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Anthropogenic effects on heavy metals and macronutrients accumulation in soil and wood of Pinus sylvestris 1/Antropogeninio poveikio itaka sunkiuju metalu ir makroelementu kaupimuisi dirvozemyje ir pusies (Pinus sylvestris L.) medienoje.

1. Introduction

Large amounts of chemical substances in the environment (air, soil and water) have an impact on humans and natural systems. Heavy metals in substantial amounts are important environmental pollutants and their toxicity is a problem for ecological, evolutionary, nutritional, and environmental reasons (Benavides et al. 2005). Heavy metals cause serious problems because of their easy spread and accumulation in soil and plants (Pantera et al. 2007; Stravinskiene 2005). Heavy metal pollution is related with the degree of industrialization and the intensity of chemical usage (Akinola et at. 2008) and is a serious problem for forest ecosystems (Stravinskiene ir Simatonyte 2008; Nuhoglu 2005). However, plants have homeostatic cellular mechanism to regulate the concentration of metal ions inside the cell to minimize the potential damage from exposure to nonessential metal ions (Benavides et al. 2005).

Trees are considered sensors that record the environmental disturbances, as they live for a long time, stay in the same place all their lives and are widespread geographically. These factors make them especially suitable biological archives. Moreover, the observed effect of metal accumulation is a time-averaged result, which is more reliable than direct determination of the pollutant concentrations in air, for a short period (Kord et al. 2009). Coniferous species filter pollutant particles from the air (Salemaa 2003) capturing pollutants directly from the atmosphere, deposition on the needles or bark, or indirectly following deposition on the soil and subsequent root uptake (Nuhoglu 2006; Watmough 1999). Generally, conifers have much higher heavy metal content than deciduous species (Trilby 2005).

Determination of trees chemical composition is one of the most frequently used method to monitor environmental pollution (Ayodele and Ahmed 2001; Yilmaz and Zengin 2003). Pinus sylvestris L. is sensitive to environmental condition changes and takes up large amounts of pollutants and translocates them into the whole plant and can be considered as a proxy about the quality of the environment and thus is a good bioindicator (Rudovica et al. 2006; Mingorance et al. 2007). It is well known that changes of metal concentrations are related to the processes involving the uptake, transformation, storage and retranslocation of the elements. In Pinus sylvestris L., some metals are accumulated in roots, especially Pb, probably due to physiological barriers against metal transport to aerial parts, while others are easily transported in plants, for example, Cd (Boruvka et al. 1997; Kabata-Pendias and Pendias 2000). The uptake of heavy metals by Pinus sylvestris L. is a complex process and the uptake rate depends on soil pH, soil organic matter content and the concentration of other elements in soil (Baltrenaite and Butkus 2007; Seregin and Ivanov 2000). Depending on the element, tree species and availability of metal in the soil, the accumulation of metals varies (Triby 2003). The main source of heavy metals entering the wood is related to the heavy metal uptake by roots from soils. On the root surface heavy metals bind to the carboxy-groups of mucilage uronic acids. The ability of mucilage to bind heavy metals decreases in the cation series: [Pb.sup.+2] > [Cu.sup.+2] > [Cd.sup.+2] > [Zn.sup.+2]. Furthermore, heavy metals enter the tree also from the atmosphere through the needles and bark with partial translocation into the wood (Scherbenko et al. 2008).

Data on the elemental composition of perennial plant organs, especially stem wood, are scarce and they are especially rare for forests under conditions of aerial pollution (Watmough and Hutchinson 1996; Saarela et al. 2005; Shcherbenko et al. 2008). The knowledge of the elemental content in the stem wood, needles and bark of Pinus sylvestris L. are of great environmental and industrial interest (Saarela et al. 2005), because it is a useful tool for analysing environmental changes and quantifying the impact of air pollution on forests (Ferretti et al. 2002).

The objectives of this study were to a) determine concentrations of heavy metals Pb, Cd, Cu, Zn and macronutrients K and Mg in the stand soil of Pinus sylvestris L. and to evaluate elements concentrations in the stemwood; b) to use Global analysis throughout principal component analysis (PCA) to identify relation among elements in soil, pinewood and soil properties and determine differences between sampling plots.

2. Materials and methods

Study site and sampling procedures

Two sampling plots (contaminated and control) located in Lithuania (Eastern Europe) were analysed in the study (Fig. 1). The first sampling plot (55[degrees]44'04"N, 24[degrees]23'30"E) is located in the city of Panevezys near the former TV manufacturing company (Fig. 2). For more than 40 years, this factory has been manufacturing glass components, colour TV-tubes and electron-gun systems. It was the only manufacturer of TV tubes in the Baltic States since 1962 ("Ekranas" Company's Annual Report 2004). Total emissions from the manufacturer have been estimated 765.7 tonnes/ years. The emission levels have varied for total nitrogen oxides (N[O.sub.x])--552.9 tonnes/ years, carbon monoxides (CO)--154.8 tonnes/years, volatile organic compounds (VOCs)--45.8 and 14.9 tonnes/years for particulate matter (PM). According, the Environmental Protection Agency (EPA), the manufacturer was one of the most polluting ones in 2004. Intensive contamination by heavy metals, Pb, Cd, Cu and Zn was caused by the manufacturer aerosol emission, significantly influencing the total soil geosanitary state. Heavy metals migrated in the soil horizons, consequently, high heavy metal and particularly Pb are dominant in the soil around the Company's sanitary zone (Strategic environmental assessment report of Panevezys city 2007).

[FIGURE 1 OMITTED]

The second sampling plot, the control one, (54[degrees]53'12"N, 24[degrees]04'35"E) is located near the city of Kaunas in the experimental forest of Dubrava Institute (Fig. 3). The control plot had a similar soil type and species as the contaminated plot, the sand and sandy loam soil type is prevailing on both plots. The control plot is outside the range of influence by nearby polluting sources, because it is in the outskirts of the city and is surrounded by a forest. The closest traffic road is 300 m away.

Sampling was carried out early in May 2009, during vegetation period when plants are most physiologically active (Hill 2002).

The contaminated plot was located at about 200 m from the Company. Southwest winds are reported to be dominant (Panevezys city municipality 2005).

Sampled trees (Pinus sylvestris L.) were at least 10 m away from each other. Ten trees of a similar diameter were randomly selected in each control and contaminated plot. Tree cores were taken at breast height (1.5 m) from the north-facing side (facing the industry) (Watmough and Hutchinson 2005) using an acid washed 10 mm stainless steel increment borer. Samples were immediately sealed in dry, plastic straws and were stored for further analysis.

40 contaminated and 20 control soil samples from the north and south direction were collected near the selected trees from 0-20 cm soil profile and stored in plastic bags.

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

Soil pH

Soil pH was measured using a soil-water ratio 1:5 and a glass electrode pH meter pH 538 WTV. 20 g of the soil was mixed with 100 ml deionised water and was shaken for 1 hour using mechanical shaker Gerhardt, Rotoshake RS 12. The mixture was left for 60 minutes to allow it to settle. The electrode of pH meter was put into the slurry and measured 3 times in one solution.

Total organic carbon in soil

TOC was determined using apparatus TOC-V by SHIMADZU at 900[degrees]C temperature. TOC content was recalculated using Eq. (1):

[W.sub.C,t] = 1000 x [m.sub.2]/[m.sub.1] x 0.2727, (1)

where [W.sub.C,t]--TOC on basis of air-dried soil, g [kg.sup.-1]; [m.sub.1]--mass (g) of test portion, g; [m.sub.2]--mass of released C[O.sub.2], g; 0.2727--conversion factor for C[O.sub.2] to C (ISO 10694:1995).

Total elements in soil

Soil samples were air-dried at room temperature for 24 hours, sieved (2 mm mesh) and weighed 0.2 g, then mixed with 9 ml HCl (37%) and 3 ml HN[O.sub.3] (65%), poured into special vessels and then placed into a Milestone ETHOS digestor and heated for 30 min. The solution was then poured into 50 ml flask and diluted with distilled water to reach the marks of 50 ml (Butkus and Baltrenaite 2007; Baltrenas et al. 2009).

The total concentrations of elements in soil and wood samples were determined by flame atomic absorption spectrophotometry (FAAS). A graphite furnace (GFAAS) was employed to determine metal concentrations when they were too low to be detected accurately by FAAS (Baltrenaite et al. 2010; Butkus and Baltrenaite 2007).

Bioavailable elements in soil

Bioavailable elements in soil were assessed by water extraction of 20 g soil in 100 ml of distilled deionised water. Soil-water samples were shaken in Gerhardt, Rotoshake RS 12 for 1 hour and left to settle. Afterwards extracts were collected by filtration through filter paper and analysed for Pb, Cd, Zn, Cu, K, Mg concentrations by FAAS or GFAAS.

The ratio (%) of bioavailable elements in soil was calculated using Eq. (2):

[C.sub.bioav,%] = [C.sub.bioav]/[C.sub.tot] x 100%, (2)

where [C.sub.bioav,%]--the percent of bioavailable elements in soil [C.sub.bioav]--mean bioavailable metal concentration in soil solution, mg kg-1; Ctot mean total metal concentration in soil, mg [kg.sup.-1] DW.

Total elements in wood

Each core was burnt for 45 min in E5CK-T muffle furnace (450 [degrees]C) and weighted, then mixed with 9 ml HCl (37%) and 3 ml HN[O.sub.3] (65%), poured into mineralization vessels and the same methods, described in total elements in soil analysis, were used.

Data analysis

Some descriptive statistics were performed, average and standard deviation (SD) and statistical significance between elements concentrations between contaminated and control plot were assessed with a Student t-test. The differences were considered significant at p < 0.05.

A multivariant analysis of the results was made using principal component analysis (PCA), based on correlation matrix. The components of PCA were rotated by a varimax rotation. The aim of using PCA was to ascertain any patters in all the samples in relation to chemical characteristics and draw a preliminary conclusion about the possible relationship among metals in soil, wood and soil properties (Jia et al. 2010). This methodology was applied in order to identify relation between variables, differences between control and contaminated plots and potential detection of anthropogenic pollutants source (Li et al. 2009). All the calculations were performed using the statistical packages Statistica 6.0 (Statsoft. Inc).

3. Results and discussion

Total elements in soil

The values of soil pH, TOC, total and bioavailable elements in the control and contaminated soil plots are summarized in Table 1.

Soil has an ability to immobilise introduced heavy metals. According to Boruvka and Drabek; Toribio and Romanya; Dube et al. soil organic carbon together with soil pH is the most important parameter controlling heavy metal immobilisation in the soil. Soil pH was found to play the most important role in determining metal speciation, solubility of metals, due to its strong effects on solubility and speciation of metals both in the soil as a whole and particularly in the soil solution. A negative correlation between soil pH and heavy metal mobility and availability to trees has been well documented. For instance, decreased soil pH increases in heavy metal desorption from soil constituents and dissolution in soil solution were observed for Cd, Pb and Zn. The mobility and bioavailability of heavy metals also increase with decreased soil pH, thus enhancing the uptake of heavy metals by trees (Zeng et al. 2010).

The results of the studied soil pH varied from 5.69 to 7.78 and 4.62 to 6.04 on contaminated and control sites, respectively, indicating moderately acid to slightly alkaline soil on the contaminated site, strongly acid to moderately acid on the control plot. Soil pH influences macronutrients uptake and tree growth. Many soil macro-nutrients change the form because of reactions in the soil that are largely controlled by soil pH, consequently trees may not be able to use these changed macronutrients (Arduini (et al. 1998). TOC ranged from 1.52 to 9.98 g [kg.sup.-1] on the contaminated plot and from 0.39 to 9.86 g [kg.sup.-1] on the control plot. The averages of soil pH and TOC at a depth of 0-20 cm of soil profile on contaminated and control plots are summarized in Table 1. Values of TOC and pH on the contaminated plot were statistically higher than on the control plot. Furthermore, average pH value of the contaminated plot approached to neutral (7.0) pH. The higher pH value can be a result of greater retention of metals and their lower solubility in the soil of the contaminated plot. Moreover, high soil pH can stabilize heavy metals, resulting in decreased leaching effects of the soils heavy metals, additionally heavy metals become stabilized due to high soil pH which may result in lower element concentration in the soil solution. This may restrain the absorbability of the elements from the soil solution and transportation into tree tissues (Malik et al. 2010).

Background and limit values of concentrations for heavy metals Pb, Cd, Cu and Zn in Lithuania are given in Table 2 (Hygiene Standard of Lithuania HN 60:2004). Although the manufacturing Company was closed (it went bankrupt in 2004) at the time of sampling and there were no emissions into the air, the concentrations for Cd, Zn and particularly Cu and Pb were higher than the background values on the contaminated plot, though limit values were not exceeded for all investigated heavy metals. The total concentrations of Pb and Cu (except Zn and Cd) under trees on the control plot were below their environmental background values, indicating that this area received minimal atmospheric heavy metals input (Kuang et al. 2007).

On the contaminated plot total heavy metal concentrations in the upper soil (0-20 cm) under the sampled trees ranged from 8.42-37.4 mg [kg.sup.-1] for Pb; 0.167-0.483 mg [kg.sup.-1] for Cd; 12.7-31.7 mg [kg.sup.-1] for Cu; 18.5-115 mg [kg.sup.-1] for Zn. Similarly, heavy metal concentrations were reported according to Watmough et al. (2005). On the control plot (0-20 cm) heavy metal concentrations ranged from 1.56-5.03 mg [kg.sup.-1] for Pb; 0.02-5.16 mg [kg.sup.-1] for Cd; 0.006-0.03 mg [kg.sup.-1] for Cu; 22.5-96.5 mg [kg.sup.-1] for Zn. Heavy metal concentrations on both studied plots confirm that the contaminated plot was affected by high heavy metal deposition (total Pb and Cu concentrations on the contaminated plot were statistically (p < 0.001) higher than those on the control plot) and present contamination and clear differences between plots by Pb and Cu.

Bioavailable metals

Results obtained in this work from analyses of bioavailable metals are listed in Table 3. The highest bioavailable concentrations were found for Zn on both sampling plots. For all the bioavailable metals, higher concentrations were found the contaminated plot than on the control plot and confirmed higher proportion of total metals that are available for trees.

The bioavailable concentrations are usually only 1-5% of the total concentrations (Tarvainen and Kallio 2002). Fig. 4 shows the percentage of bioavailable metals to total metals on control and contaminated plots.

[FIGURE 4 OMITTED]

Percentage of bioavailable Pb, Cd, Zn, K and Mg (except Cu) on the contaminated plot was significantly higher (p < 0.001) than on the control plot. In neutral soil reaction (pH 7.0) bioavailability of Cu is very low (Mazvila 2001), as a result the contaminated plot pH (6.59) approximate to neutral thus having lower bioavailable metal concentration than in control soil where pH was more acidic. The results on the contaminated plot indicated higher bioavailability of metals, to be precise, higher amount of metals that are available for tree uptake.

Total elements in wood

Heavy metals and macroelements taken by a pine tree were also accumulated in the stemwood. Elements concentrations in sampled pine stemwood on both contaminated and control plots are summarized in Figs 5 and 6.

Heavy metal concentration in wood decreased in the following order: Pb > Zn > Cu > Cd on the contaminated site and Zn > Cu > Pb > Cd in control site. The contaminated plot showed high Pb accumulation, although, Pb is rather immobile in soil and concentrates primarily in the roots and is poorly translocated to the tree parts, resulting in a comparatively low transfer factor from soil to a tree (Madejon et al. 2004). In general, Cd is absorbed by higher plant roots and accumulated in aboveground organs, having thus a comparatively high transfer soil-tree factor (Alloway 1995). Zn has relatively high transfer factor from soil to a tree and most plants can tolerate high Zn levels. Heavy metals such as Zn, Cd are readily translocated to the top of a tree, Cu is intermediate and Pb is translocated to the least extent (Alloway 1995). Pb, Cu and Zn, concentrations in pinewood on the contaminated site were significantly higher (p < 0.05 and p < 0.001) than those on the control plot.

[FIGURE 5 OMITTED]

[FIGURE 6 OMITTED]

According to Saarela et al.; Mingorance et al., Pb concentrations in wood of pine tree were very low, for instance 0.1[+ or -]0.07 mg [kg.sup.-1] or 3.72[+ or -]0.38 mg [kg.sup.-1]. The average concentration of Pb in contaminated stemwood showed extremely high values comparing to a control sample, though, Pb is not an essential element (Seregin and Ivanov 2001), however, in our case it was easily absorbed and accumulated in stemwood. The mean Pb concentrations were 34.5 ([+ or -]2.9) mg [kg.sup.-1] on the contaminated plot and low concentration was 0.01 ([+ or -]4.10) mg kg on the control plot. Although Pb accumulation in the stemwood can be explained mostly by root uptake from soil, however, in our case, the soil concentration of Pb was only 25.6[+ or -]2.3 mg [kg.sup.-1], therefore, bark uptake pathways could lead from atmospheric depositions from stack emissions, consequently influencing a higher Pb concentration in contaminated wood.

Cd tends to be very mobile in soil systems and therefore available to trees (Fritioff and Greger 2007). In most environmental conditions, Cd enters first the roots, and only small amounts are transported to the shoots, the content decreases in the order: roots>stems>seeds (Benavides et al. 2005), consequently Cd showed low concentrations in both plots. On the contaminated plot concentrations varied only 0.002-0.013 mg [kg.sup.-1], in the control plot 0.001-0.007 mg [kg.sup.-1].

In the study Cu concentrations were similar and varied 0.097-0.57 mg [kg.sup.-1] and 0.07-0.65 mg [kg.sup.-1] in the contaminated and control plots, respectively, however, no Cu contamination was found. Not only bioavailable Cu concentration can be taken by trees but also and the part of soil-immobilized Cu, because it's a necessary element for tree nutrition (Mazvila 2001).

Mean concentration of Zn in contaminated wood was 1.5 times higher than in control wood and varied from 0.775 to 2.182 mg [kg.sup.-1]; from 0.47 to 1.64 mg [kg.sup.-1], respectively. It can be concluded that airborne heavy metal pollution leads to some degree of soil pollution but it is not easily reflected in stemwood data.

Other ions such as [K.sup.+] and [Mg.sup.+2] considerably affect the uptake of heavy metals by various plant tissues (Seregin and Ivanov 2001). However, pinewood is characterized by a lower contents of macroelements in comparison with the other organs (Shcherbenko et al. 2008). The concentration of two mineral elements K and Mg are lower in the samples from the control plot K--22.9-55.5 mg [kg.sup.-1]; Mg--11.3-55.6 mg [kg.sup.-1] than in contaminated plot K--24.1-72.8 mg [kg.sup.-1]; Mg 30.5-65.8 mg [kg.sup.-1].

Global Analysis

Global analysis was carried out throughout a principal component analysis (PCA). This method is a useful tool in the examination of multivariant data (Topalian et al. 1999). The PCA applied in this study identifies five Factors that explained at least one variable and in total, explain 81.94% of the total variability: Factor 1 (47.42%), Factor 2 (14.06%), Factor 3 (11.43%), Factor 4 (4.69%) and Factor 5 (4.33%). The first two factors explained more than 60% of the total variance. Fig. 7 shows the relation between these two factors. In total we identified four groups: the first group composed for all the elements concentration in wood, total Zn and soil TOC, the second group--for total Mg, K and Cd%, the third group for total Cd, Pb% and Zn% and the fourth group for the remaining elements. From these results we can observe as expected that the majority of bioavailable elements on the control (Cn) and contaminated (C) plots depends on the pH values and the concentration of elements in wood is related with soil TOC. With the exception of Zn, Pb and Cd the percentage of elements in solution were strongly correlated with bioavailable elements (Fig. 7). The PCA also allows us to classify the differences between the control and contaminated plots. Thus we observed that the minor % of Cd extraction on the contaminated (Cn) plot in relation to the control (C) plot, decreased significantly. In relation to total concentration of K, Mg and Cd in wood no significant differences were founded. For the remaining elements the concentrations were significantly higher on the contaminated (Cn) plot. Fig. 8 represents the relation of Factor 1 and Factor 2 in consideration of all cases. The results showed that the concentration of the studied elements in contaminated (Cn) and control (C) plots is different and this suggests that some anthropogenic factor might be responsible for this difference, as observed in other studies (Li et al. 2009; Franco-Urla et al. 2009; Wu and Zhang 2010).

[FIGURE 7 OMITTED]

[FIGURE 8 OMITTED]

4. Conclusions

1. On the contaminated plot total Pb and Cu concentrations were statistically (p < 0.001) higher than those on the control plot. These higher contaminations are very likely to be due to the nearby factory. The concentrations for Cd, Zn and particularly Cu and Pb were higher than the background values on the contaminated plot, and this is a clear evidence of the manufacturer effects on the accumulation of these metals. Measures are needed to reduce the effects of this industry on the surrounding area. The total soil concentrations of Pb and Cu, except Zn and Cd on the control plot were below their environmental background values.

2. Average bioavailable concentrations of Pb, Cd, Cu, Zn, K and Mg on the contaminated plot were significantly (p < 0.001) higher than on contaminated. Thus, this indicates higher amount of heavy metals and macro-nutrients that are available for tree uptake on the contaminated plot. Moreover, total Cu concentration in contaminated soil due to high soil pH resulted in less concentration in the soil solution than on the control plot.

3. Average concentration of heavy metals Pb, Cu, Zn and macronutrients K and Mg in wood of the contaminated plot were statistically (p < 0.05 and p < 0.001) higher than on the control plot, therefore, demonstrated Pinus sylvestris L. wood as a biomonitor of heavy metals pollution.

4. The applied PCA allowed us to observe the relations between elements. The bioavailable elements are very dependent of pH, and metals concentration in Pinus sylvestris L. wood depends on soil TOC. The variability and concentration of metals between control (C) and contaminated (Cn) plot is very different, thus we can affirm that manufacturing emissions have an important effect on metals accumulation on soils and Pinus sylvestris L. wood.

doi: 10.3846/16486897.2011.557473

Acknowledgement

We gratefully acknowledge the colleagues from the Department of Environmental Protection of Vilnius Gediminas Technical University for chemical analysis as well as Giedre Raginyte and Laimonas Cvirka for assistance in the field and laboratory work.

Financial support from the Research Council of Lithuania (COST Action FA0905) is gratefully acknowledged.

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Neringa Pundyte (1), Edita Baltrenaite (2), Paulo Pereira, Dainius Paliulis (4)

Dept of Environmental Protection, Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223 Vilnius, Lithuania E-mails: (1) neringa.pundyte@vgtu.lt (corresponding author); (2) edita@vgtu.lt; (3) pereiraub@gmail.com; (4) dainius@vgtu.lt

Submitted 13 Oct. 2010; accepted 23 Dec. 2010

Neringa PUNDYTE. PhD student (2010), Dept of Environmental Protection, Vilnius Gediminas Technical University (VGTU). Master of Science (VGTU, 2010), Bachelor of Science (environmental engineering) VGTU, 2008. Research interests: heavy metals in soil and plants, environmental protection.

Edita BALTRENAITE. Dr, Assoc. Prof., Dept of Environmental Protecion, Vilnius Gediminas Technical University (VGTU). She gained her Environmental Engineering and Landscape Management Ph.D in 2007 from Vilnius Gediminas Technical University (Lithuania). Publications: author/co-author of ~ 25 scientific papers. Research interests: metal uptake and translocation in higher plants, anthropogenic impact, soil contamination, environmental management.

Paulo PEREIRA. Dr, Assoc. Prof., Dept of Environmental Protection, Vilnius Gediminas Technical University (VGTU); lecturer of Mykolas Romeris University. PhD of the GRAM (Mediterranean Environment Research Group) of University of Barcelona. Membership: European Geosciences Union (EGU), World Association of Soil and Water Conservation (WASWC), Spanish Fire Network. Research interests: Fire effects on soil and water chemistry, heavy metals on soils and water, impacts of agriculture in soil erosion, climate change and behaviour of extremes and sustainable development.

Dainius PALIULIS. Dr, Assoc. Prof., Dept of Environmental Protection, Vilnius Gediminas Technical University (VGTU). Doctor of Technological Sciences (air-cleaning devices), VGTU, 2000. Master of Science, Vilnius University (VU), 1996. Publications: co-author of more than 15 research papers. Research interests: air pollution, chemical pollutants of the environment, environmental chemistry.
Table 1. Mean soil pH, TOC and total and bioavailable metals
in control and contaminated soils. [+ or -] (Standard deviation)
SD. Data in mg [kg.sup.-1] DW in total elements

                   Control soil        Contaminated soil     P
                     (n = 20)              (n = 40)

pH              5.29 [+ or -] 0.09    6.59 [+ or -] 0.06    ***
TOC             2.47 [+ or -] 0.48    4.22 [+ or -] 0.34    ***
[Mg.sup.tot]      497 [+ or -] 73       498 [+ or -] 72     n.s
[K.sup.tot]      1282 [+ or -] 87      1107 [+ or -] 62     n.s
[Pb.sup.tot]    2.99 [+ or -] 3.24     25.6 [+ or -] 2.3    ***
[Cd.sup.tot]    0.719 [+ or -] 0.19   0.278 [+ or -] 0.13   n.s
[Cu.sup.tot]    0.01 [+ or -] 0.80     23.2 [+ or -] 0.6    ***
[Zn.sup.tot]     40.5 [+ or -] 5.5     36.9 [+ or -] 3.6    n.s

n--number of soil samples; significant differences between
plots: ***--p < 0.001; n.s.--non significant differences.

Table 2. Background and limit values of heavy metals for
Lithuanian moderate sandy soil (Hygiene Standard of
Lithuania HN 60:2004)

           Background values,     Limit values
Metal       mg x [kg.sup.-1]    mg x [kg.sup.-1]

Pb                 15                 100
Cd                0.15                 3
Cu                8.1                 100
Zn                 26                 300

Table 3. Bioavailable metals in control and contaminated soils.
[+ or -] (Standard deviation) SD. Data in mg [kg.sup.-1] DW in
bioavailable concentrations

              Control soil          Contaminated soil      P
                (n = 20)                (n = 40)

Mg (b)     2.40 [+ or -] 1.17      8.44 [+ or -] 0.83      ***
K (b)      7.81 [+ or -] 2.15      24.42 [+ or -] 1.52     ***
Pb (b)    0.007 [+ or -] 0.006     0.11 [+ or -] 0.004     ***
Cd (b)   0.00004 [+ or -] 0.001   0.009 [+ or -] 0.0007    ***
Cu (b)   0.0002 [+ or -] 0.007    0.076 [+ or -] 0.005     ***
Zn (b)     0.22 [+ or -] 0.03      0.51 [+ or -] 0.02      ***

n--number of soil samples; significant differences between
plots: ***--p < 0.001.
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Author:Pundyte, Neringa; Baltrenaite, Edita; Pereira, Paulo; Paliulis, Dainius
Publication:Journal of Environmental Engineering and Landscape Management
Article Type:Report
Geographic Code:4EXLT
Date:Mar 1, 2011
Words:5988
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