RESEARCH ARTICLE
Relationships of Zn Between Centella Asiatica and Geochemical Fractions of the Habitat Topsoils: Implications of Biomonitoring of Zn
Chee K. Yap1, *, Hishamuddin Omar1, Rosimah Nulit1, Ghim H. Ong2, Alireza R. Bakhtiari3, Ali Karami4, Salman A. Al-Shami5
Article Information
Identifiers and Pagination:
Year: 2017Volume: 3
First Page: 26
Last Page: 35
Publisher Id: BIOLSCI-3-26
DOI: 10.2174/2352633501703010026
Article History:
Received Date: 06/01/2017Revision Received Date: 03/03/2017
Acceptance Date: 06/03/2017
Electronic publication date: 29/05/2017
Collection year: 2017
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background:
Present study focused on the relationships of Zn concentrations between Centella asiatica (leaves, stems and roots) and their habitat topsoils.
Methods & Materials:
For leaves, it is found that Zn levels in the leaves significantly (P< 0.05) correlated with geochemical fractions of easily, freely, leachable or exchangeable (EFLE) (R= 0.94), acid-reducible (AR) (R= 0.63), oxidisable-organic (OO) (R= 0.85), resistant (R) (R= 0.79) and summation of all four fractions (SUM) (R= 0.83). For stems, it is found that Zn levels in the stems significantly (P< 0.05) correlated with AR (R= 0.73), R (R= 0.75) and SUM (R= 0.72). For roots, it is found that Zn levels in the roots significantly (P< 0.05) correlated with EFLE (R= 0.88), AR (R= 0.65), OO (R= 0.86), R (R= 0.77) and SUM (R= 0.82).
Conclusion:
These results indicated that the three parts of C. asiatica are able to reflect the Zn concentrations in the habitat topsoils. Based on ecological risk (Er) of the habitat topsoils, all samplings sites were categorized as ‘Low potential ecological risk’ according to Hakanson classification. Based on the positive significant relationships of Zn concentrations between plant parts and geochemical fractions of their habitat topsoils, present study indicated that C. asiatica can be used as biomonitoring plant of Zn polluted topsoils.
INTRODUCTION
Several routes involved in metal uptake by plants from soils and metal translocation within the plants were studied [1]. These routes include uptake of bioavailable metals, metal chelation and compartmentation in roots, metal translocation from root to shoot, and metal chelation and compartmentation in leaves [2, 3].
Zinc is one of the most common elements in the Earth's crust. High concentration of Zn can cause the phytotoxicity which can inhibit metabolic activities and result in growth retardation and senescence in plant. Excessive Zn can also give rise to Fe, Mn and Cu deficiency that reduce the transfer of those micronutrients from roots to shoots [4]. A medicinal plant Centella asiatica has been used widely in folk medicine for hundreds of years to treat a wide range of illness [5]. These plants were used to treat various illnesses thus awareness of the toxic effect of the medication due to the presence of excessive Zn accumulation shall be of public concern.
In Malaysia, Zn concentrations have been reported in mussels [6] and sediments [7]. However, Zn concentrations in terrestrial soils in relation to C. asiatica are lacking in the literature. Zn contamination of natural soil resources can be easily understand through this study which has emerged as an important issue due to the extension of urbanization and industrialization in Peninsular Malaysia. The objective of this study was to assess the potential of C. asiatica as a good biomonitor of Zn pollution based on correlation analysis of Zn between the plant and their topsoils collected from the field.
MATERIALS AND METHODS
In this paper, the Zn data from 9 sampling sites from Peninsular Malaysia (Fig. (1), Table 1) in the leaves, stems and roots of C. asiatica, and also their habitat topsoils were cited from [8], in which the data were presented in graphs. Briefly, the plants of 2-4 months maturity were collected and placed in plastic bags. The four geochemical fractions of Zn data in the soils were also cited from [8]. However, Ong et al. [8] did not report the relationships of Zn levels between the plant parts and different geochemical fractions of the habitat topsoils specifically.
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Fig. (1). Map showing the sampling sites for Centella asiatica in Peninsular Malaysia [8]. |
No | Sampling sites | Sampling dates | Sites descriptions |
---|---|---|---|
1. | Pontian, Johore | 9 May 10 | Near a plant agriculture area. |
2. | Kampung Simpang Renggam (KSR), Johore | 9 May 10 | Near a housing area. |
3. | Seremban, Sembilan | 4 June 10 | Near shop lots and road sides. |
4. | Kapar, Selangor | 5 June 10 | Small scale housing area. |
5. | Universiti Putra Malaysia (UPM), Selangor | 5 June 10 | Near agriculture area. |
6. | Butterworth, Penang | 12 June 10 | Near an industrial area and highway. |
7. | Kluang, Johore | 19 June 10 | Near paddy fields. |
8. | Karangan, Kedah | 12 June 10 | Near oil palm plantations. |
9. | Permatang Pauh (PPauh), Penang |
12 June 10 | Near a housing area and highway. |
Briefly, the sampling and analytical procedures of Zn can be found similarly as [9]. The separated plant parts (leaves, stems and roots) and the sediments were then dried in an oven for 72 hours at 60°C to constant dry weights. About 0.50 g of dried plant tissue parts were weighed using an analytical balance. Ten ml of concentrated nitric acid (AnalaR grade, BDH 69%) were added to a digestion tube to digest the plant tissues. For sediments, they were sieved under 63µm followed by sequential extraction technique (SET) [9]. Three replicates were done for each sampling site. Then, the digestion tubes were placed in a hot block digester at 40°C for 1 hour and 140°C for at least 3 hours [6]. After dilution, the solution was filtered through a Whatman No. 1 filter paper into an acid-washed pill box until Zn analysis by using an air-acetylene Perkin-Elmer™ flame atomic absorption spectrophotometer model AAnalyst 800. Standard solutions for Zn were prepared from 1000 ppm stock solution provided by MERCK Titrisol.
Geochemical fractions of Zn in the sediments were obtained using the modified SET (Sequential Extraction Technique) described by [10]. The four fractions ‘easily, freely or leachable or exchangeable (EFLE; F1)’, ‘acid-reducible (AR; F2)’, ‘oxidisable-organic (OO; F3)’ and resistants (R; F4) were employed [9].
Data Treatment
Geochemical Indices in the Topsoils
There are two geochemical indexes involved in this study which are geoaccumulation (Igeo) and enrichment factor (EF).
To determine the Igeo, the formula was introduced by Muller [11] as a quantitative measure of the degree of metal pollution in aquatic sediments as below:
Where;
Cn = the concentration of examined metal (n) in the soil;
Bn = the background reference values. The background references values for Fe and Zn used in the present study were 3.09% and 52.0 mg/kg, respectively, followed the upper continental crust values reported by Wedepohl [12] and the K represents for the factor of 1.5 value due to lithogenic effects [13].
To determine the EF, the formula below was used:
Where;
Me/Fe sample = the metal to Fe ratio to be analyzed.
Me/Fe background = the metal to Fe ratio which involved the background reference value, as mentioned above.
For the reading of EF, the EF which is less than 2 is the depletion to minimal enrichment. Whereas, the 2 ≤ EF < 5 categorized as moderate enrichment, 5 ≤ EF < 20 categorized as significant enrichment, and 20 ≤ EF < 40 are very high enrichment. For EF > 40, they are extremely high enrichment [14].
Ecological Risk Assessment (ERA) on the Topsoils
For the determination of potential ecological risk, the following formulas were used:
Cf= Cs/Cn
Where;
Cf= contamination factor of Zn
Cs = the examined Zn in the samples.
Cn = the background reference values of Zn, as mentioned above.
Er= Tr × Cf
Tr = the factor of toxic response; In the present study, the Tr employed for Zn was 1.00 [15].
Bioconcentration Factor
In this study, the plant metal accumulation efficiency was measured by Zn bioconcentration factor (BCF), which is a ratio of Zn concentration in plant tissue to that in the soil [16-18]:
BCF= Plant (Zn)/Soil (Zn)
where Plant (Zn) is the Zn concentration in the plant tissue (leaves, stems or roots), and Soil (Zn) is the Zn concentration in the habitat topsoil.
RESULTS AND DISCUSSION
The Zn concentrations of C. asiatica (cited from [8]) and in their habitat topsoils are presented in (Table 2). Overall, there were two sampling sites (PPauh and Butterworth) that exceeded 300 µg/g dw in the leaves and roots of the plants. For stems, higher levels of Zn were found in three sites (Butterworth, Seremban and Pontian). Therefore, Butterworth was consistently found to have highest levels of Zn in leaves, stems and roots of the plants.
leaves | Stems | Roots | F1 | F2 | F3 | R | SUM | NR | |
---|---|---|---|---|---|---|---|---|---|
PPauh | 316 | 127 | 336 | 8.45 | 39.3 | 63.8 | 112 | 224 | 60.6 |
Karangan | 172 | 133 | 208 | 2.00 | 3.14 | 12.9 | 31.9 | 49.9 | 15.9 |
Kluang | 145 | 124 | 185 | 2.43 | 4.92 | 10.8 | 24.4 | 42.6 | 76.6 |
Butterworth | 337 | 160 | 349 | 14.7 | 52.4 | 69.3 | 171 | 308 | 103 |
UPM | 145 | 124 | 208 | 0.44 | 5.13 | 36.3 | 46.6 | 88.5 | 11.4 |
Kapar | 121 | 91.3 | 134 | 1.64 | 4.36 | 5.87 | 21.3 | 33.1 | 37.6 |
Seremban | 182 | 177 | 230 | 3.17 | 67.1 | 31.6 | 108 | 210 | 102 |
KSR | 160 | 127 | 171 | 1.93 | 22.3 | 31.5 | 77.1 | 133 | 77.3 |
Pontian | 182 | 172 | 214 | 2.15 | 25.4 | 53.1 | 131 | 212 | 27.5 |
From Table 2, overall, the plant roots showed the highest Zn accumulation followed by leaves and stems. Those results were supported by [19]. It has been reported that about 30 µg/g dw of Zn is adequate for plant growth and 300 to 500 mg/kg of Zn is considered toxic to the plants [20]. The physiological disorders and metabolic abnormalities in plants could be resulted from excessive exposure of Zn [21]. Therefore, Zn might be accumulated in the roots and be unable to enter the plant by being kept in the root cells where they would be detoxified by forming complexes or sequestered into vacuoles [22]. This action greatly restricted the translocation of metals to the above-ground organs. Moreover, it could protect the leaf tissues and the metabolically active photosynthetic cells from Zn toxicity [23].
In comparison to the geochemical factions, this is well supported by the highest levels if Zn in EFLE (F1), OO (F3) and resistant (R) fractions of the topsoils. This is again supported by the total summation of all fractions and non-resistant fraction. Hence, these comparisons indicated the use of different parts of C. asiatica as a good biomonitor of Zn. In order to see the close relationships of Zn levels between the plants ad topsoils, the graphical relationships of Zn between the both parameters are presented in Fig. (2) (based on leaves), Fig. (3) (based on stems) and Fig. (4) (based on roots).
Based on the relationships of plant leaves and geochemical fractions Fig. (2), it is found that Zn levels in the leaves significantly (P< 0.05) correlated with EFLE (R= 0.94), AR (R= 0.63), OO (R= 0.85), R (R= 0.79) and SUM (R= 0.83). Based on the relationships of plant stems and geochemical fractions Fig. (3), it is found that Zn levels in the stems significantly (P< 0.05) correlated with AR (R= 0.73), R (R= 0.75) and SUM (R= 0.72). Based on the relationships of plant roots and geochemical fractions Fig. (4), it is found that Zn levels in the roots significantly (P< 0.05) correlated with EFLE (R= 0.88), AR (R= 0.65), OO (R= 0.86), R (R= 0.77) and SUM (R= 0.82). These results indicated the three parts of C. asiatica are able to reflect the Zn concentrations in the habitat topsoils. The positive relationships of Zn between soil non-resistant fractions (EFLE, AR and OO) and plant root indicated a close relationship between soil Zn concentration and root metabolism, which should be further examined to understand how soil Zn concentration can impact root Zn accumulation efficiency. According to [24], when soil metal concentration exceeds the plant tolerance, growth and metabolism will be inhibited and eventually the plant species will be excluded from the site vegetation assemblage even though there is a seed existing in the regional pool. Therefore, the roots, leaves and stems of C. asiatica are good biomonitors of Zn pollution in the environment.
Zn soil | Fe (%) soil | EF | Igeo | Cf | Er | |
---|---|---|---|---|---|---|
PPauh | 224 | 2.16 | 6.14 | 1.52 | 4.30 | 4.30 |
Karangan | 49.9 | 1.37 | 0.64 | -2.40 | 0.29 | 0.29 |
Kluang | 42.6 | 1.22 | 0.62 | -2.62 | 0.24 | 0.24 |
Butterworth | 308 | 2.79 | 6.55 | 1.98 | 5.92 | 5.92 |
UPM | 88.5 | 2.59 | 2.03 | 0.18 | 1.70 | 1.70 |
Kapar | 33.1 | 2.34 | 0.84 | -1.24 | 0.64 | 0.64 |
Seremban | 210 | 2.74 | 4.55 | 1.43 | 4.03 | 4.03 |
KSR | 133 | 2.18 | 3.61 | 0.77 | 2.55 | 2.55 |
Pontian | 212 | 2.47 | 5.10 | 1.44 | 4.07 | 4.07 |
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Fig. (2). Relationships of Zn between leaves of Centella asiatica and different geochemical fractions of habitat topsoils, based on log10 axes of x and y. |
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Fig. (3). Relationships of Zn between stems of Centella asiatica and different geochemical fractions of habitat topsoils, based on log10 axes of x and y. |
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Fig. (4). Relationships of Zn between roots of Centella asiatica and different geochemical fractions of habitat topsoils, based on log10 axes of x and y. |
The geochemical indices for EF, Igeo, CF and ER of the topsoils are presented in (Table 3). For EF, there are three sites (PPauh, Butterworth and Pontian) with classification of ‘moderate severe enrichment; 5-10’ (Table 4). Also, there are two sites with ‘moderate enrichment’, one site with minor enrichment and others with ‘no enrichment’, according to Taylor [25] classification.
A. Enrichment factor (EF) | ||
---|---|---|
EF ranges | Degree of enrichment | |
<1 | No enrichment | |
1-3 | Minor enrichment | |
3-5 | Moderate enrichment | |
5-10 | Moderately severe enrichment | |
10-25 | Severe enrichment | |
25-50 | Very severe enrichment | |
50 | Extremely severe enrichment | |
B. Geoaccumulation index (Igeo) | ||
Igeo values | Igeo Classes | Pollution intensity |
0 | <0 | Unpolluted |
1 | 0-1 | Unpolluted to moderately polluted |
2 | 1-Feb | Moderately polluted |
3 | 2-Mar | Moderately to strongly polluted |
4 | 3-Apr | Strongly polluted |
5 | 4-May | Strongly to very strongly polluted |
6 | >5 | Very strongly polluted |
C. Contamination factor (Cf) | ||
Value rangse of Cf | Description | |
Cf< 1 | Low contamination factor | |
1≤ Cf < 3 | Moderate contamination factor | |
3 ≤ Cf < 6 | Considerable contamination factor | |
Cf ≥ 6 | Very high contamination factor | |
D. Potential risk index for individual metal (Er) | ||
≤ Value ranges of Er | Description | |
Er < 40 | Low potential ecological risk | |
40 ≤ Er < 80 | Moderate potential ecological risk | |
80 ≤ Er < 160 | Considerable potential ecological risk | |
160 ≤ Er < 320 | High potential ecological risk | |
Er ≥ 320 | Very high potential ecological risk at hand for the substance in question |
For Igeo, there are four sites (PPauh, Butterworth, Seremban and Pontian) with classification of ‘moderate polluted; 1-2’ (Table 4). Also, there are two sites with ‘Unpolluted to moderately polluted’ while others with ‘unpolluted’, according to Muller [11] classification. For Cf, similarly there are four sites (PPauh, Butterworth, Seremban and Pontian) with classification of ‘Considerable contamination factor; 3-6 (Table 4). Also, there is one site with ‘Moderate contamination factor’ while others with ‘Low contamination factor’, according to Hakanson [15] classification. For Er, all sites were below 40 with ‘Low potential ecological risk’ according to Hakanson [15] classification.
Ratios of nonresistant/resistant (NR/R) are given in (Table 5). Based on NR/R, the topsoils from two sites (Kluang and Kapar) are found to have been dominated (> 1.00) by nonresistant fraction of the soils while the rests are mostly originated from natural sources (< 1.00).
NR/R | BCF-1 (Leave/Soil) | BCF-2 (Stem/Soil) | BCF-3 (Root/Soil) | |
---|---|---|---|---|
PPauh | 0.54 | 1.41 | 0.57 | 1.50 |
Karangan | 0.50 | 3.45 | 2.68 | 4.16 |
Kluang | 3.14 | 3.41 | 2.90 | 4.34 |
Butterworth | 0.60 | 1.09 | 0.52 | 1.14 |
UPM | 0.25 | 1.63 | 1.40 | 2.35 |
Kapar | 1.77 | 3.66 | 2.76 | 4.06 |
Seremban | 0.94 | 0.87 | 0.85 | 1.10 |
KSR | 1.00 | 1.21 | 0.96 | 1.29 |
Pontian | 0.21 | 0.86 | 0.81 | 1.01 |
The three values of accumulation efficiency (BCF) are presented in (Table 5). The ratios of leave/soil (BCF-1), stem/soil (BCF-2) and root/soil (BCF-3) varied from 0.87-3.66, 0.52-2.90, and 1.01-4.34, respectively. Based on six dominant plant species, Qian et al. [18] reported the ratios of BCF (root/soils) varied from 0.56 to 10.8. Higher Zn concentration in plant parts demonstrated that the amount of Zn accumulated in the plant parts is positively related to Zn concentrations in the topsoils. This was consistent with the results from other studies with other metals such as Cd, Ta and V [33] and V [18]. Hence, present three values of BCF indicated accumulation efficiency of Zn in the leaves, stems and roots of C. asiatica.
Based on some established soil sediment guidelines for Zn, four sampling sites (PPauh, Butterworth, Seremban and Pontian) had exceeded Canadian soil quality for agricultural use (200 µg/g dw) [26] while Butterworth exceeded the Chinese soil quality standard (250 µg/g dw) . The rest of the sampling sites were found to be below Target value (140 µg/g dw, the baseline concentration value that considered not affecting the natural properties of the soil) according to Dutch soil guideline [27]. Based on the present data, all sampling sites were found to be lower than middle value (430 µg/g dw, a threshold value for further investigation) and intervention value (720 µg/g dw, the maximum tolerable concentration that remediation is required), according to Dutch soil guideline [27].
In Malaysia, industries in Malaysia such as electronics, textiles, food processing and rubber based industry contribute the Zn contamination to the environment [28]. The use of fungicides and fertilizers containing organo-zinc could have caused the excess to leach into the soil [29]. Soils near highways and smelters contained high Zn concentrations as a result of deposition of Zn released in tire abrasion and stack emissions [30]. Zn level in soils was reported decreased with distance from the point source of pollution (CCME, 1999). Therefore, higher level of Zn can be found in nearby of the Zn contamination source.
Previously, Warne et al. [31] reported the Zn concentrations are found in the ranges of Zn between 150-300 µg/g dw in the polluted soils. Wang and Qin [32, 33] reported Zn ranges 53-380 μg/g dw in urban topsoil of Xuzhou (China) while Yap and Pang [7] reported the Zn ranges as 88.7-484 μg/g dw in river drainage surface sediments collected from the north western aquatic area of Peninsular Malaysia. Yap et al. [34, 35] reported the Zn ranges as 50–336 μg/g dw and 330-484 μg/g dw for intertidal and drainages in Selangor, respectively. All the above studies explained the higher or elevated levels of Zn were related to anthropogenic inputs that had increased the levels of Zn in soils. Therefore, present findings are comparable to the above reported data. This has given a strong indication of Zn contamination in Malaysian topsoils that should be given be concern from environmental management point of view [36].
CONCLUSION
Present study investigated the relationships of Zn concentrations between three parts (leaves, stems and roots) of Centella asiatica and their habitat topsoils. Based on Er of the habitat topsoils, all samplings sites were categorized as ‘Low potential ecological risk’ according to Hakanson [15] classification. Based on the positive significant relationships of Zn concentrations between plant parts and geochemical fractions of their habitat topsoils, present study indicated that C. asiatica can be used as biomonitoring plant of Zn polluted topsoils.
CONFLICT OF INTEREST
The authors confirm that this article content has no conflict of interest.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the financial support provided through Research University Grant Scheme (RUGS) [Vote no. 9322400] by Universiti Putra Malaysia, and also Fundamental Research Grant Scheme (FRGS) [Vote no. 5524953] by Ministry of Higher Education Malaysia.