described elsewhere (Alpert and Hopke, 1980; Henry, 1987; Henry et al.,. 1984; Hopke, 1988; Hopke et ai.,1976; Roscoe and Hopke, 1981), details of the factor ...
PATTERNS IN TRACE ELEMENTS IN LICHENS.
Joyce E. Sloof, H. Th. Wolterbeek Delft University of Technology Interfaculty Reactor Institute (IRI), Dept. Radiochemistry Mekelweg 15, 2629 JB Delft The Netherlands.
ABSTRACT. Epiphytic lichens were sampled in a Dutch national monitoring survey, which was carried out twice within 5 yr. The samples were analyzed by neutron activation analysis. The lichen data sets were presented in plots of geographical concentration patterns. These gave insight in the possible location of pollution sources. Comparison of the lichen data sets showed changes in the (geographical) concentration patterns with time. For all elements the areas with enhanced concentration classes increased from 1982-1983 to 1986-1987. The most striking change in concentration pattern was found for Cd. The application of factor analysis techniques in interpreting the concentration data yielded the composition of the various polluting components, which may facilitate identification of the associated sources.
i.
INTRODUCTION
Lichens have been used successfully as indicators and monitors of air pollution (see review Nieboer and Richardson, 1981). Various lichen species are rather better compared with other biological materials (e.g. grass, needles, tree rings, ferns and bark) in meeting many of the requirements of suitability for atmospheric monitoring purposes (Sloof e_!t al,, 1988). The major requirements are (i) a known relationship between concentrations observed in the monitor and the atmospheric element levels, (ii) common occurrence, (iii) averaging over a certain period of time and (iv) absence of interfering processes (De Bruin and Hackenitz, 1986; Martin and Coughtrey, 1982; Sloof et al. 1988). The relationship between element concentrations in the atmosphere and in the lichen tissue has not been quantified yet. From a number of heavy metal biomonitoring investigations in or near polluted areas it was evident that the element concentration gradients obtained from lichen data sets reflected atmospheric element concentration gradients (Addison and Puckett, 1980; Goyal and Seaward, 1981; Nieboer et al., 1972; Pilegaard, 1987; Sloof and Wolterbeek, 1990; Steinnes, 1980). As in many biomonitoring studies, in the present paper the concentrations of trace elements in lichens have been assumed to reflect
Water, Air, and Soil Pollution 57-58: 785-795, 1991. © 1991 Kluwer Academic Publishers. Printed in the Netherlands.
786
J.E. SLOOF AND H. TH. WOLTERBEEK
the averaged concentrations of these elements in dry and wet precipitation (cf. De Bruin et al., 1987; Kauppi, 1980; Rasmussen et al., 1980). In 1982-1983 a Dutch national monitoring program was set up using epiphytic lichens. In 1986-1987 the survey was repeated. Concentrations of about 40 elements in the lichen samples were determined by instrumental neutron activation analysis (INAA). The aims of the present study were (i) to verify the suitability of epiphytic lichens as biomonitor for trace-element air pollution, in realistic not a priori known situations (ii) to get insight in the character and the possible locations of pollution sources. The large data sets obtained, needing extensive mathematical treatment (see De Bruin et al., 1987; Pilegaard, 1987; Puckett and Finigan, 1980) were interpreted by target transformation factor analysis (TTFA), especially used to compile pollution source characteristics.
2.
METHODS AND MATERIALS
2.1. Sampling and analysis. In 1982-1983 the lichen species Parmelia sulcata was sampled at ii0 sampling sites from various tree species throughout the Netherlands. In 1986-1987 the national survey was repeated using a dense network of 250 sampling sites, established from a grid of ca. i0 x I0 km. The same lichen species was collected according to a standardized sampling and sample preparation method (Sloof and Wolterbeek, 1990). In addition, several samples growing on adjacent trees within a sampling site were taken in the 1986-1987 survey. The elements present in the samples were determined by the single comparator method of INAA as used routinely at IRI (De Bruin et al., 1982). Lead was determined by atomic absorption spectrometry using the graphite furnace technique (AAS), and only in the 1986-1987 survey. Quality control of the INAA and AAS results was based on the regular analysis of standard reference material NBS SRM 1571 "Orchard Leaves" (Gladney et al., 1987). The two lichen data sets are presented by plots of geographical concentration patterns. The local variation of element concentrations was checked and found not to be of influence to the concentration patterns at a national scale (Sloof and Wolterbeek, 1990). The trace element patterns obtained from the lichen data sets were used to identify the sources of the components present in various proportions in a set of samples of varying compositions. A mathematical receptor model was applied to extract the maximum information on the number and the nature of sources with no or limited a priori information other than the elemental compositional data. 2.2. Factor Analysis Factor analysis is a multivariate statistical analysis technique to simplify large and complex data sets in such a way that it may create one or more new variables (factors) each representing a cluster of interre-
PATTERNS IN TRACE ELEMENTS IN LICHENS
787
lated variables within a dataset. Especially in air pollution studies TTFA has been used to resolve the composition of atmospheric arosols into components related to emission sources. General goals of TTFA are (i) to determine the number of common factors (independent sources), which include as much as possible of the common variance and still provide a simple interpretable pattern, (ii) to determine the elemental source profiles and (iii) to calculate the contribution of each factor (source) to each sample. In our work TTFA has been applied to the lichen data sets in order to identify air pollution sources, based on the assumption that the elemental concentrations in each sample are a linear sum o~ a number of independent sources that have relatively constant compositions. A resolution of the observed elemental patterns into probable sources can thus be made. As the general mathematical procedures of TTFA have been described elsewhere (Alpert and Hopke, 1980; Henry, 1987; Henry et al., 1984; Hopke, 1988; Hopke et ai.,1976; Roscoe and Hopke, 1981), details of the factor model will not be outlined in this paper. Since the factor model is to represent physically real sources of airborne contaminants, several constraints should be applied to limit the space of possible solutions to a smaller proportion of the full space determined by the number of factors. Some contraints are (i) the predicted sources profile concentrations must be non-negative, i.e. a source cannot emit negative amounts of any element, and (ii) the model must explain the original correlations between the elements in the lichen data. The outcome of the factor model depends on choices made with respect to data standardization, number of factors, type of transformation, and correlation coefficients in the factor solution. In the TTFA applied to the lichen data sets, for each variable the mean value and variance were set to 0 and i values, respectively. The elements used in the calculations, were AI, As, Br, Cd, Co, Cr, Cs, Fe, Hg, La, Mn, Ni, Pb, Sb, Sc, Se, Th, V, W and Zn. The number of samples in the two data sets, (ii0 and 337), ensured the statistical significance of the results obtained (Henry et al., 1984). The number of factors to be retained in the model was determined on basis of the amount of variance in the data set explained. Three types of target transformations were applied, resulting in forced positive factor solutions. The target matrices used were a) the direct unrotated solution matrix of the factor analysis, b) the varimax solution matrix and c) a simple target matrix, consisting of the unit vectors. In addition, the resulting matrix was forced to be positive by an iterative procedure. The basic ideas behind these target matrices are that the TTFA should yield positive factor solutions, the original correlations between the variables should be explained and the factor solutions should be close to simple vectors containing only zero and unit values. The contributions from each of the factors to the total concentration of specific elements in individual samples were calculated and plotted geographically. The critical values for the correlation coefficients between the elements and the factors in the factor solution were determined arbitrarily: r~x . r ~ > 0.8 . r~, with rpx as the correlation coefficient between factor p and its pilot element (i.e. the element with a very high correlation coefficient), rpy as the correlation coefficient between factor p and element y, and r ~ as the significant correlation coefficient
788
J.E.SLOOFAND H.TH. WOLTERBEEK
between the elements x and y in the original correlation matrix (P=95%).
3.
RESULTS
3.1. Geographical Concentration Patterns The element concentrations of both surveys (1982-1983 and 1986-1987) are presented by plots of geographical concentration patterns. The results were consistent with the element concentration gradients obtained with a dispersion model and measured data on atmospheric concentrations and deposition (Van Jaarsveld and Onderdelinden, 1986; Sloof and Wolterbeek,
1990). Comparison of the two lichen data sets showed various changes in patterns with time, as illustrated in Figure i for the distributions of Cd concentrations.
Fig. I. Element concentration patterns of Cd in 1982-1983 and 1986-1987. The legends show the 4 concentration classes, the lowest measured concentration (infimum) and the highest measured concentation (supremum). The areas with enhanced Cd concentrations (dark indicated in Figure I) increased from 1982-1983 to 1986-1987. For all elements studied, the areas with enhanced concentration classes increased from 1982-1983 to 19861987, whereas the mean concentrations remained the same (e.g. V) or increased slightly (e.g. Cd), indicating that the overall air pollution level did not decrease within 5 yr (Sloof and Wolterbeek, 1990). Especially the areas in the south of the country may be polluted by long range transport pollutants. The number of local extremely high values decreased, suggesting that air pollution in the Netherlands has become a
PATTERNS IN TRACE ELEMENTS IN LICHENS
789
diffuse national problem.
3.2. Factor analysis TTFA was applied to the concentration patterns of 19 elements (AI, As, Br, Cd, Co, Cr, Cs, Fe, Hg, Mn, Ni, La, Sb, Sc, Se, Th, V, W, Zn) obtained from the 1982-1983 lichen data set, and for 20 elements (Pb added) of the 1986-1987 survey. These elements are of major environmental concern or were regarded as typical for certain source types. During factor analysis the linear correlation coefficient for each of the possible pairs of variables was calculated, followed by standardization of these variables and eventual determination of a reproduced correlation matrix and a first direct factor solution. In essence, the determination of the number of factors to be retained in the model should be carried out by calculation of the amount of variance, that need not to be explained by the common factors, i.e. the specific variance and the error. However, in the present paper, for practical reasons, the number of factors to be retained was determined graphically, based on the idea that a minimum of factors should explain a maximum amount of existing variance. Figure 2 shows the relation between the number of factors and the amount of variance which they explain. In the two data sets 91 ~ of the variance was explained by 7 factors (19821983) and i0 factors (1986-1987). With this number of factors, the residual concentrations not explained by these factors did not show any geographical clustering. After rotation of the factor axes to a target matrix, the factor loadings thus obtained can be interpreted as sources. The factor loadings of TTFA with the direct factor solution as target matrix are given in Table I. All three factor solutions (see section 2.2) were found to be quite similar for each of the data set. iO0
Q)
75
@ @ A
>
50
25
i
5
i
i
l
10
15
20
factors (N) Fig. 2. The variance explained by the factors as a function of the number of factors. The circles refer to the 1982-1983 data set, the triangles refer to the 1986-1987 data set.
790
J.E. SLOOF AND H. TH. WOLTERBEEK TABLE I. Loadings of the factor solution with direct solution as target matrix. 1982-1983 F1 A1 As Br Cd Co Cr Cs Fe Hg La Mn Ni Sb Sc Se Th V W Zn
0.81 0.66 0.06 0.Ii 0.67 0.81 -0.01 0.79 -0.01 0.84 0.48 0.78 0.62 0.87 0.64 0.82 0.83 0.42 0.09
F2 0.08 0.42 -0.01 0.93 0.35 0.17 0.00 0.00 -0.05 0.ii 0.16 0.15 0.45 0.06 0.23 0.08 0.I0 0.28 0.95
F3
F4
F5
0.08 0.15 0.00 0.12 0.04 0.43 0.98 0.01 0.00 -0.03 0.01 0.17 0.19 0.06 0.37 0.06 0.15 0 . 2 7 0.OO i.O0 0.00 0.39 0 . i i 0.22 0.33 0.05 0.71 0.26 0.ii -0.01 0.00 0.01 0.12 0.03 0.17 0.43 0.12 -0.01 0 . 4 1 0.26 0.14 0.14 0.12 0.01 0.60 0.26 0.14 0.03 0.ii 0.06 0.35 0.28 0.i0 0.03 0.02 0.01 -0.03
F6
F7
0.09 0.43 0.22 0.21 0.00 0.03 0.00 -0.03 0.ii 0.23 0.14 0.31 0.00 0.00 0 . 2 1 0.ii 0.00 0.60 0.00 0.34 0.81 0.06 0.13 0.32 0.22 0.19 0.04 0.31 0.25 0.00 0.04 0.42 0.19 0.08 0 . 5 1 0.55 0.05 0.06
1986-1987 F1 AI As Br Cd Co Cr Cs Fe Hg La Mn N1 Pb Sb Sc Se Th V W Zn
0.84 0.36 0.02 -0.01 0.44 0.54 0.86 0.59 0.00 o.51 0.15 0.12 -0.01 0.02 0.88 0.47 0.92 0.22 0.00 0.22
F2
F3
0.00 0.32 0.69 0.30 -0.01 -0.01 0.13 0.00 0.50 0.49 0.63 0.21 0.04 0.31 0.22 0.60 0.00 0.03 0.06 0.00 0.02 0.25 0.13 0.89 0.16 0.06 0.55 0.42 0.22 0.28 0.27 0.30 0.23 0.06 -0.02 0.81 0.46 0.00 0.88 -0.02
F4
F5
0.00 0.i0 0.24 -0.01 0.00 1.00 0.98 0.09 0.17 0.07 -0.01 0.00 0.17 0.13 0.i0 0.21 0.01 0.01 0.00 0.06 -0.01 0.00 0.09 0.11 0.00 0.14 0.21 -0.01 0.14 0.16 0.73 0.04 0.05 0.05 0.17 0.16 0.13 0.01 0.06 0.01
F6
F7
F8
0.00 0.14 0.09 0.04 0.17 -0.01 0.00 0.00 O.00 0.00 0.11 0.00 0.04 0.02 0.14 0.04 0.00 0.09 0.03 0.00 -0.01 0.18 0.06 0.07 1.00 0.01 0.03 0.00 0.84 -0.Ol 0.03 0.00 0.13 0.14 0.23 0.03 0.00 -O.01 0.97 0.05 0.32 0.16 0.04 0.06 0.07 0.02 0.06 0.17 0.00 0.02 0.08 0.00 0.16 0.22 0.05 0.08 0.00 0.00 0.00 O.13
F9
FIO
0.23 0 1 7 0.01 0.27 0.00 0.00 0.04 -0.01 O.08 0.27 O.OO 0.09 0.15 0.02 0.i0 0.13 0.00 0.02 0.00 0.02 0.09 0.93 0.00 0.01 O.02 O.02 0.31 0.24 0.14 0.i0 0.01 0.17 0.14 0.09 0.19 -0.01 0.85 0.01 0.00 0.21
The loadings of the factor solution are the correlations between the elements and the factors. Some loadings were still negative, but relatively c l o s e to z e r o a n d t h e r e f o r e can be ignored ( T a b l e I). T h e criterion rpx . r ~ > 0 . 8 . r ~ w a s u s e d t o f i n d t h e e l e m e n t s d o m i n a t i n g the factor composition. Table II shows the factor composition for both data sets, each normalized to a concentration i00 for the pilot elements.
PATI"ERNS IN TRACE ELEMENTS IN LICHENS
791
TABLE II. Normalized factor composition. 1982-1983 F1 AI 400000 As Br Cd 14 Co Cr 1600 Cs Fe 410000 Hg La 450 Mn 4300 Ni 1600 Sb Sc 100 Se 86 Th V 3000 W
Zn
F2
F3
1,5
1.9 i00
F4
F5
F6
18000 i.i 0.46
F7 360000
1.1 68
i00 57OO 0.89
100
lO0
3.9 100
80 1.1
1.4 0.86 0.47 0.73
4.0 120 3.3 1800
0.14 100
0.64
F2
F3
78
1986-1987 F1 AI 420000 As Br Cd Co Cr 2300 Cs 72 Fe 380000 Hg ha
F4
F5
F6
F7
2.3
0.36
i00
3.3.
FIO
i00 5.8
6.6 15 0.28
Ii
0.26 3200 29000 i00 100
100
9.8
Mn
Ni Pb Sb Sc Se Th V w Zn
F9
370 98000
12000 1.8
F8
4.0 43
i00
34 i00 13
I.I 0.44
i00 22 140
150 0.35 I00
9.4
4.3
38O i00
Source Apportionment
A l t h o u g h c o n s i d e r e d to r e p r e s e n t source profiles, w i t h o u t full i n f o r m a t i o n a b o u t all p o s s i b l e e x i s t i n g source c h a r a c t e r i s t i c s , f a c t o r l o a d i n g s can n o t a l w a y s be a p p o i n t e d to s p e c i f i c (types of) sources. However, a n u m b e r of f a c t o r s c o u l d be i n t e r p r e t e d in terms of (types of) sources and p r o c e s s e s , that m i g h t give rise to the o b s e r v e d c o n c e n t r a t i o n s . F i, F 4 (1982-1983) and F i (1986-1987) s t r o n g l y d e p e n d on c o n c e n t r a t i o n s of AI, Cr, Fe, Mn, Sc a n d Th, w h i c h are all typical e l e m e n t s for c r u s t a l m a t e r i a l ( E d e l m a n and De Bruin, 1986; H 0 p k e , 1988).
792
J.E. SLOOF AND H. TH. WOLTERBEEK
F 2 (1982-1983, 1986-1987), characterized by As, Cd, Sb, W and Zn, most likely represents zinc smelters and / or electronic industry (De Bruin et al., 1987). The ratiots Zn/Cd and Cd/W changed from 1982-1983 to 1986-1987 (Table II). F 3 (1982-1983) and F 5 (1986-1987) probably represent sources of coal combustion. Although coal fly ash has a composition similar to that of the crustal material, trace elements like Br, Cd, Hg, Ni, Pb, Sb, Se V, W and Zn are also emitted by coal fired plants (Mey et al., 1984; Van der Sloot et al., 1985). The ratiots between the elements are variable due to the various types of processes and coal composition. F 3 (1986-1987) depends on Ni, V and Co, which are representative elements for oil combustion processes. The ratio Ni/V ranges from i/i to 1/3, depending on the type of chemical state and / or trace element composition of the crudes. (Kleinman et. al., 1980; Olmez et al., 1988; Paeyna, 1984). F 4 (1986-1987) is related to Cd and Se. Possible sources are waste incineration, iron and steel production and the production of Cd containing alloys (Hutton, 1983). F 5, F 7 (1982-1983) and F 6 (1986-1987) depend on Hg concentrations. The kind of source represented by these factors is unknown. F 6 (1982-1983) and F i0 (1986-1987) are the only factors with a relatively strong dependence of Mn. Although not mentioned in Table II (see significance criterion, section 2.2), the loadings of F 6 and F i0 also contain As, Fe, Sb (Table I), which may indicate high temperature processes (Hampel, 1968). F 8 (1986-1987) is related to Pb, Mn, V. Pb is a tracer for particles associated with automobiles. The recent use of manganese containing additives as a substitute for Pb additives may eventually result in automotive Mn emissions (Kleinman et al., 1980). F 7 and F 9 (1986-1987) strongly related to La, and to W and V, respectively, can not be identified with specific sources. 3.4. Geographical Distribution of Factors The geographical distribution of the concentrations as well as the factor contributions can be expressed in respect of the mean, so that the calculated values may be negative or positive in a certain sampling point. Figure 3 shows the 1986-1987 distribution of total Cd concentrations, F 4 and Cd concentrations filtered for F 4 and for all factors. Of F 4 high contributions are located in areas with enhanced Cd concentration classes. Filtering for F 4 yields a new concentration pattern, with only three small areas left with enhanced Cd concentrations. Factors 2,5,7 and 9 constitute the remaining deviations from the mean Cd concentrations (Table I, Figure 3). The distribution and content of F 4 indicates that this factor is likely to represent incineration sources (Hutton, 1983). Filtering for any other factor does not alter the original Cd concentration pattern (1986-1987). Filtering for all factors together shows the fraction of the variance which can not be explained by the factors.
PATI~ERNS IN TRACE ELEMENTS IN LICHENS
CD
793
~_~
Fig. 3. Plots of geographical distributions of Cd concentrations and factor contributions (1986-1987). The upper two plots show the geographical distributions of Cd concentrations in respect of the mean (2.8 mg,kg -I) and the contribution of F 4. The lower two plots show the distribution of Cd concentrations after filtering for F 4 and all factors.
794
J.E. SLOOF AND H. TH. WOLTERBEEK
4. DISCUSSION Measurements of air particulate matter and deposition as well as estimates obtained from a dispersion model (Van Jaarsveld and Onderdelinden, 1986; National Institute of Public Health and Environmental Protection, 1987) indicate changes in atmospheric pollution levels and their distribution. From the comparable changes in the element concentration patterns obtained from both lichen data sets (Sloof and Wolterbeek, 1990), it can be concluded that lichens are able to reflect changes in the atmospheric pollution levels and their distribution within 5 yr. The application of TTFA yielded information on the number and the character of possible and significant pollution sources. Comparison of the factor outcomes from both lichen data sets showed that several factors could be interpreted as resulting from the presence of source profiles unchanged with time (soil, coal combustion, an unknown Hg source, non ferrous metal industry). The most striking change with time in concentration pattern was found for Cd. TTFA of the 1986-1987 data set resulted in a factor depending strongly on Cd and Se concentrations. The geographical distribution of the factor contributions gave insight in the possible locations of the pollution sources at a local scale, and may also be of importance with respect to the determination of source origins and validating of the model applied. The major advantage of TTFA consists of its applicability without any need for a priori assumptions, with respect to number or nature of sources; TTFA may even yield information about non-familiar types of sources, for which clear apportionments become possible only after further investigation. However, the credibility and the validity of the factor model outcomes still depend on the modeler's judgement, since for a number of constraints and criteria no well-documented mathematical procedures exist. The development of such procedures and the refining of the factor analysis technique are required to ensure the reliability of the results of source resolution studies.
REFERENCES Addison, P.A. and Puckett. K.J.: 1980, Can. J. Bot. 58, 2323. Alpert, D.J. and Hopke, P.K.: 1980, Atm. Environ. 14, 1137. De Bruin, M., Korthoven, P.J., and Bode, P.: 1982, J. Radioanal. Chem. 70, 497. De Bruin, M. and Hackenitz, E.: 1986, Environ. Pollut. Ser. B. Ii, 153. De Bruin, M., van Wijk, P.M., van Assema, R., and de Roos, C.: 1987, J. Radioanal. Nucl. Chem. Art. 112:1, 199. Edelman, Th. and de Bruin, M.: 1986, in J.W. Assink and W.J. van den Brink (eds.), 'Contaminated Soil', Background values of 32 elements in Dutch soils, determined with non-destructive neutron activation analysis. Martinus Nijhoff Publishers, Dordrecht, pp. 89-99. Gladney, E.S., O'Malley, B.T., Roelandts, I., and Gills, T.E.: 1987,NBS Special Publications 260-111. US Department of Commerce. Goyal, P. and Seaward, M.R.D.: 1981, New Phytol. 89, 631.
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Hampel, C.A. (ed.): 1968, The encyclopedia of the chemical elements. Reinhold Book Corporation, New York, pp.389-398. Henry, R.C., Lewis, C.W., Hopke, P.K., and Williamson, H.J.: 1984, Atm. Environ. 18, 1507. Henry, R.C.: 1987, Arm. Environ. '21, 1815. Hopke, P.K., Gladney, E.S., Gordon, G.E., Zoller, W.H., and Jones, A.: 1976, Atm. Environ. i0, 1015. Hopke, P.K.: 1988, Atm. Environ. 22, 1777. Hutton, M.: 1983, Sci. Total Environ. 29, 29. Kauppi, M.: 1980, Acta Universitatis Ouluensis A , Biol. i0, i. Kleinman, M.T., Eisenbud, M., Lippmann, M., and Kneip, Th.J.: 1980, Environ. Intern. 4, 53. Martin, M.H. and Coughtrey, P.J.: 1982, 'Biological Monitoring of Heavy Metal Pollution'. Appl. Sci. Publ., London. Le Blanc, F. and Rao, D.N.: 1974, Soc. Bot. Fr. Coll. Br¥ol. 237. Mey, R., van de Kooij, J., Siepman, F.G.C., and van der Sloot, H.A.: 1984, Kema Sci.Tech. Rep. 2:1, i. National Institute of Public Health and Environmental Protection (RIVM): 1988, Annual Rep. 1987, 72. Nieboer, E., Ahmed, H.M., Puckett, K.J., and Richardson, D.H.S.: 1972, Lichenologist 5, 292. Nieboer, E. and Richardson, D.H.S.: 1981, 'Lichens as Monitors of Atmospheric Deposition'. in Atmospheric Pollutants in Natural Waters S.J. Eisenreich (ed.). Ann Arbor, Mich., pp. 339-388. Olmez, I., Sheffield, A.E., and Gordon, G.E.: 1988, Japca 38, 1392. Pacyna, J.M.: 1984, Atm. Environ. 18, 41. Pilegaard, K.: 1987, Bioscience 24, i. Puckett, K.J. and Finegan, E.J.: 1980, Can. J. Bot. 58, 2073. Rasmussen, R. and Johnson, I.: 1976, Oikos 27, 483. Rasmussen, L., Pilegaard, K., and Gydesen, H.: 1980, Bot. Tidsskr 75, 93. Roscoe, B.A. and Hopke, P.K.: 1981, Comp. Chem. 5, I. Sloof, J.E., De Bruin, M., and Wolterbeek, H. Th.: 1988, in A.A. Orio (ed.) 'Proc. Int. Conf. Environ. Contamination', Critical Evaluation of some commonly used Biological Monitors for Heavy_ Metal Air Pollution. CEP Cons. Ltd., Edinburgh. Sloof, J.E. and Wolterbeek, H.Th.: 1990, Lichenologist (submitted). Steinnes, E.: 1980, J. Radioanal. Chem. 58, 387. Van der Sloot, H.A., Weyers, E.G., Hoede, D., and Wijkstra, J.: 1985, Netherlands Energy Res. Foundation Rep. 178, i. Van Jaarsveld, J.A. and Onderdelinden, D. (eds.): 1986, Model description of concentration and deposition of coal relevant components in the Netherlands, caused by emissions in Europe. Projectbeheerbureau Energie Onderzoek, Utrecht.
