Element pattern recognition and classification in sunflowers (Helianthus annuus) grown on contaminated and non-contaminated soil

Microchemical Journal 114 (2014) 164–174

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Element pattern recognition and classification in sunflowers (Helianthus annuus) grown on contaminated and non-contaminated soil A. Kötschau a,⁎, G. Büchel a, J.W. Einax c, R. Meißner b, W. von Tümpling d, D. Merten a a

Friedrich Schiller University Jena, Institute of Geosciences, Applied Geology, Burgweg 11, 07749 Jena, Germany Helmholtz Centre for Environmental Research—UFZ, Soil Physics, Lysimeter Station Falkenberg, Dorfstraße 55, 39615 Falkenberg, Germany Friedrich Schiller University Jena, Institute of Inorganic and Analytical Chemistry, Environmental Analysis, Lessingstraße 8, 07743 Jena, Germany d Helmholtz Centre for Environmental Research—UFZ, Water Analytics & Chemometrics, Brückstraße 3a, 39114 Magdeburg, Germany b c

a r t i c l e

i n f o

Article history: Received 2 December 2013 Accepted 7 December 2013 Available online 22 December 2013 Keywords: Sunflower Cluster analysis Linear discriminant analysis Display methods Element pattern Bioavailability

a b s t r a c t This study aims on identifying growth site and plant part specific element patterns in sunflower (Helianthus annuus). Sunflowers (H. annuus) were planted in small-scaled plots under field conditions on a metal-contaminated and a non-contaminated site over a vegetation period of 170 days. Nitric acid soluble contents of Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, S, Th, U, and Zn were determined in roots, stems, leaves, and heads of sunflowers harvested in regular time intervals during the vegetation period. Bioavailable and total contents of the mentioned elements were determined in the corresponding soil taken at the day of sowing and after the last harvest. At first, hierarchical agglomerative cluster analysis was used to investigate the similarities and differences between elements in sunflower parts based on 16 elements and 78 samples. Thereby two clusters were formed, containing separated from each other the sunflower samples from the contaminated and the non-contaminated site. Therein several smaller sub-clusters were found, containing the single plant parts: roots, stems, leaves, and heads. Forward stepwise linear discriminant analysis was used to verify the clusters of plant parts and to identify the elements which show the highest discriminating power in the data with respect to the sunflower parts and the plots on which the sunflowers were grown. A linear discriminant model based on Mn, Ca, Fe, Ni, U, and Zn out of the 16 measured elements allowed distinguishing between the tissue types from the contaminated and non-contaminated site (correct classification of 91.7%). By plotting the autoscaled data of these most discriminating elements the plant part and site specific element patterns could be displayed. Furthermore an explanation is given how the bioavailability of elements in soil and plant physiology influence these element patterns. © 2013 Elsevier B.V. All rights reserved.

1. Introduction The contents of essential and non-essential elements in plants reflect to some extent their bioavailability in the soil solution [1,2], but these are also regulated by the plants and their specific needs for growth and development. Macroelements like Ca, K, Mg, P, and S are required in high amounts. Microelements (Cu, Fe, Mn, Ni, Zn) are also essential, but can cause toxic effects if a certain level is exceeded. Other elements enter the plant via transporters and channels of nutrients, although they fulfill no functions in the metabolic system and/or are toxic even in trace amounts (Cd, Cr, Pb, U, Th). The bioavailability of elements in the soil is mainly influenced by organic matter content, clay content and pH value [3]. Laboratory extraction schemes were developed to estimate their bioavailability. In addition, they allow comparing the bioavailability in soils from different sites under similar conditions [4]. However, the uptake of a single element into the plant not only is a function of its bioavailability, but also depends on interactions with other elements [5,6]. For example Cu uptake seems to be influenced by ⁎ Corresponding author. E-mail address: [email protected] (A. Kötschau). 0026-265X/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.microc.2013.12.006

Ni and Zn and Cd as non-essential element hinders Fe, Mn, Zn, and Cu to be taken up [3,5]. Sunflowers (Helianthus annuus) are used as crop plants for food and feed production worldwide [7,8] as well as for producing bioenergy, e.g. biogas and briquettes [9–11]. In addition, they are potential phytoextractors. Phytoextraction is a method of phytoremediation [12]—an environmental friendly and gentle method to clean soil, water and air from organic and inorganic contaminants by means of plants. Phytoextraction plants remove contaminants from soil by bioconcentration in the shoots [13,14]. Former studies which investigated the element uptake of sunflowers grown on different soils [15–20] used univariate methods for data evaluation. This leads to a lack of information about interactions between the considered elements and makes it difficult to find out differences and similarities in plants grown on different soils. This is also the case for studies, which investigated the influence of organic or inorganic fertilizers [19,21–24] or single element amendments such as S and P, respectively on element contents in the sunflowers [6,25,26]. In this study relations between macroelements (Ca, K, Mg, P, S) and microelements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Th, U, Zn) in sunflowers grown on a metal contaminated and a non-contaminated site,

A. Kötschau et al. / Microchemical Journal 114 (2014) 164–174

respectively, were investigated by means of pattern recognition and classification (cluster analysis, display methods and linear discriminant analysis). Site related differences and similarities in the element patterns of heads, stems, leaves and roots were investigated and linked to bioavailability in soils and discussed with respect to plant physiology. 2. Materials and methods

165

samples were taken in duplicates (0.5 kg each, 0–20 cm depth) at the day of sowing and after the last harvest. They were dried at 40 °C to constant weight in an oven and stored at room temperature in polyethylene terephthalate (PET) bottles (Kautex/VWR) until further processing. In the following the samples are denotated with a combination of the plant part, the number of harvest and the sampling site. For example the sample of sunflower roots taken 108 days after sowing (4th harvest) at the GW plot is named “Root 4 GW”.

2.1. Plots 2.3. Sample preparation and analysis Sunflowers (H. annuus, variety “Peredovick”) were planted on three plots at two sites in Germany in May 2011. The contaminated plot was located at the test field “Gessenwiese” (GW), which is situated in the area of the former uranium leaching heap “Gessenhalde”. The heap “Gessenhalde” was part of a former uranium mining site (1949–1989) in the eastern part of the German federal state Thuringia near the city of Ronneburg (N 50°51′15.872″, E 12°8′49.625″). It was used as uranium ore leaching heap from the late 1970s to 1989 by the Soviet/German incorporated company (SDAG) WISMUT company [27]. The underlying heap barrier consisting of compacted loam was permeable, thus leachates enriched with U and other metals rinsed into the underlying soil [28,29]. In the early 1990s the leached material together with underlying soil was removed. Afterwards the area was recontoured and covered with allochtonic soil substrate [28]. Carlsson and Büchel [30] investigated the post-remediation situation and found a slight residual contamination of metals and radionuclides on the area of the former heap. The test field “Gessenwiese” was installed in 2004 [31]. The plot used in this study was filled with homogenized GW soil substrate to 1 m depth. The second and third non-contaminated plots were situated at the lysimeter station Falkenberg (FB) (N 52°51′36.457″, E 11°48′44.417″). There, between 1981 and 1983 small scaled lysimeters (surface area of 1 m2 and depth of 1.25 m) were installed [32]. For the recent study lysimeter 30 (Lys 30, not fertilized) and lysimeter 117 (Lys 117, fertilized) were used. The lysimeters were filled with soil substrate from a former agricultural field plot [33]. The material was separately taken as surface (0–30 cm) soil and subsurface (31–100 cm) soil, separately homogenized and filled in 10 cm layers in the lysimeters [33]. From 1983 to 1990 they were used for investigations considering crop yield maximization. Since 1991, they have been used to investigate the influence of land use on soil water and solute balance [32,34]. The applied fertilizers on lysimeter 117 from 2006 to 2010 are listed in Table 1.

2.3.1. Soil sample preparation In the dried soil samples aggregates were softly chopped with a pestle (agate) and subsequently the sample were sieved to b2 mm. The soil pH(H2O) was determined by suspending 10 g soil (b2 mm) in 25 mL ultrapure water (PureLabPlus, USF Elga). During the first hour the suspension was stirred several times and then left for 24 h at room temperature. After that time pH (H2O) was measured in the supernatant with a pH meter (pH 320 SET, WTW). Bioavailabilities of the elements in the soils were determined with a sequential extraction scheme according to [35]. The mobile fraction (F1) was extracted with 1 M NH4NO3 (p.a., Merck) solution and the specifically adsorbed fraction (F2) was extracted with 1 M NH4OAc (p.a., Merck) solution. Both fractions are hereinafter referred to as “bioavailable”. The detailed procedure is described in [29]. For total content analysis the soil (b2 mm) was ground with a mixer mill (MM 400, Retsch) for 2 min at 25 Hz using grinding jars made of zirconium oxide. Total digestion was carried out with about 100 mg ground soil in a pressure digestion system (DAS300, PicoTrace) together with HF (Suprapur, 40% Merck), HClO4 (Suprapur, 70%, Merck), and HNO3 (subboiled). 2.3.2. Sunflower sample preparation Stems, roots and heads were ground in a mixer mill (MM 400, Retsch) for 2 min at 25 Hz using grinding jars made of zirconium oxide. The leaves were ground with mortar and pestle (both made of agate). Up to 200 mg of each plant part was weighed and digested in a microwave-assisted pressure digestion system (CEM, Mars 5 XPRESS) after addition of 5 mL HNO3 (subboiled). The digested samples were transferred into 25 mL flasks filled up with ultrapure water (PureLab Plus, USF Elga) and stored in PP tubes (CELLSTAR®, Greiner Bio-One) at 4 °C until chemical analysis.

2.2. Experimental setup and sampling Sunflowers were sown on both sites at the beginning of May 2011. At the contaminated site they were sampled 34, 66, 96, 108, 140, and 170 days after sowing. At the non-contaminated plots they were harvested 28, 56, 84, 112, 140, and 168 days after sowing. At every harvest two samples consisting each of four plants (34/28 and 66/56 days old) and three plants (96/84, 108/112, 140/140 and 170/168 days old), respectively, were taken. Plants were harvested as a whole and carefully washed with deionized water. Afterwards, the sunflowers were separated into roots, stems, leaves and heads and dried at 40 °C to constant weight in an oven. Dry biomass weights of the single plant part samples were determined and the samples were stored in polypropylene (PP) tubes (CELLSTAR®, Greiner Bio-One) at room temperature until chemical analysis. Soil

2.3.3. Chemical analysis In digests and extracts of soil and plant samples Ca, Fe, K, Mg, Mn, P, and S were analyzed by ICP-OES (725 ES, Varian) and Cd, Co, Cr, Cu, Ni, Pb, Th, U, and Zn were analyzed by ICP-MS (X-Series II, Thermo Scientific). Total S contents and organic carbon contents (Corg) of the soil samples were determined with a CNS-analyzer (vario EL cube, Elementar Analysensysteme GmbH). 2.4. Multivariate statistics 2.4.1. Data pre-treatment Most of multivariate statistical methods request normally distributed datasets free of missing values. For normal distribution (tested with probability–probability-plot) the data of all elements were log10

Table 1 Applied fertilization at lysimeter 117 from 2006 to 2010. Year

2006

2007

2008

2009

2010

Fertilization in kg per ha N P K

120 30 150

45 20 150

12,000 and 30,000 (manure) 20 150

40 20 150

120 30 150

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transformed. Values below the limit of detection (LOD) were substituted by multiplication of the LOD with 0.7 [36,37]. To avoid clustering of objects due to the similar orders of magnitude of the measured variables the data were autoscaled according to: zi; j ¼

xi; j −x j sj

ð1Þ

where zi,j—autoscaled value of variable i in object j, xi,j—content of variable i in object j, x j —mean of all objects j at variable i, and sj—standard deviation of all objects j at variable i [38]. 2.4.2. Cluster analysis Hierarchical agglomerative cluster analysis was used for pattern recognition in the 78 plant part samples (objects) and 16 investigated elements (variables) of the dataset. It starts with single objects/variables, which are subsequently combined in larger clusters [39] according to their distances. Squared Euclidean distances were used to calculate the distance matrix between the objects. Ward's cluster algorithm was used for aggregation [40]. It forms clusters with a minimum increase of the within-group error sum of squares [40] and reflects the underlying structure very well [41]. Further details are explained in [38,40]. Cluster analyses were performed with Statistica 9®(Statsoft). 2.4.3. Display methods Display methods can be used to visualize and recognize pattern in data. Therefore, the autoscaled data are transformed to positive feature values and plotted in star plots, sunray plots, Chernoff faces or bar charts (used in this study) [39]. These graphical methods are good tools for displaying a limited number of variables, since the plots get too confused when a certain number is exceeded. The bar plots in this study were prepared with Statistica 9® (Statsoft). 2.4.4. Linear discriminant analysis Linear discriminant analysis (LDA) was used to statistically verify the groups of plant parts found by cluster analysis and to reveal the elements that behave most differently with respect to the sunflower parts and the growth sites. In sum, eight groups consisting of the four plant part types from the GW plot as well as from the lysimeters were predefined. LDA is based on the solution of an eigenvalue problem [39]. The calculated discriminant functions are orthogonal to each other and separate the objects into the predefined groups. Thereby the variance–covariance between the classes is maximized and the variance–covariance within the classes is minimized [42]. The forward stepwise linear discriminant method was used to find out which elements had the most discriminating power. The original dataset was split in training and validation dataset. The training dataset consisted of the root, stem, leaves and head samples of sunflowers from GW, Lys 30, and Lys 117 (66 samples of 78). Thereby one plant part sample per plot was not used, but taken for the validation dataset (12 samples of 78). The selection of the most discriminating elements based on the training dataset was stopped, when a reclassification of 100% was reached. At this point the classification of the validation dataset was tested to find out how good the unknown samples are classified. The variance radii of the groups were calculated as given in [39]. For more details about LDA see [39,43]. Forward stepwise LDA was performed with Statistica 9® (Statsoft). 3. Results and discussion 3.1. Soil characterization Originally Zeien and Brümmer [35] developed their sequential extraction method to determine the bioavailability of heavy metals in soil such as Cd, Co, Cr, Fe, Ni, Mn, Pb, Th, U, and Zn in soil. However, according to [44] this method can also be used for Ca, K, and Mg. In literature the extraction with water is described to give good estimations

about plant available P and S in soil [45,46]. Thus for P and S an extraction with water according to DIN 38414-4 [47] was carried out. The results obtained with this method (data not shown) were in good agreement with bioavailabilities of P and S determined according to [35]. Hence, just the values obtained by sequential extraction are given here. For most elements the contents in the mobile and specifically adsorbed fraction from May (data not shown) and October were not statistically significantly different in the respective plots. Exceptions were the mobile fractions of Ca, Co, Pb, and S and the specifically adsorbed fraction of Ca in GW soil, as well as K in Lys 30 and Lys 117 and Mg in Lys 30. Due to this high agreement only the results from October 2011 are given for GW and for the plots at FB in Table 3. The lower soil pH (4.4 compared with 6.6), the lower organic carbon content (Corg, 0.2% versus 1.5%), and the lower clay content (9.1% compared with 12.1%) of the GW soil (see Table 2) are explanations for the higher bioavailabilities of Cd, Co, Cu, Mg, Mn, Ni, S, and U (Table 3). A higher total content could also play a role for some elements (Table 3). For Lys 117 the former fertilization with NPK-fertilizers (Table 1) could also be of influence especially with regard to the elevated K and P bioavailabilities. Cr and Th are hardly bioavailable at all three plots. Since the bioavailable content of an element is expected to influence its content in the plant it will be taken into account during the discussion of the results of the multivariate statistical methods. 3.2. Meteorological conditions The summed-up precipitation and the temperatures (Table 2) as well as the precipitation in the single months (data not shown) from May to October were similar at GW and FB. So the meteorological conditions should have had no high influence on the element uptake in the sunflowers. 3.3. Pattern recognition in sunflowers The results of the cluster analysis of the 78 plant part samples are given as dendrogram in Fig. 1. A higher relative distance means higher dissimilarity. At a relative distance of 60% two large clusters can be identified (dotted line rectangles in Fig. 1) containing either all samples from the GW plot (see right side of Fig. 1) or all samples from the two FB lysimeters (see left side of Fig. 1). This differentiation clearly hints on the existence of sampling site specific element patterns in sunflowers. At a relative distance of 10% the root, leaves, stem and head samples of GW sunflowers formed separate clusters. An exception is the sample “Head 2 GW” (2nd harvest), which was grouped into the cluster of leaves. At the same relative distance the single plant parts of the lysimeter sunflowers also mainly formed separate clusters, but no further separation due the origin from Lys 30 and Lys 117 could be found. These groupings indicate the existence of plant part specific element patterns.

Table 2 Fine soil types, soil properties and meteorological data (May–October 2011) of the test field Gessenwiese and the lysimeter station Falkenberg.

Fine soil type according to FAO [48] Clay in % pH (H2O) Corg in w-% Sum of precipitation in mm Maximum air temperature in °C Mean air temperature in °C Minimum air temperature in °C a b c

Personal communication, Mirgorodsky. [32]. Personal communication, Stratschka.

Gessenwiese

Falkenberg

Sandy loama 9.1a 4.4 0.2 366a 21.1a 15.2a 9.6a

Sandy loamb 12.1b 6.5 1.5 394c 20.5c 15.0c 9.7c

Specifically adsorbed 267 ± 31 b25.0 (LOD) 25.5 ± 18.1 49.9 ± 1.5 b15.0 (LOD) 0.065 ± 0.001 b0.020 (LOD) 0.036 ± 0.002 0.32b 1.23 ± 0.05 9.07 ± 0.14 0.190 ± 0.049 0.183 ± 0.002 b0.004 (LOD) 0.039 ± 0.001 3.63 ± 0.09 Mobile 1530 ± 1 269 ± 7 171 ± 12 27.1 ± 3.1 b10.0 (LOD) 0.010 ± 0.003 b0.013 (LOD) b0.020 (LOD) 0.076b 0.659 ± 0.097 8.54 ± 0.86 0.091 ± 0.008 b0.005 (LOD) b0.0025 (LOD) 0.003b 1.31 ± 0.13 Total 3290 ± 50 10200 ± 100 1690 ± 30 685 ± 19 300 ± –a 0.455 ± 0.020 2.79 ± 0.17 35.6 ± 12.2 11.8 ± 0.02 10000 ± 100 330 ± 14 7.45 ± 0.08 16.9 ± 0.1 4.35 ± 0.32 1.39 ± 0.05 45.5 ± 1.2

3.4. Pattern classification in sunflowers

b

a

Equal measured values for duplicates, thus no confidence interval can be given. The measured value of only one duplicate was over the LOD, thus no confidence interval can be given.

Mobile 1630 ± 70 79.5 ± 7.4 82.9 ± 2.5 b15.0 (LOD) b10.0 (LOD) b0.0075 (LOD) b0.013 (LOD) b0.020 (LOD) b0.075 (LOD) b0.600 (LOD) 7.87 ± 0.55 0.069 ± 0.019 b0.005 (LOD) b0.0025 (LOD) b0.0025 (LOD) b0.050 (LOD) 170 100 1 19 70 0.050 0.01 6.9 0.09 120 66 0.22 0.2 0.24 0.08 0.3 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Total 3440 10300 1680 385 350 0.365 2.86 33.0 8.45 9840 359 7.30 15.8 4.52 1.34 34.1 Specifically adsorbed 30 ± 2 bLOD (25.0) 16.6 ± 0.2 b25.0 (LOD) 39.5 ± 3.5 0.017 ± 0.001 0.028 ± 0.005 b0.040 (LOD) 0.568 ± 0.058 b1.00 (LOD) 6.99 ± 1.45 0.59 ± 0.01 0.036 ± 0.01 b0.004 (LOD) 2.74 ± 0.43 0.322 ± 0.036 Mobile 527 ± 62 46.8 ± 5.4 304 ± 5 b15.0 (LOD) 179 ± 19 0.090 ± 0.020 0.361 ± 0.080 0.03b 1.25 ± 0.22 b0.600 (LOD) 83.6 ± 21.3 5.80 ± 0.13 0.035 ± 0.01 b0.0025 (LOD) 0.11 ± 0.02 1.9 ± –a Total 1230 ± 120 19900 ± 600 4070 ± 200 761 ± 68 400 ± –a 0.636 ± 0.060 27.2 ± 0.3 40.4 ± 1.60 51.2 ± 3.1 46400 ± 3200 1680 ± 390 67.9 ± 0.8 17.0 ± 0.4 8.92 ± 0.05 9.82 ± 1.42 82.2 ± 6.3 Element Ca K Mg P S Cd Co Cr Cu Fe Mn Ni Pb Th U Zn

167

The cluster analyses of the elements were separately carried out each with the data of the respective plots (Fig. 2). Due to similar dendrograms for Lys 30 and Lys 117 it was proven that the NPK-fertilization at Lys 117 (Table 1) had no significant influence on the site specific element patterns (see Fig. 2). At a relative distance of 20% three element clusters can be separated. The first cluster consists of Co, Cr, Fe, Pb, Th, and U; the second containing Ca, Cd, Mg, Mn, and Zn, the third includes Cu, K, Ni, P, and S. The cluster analysis of the elements based on GW data resulted three clusters at a relative distance of 20%, too. Again Co, Cr, Fe, Pb, Th, and U formed one cluster, but in comparison to the lysimeters, Ni and Cu were grouped together with them (see Fig. 2). Similar to the lysimeters Ca, Cd, Mg, and Mn were grouped in one cluster, but in contrast K, P, and S are added. Zn formed a single cluster at a relative distance of 20%. These different dendrograms of the variables for the contaminated and non-contaminted site underline the existence of site specific element patterns.

Specifically adsorbed 219 ± 19 b25.0 (LOD) 6.40 ± 0.45 12.6b b15.0 (LOD) 0.035 ± 0.01 b0.020 (LOD) b0.040 (LOD) b0.100 (LOD) 0.85 ± 0.08 9.31 ± 0.95 0.126 ± 0.009 0.134 ± 0.002 b0.004 (LOD) 0.030 ± 0.001 1.23 ± 0.13

Lysimeter 117 Lysimeter 30 Gessenwiese

Table 3 Comparison of total contents and bioavailable contents of macro and microelements in the soils from the test field GW and the lysimeters 30 and 117 situated at FB (October 2011), contents are given as mean ± confidence interval in µg/g (P = 95%), LOD - limit of detection.

A. Kötschau et al. / Microchemical Journal 114 (2014) 164–174

Forward stepwise LDA was used to confirm the found clusters of sunflower heads, stems, leaves, and roots (Fig. 1), thereby the most discriminating elements in descending order were found to be: Mn, Ca, Fe, Ni, U, and Zn, meaning they show the highest difference with respect to the sites and the plant parts. The inclusion of these six elements in the discriminant model was sufficient to separate the sunflower samples, regarding the plant part types and the sampling sites, with a reclassification rate of 0% (training data). The plot of the first discriminant function against the second discriminant function is given in Fig. 3 (in total six discriminant functions were calculated). The first discriminant function separates the plant parts due to their origin from GW or FB, while the second discriminant function leads to a separation between the different plant parts (root, stem, head, leaves). With the discriminant model based on Mn, Ca, Fe, Ni, U and Zn only the sample “Stem 1 Lys 117” of the validation dataset was incorrectly classified to the cluster of leaves samples. Nonetheless, the correct classification of 91.7% with only six elements is a very satisfactory result. The bar plots of Mn, Ca, Fe, Ni, U and Zn for one plant part per sunflower growth site are depicted in Fig. 4. Plotting only the values of the most discriminating variables, received from forward stepwise LDA, allowed a clear visualization and easy identification of the similar and/or different element patterns. With these plots it becomes obvious that in each plant part an unique element pattern had evolved (Fig. 4), which is also site specific. The bars of Mn (red) are higher for roots, stems, leaves and heads of GW sunflower than those of FB sunflower parts. This is in accordance with the Mn contents, which are up to two orders of magnitude higher contained in tissues of sunflowers grown at GW compared with those grown at FB (Appendix A, Table A.1, A.2 and A.3). This can be traced back to its higher bioavailability in GW soil (Table 3). The highest bars for Mn at both sites can be found in leaves, which is also true for the contents (Appendix A, Tables A.1, A.2 and A.3). This might be explained by important functions of Mn in enzymes and proteins of photosynthesis [49], which mainly takes place in leaves of plants. The bars of Ca (green, Fig. 4) equal the pattern of Mn at GW and FB, respectively, and explain their joint clustering (Fig. 2). Ca has higher values in FB sunflower samples than in GW ones. This is in accordance with higher contents of Ca in tissues of FB sunflowers, and can be traced back to the higher bioavailability of Ca in the FB soil (Table 3). As indicated by the bar plot (Fig. 4) Ca element contents in sunflower samples of GW follow the order: leaves N heads, stem N roots (Appendix A, Table A.3), and in lysimeter sunflower samples: leaves N heads N stem N roots (Appendix A, Tables A.1, A.2). Ca is used as structural component of cell walls and membranes and counter ion for several anions in the vacuole and as intra-cellular messenger [50]. With regard to this functionality, high Ca contents in leaves and following heads could be explained by subsequent formation of new cell walls and membranes (Ca binding sites) during leaf growth and formation of anions caused by synthesis of metabolic substrates in leaves and heads.

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100

60

40

20

0

Root 6 Lys 117 Root 5 Lys 117 Root 6 Lys 30 Root 5 Lys 30 Root 4 Lys 30 Root 2 Lys 117 Root 4 Lys 117 Root 3 Lys 30 Root 3 Lys 117 Root 2 Lys 30 Leaves 6 Lys 117 Leaves 3 Lys 117 Leaves 6 Lys 30 Leaves 5 Lys 30 Leaves 4 Lys 117 Leaves 5 Lys 117 Leaves 4 Lys 30 Leaves 2 Lys 117 Head 6 Lys 30 Head 5 Lys 30 Leaves 3 Lys 30 Leaves 2 Lys 30 Root 1 Lys 117 Root 1 Lys 30 Stem 6 Lys 117 Stem 6 Lys 30 Stem 5 Lys 30 Stem 5 Lys 117 Stem 4 Lys 117 Stem 3 Lys 117 Stem 4 Lys 30 Stem 3 Lys 30 Stem 2 Lys 117 Stem 2 Lys 30 Head 3 Lys 117 Head 5 Lys 117 Head 4 Lys 117 Head 4 Lys 30 Head 6 Lys 117 Head 3 Lys 30 Head 2 Lys 117 Head 2 Lys 30 Stem1 Lys 117 Leaves 1 Lys 117 Leaves 1 Lys 30 Stem1 Lys 30 Root 6 GW Root 5 GW Root 4 GW Root 3 GW Root 2 GW Root 1 GW Leaves 5 GW Leaves 4 GW Leaves 3 GW Leaves 2 GW Head 2 GW Leaves 1 GW Head 6 GW Head 4 GW Head 5 GW Head 3 GW Stem 6 GW Stem 4 GW Stem 5 GW Stem 3 GW Stem 2 GW Stem 1 GW

Relative distance in %

80

Fig. 1. Dendrogram depicting the cluster analysis of the sunflower plant part samples (roots, leaves, heads, stems) from GW, Lys 30 and Lys 117, respectively; at each of the three plots six harvests were carried out.

Relative distance in %

Fe and U are clustered together with Co, Cr, Pb, and Th, irrespectively of the sites on which the sunflowers were grown (Fig. 2). From these elements all except Fe are non-essential [51] for plants, so it was expected that the sunflowers tried to keep these elements in the roots. This was the case of the GW sunflowers, in which the roots had the highest contents of these elements, followed by leaves, stems and heads (Appendix A, Table A.3), and as can be seen very well in the height of the bars for Fe and U (dark blue and pink) in Fig. 4. Due to its redox properties Fe can also produce reactive oxygen species, which are toxic [52]. Therefore, plants store excessive Fe in the apoplast, vacuoles and ferritins (proteins). The latter ones are brought to cells with non-green plastids (chloroplast free), in shoots, root apex and also in senescent leaf tips [52]. This storage strategy of Fe in plants shall explains why it is contained to a high extend not only in the roots (GW), but also in the leaves, when bioavailablity is high (FB). Although the bioavailability of Fe in the soil of the GW plot was lower, the Fe contents in the GW plant parts were higher (especially roots) or equal compared with those from the lysimeters. This might be the result of increased uptake stimulated by Fe deficiency in soil or otherwise results from interaction with other heavy metals like Co, Cr and Ni [53,54]. The higher bioavailabilities of these elements in the GW soil and thus resulting higher contents in all plant parts could lower the translocation of Fe in the shoot and lead to an accumulation in the roots [54]. The leaves and roots of the sunflowers grown at the lysimeters had similar and highest contents of Fe (Appendix A, Tables A.1, A.2). The preferred translocation of Fe into the leaves can be explained by its participation in electron

transfer reactions in respiratory complexes and the photosynthetic apparatus of chloroplasts [55]. U follows the same pattern as Fe in sunflowers parts originating of either FB or GW. From the bar plot it can be identified, that U contents in roots, stems, leaves and heads of GW sunflowers are higher compared with those of FB sunflowers (two orders of magnitude for roots, Appendix A, Tables A.1, A.2, and A.3). The higher contents of U in plant parts of GW compared to Lys 30/Lys 117 were traced back to a higher U bioavailability at GW (Table 3). The especially high root contents of U might be caused by precipitation of U at/in the root, which mainly is known to occur at acidic soil pH [56,57], like at the GW plot (pH = 4.4) [56,57]. Whereas at higher pH no U precipitates, but it is translocated to the shoots [56,57], which would explain why the roots and leaves of sunflowers grown on the lysimeters (pH = 6.5) contained equal U contents (Appendix A, Tables A.1, A.2). Zn behaves more similarly to Mn and Ca in sunflower samples from FB (light blue bars in Fig. 4) and so do the contents: leaves N head N stem Nroot (Appendix A., Tables A.1, A.2). In sunflower samples from GW this behavior was not observed. Zn is the only element with highest contents in the stems, followed by the roots (Appendix A, Table A.3). Its different behavior in the sunflowers from FB and GW may be influenced by the differing distribution between the mobile and specifically adsorbed soil fractions (see Table 3). In FB soil the specifically absorbed fraction has a higher Zn content than the mobile fraction, and in GW soil it behaves vice-versa, which could lead to a different uptake and distribution in the sunflowers.

Lys 117

L ys 30

GW

100

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0

Cr U Th Pb Fe Co K S P Ni Cu Mg Zn Cd Mn Ca

0

Cr U Th Pb Fe Co S P K Ni Cu Zn Cd Mg Mn Ca

0

Zn Ni Cu U Th Fe Cr Pb Co P K Mg Cd S Mn Ca

Fig. 2. Dendrograms depicting the cluster analysis of the elements separately carried out for GW, Lys 30, and Lys 117.

A. Kötschau et al. / Microchemical Journal 114 (2014) 164–174

12

169

sites and how they could support the interpretation of the element uptake and distribution behavior in plants.

10

Second discriminant function

Wu 2 Lys 117

Wu 2 Lys 117 Wu 5 Lys 117 Wu Lys 117 Wu Lys 30 Wu31324Lys Lys30 117 Wu Wu 6 Lys 117 5 Lys WuWu 4 Lys 3030 Wu 1 Lys 30 Wu 6 Lys 30 Wu 5 Lys 117

8

Wu Wu1 3Lys Lys117 117

Wu 2Wu Lys4 30 Lys 117

Wu 3 Lys 30

Wu 6 Lys 117

Wu 4 Lys 30

6

Wu 5 Lys 30

4. Conclusion

Wu 1 Lys 30

Wu 6 Lys 30

4 St 3 Lys 30

St 3 Lys 30 St Lys117 30 StSt315Lys Lys 117 St30 6 Lys 30 St422Lys Lys 117 St St Lys 30 St 4 Lys 117 St 6 Lys 117 BlüSt5 5Lys Lys30117 Blü 6 Lys 117 St 1Lys Lys117 30 Blü 5 30 Blü 6 Lys 4 117 Lys 117 Bl Bl 3Blü 3 Lys Lys 30 Blü 4 Lys 30 Blü 2 Lys 117 Bla 1 Lys 117 Blü 2 Lys 30 St 3 Lys 117 St 1 Lys 117St 5 Lys 30

2

St 6 Lys 30

St 2 Lys 117

St 4 Lys 30

St 2 Lys 30

St 4 Lys 117

St 6 Lys 117

Blü 5 Lys 30

St 5 Lys 117

Blü 6 Lys 117

0

St 1 Lys 30

Blü 4 Lys 117

Blü 6 Lys 30

Blü 5 Lys 117

Bl 3 Lys 117

Bl 3 Lys 30

Blü 4 Lys 30

Bla 1 Lys 117

Blü 2 Lys 117

-2

Blü 2 Lys 30

Bla 1 Lys 30

Bla 1 Lys 30 Bla 2 Lys 30 Bla 3 5Lys 30117 Bla 2 Lys 117 Bla Lys BlaBla 5 Lys 30 6 Bla Lys 430Lys 30117 Bla 4 Lys Bla 6 Lys 117 Bla 3 Lys 117 Bla 2 Lys 30

-4

Bla 3 Lys 30

Bla 5 Lys 117

Bla 5 Lys 30

Bla 6 Lys 30

Bla 2 Lys 117

Bla 4 Lys 30

Bla 4 Lys 117

Bla 6 Lys 117

-6

Bla 3 Lys 117

-8 -10 -12 -20

-15

-10

-5

0

5

10

15

20

First discriminant function Roots FB Stems FB

Heads FB Leaves FB

Roots GW Stems GW

Heads GW Leaves GW

Fig. 3. Plot of the first discriminant function against the second discriminant function, circles mark the confidence radii, P = 95%.

The bars of Ni (yellow, Fig. 4) indicate higher contents of Ni in sunflower tissues of GW origin than in those of FB origin. This can be explained by the higher Ni bioavailability in GW soil (one order of magnitude). Ni is highest contained in leaves and roots of sunflowers grown at GW and highest contained in seeds of sunflowers grown at FB. Both can be explained by the fact that Ni activates the enzyme urease, which is known to be highly abundant in the leaves and seeds [58] of plants. The high Ni contents in leaves and especially in roots of GW sunflowers might be a strategy to avoid toxic effects on seeds in the heads [59], due to the higher bioavailability in GW soil (see Table 3). In summary it was confirmed with this study how multivariate statistics could be used to characterize plants that are grown on different

Sunflowers (H. annuus) were grown on a contaminated soil at a former uranium mining site and on non-contaminated arable soil at a lysimeter station. In a small-scaled field study the patterns and relations of the macroelements (Ca, K, Mg, P, S) and microelements (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, Th, U, Zn) in sunflower parts (heads, stems, leaves and roots) were investigated. Differences and similarities in element patterns of sunflowers with regard to the plant parts and growth sites were investigated by means of multivariate statistical methods. With cluster analysis site specific element patterns were found. Co, Cr, Fe, Pb, Th and U as well as Ca, Cd, Mg and Mn were grouped based on sunflower data of both sites, due to similar patterns at the respective sites. In contrast K, Ni, P, S, and Zn showed a different clustering, depending on the sunflowers' origin from either the contaminated or not-contaminated site. With LDA it was possible to statistically verify the clusters found and also to identify those elements that mainly lead to a differentiation of the sunflower samples: Mn, Ca, Fe, Ni, U and Zn. Display methods were used to visualize the plant parts and site specific element patterns of sunflowers. Their development was discussed with respect to the influence of the elements' bioavailable contents in soil and plant physiology. Acknowledgment This work was kindly supported by Helmholtz Impulse and Networking Fund through Helmholtz Interdisciplinary Graduate School for Environmental Research (HIGRADE) [60]. Digestions of soil and plant samples were carried out by Gerit Weinzierl, Ines Kamp, and Ulrike Buhler. Sequential soil extraction was carried out by Ulrike Buhler. Measurement with ICP-MS and ICP-OES were carried out by Dirk Merten and Ines Kamp, respectively.

FB

GW Head

Head

Mn Ca Fe Ni U Zn

Mn Ca Fe Ni U Zn

Stem

Leaves

Mn Ca Fe Ni U Zn

Stem

Leaves

Mn Ca Fe Ni U Zn Mn Ca Fe Ni U Zn

Mn Ca Fe Ni U Zn

Root

Root

Mn Ca Fe Ni U Zn

Mn Ca Fe Ni U Zn

Fig. 4. Bar plots of the positively transformed autoscaled data of the six most discriminating elements Mn, Ca, Fe, Ni, U, and Zn for the plant parts of sunflowers grown at GW (left) and FB (right).

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Appendix A Table A.1 Content of Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, S, Th, U, Zn in roots, stems, leaves, and heads of sunflower from Lys 30 plot, values are given as mean ± confidence interval in of duplicates of 3–4plants, P = 0.95%. Plant part

Days after sowing

Ca

Roots

28 56 84 108 140 168 28 56 84 112 140 168 28 56 84 112 140 168 56 84 112 140 168

5930 2870 2990 3820 4570 4420 19600 7080 5050 6020 7840 7820 28100 31100 38000 42600 41200 42200 16600 12700 13200 14100 20300

Stems

Leaves

Heads

a b

Cd ± 1570 ± 210 ± 320 ± 70 ± 240 ± 170 ± –a ± 430 ± 120 ± 200 ± 640 ± 220 ± 800 ± 2500 ± 4400 ± 2100 ± 8200 ± 20800 ± 800 ± 1100 ± 800 ± –a ± 30

0.285 0.145 0.222 0.195 0.230 0.151 0.356 0.121 0.136 0.153 0.286 0.187 0.403 0.219 0.352 0.390 0.546 0.470 0.140 0.162 0.121 0.230 0.197

Co ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.010 0.007 0.095 0.007 0.112 0.029 –a 0.027 0.014 0.032 0.119 0.018 0.082 0.067 0.008 0.032 0.057 0.097 0.041 0.036 0.036 –a 0.019

0.294 0.179 0.227 0.322 0.260 0.191 0.103 0.039 0.025 0.030 0.071 0.057 0.112 0.356 0.391 0.339 0.689 0.558 0.087 0.056 0.060 0.068 0.239

Cr ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.150 0.021 0.071 0.028 0.033 0.025 –a 0.009 0.004 0.008 0.006 0.006 0.01 0.122 0.096 0.123 0.141 0.302 0.029 0.021 0.013 –a 0.185

2.45 1.38 1.75 2.45 2.03 1.56 0.520 0.222 0.124 0.103 0.592 0.805 0.193 2.14 2.31 1.70 4.17 3.17 0.730 0.396 0.840 0.303 2.00

Cu ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.82 0.17 0.61 0.20 0.25 0.11 –a 0.068 0.041 0.010 0.136 0.866 0.033 0.97 0.51 0.67 0.94 1.94 0.240 0.242 0.291 –a 1.78

11.1 6.22 5.64 8.88 8.45 3.82 11.7 4.59 2.46 3.73 4.32 5.10 18.4 17.0 16.0 12.6 17.5 13.8 20.4 16.2 10.2 8.32 17.3

Fe ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.3 0.30 1.73 0.31 1.73 0.38 –a 0.09 0.32 0.50 2.66 3.54 4.6 0.8 2.8 3.6 10.8 1.8 1.5 0.4 2.2 –a 5.00

806 486 681 1030 814 564 83.0 18.4 15.4 12.0 66.3 56.2 140 732 985 757 1710 1290 61.0 43.7 31.9 59.8 476

K ± 475 ± 45 ± 243 ± 30 ± 143 ± 34 ± –a ± 0.2 ± 0.8 ± 4.2 ± 18.7 ± 2.6 ± 10 ± 409 ± 269 ± 369 ± 400 ± 800 ± 3.5 ± 0.3 ± 2.2 ± –a ± 416

38500 19800 18700 10800 4230 4140 47200 19600 11200 7610 5120 6560 37500 34600 23600 8260 5070 5110 42700 32200 26000 22100 20900

Mg ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

3200 1800 4600 200 2310 2270 –a 1700 2200 420 2630 4080 6400 6800 3900 140 830 1250 1100 5100 3490 –a 6200

3850 750 802 1680 1810 675 10400 2590 1700 1990 2590 1860 7970 4080 4610 5870 4800 3850 3260 3070 3690 4830 4760

± 720 ± 58 ±1 ± 20 ± 490 ± 150 ± –a ± 150 ± 500 ± 210 ± 1220 ± 190 ± 500 ± 460 ± 460 ± 750 ± 570 ± 2260 ± 140 ± 290 ± 660 ± –a ± 10

Only one sample could be analyzed, thus no confidence value could be calculated. Values below limit of detection (LOD), thus no confidence value could be calculated.

Table A.2 Content of Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, S, Th, U, Zn in roots, stems, leaves, and heads of sunflower from Lys 117 plot; values are given as mean ± confidence interval in of duplicates of 3–4 plants, P = 0.95%. Plant part

Days after sowing

Ca

Root

28 56 84 112 140 168 28 56 84 112 140 168 28 56 84 112 140 168 56 84 112 140 168

4120 2780 2900 3430 3190 3630 10300 6000 5190 6810 7770 7560 19600 33400 37400 44600 43500 43000 16700 11700 11000 12900 14300

Stem

Leaves

Head

a b

Cd ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

130 30 190 380 430 830 100 20 290 150 720 150 12100 3900 12600 5300 6500 7500 1700 2400 4200 2800 300

0.320 0.150 0.146 0.21 0.161 0.156 0.336 0.135 0.129 0.157 0.183 0.183 0.315 0.345 0.298 0.349 0.492 0.523 0.23 0.177 0.116 0.112 0.183

Co ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.030 0.007 0.002 0.027 0.016 0.021 0.058 0.021 0.030 0.031 0.035 0.001 0.146 0.010 0.043 0.011 0.213 0.111 0.06 0.018 0.007 0.003 0.073

0.228 0.165 0.193 0.220 0.151 0.133 0.065 0.023 0.022 0.028 0.032 0.050 0.067 0.153 0.122 0.274 0.317 0.158 0.052 0.052 0.045 0.055 0.097

Cr ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.040 0.005 0.012 0.018 0.056 0.022 0.009 0.001 0.001 0.004 0.003 0.016 0.034 0.036 0.085 0.04 0.069 0.010 0.01 0.027 0.021 0.007 0.010

Only one sample could be analyzed, thus no confidence value could be calculated. Values below limit of detection (LOD), thus no confidence value could be calculated.

1.69 1.29 1.50 1.79 1.15 1.06 1.05 0.074 0.080 0.064 0.220 1.41 0.157 0.78 0.36 1.51 1.66 0.528 0.569 0.409 0.484 0.981 0.615

Cu ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.45 0.09 0.03 0.15 0.44 0.10 0.76 0.018 0.035 0.005 0.181 1.36 0.075 0.452 0.264 0.33 0.42 0.062 0.223 0.072 0.189 0.344 0.437

8.56 ± 13.7 ± 4.39 ± 7.91 ± 5.80 ± 4.87 ± 6.68 ± 2.29 ± 1.90 ± 2.86 ± 3.71 ± 2.71 ± 8.69 ± 8.65 ± 4.96 ± 6.85 ± 12.1 ± 14.0 ± 13.2 ± 11.9 ± 8.64 ± 9.72 ± 14.8 ±

Fe 0.21 12.9 0.16 1.10 2.21 0.58 0.53 0.27 0.02 0.02 0.11 0.73 4.73 1.50 2.37 2.26 4.7 13.0 4.1 2.7 2.71 3.17 1.7

599 590 646 772 512 430 69.0 13.4 11.6 11.4 16.8 28.5 123 318 145 625 705 230 51.1 39.4 30.3 45.7 96.9

K ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

23 18 42 65 239 51 2.5 1.4 5.7 0.50 3.6 1.8 56 155 116 147 150 6 3.2 6.7 1.4 0.5 79.5

66200 25400 17300 20200 15400 8790 93600 26200 12500 15400 13000 15100 47000 42000 6400 14800 7630 7040 40300 29.700 27700 34800 30200

Mg ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

3500 1900 3100 100 2300 1010 800 4400 200 2900 200 1100 21400 100 3500 1700 5470 6170 4700 5700 9300 400 7300

1380 658 642 740 531 610 3520 1830 1270 1200 1010 1090 4010 4340 4540 4370 3410 4950 3460 3010 2630 2650 3700

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

60 1 39 66 39 131 70 140 90 150 330 200 2170 1760 80 870 570 1180 500 220 670 400 400

A. Kötschau et al. / Microchemical Journal 114 (2014) 164–174

Mn

Ni

32.2 ± 16.5 21.4 ± 4.4 27.6 ± 5.6 37.5 ± 4.8 32.9 ± 2.1 22.3 ± 1.3 35.6 ± –a 22.1 ± 2.0 14.8 ± 0.4 20.3 ± 6.3 32.2 ± 4.2 27.0 ± 1.7 79.2 ± 6.4 152 ± 1 154 ± 4 178 ± 36 259 ± 10 247 ± 49 53.2 ± 0.8 26.8 ± 0.8 23.2 ± 2.5 29.7 ± –a 49.8 ± 22.5

1.90 0.920 0.880 1.25 1.26 0.770 1.11 0.440 0.165 0.230 0.472 0.548 1.17 1.40 1.18 0.957 2.17 1.63 7.15 3.55 2.29 1.79 2.94

Mn

Ni

23.3 17.5 23.2 25.7 16.9 16.3 27.8 14.5 13.5 16.5 19.6 23.3 46.1 85.1 128 129 128 153 36.5 25.7 19.9 19.9 33.1

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

6.7 0.3 0.80 0.3 5.2 2.5 2.1 0.5 1.0 2.7 3.6 0.8 21 5.4 17 11 17 67 2.1 8.2 3.1 4.6 4.0

1.37 0.843 0.824 0.944 0.770 0.620 1.07 0.290 0.133 0.100 0.199 0.758 0.607 0.737 0.401 0.897 1.02 0.670 5.00 2.66 1.44 1.24 1.84

P ± 1.03 ± 0.056 ± 0.240 ± 0.11 ± 0.18 ± 0.027 ± –a ± 0.014 ± 0.007 ± 0.041 ± 0.003 ± 0.467 ± 0.04 ± 0.35 ± 0.363 ± 0.414 ± 0.17 ± 0.80 ± 0.62 ± 0.08 ± 0.92 ± –a ± 1.26

2270 1230 980 759 477 317 2670 1260 870 748 802 575 4100 2360 2380 1530 1630 1870 6380 5050 3690 2630 5330

Pb

± 40 ± 50 ± 374 ± 81 ± 283 ± 81 ± –a ± 10 ± 116 ± 347 ± 717 ± 311 ± 710 ± 460 ± 610 ± 140 ± 510 ± 1180 ± 230 ± 770 ± 660 ± –a ± 590

P ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.61 0.020 0.060 0.032 0.290 0.130 0.26 0.100 0.004 0.014 0.040 0.685 0.518 0.090 0.097 0.201 0.07 0.216 1.38 0.77 0.75 0.20 0.35

3500 2170 1120 1130 538 469 3770 1470 909 922 840 722 4220 3140 1460 1730 1650 2350 6340 5100 4040 4350 5900

S

1.24 ± 0.69 0.800 ± 0.082 1.13 ± 0.37 1.66 ± 0.13 1.50 ± 0.17 0.860 ± 0.056 0.193 ± –a 0.103 ± 0.045 0.078 ± 0.019 0.070 ± 0.022 0.198 ± 0.018 0.165 ± 0.004 0.158 ± 0.004 1.46 ± 0.87 2.01 ± 0.76 1.75 ± 0.50 3.16 ± 0.68 2.90 ± 1.32 0.071 ± 0.008 0.092 ± 0.049 0.067 ± 0.013 0.157–a 0.744 ± 0.580

Pb ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

670 140 210 30 115 154 1300 140 8 11 500 427 2580 220 1300 110 610 1430 100 660 730 1180 710

0.901 1.12 0.984 1.12 0.812 0.650 0.140 0.060 0.047 0.056 0.075 0.089 0.147 0.667 0.56 1.54 1.72 1.03 0.055 0.051 0.067 0.110 0.205

1760 667 606 579 491 319 3030 827 475 520 604 637 4290 2390 1960 1580 1590 1540 2940 2130 1850 2020 1990

Th ± 420 ± 34 ± 259 ± 11 ± 182 ± 38 ± –a ± 216 ± 54 ± 88 ± 340 ± 183 ± 320 ± 370 ± 150 ± 220 ± 260 ± 110 ± 50 ± 20 ± 390 ± –a ± 670

S ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.037 0.30 0.084 0.06 0.353 0.040 0.030 0.001 0.014 0.006 0.012 0.017 0.075 0.29 0.33 0.28 0.04 0.045 0.010 0.003 0.030 0.030 0.166

1710 584 500 568 428 392 2180 727 620 616 703 521 3660 2790 1460 1460 1600 1790 2910 2180 1710 2220 2190

171

0.199 0.123 0.216 0.346 0.247 0.191 0.018 0.009 0.009 0.009 0.016 0.020 0.016 0.264 0.340 0.252 0.555 0.450 0.006 0.023 0.004 0.011 0.187

U ± 0.129 ± 0.010 ± 0.059 ± 0.040 ± 0.006 ± 0.008 ± –a ± –b ± –b ± –b ± 0.001 ± 0.007 ± 0.004 ± 0.238 ± 0.061 ± 0.132 ± 0.071 ± 0.331 ± 0.002 ± 0.020 ± –b ± –a ± 0.193

Th ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

250 21 73 74 39 89 550 24 141 23 206 94 1660 110 1220 150 700 780 110 550 120 300 30

0.142 0.150 0.195 0.205 0.177 0.038 0.016 0.004 0.004 0.004 0.004 0.013 0.016 0.088 0.032 0.232 0.283 0.057 0.004 0.004 0.004 0.008 0.018

0.073 0.038 0.051 0.078 0.069 0.052 0.004 0.005 0.005 0.005 0.005 0.005 0.009 0.055 0.077 0.067 0.162 0.108 0.004 0.004 0.004 0.004 0.039

Zn ± 0.04 ± 0.002 ± 0.021 ± 0.011 ± 0.001 ± 0.004 ± –a ± –b ± –b ± –b ± –b ± –b ± 0.003 ± 0.030 ± 0.022 ± 0.030 ± 0.015 ± 0.068 ± –b ± –b ± –b ± –a ± 0.038

U ± 0.015 ± 0.005 ± 0.024 ± 0.019 ± 0.084 ± 0.010 ± 0.006 ± –b ± –b ± –b ± –b ± –b ± 0.009 ± 0.061 ± 0.026 ± 0.008 ± 0.151 ± 0.001 ± –b ± –b ± –b ± –b ± 0.010

0.058 0.042 0.051 0.059 0.043 0.038 0.004 0.004 0.004 0.004 0.004 0.005 0.009 0.031 0.011 0.069 0.073 0.016 0.004 0.004 0.004 0.004 0.009

45.3 ± 8.6 15.1 ± 0.2 15.6 ± 3.9 22.3 ± 4.3 38.0 ± 9.2 12.4 ± 2.1 106 ± –a 33.4 ± 0.7 24.9 ± 6.0 27.5 ± 2.3 40.0 ± 17.6 26.8 ± 1.7 70.3 ± 0.1 33.7 ± 6.4 36.8 ± 2.4 50.4 ± 23.7 53.4 ± 16.1 52.9 ± 5.7 47.5 ± 3.4 36.3 ± 3.2 26.9 ± 0.7 23.5 ± –a 50.4 ± 10.1

Zn ± 0.004 ± 0.001 ± 0.024 ± 0.004 ± 0.020 ± 0.005 ± –b ± –b ± –b ± –b ± –b ± –b ± 0.005 ± 0.010 ± 0.009 ± 0.010 ± 0.017 ± 0.001 ± –b ± –b ± –b ± –b ± 0.010

43.6 19.0 15.2 21.1 24.1 15.8 73.7 18.9 17.8 22.3 22.4 26.2 41.6 27.5 29.6 38.4 50.4 51.2 42.6 32.5 24.7 27.2 50.1

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

7.1 6.3 0.6 1.9 10.3 1.0 0.7 0.9 0.8 5.3 1.7 4.4 18.4 9.0 4.7 9.7 16.7 25.8 6.1 5.0 7.9 12.7 0.5

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Table A.3 Content of Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Ni, P, Pb, S, Th, U, Zn in roots, stems, leaves, and heads of sunflower from GW plot; values are given as mean ± confidence interval in of duplicates of 3–4 plants, P = 0.95%. Plant part

Days after sowing

Ca

Root

34 66 96 108 140 170 34 66 96 108 140 170 34 66 96 108 140 66 96 108 140 170

2960 2480 2330 2940 2730 2920 6680 4360 3780 4890 4380 5080 20800 20000 17500 17200 23200 12300 6900 6340 5290 6410

Stem

Leaves

Head

a b

Cd ± –a ± 260 ± 440 ± 70 ± 440 ± –a ± 440 ± 200 ± 510 ± 530 ± 140 ± 330 ± 1700 ± 1800 ± 1500 ± 1900 ± 6700 ± 1400 ± 30 ± 190 ± 220 ± 2440

2.31 1.70 1.42 1.79 1.24 1.05 3.74 2.20 2.12 1.93 2.03 2.00 3.68 2.52 2.32 2.03 2.90 2.28 1.29 1.17 1.05 1.31

Co ± –a ± 0.16 ± 0.19 ± 0.08 ± 0.22 ± –a ± 0.06 ± 0.40 ± 0.03 ± 0.44 ± 0.01 ± 0.18 ± 0.36 ± 0.28 ± 0.08 ± 0.04 ± 1.25 ± 0.19 ± 0.25 ± 0.15 ± 0.06 ± 0.12

13.1 9.11 6.84 5.81 5.24 5.35 3.16 2.65 2.21 1.88 2.39 2.37 3.62 4.58 3.91 4.04 5.86 2.19 1.51 1.23 1.22 1.60

± –a ± 0.87 ± 0.51 ± 1.36 ± 0.79 ± –a ± 0.26 ± 0.38 ± 0.25 ± 0.35 ± 0.29 ± 0.06 ± 0.31 ± 0.54 ± 0.94 ± 0.86 ± 0.22 ± 0.35 ± 0.22 ± 0.01 ± 0.36 ± 0.29

Only one sample could be analyzed, thus no confidence value could be calculated. Values below limit of detection (LOD), thus no confidence value could be calculated.

Cr

Cu

4.49 ± –a 3.91 ± 0.60 2.42 ± 0.01 2.05 ± 0.08 2.34 ± 0.86 1.51 ± –a 0.787 ± 0.35 0.236 ± 0.061 0.175 ± 0.108 0.220 ± 0.004 0.317 ± 0.050 0.310 ± 0.044 0.711 ± 0.237 0.759 ± 0.217 0.629 ± 0.261 1.21 ± 0.89 0.356 ± 0.045 0.470 ± 0.08 0.095 ± 0.027 0.243 ± 0.190 0.060 ± 0.039 0.163 ± 0.073

32.0 33.1 21.0 34.9 33.6 26.4 5.11 7.14 3.39 8.14 4.95 6.37 14.9 23.1 27.3 26.7 28.5 11.4 9.87 7.54 6.58 9.26

Fe ± –a ± 5.8 ± 4.7 ± 16.5 ± 4.0 ± –a ± 0.70 ± 0.35 ± 0.42 ± 4.23 ± 1.50 ± 0.96 ± 4.5 ± 5.8 ± 4.4 ± 1.9 ± 3.9 ± 1.9 ± 0.65 ± 1.19 ± 0.82 ± 3.99

4150 ± 3870 ± 2320 ± 1650 ± 2350 ± 1300 ± 339 ± 182 ± 134 ± 190 ± 68.2 ± 199 ± 388 ± 510 ± 628 ± 1110 ± 210 ± 183 ± 67.0 ± 222 ± 24.6 ± 119 ±

K –a 1090 440 60 980 –a 93 41 68 57 16.4 71 246 205 300 856b 10 50 15.0 179 0.8 65

16100 26300 18100 20500 12700 12000 19100 28100 15600 13100 14000 6080 34700 41900 46300 39900 33000 43000 33500 29400 24000 15600

Mg ± –a ± 5800 ± 2100 ± 400 ± 400 ± –a ± 1100 ± 9700 ± 1200 ± 200 ± 2500 ± 2270 ± 2500 ± 6000 ± 3300 ± 6100 ± 2400 ± 1200 ± 400 ± 3700 ± 1200 ± 3600

6290 4380 3270 3760 2450 3160 8760 6890 5380 5610 4370 4260 5440 7000 8770 5260 7660 6760 5590 4380 4000 5350

± –a ± 1750 ± 970 ± 320 ± 860 ± –a ± 970 ± 620 ± 220 ± 1410 ± 1110 ± 500 ± 790 ± 890 ± 2300 ± 300 ± 1400 ± 630 ± 930 ± 370 ± 250 ± 2180

A. Kötschau et al. / Microchemical Journal 114 (2014) 164–174

Mn

Ni

P

550 ± –a 414 ± 11 362 ± 40 370 ± 80 303 ± 69 301 ± –a 1250 ± 20 1010 ± 90 871 ± 168 639 ± 100 612 ± 38 775 ± 290 2210 ± 450 2250 ± 360 2830 ± 300 1490 ± 200 2230 ± 500 1290 ± 420 938 ± 80 386 ± 11 391 ± 103 697 ± 446

120 ± –a 98.2 ± 24.4 59.1 ± 20.1 68.1 ± 7.9 53.9 ± 5.7 71.0 ± –a 33.7 ± 0.7 26.3 ± 5.3 18.8 ± 1.4 20.5 ± 3.2 19.9 ± 3.0 27.0 ± 7.5 95.8 ± 5.5 104 ± 20 126 ± 11 133 ± 12 149 ± 10 73.4 ± 20.7 51.2 ± 22.4 44.2 ± 4.6 38.3 ± 3.9 40.7 ± 11.5

3020 3870 2420 2580 1410 1170 3890 4840 2480 2390 1600 1050 3980 3420 3180 2790 1860 6820 5420 4620 4240 4580

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

–a 370 110 1190 440 –a 310 690 320 1360 760 490 330 80 500 1100 440 330 190 720 240 670

Pb

S

Th

1.70 ± –a 1.26 ± 0.25 0.86 ± 0.091 0.661 ± 0.008 0.836 ± 0.266 0.532 ± –a 0.151 ± 0.035 0.433 ± 0.432b 0.202 ± 0.188b 0.258 ± 0.007 0.079 ± 0.012 0.254 ± 0.011 0.246 ± 0.074 0.282 ± 0.063 0.527 ± 0.101 0.856 ± 0.067 0.775 ± 0.101 0.258 ± 0.253 0.066 ± 0.022 0.125 ± 0.110 0.047 ± 0.026 0.190 ± 0.070

5820 ± –a 6350 ± 1100 4800 ± 1260 6430 ± 420 3880 ± 1250 4200 ± –a 6480 ± 590 7800 ± 1000 6240 ± 370 5980 ± 800 5380 ± 1070 2940 ± 110 8640 ± 1260 14400 ± 2300 17800 ± 30 12800 ± 2800 13000 ± 400 4520 ± 430 5310 ± 1210 4900 ± 2110 4190 ± 150 3100 ± 540

0.450 0.420 0.228 0.187 0.258 0.123 0.036 0.021 0.014 0.025 0.012 0.028 0.051 0.060 0.059 0.122 0.027 0.049 0.011 0.027 0.011 0.011

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± –a ± 0.148 ± 0.058 ± 0.001 ± 0.122 ± –a ± 0.011 ± 0.007 ± 0.007 ± 0.007 ± 0.003 ± 0.003 ± 0.017 ± 0.040 ± 0.036 ± 0.098 ± 0.003 ± 0.014 ± –b ± 0.023 ± –b ± –b

U

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5.15 ± –a 3.95 ± 1.06 2.47 ± 0.42 2.86 ± 0.43 2.62 ± 0.42 2.79 ± –a 0.155 ± 0.034 0.13 ± 0.042 0.068 ± 0.030 0.086 ± 0.040 0.047 ± 0.022 0.078 ± 0.015 0.169 ± 0.123 0.433 ± 0.156 0.201 ± 0.063 0.630 ± 0.562 0.127 ± 0.017 0.045 ± 0.023 0.009 ± 0.006 0.067 ± 0.067 0.006 ± –b 0.03 ± 0.008

156 ± –a 114 ± 9 83.0 ± 8.6 76.3 ± 0.6 64.6 ± 1.0 40 225 ± 40 158 ± 20 109 ± 4 108 ± 12 110 ± 1 107.76 ± 0.03 60.8 ± 10.9 38.9 ± 6.0 29.1 ± 5.3 50.0 ± 6.7 50.1 ± 18.2 31.2 ± 4.5 22.7 ± 3.5 24.0 ± 3.6 25.2 ± 2.4 32.2 ± 6.2

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