Marine Pollution Bulletin 212 (2025) 117587

Contents lists available at ScienceDirect

Marine Pollution Bulletin

journal homepage: www.elsevier.com/locate/marpolbul

Effectiveness of artificially planted mangroves on remediation of metals

released from ship-breaking activities
Sima Karmakar a , Khadijatul Kubra Riya a , Yeasmin N. Jolly b, Shirin Akter b, K.M. Mamun b,
J. Kabir b , Bilal Ahamad Paray c , Takaomi Arai d, Jimmy Yu e, Norhayati Ngah f,
Mohammad Belal Hossain a,e,f,*
a
Department of Fisheries and Marine Science, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
b
Atmospheric and Environmental Chemistry Laboratory, Atomic Energy Centre, Dhaka, Bangladesh
c
Department of Zoology, College of Science, King Saud University, PO Box 2455, Riyadh 11451, Saudi Arabia
d
Environmental and Life Sciences Programme, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE 1410, Brunei Darussalam
e
School of Engineering and Built Environment, Griffith University, Brisbane, QLD, Australia
f
East Coast Environmental Research Institute, Universiti Sultan Zainal Abidin, Gong Badak Campus, 21300 Kuala Nerus, Terengganu Darul Iman, Malaysia

A R T I C L E I N F O A B S T R A C T

Keywords: The pervasive and escalating issue of toxic metal pollution has gathered global attention, necessitating the
Heavy metal contamination exploration of innovative ecological strategies like phytoremediation. This study explored the extent of poten­
Mangrove species tially toxic metal contamination status and the effectiveness of three planted mangrove species (Avicennia marina,
Phytoremediation
Bruguiera gymnorhiza,and Excoecaria agallocha) in phytoremediation efforts to reduce pollution level. The results
Pollution indices
indicated that the mean concentrations of elements in the sediment of the area followed a descending sequence:
Coastal ecosystems
Fe (27,136.67 ± 929.57 mg/kg) > Ti (3371.53 ± 228.08 mg/kg) > Sr (198.59 ± 37.43 mg/kg) > Zr (159.49 ±
22.35 mg/kg) > Rb (159.11 ± 17.63 mg/kg) > Cu (82.73 ± 5.01 mg/kg) > Zn (61.29 ± 2.42 mg/kg). The
comprehensive assessment of pollution indices, encompassing enrichment factor (EF), contamination factor (CF),
pollution load index (PLI), and geo-accumulation index (Igeo), elucidated a low to medium contamination level,
particularly regarding Cu, primarily attributed to anthropogenic sources. Correlation analysis and principal
component analysis (PCA) unveiled the influence of anthropogenic activities on heavy metal distribution.
Evaluating the phytoremediation potentiality via bioconcentration factor (BCF) and translocation factor (TF)
revealed limited metal accumulation in plant tissues, yet TF values exceeding 1 demonstrated efficient metal
translocation from roots to aerial parts. Remarkably, Excoecaria agallocha exhibited the greatest phytor­
emediation potential, effectively translocating metals such as Cu and Zn to aerial parts (TF > 1). Thus, the
intricate interplay between mangrove species and their environmental setting emerges as pivotal in curbing
heavy metal transfer to neighboring estuarine and marine ecosystems.

1. Introduction contaminate the coastal and marine environments, impairing ecosystem

services vital to aquatic organisms and human well-being (Li et al.,
Heavy metals pose a significant threat to aquatic ecosystems due to 2016; Miola et al., 2016). This contamination has raised significant
their persistence, toxicity, and bioaccumulation potential (Ye et al., concerns among environmental scientists, coastal managers, policy­
2015; Huang et al., 2020a, 2020b). These metals originate from both makers, fishers, and the broader public. Beyond ecosystem impacts,
natural processes, like mineral weathering and volcanic activity these potential toxic elements pose direct risks to human health. Cd
(Larkum, 2006; Liu et al., 2018), and anthropogenic sources, including exposure is associated with lung adenocarcinoma, lung cancer, and bone
urban runoff, industrial discharge, and agricultural practices (Haynes loss, while Pb disrupts hemoglobin biosynthesis, damages the central
and Johnson, 2000; Govindasamy et al., 2011). Transported through nervous and hematopoietic systems, and causes cognitive deficits and
rivers and estuaries, heavy metals frequently accumulate and behavioral disorders, particularly in children. Cr is a toxic, non-essential

* Corresponding author at: Department of Fisheries and Marine Science, Noakhali Science and Technology University, Noakhali 3814, Bangladesh.
E-mail address: mbhnstu@gmail.com (M.B. Hossain).

https://doi.org/10.1016/j.marpolbul.2025.117587
Received 17 November 2024; Received in revised form 15 January 2025; Accepted 17 January 2025
Available online 23 January 2025
0025-326X/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

metal harmful at high concentrations, while Ni, Cu, and Zn become toxic 2. Materials and methods
when their concentrations exceed permissible limits (Sobhanardakani
et al., 2018; Taati et al., 2020; Mohebian et al., 2021). To address this 2.1. Study area description
issue, researchers are exploring a range of mitigation strategies,
including chemical and nature-based solutions, with phytoremediation Sitakunda area, one of the oldest sites of human habitation in
emerging as a promising approach. Phytoremediation involves using Bangladesh, is situated in the northwestern part of Chittagong district.
plants to absorb, immobilize, or stabilize these potential toxic elements The area is situated between 22◦ 38′43′′ N latitude and 91◦ 39′47′′ E
from sediments through their root systems, offering a cost-effective and longitude, at the mouth of the Big Feni River and in a newly formed
environmentally friendly solution (Shahraki et al., 2008; Dinaki et al., coastal area next to the Sandwip Channel, which has Bangladesh’s
2023; Baharvand et al., 2023). Mangroves, in particular, have demon­ largest planted mangrove forest. In 1976, the Chittagong Coastal
strated a notable ability to accumulate potential toxic elements in their Afforestation Division (CCAD) began a coastal afforestation endeavor in
roots, stems, and leaves, making them highly suitable for phytor­ Sitakunda, using solely plantation species. It includes plantations that
emediation applications in contaminated coastal areas (Oo et al., 2009; were established between 1976 and 2007. The plantations lie between
Costa-Boeddeker et al., 2020). 22◦ 30′ N to 22◦ 59′ N latitude and 91◦ 27′ E to 91◦ 45′ E longitude and
Mangroves are highly productive ecosystems that act as vital in­ extends from south to north with an area spreading about 6524 ha. The
terfaces between terrestrial and marine environments. They provide four beats of Sitakunda Forest Range are Bansbaria, Bhaterkhail,
critical ecological services, such as shoreline stabilization, carbon Bakhkhali and Bagachattar (with Kattoli area) covering a total existing
sequestration, nutrient cycling, and habitat for diverse species (Wang area of 3830 ha (Barua and Rahman, 2019). The climatic conditions of
et al., 2021). Mangroves also function as natural filters, trapping pol­ this area are of a tropical monsoon nature. The average annual tem­
lutants from tidal seawater and surface runoff and accumulating po­ perature is 28.21 ◦ C, with an annual rainfall of 148 mm. The land surface
tential toxic elements within their sediments (e Silva et al., 2006; Kumar is flat, stable and muddy. No rock formation occurs in this area (Haque
et al., 2016; Bastakoti et al., 2018). However, these ecosystems face et al., 2000). Sitakunda area has strong fresh water influence by Dom­
pressures from human activities, including agricultural runoff, industrial khali and Badarkhali canal.
discharge, and port operations, which increase the influx of pollutants
and threaten their integrity (Giri et al., 2015; Dudani et al., 2017). 2.2. Selection of study site and period
Despite these threats, mangroves exhibit unique biological and chemical
properties that enable them to tolerate and accumulate potential toxic The samples were collected from three stations marked as S1, S2 and
elements, underscoring their potential role in remediating contaminated S3 (Fig. 1). Samples were collected from 4 April to 6 April 2021. Each
environments (Chowdhury et al., 2015; Wang et al., 2021; Chanda et al., day samples were collected between 9 and 12 pm during low tide period
2021). with minimal disturbance. Two replicate samples were collected for
Sitakunda, situated in the Chittagong District of Bangladesh, is a sediment and each plant parts.
coastal region significantly affected by shipbreaking activities, which
contribute to the release of considerable amounts of potential toxic el­ 2.3. Sample collection, preparation and analysis
ements and petroleum hydrocarbons into the environment (Hasan et al.,
2013). Large-scale mangrove plantations have been established to pro­ A total of six surface sediment samples (two replicated samples from
tect and rehabilitate the degraded ecosystem and mitigate pollution each station) were taken from the top 0–5 cm, covering an area of 1 m2
(Chowdhury et al., 2016). Prominent mangrove species, including Son­ (Chowdhury et al., 2015). For plant sampling, three predominant
neratia apetala (Keora), Avicennia marina (Baen), Excoecaria agallocha mangrove species—Avicennia marina, Bruguiera gymnorhiza, and Excoe­
(Gewa), Ceriops decandra (Goran), and Bruguiera gymnorhiza (Kakara), caria agallocha—were selected based on specific phytoremediational and
were selected for their ability to tolerate and accumulate potential toxic ecological characteristics relevant to the study area. Avicennia marina
elementswithin their tissues. While the extent of potential toxic ele­ was chosen for its known high tolerance to salinity and strong ability to
ments contamination in this region has been well-documented (Rahman accumulate potential toxic elements, making it effective for studies on
et al., 2019; Hossain et al., 2022), the potential of some ecologically metal bioaccumulation. Bruguiera gymnorhiza is resilient in mixed-
important mangroves (Avicennia marina, Bruguiera gymnorhiza,and sediment environments, allowing it to thrive in various soil composi­
Excoecaria agallocha) to remediate pollutants through phytoremediation tions common to contaminated sites. Excoecaria agallocha was selected
remains largely unexplored. This gap in understanding limits the opti­ for its unique phytotoxic properties and ability to accumulate metals,
mization of these plantations for pollution control. Investigating the which supports its potential role in remediating highly contaminated
capacity of these species to absorb, translocate, and store potential toxic environments. Leaves, stems, and roots from each plant were collected
elements under local environmental conditions is essential to determine using sterilized knives. Only trees with a chest height > 20 cm and a
their effectiveness. Moreover, species-specific insights could guide the height > 1 m were considered, following Chowdhury et al. (2015) to
strategic implementation of mangrove-based or nature-based solutions ensure sample uniformity. A total of 54 plant samples were collected. To
for addressing contamination from industrial activities. Therefore, this remove fouling substances, plant samples were thoroughly washed
study aimed to: (1) assess the concentrations of heavy metals (Ti, Fe, Cu, before being placed in labeled plastic bags. All samples were then
Zn, Rb, Zr, and Sr) in mangrove sediments along the Chittagong coast, transported to the Atmospheric and Environmental Chemistry Labora­
(2) evaluate pollution levels and associated environmental risks, (3) tory of the Atomic Energy Centre, Dhaka (AECD), Bangladesh, for
measure the total potential toxic elements content in different parts analysis.
(leaves, stems, and roots) of three mangrove species, and (4) investigate The sediment samples were sieved in the laboratory with a plastic
the capacity of these plants to accumulate and transfer potential toxic sieve to eliminate any debris and organic matter. To analyze the po­
elements, thereby evaluating their potential for phytoremediation in tential toxic element concentration by X-ray fluorescence (XRF), plant
this ecosystem. Such research holds broader significance for enhancing and sediment samples were dried in an oven at 70 ◦ C until constant
ecosystem sustainability and informing global restoration efforts in weight was obtained. To achieve fine grain size and a consistent mixture,
similar industrially affected coastal regions. each sample was ground with an agate mortar and pestle and then
passed through a 1.0 mm (16 US mesh) nylon mesh sieve, adhering to
the USA Standard Testing Sieve specifications from W.S. Tyler Inc., USA.
The resulting dust was pressed into 25 mm pellets using a pellet maker
under approximately 10 tons hydraulic pressure for 2–5 min (Specac

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

Fig. 1. Map showing the location of the study area. a) Chittagong district, b) study area with their key elements ship breaking activities and mangrove forest.

Ltd., Orpington, Kent, UK)). For elemental analysis, these pellets were understanding of natural metal concentrations. When local data is un­
analyzed using energy-dispersive X-Ray fluorescence (EDXRF) (Epsilon available, global or regional geochemical baselines, such as average
5, PANalytical, Almelo, The Netherlands), following a previously shale values (e.g., Turekian and Wedepohl, 1961) or sediment quality
established method for similar materials (Islam et al., 2017a, 2017b). guidelines (e.g., NOAA standards), are employed. Historical data,
The pelletized sample was inserted into the sample holder of the X-Ray including pre-industrial sediment or soil records, also serve as valuable
Fluorescence (XRF) system and was bombarded by the X-Ray tube at a references for background levels. Additionally, statistical approaches,
voltage of 25 V and current of 50 μA for 100 counts or approximately 18 such as using the lower percentiles (e.g., 5th or 10th percentile) of
min. The characteristic X-Ray of the sample areas were detected by the observed metal concentrations in minimally impacted samples, can
solid-state Si–Li detector system. The spectrum analysis was carried out provide reliable estimates of background values. These methodologies
using the ADMCA and FP-CROSS Software which relates the peak area ensure the accurate evaluation of anthropogenic impacts and ecological
with concentration. risks associated with metal contamination.
Phytoremediation potential was evaluated using the bio­
concentration factor (BCF) and the translocation factor (TF). BCF was
2.4. Ecological risk assessments of potential toxic elements used to assess how efficiently plants can accumulate heavy metals from
sediment into their tissues, with separate values for leaves and roots. The
There are different types of pollution indices for sediments. Ac­ TF was calculated to determine the plants’ ability to translocate metals
cording to several studies, the selection of pollution indices is associated from roots to shoots, with values >1 indicating effective translocation.
to various goals, such as level of contamination, origin of metals, and Detailed descriptions of the calculation methods for each of these indices
potential ecological risk (Ahirvar et al., 2023; Kowalska et al., 2018; Al- are provided in Supplementary Table S1.
Anbari et al., 2015;). In the present study enrichment factor (EF),
contamination factor (CF), pollution Load Index (PLI), and geo­
accumulation index (Igeo), Nemerow pollution index (NIPI), the Metal 2.5. Statistical analysis
Accumulation index (MAI), and risk index (RI) were measured to eval­
uate the current status of sediment. The EF is considered as an effective Mathematical data were analyzed using Microsoft Excel version 10
tool to evaluate the magnitude of contaminants in the environment. The and the statistical analysis like ANOVA, correlation matrix and PCA was
CF was used to determine the degree of contamination by comparing the performed by PAST (Hammer et al., 2001) while ArcGIS (version 10.3)
concentration of metals in the sediment with background values. The was used for map plotting. The One-way analysis of variance (ANOVA)
Igeo helped to assess the level of contamination by comparing the was used to show the significant differences of potential toxic element
measured concentrations with background concentrations, incorpo­ concentrations in sediment. Before conducting ANOVA, a test for data
rating a lithospheric effect factor. The PLI was calculated as the product normality was performed. Pearson correlation was utilized to examine
of the individual CF values for each analyzed metal. Additionally, the the relationship between heavy metals, and Principal Component
NIPI and MAI provided a comprehensive assessment of pollution levels, Analysis (PCA) was conducted to determine the origin of heavy metals
while the RI was used to evaluate the associated ecological risks. In within sediment.
metal risk analyses, particularly using indices like the Geo-accumulation
Index (Igeo) and Contamination Factor (CF), background values are 2.6. Quality control and quality assurance
crucial for assessing contamination levels and ecological risks. These
values are commonly derived from local baseline studies conducted in Energy Dispersive X-Ray Fluorescence (EDXRF) was employed to
pristine or minimally impacted areas, providing a region-specific measure elemental concentrations in soil samples, with calibration

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

curves constructed using standards of similar matrices to mitigate matrix recorded during the study. The highest TDS value of 179.2 ± 4.52 mg/L
effects that could introduce errors in concentration readings (Jolly et al., was observed at station 2, while the lowest TDS of 148.5 ± 3.54 mg/L
2012). Certified reference materials (CRMs) such as Soil-7/IAEA and was observed at station 3. High or low concentrations of TDS can restrict
Montana-1/2710a were used for QA/QC purposes: Soil-7/IAEA facili­ the growth of aquatic life and may lead to mortality (Supplementary
tated calibration curve development, while Montana-1/2710a served to Materials; Table S2).
verify the accuracy of the measurements, maintaining results within an
error margin of ±10 % (Jolly et al., 2013). Regular recalibrations of the 3.2. Total metal content in sediments
instrument between batches ensured stability and prevented drift,
reducing potential instrumental errors. For sample preparation, each The concentrations of heavy metals in sediment samples from the
sample was dried, ground, and sieved to achieve consistent particle size, study area are depicted in Fig. 2. The mean concentrations of Ti, Fe, Cu,
minimizing variability due to physical inconsistencies. The pelletizing Zn, Rb, Zr, and Sr ranged from 3110.31 to 3531.18 mg/kg, 28,100 to
process, carried out under standardized pressure, further ensured sam­ 26,245 mg/kg, 87.72 to 77.71 mg/kg, 63.95 to 59.23 mg/kg, 178.71 to
ple homogeneity. Matrix effects and interferences, which can alter XRF 144.56 mg/kg, 185.21 to 144.84 mg/kg, and 233.17 to 158.84 mg/kg,
readings due to absorption and enhancement phenomena, were cor­ respectively. The study reveals that Fe has the highest concentration
rected using FP-CROSS software, accounting for spectral overlaps. (27,136.67 ± 929.57), followed by Ti (3371.53 ± 228.08), Sr (198.59
Replicate samples were analyzed to ensure measurement consistency, ± 37.43), Zr (159.49 ± 22.35), Rb (159.11 ± 17.63), Cu (82.73 ± 5.01),
while blanks were included with each batch to detect contamination or and Zn (61.29 ± 2.42). Various biogeochemical processes and anthro­
background interference. These comprehensive QA/QC measures pogenic factors significantly influence the fluctuation of heavy metal
effectively minimized sources of error and variability, ensuring the ac­ concentrations in mangrove forest sediments (Kumar et al., 2015).
curacy and reliability of the reported data within the acceptable error Additionally, these concentrations may vary across different stations
margin. due to factors such as river discharge, surface runoff, population density,
and proximity to industrial and urban areas (Islam et al., 2018; Silva
3. Results and discussion et al., 2021). Table 1 presents a comparative analysis of heavy metal
concentrations in sediment samples from this study with global findings
3.1. Characterization of studied sediment and various international standards. It was observed that the concen­
tration of Fe obtained in this study was lower than that in the ship
The pH levels of the mangrove sediment in the study area ranged breaking area, Chittagong coast (Rahman et al., 2019), but higher
from 9.11 ± 0.07 to 8.56 ± 0.16, indicating an alkaline nature (Sup­ compared to other investigated sites (Aljahdali and Alhassan, 2020;
plementary Materials; Table S2). pH is widely recognized as the primary Chowdhury et al., 2015; Defew et al., 2005; Hossain et al., 2022; Sarkar,
factor influencing the concentrations of soluble metals (Brallier et al., 2018). This higher concentration of Fe may be attributed to Fe precip­
1996). Generally, metal solubility tends to increase at lower pH levels itation as FeS, which is common in mangrove ecosystems (Sarkar, 2018).
and decrease at higher pH values (Wang et al., 2006). A broad range of Cu concentrations were higher in our study compared to various loca­
salinity levels was observed across the study area, with the salinity of the tions (Defew et al., 2005; Chowdhury et al., 2015; Kannan et al., 2016;
mangrove sediment varying from 169.23 ± 2.72 ppm to 143.91 ± 4.99 Dudani et al., 2017; Sarkar, 2018; Rahman et al., 2019; Aljahdali and
ppm, and an electrical conductivity (EC) value ranging from 280 ± 7.07 Alhassan, 2020; Hossain et al., 2022), possibly due to the release of
μS cm-1 to 215.5 ± 6.36 μS cm-1 during the study period. The total untreated industrial waste into marine environments (Mohiuddin et al.,
dissolved solids (TDS) in the mangrove sediment ranged from 179.2 ± 2012).
4.52 mg/L to 148.5 ± 3.54 mg/L, with a mean value of 167.73 ± 16.76 Additionally, our study found Zn concentrations to be lower than

Fig. 2. Potential toxic elementsconcentration in sediment sample (mg/kg) of Sitakundu coast, Chattogram.

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

Table 1
Comparison of potential toxic elements with other studies and standard values.
Ship breaking area, Ennore Mangrove Mangrove Plants Indian Sundarban Rabigh lagoon, Red Punta Mala Planted mangrove Standard
Bangladesh ( Ecosystem, East of Sundarban Wetland ( Sea (Aljahdali and Bay, Pacific forest, Chittagong values
Rahman et al., Coast India ( Wetland (Sarkar, Chowdhury et al., Alhassan, 2020) Panama ( Coast (this study)
2019) Kannan et al., 2018) 2015) Defew et al.,
2016) 2005)

Ti 5401.1–6812.7 N/A N/A N/A N/A N/A 3110.31–3531.18

Fe 62,990–75,210 N/A 2367–55,946 3019–2865 1464.82–8939.38 2827 26,245–28,100 4100a
Cu 15.4-21.95 2.95–10.3 29.09–81.7 41.55–35.03 44.26–218.50 56.3 77.71–87.72 33a
Zn 124.3-176.4 0.94–4.36 32.1–69.9 36.33–32.51 12.60–134.23 105 59.23–63.95 95b
Rb 194.5-248.2 N/A N/A N/A N/A N/A 144.56–178.71 140c
Zr 169.3-258.12 N/A N/A N/A N/A N/A 144.84–185.21 160c
Sr 121.1-177.06 N/A N/A N/A N/A N/A 158.84–233.17 300c
a
IAEA (1990). Guidebook on applications of radiotracers in industry. Technical Report Series No. 316.
b
GESAMP (1982). The Review of the Health of the Oceans. Reports and Studies No. 15. GESAMP, Geneva, 108.
c
Mason (1966) Principles of Geochemistry, 3rd edn. John Wiley & Sons, New York.

those in the ship breaking area (Rahman et al., 2019) but higher activities (Aktaruzzaman et al., 2014). Ti and Rb concentrations were
compared to other studies. The elevated Zn concentration could be lower than those reported by Rahman et al. (2019), whereas Zr and Sr
linked to the discharge of toxic waste resulting from ship breaking concentrations were higher. The lower concentrations of Ti, Zn, Zr, and

Fig. 3. Contamination risk assessment of potential toxic elementsin sediments of Sitakundu coast using different indices; a) EF value, b) CF value c) Igeo value and
d) PLI.

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

Sr may be attributed to lower heavy metal retention in the study area’s Table 3
sediments, suggesting a natural origin for these metals (Parvaresh et al., Correlation coefficients among heavy metal in sediment of the planted
2010). Furthermore, except for Cu, Rb, and Sr, other values in our study mangrove.
were below the shale value and standard value. Ti Fe Cu Zn Rb Zr Sr

Ti 1
3.3. Ecological risk assessment of potential toxic elements Fe 0.89 1
Cu 0.80 0.45 1
Zn 0.34 0.72 − 0.30 1
Enrichment Factor value for the mangrove sediment samples has Rb 0.80 0.98 0.28 0.84 1
illustrated in Fig. 3(a). The mean value of Enrichment Factor for Ti, Fe, Zr 0.67 0.93 0.09 0.93 0.98 1
Cu, Zn, Rb, Zr, and Sr were 1.2, 1, 3.20, 1.12, 1.97, 1.73 and 1.15 Sr 0.96 0.98 0.61 0.58 0.93 0.85 1
respectively. The study revealed that the mean values of Ti, Fe, Cu, Zn,
Rb, Zr, and Sr followed the increasing order of Cu < Rb < Zr < Ti < Sr <
The negative relationship of Cu and Zn in this study may be due to their
Zn < Fe. The EF value for all heavy metals was reported to be <2 at all
relationship to organic detritus, or to different transportation or distri­
stations, suggesting the minimal enrichment in the study area except for
bution patterns (Islam et al., 2017a, 2017b). (See Table 3.)
Cu. The EF value for Cu shows moderate enrichment in the study site.
Samples with an Enrichment Factor value >1.5 are considered indica­
3.3.2. Principal component analysis
tive of human influence (Birch and Olmos, 2008).
Principal Component Analysis (PCA) is a widely recognized and
The CF value for Ti, Fe, Cu, Zn, Rb, Zr, and Sr were 0.73, 0.57, 1.84,
effective method for identifying sources of pollution and understanding
0.65, 1.14, 1.00, and 0.66 (Fig. 3(b)) respectively and were found in
the underlying factors influencing environmental contamination (Bai
decreasing order as Cu > Rb > Zr > Ti > Sr > Zn Fe>. The CF value for
et al., 2011; Anju and Banerjee, 2012; Islam et al., 2015). In this study,
Ti, Fe, Zn, and Sr were found to below 1, indicating a low contamination
PCA was applied using varimax rotation to the normalized metal con­
rate. In case of Cu, Rb and Zr the values of CF were (1 ≤ CF < 3), which
centration data from mangrove sediments to identify the primary
refers moderate contamination of sediment.
sources and factors influencing the distribution of pollutants. The PCA of
The value of Geo-accumulation Index (Igeo) for the sediment samples
potentially toxic elements revealed two principal components (PC1 and
has illustrated in Fig. 3(c). The study revealed that the sediment samples
PC2), which together explained 100 % of the cumulative variance in the
of the study area were found to have practically unpolluted conditions
dataset (Fig. 4). The first principal component (PC1) was primarily
for Ti, Fe, Zn, Rb, Zr, and Sr. For Cu sediment quality can be considered
associated with metals such as Cu, Zn, and Sr, suggesting their shared
as unpolluted to moderately polluted. However, Igeo values of all metals
anthropogenic origin, likely linked to nearby industrial activities or
is in the decreasing order as Cu > Rb > Zr > Ti > Sr > Zn > Fe. The PLI
ship-breaking operations. In contrast, the second principal component
values for all sampling stations were 0.87, 0.94, and 0.80 respectively. In
(PC2) was dominated by Fe and Ti, which may indicate natural sources
the current study, PLI values in all sampling sites were below 1, implying
or geogenic influences. This analysis highlighted the distinct roles that
nil to very low level of contamination with heavy metals.
human and natural sources play in the contamination of mangrove
The Nemrow Integrated Pollution Index (NIPI) and Risk Index (RI)
ecosystems, providing valuable insights into the pollution dynamics
revealed distinct pollution patterns for the analyzed elements (Table 2).
within these coastal environments.
According to NIPI values, Fe (6.899) falls into the high pollution cate­
The first principal component (PC1), with the highest eigenvalue, is
gory, indicating significant contamination. Cu (2.616) reflects a mod­
the dominant and highly significant factor. PC1 contributes to 98.91 %
erate level of pollution, while Rb (1.300) and Zr (1.129) indicate low
of total variance. Notably, PC1 exhibits a strong positive loading with
pollution levels. In contrast, Zn (0.673), Ti (0.820), and Sr (0.755)
Fe, indicating a probable connection to local emission sources such as
remain below or near the warning threshold, suggesting minimal
metallurgical plants (Mmolawa et al., 2011). Higher variable loading
contamination. Meanwhile, all RI values were classified as low pollution
indicates a greater impact on the variation, as explained by varimax-
(RI < 150), indicating negligible contamination levels for Fe (6.53), Cu
rotated principal components (Sarkar, 2018). Conversely, PC2 ex­
(13.60), Zn (0.67), Rb (1.11), Zr (0.89), Ti (0.61), and Sr (0.67). This
plains 1.09 % of the total variance and exhibits a strong positive loading
reflects a generally clean environment with no evidence of moderate or
with Ti, while negatively associated with Fe, Zr, Rb, and Zn.
significant pollution. These results demonstrate that while NIPI high­
The hierarchical cluster analysis (HCA) dendrogram groups the po­
lights higher contamination for specific elements, the RI provides a more
tential toxic elements into several distinct clusters, reflecting varying
conservative assessment of overall pollution levels.
sources or geochemical behaviors. Fe and Ti form a closely related
cluster, suggesting a shared origin, likely linked to natural geogenic
3.3.1. Correlation coefficient
processes such as mineral weathering. Sr and Rb also cluster together,
To determine relationships among potential toxic elements and to
which may indicate their association with lithogenic inputs or natural
determine their common sources, Pearson correlation coefficient was
sedimentary processes. Cu and Zn form another distinct cluster, typically
calculated (Table 3). Kükrer et al. (2014), showed that if any correlation
associated with anthropogenic sources such as industrial or agricultural
is found among the metals, it indicates that a single factor controls the
runoff, reflecting similar behaviors in sediment binding. Zr appears to
metal. According to the correlation coefficient values, most of the po­
form its own cluster, implying a unique source or distinct geochemical
tential toxic elements determined in sediments for the study were
pathway, potentially linked to erosion of zircon-rich minerals or specific
significantly correlated with each other. The significant positive corre­
geological inputs. These smaller, well-defined clusters emphasize the
lation between Ti and Sr, Fe, Cu, Rb; Fe and Rb, Sr, Zr; Zn and Zr, Rb; Zr
diversity of sources and processes influencing heavy metal distribution
and Sr indicate that these metals may have the same origin and have the
in the sediment.
same factors controlling their distribution and pattern of accumulation.

3.4. Heavy metal distribution in mangrove plants

Table 2
NIPI and RI values for analyzed elements, indicating pollution levels in sediment
of planted mangrove. The accumulation of heavy metals varied based on the type of metal
and the specific organs of the mangrove species. The concentrations of
Fe Cu Zn Rb Zr Ti Sr
heavy metals in mangrove plants are presented in Fig. 5. The mean
NIPI 6.89 2.62 0.67 1.3 1.13 0.82 0.75 potential toxic elementconcentrations in leaf samples of Avicennia
RI 6.53 13.6 0.67 1.11 0.89 0.61 0.67
marina revealed that Fe had the highest concentration (733.94 ±

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

Fig. 4. a) Principal Component Analysis and b) Hierarchical Cluster Analysis of potential toxic elements of planted mangrove sediment.

Fig. 5. Concentration of potential toxic element in mangrove plant a) Avicennia marina; b) Bruguiera gymnorhiza; c) Excoecaria agallocha.

141.69 mg/kg), followed by Sr (26.84 ± 3.67 mg/kg), Cu (14.40 ± 0.30

± 265.6 mg/kg) in root samples, with Cu (14.94 ± 1.67 mg/kg), Sr
mg/kg), and Zn (11.47 ± 0.4 mg/kg), in stem samples the greatest mean
(13.47 ± 1.25 mg/kg), and Zn (9.90 ± 1.21 mg/kg) being the other
potential toxic elementcontent was Fe (201.88 ± 14.7 mg/kg), followed
potential toxic elementvalues. According to Caroline et al. (2015), the
by Cu (13.39 ± 4.2 mg/kg), Sr (10.61 ± 2.9 mg/kg), and Zn (9.02 ± 0.8
absorption of potential toxic element by plants had a principle that the
mg/kg), and in root samples Fe had the highest concentration (1170.17
higher the concentration of strong elements in the plant medium, the
± 161.67 mg/kg), followed by Sr (26.65 ± 1.16 mg/kg), Cu (12.15 ±
greater the metal being absorbed. The difference in concentration be­
2.36 mg/kg), and Zn (10.37 ± 0.9 mg/kg).
tween the two types of media involved in plant tissues caused mass
Potential toxic element concentrations in Bruguiera gymnorhiza plant
transfer by diffusion and osmosis. In this process, the mass of the sub­
showed that in leaf samples Fe had the highest concentration (350.36 ±
stance with a higher concentration moved to a lower region, within the
54.28 mg/kg), followed by Sr (26.45 ± 4.9 mg/kg), Cu (15.57 ± 0.73
plant media. The pH level has a crucial role in phytoremediation,
mg/kg), and Zn (10.55 ± 0.38 mg/kg). In stem, concentration of Fe was
because it affects the solubility of nutrients that allow plants to grow.
the highest (200.53 ± 10.92 mg/kg), and other potential toxic element
Moreover, high pH inhibits nutrient solubility and plant growth.
concentrations were Sr (18.26 ± 0.86 mg/kg), Cu (16.19 ± 2.58 mg/
Furthermore, the distribution and accumulation of trace metals are
kg), and Zn (8.72 ± 0.45 mg/kg), and in root samples the greatest mean
influenced by plant types, metal sources and sediment metal concen­
potential toxic element content was Fe (1152.25 ± 99.02 mg/kg), fol­
trations (Al-Solaimani et al., 2022).
lowed by Sr (18.49 ± 3.21 mg/kg), Cu (16.42 ± 1.17 mg/kg), and Zn
(9.01 ± 0.8 mg/kg).
3.5. Assessment of phytoremediation potentiality
In Excoecaria agallocha plant parts (leaf, stem and root samples) Fe
had the highest concentration (310.36 ± 17.7 mg/kg), followed by Sr
3.5.1. Bioconcentration factor (BCF)
(49.38 ± 10.52 mg/kg), Cu (17.41 ± 1.32 mg/kg), and Zn (12.08 ±
The bioconcentration factors (BCFs) for heavy metals in leaves,
0.74 mg/kg) in leaf samples, in stem Fe concentration was found to be
stems, and roots of three mangrove species, Avicennia marina, Excoecaria
the highest (209.04 ± 13.56 mg/kg), with Cu (14.97 ± 0.6 mg/kg), Sr
agallocha, and Bruguiera gymnorhiza, are shown in Fig. 6. For A. marina,
(12.90 ± 0.65 mg/kg), and Zn (8.39 ± 1.5 mg/kg) being the other heavy
the highest BCF in leaves was observed for Zn (0.20) at station 1, while
metal values, and Fe concentration was found to be the highest (1036.99
the stem and root exhibited the highest BCFs for Cu (0.20 at stations 2

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

Fig. 6. BCF value for leaf, stem and root of a) Avicennia marina; b) Excoecaria agallocha; c) Bruguiera gymnorhiza.

and 3) and Zn (0.19 at station 3), respectively. The mean BCF values for 3.5.2. Translocation factor
A. marina followed the order: Zn (0.17 ± 0.02) > Cu (0.16 ± 0.01) > Sr The translocation factor (TF) values for heavy metals in the studied
(0.11 ± 0.02) > Fe (0.03 ± 0.05) mg/kg. For E. agallocha, the highest mangrove species, as shown in Fig. 7, provide insights into the efficiency
BCF in leaves was noted for Sr (0.26 at stations 2 and 3), while the stem of metal movement from roots to aerial parts (leaves). For A. marina, the
showed the highest value for Cu (0.19 at station 2), and the root for Cu TF values were highest for Cu (1.22), followed by Zn (1.11), Sr (1.01),
(0.19 at stations 1 and 3). The mean BCF values followed the order: Cu and Fe (0.63). This indicated that Cu, Zn, and Sr were efficiently
(0.19 ± 0.01) > Zn (0.17 ± 0.03) > Sr (0.13 ± 0.10) > Fe (0.02 ± 0.01) translocated from the roots to the leaves, with Cu showing the most
mg/kg. For B. gymnorhiza, Cu showed the highest BCFs for leaves (0.20 significant translocation. Similar patterns were observed in the other
at station 2), stems (0.25 at station 3), and roots (0.21 at station 2). The mangrove species, with E. agallocha also demonstrating high TF values
mean BCFs followed the decreasing order: Cu (0.20 ± 0.005) > Zn (0.15
± 0.01) > Sr (0.08 ± 0.06) > Fe (0.02 ± 0.01) mg/kg. Statistical anal­
ysis revealed significant differences (p < 0.01) in the mean values of
BCFs for metals in A. marina. Differences in BCFs were influenced by the
type of metal (essential or non-essential) and sediment characteristics,
such as organic matter content and sulfide precipitation, which play a
critical role in metal bioavailability and uptake (Marinho et al., 2017;
Pang et al., 2017; Hossain et al., 2022).
The observed BCF values for all studied mangrove species were < 1
for potential toxic elements, indicating limited accumulation potential
in plant tissues. This result aligns with the findings of Al-Solaimani et al.
(2022), who emphasized that mangroves often immobilize metals in
sediments rather than accumulating them in significant quantities in
plant tissues. The capacity of mangroves to trap metals in sediments is
attributed to complexation with organic matter and sulfide precipitation
under reducing conditions, which reduce bioavailability (MacFarlane
et al., 2003; Abeywardhana et al., 2022). These findings suggested that
the studied mangroves are better suited for phytostabilization rather
than phytoextraction, particularly in areas with low to moderate
contamination. Their ability to immobilize metals in sediments mini­
mizes ecological risks and highlights their ecological importance in
polluted coastal ecosystems (Hossain et al., 2022). Fig. 7. TF value of a) Avicennia marina; b) Excoecaria agallocha; c) Bru­
guiera gymnorhiza.

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S. Karmakar et al. Marine Pollution Bulletin 212 (2025) 117587

for Cu, Zn, and Sr, further confirming efficient metal translocation. The Table 4
TF values exceeding one for these metals suggest that both species are Metal Accumulation Index (MAI) values for three mangrove species—A. marina,
capable of effectively moving these metals to their aerial parts, a key E. aggalocha, and B. gymnorhiza—across three plant tissues: leaf, root, and stem.
trait for their potential in phytoremediation. In contrast, the lower TF Plant species Leaf Root Stem
value for Fe (0.63 in A. marina) suggested less efficient translocation, A. marina 0.89 1.03 0.48
which may be due to Fe higher mobility limitation in plant tissues or its E. aggalocha 0.95 0.89 2.43
preferential retention in the roots. These results highlighted the capa­ B. gymnorhiza 0.72 0.99 0.57
bility of certain mangrove species, especially A. marina and E. agallocha,
to efficiently translocate specific metals, making them potentially
(BCF) and translocation factor (TF), this research highlights the prom­
valuable for metal remediation in coastal ecosystems. Copper and zinc,
ising environmental benefits of mangroves in reducing heavy metal
both essential plant micronutrients, play critical roles in physiological
loads in coastal ecosystems. Additionally, it provided valuable insights
processes. Copper is required for enzymatic activities in chloroplast re­
into the specific remediation capacities of different mangrove species,
actions, photosystem II electron transport, lignification of cell walls, and
advancing the field of ecological strategies for addressing anthropogenic
protein synthesis (Verkleij and Schat, 1990). Zinc acts as a cofactor for
contamination. Despite these contributions, the study has certain limi­
numerous enzymes and supports respiration, hormone biosynthesis, and
tations. The influence of environmental factors, such as soil composition
other growth processes (Ernst et al., 1992). The study revealed that
and tidal variations, was not fully assessed, and these variables can
E. agallocha was a stronger accumulator of metals compared to A. marina
significantly affect the effectiveness of phytoremediation. Moreover, the
and B. gymnorhiza. This finding is consistent with Chowdhury et al.
focus of the study on short-term observations necessitates long-term
(2015), who reported that E. agallocha can accumulate metals more
monitoring to fully understand the sustainability and broader applica­
efficiently, making it a promising candidate for phytoremediation. For
bility of this remediation approach. Future research should further
B. gymnorhiza, Zn and Sr showed TFs greater than one from root to leaf,
investigate the combined effects of mangrove species, environmental
whereas Cu exhibited high translocation from root to stem. These dif­
factors, and potential soil amendments to optimize the phytor­
ferences highlight the physiological and anatomical adaptations of
emediation process and enhance its long-term impact on coastal envi­
mangrove species to varying metal concentrations and their ability to
ronmental health.
regulate metal transport. The high TF values observed for Cu and Zn
suggest that these metals are prioritized for translocation due to their
CRediT authorship contribution statement
roles in enzymatic and metabolic functions (MacFarlane et al., 2003;
Abeywardhana et al., 2022) However, the observed low TF for Fe aligns
Sima Karmakar: Writing – original draft, Formal analysis, Data
with its poor mobility in plant systems, as it is often immobilized in root
curation. Khadijatul Kubra Riya: Writing – original draft, Formal
tissues through chelation or precipitation (Hossain et al., 2022).
analysis, Data curation. Yeasmin N. Jolly: Investigation, Formal anal­
Excoecaria agallocha emerged as the most effective accumulator and
ysis, Data curation. Shirin Akter: Investigation, Formal analysis, Data
translocator among the studied species, making it suitable for targeted
curation. K.M. Mamun: Formal analysis, Data curation. J. Kabir:
phytoremediation efforts, especially in environments with moderate
Investigation, Formal analysis, Data curation. Bilal Ahamad Paray:
contamination. Its ability to efficiently accumulate Cu and Zn further
Validation, Software, Resources, Funding acquisition. Takaomi Arai:
underscores its potential for phytoextraction applications, particularly
Visualization, Software, Resources, Funding acquisition. Jimmy Yu:
for micronutrient metals critical to plant health (Chowdhury et al.,
Writing – review & editing, Software, Resources. Norhayati Ngah:
2015).
Resources, Funding acquisition, Data curation. Mohammad Belal
Hossain: Writing – review & editing, Writing – original draft, Supervi­
3.5.3. Metal accumulation index (MAI)
sion, Conceptualization.
The Metal Accumulation Index (MAI) values for A. marina, E. agga­
locha, and B. gymnorhiza reveal species-specific patterns of metal dis­
Ethical clearance
tribution across leaf, root, and stem tissues (Table 4). In A. marina, the
highest MAI was recorded in the roots (1.03), followed by the leaves
Not needed.
(0.89) and stems (0.48), indicating that the species preferentially ac­
cumulates metals in the roots, likely as a strategy to mitigate toxicity in
Funding
above-ground tissues. In contrast, E. aggalocha showed the highest MAI
in the stem (2.43), with lower values in the roots (0.89) and leaves
This study was financially supported by the research cell, NSTU,
(0.95), suggesting that metals are predominantly sequestered in the
National Science and Technology (NST) Fellowship, MOST, Bangladesh.
stem, potentially for detoxification or storage. For B. gymnorhiza, the
This project was supported by Researchers Supporting Project Number
root had the highest MAI (0.99), followed by the leaf (0.72) and stem
(RSP2025R144), King Saud University, Riyadh, Saudi Arabia.
(0.57), reflecting a pattern similar to A. marina, where metal accumu­
lation is more pronounced in the roots. These results highlight the
Declaration of competing interest
different metal accumulation strategies of each species, with A. marina
and B. gymnorhiza focusing on root accumulation, while E. aggalocha
The authors declare that they have no known competing financial
favors the stem for metal storage.
interests or personal relationships that could have appeared to influence
the work reported in this paper.
4. Conclusion

Acknowledgements
This study was conducted to advance our understanding of the role of
mangrove species in phytoremediation of toxic metal pollution, partic­
The authors would like to extend their sincere appreciation to the
ularly in regions affected by ship-breaking activities in Bangladesh. The
Researchers Supporting Project Number (RSP2025R144), King Saud
findings emphasized the potential of artificially planted mangroves,
University, Riyadh, Saudi Arabia.
particularly Excoecaria agallocha, in mitigating metal contamination by
effectively translocating metals such as Cu and Zn from sediment to the
aerial plant parts. Through comprehensive analysis of pollution indices
and phytoremediation indicators, such as the bioconcentration factor

9
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