Elemental Analysis In Plants Biology Essay

Concentrations of Cu, Zn, Pb, Fe and Mn in topsoil 9 (0-5 cm) and subsoil (5-10cm) around Araucaria columnaris and in the plants of Araucaria columnaris, Cinnamomum camphora, citrus plant and Pinus elliottii obtained from elemental analysis by Flame AAS are shown in Table 5. The results in Table 5 come from duplicates of sample and most values lie within a standard deviation of less than 10 %.

3.1 Data analysis

Correlations of elements between plant and soil, leaves and bark were calculated to deduce the relationship between the two components and estimated the degree of metal accumulation within the plants. A value close to one indicates a strong correlation and a value close to zero means that the correlation is low.

The enrichment factor (EF) was also used to determine the degree of metal accumulation in contaminated plants with respect to uncontaminated (control) plants (Serbula et al., 2013). It is calculated by dividing the metal concentration in plants at contaminated site by metal concentration in plants at control site. EF value greater than 2 is usually an indication of pollution (Mingorance et al., 2007).

Table 5: Concentration of metals and average (mg/kg) in plants and soil

Plant species

Sample code

Plant part

Concentration of metals(mg/kg)

Cu

Zn

Pb

Fe

Mn

A. columnaris

A

Needle

24.2±1.6

700±63.62

324±24.0

5283±481

85.4 ±4.5

Bark

110±2.9

10135±155.6

1983±18.7

17345±190

264±7.7

Topsoil

94.9±4.0

2157±0.4

186±8.9

170626±36.1

436±28.1

Subsoil

77.0±0.01

1944±152

123±3.5

155843±2315

317±84.2

B

Needle

56.7±1.7

1659±595.9

506±183

5098

79.5

Bark

58.8±1.6

7146±188

838±2.7

9466±842

128±10.2

Topsoil

89.4±2.3

2233±47.4

214±17.8

173839±13995

416

Subsoil

64.9±0.5

982±11.7

91.9±6.6

152463±11632

257±56.1

C

Needle

23.3±1.2

1124±34.7

255±31.5

4907±39.2

81.9±0.4

Bark

55.4±0.6

9180±121

756±40.9

7884±1019

92.1±5.3

Topsoil

110±3.0

5363±131

235±1.0

143564±1273

578±9.4

Subsoil

69.9±2.9

3357±57.8

71.4±5.3

139329±2354

336±0.07

D

Needle

25.2±0.6

963±56.3

336±30.2

4320±109

105±1.0

Bark

99.4±4.8

14377±65.5

1952±63.6

14982

235±34.9

Topsoil

101±2.5

2689±53.8

205±14.3

173469±4345

554±27.0

Subsoil

226±0.2

1758

157±27.4

162345±4886

1408±298.4

A. columnaris (average and range)

Needle

32.3±16.2

1111±404

355±106

4902±417

87.9±11.6

(23.3-56.7)

(700-1659)

(255-506)

(4320-5283)

(79.5-105)

Bark

80.9±27.8

10209±3045

1382±676

12419±4476

179±82.6

(55.4-110)

(7146-14377)

(756-1983)

(7884-17345)

(92.1-264)

A.columnaris(control)

A’

Needle

4.1±0.0

44.4±0.4

7

198

145±4.1

Bark

8.4±0.4

4.0±0.9

31.9±0.8

533±115

29.8±1.4

Table 5: Concentration of metals and average (mg/kg) in plants (continued)

Plant species

Sample code

Plant part

Concentration of metals(mg/kg)

Cu

Zn

Pb

Fe

Mn

C. camphora

E

Leaves

13.0±0.03

122±1.4

66.1±8.1

368

113±5.6

Bark

11.1±0.8

6856±85.7

80.5±5.9

1124±78.1

70.6±0.7

C. camphora (control)

E’

Leaves

12.1±0.02

15.6±0.09

21.5±7.5

35.5±4.0

117±2.7

Bark

4.6±0.1

5.3±0.5

-0.3±0.01

10.4

68.8±0.06

Citrus

F

Leaves

22.5±1.8

871±356

26.0±1.6

824±356

36.9±2.3

Citrus (control)

F’

Leaves

7.4±2.2

34.2±1.8

0.3

45.3

6.6±0.7

P.elliottii

G

Needle

9.2±0.4

47.7±1.5

22.2±0.6

71.3±0.1

1373±48.8

Bark

5.5±0.01

77.5±0.3

27.4±0.9

69.8±26.8

10.6±0.03

H

Needle

6.3±0.01

54.3±0.3

32.0±1.0

54.4±1.7

1608±20.2

Bark

6.7±0.4

22.0±0.5

28.6±6.4

14.2±0.4

13.9±0.09

I

Needle

11.3±0.03

24.7±0.9

42.0±1.3

109±4.1

154±9.6

Bark

3.39±0.0

137.7±1.5

5.6±0.1

59.0±5.5

21.9±0.04

J

Needle

8.4±0.3

17.1±2.7

19.1±0.06

43.1±1.9

661±1.0

Bark

7.2±0.005

5.2±0.5

45.4

45.7±0.8

8.9±0.3

P. elliottii (average and range)

Needle

8.8±2.0

35.9±17.8

28.8±10.3

69.4±28.8

949±665

(6.3-11.3)

(17.1-54.3)

(19.1-42.0)

(43.1-109)

(154-1608)

Bark

5.6±1.7

60.4±59.6

26.7±16.3

47.1±24.0

13.8±5.7

(3.3-7.2)

(5.2-137)

(5.6-45.4)

(14.2-69.8)

(8.9-21.9)

P.elliottii (control)

G’

Needle

10.9±0.3

36.3±0.8

29.3±7.0

26.1±4.3

32.8±0.8

Bark

2.34

16.1±0.2

4.0

70.8±0.5

15.5

3.2.1 Araucaria columnaris

Araucaria columnaris also known as Coral reef araucaria is a conifer in the Araucariaceae family. It usually grows in warm climate and can attain 60 m height and can be used as a lead indicator. The levels of metal in leaves and bark are shown in figure 6.

The four plants were sampled 250 m from the smelter. In this study we observed that A. columnaris plants accumulated very high level of metals as compared to the control plants (Figure 6). The range of metal accumulation in leaves was 23.3 mg/kg (Cu) to 5283 (Fe) mg/kg and in bark 55.4 mg/kg (Cu) to 17345 (Fe) mg/kg. Plant tissue uptake in the four Araucaria columnaris was in the order of bark > leaves for Cu, Zn, Pb, Fe and Mn. The barks of A (110-17345 mg/kg) and D (99.4-14982 mg/kg) accumulated greater concentrations of all metals studied than that of B (58.8-9466 mg/kg) and D (99.4-14982 mg/kg). Whereas for leaves, the highest concentrations of Cu (56.7 mg/kg), Zn (1659 mg/kg) and Pb (506 mg/kg) were observed in B, Fe (5283 mg/kg) in A and Mn (105 mg/kg) in D. This may be due to differences in age and height of the plants and different time exposure to pollution.

The pH of soil is known to influence metal (Mn, Fe, Cu) uptake by plants (Kampfenkel et al., 1995). In this research I did not measure the pH of the soil but from literature it is known that soil pH tends to be more acidic near smelters due to SO2 emissions, thus enhancing metal uptake by plants through the roots (Kozlov et al. 1995). Since not metals are easily translocated from soil to the aboveground parts this means that the high metal levels observed in leaves and bark come mainly from atmospheric depositions on the plant surface. This observation was supported by the low correlations between the metal concentration in soil and plants. The lowest correlations were found between copper concentrations in bark and topsoil (0.041) and between Fe concentrations in leaves and topsoil (0.004). But some exceptions were observed between Fe concentrations in bark and subsoil (0.609), Pb concentrations in bark and topsoil and subsoil respectively (0.722, 0.817). Higher correlations were also observed between Mn concentrations in leaves and subsoil (0.969) and Cu concentrations in leaves and topsoil (0.532). This means that some of the Fe, Pb, Mn and Cu were taken up from the soil in addition to dust particles from the smelter but zinc (R2 = 0-0.335) came mostly from atmospheric depositions from anthropogenic activities of the smelter.

Figure 6 shows the concentration of Cu, Zn, Pb, Fe and Mn in the leaves and bark of Araucaria columnaris

The high correlation between Mn concentrations in leaves and subsoil supports Kabata-Pendias and Pendias (2001) observation that Mn is easily transported through the plant to accumulate in leaves. The very low Pb correlation (0.026) between soil and leaves of Araucaria columnaris suggests that the high Pb concentrations in foliar tissues are the result of dust particles from the smelter. Kachenko and Singh (2004), Serbula et al. 92013) and Kabata-Pendias and Pendias (2001) also concluded that Pb comes mainly from airborne emissions.

The maximum positive correlations observed for Fe-Mn (bark), Cu-Pb (bark, leaves), Cu-Mn (bark) are shown in Table 6. The strongest correlation values demonstrate that the uptake of each metal is related to the concentration already available in the plants and that they have the same origin (Serbula et al., 2013).

Table 6: Metal correlations in bark and leaves of Araucaria columnaris

Bark

Leaves

Cu

Zn

Pb

Fe

Mn

Cu

Zn

Pb

Fe

Mn

Cu

1

0.440

0.981

0.987

0.983

1

0.798

0.913

0.080

0.198

Zn

1

0.563

0.345

0.395

1

0.561

0.005

0.193

Pb

1

0.951

0.969

1

0.045

0.060

Fe

1

0.993

1

0.746

Mn

1

1

There were no significant correlations between concentrations in bark and leaves indicating that metals are not successfully translocated from bark to leaves and the high levels of metal observed come mainly from atmospheric deposition.

Araucaria columnaris has spirally arranged, overlapping leaves, they tend to accumulate higher levels of metals as compared to plants having smooth leaves, thus the deposits on the leaf surface are not easily washed out by precipitation. Elevated levels of metal were found mostly in barks which may be due to the fact that metals are better retained on their flaky surfaces despite being subjected to rain (Olajine and Ayodele, 2002).

The EF values in bark and leaves of Araucaria columnaris are shown in figure 7 and 8 and in figure 9 respectively. The highest metal enrichment in bark was observed in plants A and D; all metals had EF > 2 which is indicative of pollution from the smelter. This may have resulted from the absorption of metals over a long period of time (Serbula et al., 2013). Zinc in bark had the highest EF (1786–3594) which demonstrated that the smelter is a major source of Zn contamination. Lead also had high EFs compared to Cu, Mn and Fe (Figure 7), thus confirming that Araucaria columnaris bark is a good Pb accumulator. Mn had the lowest EF in bark (3.0-8.8); one reason could be that Mn had been leached out of the plant since the soil pH is likely to be acidic near the smelter (Lobersli and Steinnes, 1987).

Pb in leaves had the highest EFs in the range of 36.4–72.2 indicating a high enrichment of Pb due to atmospheric depositions. Kachenko and Singh (2004) also concluded that elevated levels Pb arise mostly in the form of aerial deposits on leaves. The highest EF values of Cu, Zn and Pb (13.8, 37.3, and 72.2 respectively) were observed in the leaves of plant B. Only Mn had EFs < 2 showing a deficit of Mn in the leaves and this may be because of acid rain resulting from the high levels of SO2 normally present near smelters (Kozlov et al., 1995).

Figure 7 and 8 respectively showing the EFs for Cu, Zn, Pb, Fe and Mn in leaves of Araucaria columnaris

Figure 9 showing the EFs for Cu, Zn, Pb, Fe and Mn in leaves of Araucaria columnaris

3.2.2 Pinus elliottii

Pinus elliottii also known as slash pine is a fast growing tree and grows in humid climate and damp soil. The trees usually reach a height of 18 to 30 m tall with needle-like leaves.

All four Pinus elliottii were collected approximately 2 km from the smelter. No significant differences were observed between the contaminated and control plants except for Mn in contaminated leaves which was approximately 28 times higher than the control leaves (Figure 10). The level of metals in pine was much lower as compared to A. columnaris except for Mn which had been mostly accumulated in pine leaves (Table 5, Figure 10). The order of accumulation in leaves was found to be: Mn > Fe > Zn > Pb > Cu and in bark: Zn > Fe > Pb > Mn > Cu. The highest concentrations of Zn (24.7 mg/kg), Fe (109 mg/kg), Cu (11.3 mg/kg) and Pb (42.0 mg/kg) were observed in the leaves of plant I. Plant H showed highest concentration of Mn (1608 mg/kg) in leaves. Whereas, the maximum levels of the metals analysed in bark vary within each plants. These differences may be attributable to age and height of each plant.

The strongest positive and negative correlations in bark were observed for Cu-Pb (0.895) and Cu-Zn (0.984) respectively. For pine leaves, negative correlation was observed for Cu-Mn (0.658) and positive correlations for Cu-Fe (0.684), Zn-Mn (0.777), Pb-Fe (0.641). Antagonistic interactions of Zn-Cu and Zn-Pb in pine bark occur when there is competition between the uptakes of metals. The same observations were made by Serbula et al., (2013) in their research of airborne metal pollution in pine and linden.

Table 7: Correlation in bark and leaves of Pinus elliottii

Bark

Leaves

Cu

Zn

Pb

Fe

Mn

Cu

Zn

Pb

Fe

Mn

Cu

1

0.984

0.895

0.294

0.750

1

0.311

0.166

0.684

0.658

Zn

1

0.860

0.374

0.649

1

0.000

0.017

0.777

Pb

1

0.075

0.877

1

0.641

0.164

Fe

1

0.004

1

0.329

Mn

1

1

Figure 10 shows the concentration of Cu, Zn, Pb, Fe and Mn in Pinus elliotti

It is known that Mn can be easily transported through plants but accumulates mostly in the aboveground parts (Kabata-Pendias and Pendias, 2001). The levels of Mn in needles were very high (154-1608 mg/kg) as compared to bark (8.9-21.9 mg/kg). Plant G and H accumulated more Mn in needles (1373 mg/kg and 1608 mg/kg respectively). Soil pH tend to be less acidic farther away from the smelter, thus more Mn had been able to accumulate in the foliar tissues. The EFs of Mn in needles (4.6-49.0) being high indicate that the needles of Pinus elliotti are better indicators of atmospheric deposition of Mn than the bark since the EFs in bark were less than two which show no pollution (Figure 12 and Figure 13).

Significant negative correlations between pine needles and bark were observed only for Cu (0.726) and Pb (0.816) suggesting that some of Cu and Pb had been relocated from bark to leaves.

Most pine needles were not polluted by Zn, Cu and Pb since their EFs were less than 2 but some were affected by Fe and Mn. The pine bark did not show accumulation of Cu, Mn and Fe but were enriched to some extent with Zn (EF: 0.3-8.5) and Pb (EF: 1.4-11.3). It can be deduced that the smelter does not cause major pollution up to 2 km from the smelter although the low EFs observed may be due to the fact that pine trees were not completely affected by smelter dust emission since they may not lie within the wind direction. Another reason may be that rain can easily wash out superficial deposits from the smooth surface of the pine needles.

Figure 11 and 12 respectively showing the EFs for Cu, Zn, Pb, Fe and Mn in leaves of Pinus elliottii

Figure 13 showing the EFs for Cu, Zn, Pb, Fe and Mn in bark of Pinus elliottii

Cinnamomum camphora and Citrus plant

C. camphora also known as Camphor tree is a large evergreen tree of 20 to 30 meters tall. It consists of smooth-surface leaves, a very rough bark with vertical crevices and diffused porous wood. Citrus plant can be classified as a large shrub or small tree of 5 to 15 meters tall with spiny shoot.

Camphor and citrus plants were sampled 150 m and 300 m respectively from the smelter. From Figure 14 we observed that C. camphora bark (6856 mg/kg) and citrus leaves (871 mg/kg) accumulated the highest level of Zn (Figure). Camphor bark accumulated elevated concentration compared to leaves except for Cu and Mn. Since Camphor bark is rough, metals may not be completely washed out by rainfall whereas leaves having smooth surfaces do not retain metals easily.

The order of metal accumulation in Camphor leaves was Fe > Zn > Mn > Pb > Cu; Camphor bark: Zn > Fe > Pb > Mn > Cu and Citrus leaves: Zn > Fe > Mn > Pb > Cu. Camphor and Citrus leaves had slightly the same trend of metal accumulation and since they both had high concentrations of Zn and Fe in their foliar tissues, this may suggest that slightly similar mechanisms may control transport and storage of these metals in Camphor and Citrus plants.

Despite being farther away from the smelter, Citrus leaves had accumulated higher levels of Zn (871 mg/kg) and Fe (824 mg/kg) than Camphor leaves. Zn and Fe EFs (Figure 15) for citrus leaves were much higher which support the above observation that Citrus leaves are better accumulators of these metals.

Figure 14 shows the concentration of Cu, Zn, Pb, Fe and Mn in Cinnamomum camphora and Citrus plant respectively

The EF < 2 was observed in Camphor leaves (Cu, Mn) and bark (Mn). EF > 2 was found in camphor leaves and bark (Zn, Pb and Fe) and citrus leaves (for all metals analysed). Order of EF in camphor leaves was found to be Fe > Zn > Pb > Cu > Mn; camphor bark: Zn > Pb > Fe > Cu > Mn and citrus leaves: Pb > Zn > Fe > Mn > Cu. From the above data, it can be concluded that camphor bark is a good accumulator of Zn, Pb and Fe and has been able to prove that the smelter is a source of pollution. The soil surrounding the Camphor tree may be acidic since it is closest to the smelter and this may have lead to a deficit of Mn in the plant (EFs: 0.1-1.0).

Figure 15 showing the EFs for Cu, Zn, Pb, Fe and Mn in C. camphora and Citrus plant

4.0 Conclusion and Future works

4.1 Conclusion

Cu, Zn, Pb, Fe and Mn concentrations were analysed in order to assess the degree of pollution around an iron smelter. Each species analysed had different accumulation strategies owing to their genetic differences, tolerance to pollution and metal uptake mechanisms (Zheljazkov et al., 2008; Dahmani-Muller et al., 1999).

The order of metal accumulation in Araucaria columnaris was Fe > Zn > Pb > Mn > Cu, Cinnamomum camphora: Zn > Fe > Mn > Pb > Cu, Pinus elliottii: Mn > Fe > zn > Pb > Cu and Citrus plant: Zn > Fe > Zn > Pb > Cu. The significant EFs observed support the assumption that the smelter could be an important source of pollution. The major pollutants within radius of 300 m from the smelter were Zn and Fe. Concentrations of Cu were lowest in all plants analysed suggesting that the smelter is not a major contributor of Cu. It was observed that the leaves of Camphor and Citrus plants had slightly the same trend of metals accumulation and thus there may be some similarities in their uptake mechanism. Araucaria columnaris leaves and particularly its bark were concluded to be high metal accumulators due to their high EFs and so can be used as monitor of pollution near the smelter.

EFs for Mn in most plants were the lowest observed. This may have arised from the high SO2 emission usually observed near smelters (Kozlov et al., 1995). But Mn in pine trees generated high concentrations in the foliar tissues demonstrating Mn contamination 2 km away from the smelter.

Correlations between metals were found in plants. Antagonistic interactions were observed for Zn-Cu and Zn-Pb in pine bark suggesting that uptake of one element is hindered by the other and this could account for the differences between the metal concentrations observed.

The high level of Pb observed in the foliar tissues of Araucaria columnaris resulted mostly from airborne pollution since the EFs ranged between 36.4 and 72.2 and there were very low correlations between needles and soil (0.026). Such observation was supported by Douay et al. (2008), Serbula et al. (2013) and Kabata-Pendias and Pendias (2001). Dust particles coming from the smelter also held high zinc content since EFs were high in all plants, except Pinus elliottii, and there were low correlations between the metal concentrations Araucaria columnaris and soil.

Results from this research has been able to show that prompt actions should be undertaken to reduce air pollution since air particles could be a potential hazard to the health of workers and local inhabitants living around the perimeter of the iron smelter. Since Pinus elliottii is used in medicine, Mn and some other metals may enter the food chain.

4.2 Future Works

For more reliable results and better understanding of the extent of pollution, several more factors have to be taken into account. First of all, prevailing wind direction should be determined to identify which regions are most affected by emissions from the smelter and plants should be sampled within a certain radius which could cover the whole perimeter of the smelter to assess the effects heavy metals due to distance (Serbula et al., 2013).

Soil samples should be collected around each plant to determine the transfer factor (TF) from soil to plant parts and to establish if pollution results from airborne particulates or amount of heavy metals already present in soil. The bioavailable fraction of metals using extraction procedures with ethylenediaminetetraacetic acid (EDTA) or calcium chloride can help to better understand soil to plant transfer (Kachenko and Singh, 2005). The pH of the soil can also be calculated to identify if pH enhances the uptake of particular elements from soil. Cui et al. (2004) stated that soil nutrient and properties should be considered for more reliable results.

Quantification of metal levels in the atmosphere as well as physical and chemical environment of the plant can aid in understanding the pollution extent (Kachenko and Singh, 2004; Arunachalam et al., 2009). Dendrochemistry can also be used to estimate the degree of metal accumulation over several periods of time (Vanek et al., 2010; Lukaszewski et al., 1988). Taking into account all the above facts, the impact of stack emission can be better evaluated.