Anthropic Interventions: Imposing Ecological Risk to the Natural Spawning Ground of Major Carps in Bangladesh

Bangladesh Fisheries Research Institute conducted a study to evaluate heavy metal contamination in the sediment of the Halda river. Data were collected from four locations: Khondokia Khal, Katakhali, Madari Khal, and Madarsha. Concentrations of eight heavy metals (Cd, Cr, Ni, Cu, Fe, Mn, Pb, and Zn) were measured, with Cd ranging from 0.04 to 0.96 mg kg^-¹, Cr from 18.20 to 48.14 mg kg^-¹, Ni from 0.70 to 9.10 mg kg^-¹, Cu from 6.70 to 9.10 mg kg^-¹, Fe from 14501.00 to 20323.00 mg kg^-¹, Mn from 270.00 to 430.00 mg kg^-¹, Pb from 1.83 to 8.12 mg kg^-¹, and Zn from 29.00 to 43.00 mg kg^-¹, respectively. The geoaccumulation index (I_geo) indicated Mn contamination (0.37 ± 0.02), supported by Improved Nemerow Index (IN) showing moderate contamination of heavy metals in the river Halda. The pollution load index (PLI) (0.31 ± 0.04) indicated no significant pollution, and the contamination factor (CF) also demonstrated low pollution levels. Katakhali Khal exhibited the highest degree of contamination and the modified degree of contamination was (mCd) 4.22 ± 0.45. Enrichment factor (E_F) ranged from 0.43 ± 0.10 to 4.14 ± 3.33, indicating minimal to moderate enrichment. Ecological risk factor (Eⁱ_r) (12.75 ± 0.68 to 49513.56 ± 39.23) and risk index (RI) (467.70 ± 4.53 to 641.92 ± 27.72) demonstrated varying degrees of ecological risk. The modified hazard quotient (mHQ) indicated very low to low contamination severity. Principal Component Analysis (PCA) and Cluster Analysis (CA) revealed correlations among heavy metals, suggesting similar sources. These findings emphasize the need for immediate action to address heavy metal contamination in the Halda river sediment.

Introduction

The continuous discharge of pollutants into aquatic systems from both natural and anthropogenic sources, such as rapid urbanization, industrial development, domestic sewage, mining, agriculture, electronic waste, accidents, navigation traffic, and climate change events like floods, has raised significant concerns regarding the stability of aquatic ecosystems [1-9]. Among these pollutants, heavy metals have become particularly concerning environmental contaminants, especially in developing countries were infrastructure and environmental management lag behind population growth and urbanization [10]. Heavy metals, generally defined as metals with a specific weight greater than 5 g cm-3, encompass approximately 40 different elements [11]. These metals, including cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), naturally occur in the environment [12,13] and play essential roles in ecosystems and human health [14-19]. For instance, copper (Cu) and zinc (Zn) are vital for enzyme reactions, functioning as cofactors and enzyme activators, forming complexes with enzymes and substrates, and serving as prosthetic groups in metalloproteins [20]. Despite their essential roles, even trace amounts of these metals can be harmful. Additionally, heavy metals are non-biodegradable and can bio-accumulate in organisms such as mussels, oysters, shrimp, and fish [21-33]. The accumulation of heavy metals disrupts ecosystems and causes toxic effects, leading to severe health issues or death in most living organisms [34]. These metals enter the human body through inhalation or ingestion, posing significant health risks. The accumulation and adverse effects of heavy metals on human health and aquatic life, including fish and other aquatic organisms, have thus become a global concern [11,23,35-40].

Fluvial natural water bodies play a crucial role in transporting heavy metals derived from terrestrial runoff, atmospheric deposition, sewage discharge, and other pathways [41-43]. Due to their low solubility in water, heavy metals are predominantly sequestered by fine particles, resulting in their accumulation in sediments [44-46]. Consequently, sediments serve as the principal repository for heavy metals and various chemical constituents, making them key indicators of water pollution in lakes and rivers [47-49]. These sediment matrices provide valuable insights into recent environmental perturbation. Environmental variations such as pH, oxidation-reduction potential (Eh), salinity, and organic matter content can influence the retention of heavy metals in sediments, potentially leading to their remobilization and subsequent release into the water column, thus causing secondary contamination [50-52].

Fish and sediments are recognized as primary bio-indicators for assessing heavy metal levels in natural aquatic ecosystems [40,53,54]. This underscores the precision and utility of fish in gauging habitat transformations within these ecosystems. However, Malik, et al. [54] argued that, due to their position at the base of the aquatic food chain, fish may accumulate heavy metals from sediment. Thus, identifying and quantifying heavy metals in both water and sediments are crucial environmental considerations [55-57]. Numerous global studies have examined heavy metal contamination in soils, contributing to a comprehensive understanding of this environmental concern [55-59].

In recent years, Bangladesh has experienced a noticeable increase in exposure to heavy metals and metalloids, supported by a growing body of literature [2,10,34,36,41,42,44,47]. This rise in contamination stems from diverse sources such as industrial activities, domestic waste, and agrochemical use, collectively contributing to the degradation of water quality [34,60-62]. While several studies have investigated heavy metal presence in various rivers across Bangladesh [2,10,34,36,42,44], limited attention has been focused on assessing contamination in the Halda river to date [63-65]. The Halda River holds significant ecological importance in Bangladesh as a natural breeding ground for Indian Major Carps (IMCs) during their breeding season, representing a unique natural heritage of the country. However, the river’s water quality is progressively deteriorating due to both natural factors and human activities. This study aims to evaluate the current status of heavy metals, assess their potential ecological risks, and propose mitigation measures to preserve the biodiversity of the Halda river. Initial findings from the first year of research have been previously published [63], with the present study presenting outcomes of subsequent year’s investigation.

Materials and methods

Sampling sites

The present study extends the previous year’s investigation and was conducted at the same sampling locations along the Halda river, situated between 22° 25′ 13″–22° 48′ 51.37″ N and 91° 45′ 00″–91° 52′ 33″ E [63]. Four specific points along the river-Khondokia Khal, Katakhali Khal, Madari Khal, and Madarsha-were selected for sediment sample collection.

These ‘Khals,’ local canals, are primary conduits for pollutant transport into the Halda river.

Sampling and data collection were conducted monthly over one year, from July 2021 to June 2022. The Global Positioning System (GPS) coordinates for these sampling sites are detailed in Figures 1,2 and Table 1.

Sample collection, preparations and analysis

Over the course of one year, a total of 48 surface soil samples were collected from specific locations along the Halda river. To minimize potential contamination, these samples were carefully placed in clean polythene covers. Sampling was performed using an Ekman dredge, targeting soil layers between 10 and 50 centimeters deep. The GPS coordinates for each sampling location are detailed in Table 2.

At each site, three individual samples were gathered and combined to form a composite sample. Subsequently, samples from different locations were further combined, resulting in four composite samples representing distinct areas along the river.

The analysis focused on eight common heavy metals: Cadmium (Cd), Chromium (Cr), Nickel (Ni), Copper (Cu), Iron (Fe), Manganese (Mn), Lead (Pb), and Zinc (Zn). Initially, soil samples were air-dried at room temperature. They were then processed to remove plant roots, large stones, debris, organic residues, and visible impurities. Finally, the samples were crushed, ground, and sieved through a 0.85 mm plastic sieve before being stored at 4°C until spectrophotometric analysis was conducted.

Following preliminary preparations, sediment samples were promptly transported to the Soil and Water Analysis Laboratory at the Institute of Water and Flood Management, Bangladesh University of Engineering and Technology (BUET) for analysis. Each 2-gram sample was treated equivalently to a 1-liter sample for Inductively Coupled Plasma (ICP) analysis. Consequently, concentrations were expressed as micrograms per 2 grams of sample (µg/2 g) or milligrams per 2 kilograms of sample (mg/2 kg). These values were converted to milligrams per kilogram (mg/kg) by multiplying by 0.5.

Stringent measures were employed to prevent sample contamination, including the use of clean, powder-free latex gloves and laboratory coats. Glassware was meticulously cleaned with a chromic acid solution and distilled water to eliminate residual impurities. Analyticalgrade chemicals and reagents were utilized to ensure accuracy throughout the analysis. Blank determinations were conducted to correct instrumental readings and account for background interference.

The assessment of heavy metal pollution in the surface sediments of the Halda river involved the application of various indices derived from the concentration data of these metals. These indices played a crucial role in evaluating the extent of heavy metal contamination in the river sediments.

Sediment quality assessment

Index of geo-accumulation: The geo-accumulation index (I_geo) serves as a valuable tool for mitigating the influence of human-related factors when evaluating soil contamination. It has been introduced to replace the traditional single-factor Nemerrow index [68]. I_geo was originally introduced by Müller [67] and has found extensive application in sediment geochemistry for assessing the extent of heavy metal contamination in sediments. The I_geo is defined by the following equation:

I_geo = log₂ (Cn/1:5 × Bn) (1)

The geo-accumulation index (I_geo) is calculated using the above formula, where Cn represents the concentration of elements in the sediment samples, and Bn corresponds to the geochemical background concentration for the same elements (n). The background values used for these elements in the calculation of the index are consistent with those employed in the computation of contamination factors (CFs). A factor of 1.5 is included to account for variations in the background due to lithological differences.

The I_geo index is categorized into seven distinct classes, as described in Table 3 [67]. These classes serve as a classification system for assessing the level of heavy metal contamination in sediments based on the calculated I_geo values.

I_geo is particularly valuable in mitigating the impact of natural factors such as parent rocks and human-induced effects on heavy metal contamination in soil. Consequently, it is wellsuited for assessing heavy metal contamination in areas characterized by industrial and mining activities. However, when evaluating contamination caused by a single heavy metal, I_geo alone may not offer a complete representation of the contamination status in a given area. Therefore, it’s essential to employ a comprehensive index approach. In this regard, the traditional Nemerow index (IN) has been enhanced by substituting the single-factor index with I_geo, as outlined in Table 4. The following Equation (3) was used in this improved evaluation:

I_N = √ (I_geomax² + I_geoave²)/2 (2)

I_N = 1.86 (In the present study).

Contamination factor, degree of contamination and modified degree of contamination: The Contamination Factor (CF) and Degree of Contamination (Cd) are employed to evaluate the level of pollution in sediments regarding heavy metal content [59]. The CF is calculated as a ratio, derived by dividing the concentration of each metal in the sediment by a baseline or background value [71]. The CF for each metal is computed using the following formula [72]:

$\text{CF = }\frac{\text{Concentration of measured metal}}{\text{Background concentration of the same metal}}\text{ (3)}$

To aid in pollution control efforts, Hakanson [72] introduced a diagnostic tool referred to as the ‘degree of contamination’ (Cd), which is calculated as the sum of the CF for each individual sample:

$\text{Cd = }{\displaystyle {\sum }_{i=1}^{n}CF}\text{ (4)}$

The purpose of Cd is to offer an assessment of the overall contamination level in the surface layers of a specific core or sampling site. Hakanson [72] has established four sediment grade classifications based on CF and Cd values (as shown in Table 5).

In order to calculate the degree of contamination, at least five sediment samples are required to provide a mean concentration and to compare with the background value. To avoid this constraint, a generalized index was developed [73]; named modified degree of contamination (mCd) to assess the overall heavy metal contamination of soil (Table 6). The modified degree of contamination (mCd) was estimated using the following equation:

$\text{mCd = }\frac{{\displaystyle {\sum }_{i=1}^{n}CF}}{n}\text{ (5)}$

Enrichment factor

Enrichment Factor (EF) is a convenient method to evaluate the magnitude of anthropogenic heavy metal contaminants [74] in the environment [75]. The EF was calculated using the following equation:

$EF=\frac{\left(\frac{CM}{CFe}\right)sample}{\left(\frac{CM}{CFe}\right)Eart{h}^{\prime }\text{ scrust}}\text{ (6)}$

Where, (CM/CFe) sample is the proportion of concentration of heavy metal (CM) and iron (CFe) in the sediment sample, and (CM/CFe) Earth’s crust is the proportion of heavy metal and iron in the Earth’s crust [76]. Iron (Fe) is used for the geochemical normalization to calculate the enrichment factor. Different values of EF, indicates different degrees of enrichment; where EF<1= indicates no enrichment; EF<3 = minor enrichment; EF 3-5 = moderate enrichment; EF 5-10 = moderately severe enrichment; EF 10-25 = severe enrichment; EF 25-50 = very severe enrichment; and EF>50 = extremely severe enrichment [74,77].

Pollution load index

Pollution Load Index (PLI) determines the communal effects of various pollutants in sampling sites deposited in soils and sediments [78]. The PLI for each site has been estimated by the multiplications of the nth root of the studied heavy metals [79].

$\text{PLI }{\left(\text{CF1 × CF2 × CF3 × }\mathrm{...}\mathrm{...}\mathrm{...}\text{ × CFn}\right)}^n\text{ (7)}$

where, CF is the contamination factor and n is the number of metals. The PLI of >1 indicates polluted, whereas <1 indicates no pollution [80]. This index provides a quick assessment to unskilled people to compare the pollution status of different places.

Ecological risk factor and risk index

The E_rⁱ is widely used to assess the ecological risk of heavy metals in sediments [81]. The index was calculated by the following equations [72]:

$E{r}^i=T{r}^i\times C^i\text{ (8)}$

$R{I}^i={\displaystyle \sum E{r}^i}\text{ (9)}$

where E_rⁱ is the potential ecological risk factor for a given contaminant and Tri is the toxic response factor of each element, including Cd = 30, Cr = 2, Ni=5, Cu = 5, Fe = 2.82, Mn = 1, Pb = 1 and Zn = 1 [81-87]. Risk index (RI) is the sum of E_rⁱ and represents potential toxicity response of various heavy metals in sediments. The Eri and RI values [84,87,88] are furnished in Table 7.

Probable effects level

Probable Effects Level (PEL) are guidelines widely accepted to evaluate bio-toxic risks of sediments. In view of the fact that heavy metals always occur in sediments as complex mixtures, the mean PEL quotient (m-PEL-Q) method has been proposed and used to determine the possible biological effect of combined toxicant groups by calculating the mean quotients for a range of heavy metals using the following formula:

$PEL-Q=\frac{{\displaystyle {\sum }_{i=1}^n\left(\frac{Ci}{PELi}\right)}}{n}\text{ (10)}$

where Ci is the content of measured element i, PELi is the PEL value of element i and n is the number of elements. Several classes of toxicity probability [89] for biota are presented in (Table 8).

Modified hazard quotient (mHQ)

A novel approach to assessing the risk posed by individual metals to organisms in a specific region is the modified hazard quotient [90] (Table 9). Its validity, reliability, and accuracy have been confirmed by various researchers [91-95]. As mentioned earlier, this innovative method enables the detection of contamination by contrasting metal concentrations in sediment (measured in mg/kg) with the distributions of adverse ecological impacts at slightly different Threshold Effect Levels (TEL), probable effect levels (PEL), and Severe Effect Levels (SEL), as outlined in Tables 10,11.

$mHQ=\sqrt[2]{\left[C_i\left(\frac{1}{TE{L}_i}+\frac{1}{PE{L}_i}+\frac{1}{CE{L}_i}\right)\right]}\text{ (11)}$

Equipment used

Ekman dredge (10 cm - 50 cm layer of the soil), handheld GPS, 0.85 mm plastic sieve, spectrophotometer, clean polythene covers.

Statistical analysis

Initially, the data were collected and processed using Microsoft Excel. Subsequent analyses were conducted using various statistical software packages. For instance, JMP software version 14 was employed to perform one-way analysis of variance (ANOVA) to determine if any statistically significant (p < 0.05) spatial variations existed in the concentrations of heavy metals. To create Pearson’s product-moment correlation matrix (Table 10), GraphPad Prism version 6 was utilized. Cluster Analysis (CA) was carried out to identify similarities and variations in relation to the influencing factors on the studied heavy metals [46]. A dendrogram, illustrating the similarities among the heavy metals and helping identify their sources of origin, was prepared using Past software version 4. The findings of the present study, derived from the various software tools, are presented in the form of charts and Tables.

Results and discussion

The concentrations of heavy metals in sediment samples collected from four distinct sampling locations of the river Halda have been provided in Table 10. To assess variations in the mean concentrations of these different heavy metals, a one-way ANOVA was initially conducted, followed by a Tukey-Kramer test. The results of these analyses revealed statistically significant spatial differences (p < 0.05) (Table 12). The present study observed a range of average concentrations for these heavy metals in Halda river sediments: 0.24 - 0.84 mg kg−1 for Cadmium (Cd), 20.20 – 47.27 mg kg−1 for Chromium (Cr), 1.0 - 8.40 mg kg−1 for Nickel (Ni), 6.90 - 8.70 mg kg−1 for Copper (Cu), 14520.0 - 20281.0 mg kg−1 for Iron (Fe), 274.0 - 416.0 mg kg−1 for Manganese (Mn), 1.95 - 7.87 mg kg−1 for Lead (Pb), and 32.40 - 42.10 mg kg−1 for Zinc (Zn). The relative concentrations of heavy metals, in descending order, were: Iron (Fe), Manganese (Mn), Zinc (Zn), Chromium (Cr), Nickel (Ni), Lead (Pb), Copper (Cu), and Cadmium (Cd).

The findings in the present study revealed that the concentrations of all studied heavy metals in the sediment were above the permissible limit as set by WHO [42,46], USEPA [47] and DPHE [48] (Table 13). In addition to comparing the data of the present study with some other global standards, the results were also compared with some previous works on the same river and other important river system of Bangladesh (Table 14). The results demonstrated that all most all heavy metal concentrations were lower than Buriganga, Dhaleshwari and Shitalakhya as these rivers are severely polluted by municipal and industrial effluents, sewerage and other non-treated chemicals [96-98,106]. However, the present status of the river Halda is not peasant as the condition of the river is gradually exacerbating compared to some recent investigations.

In the present study, Katakhali Khal exhibited the highest mean concentration of Cd (0.8 ± 0.1), while Madarshah had the lowest concentration (0.1 ± 0.1). Significant (p < 0.05) deviations were observed among the studied sites. The mean Cd levels observed exceeded both global and country-specific permissible limits, as outlined in Tables 13,14. A comparative analysis with prior investigations on sediment samples from various significant rivers in Bangladesh revealed divergent findings. Specifically, the Cd concentration in the Halda River was lower than that reported by Alam, et al. [63] but higher than the findings of Bhuyan, et al. [65]. Moreover, it was lower than the levels detected in the Buriganga [2,10,42], Dhaleshwari [98,99], Turag [102,103], and Shitalakhya rivers [106,117], yet higher than those in the Karnafuly [100,109], Meghna [105], and Brahmaputra River [47] (Table 14). Regional disparities in heavy metal concentrations are common, influenced by the presence or absence of various pollution sources along riverbanks and the discharge of these metals into the rivers. Notably, the current study indicates a 54% reduction in Cd concentration in the Halda River compared to the earlier study by Alam, et al. [63].

The concentration of Cr ranged from 20.20 to 47.30 mg/kg, with the highest level (47.3 ± 1.3 mg/kg) observed at Madari Khal and the lowest (20.2 ± 2.3 mg/kg) at Khondokia Khal. No significant variations were noted among the studied sites for this metal. The mean concentration surpassed the limits established by the World Health Organization (WHO) [42,43] (Table 13). In comparison to prior studies, Mohiuddin, et al. [2] and Islam, et al. [107] reported higher concentrations of Cr in the Buriganga and Shitalakhya rivers, respectively. Conversely, Ahmed, et al. [98] in Dhaleshwari, Islam, et al. [100] in Karnafuly, Banu, et al. [102] in Turag, Hassan, et al. [104] in Meghna, Bhuyan, et al. [47] in Brahmaputra, and Bhuyan, et al. [65] in Halda rivers found lower concentrations of Cr than the present study. However, it is noteworthy that the concentration of Cr in the current study was 32% lower than that was reported by Alam, et al. [63].

In the investigated regions, the concentrations of nickel (Ni), copper (Cu), and lead (Pb) ranged from 1.0 to 8.4, 7.0 to 8.7, and 2.0 to 7.9 mg/kg, respectively. Katakhali Khal exhibited the lowest concentrations (8.4 ± 1.0 and 2.0 ± 0.1 mg/kg for Ni and Cu, respectively), while Madarshah displayed the highest concentrations for Ni (8.4 ± 1.0 mg/kg) and Pb (7.9 ± 0.2 mg/kg), and Khondokia Khal showed the highest concentration for Cu (8.7 ± 0.4 mg/kg). Statistical analysis revealed significant variations (p < 0.05) in the concentrations of Ni, Cu, and Pb among the surveyed sites. The mean concentrations of these heavy metals exceeded the drinking water standards set by the World Health Organization (WHO) [42-44], USEPA [47], and DPHE [48], but remained within safe limits according to WHO [45,46] (Table 13). Furthermore, Ni concentrations were comparatively lower than those reported in previous studies of various rivers in Bangladesh, including Buriganga [97], Dhaleshwari [99], Karnofuly [101], Turag [103], Meghna [105], Shitalakhya [107], Halda [65], and Brahmaputra [47]. Cu concentrations were lower than those in Buriganga [2,10,42], Dhaleshwari [98,99], and Shitalakhya [106], but higher than Karnofuly [101], Turag [103], Meghna [105], Halda [63,65], and Brahmaputra [47]. Conversely, Pb concentration was lower than that in Buriganga [96], Dhaleshwari [99], Karnofuly [100,101], Meghna [105], Shitalakhya [107], and Halda [65] rivers but higher than Turag [103], Brahmaputra [47], and our previous studies of Halda [63] rivers.

The highest concentration of Fe (20281.0 ± 41.5) was found at Madarshah, whereas the maximum levels Mn and Zn (416.0 ± 16.4 and 42.1 ± 1.0 mg/kg) were found at Madari Khal point. Based upon statistical analysis, significant (p < 0.05) deviations were observed for these metals among the studied sites. The concentration of Mn found in the present study (345.07 mg/kg) was substantially lower than the Buriganga (4036 mg/kg) river but it was apparently higher than the level found in some other important rivers in Bangladesh by Ahmed, et al. [96,97], Islam, et al. [100], Bhuyan, et al. [47]. On the contrary, the concentration of Zn found in the present study (max 42.1 ± 1.0 mg/kg) was lower than Buriganga [2,10,42] and Shitalakhya [107] but higher than Karnafuly [100], Turag [103] and Brahmaputra [47] rivers, respectively.

A higher concentration of these metals might be outcomes of discharges of textile and paint industries and domestic sewage waste [10,42,47,103,107,108,112]. Bhuyan and Baker [65] also reported the seasonal fluctuations in the level of the heavy metals in the river Halda.

The I_geo values have been presented in Table 14. In all sampling sites, the I_geo values of all studied heavy metals except Mn configured negative values after calculation, indicating that these sites were not polluted by the heavy metals but Mn (Table 15).

The overall I_geo values of all studied heavy metals ranged from -5.08 to 1.16. Muller [83] classification of sediment quality disclosed that all sites were moderately polluted due contamination with Mn and the orderly arrangement of the sites on the basis of the concentration of this metal stand Madarikhal>Madarshah>Katakhali>Khondokia.

Mohiuddin, et al. [2] studied the I_geo values for Mn for 11 locations of Buriganga river and found the values >1.0, indicating moderately polluted sediment quality. Islam, et al. [108] found higher I_geo values for Cd and extremely contaminated sediment quality in Buriganga and Shitalakhya. Hasan, et al. [104] studied the sediment quality of the Meghna River and found positive values for Cd, Pb, Ni and Zn indicating unpolluted to moderately polluted sediment. In the Karnafuly river in Bangladesh, I_geo values of for As, Cr exposed unpolluted to extremely polluted status [109]. In the Bortala river in China, the I_geo values of Ni, Zn, Cr, As, and Cu indicated no pollution [110]. On the contrary, Malvandi [111] found higher I_geo values for As and Se and sediment class ranged from unpolluted to extremely polluted in the Zarrin-Gol River, Iran. Rahman, et al. [112] and Hassan, et al. [113] have opined that higher concentration Al and Mn originates lithogenically and associated with spinning mills and paint industries wastes. The higher I_geo values as a result of the increased concentration of Mn found in the river Halda might be the result of similar lithogenic and anthropogenic effects.

To evaluate soil heavy metal contamination in the study area, the improved Nemerow index (IN) was utilized, representing the cumulative effects of all heavy metals. The IN values ranged from 1.29 to 1.74 across the four sampling sites, indicating moderate contamination.

These findings are consistent with the results from the geo-accumulation index (I_geo). Akbor, et al. [105] reported severe contamination in certain sites along the Buriganga River in Bangladesh, as indicated by the IN. Similarly, Guan, et al. [68] observed extreme contamination at all sampling sites in a mining area in Tianjin, China. The elevated values in their studies are attributable to the higher levels of industrialization and anthropogenic activity in those regions compared to the current study area. Table 16 presents the Contamination Factors (CF), Pollution Load Index (PLI), degree of contamination, and modified degree of contamination (mCd) for heavy metals in sediment samples from the Halda River. The overall CF value for Cadmium (Cd) exceeded 1.54, indicating moderate contamination, whereas the CF values for other heavy metals indicated “low contamination.” The CF values observed in the Halda River were lower compared to those found in the Meghna River [105] and Buriganga River [10,42,96,97]. The Buriganga River is heavily polluted due to numerous industrial and sewage discharges that introduce large volumes of toxic wastes daily [114]. Similarly, the Meghna River is polluted at various sites by industries located on or near its banks, including shipyards, cement, paper, jute, super board, oil, sugar, food processing, salt, and chemical industries, which discharge wastewater and contribute domestic and agro-chemical wastes [97]. Higher CF values have been reported in other studies globally. For instance, CF values ranged from 1.3 to 5.5 in the Balok River [82], 0.14 to 6.08 in the Dikrong River [115], and 1.1 to 14.6 in the Tamaki Estuary [116].

The PLI represents the number of times by which the metal content in the sediment exceeds the background concentration and gives a summative indication of the overall level of heavy metal toxicity in a particular sample [45]. The PLI of all sampling sites presented in Table 16 was calculated according to Tomlinson, et al. [79] and the values ranged from 0.29 to 0.51 with the overall value for all four sampling sites (0.37 ± 0.10) considered to be unpolluted. Individual PLI of all sites were also <1 that must be classified as unpolluted.

The order of PLI of four sampling sites from higher to lower was Khondokia>Katakhali>Madarikhal>Madarshah. The PLI values found in the present study in the river Halda were lower than some previous studies, for instance; Ali, et al. [109] found higher PLI values in the Karnafuly river. Mohiuddin, et al. [103] reported PLI values 4.924.2 and 5.2 - 27.4 in summer and winter samples of the Buriganga river which was manifold higher than the present study. Ahmed, et al. [97] stated that 100% sampling points of Buriganga river had PLI>1, which indicated a polluted condition. In another study, Islam, et al. [108] also reported similar results. Furthermore, Abdullah, et al. [82] and Varol [71] found higher PLI values than the present study in the Balok and Tigris rivers, respectively. The reasons of this higher PLI values might be associated with the direct disposal of untreated effluents to the river from different industrial and agro-chemical sources.

In this study, Katakhali exhibited the highest level of contamination (3.79 ± 2.07), followed by three other sites. The overall contamination across the four sampling sites was measured at 2.68 ± 1.84, indicating a low degree of contamination. The mCd values for the eight analyzed elements were consistently found to be <1.5, suggesting minimal to low levels of contamination. These findings contrast with earlier studies by Sikder, et al. [96] and Akbor, et al. [105], who reported higher contamination levels in the Buriganga river. Similarly, studies by Sivakumar, et al. [73] in Tamil Nadu, India, and Abrahim and Parker [116] in New Zealand documented higher mCd values compared to this study. These variations may be attributed to differences in geographical location, with coastal areas typically experiencing higher sediment deposition rates due to river discharge. Despite variations, the average mCd values in our study indicate localized enrichment relative to geochemical background levels, potentially linked to the use of phosphate fertilizers in agricultural soils. Soil and lake acidification processes may also contribute to increased mobilization of cadmium from sediments and soils [117].

Table 17 presents the Enrichment Factor (EF) values for heavy metals analyzed in the sediments of the river Halda. The results indicate that the mean EF value for cadmium (Cd) was >4, indicating moderate enrichment in the river. Conversely, the EF values for other metals studied at all sites indicated “minor enrichment” (Table 17). Previous studies, such as those on the Luanhe river (for Cr, Ni, and Zn) and the Bortala river (for As, Ni, and Cu) [110,118], reported mean EF values >1.5. Abdullah, et al. [82] and Varol [71] suggested that these elevated EF values predominantly originate from natural processes or crustal materials. This factor may also influence the EF values observed in the current study.

To evaluate the ecological risk of elements in the Halda River, potential ecological risk indices (E^r_i and RI) were measured and are detailed in Table 18. The ranking of potential ecological risk factor (E^r_i) for heavy metals in the river Halda sediments was Fe > Mn > Cr >> Zn > Cu > Ni > Pb > Cd. The average concentrations of Fe and Mn across the four sites indicate a serious ecological risk, while Cr poses a moderate ecological risk. The mean potential ecological risk coefficients for Cd, Ni, Cu, Pb, and Zn were all below 40, classifying them as low ecological risk. Additionally, the RI values at all sites were greater than 400, indicating a considerable ecological risk. Overall, the E^r_i and RI indices for the studied elements in the Halda River surface sediment suggest a potential ecological risk.

Rahman, et al. [112] reported lower E^r_i and RI indices in an adjacent area of the Dhaka Export Processing Zones compared to this study, likely due to their study being conducted in a floodplain area and a river in Savar upazila, which receives less municipal effluent than Dhaka. In contrast, Islam, et al. [119] found significantly higher E_ri and RI indices in the Buriganga River compared to the Halda River. Malvandi, et al. [111] observed lower indices in the Zarrin-Gol River in Iran, while Soliman, et al. [84] found higher Eri for Cd in Egypt, with other metals showing lower values than in this study. Sivakumar, et al. [73] reported lower RI values than those observed in this study. These discrepancies in E^r_i and RI indices with local and international studies may be due to different types of contaminants from various anthropogenic sources, resulting in variations in metallic element concentrations.

The mean Probable Effects Level (PEL) for the four sampling sites was calculated for metals including Cd, Cr, Ni, Cu, Fe, Mn, Pb, and Zn to assess potential risks to aquatic life. The PEL values ranged from 0.04 to 0.44, while the mean Probable Effects Level Quotient (PEL-Q) ranged from 0.16 to 0.20, with an overall value of 0.18 ± 0.00 (Table 19). These results suggest that the combined presence of heavy metals has a 21% probability of being toxic (Table 8). No literature reports on the PEL and PEL-Q of heavy metals in any native river, making it difficult to compare the current study’s results with previous ones. However, Li, et al. [120] reported a similar probability of toxicity on the Weihai coast in China, and Soliman, et al. [84] found a 30% probability of toxicity on the Mediterranean coast in Egypt.

The concentrations of heavy metals in sediment samples (mg/kg) were compared with sediment quality guidelines (SQGs): Threshold Effect Level (TEL), Probable Effect Level (PEL), and Severe Effect Level (SEL). Cd concentrations in Katakhali Khal exceeded the TEL (0.596 ppm), while other sites had lower TELs. No heavy metals exceeded PEL or SEL values. Islam, et al. [121] reported higher Cd and Ni levels than TEL, PEL, and SEL in the Old Brahmaputra River, Bangladesh. Agah [122] found TEL, PEL, and SEL values for heavy metals in Chabahar Bay, Makoran, Iran. Factors such as manufacturing releases, land use practices, urban discharges, and urbanization may contribute to elevated metal levels. [94,123]. Apart from that, the production facility is likely to be a significant generator of Cd [124]. Production facilities, particularly those manufacturing phosphate fertilizers and pesticides, are significant Cd sources. The elevated Cd in Katakhali Khal is likely due to these factors [125]. The higher amount of Cd in the Katakhali Khal might be associated with the above reasons.

A pollution index pertaining to contamination levels, termed the modified hazard quotient (mHQ), was recently introduced by Benson, et al. [91]. The mHQ assesses pollution levels by comparing each metal(oid) concentration in sediments with thresholds for adverse environmental effects, such as TEL, PEL, and SEL. Evaluating the mHQ is critically important as it gauges the threat posed by individual metal(oid)s to biota and the aquatic environment [126]. The mHQ was computed using equation 11, and the specific contributions of each metal are presented in Tables 10,11. Among the analyzed heavy metals, only Cr and Mn showed a Low Severity of Pollution (1.5>mHQ≥1), while other heavy metals exhibited Very Low Severity of Pollution (1>mHQ≥0.5) (Table 10). The elevated levels of Cr and Mn may originate from chemical industry activities along the riverbank [127].

Heavy metals in sediments typically originate from various natural and anthropogenic sources [132]. Organic matter and grain size are key factors influencing heavy metal distribution in sediments [133]. The correlation among metals in sediments provides critical information on their sources and pathways in aquatic environments. Correlation analyses, supported by PCA and CA, revealed strong relationships among certain metals, indicating similar origins, particularly from industrial effluents, municipal wastes, and agricultural inputs. A correlation matrix was applied to identify relationships among elements and potential common metal sources in the Halda River. The Pearson correlation matrix (95% confidence level, p = 0.05) showed significant correlations among several metals (Table 20). Cd correlated closely with Ni and Fe; Cr with Fe, Mn, and Zn; Ni with Cu and Pb; Fe with Mn and Zn; and Mn with Zn, suggesting common sources, likely anthropogenic [134]. In contrast, other metal pairs showed no significant correlation, indicating distinct pollution sources. Similar associations were found by Alam, et al. [63], Bhuyan and Bakar [47], and Hossain, et al. [135] in the Halda River, and by Hassan, et al. [104] and Akbor, et al. [105] in the Buriganga River, where most metals showed positive correlations except a few.

The Principal Component Analysis (PCA) revealed three components (Eigen values > 1) explaining 94% of the total variance (Table 21). The first component (PC1) accounted for 50% of the variance with high loadings for Fe, Zn, Mn, Cr, Ni, and Pb. The second component (PC2) explained 30% of the variance with high loadings for Ni, Cu, and Pb. The third component (PC3) accounted for 14% of the variance with high loadings for Cu and Cr. Pearson’s Correlation Matrix indicated significant positive correlations among these elements, suggesting lithogenic (natural) origins for elements in PC1 (Fe, Zn, Mn, Cr, Ni, and Pb) [136,137]. Potential sources include industrial discharges, municipal waste, household garbage, and urban runoff [105]. Elements in PC2 (Ni, Cu, and Pb) and PC3 (Cu and Cr) indicate natural or anthropogenic origins. Previous studies by Alam, et al. [63], Bhuyan and Bakar [47], Bhuyan, et al. [64], and Akbor, et al. [105] found similar results in the Halda and Buriganga rivers. Soliman, et al. [84] and Li, et al. [120] observed similar patterns on the Mediterranean coast, Egypt, and Weihai coast, China, respectively. Variations in the number of components and element loadings are likely due to spatial differences and analytical procedures (Figures 3,4).

Conclusion and recommendations

The investigation reveals critical information regarding metal contamination in the sediments of the Halda River. The heavy metals are distributed in the following order: Fe > Mn > Zn > Cr > Ni > Pb > Cu > Cd (mg/kg). These levels exceed the certified reference values set by WHO and USEPA. Although the eight elements displayed different distribution characteristics, lithogenic and anthropogenic sources were the primary contributors. The impact on aquatic biota was evaluated using various indices. The average I_geo values indicated pollution from Mn (0.37 ± 0.02). The average PLI (0.37 ± 0.10) showed no significant pollution across the sites, while the average mCd values indicated a nil to very low degree of contamination. The average EF (0.43 ± 0.10 to 4.14 ± 3.33) suggested none to moderate enrichment. However, average Eri (12.75 ± 0.68 to 49513.56 ± 39.23) and RI (467.70 ± 4.53 to 641.92 ± 27.72) revealed low to serious ecological risk. The PEL-Q indicated a 21% probability of sediment toxicity. The modified hazard quotient (mHQ) showed very low to low levels of contamination.

Multivariate statistical analysis revealed significant correlations among the studied heavy metals, indicating similar sources and/or lithogenic/anthropogenic processes regulating their occurrence. The study of the Halda River in Bangladesh provides crucial insights into the current state of metal pollution in the river. This research is a valuable resource for academics, researchers, and government authorities in Bangladesh. It will aid in developing future management strategies aimed at conserving and restoring the Halda River, which is recognized as the only natural breeding ground for Indian major carps and has been designated as the Bangabandhu Fisheries Heritage.

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