6, 8, 9). Statistical Treatment of Data. Geometric mean was calculated from the concentration data of each transect. Normalized bar diagrams of the a-HCH, HCB, 7-HCH, p,p'-DDE, and p,p'-DDT concentrations in pine needle samples were then obtained from the geometric mean values for each transect. Bar diagrams of the relative composition of the contaminants were plotted in order to have a comparative representation of the levels of the compounds for the different areas. Correspondence factor analysis (CFA) was used for the treatment of the data after converting the mass units of the contaminants in the concentration values from nanograms to picomoles. The analysis was performed according to a statistical computer program STAT-ITCF version 3.0 (1987) originally produced by ECOSOFT and translated and corrected by the Institut Technique des CBr6ales et des Fourrages, Paris. Results and Discussion
Pine Needle Contamination Levels of Chlorinated Hydrocarbons in Europe. The geometric mean of the contamination level of the chlorinated hydrocarbons analyzed for each transect is reported in Table 1. The results of the measurement of the contamination levels in foliage by chlorinated hydrocarbons are in accordance with the data published in the literature.
Finlend
Italy-Lake Garda
a) lichens in SpWxqen(7)
b) lichens in Sweden (5)
Holland
e) pine needles in canhal Sweden (9)
f) pine needles in tha south of France (9)
Italy-Milan
c) lidrens in Norway ( 3 )
Czechoslovakia
g) pine needles in Poland (9)
Greece
d) pine needles in Germany ( 6 )
Y C H
HCB
*
WOOE
PPrnT
Austria
Flgure 2. Bar diagrams of the geometric means of a-HCH, HCB, y-HCH, p,p’-DDE, p,p‘-DDT for the seven European transects.
Previous research of this same group ( 4 , 7) and other shows almost the same state of relatively authors (3,5,6,9) high contamination levels with many variations among geographical zones. The comparison of the absolute values of the contamination levels among the different countries gives a quite confused description of the main trends. A different approach is therefore required for both the representation and the discussion of the contamination levels by chlorinated hydrocarbons in use areas. Bar Diagrams of Mean Contamination Level for European Transects. Typical trends of the contamination pattern can be represented by the bar diagrams of the geometric mean of all the compounds analyzed for each transect and are reported in Figure 2, in a normalized way as a “fingerprint”. The compounds included are a-HCH, HCB, y-HCH, p,p’-DDE, and p,p’-DDT because the two o,p’-isomers of DDE and DDT seem to be strictly related to the p,p’-isomers and so are not characteristic of one area as opposed to an other. At first glance, very different regional patterns can be recognized by the geometric mean concentration bar diagrams of the chlorinated hydrocarbons analyzed. Finland is characterized by a relatively high level of a-HCH and low levels of the others: the general contamination level is low. Holland has the same low level of contamination but is characterized by the high y H C H values. Czechoslovakia seems to be more contaminated for the other compounds; DDT contamination seems to conform to an “ o l d pattern with the prevalence of DDE over DDT. Austria has the same pattern as Holland but a t a higher
Flgure 3. Bar diagrams of a-HCH, HCB, */-HCH, p,p’-DDE, p,p’-DDT concentration data in foliage from the literature. The bar diagrams refer to (a) the C,, values of 20 samples of lichens and mosses in Spitsbergen (7), (b) the mean values of three samples of lichens In Sweden (3, (c) the geometric mean of 11 samples in lichens in Norway (4, (d) the mean values of at least two samples of pine needies in (e) the mean of 15 samples of pine needles in central Germany (4, Sweden (9),(f) the mean of four samples of pine needles in the south of France (9,and (9) the mean of two samples of pine needles In Poland (9).
level of contamination, almost twice as much. In the two transects of Italy, p,p’-DDT is the most abundant pollutant while the others are also present in high levels; the city of Milan shows almost a double level of contamination and a larger abundance of HCB. Greece is not characterized by any typical compound but has a diffuse contamination for all the pollutants. The distribution patterns are sufficiently different to confirm that for each area there is a typical distribution. The two transects of Italy are very close despite the differences in concentrations, confirming previous observations. Czechoslovakia is the most contaminated place. Holland appears to be similar to Austria. Finland, Holland, and Greece are less contaminated but with a different pattern. The specificity of the contamination seems to be related more to the relative composition of the contamination pattern than to the abundance of the pollutants alone. This consideration is still more evident comparing the present data of contamination levels in foliage to other data available in the literature. Several bar diagrams of the distribution pattern of the contamination levels in European foliage in the literature are reported in Figure 3. Data from Jensen et al. (9) are difficult to be considered in the way they represent only a fraction of chlorinated hydrocarbons present in needle waxes. Reischl et al. (6) show each compound to have a Environ. Sci. Technol., Vol. 28, No. 3. 1904
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Flgure 4. Correspondence factor analysis of the 39 transect samples by five variables: CY-HCH,HCB, y-HCH, p,p‘-DDE, and p,p‘-DDT. The graphical display is obtained by the projection of the points an the first and the second axis. Samples are identified by the codes as follows: FI = Finland; HL = Holland; AU = Austria; CE = Czechoslovakia; M I = Milan, Italy; GA = Lake Garda, Italy; GR = Greece.
specific soluble cuticular lipids-remaining needle concentration ratio: the ratio is lower than 1(average of 0.2-0.4) for HCH isomers and HCB and is 0.5-1 for DDT and DDE. Following these indications, the data of Jensen et al. (9) have been recalculated and expressed in a comparable way with the other data. Although considering transformations and approximations, the bar diagrams of data from the literature show similar type of information as that of Figure 2: all the contamination patterns in the foliage of the northern regions are characterized by very close distribution bar diagrams, such as the present data from Finland, and the other areas such as Germany, France, and Poland have their own typical fingerprint. Correspondence Factor Analysis of Pine Needle Contamination Level in Europe. The characterization of the presence of different regional patterns, defined by bar diagrams of the geometric means, was obtained also by the correspondence factor analysis (CFA) of the single data. CFA allows as many representations of observations as combinations obtained by the axes. From an analysis of the axes, the percentage of the total variance carried by the axes is deduced. The axes with the higher variance percentage are more explicative for the relative differences in the composition of the samples (IO). The importance of the variables in explaining the total variability is reported in the analysis of the variables. The most important variables and the ones already explained are deduced in this part of the CFA. o,p’-DDT was not included in the CFA because it is related to p,p’-DDT: the correlation coefficient is 0.913. The exclusion of o,p’DDT in the CFA was validated through the repetition of the analysis with and without o,p’-DDT. The variables considered for the characterization of the sampled areas are a-HCH, HCB, y-HCH, p,p’-DDE, and p,p’-DDT; among them the two isomers of HCH, p,p’432
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DDT and p,p’-DDE, are the most explicative in the characterization of the samples. Figure 4 reports the most significant graphical representation of this statistical analysis. The most significant representation is obtained by the use of the first and second axes which explain 68 and 16% of the total variability, respectively; the axes select the observation: the first one for y H C H andp,p’-DDT and the second one for a-HCH, y H C H , and p,p’-DDE. This representation allows a distinctive separation among the samples with a grouping inside the transects and allows the identification of the variable most associated with a transect. Finnish samples are very closely separated from the others, showing very homogeneous and diverse contamination patterns connected with the a-HCH variable. Those of Austria and Holland lie inside a cloud characterized by the proximity to the y-HCH variable. Czechoslovakian samples are more diffused but well distinguished between y-HCH and p,p’-DDE variables. Italian samples are very spread out in half of the graph, but they are tied to the p,p’-DDT variable. The spread cloud of the Italian samples can be expressive of the high variability of the contamination data that are typical of very different zones of a use area, e.g., the city of Milan and a lakeside. Samples of Greece are depicted in the middle of the picture with one sample closer to p,p’-DDE; the Greek results are not very homogeneous, and they are not characterized by a particular compound, indicating indirect and diverse contamination sources. Four main pieces of information can be visually derived from the graphical display of the CFA: a simultaneous multiresidual representation of the data, the individual separation in clouds of points, the homogeneity inside the clouds, and the relative association between observations and variables. A synthetic identification and classification of the samples is obtained by their similarities in the contamination pattern.
n FI-ZFI-1
FI-4 FI-5
J
GR-3
Flgure 5. Correspondence factor analysis of the 39 transect samples with the addition of the four single samples by five variables: a-HCH, HCB, T-HCH, p,p’-DDE, and p,p‘-DDT. The graphical display is obtained by the projection of the points on the first and the second axes. Samples are identified by the codes as follows: F I = Finland; HL = Holland; AU = Austria; CE = Czechoslovakia; M I = Milan, Italy; GA = Lake Garda, Italy; GR = Greece; NP = Northern Po Valley, Italy; VG = Val Grande, Italy; NA = Napoli, Italy.
The characterization of clouds of observations that have the same fingerprint of use allows the diagnosis of single samples that can be different in the level of single compounds but similar in composition and in ratios of all the pollutants, e.g., vegetation samples of different species or samples distant in space and time. Figure 5 reports the CFA of the transect samples together with the last four Italian samples (NA-1,NP-1, NP-2, andVG-11, elaborated by the same computer program that adjusts for the introduction of additional observations. This program does not include the additional variability of the supplementary samples in the analysis, revealing only the position of single samples on the graph composed by the clouds of samples coming from the transect-sampling mode. The graph shows the position of the single samples that fall inside the Italian cloud; in particular NA-1 and VG-1 lie just in the middle although deriving from areas far away from the actual transects.
Conclusions From the data of this work and that of the literature, a new element has been added in the comprehension of the global contamination; as in remote areas, physicochemical properties in combination with climatic and meteorological factors are the most important factor; in use areas, present and past use seems to be the most important element in determining the distribution pattern of these chemicals. Countries very different in the environmental features such as Austria and Holland show the same distribution pattern, while areas much more similar such as Austria and Czechoslovakia reveal a very different contamination status. Bar diagrams of geometric means of homogeneous data, coming from a transect of samples, are a suitable method
for the representation of the contamination pattern of the vegetation of an area. The typical distribution pattern of an area, dealing with the history of use of these pesticides, is proposed to be called the “fingerprint” of the area. By the fingerprint of the history of use, information about the socioeconomiclevel of a country can be derived; it is possible to outline several observations regarding the technological level of the agriculture (r-HCH dominance), the effectiveness of the bans (intensity of the DDT contamination, relative ratios of the contaminants), the used quantities of “old technological” insecticide (DDT and technical HCH), the age of the contamination (DDE/ DDT ratio, variability of the contamination levels), direct or indirect contamination (comparison of the typical contamination pattern of an area with those of the surrounding areas), limited local contamination (difference in the fingerprint of a restricted area in comparison to a wider one), and the role of long-range transport. Correspondence factor analysis provides many advantages for the treatment of multiresidual contamination data. CFA allows a graphical representation of the crude data without any elimination of outliers. By CFA analysis, samples are selected by similarities of the contamination pattern: the homogeneity within the samples of the same contamination pattern is visually evident; the associations of the clouds of samples to one or more variables can be deduced; the identification and classification of the contamination patterns of different transects can be obtained; single samples can be identified from relative Bimilarities.
Acknowledgments We thank all the people that helped in the pine needle collection. The research was supported by funds from Environ. Scl. Technol., Vol. 28, No. 3, lQ94
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the Italian Minister0 dell'Universiti e della Ricerca Scientifica e Tecnologica, (1991/40%1. Literature Cited Risebrough, R. W.; Huggett, R. J.; Griffin, J. J.; Goldberg, E. D. Science 1968, 159, 1233-1236. Risebrough, R. W. In Long Range Transport ofpesticides; Kurtz, D. A., Ed.; Lewis Publishers: Chelsea, MI, 1990; p p 417-426. Carlberg, G. E.; Ofstad, E. B.; Drangsholt, H.; Steinnes, E. Chemosphere 1983,12, 341-356. Gaggi, C.; Bacci, E.; Calamari, D.; Fanelli, R. Chemosphere 1985,14, 1673-1686. Villeneuve, J. P.; Holm, E.; Cattini, C. Chemosphere 1985, 14, 1651-1658.
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(6) Reischl, A.; Reissinger, M.; Hutzinger, 0.Chemosphere 1987, 16, 2647-2652. (7) Calamari, D.; Bacci, E.; Focardi, S.; Gaggi, C.; Morosini, M.; Vighi, M. Environ. Sei. Technol. 1991,25, 1489-1495. (8) Schrimpff, E. Water, Air, Soil Pollut. 1984, 21, 279-315. (9) Jensen, S.; Eriksson, G.; Kylin, H.; Strachan, W. M. J. Chemosphere 1992, 22, 229-245. (10) Devillers, J.; Karcher, W. In Practical Applications of
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Received for review May 18,1993. Revised manuscript received September 7, 1993. Accepted November 4, 1993." Abstract published in Advance ACS Abstracts, December 15, 1993. @
