Accumulation and Phytoremediation Potentiality of Trace and Heavy Metals in Some Selected Aquatic Plants from a Highly Urbanized Subtropical Estuary

Abstract

The global concern over trace and heavy metal contamination in aquatic environments necessitates the development of effective remediation strategies. Using aquatic plants for heavy metal removal is a relatively economical and sustainable technology worldwide. This study involved collecting sediment and aquatic plant samples (Acanthus ilicifolius, Typha elephantina, and Cynodon dactylon) from a highly urbanized estuary to analyze metal concentrations in sediment, assess ecological risks, and explore the phytoremediation potential. Trace and heavy metals were detected using Energy Dispersive X-ray Fluorescence Spectroscopy (EDXRF). The sediment metal concentrations were found in decreasing order of Fe, Ti, Mn, Rb, Zr, Zn, Sr, Cu, Co, and As. Fe, Sr, and As concentrations were below certified values, whereas Cu, Zn, and Rb exceeded them. Cumulatively, the pollution load index (PLI) values were close to 1 (0.845), indicating that the study area is likely experiencing metal pollution. The Contamination Factor (CF) values, ranging from 1 to 3, indicated a moderate degree of sediment pollution for Ti, Mn, Cu, Zn, and Rb. The Enrichment Factor (EF) values similarly showed moderate enrichment for these metals, with Cu exhibiting the highest degree of enrichment. Ecological risk assessment highlighted the only metal, Cu, as posing the greatest risk among the studied metals. In terms of phytoremediation potential, the bioconcentration factor (BCF) followed the decreasing order of C. dactylon > A. ilicifolius > T. elephantina for most metals, with low BCF values (<1) indicating low accumulator potential. However, the translocation factor (TF) values for Zn (1.464) and Rb (1.299) in A. ilicifolius species were greater than 1, indicating low accumulation potential but hyper-metabolizing capabilities, allowing the plant to accumulate metals in its aerial parts, making it effective for phytostabilization.

1. Introduction

Heavy metals, exceeding natural concentrations, pose significant challenges for environmental and public health monitoring due to their persistence in the environment and tendency to bioaccumulate in the food chain [1]. Aquatic ecosystems and human health are at serious risk from the presence of heavy metals including Fe, Ti, Mn, Rb, Zr, Zn, Sr, Cu, Co, and As. Aquatic life may suffer from toxic algal blooms and oxygen loss in water bodies caused by high Fe levels. Although the toxicity of Ti has not been thoroughly investigated, its buildup can upset aquatic food webs. When Mn concentrations are excessive, they can be hazardous to aquatic life and cause neurological problems in fish. Although their long-term effects on the environment are unclear, Rb and Zr are usually thought to be less harmful. Smaller doses of Zn are necessary, but larger concentrations can be hazardous and damage aquatic species’ ability to develop and reproduce [2]. Both human and aquatic life may experience health problems when Sr replaces calcium in the bones. At high concentrations, aquatic organisms are fatally affected by Cu, which has a profoundly destructive effect on their metabolism. In aquatic species, Co can lead to DNA damage and oxidative stress [2]. Arsenic (As) is a highly poisonous and carcinogenic substance that poses serious health concerns to people, such as skin sores, cancer, and cardiovascular problems. Chronic exposure to excess levels of Cu in the body is associated with various diseases, including liver conditions such as cirrhosis, hepatitis, and gastroenteritis, as well as neurological disorders [3].

Plant or nature-based cleanup or removal of pollutants is one of the most promising eco-friendly approaches for sustainable ecosystem management [1]. Phytoremediation is a plant-based technique that uses plants to eliminate or reduce the bioavailability of elemental contaminants in the soil [2]. This method is highly effective for treating contaminated soil and sequestering environmental contaminants, such as heavy metals [3]. Since plant cultivation and harvesting are relatively inexpensive processes, phytoremediation may provide an attractive alternative for the cleanup of heavy metals in coastal sediments [4]. Heavy metals moving from soils to plants have been demonstrated as an effective method for removing these metals with harvestable plant components like root systems, stems, and leaves [5]. However, variations in metal concentration exist among different plant species and their parts, demonstrating their varying abilities to absorb metals.

The effectiveness of a phytoremediation process depends on the selection of appropriate plants for a particular environment [6]. It is essential to employ indigenous aquatic flora from polluted areas for phytoremediation. Aquatic plants inherently adapted to environmental stresses demonstrate superior capabilities in survival, expansion, and reproduction in contrast to those introduced from different habitats. There remains a persistent interest in identifying indigenous plants exhibiting resilience to heavy metals. In this study, the aquatic plants Hargosa (Acanthus ilicifolius), Hogla (Typha elephantina), and Durva grass (Cynodon dactylon) were selected to investigate their phytoremediation capabilities because of their widespread presence, adaptability, and established efficacy in removing pollutants. These plants are frequently encountered in a wide range of aquatic and semi-aquatic habitats, which makes them easily accessible. Hargosa (Acanthus ilicifolius), Hogla (Typha elephantina), and Durva grass (Cynodon dactylon), hold promise for accumulating heavy metals within their tissues as well [7,8]. A. ilicifolius, commonly known as Holy Mangrove, typically occurs in mangrove habitats [9]. T. elephantina, known as Elephant Grass, has an extensive rhizome system and reproduces by producing large quantities of pollen [10]. C. dactylon, or Bermuda Grass, is found on almost all soil types [11]. These selected species generally thrive in disturbed areas and are well adapted to environmental changes.

The Karnaphuli River, an urbanized estuary, located in Bangladesh’s second-largest metropolitan area, holds considerable importance for various activities such as boating, transit, fishing, docking, and industrial utilization of the river ecosystem [12]. It also supports port activities and industrial operations, including cooling and processing functions [13]. However, contamination in the Karnaphuli River estuary is steadily increasing, consistently exceeding safe limits [14]. Around 800 industrial units, including textile and cement industries, ship recycling, oil refineries, tanneries, paint manufacturing, dyeing plants, paper and rayon mills, naval and merchant ships, steel and engineering factories, fertilizer, and other chemical industries, are situated along the riverbank [15]. The direct discharge of solid and sewage wastes from these industries into the Karnaphuli River adversely affects coastal fisheries and the well-being of local communities [14,16]. Depending on the sewage treatment process and sludge stabilization methods employed, sludge can contain significant levels of hazardous heavy metals [17]. The presence of harmful pollutants in rivers and marine ecosystems poses a risk to aquatic organisms, particularly fish [18]. The invertebrate fauna experiences disruption due to the elimination of specific species, with various taxa universally impacted by metal mining and related activities. Mollusks, crustaceans, platyhelminths, oligochaetes, and certain groups exhibit inconsistent responses to metal contamination [19,20]. Plants capable of accumulating heavy metals from sediment have been employed as a technique for monitoring environmental quality [10].

Due to the environmental concerns, and long-term solutions of heavy metal pollution, aquatic flora-dependent bioremediation has been extensively studied throughout the world [21,22,23,24,25]. Despite a good number of published studies on the accumulation of metals by aquatic flora from sediments in the coastal areas [7,26,27], studies on the concentration of heavy metals in aquatic plants from sediments of Karnaphuli River estuary, Bangladesh, have received limited attention. Examining the presence of heavy metals in sediment and aquatic plant samples from a heavily urbanized estuary is crucial because urbanization frequently causes a rise in industrial operations and human settlements, which in turn leads to higher levels of heavy metals in aquatic environments. These contaminants can build up in sediment and living organisms, which can lead to substantial ecological hazards and potential damage to human health by accumulating in the food chain. Evaluating the levels of metal concentrations in sediment aids in comprehending the degree of contamination and its origins, while examining aquatic plants offers valuable information regarding their capacity to accumulate and perhaps alleviate metal pollution through phytoremediation mechanisms. Therefore, it is essential to assess the heavy metal contamination levels, accumulation, and remediation potential of local aquatic plants. This study aimed to answer the following questions: what are the accumulation levels and contamination status of trace and heavy metals in sediments and aquatic plants in an urbanized estuary, and what ecological risks do these metal concentrations pose? Additionally, what is the phytoremediation potential of native aquatic plants (Acanthus ilicifolius, Typha elephantina, and Cynodon dactylon? The results provided valuable knowledge for global initiatives in comprehending and controlling metal pollution in aquatic ecosystems. These findings emphasized strategies for sustainable environmental preservation and nature-based remediation.

2. Materials and Methods

2.1. Study Area

The Karnaphuli River stands as a significant watercourse in hilly areas of Chittagong emerging from the Lushai Hills of Mizoram in India; it covers a watershed of approximately 11,000 km² [28]. The river flows across 180 km of mountainous terrain in Rangamati, Bangladesh, followed by a 170 km stretch through the major seaport of Chittagong, ultimately discharging towards the Bay of Bengal. The residents of Chittagong City rely on the Karnaphuli River for their drinking water and various domestic purposes. From a geological perspective, the entire river catchment consists of tertiary rocks beneath a layer of alluvial sediments. The overlying sediments consist of subsequent strata of mud and sand [29]. Samples were collected from the KR estuary, situated between latitude 22°14′39″ N and longitude 91°50′10″ E close to Patenga in Chittagong city (Figure 1). This estuary holds significant importance within the context of Bangladesh. The estuary demonstrates semidiurnal tides varying between 2 and 4 m and upholds an average depth of the channel being 8–10 m in the outer zone [30]. Because of the substantial influence of the Indian monsoon, the environmental conditions in the KR estuary experience seasonal variations [31]. In the dry season, rainfall is absent, leading to a decrease in river water levels. Conversely, in the wet season, heavy rainfall results in rising water levels in rivers. In the studied river, the water flow during the rainy season exceeded that of the dry period, potentially causing variations in metal concentrations in the sediment.

2.2. Sample Collection, Preparation and Analysis

Sediments and plant samples in three replications were collected in May 2022 (dry season) and August 2022 (wet season) during low tide conditions from the Karnaphuli estuary (Figure 1). In total, 16 surface sediment samples (8 samples from each season) were taken at depths of 0–5 cm, covering an area of 10 m × 10 m, and also 30 plant samples in total (15 samples in each season) were collected. Three plant species (Acanthus ilicifolius (leaves, stems and roots), Typha elephantina and Cynodon dactylon) were selected for this study. Leaves, stems and roots were collected from Acanthus ilicifolius trees using a sharp sterilized knife and all the samples were washed to remove any adhering dirt and then they were transferred to plastic bags with zip locks. Plant samples were fully dried in air and transferred into the laboratory.

The collected plant samples were segmented into appropriate sizes using a stainless steel knife, washed with tap water multiple times, followed by distilled water, and subsequently dried in air. All the dried plant samples were individually placed in porcelain dishes (Model: MA-276; Gilson Company, Lewis Center, OH, USA); marked with distinguishing numbers and oven-dried (Model: 3618-5; Thermo Fisher Scientific, Waltham, MA, USA) around 60 °C for overnight drying, then, they were finely crushed into powder using a carbide mortar (Model: HM-109; Gilson Company, USA) and grinder. Pellets with a diameter of 2.5 cm were prepared using a hydraulic press pelletizing device (Model: Z803103; Specac Ltd., Fort Washington, PA, USA) under a pressure of 5 tons. For analyzing metals, the procedure described in Rahman et al. [7] was followed. Energy Dispersive X-ray Fluorescence (EDXRF) (Shimadzu, Chiyoda-ku, Tokyo, Japan) was used to carry the metal analysis. This analytical approach is non-destructive and includes exposing the sample to X-rays. This exposure causes the atoms in the sample to generate secondary X-rays, also known as fluorescence X-rays, with certain energies that are distinct for each element. The EDXRF system is capable of detecting and quantifying these energy levels, enabling the identification and measurement of the concentration of metals in the sample. The approach is beneficial since it allows for quick analysis, requires minimal sample preparation, and has the capability to simultaneously analyze a diverse variety of components.

For quality control and assurance, a calibration curve was constructed using commercially available certified reference materials (CRMs), namely Spinach/NIST 1570a (Sigma-Aldrich, Meguro-ku, Tokyo, Japan) for vegetables and lake sediment for soil, and their validity was scrutinized by running another respective CRM “Orchard Leaf/NIST 1571 (Sigma-Aldrich, Meguro-ku, Tokyo, Japan) ” and “Marine sediment IAEA 433 (International Atomic Energy Agency, Vienna, Austria)”, where the measured value was found to match with the certified value (±10%) [32]. The Limit of Detection (LOD) ranged from 0.1 to 0.001.

2.3. Ecological Risk Assessments of Heavy Metals

To assess the present contamination level of the sediment, various indices including the contamination factor (CF), pollution load index (PLI), geo-accumulation index (I_geo), enrichment factor (EF), and ecological risk index were employed.

2.3.1. Contamination Factor (CF)

To evaluate the degree of heavy metal contamination, the contamination factor, and pollution load index were employed [33]. The contamination Factor (CF) parameter is articulated as follows:

(1)$\mathrm{CF}=\frac{C_{metal}}{C_{background}}$

where CF is the contamination factor, C_metal is the concentration of pollutant in sediment, C_background is the background value of metal and n is the number of metals. The CF reflects the metal enrichment in the sediment. The background values for the metals were derived from the averages in the continental crust [34]. The CF was categorized into the following four groups: CF < 1 indicates low contamination; 1 ≤ CF < 3 signifies moderate contamination; 3 ≤ CF ≤ 6 indicates considerable contamination, and CF > 6 suggests very high contamination.

2.3.2. Pollution Load Index (PLI)

The level of metal pollution at each roadway site was assessed using the pollution load index (PLI) method [35].

PLI = (CF1 × CF2 × CF3 × CFn)^(1/n)(2)

where n represents the number of metals analyzed, and CF stands for the contamination factor, calculated as described previously. The pollution load index (PLI) offers a straightforward yet comparative method for evaluating site quality. A PLI value below 1 suggests an ideal condition, while a PLI value of 1 indicates the presence of only baseline pollutant levels. A PLI exceeding 1 would indicate a decline in site quality [22].

2.3.3. Geo-Accumulation Index (I_geo)

The estimation of heavy metal contamination in sediment samples can be carried out using the geo-accumulation index (Igeo), computed using the equation devised by Muller [36].

(3)${\mathrm{I}}_{\mathrm{geo}}={\log }_2 \left[\frac{\mathrm{C}\mathrm{n}}{1.5 \mathrm{B}\mathrm{n}}\right]$

In the equation, the concentration of the heavy metal in the samples is represented by Cn, while Bn indicates the content of the respective heavy metal in the geochemical background. To address lithospheric effects, a factor of 1.5 was incorporated. This adjustment accounts for possible disparity in background values due to differences in lithology. I_geo functions as an indicator of the contamination level present in various sssediments and soils. According to Muller (36), I_geo values are categorized into the following seven groups: unpolluted (I_geo < 0), unpolluted to moderately polluted (0 < I_geo < 1), moderately polluted (1 ≤ Igeo < 2), moderately to strongly polluted (2 ≤ I_geo < 3), strongly polluted (3 ≤ I_geo < 4), strongly to extremely polluted (4 ≤ I_geo < 5), and extremely polluted (I_geo ≥ 5).

2.3.4. Enrichment Factor (EF)

The enrichment factor (EF) was utilized to evaluate the impact of human actions on metal concentrations in sediment can be analyzed using the following equation [37]:

EF = [M_x/Fe_x]/[M_ref/Fe_ref] (4)

where [M_x/Fe_x] represents the number of heavy metals in the examined sediment samples, while [M_ref/Fe_ref] indicates the geochemical background concentration of the metal and iron. Samples with an EF value surpassing 1.5 are considered to have originated from human activities [38]. The following five pollution categories are differentiated according to the extent of contamination [39]: minimal enrichment (EF < 2), moderate enrichment (2 < EF < 5), significant enrichment (5 < EF < 20), very high enrichment (20 < EF < 40) and extremely high enrichment (EF > 40).

2.3.5. Assessment of Potential Ecological Risk Index $\left({\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}\right)$

The risk index (RI) is employed as an indicator to examine the extent of metal contamination in sediment, taking into account the metal’s toxicity and its impact on the environment [33]. The steps for examining the risk index (RI) are delineated below:

(5)${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}={\mathrm{T}}_{\mathrm{r}}^{\mathrm{i}}{\mathrm{C}}_{\mathrm{f}}^{\mathrm{I}}; {\mathrm{C}}_{\mathrm{f}}^{\mathrm{I}}={\mathrm{C}}_{\mathrm{n}}^{\mathrm{I}}∕{\mathrm{C}}_0^i; \mathrm{RI}=\sum {\mathrm{E}}_{\mathrm{r}}^{\mathrm{I}}$

where summing up all risk factors determines the RI, ${\mathrm{E}}_{\mathrm{r}}^{\mathrm{I}}$ denotes the individual index for the ecological risk index, and ${\mathrm{T}}_{\mathrm{r}}^{\mathrm{I}}$ represents the toxicity factors (2, 1, 5, 1, 10, 5, 5, and 30) corresponding to Cr, Cu, Mn, As, Zn, Co, Pb, and Cd, respectively [33]. Hakanson [33] categorized the ecological risk index into five distinct classes.

2.3.6. Bioconcentration Factor (BCF)

To assess the phytoextraction capability of the studied plants, the bioconcentration Factor (BCF) was measured using the following three equations [40]:

(6)${\mathrm{BCF}}_{\mathrm{leaf}}=\frac{C_{leaf}}{C_{sediment}}$

(7)${\mathrm{BCF}}_{\mathrm{stem}}=\frac{C_{stem}}{C_{sediment}}$

(8)${\mathrm{BCF}}_{\mathrm{root}}=\frac{C_{root}}{C_{sediment}}$

2.3.7. Translocation Factor (TF)

The transfer factor (TF) was calculated to assess the phytoremediation capability of a plant species, using the following two equations adapted from previous studies [41,42].

(9)${\mathrm{TF}}_{\mathrm{leaf}}=\frac{C_{leaf}}{C_{root}}$

(10)${\mathrm{TF}}_{\mathrm{stem}}=\frac{C_{stem}}{C_{root}}$

where C_leaf, C_stem and C_root represent the concentrations of trace metals in the leaf, stem and root, respectively.

2.3.8. Assessment of Phytoremediation Potential

Native plants in the selected study region demonstrate the potential to withstand and gather heavy metals presenting an opportunity for phytoremediation at metal-polluted sites. BCF and TF are useful metrics for assessing a plant’s phytoremediation capacity [42]. Since absorbed contaminants by the plant are not swiftly metabolized, pollutants tend to accumulate within the plant [43]. Comparing the bioconcentration factor (BCF) and transfer factor (TF) of indigenous plants enables the assessment of their phytoremediation potential.

2.4. Statistical Analysis

This study employed one-way ANOVA to determine whether there were statistically notable variations in the concentration of heavy metals among three aquatic plant species. Principal component analysis (PCA) and correlation matrix were used to identify the correlations and link among heavy metals in the examined sediment samples [44]. Utilizing the correlation matrix for heavy metal analysis may yield both positive and negative correlations among the elements [45]. Hierarchical cluster analysis (HCA) stands as one of the most extensively employed algorithms, generating clusters by sequentially adding variables or individuals based on the hierarchical structure of the cluster [46]. Metals sharing similar properties were grouped or clustered, whereas different groups of elements were depicted in distinct clusters. This differentiation helped elucidate the pollution level of the samples [47]. Finally, the analysis of linear regression was conducted to understand the connection between metal content in sediment and plant tissue. ANOVA, correlation matrix, and regression analysis were executed using Microsoft Excel 2016, while PCA and HCA were performed using Past 4.01 software.

3. Results and Discussion

3.1. Metal Concentration in Sediment

In this study, ten metals (Ti, Mn, Fe, Cu, Co, As, Zn, Rb, Sr and Zr) were examined in the sediments of the Karnaphuli River (KR) estuary using an Energy Dispersive X-ray Fluorescence (EDXRF) system. The mean concentrations of the investigated heavy metals showed the descending order of Fe (20,698.13 ± 138.77), Ti (4065.48 ± 553.25), Mn (460.22 ± 62.18), Rb (183.06 ± 3.95), Zr (140.72 ± 4.10), Zn (113.00 ± 0.75), Sr (107.80 ± 0.66), Cu (97.11 ± 4.64), Co (6.14 ± 0.32); As (5.93 ± 0.51) (Figure 2). Elevated concentrations of Fe, Zn, Cu, and Rb might result from human activities [48]. The highest concentrations of these elements might originate from runoff in agricultural fields, semi-urban areas, and untreated domestic wastewater discharged from housing and industrial regions [48].

Table 1 presents a comparison of heavy metal levels in sediment samples from this study with global findings and various international standards. The concentrations of Fe, Sr, and As were found to be below various international guidelines and certified values, whereas Cu, Zn, and Rb exceeded these certified values. Specifically, the concentration of Fe in the sediment (19,540–22,100 mg/kg) in this study was notably higher compared to levels observed along the Mediterranean coast of Egypt (13,256 mg/kg) [49], Tirumalairajan river, India (2346 mg/kg) [50], and Meghna river estuary, Bangladesh (1290 mg/kg) [51], but lower than that in sediments of the ship-breaking area, Chittagong, Bangladesh (68,260 mg/kg) [7], coastal sediment of Dumai, Indonesia (30,100 mg/kg) [52]. The concentration of Mn in this study was found to be lower than various international guidelines, indicating relatively low Mn contamination in the study area. However, Mn levels were higher compared to those reported for the Southeast coast of India and Elckie Lake, Poland [53,54]. Conversely, the concentration of Cu notably exceeded the standard value set by IAEA [55], surpassing levels found in the coastal sediment of urban areas in Semarang, Indonesia, and the Langkawi coastal area of Malaysia, but remaining lower than levels observed in sediments from the Suez Gulf, Egypt, and the Alang-Sosiya coast of India [23,56,57]. The mean concentration of Zn in the sediment was consistent with levels in the east coast of Bay of Bengal and Umeda River, Japan, but higher compared to the Yangtze river, China; Meghna river estuary, Bangladesh; northern Bohai and Yellow Seas, China; and lower than those in the sediments of the ship-breaking area, Chittagong, Bangladesh, and Fujian coast, China [7,51,58,59,60,61]. The Zn concentration exceeded the certified value for the metal [62]. The mean concentration of As in the sediment was much lower than the standard value, being higher than in the sediment of Paraiba do Sul river delta, Brazil, but lower than in the coastal sediment of the estuary of Yalu River, China; Ulsan Bay, South Korea; Jinzhou Bay, China; and Port Klang coastal area, Malaysia [60,61,63,64,65]. Additionally, Ti and Rb levels exceeded the recommended levels [66].

3.2. Assessment of Metal Pollution in Sediment

The contamination factor (CF) indicated that Fe, Co, As, Sr, and Zr had values below 1, suggesting minimal contamination. For Ti, Mn, Cu, Zn, and Rb, CF values ranged from 1 to 3, indicating a moderate degree of sediment pollution. Overall, CF values for all heavy metals followed a descending order of Cu, Ti, Rb, Zn, Mn, Zr, As, Fe, Co, and Sr (Figure 3A). The pollution load index (PLI) provided an overall heavy metal contamination status in the sediment sample [68]. This study revealed that PLI values for both seasons were found to be just below 1 (0.835 and 0.827, respectively), indicating that the study area is likely experiencing environmental pollution due to the increased discharge of untreated industrial waste into the Karnaphuli River estuary (Figure 3B). Almost similar concentration of metals was found in both seasons, although the dry season showed a slightly higher concentration than the wet season.

The Igeo method was used to evaluate the level of metal contamination in sediment samples from the study area. This method includes seven categories, from unpolluted to highly polluted [69]. Using the Igeo equation [3], the findings showed that the sediment samples were largely unpolluted (Igeo < 0) for all metals except for cu, which indicated moderate pollution (1 < Igeo < 2) (Figure 3C).

3.2.1. Enrichment Factor (EF)

This study demonstrated that EF values for Fe, Co, As, and Sr consistently remained below 2 across both seasons, indicating minimal to negligible enrichment in the area. However, Ti, Mn, Cu, Zn, Rb, and Zr showed distinct trends (Figure 3D). Specifically, EF values for Ti, Mn, Zn, Rb, and Zr ranged between 2 and 5, indicating moderate enrichment, while Cu exhibited significant enrichment (EF > 5 and <20), highlighting potential environmental concerns in the near future.

3.2.2. Ecological Risk Index

The ecological risk factor for individual metals was observed to increase in the order of Mn < Zn < Co < As < Cu (Table 2). Among these metals, Cu presented the highest ecological risk compared to others. The highest risk was likely attributed to increasing Cu concentrations in sediments within the Karnaphuli River estuary, particularly near harbors and shipping lanes, due to its widespread use in construction materials and electronic products [70]. The ecological risk index (${\mathrm{E}}_{\mathrm{r}}^{\mathrm{i}}$) values for all metals analyzed were below 40, indicating a low-risk condition in the study areas. Additionally, the risk index values consistently remained below 150 across all cases, confirming the overall low-risk status of the surrounding environment.

3.3. Concentration of Metals in Aquatic Plants

In this study, concentrations of trace and heavy metals were evaluated in three aquatic plant species (Acanthus ilicifolius, Typha elephantina, and Cynodon dactylon) during both the dry and wet seasons. Cynodon dactylon exhibited the highest metal accumulation, while Typha elephantina showed the lowest except for Fe (which was lower than Cynodon dactylon but higher than Acanthus ilicifolius). Metal accumulation among the plant species followed the order of Cynodon dactylon > Acanthus ilicifolius > Typha elephantina (Figure 4). Specifically, Cynodon dactylon recorded the highest Fe levels (6376.89 mg/kg), whereas the stem part of Acanthus ilicifolius exhibited the lowest Fe levels (231.23 mg/kg) during the dry season. Regarding Cu, the root of Acanthus ilicifolius contained the highest concentration (23.21 mg/kg), while Typha elephantina showed the lowest (8.89 mg/kg) during the wet season. For Zn, the highest concentration was found in the leaf of Acanthus ilicifolius (69.31 mg/kg) in the dry season, and the lowest was found in the stem of Acanthus ilicifolius (25.52 mg/kg) in the wet season. For Rb, the highest concentration was found in Cynodon dactylon (16.44 mg/kg), and the lowest was found in the stem of Acanthus ilicifolius (6.49 mg/kg) in the dry season. For Sr, the root of Acanthus ilicifolius (80.16 mg/kg) in dry season showed the highest accumulation, and Cynodon dactylon (17.09 mg/kg) in the wet season showed the lowest. In the case of Mn and Co, Cynodon dactylon showed the highest accumulation and Acanthus ilicifolius and Typha elephantina showed the lowest accumulation (minimum detection limit was found for both the metals) and there was no significant variation in metal absorption among the three species (F = 0.64068, p > 0.05).

The present study unveiled that the concentrations of most accumulated heavy metals in aquatic plants of Karnaphuli estuary were higher than their respective concentrations in the plants of coastal area worldwide- Mongla Sundarban, Bangladesh (26), Egyptian Lake, Burullus [71] Wastewater irrigated area, Asmara, Eritrea [72] but lower than Wetland, Kwekwe, Zimbabwe [73] (Table 3).

3.4. Phytoremediation Potential

Phytoremediation utilizes metal-accumulating plants to address contamination in primary sources such as soil and water [75]. Selected plants for phytoremediation are chosen based on their ability to absorb high levels of heavy metals into their roots, tolerate diverse metal types, and adapt physiologically to varying environmental conditions [76]. The effectiveness of a plant’s phytoremediation capacity can be evaluated using both bioaccumulation factor (BCF) and translocation factor (TF) metrics [42]. Plants with TF, and particularly BCF values less than one, are considered less effective for phytoextraction [77]. However, factors such as metal availability, media pH, chemical composition of co-pollutants, and interactions with rhizospheric organisms all influence the uptake of pollutants by plants [78]. In the study, all metals had BCF values below one in plants, except for Sr (0.747) in the root of A. ilicifolius during the dry season, which was close to one (Figure 5A–C). However, TF values for Zn (1.464) and Rb (1.299) exceeded one in A. ilicifolius species during both the dry and wet season (Figure 5D,E). This indicates the plant’s low accumulation potential but hyper-metabolizing capabilities, allowing it to accumulate metals in its aerial parts, making it effective for phytostabilization. Comparing BCF values across the aquatic plant species in this study, C. dactylon, A. ilicifolius, and T. elephantina followed a decreasing order for all metals except Sr. A. ilicifolius, which showed the highest accumulation of Sr (0.568 in dry season and 0.347 in wet season), while C. dactylon exhibited the lowest accumulation (0.261 in dry season and 0.158 in wet season). This study reveals that C. dactylon is a more effective accumulator of metals compared to A. ilicifolius and T. elephantina, as indicated by BCF values. Additionally, A. ilicifolius demonstrated hyper-accumulating capabilities. These findings support the phytoremediation potential of these plants for most metals, consistent with previous research [7].

3.5. Relationship of Metal Concentrations in Sediment with Plant Tissue

The linear regression model depicted in Figure 6 illustrated the correlation between metal concentrations in sediment and plant tissue. Analysis of log-transformed metal concentrations revealed a pronounced positive correlation between sediment and aquatic plants. However, the strength of this relationship varied and was moderate for leaf and stem tissues (R² = 0.58 and R² = 0.57, respectively), and very strong and significant for root and sediment (R² = 0.80–0.93), highlighting the robust relationship between metal concentrations in sediment and plant tissues. The high coefficients of determination (ranging from 0.58 to 0.93) for both sediments and plant tissues indicate a moderate to strong association of metals. Significant positive correlations were observed between sediment metal concentrations and plant tissues of A. ilicifolius (leaf and stem), and a very strong significant positive correlation was found in A. ilicifolius (root), T. elephantina and C. dactylon. All the plants showed elevated co-efficient of determination, indicating a significantly higher association (p < 0.05).

3.6. Pollution Source Identification

Principal Component Analysis (PCA) and Cluster Analysis for Source identification

The extraction technique was employed to identify the principal components in PCA, specifically through the determination of Eigenvalues. Principal component analysis (PCA) of sediment samples revealed that most heavy metals originated from natural geological sources, with PC1 showing high loadings for Mn, Zn, Rb, and Sr. PC2 indicated the presence of heavy minerals like Ti and Zr. Fe, influenced by both natural and industrial sources, dominated PC3 [79]. PC4 linked Zn to industrial or agricultural runoff. PC5 highlighted the natural sources of Rb and Zr [80]. PC6 associated Cu and Sr with industrial activities and urban runoff [22]. PC7 indicated a minor specific influence on Mn distribution. The prevalence of Cu-contaminated areas continues to rise, attributed to various factors, including mining activities and its use as a pesticide in both conventional and organic agriculture [80]. While Zr is naturally present in the environment, anthropogenic sources include industrial by-products containing zirconium and emissions from sponge zirconium processing [81] and agriculture stands out as the most significant source of anthropogenic Sr [82]. Overall, the analysis underscores significant anthropogenic impacts on Fe, Zn, Cu, and Sr, alongside natural geological contributions.

Cluster analysis is primarily employed to depict groups of sampling sites with spatial variability, categorizing them based on concentration into similar and dissimilar clusters. This aids in identifying specific areas with distinct characteristics [47,83,84]. Hierarchical cluster analysis (HCA) was employed to assess the connection between the variables of metal concentration, shedding light on the analyzed criteria and potential sources [30]. The HCA was set at (Dlink/Dmax) < 2.5, utilizing the Euclidean distance of similarities in variables, delineating two distinct clusters (Figure 7). Cluster 1, comprising Fe and Ti, could potentially stem from various sources such as domestic sewage, land runoff, river discharge, and shipping activities [85] and cluster 2 (including Cu, Sr, Zn, Rb, Zr and Mn) could have been originated from anthropogenic, arising from agricultural activities, industrial effluents, municipal wastes, and mining operations near the study area [86].

4. Conclusions

This study illustrated the contamination levels of various trace and heavy metals in the sediments and aquatic plants of the Karnaphuli River estuary. It explored their current status, and environmental contamination levels, and assessed the phytoremediation capabilities of three aquatic plants in mitigating pollutants. Except for Fe, Sr, and As, the concentrations of most metals were observed to exceed standard values. The PLI value reflected that the study area likely experiences environmental pollution due to the heightened discharge of untreated industrial waste into the Karnaphuli River estuary. CF, I_geo, and EF analyses indicated low to moderate levels of pollution of sediment samples in this study area, particularly by Cu. The ecological risk assessment index suggested that the study area did not pose severe risks to organisms; however, the ongoing rise in Cu levels raises concerns for the environment. Mn-Rb and Rb-Sr showed a strong positive correlation; Mn-Zn showed a moderate positive and Fe-Zr, Cu-Rb showed a moderate negative correlation. The hierarchical cluster analysis also revealed that Cu, Sr, Zn, Rb, Zr, and Mn could have been reflecting the same sources generated by human activity, including industrial effluents, municipal waste, and agricultural inputs. However, the plant species A. ilicifolius, T. elephantina, and C. dactylon showed BCF value of less than one, suggesting limited capability for heavy metal accumulation. Regarding TF, A. ilicifolius exhibited values exceeding one for Zn and Rb, indicating the plant’s capacity to translocate metals from roots to leaves, potentially serving as a phytoremediator in the study area. C. dactylon showed the highest BCF value of all the metals compared to A. ilicifolius and T. elephantina, except Sr, which indicated that C. dactylon was found to be a greater accumulator than A. ilicifolius and T. elephantina.

Author Contributions

Conceptualization, supervision, project administration, resources, M.B.H. and M.M.R.; methodology, investigation, data collection, formal analysis, writing—original draft, F.T., Y.N.J., S.A. and M.B.H.; methodology, investigation, formal analysis, writing—original draft, F.T. and K.K.R.; data collection, writing—original draft, F.T. and M.M.R.; reviewing and editing: M.B.H., M.F.A., J.Y. and T.A.; funding: M.B.H., M.F.A. and T.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be available upon request.

Acknowledgments

Laboratory assistance was provided by LEEB and Bangladesh Atomic Energy Commission. Thanks are due to Researchers Supporting Project Number (RSP2024R436), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Map showing the study area, Karnaphuli River estuary (red circled).

Figure 2. Trace and heavy metal content of sediment samples within the study area (dry season and wet season).

Figure 3. Contamination assessment of heavy metals: (A) contamination factor; (B) PLI; (C) Igeo values; (D) EF values of heavy metals in the sediment samples.

Figure 4. Variation in the abundance of heavy metals in the Karnaphuli River estuarine plants: (A) dry season and (B) wet season. Different letters a, b indicated significant differences at 0.5 level.

Figure 5. Bioconcentration factor (BCF) of heavy metals in the plant species (A) A. ilicifolius (B) T. elephantina, (C) C. dactylon; and translocation factor (TF) of metals in (D) A. ilicifolius (leaf) and (E) A. ilicifolius (stem).

Figure 5. Bioconcentration factor (BCF) of heavy metals in the plant species (A) A. ilicifolius (B) T. elephantina, (C) C. dactylon; and translocation factor (TF) of metals in (D) A. ilicifolius (leaf) and (E) A. ilicifolius (stem).

Figure 6. Linear regression analysis between metal concentration in sediment and the metal concentration in plants: (A) A. ilicifolius (leaf), (B) A. ilicifolius (stem) (C) A. ilicifolius (root), (D) T. elephantina and (E) C. dactylon.

Figure 7. Source identification of heavy metals in the sediments of Karnaphuli river estuary through multivariate (A) PCA and (B) HCA.

Table 1

Comparison of heavy metals in sediments from the present study with certified values of standard reference materials and other studies in the world (in mg/kg dry weight).

Location	Fe	Cu	Zn	Sr	As	Rb	Reference
Ship-break area, Sitakunda, Chittagong	68,260	18.7	151.5	142	10.55	226.4	[7]
Tirumalairajn river, India	2342	20.7	35.8	-	-	-	[50]
Mediterranean coast, Egypt	13,256	8.46	22.19	-	-	-	[49]
Coastal sediment of Dumai, Indonesia	30,100	6.08	53.89	-	-	-	[52]
Yangtze estuary, China	-	29	93	-	10.1	-	[67]
Meghna river estuary, Bangladesh	1290	6.22	42.41	-	-	-	[51]
Karnaphuli River estuary, Bangladesh (dry season)	20,600	100.39	113.53	107.33	6.29	185.86	Present study
Certified values	47,200 a	33 b	95 c	300 d	13 d	140 d	-

^a [68], ^b [55], ^c [62], ^d [66].

Table 2

Potential ecological risk indices of heavy metals in the sediment of Karnaphuli River estuary.

Sampling Season	$\mathbf{Ecological} \mathbf{Risk} \mathbf{for} \mathbf{Single} \mathbf{Metal} \mathbf{\left(}{\mathbf{E}}_{\mathbf{r}}^{\mathbf{i}}\mathbf{\right)}$					RI
	Mn	Cu	Zn	As	Co
Dry season	1.10	15.21	1.20	4.84	2.023	24.37
Wet season	0.91	14.22	1.18	4.28	2.18	22.77
Mean	1.00	14.71	1.19	4.56	2.10

Table 3

Comparison of metals in plants from this study with findings from other studies worldwide (in mg/kg dry weight).

Site	Species	Fe	Cu	Zn	Rb	Sr	Reference
Mangrove forest of KVCR, India	Acanthus ilicifolius	1163.67	144.8	22.7	-	-	[74]
Mongla Sundarban, Bangladesh	Avicennia officinalis	619.39	19.79	12.54	-	19.88	[26]
Karnaphuli River estuary, Bangladesh	Acanthus ilicifolius	784.64	14.29	45	9.19	60.98	Current study
Egyptian lake Burullus	Typha domingensis	29.9	8.9	7.7	-	-	[71]
Wetland, Kwekwe, Zimbabwe	Typha capensis	9413	35	162	-	-	[73]
Karnaphuli River estuary, Bangladesh	Typha elephantina	1531.52	11.96	37.19	11.46	39.6	Current study
Nakivubo channelized stream, Uganda	Cynodon dactylon	4018.77	73.75	148.38	-	-	[12]
Karnaphuli River estuary, Bangladesh	Cynodon dactylon	6376.89	15.71	55.97	16.44	28.02	Current study
Wastewater irrigated area, Asmara, Eritrea	Cynodon dactylon	1138.82	14.26	45.55	-	-	[72]

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