Abstract
Disposal of industrial wastewater has resulted in increased concentration of heavy metals (HMs) along the coastline of Malaysia. However, little is known about the accumulation capacity of HMs by
Introduction
Mangroves found at the interface of land and sea are highly productive wetlands of tropical and sub-tropical regions (Alongi Daniel, 2002; Chmura et al., 2003; Gandaseca et al., 2011; Praveena et al., 2008). Mangroves help reduce soil erosion and maintain stability of adjoining landforms (Alongi Daniel, 2002; Clark et al., 1997; Murin, 1995). It has been documented that mangrove sediments act as a sink for heavy metals (HMs) of anthropogenic origin (Peters et al., 1997; Tam & Wong, 1997).
The elevated level of HMs in the environment has become an important issue due to their persistence, toxicity and bio-accumulation in the food web (Haris et al., 2017; Shafie et al., 2013; Soliman et al., 2015). Major sources of HM contamination include agricultural chemicals and industrial effluents. Therefore, high concentration of HMs are found in mangrove areas adjacent to agricultural and industrial zones (Vane et al., 2009). These HMs include Copper (Cu), Nickel (Ni), Zinc (Zn), Lead (Pb), Arsenic (As), Cadmium (Cd) and Manganese (Mn; Chowdhury et al., 2015). In this scenario, phytoextraction is being used to treat HM contamination of soil and water since decades (Kaewtubtim, 2016).
Phytoremediation is a green technology that utilizes different types of plants, called phytoremediators, that extract HMs from the soil (Ismail, 2012). In this regard,
Methods
Study Area
The study was conducted in MMFR in Perak state, Peninsular Malaysia (Figure 1). The total area of MMFR is 40,600 ha and is divided into mainland forest (30%) and island forest (70%).

Study Site (A) Matang Mangrove Forest Reserve. Compartments for sampling are shown (yellow dots) by overlaying on imagery from Google Earth (Source: Google Earth Pro V 7.3.2.5776, July 13 2019, lat 4.838128 lon 100.624829, Eye alt 3.52 km, Maxar Technologies, CNES/Airbus). Compartment 42 represents 80 year old trees and compartment 31 represents 15 year old trees.
Sampling Procedure
In this study, 15-year-old (compartment 31, 04° 50.503’ N,100° 36.195' E) and 80-year-old (compartment 42, 04° 50' 04.5'' N, 100° 38' 05.5'' E) trees were selected to obtain samples of leaves, root and sediment (Figure 2). A total of 30 healthy trees (15 trees per age group) were sampled randomly . A metal ball was tied to a rope and launched towards the tree canopy to collect 250 g of fresh leaf samples . To sample roots 7 cm of root section was collected. 400 g of sediment samples were taken at a depth of 10 cm using peat auger during the dry season of 2016. All samples were collected at the same time. Each sample was placed in zip-lock plastic bags, tagged and stored in an ice chest at 4 °C during a five-hour transportation. In the laboratory, leaf and root samples were washed together with deionized water to remove dust, soil particles and algal traces and were oven-dried to a constant weight (Arianto et al., 2015). Subsequently, each leaf and root sample were grinded using porcelain pestle mortar and sieved using mesh size of 2 mm (Tam & Wong, 2000).

Trees of
Heavy Metal Assessment
2 g of each sample (leaves, roots and sediment) was placed in volumetric flasks (250 ml) and 20 ml of aqua regia solution was added (Walsh et al., 1997; HCL: HNO3 in 3:1 ratio). The solution was heated until it became clear and was filtered through ash-less filters (Whatman filter paper no. 2) into 100 ml volumetric flask. The solution was diluted with 40 ml distilled water and analyzed in atomic absorption spectrometer (AAS) to determine the elemental concentration of Mn, Fe, Zn and Cu (Brooks, 1983; Fletcher, 1981; Van Loon, 1985). Sediment physiochemical properties can be found in Supplementary Material.
Translocation and Bioconcentration Factor
Bioconcentration factor (BCF) indicates the capability of plants to take up heavy metals from soil (Qiu et al., 2011; Usman et al., 2012). Translocation factor (TF) shows the efficiency of plants in transporting HMs from belowground to aboveground organs (Usman et al., 2012). TF was calculated using the following equation (Mahdavian et al., 2017).
Where
Where
Statistical Analysis
Data corresponding to sediments was tested using one -way ANOVA to study the effect of location (compartment). Significant difference between the calculated mean of HM concentration in leaves, root and sediment samples were compared using post-hoc Tukey’s HSD tests. All means are presented with their standard errors and were taken at a significance level of 95% (p < 0.05). The statistical analysis was conducted by using SAS version 9.4.
Results
Sediment Characteristic
Results showed that the sediment fractions were 44.4/0.5/55 versus 41/0.5/58.4 (sand/silt/clay) for 15- and 80-year-old compartments, respectively (Supplementary Material Figure 1). No significant difference was found for pH and salinity between the two compartments (p > 0.05). The pH of sediments collected from 15-year-old and 80-year-old compartments was 4.5 and 5.1 respectively. Soil salinity came out to be 18.8 and 19.1 ppt for 15 and 80-year old compartments respectively.
Heavy Metal Concentration in Sediments and Plant Tissue
Results were described in two ways, firstly comparison of concentration of HMss between age groups and samples (Table 1). Highest concentration of Mn (5 mg kg−1) was found in the leaves of 80-year-old trees followed by 15-year-old trees leaves (3.1 mg kg−1), 80-year-old compartment roots (2.06 mg kg−1) and sediment (2.2 mg kg−1; Table 1). Sediments of 80-year-old compartment had the highest concentration of Zn (0.9 mg kg−1), Cu (0.13 mg kg−1) and Fe (46 mg kg−1). Furthermore, Zn concentration was lowest in the roots of 80-year-old compartment. In case of Fe and Cu the lowest concentrations (3.75 mg kg−1 and 0.05 mg kg−1) were observed respectively in the leaves of 15-year-old trees.
Heavy Metal Concentration of Both Compartments in Leaf, Root and Sediment Samples in mg kg−1.
Bold values indicate the significant differences (
Secondly, concentrations of HMs were compared only between samples (Table 2). Each concentration in Table 2 was the mean taken from the concentration in the 15- and 80-years old compartments. The highest content of Mn (4.26 mg kg−1) was found in leaves followed by roots (1.56) and sediment (1.65) (Table 2), indicating a high metal translocation efficiency from sediments to leaves. Along this line, iron (Fe) content was highest in sediment (42.7 mg kg−1), while leaves (5.44 mg kg−1) and roots (6.02 mg kg−1) contained much less Fe. Hence,
Mean Heavy Metals Accumulation in Leaves and Roots of
Different superscripts letters indicate the significant differences between both groups (p < 0.05).
Bioconcentration and Translocation Factor
In the present study, BCF of Mn, Zn and Cu were 3.52, 1.88 and 1.33, respectively. BCF of Fe as 0.26 indicated insignificant uptake. Along the same line, Mn (TF = 2.73) and Zn (TF = 1.28) are efficiently translocated from roots to leaves (Figure 3). Whereas Fe and Cu were equally distributed among leaves and roots, which also requires moderate transport.

Bioconcentration (A) and Translocation Factor (B) of the of
Discussion
In this study, we measured and compared the concentration of Fe, Cu, Mn and Zn in the leaves, roots and sediment samples of 15- and 80-year old compartments of
The concentrations of Fe and Cu were higher in roots than in shoots, suggesting efficient uptake and accumulation that might be linked to better oxidation in the rhizosphere (Marchand et al., 2011). However little internal transport of these elements was noticed. High accumulation of HMs in roots is regarded as metal exclusion strategy in plants which is employed to avoid metal toxicity (Marques et al., 2009). Low accumulation of Cu is in line with the findings of Paz-Alberto et al. (2015) where Cu was not detected in

Heavy Metal Concentration in Shoot (Leaves + Roots) of

Heavy metal Concentrations in Sediments at Two Different Sites (15 and 80 Years).
BCF and TF are used to estimate the phytoremediation potential of a certain specie (Qiu et al., 2011; Usman et al., 2012). In this study we were able to demonstrate through high metal concentrations in roots combined with TF of more than 1 that
Implications for Conservation
Supplemental Material
sj-xlsx-1-trc-10.1177_1940082920947344 - Supplemental material for Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia
Supplemental material, sj-xlsx-1-trc-10.1177_1940082920947344 for Phytoextraction Potential of
Supplemental Material
sj-pdf-2-trc-10.1177_1940082920947344 - Supplemental material for Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia
Supplemental material, sj-pdf-2-trc-10.1177_1940082920947344 for Phytoextraction Potential of
Supplemental Material
sj-pdf-3-trc-10.1177_1940082920947344 - Supplemental material for Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia
Supplemental material, sj-pdf-3-trc-10.1177_1940082920947344 for Phytoextraction Potential of
Supplemental Material
sj-pdf-4-trc-10.1177_1940082920947344 - Supplemental material for Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia
Supplemental material, sj-pdf-4-trc-10.1177_1940082920947344 for Phytoextraction Potential of
Supplemental Material
sj-pdf-5-trc-10.1177_1940082920947344 - Supplemental material for Phytoextraction Potential of Rhizophora Apiculata: A Case Study in Matang Mangrove Forest Reserve, Malaysia
Supplemental material, sj-pdf-5-trc-10.1177_1940082920947344 for Phytoextraction Potential of
Footnotes
Acknowledgments
The authors would like to acknowledge “
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Forest Research Institute Malaysia (FRIM), UPM Geran Putra and Fundamental Research Grant scheme (FRGS) (Vot. No): (6300835, 9522100, 5540232).
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
Please find the following supplemental material available below.
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