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Trace metals in fish used for time trend analysis and as environmental indicators
Vol.28. No. 4, pp. 235-243, 1994 Copyright© 1994ElsevierScienceLtd Printed in Great Britain.All rightsreserved 0025-326X/94 $7.00+0.00
Marine Pollution Bulletin,
Pergamon
Trace Metals in Fish used for Time Trend Analysis and as Environmental Indicators L. A. J O R G E N S E N * t and B. PEDERSEN ~:
*Department of Policy Analysis, National Environmental Research Institute, Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark ~Department of Marine Ecology and Microbiology, National Environmental Research Institute, Frederiksborgvef 399, P.O. Box 358, DK-4000 Roskilde, Denmark tPresent address: Environmental Statistics Office, Danmarks Statistics, Sejrogade 11, DK-2100 Copenhagen, Denmark
A series of regressive models described in the ICES guide-lines for the time-trend investigation in levels of contaminants in fish, has been tested and evaluated on the basis of 11 years of monitoring data. Metal concentrations (Cd, Cu, Hg, Pb, and Zn) in plaice (Pleuronectes platessa) from three stations along the Jutland coast of the North Sea and concentrations in flounder (Platichthys flesus) from one station in the Sound and the Great Belt, respectively, have been used. A new graphic method that facilitates the statistical understanding has been developed. Time trends analysis showed a recent decrease in Pb concentrations, while the yearly variation among the rest of the concentrations was high. The largest Hg concentrations were found in the Sound. The differences in concentrations between species were small, except for Cu. An environmental index that incorporates all trace metal concentrations in the fish is suggested and problem areas are pinpointed.
M a t e r i a l s and M e t h o d s
Sampling and chemical analysis Pleuronectes platessa was caught in the autunm along the Jutland West Coast, and Platichthys flesus in the Sound and the Great Belt (Fig. 1), during the period 1979-89, as part of the Danish national environmental monitoring programme. The salinity at the sampling stations, see Fig. 1, increases from station J05 to J07 from 30.9 to 32.0 psu measured during the Danish routine monitoring. In the
iStavanger
The OECD has recommended the use of environmental indicators to describe the state of the environment to the public and the politicians. Heavy metal concentrations in fish can act as environmental indicators (OECD, 1991). Trace metal concentrations in stationary fish could be possible indicators in areas affected by human activities. The use of fish for trend monitoring of trace metals has been recommended by ICES (International Council for Exploration of the Sea). H E L C O M (Helsinki Commission), and OSPARCOM (Oslo and Paris Commission, 1990) have also agreed upon using this monitoring technique in their programmes for assessing the efficiency of control measures. A series of regressive models (Nicholson, 1985) have been evaluated on the basis of 11 years data from the Danish environmental monitoring programme and an environmental index has been applied.
-__
TheNorthSea
?
30.a
....
Skagen',
.4,,.
J07 -.~/"
// ~ ~
~
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Kattegat ~.. ~" ~k ' ~
'DENMARK ~ , t ' .
~X~',~
Fig. 1 Sampling stations and currents in the Danish marine area. The dashed arrow shows a subsurface current.
235
Marine Pollution Bulletin
Sound and the Great Belt (both stations in the inner Danish marine area) a estuarine two layer flow exists with brackish Baltic water oufflowing at the top and a saline bottom current with water originating from the Skagerrak. The average salinity in the top layer is 15.4 and 28.5 psu in the bottom. The fish were caught in nets located above the halocline in the Great Belt and the Sound. The number of occasions on which fish have been caught, varied for each particular area, trace metal and species are indicated in Table 1. The flounders from the Sound and the Great Belt were always caught by gill nets by the staff of the institute. The plaice was usually caught by local fishermen, under the supervision of the local division of the national Fisheries Inspectorate. The concentrations of mercury in fish muscle and cadmium, zinc, lead and copper in fish liver were measured in each fish. The length, weight, sex, age, liver weight, and liver dry wt were also recorded, according to the recommended sampling procedure of ICES (Anon., 1984). The analysis method used for the heavy metals, has been published previously (Jensen & Cheng, 1987). Pb concentrations are given on a dry wt basis, all other metals on a wet wt basis, because of better length correction when the Pb wet wt concentrations are used. The recovery and the standard deviation of the reference material and the fish samples are given in Table 2. The standard materials were supplied from the Standard Reference Materials from the NIST (NBS), the National Research Council of Canada and the Danish National Reference Material.
Statistical methods--covariables Prior to the statistical trend analysis the whole data set was subjected to a preliminary analysis to determine any covariates as to age, sex, liver, weight, length and weight of the trace metal concentration. This was done in order to select the most important variable to be TABLE 1
Total number of fish used for analysis from the different stations. A maximum of 25 fish were sampled in most of the years 1979-89. The mean dry wt pct of the fish are given in the last column. Station
Cd
Cu
Hg
Pb
Zn
Dry wt
J05 J06 J07 31 39
188 195 215 254 223
188 197 215 258 226
188 197 213 258 224
172 188 190 253 228
188 197 215 258 227
23.7 24.4 25.4 29.0 31.2
used further on in the statistical methods and also to get a first estimate of any covariates. A series of ordinary linear regression models with the trace metal concentration as the dependent variable and the covariable as the independent axis was made. This was performed for each covariable and for each metal concentration, both on dry and wet wt basis, to single out the most important covariable. The Hg concentration could not be performed on a dry wt basis since the dry wt is not measured in the muscle. A summary of the results is given in Table 3.
Normal distribution The data were also tested to examine the basic trace metal data distribution. Several authors have argued for a log~ transformation of the concentration variables, since many biological variables have been found loge normal distributed in nature. The hypothesis of a normal distribution was therefore tested on both the loge transformation of the metal concentrations and on the raw data material. Trend analysis The statistical analysis is built around a series of descriptive models, designed to test the effects of several hypotheses concerning the relationships of biological covariables and the concentrations of the metals (Nicholson, 1985). The contaminant concentration of each fish (I0, i.e. Cd, Cu, Hg, Pb and Zn, and the biological parameters (x, x l , . . . , Xp), i.e. fish length, fish wt, liver wt, dry wt, are used in the models. These variables are measured every year, over a number of years, T, and each year a number of fish, nt, is measured giving the total number of observations by summing up. For each station, each metal and each species, the series of models are: Model 1: tog~[Y] = ~tt + b t "Xl,
(1)
where xl is the individual fish length, and the intercept, ~tt, as well as the slope, bt, have different values each year.
Model 2: Log~[ Y] = ~tt + b- xl,
(2)
where ~tt and xl are the same as in equation (1), but the slope b has the same value every year.
Model3: Log~[ I1] = ~t + b" Xl,
(3)
where xl, and b are the same as in equation (2), but the intercept ~t has the same value every year.
TABLE 2 Recovery (RE), standard deviation of analysis of reference material (SD~) and number of analysis of reference material, and the range of the standard deviation of sampled material (SDFIsH) at stations with 25 fish in the period 1979-89. Cd
Cu
Hg
Pb
Zn
92.5 0.03 ( n = 103) 0.10--0.90
100 0.5 (nz60) 2-8
93.5 0.007 (n~72) 0.01--0.07
99.3 0.05 (n=94) 0.05-0.40
99.5 3 (n=83) 20-40
TABLE 3 Upper quartile from two unpolluted areas for Platichthys flesus from station 39 (the Great Belt) and Pleuronectes platessa from station J07 (the Skagerrak). The unit is ppm wet wt in fish liver for Cd, Cu and Zn, ppm dry wt in liver for Pb and ppm wet wt in muscle for Hg. Liver
RE, % SDRE SDFIsH
236
Station 39 J07
Cd
Cu
Pb
Zn
Muscle Hg
0.14 0.09
15 2.5
0.26 0.37
45 30
0.07 0.05
Volume 2 8 / N u m b e r 4 / A p r i l 1994
Model 4: Log~[ Y] = a0 + al "t + b" Xl,
(4)
where b, xl are the same as in equations (2) and (3), but with a linear trend in the intercepts, expressed as a 1 multiplied by t, where t is the time, e.g. in years after the first sampling year and a0 is a new intercept. The next model includes all covariables as length, weight, liver weight, sex, age etc. according to:
Model 5: Log,[ Y] = ~tt +
b t l • x 1 + bt2 " x 2 + . . . + b t p "Xp, ( 5 )
where the parameters ~tt, btl to btp are calculated each year. The last model has only a yearly effect, the yearly mean is calculated.
Model 6: Log.[ Y] = ~t.
(6)
An even simpler model, where the mean value of all years is calculated, is not included in the ICES statistical guide-lines, but can be formulated as:
Model 7: Loge[ Y] = ~t,
(7)
where no covariables are calculated. In model 5 one covariable is selected at a time. More covariables are included in the model, if the F-statistics reflecting the covariable's contribution to the model improves the F-statistics, with more than 0.50. This procedural evaluation is repeated and the procedure ends when no remaining covariable produces a significant improvement in the F-statistics. A covariable once selected, remains in the model throughout the procedure (Hocking, 1976). The different statistical models are compared by calculating: RSS b - RSS a
F(dfb - dfa, df~) -
dfb -- dfa RSSa
,
(8)
dr.
where the residuals sum of squares and the degree of freedom for a complex model (model a) is compared with a more simple model (model b). Model a is accepted if F(dfa, dfb) is significantly large compared to an F distribution with (dfb -- dfa, df,) degrees of freedom (Nicholson, 1985). The analysis in the ICES guide-lines is designed in the expectation that model 2 will be appropriate. The geographical variation is tested against the basic hypothesis that all stations have the same concentration. Since the year to year variation is large the test is performed on a yearly basis. The method is the same as described for the time-trend analysis (Gabriel, 1978).
Graphical method When concentration measurements are used for yearly calculation of the mean value and these means have to be compared with those of previous years, it is very important that both statistical tests and graphical methods can be used in the interpretation of the data. It has been suggested (Nicholson, 1985) that plots should
be made of the log~ adjusted concentrations with pairwise significance bands for each year. The band could be calculated according to: exp(Yadjusted) "1- t0.975
"
SE ~2'
(9)
where exp is the exponential function, Y is the 1Oge length adjusted mean concentration, SE is the standard error of the mean and t is the students' t-statistic for the two-sided 0.975 level with the appropriate degrees of freedom. However, this method fails when the comparison includes means calculated from fish samples, where the number of samples are not the same in every year. Although the standard sampling procedure of the monitoring programme (Anon., 1984) describes that 25 fish should be included in the analysis, the number of successful measurements will normally differ from year to year. The pair-wise bands calculated from equation (9) can only be used when the Y are overlapping, in this case the means are not significantly different. When the bands do not overlap no statistical judgement can be made from the graphic plots. The same problem exists in the box and whiskers plot. The basis for testing if several different means are equal or not, demands an analysis of the variance. The t-test cannot be used when there are more than two means to be compared. One could of course compare several means, by applying the t-test between all different means, in total k ( k - 1 ) / 2 where k is the number of means. To compare five means, e.g. in this case a total of 10 comparisons have to be performed. If the t-test is performed at the 0.05 level the probability of making at least one type 1 error (the experiment-wise error rate) is 1 - (1 - 0.05)10=0.401. When the number of means to be compared increases, the probability of getting two means that are equal, tested to be different by pure chance, increases and approaches to one. This means that the probability that two mean values that in reality are equal, in the test will turn out to be different, approaches to one--more than two means will ruin the test. Most of the methods for making these simultaneous inference comparisons (Miller, 1981) involve calculations of all individual measurements in the two samples that are to be compared, and in this way it is impossible to calculate a pair-wise band around the mean value to get an easy graphical interpretation of a trend. The methods are known under their names, e.g. Tukey or Duncan. However, the problem can be solved by using a method based on the studentized maximum modules (Gabriel, 1978) and the bands can be calculated by Y~ -
m(a; k, v) "s 2 "n~
,
(10)
where Yi is the mean in a specific year with n i measurements, s the root mean square error and m(et; k, v) the studentized maximum module based on k independent normal random variables (the number of means) with v 237
Marine Pollution Bulletin
degrees of freedom and a-level critical value (0.95). This method of plotting has been applied in Fig. 3 where the bands are calculated according to the method described above. Another advantage of this method is that a new year can be calculated without a recalculation of all the other years. The individual measurements are also given in Fig. 3 using a jittering principle, by adding a small amount of random noise along the x-axis (Cleveland, 1985).
Environmental index The purpose of calculating an environmental index is to be able to present all results from the metal concentrations as one value if possible. This implies that the five metal concentrations must be normalized to make it possible to sum up and average the different metal concentrations into one value. We have chosen a statistical value, the upper quartile or the 75 percentile (Q75) as a normalizer or reference value. The upper quartile is used to account for the biological variation in a non-polluted area. Different Q75-values have been calculated for plaice and flounder, respectively, using all the data from the least contaminated areas for the two species (J07 for plaice and 39 for flounder). The values are given in Table 3. The Q75-values calculated here are found to be in good agreement with those given by ICES (1988), except for Cd, where the values found by ICES are higher by a factor of 2-3. The Q75-value is only a statistical value and does not relate to any effect data. It is to be noted, however, that for blue mussels, the ICES (1988) Q75-values are in the same range as the values suggested in Norway (Knutzen, 1992) to discriminate between a less contaminated area from a more contaminated. There exist no similar reference values for Cu, Cd, Pb and Zn in fish liver, that could have been used as a reference concentration. The environmental trace metal index is calculated according to: 1
K L
Index = - - . ~, Z metalconci K i=i i= 1 upper quartilei ' where K is equal to 5 metals and L is the number of fish measured in a year, usually 25. This calculation is performed for each year, in Fig. 9. If this combined index is above 1 the concentrations of trace metal could be considered elevated. One could also have selected the mean concentration as a reference value, but this would remove the possibility of distinguishing between clean and polluted areas. By this approach, measurements at a new station can more easily be classified into the two mentioned categories.
the North Sea, in the Kattegat and the Belts. The counter-clockwise flow in the North Sea is known to transport pollutants from the large European rivers, especially from the Ems and the Elbe along the Jutland coast, see Fig. 1.
Covariables The results in Table 4 show that the length is the most important covariable and therefore the length will be used (as the covariable (Xl) in all calculations) in models 1-7.
Normal distribution The original data (pooled for all years on each separate station) were tested for normal distribution. The same procedure was applied to the data transformed according to model 1. The test showed that the data follow a normal distribution, both the original and the transformed data and if the data are divided into yearly populations, these are also following normal distributions. The highest calculated probability for rejection of the hypotheses that the population follows a normal distribution was 69% for Pb at station J05 for the measured data and the mean probability was 20%. A normal distribution was also found for metals, PCB, PAH, pp'DDE and lindane in flounder and blue mussels from Dutch estuaries (Stronkhorst, 1992). Other researchers are using non-parametric statistics without testing the distribution type (Hellon et al., 1992; Monteiro & Lopes, 1990).
Test of length dependence First it is necessary to investigate if the relationship between the metal concentration and the length is the same every year. Model 1 was therefore tested against model 2 according to equation (8). The test showed that model 2 is the best model in 13 out of 25 cases (the total number of 25 is given by five metals at five stations). Model 2 does not hold for Hg at stations 39 and J06, Cd at stations 31, J05, J06 and J07, Cu at stations 39 and J07, Pb at stations 31, 39, J06 and J07 and Zn at stations J05, J06 and J07. Figure 2 shows the year to year variation in the regression line of Hg vs fish length, in two selected years. Model 1, showing different slopes and different intercepts each year was therefore chosen. The results TABLE 4 N u m b e r and type of significant covariables (P > 95%) of logo concentration of trace metals in Platichthys flesus and Pleuronectes platessa from five stations at the Danish coastal area, e.g. the first '2' in row one indicates that there are two positive correlations between Cu concentration and age at the five stations. The calculation is performed on both dry wt (dw) as well as wet vet (ww) concentrations except for Hg.
Results and Discussion
The collection sites were in coastal or open sea areas, far from any point source of discharge, except for the site in the Sound where the city of Copenhagen and three larger Swedish cities with chemical industries have given an anthropogenic load of trace metals. A chemical plant is located at the shoreline of the station J06. The sampling areas are influenced by the currents in 238
Covar.
dw
Cd ww
dw
Cu ww
Hg ww
Age Length Weight Liver
1 4 2 1
1 2 1 3:~
2 4 3 2
2 3 1 1
3 4 5 4
Pb dw 2t 3~: 3* 5*
Zn ww
dw
ww
2 3* 3* 5*
2 5 5 5
3 5 5 3
*Negative correlation. t O n e negative correlation. ~:Two negative correlations, no sign = positive correlation.
Volume 28/Number 4/April 1994 H g versus
TABLE 5
length
Results of linear time trend analysis by comparing model 1, model 3 and model 4. A '--' indicates a decrease in concentation, a %' an increase. The value in the table is the F-value calculated after equation (8). The F-value is significant at the 95% level if F is larger than 6.76. 0.1
to Station o
0 200
I 300
I 400
I 500
Lengthin m m
J05 J06 J07 31 39
Cd
Cu
Hg
+95.2 +0.13 -2.98 +10.1 +7.01
-11.0 -28.5 -6.68 -0.81 +12.0
-18.0 +1.68 +0.69 -0.62 -2.70
Pb +0.13 +7.37 -46.1 -85.3 -125.0
Zn -48.9 -46.0 -42.0 +0.12 +32.5
Fig. 2 Hg concentrationsvs lengthfor two differentyears. are in contrast to what was found early (Jensen & Cheng, 1987), where data from the first 5 years from two of the stations were analysed. The reason for this may be that this study includes measurements from 11 years. In the further analysis the b in equations (3) and (4) has been replaced by a bt, since the length correction is made on a yearly basis and not according to an average concentration vs length relationship. The degrees of freedom have been changed accordingly.
1.0 ] 0.8
@ ,
ol
OIJan79
Trend analysis The time trend analysis is made by calculating the F-statistics according to equation (8) using the residuals from models 4, 3 and 1. The results are presented in Table 5. Table 5 shows a significant increase in the metal concentration in fish on six occasions and a significant decrease on nine occasions in the period 1979-1989. The trend for Cd shows a general increase at stations J05 and 31. The Cu concentration shows a general decrease in the North Sea and an increase in the Sound. The Hg concentration is decreasing at the most southward station in the North Sea and the rest of the concentrations are not changing significantly. The Pb concentration is decreasing in the inner Danish marine area and in the Skagerrak. An increase is found at station J06. This station has low concentrations which increases the risk of contamination problems which might again have an effect on the trend analysis. The Zn concentration is decreasing at all stations in the North Sea and increasing in the Great Belt. The more simple linear regression is not applicable to this type of data distribution. If used, it would fail to detect a significant change in concentration in two cases (Cu at station J07 and Pb at station J06). The time-trend and the year to year variation can also be derived from the plots made by the graphical method. An example of a time-trend analysis made according to the graphical method described earlier is shown in Fig. 3. The figure shows that the concentration in the last part of the 1980s is significantly lower than in those of 1979-1985. Similar plots of all the other metal concentrations have been used to derive the following conclusions with special reference to the last 5 years. Cd. There has been an increase in Cd concentrations in the Sound and the Great Belt for the last 5 years measured. An almost linear trend in the mean concentrations can be found. The same increase can be
Pb in flounder liver versus year Areacode=31 (.3
o
'OlJan83
'Ollan87 '
OlJan90
Year Fig.
3 Pb concentrations in ppm dry wt in fish liver in the Sound together with bands for significance levels and mean values. The samples are taken in the late autumn.
seen in the North Sea for plaice and flounders during the last 4 years, see Fig. 4. The same increase is found in flounder livers from two Dutch estuaries (Stronkhorst, 1992). This trend is also found in the German Bight in 1985-1988, while a constant level or a decrease is found towards the English Channel (ICES, 1991). Cu. Stations 31 and 39 have had constant concentrations during the last 5 years. At station J05 there is a decrease, the value at station J06 remains constant and there is an increase at station J07, see Fig. 5. The concentrations of Cu in both plaice and flounder are also constant in the Skagerrak and the German Bight (ICES, 1991). Hg. Two of the stations in the North Sea, J06 and J07 showed a small increase in 1988 and 1989, while the concentration at J05 had lower means at the end of the 1980s than at the beginning. In the Great Belt the concentration over the last 5 years is rather constant, while the concentration in the Sound at the end of the 1980s had not reached the local maximum in Hg concentration found in 1983 and 1984, see Fig. 6. In the Ems Dollard and in the Western Scheldt the concentrations of Hg in blue mussels and flounders remained constant in the period 1985-90 (Stonkhorst, 1992). Pb. In the inner Danish marine area, there has been a decrease in the concentrations when the last 5 years of the 1980s are compared with the beginning of the 1980s. This decrease cannot be detected in the North Sea, station J07 even shows the highest concentrations ever in 1989. The liver of plaice is relatively smaller 239
Marine Pollution Bulletin
0.41- J06
~i~f J07
0.3[" 0.2
0.2[" 0.1
o,I,. ,I ol
I |llnnii ~
0.4
J05
/
79
84
79 ::ar 89~
~
89 ~
r
0.3[ 0.2
o 7,
~
~-
i,,,.ll! Year
~
L . : ~
~"z
~
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~...'~M.~,. \
~ ~
.-~
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79 84 sgL,'
OAf
-
~ 0 . 2 ~
,
. I
Year
~.,..._
Year
31
o.3F
4 , .
Fig. 4 Cd concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
20f JO7 10]
20[ J06
°t 20f
J05
~ o
7;::::="
~
1o
o I h h hi 79 84 89 Year
>
31
llillli,lt 79
84 89 Year
Fig. 5 Cu concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
(mean = 4.9 g, SD = 5) than that of flounder (mean = 11 g, SD----9). This gives less material for analysis and therefore the contamination problem may have a larger influence on the results of plaice compared to those of flounder, see Fig. 7. The emissions of Pb from gasoline 240
have decreased from 1000 t in 1977 to 100 t in 1991 in Denmark. The decrease in Pb concentrations can be found overall in the North Sea and the Baltic (ICES, 1991). In the Rhine delta, there was a decrease by a factor of three (i-Iendriks & Pieters, 1993).
Volume 28/Number 4/April 1994 0.3[ J07 0.3
J06
t
0.2 0.1
O.
o I idldl ~
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s4
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Hg concentrations in ppm wet wt in fish muscle in the Danish marine area, flounder in the Belts and plaice in the North Sea.
o.6t
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~
Year
=r
, I: 84 89, Year
~-'~--~--" ~
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dlJa,, Year
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Pb concentrations in ppm dry wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
Zn. In the Sound in the last 4 years, there have been 2 years with high concentrations (1986 and 1989) and 2 with low concentrations (1987 and 1988). In the Great Belt there has been a general increase, but the concentrations were low in •988. In the North Sea the
last 5 years experienced low concentrations especially compared with the beginning of the measuring period. There has been an increase in flounder and plaice in the German Bight and a decrease in plaice from the Skagerrak (ICES, 1991). 241
Marine Pollution Bulletin
The within years variation can be tested by comparing model 7 with model 6 or comparing model 7 with model 2. The result will in both cases be that there is a significant F-value by including the year-to-year variation in the analysis. It is important to further investigate this yearly fluctuation which can be up to a factor of 25 for some metals, but usually the standard deviation is 60% of the mean, see Fig. 3.
Covariables Other covariables than length could also be used in the analysis. A statistical method for making this is to compare model 5 with model 2. The result is given in Table 4, and a statistical analysis according to equation (8) shows that the residuals can be reduced by introducing more biological covariables into the models. However, in some cases the best covariable will be the length or the age, and in others the weight or the liver weight. This means that the type of biological covariable will differ from station to station and that the number of biological covariables will also change between stations and metals, if for example the age should be included as a covariable this could be done in 18 out of 25 cases, according to Table 4. This implies that the introduction of several covariables will not give any improvement to strengthen the trend analysis. Geographical distribution Concerning the difference in concentration between the various geographical positions the five metals are treated separately. Cd: The concentrations as seen in Fig. 4 are varying, with the largest concentration in most of the years in the inner marine Danish area. There is a tendency for a
decrease in the concentration along the coast line of the Jutland, with the highest concentration in the south. However, there is no statistically significant difference between the stations in the North Sea. Cu: The copper concentrations in flounder are always much higher (stations 31 and 39) than those found in plaice (stations J05, J06 and J07), as shown in Fig. 5. Cu is presumably regulated differently in the two species. The Cu concentrations in the Sound are in 5 years significantly higher than the Cu concentration in the Great Belt, which in turn is significantly higher in 3 years. In 3 years the concentrations are equal. There is no significant difference between the concentration at the different stations along the Jutland West Coast. Hg: The mercury concentration in the Sound is always significantly higher than the concentration at all other stations. The stations in the North Sea all fall in the same group in each year except for 1985, where the concentration at J07 had a significantly lower concentration than the other two stations as seen in Fig. 6. Concentrations at the station in the Great Belt are comparable to those in the North Sea. Pb: The concentrations are lower in the flounders from the Great Belt (station 39) compared to those from the Sound (station 31). At the Jutland West Coast, the largest concentrations are mainly found in plaice from the most southern station in the North Sea shown in Fig. 7. Zn: The concentration of zinc in the Sound and the Great Belt has been fluctuating around the same mean values during the whole period as shown in Fig. 8. In the period from 1984 to 1987 the concentration was larger at J05 than the concentrations of Zn at the other two stations in the North Sea. This was not true in 1988
4~[ JOt
:IHlht
4~i JO7
IIIllll
I
JO5
79
84 89 Year
31 ~
_"~
601" ~'139
~
~
~
" 40 2
Year
Fig. 8 Zn concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
242
89
Volume 28/Number 4/April 1994
and 1989, where the concentrations did not differ significantly among the stations in the North Sea.
2,0
Comparisons between species
1,4
There are only a few stations where measurements have been made for more than one species, all in the year 1979 and the number of samples is given in Table 6. The same statistical method as described for the comparison between years (equation 10) has been applied when comparing the concentrations between species. The results show that for Cd, male Cu, Pb and Zn the hypothesis that all eight groups have the same mean concentrations cannot be rejected. For female Cu concentrations, there is a significant difference in the means between the species• For Hg, all species of different fish from the same station have the same concentration• However the number of fish in each group is limited and the mean concentrations can vary between the groups by 20-30% and still be regarded as not significantly different. A special study must be conducted to investigate any differences in concentrations between species at the same location to obtain more usable results.
Environmental index The values in the environmental index are shown in Fig. 9. The figure shows that the burden of heavy metals is large in the Sound with increased concentrations in all years (station 31). The main component of the index is the Hg concentration. The rest of the concentrations tend to vary to a smaller extent. In 1988 the burden was the same at nearly all stations. The high Pb concentrations in the Great Belt were excluded in 1984. It is interesting that the index shows the same year to year variation in the Great Belt and in the Sound• The index has in the North Sea a low and rather constant value with small year to year variations. The last year (1989) shows an increase in the index mainly caused by an increase in Cd concentrations. TABLE 6
Number of samples of fish at the five different main stations, used when comparing the concentrations in different species. Species
L imanda limanda Platichthysflesus Limanda limanda Platichthys flesus Platichthys flesus Pleuronectes platessa Limanda limanda Platichthysflesus
Station
Number
J05 J05 J04 J04 J07 J07 39 39
18 22 14 20 6 10 10 5
Metal index versus time
1.8
•~ 1.2
. . . . . . _'h. . . .
~
~'"
1o11".....:: g' :-~-,.l,.~ °.'~.,- ~ 7. .... "t~". ".." o.8- • / .....
1979
,....x.V"--e /
~.. "::~
o.6~,~ -0.41-
,, _ .xCr
I
J
1982
1985 Time
o31
e39
OJ05
"et"
~J06
a 1988
t 1991
AJ07
Fig. 9 The environmental index including Cd, Cu, Hg, Pb and Zn concentrations at five different stations.
The authors want to thank MSc. Lotte Mindedal for assisting in the data handling, the technical staff at the laboratory in the year 1979-89 for the analysis of the samples, and the National Fishery Inspectorate, Jan Damgaard Nielsen and Kjeld Moser for collecting the fish samples.
Anon. (1984). The ICES Coordinated Monitoring Programme for contaminants in fish and shellfish, 1978 and 1979 and six-years Review of ICES Coordinated Monitoring Programme. ICES Coop. Res. Rep. 126. Cleveland, W. S. (1985). The Elements of Graphing Data. Wadsworth, Monterey, California. Gabriel, K. R. (1978). A simple method of multiple comparisons of means. J. Am. Stat. Ass. 73,364. Hellon, J., Warren, W. G., Payne, J. E, Belkhode, S. & Lobel, P. (1992). Heavy metals and other elements in three tissues of cod, Gadus morhua from the Northwest Atlantic. Mar. Pollut. Bull. 24, 452458. Hendriks, A. J. & Pieters, H. (1993). Monitoring concentrations of microcontaminants in aquatic organisms in the Rhine delta: a comparison with reference values. Chemosphere 26,817-836. Hocking, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics 32, 1-50. ICES (1988). Results of 1985 baseline study of contaminants in fish and shellfish. Cooperative Research Report No. 151. ICES (1991). Statistical analysis of the ICES Cooperative Monitoring Programme Data on Contaminants in Fish Liver Tissue and Mytilus edulis (1978-1988) for determination of temporal trends. Cooperative Research Report No. 176. Jensen, A. & Cheng, Z. (1987). Statistical analysis of trend monitoring data of heavy metals in flounder (Platichthys flesus). Mar. Pollut. Bull. 18,230-238. Knutzen, J. (1992). Vannkvalitetskriterier for Marine Omr/ider. Miljogifter. SFT. Oslo, Norway. Miller, R. G., Jr. (1981). Simultaneous Statistical Inference. Springer, New York. Monteiro, L. R. & Lopes, H. (1990). Mercury content of swordfish, Xiphias gladius, in relation to length, weight, age and sex. Mar. Pollut. Bull. 21,293-296. Nicholson, M. D. (1985). The treatment of time-effects in the statistical analysis of contaminant monitoring data. ICES, C.M.1985/E: 31. Organization for Economic Cooperation and Development (OECD) (1991). The State of the Environment. Paris. Oslo and Paris Commissions (1990). Principles and Methodology of the Joint Monitoring Programme. Stronkhorst, J. (1992). Trends in pollutants in blue mussel Mytilus edulis and flounder Platichthys flesus from two Dutch estuaries, 1985-1990. Mar. Pollut. Bull. 24,250-258.
243
Marine Pollution Bulletin,
Pergamon
Trace Metals in Fish used for Time Trend Analysis and as Environmental Indicators L. A. J O R G E N S E N * t and B. PEDERSEN ~:
*Department of Policy Analysis, National Environmental Research Institute, Frederiksborgvej 399, P.O. Box 358, DK-4000 Roskilde, Denmark ~Department of Marine Ecology and Microbiology, National Environmental Research Institute, Frederiksborgvef 399, P.O. Box 358, DK-4000 Roskilde, Denmark tPresent address: Environmental Statistics Office, Danmarks Statistics, Sejrogade 11, DK-2100 Copenhagen, Denmark
A series of regressive models described in the ICES guide-lines for the time-trend investigation in levels of contaminants in fish, has been tested and evaluated on the basis of 11 years of monitoring data. Metal concentrations (Cd, Cu, Hg, Pb, and Zn) in plaice (Pleuronectes platessa) from three stations along the Jutland coast of the North Sea and concentrations in flounder (Platichthys flesus) from one station in the Sound and the Great Belt, respectively, have been used. A new graphic method that facilitates the statistical understanding has been developed. Time trends analysis showed a recent decrease in Pb concentrations, while the yearly variation among the rest of the concentrations was high. The largest Hg concentrations were found in the Sound. The differences in concentrations between species were small, except for Cu. An environmental index that incorporates all trace metal concentrations in the fish is suggested and problem areas are pinpointed.
M a t e r i a l s and M e t h o d s
Sampling and chemical analysis Pleuronectes platessa was caught in the autunm along the Jutland West Coast, and Platichthys flesus in the Sound and the Great Belt (Fig. 1), during the period 1979-89, as part of the Danish national environmental monitoring programme. The salinity at the sampling stations, see Fig. 1, increases from station J05 to J07 from 30.9 to 32.0 psu measured during the Danish routine monitoring. In the
iStavanger
The OECD has recommended the use of environmental indicators to describe the state of the environment to the public and the politicians. Heavy metal concentrations in fish can act as environmental indicators (OECD, 1991). Trace metal concentrations in stationary fish could be possible indicators in areas affected by human activities. The use of fish for trend monitoring of trace metals has been recommended by ICES (International Council for Exploration of the Sea). H E L C O M (Helsinki Commission), and OSPARCOM (Oslo and Paris Commission, 1990) have also agreed upon using this monitoring technique in their programmes for assessing the efficiency of control measures. A series of regressive models (Nicholson, 1985) have been evaluated on the basis of 11 years data from the Danish environmental monitoring programme and an environmental index has been applied.
-__
TheNorthSea
?
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....
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.4,,.
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// ~ ~
~
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Fig. 1 Sampling stations and currents in the Danish marine area. The dashed arrow shows a subsurface current.
235
Marine Pollution Bulletin
Sound and the Great Belt (both stations in the inner Danish marine area) a estuarine two layer flow exists with brackish Baltic water oufflowing at the top and a saline bottom current with water originating from the Skagerrak. The average salinity in the top layer is 15.4 and 28.5 psu in the bottom. The fish were caught in nets located above the halocline in the Great Belt and the Sound. The number of occasions on which fish have been caught, varied for each particular area, trace metal and species are indicated in Table 1. The flounders from the Sound and the Great Belt were always caught by gill nets by the staff of the institute. The plaice was usually caught by local fishermen, under the supervision of the local division of the national Fisheries Inspectorate. The concentrations of mercury in fish muscle and cadmium, zinc, lead and copper in fish liver were measured in each fish. The length, weight, sex, age, liver weight, and liver dry wt were also recorded, according to the recommended sampling procedure of ICES (Anon., 1984). The analysis method used for the heavy metals, has been published previously (Jensen & Cheng, 1987). Pb concentrations are given on a dry wt basis, all other metals on a wet wt basis, because of better length correction when the Pb wet wt concentrations are used. The recovery and the standard deviation of the reference material and the fish samples are given in Table 2. The standard materials were supplied from the Standard Reference Materials from the NIST (NBS), the National Research Council of Canada and the Danish National Reference Material.
Statistical methods--covariables Prior to the statistical trend analysis the whole data set was subjected to a preliminary analysis to determine any covariates as to age, sex, liver, weight, length and weight of the trace metal concentration. This was done in order to select the most important variable to be TABLE 1
Total number of fish used for analysis from the different stations. A maximum of 25 fish were sampled in most of the years 1979-89. The mean dry wt pct of the fish are given in the last column. Station
Cd
Cu
Hg
Pb
Zn
Dry wt
J05 J06 J07 31 39
188 195 215 254 223
188 197 215 258 226
188 197 213 258 224
172 188 190 253 228
188 197 215 258 227
23.7 24.4 25.4 29.0 31.2
used further on in the statistical methods and also to get a first estimate of any covariates. A series of ordinary linear regression models with the trace metal concentration as the dependent variable and the covariable as the independent axis was made. This was performed for each covariable and for each metal concentration, both on dry and wet wt basis, to single out the most important covariable. The Hg concentration could not be performed on a dry wt basis since the dry wt is not measured in the muscle. A summary of the results is given in Table 3.
Normal distribution The data were also tested to examine the basic trace metal data distribution. Several authors have argued for a log~ transformation of the concentration variables, since many biological variables have been found loge normal distributed in nature. The hypothesis of a normal distribution was therefore tested on both the loge transformation of the metal concentrations and on the raw data material. Trend analysis The statistical analysis is built around a series of descriptive models, designed to test the effects of several hypotheses concerning the relationships of biological covariables and the concentrations of the metals (Nicholson, 1985). The contaminant concentration of each fish (I0, i.e. Cd, Cu, Hg, Pb and Zn, and the biological parameters (x, x l , . . . , Xp), i.e. fish length, fish wt, liver wt, dry wt, are used in the models. These variables are measured every year, over a number of years, T, and each year a number of fish, nt, is measured giving the total number of observations by summing up. For each station, each metal and each species, the series of models are: Model 1: tog~[Y] = ~tt + b t "Xl,
(1)
where xl is the individual fish length, and the intercept, ~tt, as well as the slope, bt, have different values each year.
Model 2: Log~[ Y] = ~tt + b- xl,
(2)
where ~tt and xl are the same as in equation (1), but the slope b has the same value every year.
Model3: Log~[ I1] = ~t + b" Xl,
(3)
where xl, and b are the same as in equation (2), but the intercept ~t has the same value every year.
TABLE 2 Recovery (RE), standard deviation of analysis of reference material (SD~) and number of analysis of reference material, and the range of the standard deviation of sampled material (SDFIsH) at stations with 25 fish in the period 1979-89. Cd
Cu
Hg
Pb
Zn
92.5 0.03 ( n = 103) 0.10--0.90
100 0.5 (nz60) 2-8
93.5 0.007 (n~72) 0.01--0.07
99.3 0.05 (n=94) 0.05-0.40
99.5 3 (n=83) 20-40
TABLE 3 Upper quartile from two unpolluted areas for Platichthys flesus from station 39 (the Great Belt) and Pleuronectes platessa from station J07 (the Skagerrak). The unit is ppm wet wt in fish liver for Cd, Cu and Zn, ppm dry wt in liver for Pb and ppm wet wt in muscle for Hg. Liver
RE, % SDRE SDFIsH
236
Station 39 J07
Cd
Cu
Pb
Zn
Muscle Hg
0.14 0.09
15 2.5
0.26 0.37
45 30
0.07 0.05
Volume 2 8 / N u m b e r 4 / A p r i l 1994
Model 4: Log~[ Y] = a0 + al "t + b" Xl,
(4)
where b, xl are the same as in equations (2) and (3), but with a linear trend in the intercepts, expressed as a 1 multiplied by t, where t is the time, e.g. in years after the first sampling year and a0 is a new intercept. The next model includes all covariables as length, weight, liver weight, sex, age etc. according to:
Model 5: Log,[ Y] = ~tt +
b t l • x 1 + bt2 " x 2 + . . . + b t p "Xp, ( 5 )
where the parameters ~tt, btl to btp are calculated each year. The last model has only a yearly effect, the yearly mean is calculated.
Model 6: Log.[ Y] = ~t.
(6)
An even simpler model, where the mean value of all years is calculated, is not included in the ICES statistical guide-lines, but can be formulated as:
Model 7: Loge[ Y] = ~t,
(7)
where no covariables are calculated. In model 5 one covariable is selected at a time. More covariables are included in the model, if the F-statistics reflecting the covariable's contribution to the model improves the F-statistics, with more than 0.50. This procedural evaluation is repeated and the procedure ends when no remaining covariable produces a significant improvement in the F-statistics. A covariable once selected, remains in the model throughout the procedure (Hocking, 1976). The different statistical models are compared by calculating: RSS b - RSS a
F(dfb - dfa, df~) -
dfb -- dfa RSSa
,
(8)
dr.
where the residuals sum of squares and the degree of freedom for a complex model (model a) is compared with a more simple model (model b). Model a is accepted if F(dfa, dfb) is significantly large compared to an F distribution with (dfb -- dfa, df,) degrees of freedom (Nicholson, 1985). The analysis in the ICES guide-lines is designed in the expectation that model 2 will be appropriate. The geographical variation is tested against the basic hypothesis that all stations have the same concentration. Since the year to year variation is large the test is performed on a yearly basis. The method is the same as described for the time-trend analysis (Gabriel, 1978).
Graphical method When concentration measurements are used for yearly calculation of the mean value and these means have to be compared with those of previous years, it is very important that both statistical tests and graphical methods can be used in the interpretation of the data. It has been suggested (Nicholson, 1985) that plots should
be made of the log~ adjusted concentrations with pairwise significance bands for each year. The band could be calculated according to: exp(Yadjusted) "1- t0.975
"
SE ~2'
(9)
where exp is the exponential function, Y is the 1Oge length adjusted mean concentration, SE is the standard error of the mean and t is the students' t-statistic for the two-sided 0.975 level with the appropriate degrees of freedom. However, this method fails when the comparison includes means calculated from fish samples, where the number of samples are not the same in every year. Although the standard sampling procedure of the monitoring programme (Anon., 1984) describes that 25 fish should be included in the analysis, the number of successful measurements will normally differ from year to year. The pair-wise bands calculated from equation (9) can only be used when the Y are overlapping, in this case the means are not significantly different. When the bands do not overlap no statistical judgement can be made from the graphic plots. The same problem exists in the box and whiskers plot. The basis for testing if several different means are equal or not, demands an analysis of the variance. The t-test cannot be used when there are more than two means to be compared. One could of course compare several means, by applying the t-test between all different means, in total k ( k - 1 ) / 2 where k is the number of means. To compare five means, e.g. in this case a total of 10 comparisons have to be performed. If the t-test is performed at the 0.05 level the probability of making at least one type 1 error (the experiment-wise error rate) is 1 - (1 - 0.05)10=0.401. When the number of means to be compared increases, the probability of getting two means that are equal, tested to be different by pure chance, increases and approaches to one. This means that the probability that two mean values that in reality are equal, in the test will turn out to be different, approaches to one--more than two means will ruin the test. Most of the methods for making these simultaneous inference comparisons (Miller, 1981) involve calculations of all individual measurements in the two samples that are to be compared, and in this way it is impossible to calculate a pair-wise band around the mean value to get an easy graphical interpretation of a trend. The methods are known under their names, e.g. Tukey or Duncan. However, the problem can be solved by using a method based on the studentized maximum modules (Gabriel, 1978) and the bands can be calculated by Y~ -
m(a; k, v) "s 2 "n~
,
(10)
where Yi is the mean in a specific year with n i measurements, s the root mean square error and m(et; k, v) the studentized maximum module based on k independent normal random variables (the number of means) with v 237
Marine Pollution Bulletin
degrees of freedom and a-level critical value (0.95). This method of plotting has been applied in Fig. 3 where the bands are calculated according to the method described above. Another advantage of this method is that a new year can be calculated without a recalculation of all the other years. The individual measurements are also given in Fig. 3 using a jittering principle, by adding a small amount of random noise along the x-axis (Cleveland, 1985).
Environmental index The purpose of calculating an environmental index is to be able to present all results from the metal concentrations as one value if possible. This implies that the five metal concentrations must be normalized to make it possible to sum up and average the different metal concentrations into one value. We have chosen a statistical value, the upper quartile or the 75 percentile (Q75) as a normalizer or reference value. The upper quartile is used to account for the biological variation in a non-polluted area. Different Q75-values have been calculated for plaice and flounder, respectively, using all the data from the least contaminated areas for the two species (J07 for plaice and 39 for flounder). The values are given in Table 3. The Q75-values calculated here are found to be in good agreement with those given by ICES (1988), except for Cd, where the values found by ICES are higher by a factor of 2-3. The Q75-value is only a statistical value and does not relate to any effect data. It is to be noted, however, that for blue mussels, the ICES (1988) Q75-values are in the same range as the values suggested in Norway (Knutzen, 1992) to discriminate between a less contaminated area from a more contaminated. There exist no similar reference values for Cu, Cd, Pb and Zn in fish liver, that could have been used as a reference concentration. The environmental trace metal index is calculated according to: 1
K L
Index = - - . ~, Z metalconci K i=i i= 1 upper quartilei ' where K is equal to 5 metals and L is the number of fish measured in a year, usually 25. This calculation is performed for each year, in Fig. 9. If this combined index is above 1 the concentrations of trace metal could be considered elevated. One could also have selected the mean concentration as a reference value, but this would remove the possibility of distinguishing between clean and polluted areas. By this approach, measurements at a new station can more easily be classified into the two mentioned categories.
the North Sea, in the Kattegat and the Belts. The counter-clockwise flow in the North Sea is known to transport pollutants from the large European rivers, especially from the Ems and the Elbe along the Jutland coast, see Fig. 1.
Covariables The results in Table 4 show that the length is the most important covariable and therefore the length will be used (as the covariable (Xl) in all calculations) in models 1-7.
Normal distribution The original data (pooled for all years on each separate station) were tested for normal distribution. The same procedure was applied to the data transformed according to model 1. The test showed that the data follow a normal distribution, both the original and the transformed data and if the data are divided into yearly populations, these are also following normal distributions. The highest calculated probability for rejection of the hypotheses that the population follows a normal distribution was 69% for Pb at station J05 for the measured data and the mean probability was 20%. A normal distribution was also found for metals, PCB, PAH, pp'DDE and lindane in flounder and blue mussels from Dutch estuaries (Stronkhorst, 1992). Other researchers are using non-parametric statistics without testing the distribution type (Hellon et al., 1992; Monteiro & Lopes, 1990).
Test of length dependence First it is necessary to investigate if the relationship between the metal concentration and the length is the same every year. Model 1 was therefore tested against model 2 according to equation (8). The test showed that model 2 is the best model in 13 out of 25 cases (the total number of 25 is given by five metals at five stations). Model 2 does not hold for Hg at stations 39 and J06, Cd at stations 31, J05, J06 and J07, Cu at stations 39 and J07, Pb at stations 31, 39, J06 and J07 and Zn at stations J05, J06 and J07. Figure 2 shows the year to year variation in the regression line of Hg vs fish length, in two selected years. Model 1, showing different slopes and different intercepts each year was therefore chosen. The results TABLE 4 N u m b e r and type of significant covariables (P > 95%) of logo concentration of trace metals in Platichthys flesus and Pleuronectes platessa from five stations at the Danish coastal area, e.g. the first '2' in row one indicates that there are two positive correlations between Cu concentration and age at the five stations. The calculation is performed on both dry wt (dw) as well as wet vet (ww) concentrations except for Hg.
Results and Discussion
The collection sites were in coastal or open sea areas, far from any point source of discharge, except for the site in the Sound where the city of Copenhagen and three larger Swedish cities with chemical industries have given an anthropogenic load of trace metals. A chemical plant is located at the shoreline of the station J06. The sampling areas are influenced by the currents in 238
Covar.
dw
Cd ww
dw
Cu ww
Hg ww
Age Length Weight Liver
1 4 2 1
1 2 1 3:~
2 4 3 2
2 3 1 1
3 4 5 4
Pb dw 2t 3~: 3* 5*
Zn ww
dw
ww
2 3* 3* 5*
2 5 5 5
3 5 5 3
*Negative correlation. t O n e negative correlation. ~:Two negative correlations, no sign = positive correlation.
Volume 28/Number 4/April 1994 H g versus
TABLE 5
length
Results of linear time trend analysis by comparing model 1, model 3 and model 4. A '--' indicates a decrease in concentation, a %' an increase. The value in the table is the F-value calculated after equation (8). The F-value is significant at the 95% level if F is larger than 6.76. 0.1
to Station o
0 200
I 300
I 400
I 500
Lengthin m m
J05 J06 J07 31 39
Cd
Cu
Hg
+95.2 +0.13 -2.98 +10.1 +7.01
-11.0 -28.5 -6.68 -0.81 +12.0
-18.0 +1.68 +0.69 -0.62 -2.70
Pb +0.13 +7.37 -46.1 -85.3 -125.0
Zn -48.9 -46.0 -42.0 +0.12 +32.5
Fig. 2 Hg concentrationsvs lengthfor two differentyears. are in contrast to what was found early (Jensen & Cheng, 1987), where data from the first 5 years from two of the stations were analysed. The reason for this may be that this study includes measurements from 11 years. In the further analysis the b in equations (3) and (4) has been replaced by a bt, since the length correction is made on a yearly basis and not according to an average concentration vs length relationship. The degrees of freedom have been changed accordingly.
1.0 ] 0.8
@ ,
ol
OIJan79
Trend analysis The time trend analysis is made by calculating the F-statistics according to equation (8) using the residuals from models 4, 3 and 1. The results are presented in Table 5. Table 5 shows a significant increase in the metal concentration in fish on six occasions and a significant decrease on nine occasions in the period 1979-1989. The trend for Cd shows a general increase at stations J05 and 31. The Cu concentration shows a general decrease in the North Sea and an increase in the Sound. The Hg concentration is decreasing at the most southward station in the North Sea and the rest of the concentrations are not changing significantly. The Pb concentration is decreasing in the inner Danish marine area and in the Skagerrak. An increase is found at station J06. This station has low concentrations which increases the risk of contamination problems which might again have an effect on the trend analysis. The Zn concentration is decreasing at all stations in the North Sea and increasing in the Great Belt. The more simple linear regression is not applicable to this type of data distribution. If used, it would fail to detect a significant change in concentration in two cases (Cu at station J07 and Pb at station J06). The time-trend and the year to year variation can also be derived from the plots made by the graphical method. An example of a time-trend analysis made according to the graphical method described earlier is shown in Fig. 3. The figure shows that the concentration in the last part of the 1980s is significantly lower than in those of 1979-1985. Similar plots of all the other metal concentrations have been used to derive the following conclusions with special reference to the last 5 years. Cd. There has been an increase in Cd concentrations in the Sound and the Great Belt for the last 5 years measured. An almost linear trend in the mean concentrations can be found. The same increase can be
Pb in flounder liver versus year Areacode=31 (.3
o
'OlJan83
'Ollan87 '
OlJan90
Year Fig.
3 Pb concentrations in ppm dry wt in fish liver in the Sound together with bands for significance levels and mean values. The samples are taken in the late autumn.
seen in the North Sea for plaice and flounders during the last 4 years, see Fig. 4. The same increase is found in flounder livers from two Dutch estuaries (Stronkhorst, 1992). This trend is also found in the German Bight in 1985-1988, while a constant level or a decrease is found towards the English Channel (ICES, 1991). Cu. Stations 31 and 39 have had constant concentrations during the last 5 years. At station J05 there is a decrease, the value at station J06 remains constant and there is an increase at station J07, see Fig. 5. The concentrations of Cu in both plaice and flounder are also constant in the Skagerrak and the German Bight (ICES, 1991). Hg. Two of the stations in the North Sea, J06 and J07 showed a small increase in 1988 and 1989, while the concentration at J05 had lower means at the end of the 1980s than at the beginning. In the Great Belt the concentration over the last 5 years is rather constant, while the concentration in the Sound at the end of the 1980s had not reached the local maximum in Hg concentration found in 1983 and 1984, see Fig. 6. In the Ems Dollard and in the Western Scheldt the concentrations of Hg in blue mussels and flounders remained constant in the period 1985-90 (Stonkhorst, 1992). Pb. In the inner Danish marine area, there has been a decrease in the concentrations when the last 5 years of the 1980s are compared with the beginning of the 1980s. This decrease cannot be detected in the North Sea, station J07 even shows the highest concentrations ever in 1989. The liver of plaice is relatively smaller 239
Marine Pollution Bulletin
0.41- J06
~i~f J07
0.3[" 0.2
0.2[" 0.1
o,I,. ,I ol
I |llnnii ~
0.4
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/
79
84
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~
89 ~
r
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o 7,
~
~-
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~
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~
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-
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,
. I
Year
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Fig. 4 Cd concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
20f JO7 10]
20[ J06
°t 20f
J05
~ o
7;::::="
~
1o
o I h h hi 79 84 89 Year
>
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llillli,lt 79
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Fig. 5 Cu concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
(mean = 4.9 g, SD = 5) than that of flounder (mean = 11 g, SD----9). This gives less material for analysis and therefore the contamination problem may have a larger influence on the results of plaice compared to those of flounder, see Fig. 7. The emissions of Pb from gasoline 240
have decreased from 1000 t in 1977 to 100 t in 1991 in Denmark. The decrease in Pb concentrations can be found overall in the North Sea and the Baltic (ICES, 1991). In the Rhine delta, there was a decrease by a factor of three (i-Iendriks & Pieters, 1993).
Volume 28/Number 4/April 1994 0.3[ J07 0.3
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t
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Hg concentrations in ppm wet wt in fish muscle in the Danish marine area, flounder in the Belts and plaice in the North Sea.
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-
Pb concentrations in ppm dry wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
Zn. In the Sound in the last 4 years, there have been 2 years with high concentrations (1986 and 1989) and 2 with low concentrations (1987 and 1988). In the Great Belt there has been a general increase, but the concentrations were low in •988. In the North Sea the
last 5 years experienced low concentrations especially compared with the beginning of the measuring period. There has been an increase in flounder and plaice in the German Bight and a decrease in plaice from the Skagerrak (ICES, 1991). 241
Marine Pollution Bulletin
The within years variation can be tested by comparing model 7 with model 6 or comparing model 7 with model 2. The result will in both cases be that there is a significant F-value by including the year-to-year variation in the analysis. It is important to further investigate this yearly fluctuation which can be up to a factor of 25 for some metals, but usually the standard deviation is 60% of the mean, see Fig. 3.
Covariables Other covariables than length could also be used in the analysis. A statistical method for making this is to compare model 5 with model 2. The result is given in Table 4, and a statistical analysis according to equation (8) shows that the residuals can be reduced by introducing more biological covariables into the models. However, in some cases the best covariable will be the length or the age, and in others the weight or the liver weight. This means that the type of biological covariable will differ from station to station and that the number of biological covariables will also change between stations and metals, if for example the age should be included as a covariable this could be done in 18 out of 25 cases, according to Table 4. This implies that the introduction of several covariables will not give any improvement to strengthen the trend analysis. Geographical distribution Concerning the difference in concentration between the various geographical positions the five metals are treated separately. Cd: The concentrations as seen in Fig. 4 are varying, with the largest concentration in most of the years in the inner marine Danish area. There is a tendency for a
decrease in the concentration along the coast line of the Jutland, with the highest concentration in the south. However, there is no statistically significant difference between the stations in the North Sea. Cu: The copper concentrations in flounder are always much higher (stations 31 and 39) than those found in plaice (stations J05, J06 and J07), as shown in Fig. 5. Cu is presumably regulated differently in the two species. The Cu concentrations in the Sound are in 5 years significantly higher than the Cu concentration in the Great Belt, which in turn is significantly higher in 3 years. In 3 years the concentrations are equal. There is no significant difference between the concentration at the different stations along the Jutland West Coast. Hg: The mercury concentration in the Sound is always significantly higher than the concentration at all other stations. The stations in the North Sea all fall in the same group in each year except for 1985, where the concentration at J07 had a significantly lower concentration than the other two stations as seen in Fig. 6. Concentrations at the station in the Great Belt are comparable to those in the North Sea. Pb: The concentrations are lower in the flounders from the Great Belt (station 39) compared to those from the Sound (station 31). At the Jutland West Coast, the largest concentrations are mainly found in plaice from the most southern station in the North Sea shown in Fig. 7. Zn: The concentration of zinc in the Sound and the Great Belt has been fluctuating around the same mean values during the whole period as shown in Fig. 8. In the period from 1984 to 1987 the concentration was larger at J05 than the concentrations of Zn at the other two stations in the North Sea. This was not true in 1988
4~[ JOt
:IHlht
4~i JO7
IIIllll
I
JO5
79
84 89 Year
31 ~
_"~
601" ~'139
~
~
~
" 40 2
Year
Fig. 8 Zn concentrations in ppm wet wt in fish liver in the Danish marine area, flounder in the Belts and plaice in the North Sea.
242
89
Volume 28/Number 4/April 1994
and 1989, where the concentrations did not differ significantly among the stations in the North Sea.
2,0
Comparisons between species
1,4
There are only a few stations where measurements have been made for more than one species, all in the year 1979 and the number of samples is given in Table 6. The same statistical method as described for the comparison between years (equation 10) has been applied when comparing the concentrations between species. The results show that for Cd, male Cu, Pb and Zn the hypothesis that all eight groups have the same mean concentrations cannot be rejected. For female Cu concentrations, there is a significant difference in the means between the species• For Hg, all species of different fish from the same station have the same concentration• However the number of fish in each group is limited and the mean concentrations can vary between the groups by 20-30% and still be regarded as not significantly different. A special study must be conducted to investigate any differences in concentrations between species at the same location to obtain more usable results.
Environmental index The values in the environmental index are shown in Fig. 9. The figure shows that the burden of heavy metals is large in the Sound with increased concentrations in all years (station 31). The main component of the index is the Hg concentration. The rest of the concentrations tend to vary to a smaller extent. In 1988 the burden was the same at nearly all stations. The high Pb concentrations in the Great Belt were excluded in 1984. It is interesting that the index shows the same year to year variation in the Great Belt and in the Sound• The index has in the North Sea a low and rather constant value with small year to year variations. The last year (1989) shows an increase in the index mainly caused by an increase in Cd concentrations. TABLE 6
Number of samples of fish at the five different main stations, used when comparing the concentrations in different species. Species
L imanda limanda Platichthysflesus Limanda limanda Platichthys flesus Platichthys flesus Pleuronectes platessa Limanda limanda Platichthysflesus
Station
Number
J05 J05 J04 J04 J07 J07 39 39
18 22 14 20 6 10 10 5
Metal index versus time
1.8
•~ 1.2
. . . . . . _'h. . . .
~
~'"
1o11".....:: g' :-~-,.l,.~ °.'~.,- ~ 7. .... "t~". ".." o.8- • / .....
1979
,....x.V"--e /
~.. "::~
o.6~,~ -0.41-
,, _ .xCr
I
J
1982
1985 Time
o31
e39
OJ05
"et"
~J06
a 1988
t 1991
AJ07
Fig. 9 The environmental index including Cd, Cu, Hg, Pb and Zn concentrations at five different stations.
The authors want to thank MSc. Lotte Mindedal for assisting in the data handling, the technical staff at the laboratory in the year 1979-89 for the analysis of the samples, and the National Fishery Inspectorate, Jan Damgaard Nielsen and Kjeld Moser for collecting the fish samples.
Anon. (1984). The ICES Coordinated Monitoring Programme for contaminants in fish and shellfish, 1978 and 1979 and six-years Review of ICES Coordinated Monitoring Programme. ICES Coop. Res. Rep. 126. Cleveland, W. S. (1985). The Elements of Graphing Data. Wadsworth, Monterey, California. Gabriel, K. R. (1978). A simple method of multiple comparisons of means. J. Am. Stat. Ass. 73,364. Hellon, J., Warren, W. G., Payne, J. E, Belkhode, S. & Lobel, P. (1992). Heavy metals and other elements in three tissues of cod, Gadus morhua from the Northwest Atlantic. Mar. Pollut. Bull. 24, 452458. Hendriks, A. J. & Pieters, H. (1993). Monitoring concentrations of microcontaminants in aquatic organisms in the Rhine delta: a comparison with reference values. Chemosphere 26,817-836. Hocking, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics 32, 1-50. ICES (1988). Results of 1985 baseline study of contaminants in fish and shellfish. Cooperative Research Report No. 151. ICES (1991). Statistical analysis of the ICES Cooperative Monitoring Programme Data on Contaminants in Fish Liver Tissue and Mytilus edulis (1978-1988) for determination of temporal trends. Cooperative Research Report No. 176. Jensen, A. & Cheng, Z. (1987). Statistical analysis of trend monitoring data of heavy metals in flounder (Platichthys flesus). Mar. Pollut. Bull. 18,230-238. Knutzen, J. (1992). Vannkvalitetskriterier for Marine Omr/ider. Miljogifter. SFT. Oslo, Norway. Miller, R. G., Jr. (1981). Simultaneous Statistical Inference. Springer, New York. Monteiro, L. R. & Lopes, H. (1990). Mercury content of swordfish, Xiphias gladius, in relation to length, weight, age and sex. Mar. Pollut. Bull. 21,293-296. Nicholson, M. D. (1985). The treatment of time-effects in the statistical analysis of contaminant monitoring data. ICES, C.M.1985/E: 31. Organization for Economic Cooperation and Development (OECD) (1991). The State of the Environment. Paris. Oslo and Paris Commissions (1990). Principles and Methodology of the Joint Monitoring Programme. Stronkhorst, J. (1992). Trends in pollutants in blue mussel Mytilus edulis and flounder Platichthys flesus from two Dutch estuaries, 1985-1990. Mar. Pollut. Bull. 24,250-258.
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