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Characterization of the element content in lacustrine ecosystems in Terra Nova Bay, Antarctica
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Characterization of the element content in lacustrine ecosystems in Terra Nova Bay, Antarctica
Ornella Abollino*, Mery Malandrino, Isabella Zelano, Agnese Giacomino, Sandro Buoso, Edoardo Mentasti
Department of Analytical Chemistry, University of Torino, Via Giuria 5, 10125 Torino (Italy)
Abstract
The distribution of a series of elements in lacustrine environments from Terra Nova Bay, Northern Victoria Land, Antarctica, has been examined, in order to gain insight into the natural processes regulating species distribution, and to detect possible present or future local and/or global anthropogenic contamination. Attention was focused on freshwaters, algae, mosses and lichens (one site only). Lake water composition was found to be influenced by marine spray, lake geographical position and meltwater input. Bioaccumulation of elements by algae was observed. Higher element concentrations in algae than in mosses have been interpreted taking into account this phenomenon. Data processing by chemometric techniques showed correlations between elements and similarities among samples. No evidence of a detectable metal contamination was found in the investigated area.
Keywords: lake, Antarctica, freshwater, algae, mosses, metals
1. Introduction
The terrestrial ecosystems of the Antarctic region are among the most uncontaminated on Earth and are considered as one of the most suitable places for estimating the natural presence, concentration and variability of organic and inorganic compounds with a minimum level of anthropogenic influence. From these ecosystems it is therefore possible to gain insight into the natural processes
regulating organic and inorganic species distribution in different environmental compartments. Antarctic lake waters have been less extensively studied than other matrices, such as seawater or snow [e.g. 1-5]. Most available data on Antarctic lake waters derive from the McMurdo Dry Valleys area in southern Victoria Land, West Antarctica, in particular Lake Vanda and Lake Wilson, and the Larsemann Hills, East Antarctica [e.g. 6-10].
The lacustrine Antarctic ecosystems of the Terra Nova Bay area (Northern Victoria Land) have been studied since 1985 within the framework of the Italian National Research Program in Antarctica (PNRA) [e.g. 11-21]. The comparison of results obtained over a relatively long period of time (1993-2001) has demonstrated that human activity has not resulted in detectable metal contamination in that area [20]. The monitoring program at Terra Nova Bay is useful to establish contaminant baseline levels and to detect the occurrence of possible local and /or global anthropogenic contamination [22-23].
In this study, the distribution of a series of elements in lacustrine compartments from Terra Nova Bay has been examined. Attention was focused on freshwaters, algae, mosses and (for one site) lichens, from Edmonson Point (EP) lakes 13, 13A, 14, 15, 15A, 16, Carezza (CA), Gondwana (GW), Tarn Flat (TF) and Inexpressible Island (II). The lakes are located in the vicinity of the “Mario Zucchelli” Italian station. Element concentrations have been determined by optical emission, atomic absorption spectroscopy and ICP mass spectrometry. The experimental results have been processed by multivariate chemometric techniques.
The study has two main aims. First, to verify whether anthropogenic trace metal contamination is present in the area around Terra Nova Bay. Second, to shed some light over the biogeochemical cycles of the investigated elements, taking into account the relationships among the different matrices.
*
Corresponding author. Tel: +390116705243.
2. Experimental
2.1. Location and setting
Fig. 1 shows a map of the investigated area, which stretches from about 74°10’ to 75°20’S and from 162°00’ to 165°50’ E around Terra Nova Bay, in the Ross Sea. Terra Nova Bay is relatively ice-free in summer owing to the presence of western katabatic winds blowing from the plateau seawards. More than a hundred small lakes and ponds, as well as meltwater and periglacial lakes, are present in this ice-free area [12,20,21]. Table 1 shows the main geographical features of the ten investigated lakes together with the location of each site.
Such lakes are small, shallow and are usually unfrozen only for a few months during the Antarctic summer. They are fed by surface streams deriving from glacial or snow meltwater and/or by groundwater and have no definite outflow, but water is lost by evaporation, sublimation or spraying induced by the persistent katabatic winds; the latter result in the lakes being well-mixed during the summer. In a previous study we identified the major mineralogical species present in the regoliths catchements (Table 2) [21]; in summary, these include: 1) quartz, alkaline feldspars, plagioclase, amphibole, biotite, chlorite and muscovite in CA, II and TF and 2) plagioclase, alkaline feldspars, pyroxene and olivine at EP. We concluded that: 1) for CA, II and TF, the soils and the sediments derive from intrusive rocks, in particular from granitic (CA and II) and granodioritic (TF) rocks and 2) the soils and the sediments of EP lakes derive from effusive rocks (basalts, rhyolites and pumice fragments). Each investigated site has some specific features. In particular, Edmonson Point is close to Mount Melbourne, a dormant volcano, and has a relatively abundant terrestrial plant community, favoured by the availability of marine- and bird-derived nutrients (a penguin rookery is present in that area). Inexpressible Island is almost completely free from glaciers and the lake considered in this study is the closest to the sea. Tarn Flat area is heavily affected by glacial erosion, which generated undulations with hilly reliefs and depressions; the lake sampled in this study lies at −70 m below sea level and is characterized by a noticeable amount of inflowing and outflowing water. It is fed by the waters generated by a small glacier (Mt. Gerlache) and by surface waters produced by the
melting of permafrost and small snowfields. The water outflow feeds a nearby lake at −80 m below sea level. Carezza lake lies in the Northern Foothills, an area characterized by rounded hilly reliefs with small local glaciers and large amounts of snow. It is the closest to the Italian station; Gondwana Lake is located near the former German station. Further details on Terra Nova Bay can
be found in [21]
.
2.2. Sampling and sample pre-treatment
2.2.1. Lake waters
Water samples were collected with previously cleaned 500 ml-polyethylene containers, immediately filtered through acid-washed 0.45 μm cellulose acetate filters and frozen at -20°C. All samples were maintained at -20°C during all stages of storage and transportation to laboratory. Before analysis, the water samples were unfrozen and acidified with 500 µl of purified nitric acid. The sample blanks were prepared similarly to samples in the laboratory without the field sampling step. These pretreatment steps were carried out under a Class-100 laminar flow bench-hood placed in a controlled atmosphere laboratory.
2.2.2. Algae, mosses, lichens
Mosses and lichens were sampled near lakes and, where possible, close to brooks generated by ice and snow melting. Mosses were collected with a scoop, whereas lichens were hand-picked. Algae were sampled near banks, where lakes are shallower, by using a scoop. For each sample 250 g were collected in polycarbonate jars. All samples were maintained at -20°C during all stages of storage and transportation to the laboratory. After thawing, soil residues and stones were removed with the aid of tweezers, until no debris was visible. Then the samples were split into two parts: the first one was washed with deionized water, the second one was left untreated. Samples were then frozen with liquid nitrogen and freeze-dried. Roots were removed from mosses and lichens after lyophilisation.
All samples were ground by hand in an agate mortar and transferred into polycarbonate jars until analysis. No substantial differences between element concentrations in washed and unwashed samples were found during the analyses, confirming the efficiency of the removal of stones and soil residues.
2.3. Apparatus and reagents
A Milestone MLS-1200 Mega microwave laboratory unit was used for the dissolution of the solid samples.
Analyses were carried out with the following instruments:
- Thermo Finnigan Element 2 Inductively Coupled Plasma-Mass Spectrometer (ICP-MS), able to work at low, medium and high resolution;
- Varian Liberty 100 Inductively Coupled Plasma-Optical Emission Spectrometer (ICP-OES) provided with a Czerny-Turner monochromator, a Sturman- Masters spray chamber, a V-groove nebuliser and a radio frequency (RF) generator at 40.68 MHz;
- Perkin Elmer 5100 Graphite Furnace Atomic Absorption Spectrometer (GF-AAS) equipped with Zeeman-effect background correction, a HGA 600 graphite furnace and an AS-60 autosampler. Pyrocoated graphite tubes with L’Vov platform were used throughout.
Table 3 shows the detection limits of the analytes of interest with the technique used for their determination (see section 2.4).
The reagents used were all of analytical purity. The nitric acid used to acidify samples before analysis was further purified by sub-boiling distillation in a quartz apparatus. Water was purified in a Milli-Q system, resulting in high purity water (HPW) with a resistivity of 18 MΩ cm. Intermediate metal standard solutions were prepared from concentrated (1000 mg/l) stock solutions (Merck Titrisol) and acidified to pH = 1.5.
2.4. Procedures
2.4.1. Lake waters
Elements in freshwater samples were determined by ICP-MS with the exception of Na and Mg (ICP-OES) and Cr (GF-AAS). A Certified Reference Material (CRM), namely natural water SRM 1640, supplied by the National Institute of Standards and Technology (NIST), was analyzed in order to check the accuracy of the experimental procedure. The recoveries ranged between 93 and 107% for most certified analytes; the results obtained for the CRM determined the choice of the analytical technique adopted for each element in the subsequent analyses, so as to achieve the best accuracy.
The standard solutions for the instrumental calibration were prepared in aliquots of calibration blank. The difference between the sample blank and the calibration blank was always lower than the detection limit of the instrument used, so we subtracted the signal of the calibration blank, prepared daily in connection with instrumental calibration, from the sample concentrations.Such blank is unavoidably over-estimated because it contains HPW, which is not present in the samples. However, the contribution of HPW to the blank can be considered small in comparison to that of other sources of contamination, such as nitric acid and sample manipulation during acidification and analysis. This hypothesis is supported by the good accuracy observed in the analysis of SRM 1640. The repeatability was within 10% for more than 75% of the data, as shown by the values of the standard deviation (see section 3.1).
2.4.2. Algae, mosses, lichens
Algae, mosses and lichens samples were digested in polytetrafluorethylene bombs in the microwave oven with 8 ml of HNO3 per 0.5 g of dried sample. The following heating program was adopted: 250 W, 5 min; 400 W, 5 min; 600 W, 5 min; 250 W, 5 min; ventilation, 25 min. Blanks were prepared following the same procedure without the sample material, and the resulting solutions were also used to prepare calibration standards.
Sea lettuce BCR 279, obtained from the Institute of Reference Materials and Measurements (IRMM), was analysed to check the accuracy of the procedure; the recoveries ranged between 86 and 98 % for all certified analytes, with the exception of Cr (106%). All the analytes in the samples were determined by ICP-MS, with the exception of Ca, Cr, Fe, K, Mg and P, which were determined by ICP-OES. The repeatability was within 10% for most of the data (more than 80% for algae and more than 70 % for mosses and lichen), as shown by the values of the standard deviation (see section 3).
2.4.3. Chemometric treatments
Experimental results were processed by Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) with the aid of XLSTAT, an add-on of Microsoft Excel. Information on the principles of the two techniques can be found elsewhere [24-27]. For lake water data, the analytes whose concentrations were below the detection limit in more than two samples were not included in the calculations. In the other cases, the values below detection limit were substituted with a random number between zero and that limit. The data were preprocessed by column standardization, i.e. by mean-centering (for each variable) and dividing by the corresponding standard deviation. The first two PCs were considered in PCA. The interpretation of the loading plots was aided by the examination of the Pearson’s correlation matrix (not reported)Only significant correlations (tested at a significance level of 5%) were considered. HCA was carried out using the Euclidean distance and Ward’s agglomeration method; the results were reported in a dendrogram.
3. Results and discussion 3.1.Lakewaters
The concentrations of major, minor and trace elements in waters of the investigated lakes are reported in Table 4 together with their ranges, mean and median values. A total of 33 elements was
determined, namely Al, As, B, Ba, Ca, Cd, Ce, Co, Cr, Cu, Fe, Gd, K, La, Mg, Mn, Mo, Na, Nd, Ni, Pb, Rb, Sb, Sc, Si, Sr, Ta, Ti, V, W, Y, Zn and Zr.
Na is the most abundant cation in all studied lakes, followed by Mg and Ca, with the exception of EP15A, EP16 and GW where Ca has the highest concentrations. These results suggest that marine spray has a very important influence on lake water chemistry; its contribution can be estimated by assuming that Na and Mg have mainly a marine origin, whereas the origin of Ca is mainly terrestrial
(natural erosion processes), and by calculating Na/(Na+Ca) and Mg/Ca ratios as reported in Table 5. Mg/Ca values range from 0.04 to 5.51 with an average of 1.25 and decrease in the following order: EP13 > EP15 > II > EP14 > TF > CA > GW > EP16 > EP15A > EP13A. Na/( Na+Ca) values range from 0.41 to 0.98 with an average value of 0.72, and decrease in the order: EP13 = EP15 > II > EP14 > CA > TF > EP13A > GW > EP16 > EP15A. Therefore a similar trend emerges from both ratios. As it can be noticed, substantial differences exist among lakes located at almost the same distance from the sea, as well as between lakes situated less than one kilometre from each other, like EP14 and EP15A. This phenomenon can be explained considering that the marine spray contribution is a function not only of the distance from the sea but also of the different geomorphological conditions of each lake [10], such as the total surface area and elevation (Table 1). Furthermore EP13A and EP15A were almost completely frozen during the sampling expedition, so that sea spray probably deposited onto the ice cover and only partially could reach the waters; on the other hand, the penguins from the rookery close to EP13 might contribute to the increasing of some marine elements. All these factors result in variable contributions of marine spray, that primarily influence alkali and alkaline earth metal concentrations. The amount and source of meltwater flowing into the lakes and the mineralogical and chemical characteristics of soils surrounding the lakes (Table 2) also influence the above mentioned ratios [20]: meltwater may carry elements of marine origin previously deposited onto ice and snow, as well as calcium leached from soils and rocks. For instance CA lake, although being close to the cost, has quite low Na/(Na+Ca) and Mg/Ca values, probably because the influx of sea spray is partly balanced by dilution with
surface meltwater and shallow level groundwater that flow into the lake from the nearby glacier.
The lakes in Edmonson Point area receive meltwater streams from Mount Melbourne glacier, but they show differences in the concentrations of lithogenic (Ti, Al, Si and Sc) as well as in marine (Na and Mg) elements.
With regard to minor and trace metals, in our previous study we concluded that their concentrations in Terra Nova Bay lakes are mainly controlled by natural water-sediment and water-bedrock interaction, and shallow-level groundwater and surface water input [20]. In the present study, we found the highest concentrations of such elements in TF and II. Metal sources for TF waters probably include the relatively easy weathering of rocks and sediments containing muscovite, biotite, chlorite and fluorite: the lake receives a substantial contribution of meltwater supplied from a drainage area of 2 km2 and from Gerlache Mount glacier. In the case of II, we hypothesize a contribution from marine spray also for minor and trace elements; the high concentrations of Al, K and Fe can be due to erosion of regolith containing biotite. The lowest concentrations are present in EP and CA lakes, with the differences among EP lakes presumably due to the effect of geographic features and biological activity, as discussed above.
The comparison between the new data and the results obtained in previous investigations on the same lakes [18-20] shows that slightly higher concentrations of several elements have been found in this study. These trends, however, cannot be related to anthropogenic contamination, since the increment in concentrations involved both metals like Pb, Zn, Cu, and Cd, which are typical markers of human activity, and lithogenic elements such as Ti, Zr and rare earth elements. A wide range of natural variability in metal concentrations has been observed in our previous studies as well as elsewhere in Antarctica [28]. The data in Table 4 show that typical geogenic elements such as Ti, Al and Zr exhibit a higher variability than some potentially toxic elements like Cd and Pb: this suggests that the differences among the lakes are mainly due to natural processes.
Overall, our new results confirm that anthropogenic contamination in the investigated Antarctic region is still below detection. Similar conclusions where reached by Gasparon and Burgess [10] for
the Larsemann Hills, a potentially “high environmental risk” area, due to the presence of four research stations and an ice runaway.
3.2. Algae, mosses, lichens
Tables 6-7 report the results obtained for the investigated vegetation samples coming from the different lakes; all concentrations are expressed on a dry weight basis. The sample labelled as EP13/13A is a moss collected in a zone between lakes EP13 and EP3A. A total of 18 elements was determined, namely As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, Ni, P, Pb, Sr and Zn. Unfortunately, we could not obtain samples of all matrices from all lacustrine systems, due to logistic difficulties and high sampling costs.
The metals present at the highest levels in both algae and mosses are macronutrients, namely Ca, K and Mg, followed by Na and Fe, while Pb, Cu, Zn, Cd and Mo are present at very low concentrations, as already observed for freshwaters. Only for GW it is possible to compare the three types of vegetation samples: the concentrations increase in the order lichens < mosses < algae. In general, algae have higher concentrations of trace elements than mosses from the same lacustrine system, probably because they can assimilate nutrients and other elements from waters and live in less severe conditions than mosses: the latter are exposed to a dry climate with extremely low temperatures.
Algae are able to bio-accumulate metals present in lake waters thanks to their high metal-binding capacity; this is due to the presence of various functional groups such as carboxyl, amino, sulphate and hydroxyl groups which can act as binding sites for metals [29-32]. However, the presence of some functional groups does not necessarily guarantee biosorption, perhaps due to steric, conformational or other barriers [30]
.
The mechanism of bioaccumulation is complex and involves complexation, ion exchange and physical adsorption [29-30]. In particular, metals can form complexes with lipidic and glicidic biopolymers present in cellular walls [10]. Several studies have been made about the adsorption kinetics of metals from aqueous solution and the maximum uptakecapacity of algae [33-34]; adsorption is often described by Langmuir and Freundlich isotherm models, but adsorption isotherms may exhibit an irregular pattern due to the complex nature of both the sorbent material and its varied multiple active sites, as well as the complex solution chemistry of some metallic compounds [35].
Bioaccumulation is fundamental for getting essential nutrients but it is not selective, and algae can assimilate also potentially toxic elements like Cd and Pb. This property has been exploited in various applications: algae were used as biomonitoring tools in aquatic ecosystems [36-37], as indicators of pollution in environmentally impacted sites [38-39], and in phytoremediation of polluted waters [40-41]. Table 8 shows enrichment factors for some of the studied metals, calculated as the ratio between the element concentration in algae and in lake water. Almost all values range between 102 and 104. Wide ranges of enrichment factors are observed both for the same element in different lakes and for different elements in the same lake. According to the above described bioaccumulation mechanism, it is expected that elements that form stable complexes, such as Fe and Cu and in general transition metals, should have higher enrichment factors than alkali and alkaline earth metals. Overall, this trend can be observed in Table 8, with some exceptions. To our knowledge, no data on bioaccumulation by algae in Antarctic lakes is reported in the literature, even if this phenomenon is mentioned in [42].
Like algae, mosses and lichens are considered good biomarkers of environmental pollution, since they assimilate most nutrients from the atmosphere and are also able to bioaccumulate elements [14, 43,44]: the low metal concentrations present in these matrices can be regarded as a further confirmation that Antarctica is little affected by anthropogenic atmospheric pollution and global contamination. Similarly, the lack of significant differences between washed and unwashed samples (see section 2.2.2) suggests that atmospheric contamination by heavy metals in Terra Nova Bay is currently not detectable.
3.3. Chemometric data processing
3.3.1. Lake waters
PCA and HCA were applied to the analytical data to obtain a visual representation of the metal distribution in the Antarctic lake waters, and to highlight similarities among samples and correlations among variables.
For PCA, Fig. 2-a shows the plot of loadings on the first two principal components which retain most (64 %) of the variance of the original dataset, while Fig. 2-b shows the score plot. For the loadings, two main groups of variables can be identified. The first one includes Ca, Ni, K, Cu, Sc and Si, while the second one comprises Mg, Na, B, Ba, Fe, Zn, Ti, Ta, Pb, Al and Y. Na is correlated with B, Mg, Sr and Mn, suggesting a common marine origin of these elements. On the other hand, Na is not correlated to K and Ca, which confirms that these two metals are mainly geogenic. Considering Al and Si as representative of lithogenic elements, it can be observed that the former is correlated to Ti, Zn, Y, Ba, Ta, Pb and Fe, while the latter is correlated to Ca, Sc, Co, Ni, Cu and V. The correlation between Pb and lithogenic elements such as Al, Fe and Ti, confirms that this element primarily derives from as well as natural processes, presumably weathering, and not from local or global pollution. A similar conclusion can be drawn for As, which is correlated to Ca, K, Cd, Co and Cu. Regarding transition metals, in particular Co, Cu, Ni and V, their correlation with Si suggests that their concentrations mainly depend on the composition of the underlying soil and basement rock. In Fig. 2-b it is possible to define four groups of lakes: 1) EP13, EP13A, EP15, EP 15A and CA, which show low concentrations of Si, as well as of elements correlated to it (see above in this section) and K, with the exception of EP13; 2) EP14, EP16 and GW, characterized by higher levels of such elements as well as As; these lakes also have a very high concentration of K and Rb, likely due to alteration of biotite, which is rich in these two elements and is abundant in these lakes’ catchements; 3) TF and 4) II, both distinctly different from the other investigated lakes. These last two lakes have very high concentrations of the elements correlated to Al; the position of II in the score plot derives from the fact that it has the highest levels of several elements This
grouping shows that EP lakes, although being in the same area, have several different features, which evidences how water composition depends not only on the characteristics of the underlying soils, but also on other parameters such as geographic features (see section 3.1) and extent of biological activity: for instance the penguin rookery close to EP13 is a significant source of marine-derived elements. It is interesting to note the similarity between lakes EP13A and EP15A, which were both still partially frozen during sampling.
Fig. 3 shows a dendrogram obtained by HCA which, like PCA, provides a classification of the samples, while at the same time preserving the information of the original data. It shows results similar to the ones obtained by PCA, highlighting the difference of II from the other lakes and the similarity between EP13A and EP15A.
3.3.2. Algae, mosses, lichens
A chemometric study was also carried out on the vegetation data. Fig. 4-a shows the plot of loadings on the first two principal components, while Fig. 4-b reports the score plot. There are no sharp groupings among variables. Pearson’s correlation matrix shows that Cu is correlated to Ba,
Ca, Co, Cr, Mn, Ni, Pb and Zn: this correlation involves transition metals, alkali earth metals and
lead, therefore it is unlikely to be due to similar chemical properties, but it might be indicative of a common geogenic source. The correlation between P (a typical nutrient) and Cd can be explained by the fact that Cd can replace Zn in several metal-enzyme systems: also in seawater cadmium is often found to be correlated with phosphates and has a nutrient-type behaviour.
Fig. 4-b shows that samples from EP lakes are mainly divided into two groups: 1) algae and mosses from EP15A, mosses from EP13/13A and algae from EP16 and 2) mosses from EP14, EP15 and EP16 and, to some extent, algae from EP14. The samples from the other lakes are all different from each other. The dendrogram (Fig. 5) shows that EP specimen are clustered separately from nearly all the other samples, and indicates a similarity between mosses collected near CA and GW and, to a lesser extent, between algae from TF and GW. As for GW, there is a higher degree of similarity
between mosses and algae than between mosses and lichen, whereas the opposite behaviour was expected, since mosses and lichens are terrestrial vegetation.
In PCA, the relative positions of scores and loadings allow us to detect, at a glance, the presence of high or low concentrations of some elements in the samples. However, it is necessary to examine the dataset in order to confirm such observations, or to have a broader view on the actual composition of the sample, since only a part of the total variance is explained in PCA. Considering Fig- 4-a and 4-b simultaneously, it can be seen that GW and (to a lesser extent) TF algae have high concentrations of several transition metals. On the other hand, mosses from GW have lower concentrations of such elements but high levels of Fe and K, whereas lichens have lower concentrations of most elements. It is interesting also to observe that Mo is the element that
characterizes EP lakes with respect to the other lakes. An enrichment of Mo is typical of volcanic soils, and vegetation of the area reflects its proximity to Mt Melbourne volcano.
In summary, PCA and HCA did not show sharp differences among algae, mosses and lichens. The similarities between samples are due both to the features of the area of origin and to the type of sample itself. It can therefore be concluded that the elemental composition of the vegetation depends on their own biochemical characteristics as well as on the characteristics of the ecosystem (water, air and soil) and on the morphology of the area.
5. Conclusions
The element composition of the investigated lake systems in Terra Nova Bay depends on different factors. Marine spray has a significant influence on water composition, especially for Na and Mg, but in turn its contribution depends on the distance of each lake from sea, on its surface area, depth and elevation, on the exposure to wind and presence of local biological activities. Surface water input, rock- and sediment-water interaction processes, as well as weathering and erosional processes operating in the region, are, on the other hand, the main origin of minor and trace elements. Algae are able to bio-accumulate metals present in lake waters and show high enrichment factors, with
respect to waters, not only for nutrients but also for potentially toxic elements like Cd and Pb. In general, algae have higher trace element concentrations than mosses and lichens, probably because they have a higher availability of nutrients and are less extensively affected by extreme climate conditions. Several conclusions have been drawn from the chemometric study. In particular, the correlation of Na with B, Mg, Sr and Mn in lake waters suggests a common marine origin of these elements, and its lack of correlation with K and Ca confirms that these two metals are mainly geogenic. Pb and As are correlated with lithogenic elements, which indicates that their presence in lake waters primarily derives from natural processes, and not from local or global pollution. In terms of mosses and algae, the EP specimens are clearly different from the other samples, and are characterized by the presence of Mo, an element typically abundant in volcanic soils. Finally, the outcomes of PCA and HCA allowed us to conclude that the elemental composition of the vegetation depends on its own biochemical characteristics as well as on the characteristics of the ecosystem (water, air and soil composition) and on the morphology of the area in which it lives.
Analyses of washed and unwashed samples of mosses and lichens showed that atmospheric contamination by heavy metals in Terra Nova Bay is currently below detection. No other evidence of anthropogenic contamination was found, so it can be assumed that element distribution in such ecosystems still represents the result of natural processes. The temporal and spatial variability of natural background values, however, needs to be defined for a proper assessment of the levels of human impact.
Acknowledgements
This research was supported by the Italian National Research Program for Antarctica (PNRA). We are grateful to the Italian participants to 2004-2005 expedition to Antarctica for carrying out the samplings.
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Captions to figures
Fig. 1. Map of Terra Nova Bay showing the position of the investigated lakes: 1) Edmonson Point; 2) Gondwana; 3) Carezza; 4) Inexpressible Island; 5) Tarn Flat. The inset map of Antarctica shows the approximate location of the investigated area.
Fig. 2. Plots of loadings (a) and scores (b) obtained by PCA for element concentrations in lake waters.
Fig. 3. Dendrogram obtained by HCA for element concentrations in lake waters.
Fig. 4. Plots of loadings (a) and scores (b) obtained by PCA for element concentrations in algae (AG), mosses (MS) and lichens (LC).
Fig. 5. Dendrogram obtained by HCA for element concentrations in algae (AG), mosses (MS) and lichens (LC).
Zn Y W V Ti Ta Sr SiSc Sb Rb Pb Ni Na Mo Mn Mg K Fe Cu Co Cd Ca Ba B As Al -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 F1 (44,98 %) F 2 (18,9 3 %) II TF GW CA EP16 EP15A EP15 EP14 EP13A EP13 -4 -3 -2 -1 0 1 2 3 4 5 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 F1 (44,98 %) F 2 (18,9 3 %) Fig. 2
(b)
(a)
CA EP15 EP15A EP13A TF EP13 GW EP16 EP14 II 0 10 20 30 40 50 60 70 80 Diss imilar it y Fig. 3
Zn Sr Pb P Ni Mo Mn Mg La K Fe Cu Cr Co Cd Ca Ba As -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 -1 -0,75 -0,5 -0,25 0 0,25 0,5 0,75 1 F1 (46,01 %) F 2 (13,0 8 %) TF AG GW AG EP16 AG EP15A AG EP14 AG GW LC GW MS CA MS EP16 MS EP15A MS EP15 MS EP14 MS EP13 MS -4 -3 -2 -1 0 1 2 3 4 5 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 F1 (46,01 %) F 2 (13,0 8 %) Fig. 4
(a)
(b)
EP14 MSEP15 MSEP15A MSEP13 MS EP15A AGEP16 MS GW LC EP14 AG EP16 AG GW AG TF AG CA MS GW MS 0 10 20 30 40 50 60 70 80 90 Diss imilar it y Fig. 5
Table 1
Geographic features of the investigated lakes and summary of the available lacustrine matrices.
Lake Coordinates Altitude (m a.s.l.) Depth (m) Surface (m2) Distance from the
sea (Km) Water Algae Moss Lichen
EP13
74°19’S ;
165°08’E 4 n.a. 17800 ~0,1 X X
EP13A
74°19’S ;
165°08’E n.a. n.a. n.a. ~0,3 X X
EP14 74°19’S ; 165°08’E 20 1,5 4000 ~0,5 X X X EP15 74°19’S ; 165°08’E 2 ~2 3600 ~0,2 X X EP 15A 74°18’S ; 165°04’E 3 4 4600 ~0,2 X X X EP16 74°18’S ;
165°04’E n.a. n.a. n.a. ~0,5 X X X
CA 74°42’S ; 164°02’E 175 1,5 7900 1 X X II 74°54’S ; 163°40’E 32 2,5 6800 ~0,8 X TF 74°58’S ; 162°30’E -70 3,9 17700 35 X X GW 74°36’S ; 164°12’E 86 ~2,0 3000 n.a. X X X X
Table 2
Mineralogical species detected by XRD in regoliths close to the borders of the investigated lakes [21].
Quartz Feldspars Pyroxenes Amphyboles Biotite Chlorite Muscovite Calcite Fluorite Plagioclase Olivine
TF ● ● ● ● ● ● ● ●
CA ● ● ● ● ● ●
GW ● ● ● ● ●
II ● ● ● ● ● ● ●
Table 3
Limits of detection (LoD) for the investigated elements. All limits are expressed in nmol/l and refer to ICP-MS, unless otherwise stated.
Element LoD Element LoD Element LoD Element LoD
27Al 3.4 Cr** 1.9 96Mo 0.15 28Si 35.7 75As 0.22 Cr* 0.23 Na* 2.3 29Si 28.4 10 B 89.0 63Cu 1.10 142Nd 0.013 86Sr 1.39 11B 41.8 54Fe 1.61 143Nd 0.013 88Sr 0.99 135Ba 0.24 Fe* 0.95 144Nd 0.012 181Ta 0.009 137Ba 0.29 155Gd 0.060 146Nd 0.012 47Ti 0.50 138 Ba 0.36 157Gd 0.026 62Ni 2.01 51V 0.03 44Ca 50.0 158Gd 0.016 P* 1.52 182W 0.01 Ca* 4.9 39K 201 206Pb 0.029 183W 0.02 111 Cd 0.008 K* 4.4 207Pb 0.021 89Y 0.02 112Cd 0.009 139La 0.005 208Pb 0.021 64Zn 2,13 114Cd 0.012 Mg* 3.1 85Rb 0.08 66Zn 2,13 140 Ce 0.012 55Mn 1.34 121Sb 0.07 90Zr 0,025 59Co 0.05 95Mo 0.13 45Sc 0.02 91Zr 0,025
* LoD for ICP-OES, expressed in µmol/l, measured at the following wavelengths: 317.933 nm (Ca); 267.716 nm (Cr); 769.896 nm (K); 285.213 nm (Mg); 589.592 nm (Na); 214.914 nm (P)
Table 4
Element concentrations (mmol/l, mol/l and nmol/l) in lake water samples and descriptive statistics (minimum, maximum, mean, median).
EP13 EP13A EP14 EP15 EP15A EP16 CA GW TF II Min Max Mean Median
mmol/l Na 25.0 ± 0.2 0.15±2x10-3 9.96 ± 0.17 3.68 ± 0.04 0.15 ± 0.01 0.74 ± 0.004 2.70 ± 0.03 0.60 ± 0.004 1.48 ± 0.01 16.5 ± 0.2 1.48 25.0 6.09 2.09 mol/l Al 0.91 ± 0.03 4.80 ± 0.17 0.42 ± 0.01 0.51 ± 0.01 0.95 ± 0.02 1.88 ± 0.02 1.36 ± 0.003 1.26 ± 0.03 21.2 ± 0.1 39.7 ± 0.03 0.42 39.7 7.31 1.31 Ca 267 ± 29 58.9 ± 7.2 353 ± 48 47.4 ± 7.2 118 ± 20 559 ± 14 416 ± 39 431 ± 37 390 ± 18 436 ± 13 47.4 559 307 371 B 6.85 ± 0.67 n.d.a 0.33 ± 0.03 0.44 ± 0.01 0.42 ± 0.06 0.65 ± 0.04 0.50 ± 0.01 1.36 ± 0.16 0.76 ± 0.03 6.34 ± 0.73 0.33 6.85 1.96 0.65 Fe 1.11 ± 0.02 0.78 ± 0.08 0.77 ± 0.01 0.64 ± 0.01 1.01 ± 0.01 1.50 ± 0.07 1.84 ± 0.11 14.2 ± 0.8 12.9 ± 0.4 25.8 ± 0.6 0.64 25.8 6.05 1.31 K 222 ± 15 43.2 ± 2.9 355 ± 24 53.1 ± 3.8 53.2 ± 3.9 281 ± 21 76.5 ± 6.3 219 ± 16 54.0 ± 4.3 464 ± 38 43.2 464 182 148 Mg 2,425 ± 19 4.36 ± 0.12 621 ± 10 249 ± 2 11.8 ± 0.1 62.3 ± 0.4 227 ± 2 58.8 ± 0.4 229 ± 1 1,304 ± 13 4.36 2,425 519 228 Si 7.16 ± 0.32 17.6 ± 1.1 181 ± 9 7.76 ± 0.39 22.3 ± 0.9 281 ± 7 70.5 ± 2.0 378 ± 15 265 ± 6 247 ± 3 7.16 378 147 126 Sr 1.39 ± 0.07 0.11 ± 0.01 0.80 ± 0.05 0.22 ± 0.02 0.18 ± 0.03 0.90 ± 0.03 0.89 ± 0.03 0.70 ± 0.04 0.40 ± 0.03 1.30 ± 0.07 0.11 1.39 0.69 0.75 nmol/l As 30.3 ± 0.01 5.74 ± 0.13 57.1 ± 0.1 13.2 ± 0.1 8.54 ± 0.13 68.1 ± 2.8 13.5 ± 0.9 37.1 ± 0.7 7.88 ± 0.40 40.7 ± 2.7 5.74 68.1 28.2 21.9 Ba 4.22 ± 0.51 5.17 ± 0.44 6.85 ± 0.55 2.99 ± 0.36 2.77 ± 0.36 10.3 ± 0.6 7.94 ± 0.44 9.39 ± 0.73 32.1 ± 1.7 72.7 ± 3.8 2.77 72.7 15.4 7.39 Cd 0.27 ± 0.03 0.18 ± 0.01 0.27 ± 0.03 0.09 ± 0.01 0.27 ± 0.01 0.44 ± 0.09 0.09 ± 0.01 0.36 ± 0.05 0.09 ± 0.01 0.36 ± 0.03 0.09 0.44 0.24 0.27 Ce 0.29 ± 0.01 n.d. n.d. n.d. 0.71 ± 0.01 0.43 ± 0.01 0.29 ± 0.02 2.57 ± 0.07 14.0 ± 0.6 32.4 ± 0.1 0.29 32.4 7.25 0.71 Co 2.21 ± 0.02 0.51 ± 0.02 3.56 ± 0.08 0.34 ± 0.03 0.85 ± 0.02 10.9 ± 0.3 2.21 ± 0.17 7.98 ± 0.17 5.77 ± 0.17 12.6 ± 0.5 0.34 12.6 4.68 2.88 Cr 0.19 ± 0.08 1.35 ± 0.19 0.96 ± 0.19 n.d. n.d. 1.15 ± 0.02 n.d. n.d. 12.1 ± 0.2 33.1 ± 0.2 0.19 33.1 8.14 1.25 Cu 23.8 ± 2.2 5.82 ± 0.79 27.4 ± 1.9 3.78 ± 0.16 5.04 ± 0.63 69.7 ± 6.4 13.8 ± 0.8 68.5 ± 8.5 15.9 ± 1.7 70.3 ± 5.8 3.78 70.3 30.4 19.8 Gd n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. 2.03 ± 0.32 3.12 ± 0.45 2.03 3.12 2.58 2.58 La n.d. n.d. n.d. n.d. 0.36 ± 0.02 n.d. n.d. 1.08 ± 0.04 10.1 ± 0.2 18.1 ± 0.3 0.36 18.1 7.40 5.58 Mn 683 ± 16 32.4 ± 0.9 169 ± 3 29.5 ± 0.5 229 ± 4 224 ± 4x10-1 55.3 ± 0.7 289 ± 4 235 ± 1 382 ± 4 29.5 683 232 227 Mo 14.5 ± 1.5 21.7 ± 1.1 35.4 ± 2.3 4.79 ± 0.52 27.3 ± 2.0 18.8 ± 1.7 4.90 ± 0.52 21.5 ± 1.6 3.65 ± 0.21 14.6 ± 1.7 4.65 35.4 16.7 16.7 Nd n.d n.d. n.d. n.d. 0.14±4x10-3 0.07 ±5x10-3 n.d. 1.32 ± 0.02 7.76 ± 0.14 15.7 ± 0.3 0.07 15.7 5.01 1.32 Ni 18.6± 0.2 7.00 ± 0.17 24.4 ± 0.2 0.85 ± 0.02 4.94 ± 0.05 49.1 ± 0.5 21.8 ± 1.2 89.1 ± 9.4 21.3 ± 0.2 57.1 ± 0.5 0.85 89.1 29.4 21.5 Pb 1.06 ± 0.05 1.69 ± 0.05 0.92 ± 0.03 1.01 ± 0.05 0.92 ± 0.05 1.06 ± 0.05 0.97 ± 0.05 1.35 ± 0.05 1.98 ± 0.19 2.03 ± 0.19 0.92 2.03 1.30 1.06 Rb 70.9 ± 2.6 54.2 ± 1.5 144 ± 6 14.5 ± 0.80 28.2 ± 1.9 104 ± 10 6.32 ± 0.82 19.0± 1.2 27.1 ± 3.0 51.8 ± 6.4 6.32 144 52.0 40.0 Sb n.d. 2.35 ± 0.02 2.35 ± 0.24 n.d. 0.78 ± 0.08 1.57 ± 0.16 0.78 ± 0.02 1.57 ± 0.08 0.78 ± 0.08 0.78 ± 0.03 n.d. 2.35 1.10 0.78 Sc 0.44 ± 0.07 0.22 ± 0.02 8.90 ± 0.89 0.44 ± 0.02 1.33 ± 0.09 14.9 ± 0.9 3.34 ± 0.22 22.9 ± 2.0 12.2 ± 1.1 14.9 ± 0.9 0.22 22.9 7.96 6.12 Ta 0.72 ± 0.22 0.72 ± 0.17 0.77 ± 0.17 0.72 ± 0.22 0.72 ± 0.22 0.77 ± 0.22 0.77 ± 0.22 0.77 ± 0.22 1.22 ± 0.39 1.33 ± 0.44 0.72 1.33 0.85 0.77 Ti 29.2 ± 3.8 19.2 ± 1.3 34.3 ± 2.5 3.97 ± 0.21 20.9 ± 1.9 90.9 ± 4.4 29.2 ± 1.5 77.3 ± 5.2 196 ± 11 524 ± 27 3.97 524 102 31.7 V 5.10 ± 0.79 21.8 ± 0.6 40.0 ± 1.7 7.07 ± 0.39 29.4 ± 1.6 43.4 ± 3.3 33.4 ± 3.5 83.2 ± 4.7 65.6 ± 6.1 61.8 ± 6.3 5.10 83,2 39.1 36.7 W 1.03 ± 0.11 2.77 ± 0.27 8.98 ± 0.87 0.11±4x10-3 2.50 ± 0.22 1.69 ± 0.16 0.49 ± 0.05 0.82 ± 0.05 5.44 ± 0.54 0.16 ± 0.02 0.11 8.98 2.40 1.36 Y 0.11 ± 0.01 0.11±1x10-3 0.07±4x10-4 0.11 ± - 0.22±1x10-3 0.22±1x10-3 0.11±1x10-3 0.45±3x10-3 0.79±4x10-3 1.24 ± 0.01 0.07 1.24 0.34 0.17 Zn 87.2 ± 7.5 60.4 ± 6.7 54.6 ± 6.1 11.6 ± 0.3 119 ± 13 106 ± 10 42.2 ± 4.9 167 ± 6 91.8 ± 6.0 526 ± 35 11.6 526 127 89.5
Zr n.d. 0.44 ± 0.11 2.08 ± 0.11 n.d. n.d. 2.08 ± 0.22 0.77 ± 0.11 3.07 ± 0.11 21.9 ± 0.8 24.7 ± 0.8 n.d. 24.7 7.86 2.08 an.d. = not detected
Instrumental conditions used for ICP-MS: Ar flow in the nebuliser 0.9 l/min; Ar auxiliary flow 1.1 l/min; Ar flow in plasma 16 l/min; plasma power 1.3 kW; peristaltic pump rate 8 rp m (about 1 ml/min); integration time 10 s. Low (LR) and medium (MR) resolution were utilized; number of scans 15 (LR) and 18 (MR).
Instrumental conditions used for ICP-OES: height of plasma reading 8mm/5 mm; plasma power 1.2 kW; nebuliser pressure 180 kPa; Ar flow in plasma 15 l/min; integration time 1 s; peristaltic pump rate 1 ml/min; photomultiplier voltage 650 or 850 V; argon auxiliary flow 1.5 l/min.
Table 5
Na/(Na+Ca), Mg/ ratios as indicators of marine spray contribution in lake waters.
Ratio EP13 EP13A EP14 EP15 EP15A EP16 CA GW TF II Na/(Na+Ca) 0.99 0.72 0.97 0.99 0.56 0.57 0.87 0.58 0.79 0.97
Table 6
Element concentrations (mmol/kg and mol/kg) in lake algae (AG) samples and descriptive statistics (minimum, maximum, mean, median).
Instrumental conditions used for ICP-MS: Ar flow in the nebuliser 0.9 l/min; Ar auxiliary flow 1.1 l/min; Ar flow in plasma 16 l/min; plasma power 1.3 kW; peristaltic pump rate 8 rpm (about 1 ml/min); integration time 10 s. Low (LR) and medium (MR) resolution were utilized; number of scans 15 (LR) and 18 (MR).
Instrumental conditions used for ICP-OES: height of plasma reading 8mm/5 mm; plasma power 1.2 kW; nebuliser pressure 180 kPa; Ar flow in plasma 15 l/min; integration time 1 s; peristaltic pump rate 1 ml/min; photomultiplier voltage 650 or 850 V; argon auxiliary flow 1.5 l/min.
EP14 AG EP15A AG EP16 AG GW AG TF AG Min Max Mean Median
mmol/kg Ca 211 ± 6 107 ± 2 395 ± 65 3.1x104±204 131 ± 1 107 3.1x104 6385 211 Fe 67.0 ± 0.4 135 ± 13 97.7 ± 4.7 152 ± 1 111 ± 1 67.0 152 112 111 K 50.8 ± 1.2 51.4 ± 5.9 94.4 ± 10.7 219 ± 3 55.5 ± 2.2 50.8 219 94.3 55.5 Mg 393 ± 4 97.0 ± 4.4 388 ± 13 394 ± 4 73.7 ± 0.6 73.7 394 269 388 Mn 15.1 ± 0.3 3.55 ± 0.22 5.84 ± 0.22 18.9 ± 0.5 9.43 ± 0.15 3.55 18.9 10.6 9.43 P 61.0 ± 1.1 38.7 ± 1.9 254 ± 9 41.7 ± 0.8 0.08 ± 0.28 0.08 254 79.2 41.7 mol/kg As 59.3 ± 3.2 12.4 ± 0.9 37.9 ± 2.8 41.2 ± 0.5 66.5 ± 1.9 12.4 66.5 43.5 41.2 Ba 102 ± 4 78.6 ± 5.8 72.1 ± 3.1 352 ± 6 395 ± 12 72.1 395 200 102 Cd 1.96 ± 0.09 4.36 ± 3.11 5.07 ± 0.98 2.94 ± 0.09 2.05 ± 0.09 1.96 5.07 3.27 2.94 Co 76.4 ± 4.1 23.2 ± 2.4 25.8 ± 0.8 360 ± 15 193 ± 11 23.2 360 136 76.4 Cr 97.7 ± 4.2 137 ± 19 52.1 ± 22.1 733 ± 24 362 ± 21 52.1 733 276 136 Cu 129 ± 6 41.7 ± 6.14 76.3 ± 7.1 675 ± 24 557 ± 21 41.7 675 296 129 La 45.6 ± 2.2 83.5 ± 0.4 37.4 ± 5.7 99.3 ± 1.3 182 ± 6 37.4 182 89.6 83.5 Mo 4.38 ± 0.10 5.63 ± 0.10 3.44 ± 0.10 2.61 ± 0.52 2.29 ± 0.21 2.29 5.63 3.67 3.44 Ni 69.9 ± 0.8 24.2 ± 3.4 21.0 ± 4.8 424 ± 16 117 ± 5 21.0 424 131 69.9 Pb 3.28 ± 0.19 5.55 ± 3.43 5.07 ± 0.34 21.4 ± 1.0 39.6 ± 2.4 3.28 39.6 15.0 5.55 Sr 721 ± 4.79 196 ± 14 439 ± 6 678 ± 18 630 ± 20 1960 721 533 630 Zn 393 ± 31 220 ± 14 483 ± 12 1.0 x103±22 1.1 x104±16 220 1.1 x104 650 483
Table 7
Element concentrations (mmol/kg and mol/kg) in mosses (MS) and lichens (LC) samples and descriptive statistics (minimum, maximum, mean, median).
Instrumental conditions used for ICP-MS: Ar flow in the nebuliser 0.9 l/min; Ar auxiliary flow 1.1 l/min; Ar flow in plasma 16 l/min; plasma power 1.3 kW; peristaltic pump rate 8 rpm (about 1 ml/min); integration time 10 s. Low (LR) and medium (MR) resolution were utilized; number of scans 15 (LR) and 18 (MR).
Instrumental conditions used for ICP-OES: height of plasma reading 8mm/5 mm; plasma power 1.2 kW; nebuliser pressure 180 kPa; Ar flow in plasma 15 l/min; integration time 1 s; peristaltic pump rate 1 ml/min; photomultiplier voltage 650 or 850 V; argon auxiliary flow 1.5 l/min.
EP13/13A MS EP14 MS EP15 MS EP15A MS EP16 MS CA MS GW MS Min Max Mean Median GW LC
mmol/kg Ca 147 ± 6 205 ± 12 124 ± 0.1 147 ± 4 174 ± 15 136 ± 5 149 ± 10 124. 205 154 147 322 ± 11 Fe 188 ± 13 82.1 ± 4.0 31.9 ± 1.2 137 ± 14 65.0 ± 12 265 ± 5 261 ± 9 31.9 265 147 137 35.2 ± 1.3 K 54.2 ± 2.0 26.1 ± 1.2 35.8 ± 0.3 47.7 ± 2.3 139 ± 4 158 ± 10 179 ± 4 26.1 179 91.5 54.2 149 ± 4 Mg 141 ± 10 126 ± 4 127 ± 1 126 ± 16 118 ± 7 251 ± 6 284 ± 3 117 284 167 127 226 ± 12 Mn 4.71 ± 0.82 3.55 ± 0.15 1.63 ± 0.05 4.88 ± 0.31 2.89 ± 0.13 3.42 ± 0.29 4.08 ± 0.26 1.63 4.88 3.59 3.55 0.71 ± 0.05 P 47.6 ± 1.8 31.8 ± 2.9 24.1 ± 1.09 40.2 ± 0.5 43.7 ± 1.0 174 ± 2 32.5 ± 0.9 24.1 174 56.3 40.2 9.88 ± 0.27 mol/kg As 11.1 ± 1.5 12.4 ± 0.5 14.9 ± 0.5 10.0 ± 0.1 44.4 ± 1.6 20.7 ± 2.8 19.5 ± 2.8 10.0 44.4 19.01 14.9 11.9 ± 0.7 Ba 71.2 ± 8.7 101 ± 2.26 57.1 ± 5.7 123 ± 9 21.4 ± 5.2 622 ± 31 420 ± 12 21.4 621 202 101 48.3 ± 2.6 Cd 1.78 ± 0.44 2.40 ± 0.27 3.11 ± 0.27 6.05 ± 3.74 2.94 ± 0.27 4.71 ± 3.65 2.14 ± 0.09 1.78 6.05 3.30 2.94 0.89 ± 0.01 Co 37.2 ± 5.6 43.8 ± 4.9 11.7 ± 0.5 32.9 ± 5.3 17.5 ± 0.3 80.1 ± 5.9 117 ± 6 11.7 117 49 37.2 25.6 ± 2.7 Cr 69.2 ± 49.0 55.2 ± 3.1 12.3 ± 4.4 57.7 ± 36.9 285 ± 37 105 ± 24 335 ± 18 12.3 335 131 69.2 78.7 ± 4.0 Cu 63.3 ± 1.4 72.2 ± 3.9 45.9 ± 21.2 63.3 ± 4.9 43.6 ± 5.5 192 ± 2 255 ± 10 43.6 255 105 63.3 146 ± 5 La 143 ± 4 51.0 ± 1.6 26.9 ± 1.0 73.4 ± 6.5 14.5 ± 1.2 173 ± 15 58.7 ± 9.0 14.5 173 77.2 58.7 42.9 ± 2.4 Mo 28.0 ± 1.5 23.9 ± 3.3 26.0 ± 1.2 38.0 ± 2.5 3.75 ± 0.10 4.48 ± 0.10 3.23 ± 0.10 3.23 38.0 18.2 23.9 1.25 ± 0.01 Ni 26.6 ± 0.7 39.9 ± 3.1 17.7 ± 1.4 23.8 ± 4.9 22.8 ± 0.2 151 ± 20 148 ± 4 17.7 151 61.4 26.6 42.3 ± 2.0 Pb 4.54 ± 0.34 2.85 ± 0.29 1.54 ± 0.05 3.72 ± 0.53 0.39 ± 0.10 10.6 ± 1.2 10.3 ± 2.5 0.39 10.6 4.85 3.72 16.2 ± 3.2 Sr 261 ± 35 698 ± 61 766 ± 56 434 ± 23 449 ± 9 520 ± 39 816 ± 83 261 816 563 520 301 ± 20 Zn 283 ± 47 332 ± 8 216 ± 10 321 ± 24 613 ± 38 609 ± 70 656 ± 23 216 656 433 332 385 ± 18
Table 8
Enrichment factors of some elements in algae.
EP14 EP15A EP16 GW TF Mean Range
As 1.04x103 1.45x103 5.57x102 1.11x103 8.44x103 2.52x103 5.57x102 - 8.44x103 Ba 1.49x104 2.84x104 6.97x103 3.75x104 1.23x104 2.00x104 6.97x103 - 3.75x104 Ca 5.97x102 9.04x102 7.07x102 7.22x104 3.37x102 1.49x104 3.37x102 - 7.22x104 Cd 7.33x103 1.63x104 1.14x104 8.25x103 2.30x104 1.33x104 7.33x103 - 2.30x104 Co 2.14x104 2.74x104 2.38x103 4.51x104 3.35x104 2.60x104 2.38x103 - 4.51x104 Cu 4.71x103 8.28x103 1.09x103 9.86x103 3.50x104 1.18x104 1.09x103 - 3.50x104 Fe 8.74x104 1.33x105 6.50x104 1.07x104 8.58x103 6.10x104 8.58x103 - 1.33x105 K 1.43x102 9.66x102 3.36x102 9.99x102 1.03x103 6.95x102 1.43x102 - 1.03x103 Mg 6.32x102 8.24x103 6.23x103 6.72x103 3.22x102 4.43x103 3.22x102 - 8.24x103 Mn 8.93x104 1.55x104 2.61x104 6.54x104 4.02x104 4.73x104 1.55x104 - 8.93x104 Mo 1.24x102 2.06x102 1.83x102 1.21x102 6.29x102 2.53x102 1.21x102 - 6.29 x102 Ni 2.87x103 4.90x103 4.27x102 4.76x103 5.51x103 3.69x103 4.27x102 - 5.51x103 Pb 3.58x103 6.05x103 4.77x103 1.59x104 2.00x104 1.01x103 3.58x103 - 2.00x104 Sr 9.04x102 1.09x103 4.90x102 9.69x102 1.58x103 1.01x103 4.90x102 - 1.58x103 Zn 7.20x103 1.85x103 4.55x103 6.25x103 1.21x104 6.40 x103 1.85x103 - 1.21 x104 Average 1.61x104 1.70x104 8.74x103 1.91x104 1.35x104 Range 1.24x102- 8.93 x104 2.06x102- 1.33 x105 1.83x102- 6.50 x104 1.21x102- 7.22x104 3.22x102- 4.02x104
