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Keywords:

  • acid deposition;
  • Lapland;
  • modern calibration data set;
  • pH history;
  • transfer function

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

1. A considerable proportion of the total deposition of sulphur in northernmost Europe originates from the large non-ferrous smelters of the Kola Peninsula, Russia. Potential long-term effects of this point source pollution on sensitive subarctic lakes were evaluated using palaeolimnological techniques.

2. Multivariate analysis of a diatom and water chemistry data set from 45 small headwater lakes located in north-eastern Finnish Lapland demonstrated that pH, calcium and silica were the three most powerful chemical variables in explaining the variance in the diatom data. From these, lake water pH was shown to be the strongest determinator by variance partitioning.

3. Weighted averaging partial least squares regression (WA-PLS) was used to develop a diatom-based prediction model for inferring lake water pH from sediment core diatom assemblages. The performance of the model was assessed by leave-one-out cross-validation.

4. The prediction model was applied to radiometrically dated sediment cores taken from three headwater lakes receiving different amounts of acid fallout from the Kola Peninsula smelter industry.

5. Stable diatom assemblages and results of pH reconstructions suggested that no substantial changes in the acidification status of the lakes have occurred within the last century despite the very high local acid deposition.

6. The pollution levels in the study area have not increased to the point where the biology of the lakes has been influenced significantly.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

Arctic ecosystems are fragile and particularly vulnerable to anthropogenic risk factors, such as arctic haze (Kerr 1981), acid rain (Koerner & Fisher 1982) and other pollutants. In general, slow growth and low species diversity renders the ecosystems sensitive to any perturbations, and they are also slow to recover. Fortunately, most of the Arctic lacks point pollution sources, yet the Kola Peninsula, north-west Russia, is exceptional as an area where massive emissions of sulphur dioxide and toxic metals are produced by the metallurgical processes (Iversen et al. 1990; Traaen et al. 1991; Tuovinen et al. 1993). Sulphur deposition in arctic Eurasia and northern Fennoscandia is predominantly due to industry on the Kola Peninsula, particularly to emissions from the two large copper–nickel smelters at Nikel-Zapolyarnyi (Traaen et al. 1991) (Fig. 1), which were established in this region in the early 1940s. Annual sulphur emissions from each of these smelters equals Finland's total sulphur deposition (Tuovinen et al. 1993), and together the sulphur deposition in the vicinity of the smelters has been estimated to exceed 3·0 g S m–2 year–1, affecting the environment adversely around the sites for many kilometres (Rühling et al. 1992; Turunen et al. 1994).

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Figure 1. Estimated sulphur deposition in northern Fennoscandia and the Kola Peninsula (g m–2 year–1) from June 1990 to June 1991. Deposition data are adapted from Tuovinen et al. (1993). The three study sites are indicated by asterisks: I = Lake 222, II = Pieni Kokkoselkä (PK), III = Sarvijärvi (SJ). The 45 lakes in the local diatom-pH calibration data set are marked by small dots; the large dots refer to towns. The shaded areas indicate the main structural units of the Precambrian rocks; modified after Simonen (1980). A = Apatity, Ka = Kandalaksha, Ke = Kemijärvi, Ko = Kovdor, Mo = Monchegorsk, Mu = Murmansk, N = Nikel, O = Olenogorsk, Z = Zapolyarnyi.

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The highest total estimated annual sulphur deposition in northern Finland, 1·0 g S m–2, has been recorded in north-eastern Lapland, close to the Russian and Norwegian borders, from where the values decline gradually towards the south-west, being 0·3 g S m–2 in central Lapland (Fig. 1). Atmospheric sulphur dioxide concentrations in Lapland are highest in winter, causing a considerable acid stress on lakes when the acid compounds accumulated in snow are liberated as the snow melts (Kähkönen 1996). In general, SO2 concentrations in northern Fennoscandia show marked temporal fluctuations, the highest hourly concentrations reaching 400 g m–3, while during the most polluted periods they commonly exceed 100 g m–3. Because in Finnish Lapland concentrations of neutralizing ions are low, about two-thirds of sulphate and nitrate deposition has been suggested to be in acid form (Tuovinen et al. 1993).

During the past few years, global concern has arisen regarding whether the acid emissions from the Kola Ni-Cu smelters have affected terrestrial and aquatic ecosystems in areas close to these sources. In northern Finland, the acid-sensitivity of lakes is highest in north-eastern Lapland, where oligotrophic, slowly weathering granitic rocks predominate, and these lakes are situated closest to the point emission sources on the Kola Peninsula (Kähkönen 1996). This area is characterized by the highest lake density in Finland (> 800 lakes 100 km–2), the overwhelming majority of the lakes being small water basins in the broken bedrock topography. These lakes are vital resources for settlement pattern, food production, recreation and tourism, for which reason their water quality is of great significance for the population as well as for the general ecology of the area (Henriksen et al. 1997). It is estimated that sulphur deposition in north-eastern Finnish Lapland exceeds the critical load set for lakes in more than 50% of the lakes analysed (Kinnunen 1992). However, the lack of sufficiently long monitoring records has seriously limited a more holistic impact assessment.

In the absence of direct long-term monitoring data, indirect proxy records can be used to establish the cause–effect relationship between acidic deposition and lake acidification (Battarbee & Charles 1987; Jones et al. 1993). Diatoms preserved in lake sediments are particularly useful for this purpose, because they are a group of aquatic organisms known to respond rapidly and in a quantifiable manner to environmental changes (Dixit et al. 1992; Moser, MacDonald & Smol 1996). Their distribution is generally highly related to lake water acidity and for this reason they have been used successfully in numerous previous acidification studies (Birks et al. 1990; Korhola & Tikkanen 1991; Jones et al. 1993; Korhola et al. 1996). However, it is only recently that quantitative diatom-based transfer functions have been developed by means of modern calibration data sets to reconstruct past changes in pH. Such transfer functions not only provide the means of deriving quantitative estimations of past pH fluctuations, but also enable the timing, magnitude and rates of acidification to be assessed for a particular site. Here we present the first data on palaeolimnological diatom analyses from small arctic lakes in the vicinity of Kola smelter industry in north-eastern Finnish Lapland to document lake responses to the increased atmospheric contamination in this extreme environment. We have collected a local calibration data set to create a diatom-based transfer function for pH and applied it to diatom records from three sites in north-eastern Finnish Lapland.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

Calibration data set

The local calibration data set consisted of surface-sediment diatom assemblages and the corresponding water chemistry data collected from 45 lakes located close to the Russian border in north-eastern Finnish Lapland (Fig. 1). The lakes were selected semi-randomly so that small, undisturbed, moderate deep and bathymetrically simple headwaters were generally favoured. The lakes are mostly clear, oligotrophic, dilute (low ionic strength), circumneutral (pH range 6·3–7·6) waters with no land-use or direct human impact in their catchments. For further data on their water quality, see Table 1.

Table 1.  Summary data on selected physical and chemical characteristics of 45 lakes in the north-eastern Lapland calibration data set
 Area (ha)Maximum depth (m)Secchi (m)pH (units)Conductivity (µS cm–1)Alkalinity (µeq l–1)K+ (µeq l–1)Ca2+ (µeq l–1)Na+ (µeq l–1)Mg2+ (µeq l–1)DOC (mg l–1)SO42– (µeq l–1)
Mean17·08·36·47·123·5114·75·1124·843·532·93·447·9
Median10·67·06·27·123·798·35·1124·843·524·73·047·9
Minimum2·32·32·36·311·316·42·625·017·424·71·012·5
Maximum115·723·910·77·638·2278·610·2289·473·9106·96·483·3
Range113·421·68·41·326·9262·27·6264·456·582·25·470·8

Water and surface-sediment samples were collected by helicopter over 2 days at the end of July 1996. Sediment samples (0–1 cm) were collected from the deepest basin of the lakes using a Limnos type gravity corer, operated from helicopter pontoons. Epilimnetic water temperature, lake depth and Secchi disc transparency were measured in the field. Water quality data were collected for the following determinants: pH, conductivity, alkalinity, K+, Ca2+, Na+, Mg2+, Fe2+, Si, Cl, Mn, NH4, SO42–, NO3, total organic carbon (TOC), dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC). All analytical work, except for the determination of DOC, TOC and DIC, was carried out by the Laboratory of Physical Geography, University of Helsinki, Finland, with extensive quality assurance/quality control routines. Carbon compounds were determined at the Lammi Biological Station after the method described by Salonen (1979). Location, altitude, lake and catchment dimensions, as well as the distance of each lake to the closest (coniferous) tree line, were determined from topographic maps. In total, 26 physical and chemical environmental variables were determined.

Diatoms were prepared from sediment samples using H2O2 digestion, and cleaned diatoms were mounted in Naphrax®. At least 500 diatom valves per slide were counted on random transects. Standard floras were used in diatom identification. For a more detailed description of the methods used, see Weckström, Korhola & Blom (1997a,b).

Data analysis

The diatom data for the calibration data set were expressed as percentages of the total frustule count. Multivariate analyses and inference models were performed on square-root transformed species percentage values in order to stabilize the variance. In all ordinations, rare taxa were downweighted in proportion to their frequency. The following determinants had skewed distributions and were log(x + 1) transformed prior to statistical analyses: alkalinity, conductivity, K+, Na+, Mg2+, Ca2+, Fe2+, Si, Cl, Mn, NH4, SO42–, NO3, TOC, DOC, DIC, altitude, lake area, catchment area, lake depth and water temperature.

Because many of the environmental variables in our data set were highly correlated with each other, canonical correspondence analysis (CCA) (ter Braak 1986) with forward selection and associated Monte Carlo permutation tests (199 unrestricted permutations) was used to identify a subset of variables that best explained the variation in the diatom taxon data. The independence and relative statistical strength of the most meaningful environmental gradients, as determined using the forward selection operation, was assessed by performing a series of variance partitioning analyses by means of partial CCAs (Borcard, Legendre & Drapeau 1992). At each step, the analysis was done by constraining the first ordination axis to the environmental gradient of interest and by using other relevant variables as covariables. The mode of response (i.e. gradient lengths) of diatom assemblages along the pH gradient was assessed using detrended canonical correspondence analysis (DCCA), with pH as the sole explanatory variable. The presence of any significant secondary gradients in the calibration data set was assessed by comparing the size and length of the second unconstrained detrended correspondence analysis (DCA) axis with the corresponding values of the constrained gradient. All ordination analyses were implemented by the program canoco version 3.12 (ter Braak 1987, 1990).

The diatom–pH transfer function was developed using weighted averaging partial least squares regression (WA-PLS) (ter Braak & Juggins 1993). Data screening and outlier detection followed Weckström, Korhola & Blom (1997b). The statistical performance of the transfer function is reported in terms of the coefficient of determination (r2) between the observed and the inferred pH values, the root mean square of the error (RMSE) (observed – inferred) and the coefficient of determination (r2jack) and RMSE of prediction (RMSEP), both obtained by leave-one-out cross-validation (jack-knifing) (ter Braak & Juggins 1993). The optimum number of WA-PLS orthogonal components was estimated by cross-validation. Following the guidelines given in Birks (1998), additional components were considered ‘useful’ only if they gave a reduction in prediction error of 5% or more of the RMSEP for the simplest one-component WA-PLS model. All WA-PLS procedures were performed using the computer program calibrate (S. Juggins and C.J.F. ter Braak, unpublished program).

Study sites and sediment coring

Three small headwater lakes were selected to study the acidification histories. Two of the lakes, Lake 222 and Lake Pieni Kokkoselkä (PK), are situated in the area of heaviest pollution, while one (Lake Sarvijärvi, SJ) is a reference lake in a slightly cleaner area in western Lapland (Fig. 1). Lake 222 is a small (24 ha, maximum depth 22 m) lake near the Norwegian and Russian borders, approximately 40 km west from the Nikel smelter area. It is located on barren granitic bedrock above the tree limit, whereas PK and SJ lie on granulite rocks. The lakes were selected because they exhibit many features typical of acidification-prone lakes, such as small watersheds, a poorly weathering bedrock in their drainage area, thin or absent soils, no agricultural fields, low base cation concentrations, and small ratios of catchment to surface area (< 10). The sites are extremely clear (colour < 10 mg Pt l–1) and characterized by low conductivity. Their SO4–2 concentrations are relatively high (with the exception of PK in autumn 1991), reflecting the high level of sulphur deposition in the area. The low levels of Ca2+, particularly at PK and SJ (< 40 µeq l–1), refer to limited buffer capacity and general susceptibility of these lakes to acidification. According to information obtained from the national water quality database of the Finnish Environment Institute, water pH in the study lakes during summer and the autumnal circulation period is around 6·7–6·9 (Lake 222), 6·6 (PK) and 6·5–6·6 (SJ) (Table 2). The pH drops to around 5·6 during the period of snow melt in PK, for which water chemistry data are available also for spring; no alkalinity exists in the lake during the heaviest acid pulse. For further data on the water chemistry and physical characteristics of the sites, see Table 2.

Table 2.  Physical and chemical characteristics for the three lakes studied for acidification history (water chemistry data adapted from the Water Quality Data Base, Finnish Environment Institute). Values are sea-salt corrected. Unless otherwise indicated values are expressed as microequivalents per litre (µeq l–1). Catchment area excludes lake area
 Lake 222Pieni KokkoselkäSarvijärvi 
Latitude6927′N6826′N 6806′N
Longitude2907′E2823′E 2406′E
Altitude (m a.s.l.)222148 327
Lake area (ha)247 32
Catchment area (ha)22442 270
Catchment lake9·36 8·4
Maximum depth (m)218 3·5
AutumnSpringAutumnAutumn
1995199519911995
————————— 
pH6·7–6·95·66·66·5–6·6
Alkalinity (µeq l–1)6002020
Conductivity (mS m–1)1·81·41·01
Colour (mg Pt l–1)00105
Ca2+62·523·119·939·0
Mg2+11·716·624·78·3
K+7·14·75·12·3
Na+12·46·230·43·1
SO42– (µg l–1)53·543·715·040·6
NO2 + NO3-N (µg l–1)0·12·45·00·1

The cores were collected near the deepest point in each lake using a Limnos-type gravity corer, and extruded at 0·5-cm intervals for the topmost section (3–7 cm), and below that at 1-cm intervals. Organic content was determined by loss-on-ignition (LOI) at 550 °C. Sediments were dated by their β-activity profiles. This method can be used to track overall trends in the accumulation of radionuclides in lake sediments. Among these 137Cs is one that is easy to detect due to its quite high-energetic γ and β ray emission. A β-multicounter system by the Dating Laboratory of the University of Helsinki, Finland, was used for detection of the radionuclides; for the exact methods and equipment used in the analysis, see Jungner (1998). According to Jungner (1998), the Chernobyl accident is usually clearly visible in the β-activity profiles of Finnish lakes, whereas the bomb test period is more difficult to distinguish. For Lake 222, sedimentation rate was estimated additionally by the spheroidal carbonaceous particle (SCP) stratigraphy; 210Pb results from a previously dated surface-sediment core were also available for comparison (Vartiainen et al. 1997). Carbonaceous particle analysis followed the method described in Rose (1990). SCP are formed from the incomplete combustion of fossil fuels, and their application to chronology is based on comparison between the total area fossil fuel combustion history and the SCP concentration (Renberg & Wik 1985; Rose et al. 1995). In studies with exact time control (annually laminated sediments), the method has been shown to give fairly reliable age determinations for Finnish lakes (Tolonen, Haapalahti & Suksi 1990). We also compared the SCP profile with another SCP profile from a radiometrically dated site in Finnish Lapland (Sorvari & Korhola 1998; Korhola et al., in press).

Results and discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

Calibration data set and ph transfer function

A total of 266 diatom taxa representing 34 genera were identified in samples from the 45 subarctic north-eastern Lapland sites, comprising predominantly benthic and periphytic species. Acidic sites were mainly characterized by Brachysira brebissonii Ross in Hartley, Brachysira vitrea (Grun.) Ross in Hartley and Frustulia rhomboides var. saxonica (Rabenh.) De Toni, whereas more alkaline sites showed a dominance of Cyclotella rossii Håkansson, Achnanthes minutissima Kütz., Achnanthes levanderi Hust., Aulacoseira italica f. crenulata (Ehrenb.) Ross in Hartley, and particularly Fragilaria spp.

After deleting highly collinear variables (latitude, conductivity, DIC, TOC) on the basis of their high variance inflation factors (Weckström, Korhola & Blom 1997b), forward selection and associated Monte Carlo permutation tests indicated that four of the remaining 22 environmental variables made statistically significant contributions to explaining the variance in the diatom species data. These four variables were Ca, pH, Si and maximum lake depth, which together accounted for 26·3% of the total variance in the diatom data.

The results of the variance partitioning are summarized in Table 3 and 4. Following the guidelines given in Okland & Eilertsen (1994), only the subset of significant variables was included in the partial CCAs. First, the variance in the diatom data was partitioned amongst the chemical and physical components. The three most meaningful chemical variables (pH, Ca, Si), as determined on the basis of the forward selection procedure (see above), independently accounted for a statistically significant (P≤ 0·05) proportion (18·0%) of the variance in the diatom data, whereas the most influential physical variable (lake depth) independently captured a statistically significant proportion of 7·5% of the variance (Table 3). There was only a small conditional effect between the two sets of variables that contributed an additional 0·8% of the variance. We thus conclude that the major chemical variables affecting the diatoms in the data set are not significantly confounded by the existing physical gradients.

Table 3.  Results of partial canonical correspondence analysis (CCA) partitioning the total variance in the calibration set diatom data between the most influential chemical (pH, Ca, Si) and physical (lake depth) gradients. P = significance level of Monte Carlo permutation test (99 unrestricted permutations)
Source of variationVariance explained (%)P
Chemical vs. physical variables
 Independent contribution of pH, Ca and Si18·00·01
 Independent contribution of lake depth7·50·01
Covariance between chemical and physical variables0·8
Explained variance26·30·01
Unexplained variance73·70·01
Table 4.  Results of partial ordination (CCA) of diatom assemblages in 45 north-eastern Lapland lakes. P = significance level of Monte Carlo permutation test (99 unrestricted permutations)
VariableCovariableVariance explained (%)P
pHNone10·00·01
pHCa5·40·01
pHSi4·60·01
CaNone10·90·01
CapH6·40·01
CaSi5·80·01
SiNone10·00·01
SipH4·70·01
SiCa4·80·01

Secondly, we partitioned the explained variance between the three most significant chemical components. This analysis indicated the significant and unique responses of diatoms to pH, Ca and Si, respectively, regardless of the covariables used in each analysis (Table 4). On the basis of all the ordination analyses, pH, Ca and Si were therefore identified as strong predictor variables in explaining the diatom composition in our 45-lake calibration data set. Although reliable inference models could, at least in theory, be developed for each of these three chemical variables, we present in this connection only the prediction model for reconstructing trends in lake water pH. When interpreting the results, however, one should bear in mind that, although there is an independent and statistically significant response of diatoms to lake water acidity, pH is at least partially confounded by other chemical variables, as indicated by Table 4 (e.g. almost half of the variance explained by pH is conditional on Si). Subsequent reconstructions for pH can therefore not be considered totally independent of other chemical gradients.

In relation to pH, diatom data had a gradient length of 2·17 standard deviation (SD) units. In a DCCA with pH as the sole constraining variable, pH explained 10·0% of the variance in the diatom data, whereas the second unconstrained axis explained 9·6%; the gradient length of the second unconstrained axis was 2·02 SD units. These results support our view that pH is a strong explanatory variable, but there also exist strong secondary gradients and hence much variation in the diatom data that is not related to this particular variable. We nevertheless conclude that the relationship between the diatom assemblages and lake water pH is strong enough for developing a statistically robust and biologically meaningful predictive model from these data for lake water pH.

Results of WA-PLS regression for lake water pH using the 45-lake data set are shown in Fig. 2. The transfer function indicated a close agreement between measured and diatom-inferred pH (r2 = 0·73), and the model had a low RMSE of 0·17 pH units (Fig. 2a). After leave-one-out cross-validation, the first component WA-PLS model provided a jack-knifed r2 of 0·60 and a RMSEP of 0·20 pH units (Fig. 2b). As indicated by the residuals, the model typically tended to over-estimate values slightly at the low end of the pH range and under-estimate the high values, the reasons for which are discussed, for example, by Lotter et al. (1997). No improvement on the predictions was achieved by using further components in WA-PLS or by deletion of unusual samples. In general, the statistical performance of the predictive model compares well with the other previously developed diatom–pH transfer functions (Hall & Smol 1995; Korsman & Birks 1996).

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Figure 2. Relationship between measured and diatom-inferred lake water pH in 45 north-eastern Lapland lakes using a one-component weighted-averaging partial least squares (WA-PLS) model. (a) Inferred pH (apparent relationship) without leave-one-out cross-validation (r2 = 0·73; RMSE = 0·17 pH units). (b) Predicted pH with leave-one-out cross-validation (r2 = 0·60; RMSEP = 0·20 pH units). Distribution of residuals is also shown.

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Core applications

The LOI, β-activity and SCP profiles for Lake 222 are shown in Fig. 3a, together with the previously published 137Cs activity profile as well as the age–depth curve obtained by 210Pb dating. The SCP profile showed trends similar to those noted in sediment cores previously taken from north-western Finnish Lapland (Sorvari & Korhola 1998; Korhola et al., in press) and lakes close to the pollution sources on the Kola Peninsula (Rose 1995). There is first a slow but steady increase in concentration from approximately 7·5 cm until a period of more rapid increase at approximately 4·0 cm. This is followed by a peak at 1·5 cm, then a decline, and another concentration peak right at the core top. Sediment metal records from lakes in Kola Peninsula document increases above background levels around the 1920s and 1930s (Norton et al. 1992). The increase in SCP at 4·0 cm may thus date from this time (Sorvari & Korhola 1998; Korhola et al., in press). Maximum concentrations of Ni and Cu in the sediments occurred in the latter half of 1970s (Norton et al. 1992), coinciding with the period of maximum emissions of sulphur dioxide from the area (Traaen et al. 1991). This date may also correspond with the SCP concentration peak at 1·5 cm. If the increase observed in β-activity at 0·5 cm is interpreted as being caused mainly by the nuclear reactor failure in Chernobyl in 1986, then the SCP and β-activity chronologies match well with each other. However, the activity peak at Lake 222 is rather weak, indicating either that the Cs in this lake has not been effectively removed from the water and fixed to the sediment or that the activity measurements are actually detecting radionuclides other than 137Cs. Nevertheless, the established chronology based on SCP and β-activity is in good agreement with the 137Cs activity profile and 210Pb dates obtained from a parallel core in the lake (Fig. 3a).

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Figure 3. (a) Depth profiles of loss-on-ignition (LOI), spheroidal carbonaceous particles (SCP) and β-activity for Lake 222 core. 137Cs activity and the age–depth curve obtained by 210Pb dating another sediment core from the site are also shown [137Cs and 210Pb results adapted from Vartiainen et al. (1997)]. (b) The beta count rates for the sediments from Lake Sarvijärvi (SJ). (c) The beta count rates for the sediments from Lake Pieni Kokkoselkä (PK).

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The β-activity profiles for SJ and PK are shown in Fig. 3b,c. At PK, the β-activity rises from a sediment depth of 12 cm and reaches its highest value at 4·0 cm, suggesting a possible Chernobyl signal in 1986. At SJ, the activity starts to rise at 3 cm, with the activity maximum at the top of the core (0–0·5 cm). As we found only small compaction, we assumed that the sedimentation rates of the upper part of the cores (0–3 cm) were only slightly higher (10%) than those of the lower parts, a view that is supported by the 210Pb dating of Lake 222 (Fig. 3a). Thus, average sedimentation rates can be estimated for each of the three study lakes ranging from a low of 0·5 mm year–1 and 0·6 mm year–1 (Lake 222 and SJ, respectively), to a high of 4·0 mm year–1 (PK). In general, sediment accumulation rates in lakes vary according to the input of both organic and inorganic material from the lake catchment and biogenic material from the lake itself. In oligotrophic systems, like the lakes studied here, material derived from the catchment usually dominates. The extremely slow sedimentation rates observed at Lake 222 and SJ are typical for arctic sites with rocky catchments and low primary production (Douglas, Smol & Blake 1994; Sorvari & Korhola 1998). The considerably faster sediment accumulation rate at PK is most probably related to the location of the site at a lower elevation (148·0 m a.s.l.) and the resulting higher percentage of organic soils and denser vegetation cover in its catchment. The sediment of PK also had much higher LOI values (70–80%) than the sediments of lakes 222 and SJ (20–30% LOI). In all, the data demonstrate that a continuous stratigraphic record is present in each of the cores over the period of interest.

Each of the studied cores featured surprisingly monotonous diatom stratigraphies (Fig. 4). The diatom floras of SJ and PK were dominated by acidophilous, benthic species, such as Brachysira brebissonii and Frustulia rhomboides var. saxonica as well as various Eunotia and Navicula species. Lake 222's development was dominated by small centric Cyclotella spp., particularly C. rossii, which constituted approximately 60% of the total flora throughout the core. The high abundance of Cyclotella is typical for deeper lakes in Finnish Lapland (Weckström, Korhola & Blom 1997a,b; Sorvari & Korhola 1998). Diatom-inferred pH (DpH) was more or less stable in each core. No major response to the onset of operations at the Kola smelters and an associated increase in acid deposition was observable either in species compositions or in the inferred DpH (Fig. 4). At each site, the DpH for the uppermost subsample corresponded relatively well with the known pH value of the lakes (7·0, 6·5, 6·8 and 6·7–6·9, 6·6, 6·5–6–6, respectively), suggesting that the pH reconstruction technique used is valid.

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Figure 4. Relative frequency diagrams of the most dominant diatom taxa recorded in the sediments of the three study lakes and pH reconstructions (DpH). (a) Lake 222, (b) Pieni Kokkoselkä (PK), (c) Sarvijärvi (SJ). The commencement of operations at the Kola smelters in the early 1940s is indicated in the diagrams by a solid line.

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Although the diatom stratigraphies look fairly featureless, there might still be changes that are not observable by visual inspection. The potential ‘hidden’ trends in the diatom assemblages can be analysed more sensitively using community ordination techniques that allow the interrelatedness of the samples to be detected. The technique used here was CCA, a direct gradient analysis method where the ordination axes are constrained to be linear combinations of environmental variables (ter Braak 1986). If the fossil assemblages are entered passively in a CCA of the modern diatom–environment data set used for pH reconstruction, changes in the diatom compositions can be compared directly to the environmental gradients within this data set (Birks, Juggins & Line 1990; Allott, Harriman & Battarbee 1992). In Fig. 5 it can be seen that the stratigraphic samples (i.e. diatom assemblages) of each core are clustered closely around each other in the CCA space and are not chronologically aligned with the pH or any other environmental gradient. The overall CCA results thus suggest that no major shifts in abundance of the diatom taxa are present in the records to indicate recent acidification of the lakes, leading us to conclude that the studied ecosystems have not so far responded biologically to the increased acid loading.

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Figure 5. Canonical correspondence analysis (CCA) ordination diagram showing the positioning of the samples of the three studied cores in relation to environmental gradients in the calibration data set used for pH reconstruction. Core samples were treated passively in the ordination analysis.

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To assess further the sensitivity of the selected lakes to acidification we calculated their baseline, site-specific critical sulphur load by means of an empirical diatom-based palaeolimnological model calibrated using sites and data from the UK (Battarbee et al. 1996). This model is based on a dose–response function that can be used to set critical load values for a site from a knowledge of the ratio of Ca2+ of the water to the modelled S deposition at the site. Such a test revealed that for our reference lake (SJ) the critical load has not yet been exceeded (critical load: 0·37 keq S ha–1 year–1; current deposition: 0·19 keq S ha–1 year–1). However, Lake 222 has already reached its critical load limit (0·59 keq S ha–1 year–1, 0·62 keq S ha–1 year–1, respectively), whereas in the case of PK the level is exceeded by more than twofold (0·19 keq S ha–1 year–1, 0·44 keq S ha–1 year–1, respectively). The lack of any pH decline in PK is therefore most revealing regarding the discrepancy between our palaeolimnological evidence and the empirical diatom model based on the diatom–water chemistry data from the UK. These results suggest that the Ca : S ratio of 94 : 1 used to predict critical load exceedance for sites in the UK (Battarbee et al. 1996) is probably not directly applicable to assess the acid sensitivity of lakes in Finnish Lapland. The results further highlight the need to calibrate the diatom model specifically for north-eastern Lapland lakes by means of a larger data set of inferred acidification profiles from dated lake sediment cores throughout the region. Such work is currently in progress.

Because of the importance of organic matter to Finnish lakes, the influence of organic anions on lake acidity and calculations of critical loads should consequently be given special attention (Forsius, Kämäri & Posch 1992). In the Barents Sea region, the most acid lakes are found on the Russian side of Kola, where the flat topography favours the occurrence of peatlands and where the organic carbon content of surface waters is highest (Henriksen et al. 1997; Blom et al. 1999). In contrast, the northernmost areas of the Finnish Lapland are characterized by less organic soils, and the concentrations of humic substances in waters are much lower. In our northern Finland lake calibration data set, the median value for TOC was 4·8 mg l–1 (n = 98) and the median value for DOC was 3·0 mg l–1 (n = 45) (Blom, Korhola & Weckström 1998), whereas the median TOC for all Finnish lakes under regular monitoring is 12·0 mg l–1 (n = 987) (Forsius et al. 1990). In addition to soils, this decreasing south to north trend in TOC values in Finland apparently reflects variations in climate, primary production and decomposition rates (Kortelainen 1993).

Organic carbon may buffer lakes against acidification, but may also add acidity to surface waters (Kullberg et al. 1993). It has been shown that in humic lakes the effect of strong mineral acids is superimposed on organic acid contributions to acidity (Gorham et al. 1986; Brakke, Henriksen & Norton 1987). As a consequence, organic acids make humic lakes generally more sensitive to acidification than clear-watered lakes (Brakke, Henriksen & Norton 1987). Biological communities and processes are also affected by humic substances, either directly through interfering with metabolic processes or indirectly by altering the bioavailability of nutrient or toxicants (Petersen 1991; Kullberg et al. 1993). The biological response on acid loading in clear-water lakes may therefore differ substantially from that observed in humic lakes, which may, at least in part, explain the greater ability of northern lakes to resist acid deposition. Separate, geographically restricted dynamic models for defining critical loads and predicting ecological impacts in clear-water ecosystems in northernmost Lapland are therefore needed.

The observed stability in pH development during the 20th century is not unique to the three cores studied. We have obtained a similar pH record from an arctic lake that we have studied in western Finnish Lapland (Sorvari & Korhola 1998). In contrast, diatom floras in sediments of some mountain lakes of the Kola north have clearly undergone changes during recent times, providing evidence of the development of water acidification (Moiseenko, Dauvalter & Kagan 1997). This is not surprising per se, because the prevailing north-easterly and northerly winds transport higher concentrations of acid compounds to the areas north of the Kola smelters, particularly during winter (Tuovinen et al. 1993). On the other hand, the interpretations of the increased acidity in the lakes of the Kola north is based predominantly on the knowledge of diatom ecology from more southerly locations, including the reconstruction of lake water pH by means of the ‘index B’ developed for the lakes of Sweden (Moiseenko, Dauvalter & Kagan 1997). The transformation of such information to northern algal communities and lake situations can, according to our own experiences, be highly misleading (Weckström, Korhola & Blom 1997b).

The currently available palaeolimnological data indicate that the watershed and in-lake alkalinity-generating processes are still effectively opposing the acidification in many of the clear-water lakes in Finnish Lapland. This is in agreement with other palaeolimnological data showing that there is often a long time-lag between the onset of contamination by atmospheric pollution and its first effects on the biology and chemistry of surficial waters (Kingston et al. 1990). However, the present study is based on three lakes only, from which only two would be expected to respond to the current levels of acid deposition. Clearly, further data are needed to make effective generalizations about the acidification trends and status of the numerous small lake basins in the study area. This includes, for example, additional palaeolimnological studies on lakes in the region, as well as the development of the local ‘diatom model’ to assess critical loads of acidity for these unique subarctic lakes.

Conclusions

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

Although these remote sites have been contaminated by atmospheric pollution from the Kola smelters over several decades, the pollution levels have obviously not increased to the point where the chemistry and biology of the lakes have been influenced significantly. This is in agreement with general palaeolimnological knowledge indicating that there is often a long time-lag between the commencement of contamination by atmospheric fallout and its first effects on the aquatic biology and chemistry. However, in order to prevent any future acidification processes, very strict emission reductions measures are needed at the Kola smelters. Our finding that aquatic ecosystems 30–40 km away from the Kola smelters have not yet been severely damaged is encouraging in demonstrating that arctic ecosystems may be more resistant to environmental changes than has hitherto been thought.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References

This study was funded by the Academy of Finland (Grant 101 7383) and Nordic Council of Ministers (Grant FS/HFj/X-96005). The research contributes to the development of the European Diatom Database (EDDI), funded by the EC Environment and Climate Research Programme (ENV4-CT97-0562). We are grateful to the Lapland Regional Environmental Centre for support and logistic help, H. Jungner for the β-activity measurements, I. Renberg and H. J. B. Birks for critical comments to an earlier version of the manuscript, S. Juggins and C. J. F. ter Braak for a pre-release version of their calibrate program, and two anonymous referees for their constructive comments.

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  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results and discussion
  6. Conclusions
  7. Acknowledgements
  8. References
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Received 11 February 1999; revision received 17 June 1999