Comments Concerning the National Swedish Monitoring Programme in Fresh Water Biota 2001

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Comments Concerning the National Swedish Monitoring Programme in Fresh Water Biota 2001

2002-10-25

Compiled by Anders Bignert

Contaminant Research Group at the Swedish Museum of Natural History

Chemical analysis:

Trace metals

Institute of Applied Environmental Research at the University of Stockholm Centre for Environmental Monitoring at the University of Agriculture Organochlorines

Institute of Applied Environmental Research at the University of Stockholm

1 Introduction

This report gives a summary of the monitoring activities within the national Swedish contaminant programme in fresh water biota. It is the result from the joint efforts of: the Institute of Applied Environmental Research at Stockholms University (analyses of organochlorines and retrospective analyses of trace metals), the Centre for Environmental Monitoring at the University of Agriculture (trace metals) and the Contaminant Research Group at the Swedish Museum of Natural History (co-ordination, sample collection administration, sample preparation, recording of biological variables, minor additional analyses of organochlorines, storage of frozen biological tissues in the Environmental Specimen Bank for retrospective studies, data preparation and statistical evaluation). The monitoring programme is financed by the Environmental Protection Agency in Sweden.

Analyses of char and perch from lake Vättern, 1996, are also included in the report. The samples are collected and treated as samples within the national contaminant monitoring programme in biota. These analyses were financed by Vätternvårdsförbundet.

The data of concern in this report represent the bioavailable part of the investigated contaminants i.e. the part that has virtually passed through the biological membranes and might cause biological effects. The objectives of the monitoring program in fresh water biota could be summarised as:

• to estimate the levels and the normal variation of various contaminants in fresh water biota from several representative sites, uninfluenced by local sources, in order to describe the general contaminant status and to serve as reference values for regional and local monitoring programmes

• to monitor long term time trends and to estimate the rate of found changes

quantified objective: to detect an annual change of 10% within a time period of 10 years with a power of 80%

• to estimate the response in fresh water biota of measures taken to reduce the discharges of various contaminants

• to detect incidents of regional influence or widespread incidents of ‘Chernobyl’- character and to act as watchdog monitoring to detect renewed usage of banned contaminants

• to indicate large scale spatial differences

• to explore the development and regional differences of the composition and pattern of e.g. PCB’s, HCH’s and DDT’s as well as the ratios between various contaminants

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• the perfectly unique material of high quality, long time series is further used to explore relationships among biological variables and contaminants concentrations in various tissues; the effects of changes in sampling strategy, the estimates of variance components and the influence on the concept of power etc. It could furthermore be used as input of

‘real’ data in the ongoing model building activities concerning fresh water ecosystems in general

The present report displays the time series of analysed contaminants in biota and summarises the results from the statistical treatment. It does not in general give the background or explanations to significant changes found in the time series. Increasing concentrations thus, urge for intensified studies.

Short comments are given for temporal trends as well as for spatial variation and, for some contaminants. In the temporal trend part, an extract of the relevant findings are summarised in the 'conclusion'-paragraph. It should be stressed though, that geographical differences might not reflect anthropogenic influence but might be due to factors like productivity, temperature etc.

The report is continuously updated. The date of the latest update is reported in the beginning of each contaminant chapter. The creation date of each figure is written in the lower left corner.

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2 Summary

A short summary of the latest results is given below. Graphical presentations, tables and details are given in the following chapters.

• Decreasing trends of fat content in pike muscle are indicated both from L. Bolmen (- 1.2%, p< 0.001) and L. Storvindeln (-0.97%, p < .004).

• Extremely high concentrations of cadmium were found in L. Stensjön. The

concentrations are around 20 times higher than the other reference lakes. However, only one year of analyses is yet available.

• Lead concentrations in arctic char liver from L. Abiskojaure show a significant increasing trend of about 5.5% a year.

• Zinc concentrations in both arctic char liver from L. Abiskojaure and pike liver from L.

Storvindeln show a significant decreasing trend of about 2% a year. These are the only time series of zinc longer than three years in the programme.

• The PCB concentrations during the last ten years period, seems to continue down in the north of Sweden (L. Storvindeln and L. Abiskojaure) but there is no indication of decreasing trends in the south (L. Bolmen). The time-series are however today, too few to make any firm conclusions for the recent ten year period.

• The PCB concentration in pike is significantly higher in L. Bolmen, about two times, compared to L. Storvindeln, still 1998 (three times for CB-153).

• The estimated concentrations of sPCB in perch from L. Skärsjön are considerably higher compared to Stensjön, Rotehogstjärn and Allgjuttern. It is even more than three times higher compared to L. Bälgsjön sampled in the middle of the eighties.

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3 Sampling

3.1 Collected specimens

In general, older specimens show a greater within-year variance and hence relatively young specimens are collected. However, the size of the individual specimens has to be big enough to allow individual chemical analysis. Thus the size and age of the specimens vary between species and lakes. To avoid possible contribution of between-year variance due to sex differences the same sex (females) is analysed each year in most time series. Only healthy looking specimens with undamaged skin are collected.

Sampling of the various fish species is carried out in autumn, outside the spawning season except for pike which is sampled during or close after spawning.

The collected specimens are placed individually in polyethene plastic bags, deep frozen as soon as possible and transported to the sample preparation laboratory.

3.2 Number of samples and sampling frequency

In general 10 individual specimens are analysed annually from each lake.

The sampling recommendation prescribes a range for age and/or weight of the samples. In a few cases it has not been possible to achieve the required number of individuals within that range. In order to reduce the between-year variation due to sample differences in age composition, only specimens within the range of age/weight classes given in brackets after species name in the figures, are selected in this presentation.

Sampling is carried out annually in all time series. A lower frequency would certainly result in a considerably loss in statistical and interpretational power. Because of reduced analytical capacity, several of the collected samples have not been analysed during recent years but are stored in the Environmental Specimen Bank.

3.3 Sample preparation and registered variables

For each specimen total body weight, total length, body length, sex, age (see chap. 3 for various age determination methods depending on species), reproductive stage, state of nutrition, liver weight and sample weight are registered.

The epidermis and subcutaneous fatty tissue are carefully removed. Muscle samples are taken from the middle dorsal muscle layer.

The liver is completely removed and weighted in the sample container.

See TemaNord (1995) for further details about sample preparation.

Fish muscle tissue are analysed for organochlorines (DDT's, PCB's, HCH's and HCB), PBDE (Poly Brominated Diphenyl Ethers) and mercury. Fish liver is used for lead, cadmium, nickel, chromium, copper and zinc.

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3.4 Data registration

Data are stored in a flat ASCII file in a hierarchical fashion where each individual

specimen represents one level. Each measured value are coded and the codes are defined in a codelist (Persson, 1998). The primary data files are processed through a quality control program. Suspected values are checked and corrected if appropriate. Data are retrieved from the primary file into a table format suitable for further import to database or statistical programs.

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4 Sample matrices

4.1 Pike (Esox lucius)

The pike males become sexually mature between 1-3 years and the females between 2-5 years. The spawning takes place during the period March - May. The adult pike feeds on fish but also on snakes, frogs and young birds.

Pike are collected from two sites: L. Bolmen (Småland) since 1967 and from L.

Storvindeln (Västerbotten) since 1968. These two time series are probably the longest series of frozen stored fish in the world and has been used for retrospective studies of contaminant concentrations for several contaminants.

The specimens from L. Bolmen are collected during March - May. From L. Storvindeln, the specimens are collected in the middle of May with few exceptions.

The pike specimens are age determined by operculum.

Table 3.1. Number of samples, n of years, average age, weight and length with 95% confidence interval for the pikes collected at L. Bolmen and L. Storvindeln.

Bolmen Storvindeln

n tot n of years

geom.

mean

95% c.i. n tot n of years

geom.

mean

95% c.i.

Age 161 18 4.3 3.9-4.8 199 21 4.6 4.0-5.2

Length (cm) 177 18 53 51-55 184 19 56 53-60

Weight (g) 296 29 956 424 29 1060 922-

4.2 Arctic char (Salvelinus alpinus)

The arctic char becomes sexually mature between 3-5 years. The spawning takes place during the period August - October.

Arctic char are collected in the autumn from two sites: L. Abiskojaure (Norrbotten) since 1981 and from L. Tjulträsk (Västerbotten) since 1982.

Char samples from L. Vättern in 1996 are also included in the report.

The specimens are age determined by otoliths.

4.3 Roach (Rutilus rutilus)

The roach becomes sexually mature between 2-5 years. The spawning takes place during the period April - June when the water temperature has reached at least 10 degrees.

Roach are collected in the autumn from four sites: L. Ämten (Örebro län), L. Horsan (Gotland), L. Krankesjön (Malmöhus län) and L. Svartsjön (Skaraborgs län).

The roach specimens are age determined by scales.

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4.4 Perch (Perca fluviatilis)

The perch males become sexually mature between 2-4 years and the females between 3-6 years. The spawning takes place during the period April - June when the water temperature reaches about 7-8 degrees. Perch is also used as an indicator species for contaminant monitoring within the national monitoring in of contaminants in marine biota.

Perch samples are collected in late summer or autumn, each year from five lakes: Stensjön, Rotehogstjärn, Allgjuttern, Skärsjön and Skärgölen (Kalmar län).

Perch samples from Lake Vättern in 1996 and 1998 as well as from Lake Munksjön 1998 are also included in the report.

The perch specimens are age determined by operculum.

Table 4.4. The range of weeks when collection of samples has been carried at a specific location and the age classes selected in the presented time series below. Intervals for the yearly means of total body weight, total length, liver weight and liver and muscle dry weight are also given.

Sampling week

age body

weight

length liver weight liver dry weight

muscle dry weight

Muscle fat weight

year g cm g % % %

Stensjön 34 6-8 52-62 17-19 0.3-0.6 19-21 0.55

Rotehogstjärn 36 5-8 50-58 16-18 0.2-0.5 19-21 0.65

Allgjuttern 36-37 4-6 60-70 17-20 0.3-0.7 20-21 0.65

Skärsjön 36 5-7 55-69 16-19 0.3-0.6 17-21 0.49

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5 Sampling sites

The location and names of the sample sites are presented in figure 4.1. The sampling sites are selected following a number of criteria:

• the lakes must not be influenced by local contamination

• the land use in the areas surrounding the lake should be under control and intensive rural areas avoided

• the lake should preferable be placed high in the drainage system

• influence of liming activities should be avoided

• the lakes should have some protection against future exploitation

• in general, fish from eutrophic lakes would show a slower response to changes in the amount of discharges to the lakes compared to oligotrophic ones which are thus preferred for monitoring activities

• to facilitate regional comparisons the selected lakes should preferable be as similar as possible concerning factors that could influence the concentration of various contaminants in the analysed biological tissues

Collection of samples is carried out in 13 lakes distributed from north to south in Sweden, see map. Due to reduced resources, the originally intended analytical programme has been severely cut down. In order to give priority to the best fitted lakes from a monitoring point of view, the lakes have been scored according to the criteria given above together with some additional criteria in table 5.1 below.

Table 4.1. (1) Start year, (2) number of years of collected samples, (3) n of years of analytical results, (4) suitability as reference site, (5) between-year variation, (6) within-year variation (CV), (7) distance to the closest neighbour site(a-m), (8) integration with other monitoring activities, (9) protection status, (10) n of plants or other establishments potentially hazardous to the environment, within a radius of 30 and 50 km respectively. (11) index for the degree of potential influence of these establishments.

1 2 3 4 5 6 7 8 9 10 11

k) Abiskojaure 81 14 13 1 .31 20-93 284 l) V NP 0 0.00

l) Tjulträsk 82 13 6 1 .14 19-38 56 f) TV NR 0 0.00

f) Storvindeln 68 28 27 1 .38 21-71 56 l) V 0/1 0.00

m) Stormyrtjärn 90 4 0 1 - - 295 g) V 1/5 0.03

g) Ämten (81) 11 2 1 - 26-28 119 j) TV NR 6/26 0.35

b) Bylsjön 82 13 5 3 .14 36-61 149 h) TV NP 24/41 0.78

j) Svartsjön 82 13 4 1 .23 16-42 119 g) TV 2/14 0.11

c) Kvarnsjön 81 14 6 3 .36 14-67 112 a) T 19/54 1.05

d) Skärgölen 81 14 6 1 .18 19-36 135 j) TV 3/8 0.13

h) Horsan 80 15 5 1 .26 20-29 149 b) TM 1/2 0.04

a) St Bälgsjön, Pipsjön 84 11 3 3 .49 16-98 61 e) T 4/13 0.09

e) Bolmen 67 29 28 1 .28 16-84 61 a) 0/2 0.02

i) Krankesjön 80 15 12 2 .36 33-177 141 e) T 21/43 0.54

(8) T = Contaminant monitoring in terrestrial biota, V = Vegetation monitoring (9) NP = National Park, NR =Nature Reserve

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Abiskojaure

Tjultr.

Stensj.

Rotehogstj.

Allgjuttern

Balgsj.

Bolmen

Horsan Amten

Skarg.

Storvindeln

Kvarnsj.

Svartsj.

Bylsj.

Stormyrtj.

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Table 4.2. Lakes included in the program for a shorter or longer time, coordinates and the period during which collection of samples has been carried out and where samples are available in the Environmental Specimen Bank

Abiskojaure 7582080 1617490 1981 - 2000

Allgjuttern 6424890 1517240 1997 – 1999

Bolmen 6317500 1376100 1967 - 2000

Bylsjön 6564340 1640300 1982 - 1996

Bysjön 6580860 1302640 2000 - 2000

Bälgsjön 6332550 1316190 1984 - 1996

Degervattnet 7085120 1520860 2000 - 2000

Fiolen 6330250 1422670 2000 - 2000

Hjärtsjön 6325150 1466750 2000 - 2000

Horsan 6420080 1680130 1980 – 2000

Krageholmsjön 6153750 1370870 2000 – 2000

Krankesjön 6177600 1351950 1980 – 2000

Kvarnsjön 6438190 1278190 1981 – 1996

Pipsjön 6331070 1316620 1987 – 1996

Remmarsjön 7086190 1621320 2000 – 2000

Rotehogstjärn 6529020 1257830 1997 – 1999

Skärgölen 6406090 1486730 1981 – 2000

St. Envättern 6555870 1588690 2000 – 2000

St. Skärsjön 6286060 1332050 1997 – 1999

Stensjön 6836730 1540830 1997 – 2000

Stormyrtjärn 6904980 1523030 1990 – 1996

Storvindeln 7289600 1560800 1968 – 2000

Svartsjön 6516090 1408390 1982 – 2000

Tjulträsk 7317990 1511960 1982 – 2000

Tärnan 6606880 1644780 2000 – 2000

Ämten 6612060 1479010 1981 – 2000

Övre Skärsjön 6635320 1485710 2000 - 2000

Below, each site is presented on more detailed maps indicating the distance to industries or other establishments that possibly could influence the contaminant load to their

surrounding lakes. Lakes included in other monitoring programmes are also indicated in the maps.

Solid star: freshwater contaminant monitoring programme in biota (NRM).

Large thin open circles: terrestrial contaminant monitoring programme in biota (NRM).

Medium sized open circles: contaminant monitoring program in water (University of Agriculture).

Small open circles: lakes were at least one single or a few water analyses have been carried out (University of Agriculture).

Large thick open circles: Industries or other establishments potentially hazardous to the environment.

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5.1 Abiskojaure, Norrbotten Co-ordinates

RAK: 758208 / 161749 (30I6d 2584) Greenwhich: 68 18’ N / 18 39’ E

Main river system: Torne älv

Physical geography: High mountain region of Lappland (36B)

Altitude: 487 m

Lake area: 2.7 km²

Max. depth: 35 m

Protection national park Sample matrix Arctic char

Start 1981

Other monitoring activities:

Fish population investigated by the Inst. of Freshwater Research

Abiskojaure Abiskojaure

30 31

I J

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5.2 Tjulträsk, Ammarnäs, Västerbotten Co-ordinates:

RAK: 731799 / 151196 (25G3b 2422) Greenwhich: 65 58’ N / 16 4’ E

Main river system: Ume älv Physical geography: (36A)

Altitude: 539 m

Volume 114 Mm³

Protection nature reserve Sample matrix Arctic char

Start 1982

See Figure 5.3.

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5.3 Storvindeln, Västerbotten Co-ordinates

RAK: 728271 / 157578 (24H7c 2422) Greenwhich: 65 42’ N / 17 8’ E

Sorsele (2422), (Close to the border of Norrbotten) Main river system: Ume älv/ Vindelälven (28000) Sample matrix Pike

Start 1968

Together with L. Bolmen, the longest time series of contaminants in freshwater fish, in the world.

Tjultrask

Storvindeln

24 25

26 G H I

TISS - 98.02.10 09:34, stvibw2

Figure 5.3. The sampling sites at L. Tjulträsk and L Storvindeln.

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5.4 Stormyrtjärn, Västernorrland

Co-ordinates (RAK): 690498 / 152303

Ca 60 km VSV Sundsvall, close to the border to Gävleborgs län.

Sample matrix Perch

Start 1990

5.5 Stensjön, Gävleborgs län

Co-ordinates (RAK): 683673 / 154083

Main river system: Ljusnan (48)

Altitude: 268 m

Max. depth: 8.5 m

Start 1997

Stensjon Stormyrtjarn

15 16 17

18 F G H I

TISS - 98.02.10 09:37, storbw2

Figure 5.5. The sampling sites at L. Stormyrtjärn and L. Stensjön.

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5.6 Ämten, Grimsö, Örebro län

Co-ordinates (RAK): 661206 / 147901 11F2f 1885

Protection NR

Other monitoring activities:

Terrestrial contaminants, air

Sample matrix Roach

Start 1981

St Envattern Ovre Skarsjon

Amten

10 11 12

13 E F G H

TISS - 98.02.10 09:41, amtbw2

Figure 5.6. The sampling site at L. Ämten.

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5.7 Bylsjön, Tyresta, Stockholms län

Co-ordinates (RAK): 656434 / 164030 (10I3i 0136) 59 10' 18 15'

Main river system: Tyresån (98000)

Altitude 41 m

Lake area 0.05 km²

Drainage area 340 ha

Protection National Park Other monitoring

activities:

terrestrial contaminants, air, soil

Start 1982

St Envattern

Bylsjon

09 10

11 H I J

TISS - 98.02.10 09:45, bylsbw2

Figure 5.7. The sampling site at L. Bylsjön.

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5.8 Rotehogstjärn, Göteborgs o Bohuslän

Co-ordinates (RAK): 652902 / 125783

Main river system: Enningdalälven (112)

Altitude: 120 m

Start 1997

5.9 Svartsjön, Tiveden, Skaraborgs län

Co-ordinates (RAK): 651609 / 140839 09E3b 1663

Main river system: Motala ström (108138)

Altitude: 125 m

Lake area: 0.08 km² Max. depth:

Sample matrix Roach

Start 1982

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Svartsjon

08 09 10

D E F

TISS - 98.02.10 09:49, svarbw2

Figure 5.9 The sampling site at L. Svartsjön.

5.10 Kvarnsjön, Svartedalen, Göteborgs- and Bohuslän Co-ordinates (RAK): 643819 / 127819 (07B7f 1482)

Main river system: Göta älv /Bäveån Start: 1981

Sample matrix: Perch

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Fracksjon

Harsvattnet Kvarnsjon

07 08

B C

TISS - 98.02.10 09:54, kvarbw2

Figure 5.10. The sampling sites at L. Kvarsjön.

5.11 Allgjuttern, Kalmar län

Co-ordinates (RAK): 642489 / 151724

Main river system: Botorpsströmmen (71)

Altitude: 163 m

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5.12 Skärgölen, N. Kvill, Kalmar län

Co-ordinates (RAK): 640609 / 148673 07F1h 0884

Main river system: Motala ström

Fish fauna: Pike, perch Start: 1981

Sample matrix: Perch

Allgjuttern

Kvadofj.

Skargolen

06 07 08

E F G H

TISS - 98.02.10 09:56, skarbw2

Figure 5.12. The sampling sites at L Allgjuttern and L. Skärgölen

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5.13 Horsan, Fleringe, Gotland

Co-ordinates (RAK): 642008 / 168013 (07J4g 0980)

Main river system: (118117)

Protection status: No protection Start: 1980

Sample matrix: Roach

SO Gotland St.Karlso

Kvadofj.

Horsan

05 06 07

H I J K

TISS - 98.02.10 10:43, horsbw2

Figure 5.13. The sampling site at L. Horsan.

5.14 Bälgsjön, St Pipsjön, Boaberg, Hallands län

Co-ordinates (RAK): 633255 / 131619 05C6d 1382 (633107/131662)

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5.15 Stora Skärsjön, Hallands län

Co-ordinates (RAK): 628606 / 133205

Main river system: Genevadsån (99)

Altitude: 60 m

Start 1997

St Skarsjon

Fiolen Balgsjon

Bolmen

04 05 06

B C D E

TISS - 98.02.10 10:20, balgbw2

Figure 5.15. The sampling sites at L. Bälgsjön, L Bolmen and L. St. Skärsjön.

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5.16 Bolmen, Kronobergs, Hallands, Jönköpings län

Co-ordinates (RAK): 629511 / 136866

Main river system: Lagan (098) /Bolmån

Altitude: 141 m

Lake area: 184 km²

Volume 1260 Mm³

Sample matrix Pike

Start 1967, The longest timeserie of contaminants in freshwater biota in the world.

5.17 Krankesjön, Malmöhus län

Co-ordinates (RAK): 6177697 / 135339 (02D5a 1281)

Main river system: Kävlingeån (92000)

Altitude: 19 m

Fish fauna Roach, pike, perch Sample matrix Roach

Start 1980

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St Skarsjon

Krageholmssjon Krankesjon

01 02 03 04

C D E F

TISS - 98.02.10 10:23, kranbw2

Figure 5.17. The sampling site at L. Krankesjön.

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6 Analytical methods

The analytical methods applied are described elsewhere (organochlorines: Jensen et al., 1983, Eriksson et al., 1994; metals in liver: Borg et al.,1981, mercury: May & Stoeppler, 1984, Lindsted & Skare,1971) and follows the recommendation for quality assurance concerning Certified Reference Material and intercalibration exercises. Detection limits and other comments are reported under each contaminant description.

7 Statistical treatment and graphical presentation

The analytical results from each of the investigated elements are displayed in figures. Each site/species is represented by a separate plot.

The plot displays the geometric mean concentration of each year (circles) together with the individual analyses (small dots) and the 95% confidence intervals of the geometric means.

The overall geometric mean value for the time-series is depicted as a horizontal, thin, dashed line.

The trend is presented by one or two regression lines (plotted if p < 0.10, two-sided regression analysis); one for the whole time period and one for the last ten years (if the time series is longer than ten years). Ten years is often too short a period to statistically detect a trend unless it is of considerable magnitude. Nevertheless the ten year regression line will indicate a possible change in the direction of a trend. Furthermore, the residual variance around the line compared to the residual variance for the entire period will

indicate if the sensitivity have increased as a result of e.g. an improved sampling technique or that problems in the chemical analysis have disappeared.

A smoother is applied to test for non-linear trend components (see below). The smoothed line is plotted if p < 0.10.

The log-linear regression lines fitted through the geometric mean concentrations follow smooth exponential functions.

A cross inside a circle, indicate that the current geometric mean is too far from the

regression line considering from what could be expected from the residual variance around the line. The procedure to detect the suspected outliers are described by Hoaglin and

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ICES (Nicholson et al., 1995). A broken line or a dashed line segment indicate a gap in the time series with a missing year.

Below the header of each plot (with name of sampling locality and selected age class), the results from several statistical calculations are reported:

n(tot)= The first line reports the total number of analyses included together with the number of years ( n(yrs)= ).

m= The overall geometric mean value together with its 95% confidence interval is reported on the second line of the plot (N.B. d.f.= n of years - 1).

slope= The slope, expressed as the yearly percentual change together with its 95%

confidence interval is reported on the third line. A slope of 5% implies that the

concentration is halved in 14 years whereas 10% corresponds to a similar reduction in 7 years and 2% in 35 years. See table 7.1 below.

Table 7.1. The approximate number of years required to double or half the initial concentration assuming a continuous annual change of 1, 2, 3, 4, 5, 7, 10, 15 or 20% a year.

1% 2% 3% 4% 5% 7% 10% 12% 15% 20%

Increase 70 35 24 18 14 10 7 6 5 4

Decrease 69 35 23 17 14 10 7 6 4 3

SD(lr)= reports the square root of the residual variance around the regression line, as a measure of between-year variation, together with the lowest detectable change in the current time series with a power of 80%, one-sided test, α=0.05. The last figure on this line is the estimated number of years required to detect an annual change of 5% with a power of 80%, one-sided test, α=0.05.

power= reports the power to detect a log-linear trend in the time series (Nicholson &

Fryer, 1991). The first figure represent the power to detect an annual change of 5% with the number of years in the current time series. The second figure is the power estimated as if the slope where 5% a year and the number of years were ten. The third figure is the lowest detectable change for a ten year period with the current between year variation at a power of 80%.

r²= reports the coefficient of determination (r²) together with a p-value for a two-sided test (H0: slope = 0) i.e. a significant value is interpreted as a true change, provided that the assumptions of the regression analysis is fulfilled.

y(95)= reports the concentration estimated from the regression line for the last year together with a 95% confidence interval, e.g. y(95)=2.55(2.17,3.01) is the estimated concentration of year 1995 where the residual variance around the regression line is used to calculate the confidence interval. Provided that the regression line is relevant to describe the trend, the residual variance might be more appropriate than the within-year variance in this respect.

tao= The regression analyses presupposes, among other thing, that the regression line gives a good description of the trend. The leverage effect of points in the end of the line is

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also a well known fact. An exaggerated slope, caused 'by chance' by a single or a few points in the end of the line, increases the risk of a false significant result when no real trend exist. A non-parametric alternative to the regression analysis is the Mann-Kendall trend test (Gilbert, 1987, Helsel & Hirsch,1995, Swertz,1995). This test has generally lower power than the regression analysis and does not take differences in magnitude of the concentrations into account, it only counts the number of consecutive years where the concentration increases or decreases compared with the year before. If the regression analysis yields a significant result but not the Mann-Kendall test, the explanation could be either that the latter test has lower power or that the influence of endpoints in the timeserie has become unwarrantable great on the slope. Hence, the eighth line reports Kendall's 'τ', and the corresponding p-value. The Kendall's 'τ' ranges from 0 to 1 like the traditional correlation coefficient ‘r’ but will generally be lower. ‘Strong’ linear correlations of 0.9 or above corresponds to τ-values of about 0.7 or above (Helsel and Hirsch, 1995, p. 212).

This test was recommended by EPA for use in water quality monitoring programmes with annual samples, in an evaluation comparing several other trend tests (Loftis et al. 1989).

SD(sm)= The ninth line reports the square root of the residual variance around the smoothed line. The significance of this line could be tested by means of an Analysis of Variance where the variance explained by the smoother and by the regression line are compared with the total variance (Nicholson et al., 1995). The p-value is reported for this test. A significant result will indicate a non-linear trend component.

Below these nine lines are additional lines with information concerning the regression of the last ten years, see explanations for line 3, 4, 5 and 7.

In some few cases where an extreme outlying observation may hazard the confidence in the regression line, the ordinary regression line is replaced by the ‘Kendall-Theil Robust line’, see Helsel and Hirsch (1995) page 266. In these cases only the ‘Theil’-slope and Kendall’s ‘τ‘ are reported.

Values reported below the detection limit is substituted using the ‘robust’ method suggested by Helsel & Hirsch (1995) p 362, assuming a log-normal distribution within a year.

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8 The power of the programme

Before starting to interpret the result from the statistical analyses of the time series it is essential to know with what power temporal changes could be detected (i.e. the chance to reveal true trends with the investigated matrices). It is of cause crucial to know whether a negative result of a trend test indicate a stable situation or that the monitoring programme is to poor to detect even serious changes in the contaminant load to the environment. One approach to this problem would be to estimate the power of the time series based on the

‘random’ between-year variation. Alternatively the lowest detectable trend could be estimated at a fixed power to represent the sensitiveness of the time-series.

The first task would thus be to estimate the ‘random’ between-year variation. In the results presented below this variation is calculated using the residual distance from a log-linear regression line. In many cases the log-linear line, fitted to the current observations, seems to be an acceptable ‘neutral’ representation of the true development of the time-series. In cases where a significant ‘non-linear’ trend has been detected (see above), the regression line may not serve this purpose, hence the sensitiveness- or power-results based on such time series are marked with an asterix in the tables below. These results are also excluded from estimations of median performances.

Another problem is that a single outlier could ruin the estimation of the between-year variation. In the presented results also suspected outliers are included which means that the power and sensitiveness are under estimated.

Table 7.1. Lowest detectable trend within a 10 year period with a power of 80% for various variables in various matrices at various sites. A * indicates a significant non-linear trend component that may explain the low sensitiveness. # indicate presence of one extreme year.

Fat % Hg sPCB sDDT ^α-HCH Lindane HCB Pike muscle

Bolmen 4.4 5.2 8.3 14 10 5.6 12

Storvindeln 4.2 6.6 9.7 11 19 *23 11

Arctic char muscle

Abiskojaure 10 5.0 9.9 12 7.9 #20 6.0

Tjulträsk 7.5 - 19 19 - - -

Roach muscle

Svartsjön 5.0 4 7.1 6.0 - - -

Horsan 6.8 14 7.5 8.5 11 *14 9.4

Krankesjön 6.4 14 7.9 10 #35 *20 6.0

Perch muscle

Bälgsjön - 10 - - - - -

Bylsjön 1.9 - 23 19 - - -

Kvarnsjön 2.9 - 8.3 9.2 - - -

Skärgölen 1.4 6.8 22 23 - - -

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9 Fat content

Updated 99.04.14

The fat content is determined in samples that are analysed for organochlorines.

In general, an extremely low fat content, due to e.g. starvation, may cause elevated concentrations of organochlorines expressed on a fat weight basis.

The result of the fat determination may vary considerable depending on the extraction method used.

The fat content in the present investigation are determined after extraction with acetone and hexane with 10% ether without heating (Jensen et al. 1983).

Temporal variation

Decreasing trends for pike muscle are indicated both from lake Bolmen (-1.2%, p< 0.001) and lake Storvindeln (-0.9%, p < .02).

These decreasing trends have to be considered when evaluating the time series of organochlorines.

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Table 9.1. Geometric mean fat content (%) and the corresponding 95% confidence interval in various matrices & sites. The total number of analyses and the number of years covered by the various time-series are also presented.

Fat content (%)

^{n total} ^{n of} years

Years geom.

Mean

95 % c.i.

Pike muscle

Bolmen 238 30 67-98 0.59 .55 .63

Storvindeln 298 29 70-98 0.61 .57 .65

Arctic char muscle

Abiskojaure 165 17 81-97 1.4 1.2 1.7

Tjulträsk 65 7 82-95 1.2 .88 1.7

Roach muscle

Ämten 20 2 84-85 1.1 *.32 4.1

Svartsjön 40 4 82-85 0.77 .62 .95

Horsan 159 16 81-96 0.82 .74 .91

Krankesjön 160 16 81-96 0.87 .77 .97

Perch muscle

Bälgsjön 30 3 84-87 0.79 .67 .93

Bylsjön 50 5 82-86 0.62 .58 .66

Kvarnsjön 59 6 81-86 0.76 .70 .84

Skärgölen 59 6 81-87 0.78 .74 .81

Stensjön 10 1 97 0.55

Rotehogstjärn 10 1 97 0.65

Allgjuttern 10 1 97 0.65

St.Skärsjön 10 1 97 0.49

* confidence interval based on only two years, d.f = 1

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Fat %, pike muscle

Bolmen (0.5-1.5 kg)

.8 1.0 1.2 1.4 1.6 1.8

n(tot)=238,n(yrs)=30 m=.590 (.552,.630) slope=-1.2%(-1.8,-.57) SD(lr)=.14,.80%,10 yr power=1.0/.89/4.4%

y(98)=.496 (.448,.551) r2=.36, p<.001 * tao=-.42, p<.001 * SD(sm)=.11, p<.011 *

slope=-4.5%(-7.2,-1.8) SD(lr)=.11,3.3%,8 yr power=.99/.99/3.3%

r2=.65, p<.005 *

Storvindeln (0.2-2.0 kg)

.8 1.0 1.2 1.4 1.6 1.8

n(tot)=298,n(yrs)=29 m=.607 (.572,.645) slope=-.97%(-1.6,-.34) SD(lr)=.14,.80%,9 yr power=1.0/.92/4.2%

y(98)=.530 (.479,.588) r2=.27, p<.004 * tao=-.35, p<.008 * SD(sm)=.13, p<.254

slope=-1.9%(-7.4,3.5) SD(lr)=.21,6.6%,12 yr power=.60/.60/6.6%

r2=.08, p<.440

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Fat %, arctic char muscle

Abiskojaure (50-400 g)

.0 .4 .8 1.2 1.6 2.0 2.4 2.8 3.2 3.6

81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 n(tot)=165,n(yrs)=17

m=1.43 (1.20,1.69) slope=1.4%(-2.2,4.9) sd(lr)=.33,4.4%,16 yr power=.90/.33/10%

y(97)=1.59 (1.14,2.22) r2=.04, p<.429 tao=.10, p<.592 sd(sm)=.28, n.s.

slope=6.5%(-1.6,15) sd(lr)=.32,9.9%,16 yr power=.35/.35/9.9%

r2=.30, p<.100

Contaminant Research Group /NRM, Inst. of Appl. Environmental Research /SU 99.04.21 13:23, fats1

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10 Mercury

Updated 99.04.21

Mercury was first included in the old monitoring programme, starting already in 1967/68 in pike. After 1975, however mercury was no longer analysed.

Mercury has now been retrospectively analysed, but so far only in pike muscle from lake Storvindeln, arctic char muscle from lake Abiskojaure and perch muscle tissue from two sites: lake Bälgsjön (Hallands län) and lake Skärgölen (Kalmar län). During 1995 and 1996, mercury analyses was also carried out in samples of arctic char from lake

Abiskojaure and lake Tjulträsk and in 1996 also in pike muscle from lake Bolmen and lake Storvindeln.

Temporal variation

Mercury concentrations in perch muscle tissue from L. Skärgölen show a significant increasing trend of about 5% a year. No trend were shown in the time-series of pike from lake Storvindeln nor in perch muscle from L. Bälgsjön or in char from L. Abiskojaure.

Spatial variation

The estimated concentration 1994/95 is significantly higher in perch muscle from L.

Skärgölen compared to samples from L. Bälgsjön.

The mercury concentration found arctic char muscle from L. Tjulträsk was about 4 times higher compared to samples from L. Abiskojaure. This difference cannot be explained by a difference in age of the analysed specimens between the two lakes. However, L. Tjulträsk have yet only been analysed for mercury for two years.

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Table 9.1. Geometric mean mercury concentrations (ng/g fresh wt) in various matrices & sites and the estimated mean concentration for the last year. The total number of analyses and the number of years covered by the various time-series are also presented.

Matrix/ Lake n n of

years

year Mean 95% ci last year

95 % ci Pike muscle

Bolmen 117 11 67-75,96-98 301 223-406 301 223-406

Storvindeln 307 28 68-98 308 283-335 308 283-335

Arctic char

Abiskojaure 170 17 81-97 31 28-34 31 28-34

Tjulträsk 20 2 95-96 - - 128 118-139

Vättern, Röknen 5 1 96 - - 197 133-294

Vättern, Visingsö 5 1 96 - - 223 177-281

Perch muscle

Bälgsjön 120 11 84-94 261 208-327 213 141-324

Skärgölen 150 15 81-95 291 244-347 421 333-533

Vättern, Röknen 10 1 96 - - 77 66-89

Vättern, Visingsö 10 1 96 - - 49 41-59

Stensjön 10 1 97 312

Rotehogstjärn 10 1 97 382

Allgjuttern 10 1 97 184

St. Skärsjön 10 1 97 156

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Hg, ng/g wet wt., pike muscle

Bolmen

0 100 200 300 400 500 600 700 800 900 1000

68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 n(tot)=177,n(yrs)=22

m=326 (280 ,380 ) slope=.73%(-.72,2.2) SD(lr)=.34,3.0%,16 yr power=1.0/.32/11%

y(99)= 367 ( 277, 487) r2=.05, NS tao=-.04, NS

Storvindeln

0 100 200 300 400 500 600 700 800 900 1000

68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 n(tot)=317,n(yrs)=29

m=311 (286 ,338 ) slope=.61%(-.26,1.5) SD(lr)=.21,1.2%,12 yr power=1.0/.60/6.6%

y(99)= 342 ( 292, 400) r2=.07, NS tao=.22, p<.091 SD(sm)=.16, p<.009 *

Cont.Res.Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 00.11.27 13:58, hge

Hg, ng/g fresh w., perch muscle

Balgsjon, Boaberg

300 400 500 600 700

n(tot)=120,n(yrs)=11 m=261 (208 ,327 ) slope=-4.0%(-11,3.1) sd(lr)=.33,8.6%,16 yr power=.42/.34/10%

y(94)= 213 ( 141, 324) r2=.15, p<.230 tao=-.24, p<.350 sd(sm)=.35, n.s.

Skargolen, N Kvill

300 400 500 600 700

n(tot)=150,n(yrs)=15 m=291 (244 ,347 ) slope=5.3%(2.4,8.1) sd(lr)=.22,3.5%,12 yr power=.98/.57/6.8%

y(95)= 421 ( 333, 533) r2=.55, p<.002 * tao=.58, p<.003 * sd(sm)=.14, p<.027 *

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11 Lead, Cadmium, Nickel, Chromium, Copper, Zinc and Aluminium

Updated 01.06.25

The concentration of the above mentioned metals in fish liver is determined using atomic absorption spectrometry with graphite furnace (to 1997) or ICP-masspectrometry (from 1998-).

Retrospective studies of pike from L. Storvindeln (1969-1994) and in arctic char from L.

Abiskojaure (1981-1993) have been carried out at the Institute of Applied Environmental Research, (ITM), Stockholm University .

Analyses of trace metals were introduced in the ordinary programme in 1995 and were carried out at the Department of Environmental Assessment at the University of Agriculture, Uppsala until 1997 and by ITM from 1998 and onwards.

Analyses carried out in char and perch for Vätternvårdsförbundet, 1996, using comparable samples are also included in the tables.

Spatial variation

Comparison should be interpreted with great caution since only one or two years of data is yet available and hence no estimation of the between-year variation is possible to carry out.

11.1 Lead

No linear trends of lead concentrations for the longer time series of arctic char liver from L. Abiskojaure nor from pike liver from L Storvindeln could be seen.

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Table 10.1. Lead, geometric mean concentrations (ng/g dry and fresh weight) in liver of various fish species and sites, presented together with the total number of analyses and the number of years of the time-series.

Matrix/ Lake n n of years

year dry w mean

95% c.i. fresh w mean

95% c.i.

Pike

Bolmen 40 4 96-98 52 44-61

Storvindeln 196 27 69-00 31 27-36

Arctic char

Abiskojaure 117 18 81-00 28 23-34

Tjulträsk 20 2 95-96 45 36-56 11 8.8-13

Vättern, Röknen 5 1 96 48 6-399 19 1.2-304

Vättern, Visingsö 5 1 96 52 25-108 20 9.3-42

Perch

Stensjön 10 1 97 140 110-170

Rotehogstjärn 10 1 97 140 100-180

Allgjuttern 10 1 97 86 68-110

St.Skärsjön 10 1 97 100 89-110

Vättern, Röknen 10 1 96 98 76-127 20 16-27

Vättern, Visingsö 10 1 96 56 36-85 12 8.3-19

Pb, ug/g dry wt., pike liver Storvindeln

.04 .06 .08 .10 .12 .14 .16

.18 n(tot)=196,n(yrs)=27 m=.031 (.027,.036) slope=-.98%(-2.4,.49) SD(lr)=.34,2.2%,16 yr power=1.0/.32/11%

y(99)=.027 (.020,.035) r2=.07, NS

tao=-.26, p<.057 SD(sm)=.27, p<.043 *

Bolmen

.04 .06 .08 .10 .12 .14 .16

.18 n(tot)=40,n(yrs)=4 m=.052 (.044,.061)

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Pb, ug/g dry wt., arctic char liver, Abiskojaure

.00 .02 .04 .06 .08 .10 .12 .14 .16 .18

81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 n(tot)=117,n(yrs)=18

m=.028 (.023,.034) slope=1.8%(-1.8,5.3) SD(lr)=.40,4.8%,18 yr power=.84/.26/13%

y(00)=.033 (.022,.049) r2=.07, NS

tao=.32, p<.063 SD(sm)=.31, p<.055

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 01.06.28 11:20, pbs1

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11.2 Cadmium

Pike liver from L. Bolmen showed significantly higher concentrations of cadmium (about 4.5 times) compared to samples from L. Storvindeln. Arctic char liver from L. Abiskojaure showed significantly higher cadmium concentrations compared with char samples from the other investigated sites (2 times or more). Extremely high concentrations of cadmium were found in L. Stensjön. The concentrations are around 20 times higher compared to the reference lakes Allgjuttern and Rotehogstjärn. However, only one year of analyses is yet available.

Table 11.2. Cadmium, geometric mean concentrations (µg/g dry and fresh weight) in liver of various fish species and sites, presented together with the total number of analyses and the number of years of the time- series.

Matrix/ Lake n n of years

year dry w.

Mean

95% c.i. fresh w.

mean

95% c.i.

Pike

Bolmen 30 3 96-98 0.23 .16-.32

Storvindeln 276 28 69-98 0.049 .044-.055 Arctic char

Abiskojaure 190 19 81-00 1.0 .80-1.3

Tjulträsk 20 2 95,96 0.31 .26-.38 .075 .062-.091

Vättern, Röknen 5 1 96 0.14 .075-.27 .053 .035-.082 Vättern, Visingsö 5 1 96 0.097 .071-.13 .037 .029-.047 Perch

Stensjön 10 1 97 17

St Skärsjön 10 1 97 5.8 4.5-7.5

Vättern, Röknen 21 2 96,98 4.5 3.5-5.8 Vättern, Visingsö 19 2 96,98 1.5 1.1-2.0

Munksjön 10 1 98 0.42 .27-.58

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Cd, ug/g dry wt., pike liver

Storvindeln

.0 .1 .2 .3 .4 .5 .6

68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

n(tot)=276,n(yrs)=28 m=.049 (.044,.055) slope=-.34%(-1.7,.99) sd(lr)=.31,1.9%,15 yr power=1.0/.37/9.6%

y(98)=.047 (.037,.059) r2=.01, p<.607 tao=-.03, p<.859 sd(sm)=.21, n.s.

Bolmen

.0 .1 .2 .3 .4 .5 .6

96 98

n(tot)=30,n(yrs)=3 m=.228 (.161,.322)

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 99.04.21 13:26, cde

Cd, ug/g dry wt., arctic char liver, Abiskojaure

.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5

81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 n(tot)=190,n(yrs)=19

m=1.01 (.798,1.28) slope=-1.1%(-5.4,3.2) SD(lr)=.50,5.5%,20 yr power=.74/.20/16%

y(00)= .90 ( .55,1.48) r2=.02, NS

tao=-.10, NS

SD(sm)=.32, p<.007 *

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 01.06.27 11:16, cds1

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11.3 Nickel

The time series from pike liver (24 years long) show more or less stable concentrations neither do the other series from arctic char (17 years) show any trend.

No significant differences between comparable samples were found.

Table 11.3. Nickel, geometric mean concentrations (µg/g dry and fresh weight) in liver of various fish species and sites, together with the total number of analyses and the number of years of the time-series.

Matrix/ Lake n N of years

year dry w.

Mean

95% c.i. fresh w.

mean

95% c.i.

Pike

Bolmen 30 3 96-98 0.088 .067-.12

Storvindeln 82 24 68-98 0.070 .060-.082 Arctic char

Abiskojaure 165 17 81-97 0.20 .15-.25

Tjulträsk 20 2 95-96 0.20 .16-.24 .048 .040-.058

Vättern, Visingsö 2 1 96 0.30 .16-.57 .11 .023-.48

Perch

Stensjön 10 1 97 0.11

St. Skärsjön 10 1 97 0.097

Vättern, Röknen 3 2 96,98 0.17 .044-.65 .050 .026-.094

Vättern, Visingsö 4 2 96,98 0.10 .011-.97 -

Munksjön 5 1 98 0.058 .048-.069

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Nickel, ug/g dry wt., pike liver

Storvindeln

.0 .1 .2 .3 .4 .5 .6

69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99

n(tot)=85,n(yrs)=25 m=.068 (.058,.080) slope=-.62%(-2.3,1.1) SD(lr)=.39,2.8%,18 yr power=1.0/.27/12%

y(99)=.062 (.045,.084) r2=.02, NS

tao=-.21, NS

Bolmen, pike

.0 .1 .2 .3 .4 .5 .6

96 98 n(tot)=40,n(yrs)=4 m=.081 (.065,.101)

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 0e

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11.4 Chromium

The time series from pike liver (28 years long) show more or less stable concentrations neither do the other series from arctic char (17 years) show any trend.

No significant differences between comparable samples were found. (If only the last three years are compared there is no significant difference between L. Bolmen and L.

Storvindeln)

Table 10.4. Chromium, geometric mean concentrations (µg/g dry and fresh weight) in liver of various fish species and sites, together with the total number of analyses and the number of years of the time-series.

Matrix/ Lake n N of years

year dry w.

mean

95% c.i. fresh w.

mean

95% c-i.

Pike

Bolmen 30 3 96-98 .23 .20-.25

Storvindeln 222 28 69-98 .15 .14-.16

Arctic char

Abiskojaure 150 17 81-97 .17 .16-.19

Tjulträsk 20 2 95-96 .33 .27-.40 .078 .065-.095

Vättern, Röknen 5 1 96 .14 .096-.20 .051 .028-.093

Vättern, Visingsö 5 1 96 .17 .14-.20 .063 .044-.089

Perch

Stensjön 10 1 97 .37

Rotehogstjärn 10 1 97 .27

Allgjuttern 10 1 97 .25

St. Skärsjön 10 1 97 .29 .22-.38

Vättern, Röknen 15 2 96,98 .16 .14-.19 Vättern, Visingsö 14 2 96,98 .16 .14-.18

Munksjön 10 1 98 .19 .17-.21

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Chromium, ug/g dry wt., pike liver

Storvindeln

.0 .1 .2 .3 .4 .5 .6

68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98

n(tot)=232,n(yrs)=29 m=.150 (.138,.163) slope=-.68%(-1.6,.20) SD(lr)=.22,1.2%,12 yr power=1.0/.59/6.6%

y(99)=.135 (.115,.158) r2=.08, NS tao=-.26, p<.051 SD(sm)=.20, n.s.

Bolmen, pike

.0 .1 .2 .3 .4 .5 .6

96 98 n(tot)=40,n(yrs)=4 m=.228 (.210,.249)

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 0e

Chromium, ug/g dry wt., arctic char liver

Abiskojaure

.0 .1 .2 .3 .4 .5 .6 .7 .8 .9

81 83 85 87 89 91 93 95 97 99

n(tot)=170,n(yrs)=19 m=.175 (.159,.192) slope=.29%(-1.5,2.1) SD(lr)=.20,2.2%,12 yr power=1.0/.65/6.2%

y(00)=.180 (.147,.220) r2=.01, NS tao=.18, NS SD(sm)=.19, n.s.

Contaminant Research Group /NRM, Inst.Appl.Env.Res./ITM, Dep.Env.Assess./SLU 01

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Lead

.00 .05 .10 .15 .20 .25 .30 .35 .40 .45 .50 .55

0 2 4 6 8 10 12 n(tot)=112,n(yrs)=12 m=.057 (.031,.103)

Cadmium

0 5 10 15 20 25

0 2 4 6 8 10 12 n(tot)=130,n(yrs)=12 m=4.01 (1.63, 9.9)

Nickel

.00 .02 .04 .06 .08 .10 .12 .14 .16 .18

0 2 4 6 8 10 12 n(tot)=94,n(yrs)=12 m=.057 (.045,.073)

Chromium

.00 .05 .10 .15 .20 .25 .30 .35 .40 .45

0 2 4 6 8 10 12 n(tot)=130,n(yrs)=12 m=.184 (.167,.202)

Contaminant Research Group /NRM, Inst. of Appl. Environmental Research /SU 01.06.27 09:46, pt2

Figure 10.4. Concentrations of lead, cadmium, nickel and chromium (ug/g dry weight, in perch liver) during 1998-2000. From north to south: 1=Remmarsjön, 2=Degervattnet, 3=Stensjön, 4=Övre Skärsjön, 5=Bysjön, 6=St Envättern, 7=Rotehogssjön, 8=Allgjuttern, 9=Fiolen, 10= Hjärtsjön, 11= St Skärsjön,

12=Krageholmsjön

11.5 Aluminium

Aluminium

20 30 40 50 60 70 80 90

n(tot)=123,n(yrs)=12 m=15.1 (8.27,27.6)

Comments Concerning the National Swedish Monitoring Programme in Fresh Water Biota 2001