{"paper_id":"0b6103fd-3b79-4cd0-8dbb-9f5e429cb6b6","body_text":"Comparative Environmental and Human Health Risk Assessment of Heavy Metal Contamination near Dhaka’s Tannery Estates | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Environmental and Human Health Risk Assessment of Heavy Metal Contamination near Dhaka’s Tannery Estates Thamina Acter, Tony Thomas Nokrek, Md. Masud Rana, Durjoy Chakraborty, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7387758/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study studies contamination outlines, pollution causes, and allied health influences in two tannery estate locations in Dhaka, Bangladesh: Hemayetpur, Savar (new) and Hazaribagh (old). Soil and vegetable samples were collected from industrial areas (IA) and residential/agricultural areas (RA). Four heavy metals—chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn)—were enumerated using flame-polarized atomic absorption spectrophotometry (AAS). Statistical tools, including pollution indices, bioconcentration factors (BCF), correlation analysis, and principal component analysis (PCA), were applied to assess health risks. Non-carcinogenic and carcinogenic health risks were evaluated according to WHO/FAO guidelines. Heavy metal concentrations in soils (mg/kg) were: Hazaribagh – Cr: 608.53, Cd: 2.89, Pb: 3.59, Mn: 1.81; Hemayetpur – Cr: 745.93, Cd: 2.93, Pb: 35.47, Mn: 3.23. In vegetables, B. alba contained Cr: 219.86, Cd: 7.73, Pb: 5.23, Mn: 0.56; L. siceraria contained Cr: 391.37, Cd: 8.11, Pb: 5.49, Mn: 2.67. Industrial soils exceeded WHO/FAO permissible limits for Cr and Cd by up to 42 and 14 times, respectively. Cr was the dominant pollutant, followed by Cd, Pb, and Mn. Pollution indices indicated severe contamination, particularly in IA-2 (Hemayetpur). Vegetables revealed high Cd BCFs, with root concentrations exceeding safety threshold levels. Dietary exposure was the primary health risk pathway, with Cd posturing the uppermost ecological risk and Cr the highest carcinogenic risk. Industrial activities, primarily tanning and insufficient effluent treatment via the Common Effluent Treatment Plant (CETP), are the major contamination sources (Cr > Cd > Pb). While RA soils exhibited lower contamination, diffuse pollution was apparent. Heavy Metals Soil Contamination Tannery Estate Environmental Monitoring Food Safety Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction As technology advances rapidly, environmental pollution, including heavy metal pollution, has simultaneously become a worldwide concern in the twenty-first century (Han et al. 2020 ). Every sector of the environment—soil (Ruan et al. 2023 ; Noli and Tsamos 2016 ; Lima et al. 2024 ; Xiang et al. 2021 ; Liu et al. 2022 ; Deng et al. 2020 ; Yang et al. 2022 ; Palansooriya et al. 2020 ), water (Noli and Tsamos 2016 ; Wang et al. 2022 ), sediments, air, and food (Ruan et al. 2023 ; Noli and Tsamos 2016 ; Lima et al. 2024 ; Xiang et al. 2021 ; Deng et al. 2020 ; Yang et al. 2022 ; Hassan et al. 2024 ; Kumar et al. 2019 ; Rai et al. 2019 ) is affected by heavy metal pollution. In this chain of events, heavy metal contamination in soil-vegetable systems presents significant environmental and health concerns globally (Ruan et al. 2023 ; Noli and Tsamos 2016 ; Lima et al. 2024 ; Hassan et al. 2024 ; Kumar et al. 2019 ; Rai et al. 2019 ; Hossain et al. 2021 ; Ahmed et al. 2022 ), especially in regions with high industrial activity (Ruan et al. 2023 ; Noli and Tsamos 2016 ; Lima et al. 2024 ; Hassan et al. 2024 ). Among the various industries contributing to heavy metal contamination, the tannery sector holds particular significance due to its use of metal-based chemicals in leather processing (Hossain et al. 2021 ; Ahmed et al. 2022 ). Dhaka, Bangladesh, a densely populated urban center, is no exception, with its tannery industry contributing to heavy metal pollution in the surrounding environment (Hossain et al. 2021 ; Ahmed et al. 2022 ; Rahman et al. 2022 ; Mizan et al. 2023 ). Tanneries are known to release a variety of heavy metals such as chromium, lead, and cadmium, which can accumulate in soil and subsequently be taken up by plants, potentially entering the food chain and posing risks to human health (Ahmed et al. 2022 ). Within the urban landscape of Dhaka, two prominent tannery estates, Savar and Hazaribagh, stand out as significant industrial hubs, housing numerous tanneries engaged in leather production and thus have garnered attention for their potential impact on environmental quality and public health (Hossain et al. 2021 ; Ahmed et al. 2022 ; Rahman et al. 2022 ; Mizan et al. 2023 ). These estates, characterized by intensive industrial activities, inadequate waste management practices, and proximity to residential areas, present a critical environmental challenge in terms of heavy metal pollution (Mizan et al. 2023 ; Ahmed et al. 2022 ). Savar and Hazaribagh are both located within the urban landscape of Dhaka, but they differ significantly in terms of tannery operations, waste management practices, and proximity to residential areas. These variations can potentially result in differing levels of heavy metal pollution in the soil-vegetable systems surrounding these estates (Hossain et al. 2021 ; Ahmed et al. 2022 ; Rahman et al. 2022 ; Mizan et al. 2023 ). Heavy metals such as Cr, Cd, Pb, and Mn, pertain to known toxicity, can be prevalent in industrial effluents, and can accumulate in soil and plant tissues (Ruan et al. 2023 ; Yang et al. 2022 ; Haque et al. 2024 ; Chowdhury and Alam 2024 ; Haque et al. 2021 ). The tannery sector extensively employs Cr-based chemicals in leather processing. Chromium, primarily present in the form of hexavalent chromium [Cr (VI)], is a known carcinogen and poses significant health risks when ingested through contaminated food and water (Kerger et al. 1996 ; Kerger et al. 1997 ). Cadmium, a byproduct of various industrial processes, is highly toxic and can accumulate in the human body, leading to severe health effects, including kidney damage and bone diseases (Alissa and Ferns 2011 ). Lead, a ubiquitous environmental pollutant, is associated with neurological impairments and developmental disorders, especially in children (Brochin et al. 2008 ; Joint et al. 1972 ), while manganese, although an essential micronutrient, can exert toxic effects at elevated concentrations, particularly on the nervous system. Excessive exposure to these metals can result in a range of adverse health effects including neurological disorders, respiratory issues, cardiovascular problems, reproductive issues, developmental abnormalities, and damage to the liver and kidneys (Joint et al. 1972 ; Jaishankar et al. 2014 ). Although the previous toxicological studies in the old Tannery Estate in Hazaribagh, Dhaka helped the government to relocate most of the unplanned tanneries in various parts of the country, including those at Hazaribagh under Dhaka metropolis to the new Tannery Estate in Hemayetpur, Savar, Dhaka for eradicating the heavy metal pollution from the urban areas near the river Buriganga and its aquatic life (Islam et al. 2014 ; Whitehead et al. 2019 ), dozens of small rawhide processing units are still unlawfully in operation in Hazaribagh, Dhaka (Sarker 2022 ; Hasan 2022 ). Besides, the new estate equipped with modern Common Effluent Treatment Plant (CETP) is being operated by the Dhaka Tannery Industrial Estate Wastage Treatment Plant Company Ltd from June 2021. However, numerous electromechanical components of CETP have sustained damage over time due to lack of pre-treatment of liquid waste and the excessive use of water, resulting in diminishing the efficiency of key units significantly, rendering it impractical to adequately treat effluents to the required standards (Wardad 2024 ). As a result, both the area has the probability to bring similar risks to the inhabitants, aquatic life of the Dhaleswari and Buriganga Rivers as well as the total environment of that region. Therefore, it is of utmost importance to conduct consecutive toxicological studies of the soil and vegetables of both areas regularly. A study (Hossain et al. 2021 ) first systematically assessed heavy metal pollution (Cr, Pb, Ni, Cu, Cd) in soil and vegetables in the relocated tannery estate in Hemayetpur, Savar, Dhaka. The current study expands to include both Hemayetpur and Hazaribagh to understand pollution dynamics of heavy metals commonly associated with tannery waste over time, through systematic sampling and analysis of soil and vegetable samples. Therefore, this study was designed to systematically conduct a comparative assessment of heavy metal contamination in soil–vegetable systems in two prominent tannery estates of Dhaka, Bangladesh—Hazaribagh and Hemayetpur (Savar). First, to systematically evaluate the concentrations of selected heavy metals—chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn)—in soil and vegetable samples collected from the two tannery-affected sites. Second, to assess the level of heavy metal contamination in soil and vegetable tissues using established pollution indices. Third, to evaluate both the potential ecological risks posed by metal accumulation in the soil–plant system and the associated human health risks. Materials and methods Selection of study area and samples: This study was conducted in two tannery-impacted locations in Dhaka, Bangladesh, selected based on the intensity of surrounding anthropogenic activities and historical relevance to leather processing. The first sampling site, designated as Industrial Area 1 (IA-1), is situated in Hazaribagh, which historically functioned as the principal center for tannery operations in Dhaka. Although official tannery activities were discontinued in this location in 2017 following government directives, the area continues to pose environmental concerns due to residual contamination associated with decades of industrial usage. The second site, identified as Industrial Area 2 (IA-2), encompasses the relocated tannery estate in Hemayetpur, Savar. This facility has been operational since its establishment in 2017 and currently accommodates more than 150 active tanning units. The estate is estimated to produce approximately 40,000 cubic meters of industrial effluent per day. However, the Central Effluent Treatment Plant (CETP) servicing the site has a maximum treatment capacity of only 25,000 cubic meters per day, indicating the potential for substantial volumes of untreated or partially treated effluent being discharged into the surrounding environment. As a comparative control site for evaluating pollution levels in the industrial study areas, a non-industrial location situated along Katasur Road in the Mohammadpur area of Dhaka was selected. This site, referred to as the Residential Area (RA), is geographically distant from both leather-processing zones and other major industrial operations and thus presumed to be minimally influenced by tannery-related pollution. Two edible vegetable species were selected from all three areas: Malabar Spinach ( Basella alba ) and Bottle Gourd ( Lagenaria siceraria ). These were selected based on their widespread local cultivation, fast-growing nature, and known potential to bioaccumulate heavy metals. Additionally, both species are frequently consumed as part of the local diet, thereby offering relevance for evaluating potential risks to human health arising from environmental contamination. Soil and vegetable samples were collected from IA-1 and IA-2 within the coordinates of 22°47′17.0″N to 22°46′20.7″N and 90°14′39.4″E to 90°14′31.2″E. RA samples were collected between 23°44′11.7″N and 23°45′10.5″N and 91°22′40″E to 90°21′44″E (Fig. 1 ). Sample collection and pretreatment: Three surface soil samples were collected from solid waste dumping areas adjacent to tanneries in IA-1 and IA-2, and from cultivated fields in RA. All samples were collected at a uniform depth of 10 cm using a stainless-steel auger. Each plant species was collected from three areas, making a total of 6 vegetable samples. After a growth period of two and a half months, vegetables were harvested. Plant parts (leaves, stems, roots) were separated, thoroughly washed with deionized water, chopped, and oven-dried at 70 ± 5°C for 48 hours to constant weight, following standard protocols for minimizing thermal decomposition. The dried samples were then ground using a stainless-steel grinder and sieved through a 2 mm mesh. All processed materials were stored in zip-lock polyethylene bags and preserved at − 20°C until further digestion. Sample Digestion Sample digestion procedures were followed according to Hossain et. Al’s study, 14 which was adopted from previous studies (Hseu et al. 2002 ). For the digestion of soil, 1 g of each soil sample was soaked in 10 mL HNO 3 for 24 h. Then the mixture was allowed to be heated for 2h from 80°C to 100°C. After heating, 5 mL HClO 4 was added and heated at 150°C for 1 h using a hot plate and then at 180°C for the next 1 h. Finally, the solution was diluted to 50 mL and filtered. For the digestion of vegetable samples, 0.5 g of vegetable from each sample was soaked in 5 mL HNO 3 and 3 mL H 2 O 2 for 24 h. The mixture was then heated for 4 h with a gradual increase in temperature from 80°C to 180°C. Finally, the solution was diluted to 50 mL and filtered. The filtrate of digested soil and vegetable samples was collected and stored in vial freezing condition (20°C) for the AAS analysis. Analysis of samples: The digested soil and vegetable samples were analyzed using an Atomic Absorption Spectrophotometer (AAS) (Model: ZA3300, Hitachi High Technologies Corporation, Japan) to determine the concentrations of four heavy metals: chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn). AAS was selected for the analysis of heavy metals due to its high sensitivity, accuracy, and reliability in detecting trace metal concentrations in environmental samples (Chowdhury et al. 2024 ; Noli and Tsamos 2016 ). Furthermore, the method is cost-effective and suitable for routine monitoring of heavy metal pollution in developing-country contexts such as Bangladesh (Hossain et al. 2021 ; Kashem and Singh 1999 ), where resources and access to advanced instrumentation may be limited. The analytical wavelengths used for the respective hollow cathode lamps were 359.3 nm for Cr, 228.8 nm for Cd, 283.3 nm for Pb, and 279.5 nm for Mn. The slit width was maintained at 0.7 nm for all elements. Lamp current settings were 2 mA for Cr, Cd, and Pb, and 2.5 mA for Mn, optimized to enhance analytical sensitivity and signal stability. The detection limits (LOD) of the instrument for the analyzed metals were as follows: Cr – 0.01 mg/L, Cd – 0.001 mg/L, Pb – 0.005 mg/L, and Mn – 0.01 mg/L. Quality assurance, precision and accuracy All samples were analyzed in triplicate, and results were expressed as mean values. Calibration curves were established using certified reference standards. For lead (Pb), calibration was performed in the range of 0.5 to 2.5 mg/L, and for chromium (Cr), from 0.25 to 2.0 mg/L. The method detection limits (MDL) were calculated as follows: Pb – 0.27 mg/L; Cr – 0.15 mg/L. The limit of quantifications (LOQ) was estimated as three times the MDL values: Pb – 0.81 mg/L; Cr – 0.45 mg/L. Spike recovery tests were conducted by adding known concentrations of metals to both soil and plant matrix samples. The average recovery rates exceeded 90% for both analytes in both matrices. Analytical precision was assessed through repeated measurements and expressed as relative standard deviation (RSD). The standard deviations (SD) were calculated to be within 6%. For Pb, RSD values were 3.62%, and for Cr, 3.71%. Accuracy was further confirmed using procedural blanks and standard additions. No significant contamination was detected in blank samples. Assessment criteria: After obtaining the analyzed metal concentrations from the studied soils and vegetables, different indices as described in Table 1 were used for evaluation of heavy metal pollution for the respective IA areas. Heavy metals pollution indices: The heavy metals pollution in the studied soils and vegetables was assessed for the level of metal pollution using standard pollution indices such as Contamination Factor (CF) and Pollution Load Index (PLI). The permitted limits in soil and vegetables suggested by FAO/WHO,25 and the established pollution indices are described in Table 1 . Ecological risk indices: The exposure possibility of the studied metals from the environment (e.g., soil) to the two specific plant tissues was evaluated by observing the bioaccumulation of the studied metals in plant tissues. Then the potential ecological risk of the studied metals on the plant species was assessed with equitation suggested by Håkanson (Hakanson 1980 ) (Table 1 ). Human health hazard indices: As the older tannery estate was located near the residential areas and the present tannery estate is surrounded with by paddy and vegetable fields, it is highly desirable that the residents in those areas could be chronically exposed to heavy metal pollution from the studied soil vegetable system via the following four major metal exposure pathways: (Liu et al. 2013 ) (1) direct oral ingestion of soil particles, (2) dermal absorption of trace metals in particles adhered to exposed skin (3) diet through the vegetables grown in pollutant soils, and (4) inhalation suspended particulates emitted from the soil by air through mouth and nose. In particular, Cd, Cr, and Pb are known to have both non-carcinogenic and carcinogenic risks (Oni et al. 2022 ; Zheng et al. 2020 ), but Mn has a low carcinogenic effect (Assem et al. 2011 ). Therefore, the analyzed metal concentrations from the studied soils and vegetables were also utilized to evaluate the non-carcinogenic and carcinogenic risks of human health with exposure parameters for dietary intake (e.g., daily vegetable consumption, body weight, and exposure frequency) using Table S1 . Multivariate statistical analysis Multivariate statistical analysis for the studied samples was performed to figure out the correlation among the sources of the studied metals and identify the potential metals in the respective areas. Pearson correlation tests were performed using SPSS software.40 The significance of differences between metal concentrations in RA and IA soil as well as the accumulation of metals in two different varieties of vegetables grown in those soils were studied by two-way ANOVA41 and Tukey HSD at 0.05% level using R-studio (Wickham and Grolemund 2017 ). Due to the limited number of heavy metals (n = 4) and constrained sample size, the factor analysis, i.e., Principal component analysis (PCA) (Jolliffe and Cadima 2016 ) was applied in an exploratory capacity to assess potential grouping patterns and source indications using both of the software where Varimax rotation was used for maximizing the sum of variance of the factor coefficients. Results and discussion Heavy metals quantification in soil and plants Figure S1 shows the concentrations of the four studied metals (Cr, Cd, Pb, and Mn) in three different types of soils collected from three different areas, i.e., industrial area-1 (IA-1), industrial area-2 (IA-2), residential area (RA) and their permitted limits in soil suggested by FAO/WHO. The raw data to draw the Figure S1 is tabulated in Table 2 . The pH of the industrial soils is slightly basic, where the range of pH for IA-1 and IA-2 is 7.4–7.67 and 7.5–7.7 respectively. The pH of the residential soil is almost neutral, and its pH range is 6.5–6.7. Among all the studied metals, the concentration of the two metals (Cr and Cd) in two types of IA soils was higher than that of the RA soil and their recommended levels suggested by WHO/FAO ( Figure S1 ). Therefore, the IA-soils were more contaminated than RA soil in terms of the concentration of Cr and Cd. It was noticeable that the concentration of Cr in both IA-1 and IA-2 soils respectively (608.53 and 745.93 mg/kg), were 4–5 times greater than that of the safe limit (150 mg/kg). As the so-called CETP set up in the Dhaka Tannery Industrial Estate at Hemayetpur in Savar has been showing poor performance in processing solid waste since its inception in 2021 (Rahaman 2023 ), it is expected to have higher Cr concentration in IA-2 soils disposed of with tanning agents such as basic chromium sulphate [Cr 2 (SO4) 3 ] from leather-processing industries (Hasan et al. 2021 ; Hossain et al. 2021 ). However, the concentration of Cr in the IA-2 soil was significantly lower than in the previous studies, i.e., from 200 mg/kg (Hasan et al. 2021 ) or 10573 mg/kg (Hossain et al. 2021 ) to 745 mg/kg (this study). The reason behind this decreasing trend in Cr contamination in the Hemayetpur tannery estate in this study may be the continuous flashing out of Cr from the soil surface due to heavy rains in monsoon and removal of some Cr by solid waste processing using poor functioned CETP. It is a matter of warning that IA-1 soil has still higher Cr loadings although the tannery industries were removed from Hazaribagh in 2017. A wide range of Cr (113.7–37,000 mg/kg) in the old tannery estate Hazaribagh Dhaka Bangladesh has also been reported in other studies (Alam et al. 2020 ; Juel et al. 2020 ). The higher Cr concentration in IA-1 soil indicates the presence of rawhide processing units running in that area. The order of concentration of all the metals in the three studied areas decreased as follows: IA-1 & IA-2 soil: Cr > Cd > Pb > Mn and RA soil: Cd > Pb > Cr > Mn. The second most concentrated metal in IA soils was Cd. Although it is noticed that Cd in IA-2 soil was less than that of Hossain et. al’s study (Hossain et al. 2021 ) (Table 2 ), its concentration was still two times higher than the safe limit (Fig. 2 ). The concentration of Pb was observed as two times higher in IA-2 soil ( Figure S1 ) than that of Hossain et. al’s study (Hossain et al. 2021 ) (Table 2 ). In this study, the highest Pb concentration was noticed for RA soil. The higher Pb concentration in soil and dust of the Mohammadpur area (RA in this study) was also reported in the literature (Sultan et al. 2022 ), which may be the contributions of heavy traffic activities and exhaust and non-exhaust parts of vehicles (Caravanos et al. 2006 ; Ashraf et al. 2019 ). Although the RA soil has a higher Mn concentration than the IA soils, the concentration of Mn in both RA and IA soils was lower than the safe limit. The quantification results in this study indicated that the studied soils were contaminated with the studied metals, which can ultimately result in the growth of plants containing elevated levels of those metals. Therefore, the quantification of the studied heavy metals in two types of plants ( B. alba and L. siceraria ) was performed by analyzing all the portions of the vegetables, i.e., leaves, stems, and roots using AAS and the results were presented in Fig. 2 b. The three metals, Cr, Cd, and Pb exceeded the safe limit concentration for vegetables (Table 2 ), which indicates their toxicity in plants. The highest concentrated metal was Cr in both plants. The second most concentrated metals were Cd and Pb. The order of concentration of all the metals in the two studied vegetables decreased as follows: Cr > Cd > Pb > Mn. It is found in the literature that the Cr availability to plants is facilitated by Cr complexation with them.51 The Cr and Pb toxicity in plants was also found in previous study (Hossain et al. 2021 ). More specifically, the edible parts (leaves and stems) of the B. alba in the IA-2 soil of this study had comparatively higher Cd but lower Cr and Pb than that of Hossain et al’s study (Hossain et al. 2021 ) (Table 2 ). By comparing the concentration of different parts of the vegetables, it was observed that most of the studied metals (except Cd) were highly loaded in roots, followed by the stems and leaves of both vegetables in all types of soil. The metal concentration in the parts of the vegetable decreased in the following order: root > stems > leaves. The roots of both the two vegetables accumulated 3 times more Cr and 10 times more Pb than its leaves in all types of soils signifying a low rate of translocation of these metals from the roots to leaves. These findings agreed with previous studies where roots were considered as the primary site for Cr and Pb accumulation in most aquatic plants, rather than shoots (Gil-Cardeza et al. 2014 ; Zulfiqar et al. 2019 ; Tiwari et al. 2013 ). Higher Cr and Pb accumulation can cause toxic effects on the plant's metabolic activity and translocation of nutrients by reducing the uptake of essential elements, i.e., Fe, K, Mg, Mn, P, and Ca (Zulfiqar et al. 2019 ; Tiwari et al. 2013 ). It should be mentioned that the amount of Mn found in this study was low. Table 2 Concentration of the metals in two types of vegetables and soils of the three studied locations measured using AAS. Studied Area Analyzed parts Concentration of Studied heavy metals (mg/kg) Cr Cd Pb Mn IA-1 (Hazaribagh) B. alba Leaves 36.5 ± 1.25 2.54 ± 0.03 0.24 ± 0.02 0.19 ± 0.001 B. alba Stems 44.26 ± 1.17 2.46 ± 0.02 2.17 ± 0.2 0.17 ± 0.008 B. alba Roots 139.1 ± 1.9 2.73 ± 0.07 2.82 ± 0.22 0.2 ± 0.02 IA-2 (Hemayetpur) B. alba Leaves 64.41 ± 2.08 2.67 ± 0.02 0.29 ± 0.04 0.27 ± 0.007 B. alba Stems 104.76 ± 0.75 2.5 6 ± 0.03 0.25 ± 0.02 0.27 ± 0.007 B. alba Roots 222.2 ± 4.13 2.88 ± 0.03 4.95 ± 0.43 2.13 ± 0.029 IA-1 (Hazaribagh) L. siceraria Leaves 37 ± 0.98 2.42 ± 0.02 0.22 ± 0.01 0.06 ± 0.01 L. siceraria Stems 43.64 ± 1.96 2.49 ± 0.03 2.22 ± 0.23 0.015 ± 0.0005 L. siceraria Roots 141.03 ± 2.02 2.7 ± 0.05 2.67 ± 0.41 0.12 ± 0.01 IA-2 (Hemayetpur) L. siceraria Leaves 59.87 ± 0.4 2.65 ± 0.02 0.18 ± 0.006 0.18 ± 0.005 L. siceraria Stems 86.65 ± 4.07 2.51 ± 0.01 0.19 ± 0.02 0.24 ± 0.004 L. siceraria Roots 212.76 ± 1.45 2.76 ± 0.02 3.89 ± 0.73 0.16 ± 0.011 RA (Mohammadpur) B. alba Leaves 1.47 ± 0.1 1.18 ± 0.02 0.41 ± 0.06 0.15 ± 0.0006 B. alba Stems 2.14 ± 0.18 1.2 8 ± 0.02 0.9 ± 0.03 0.064 ± 0.0017 B. alba Roots 5.32 ± 0.11 1.01 ± 0.07 14.91 ± 0.32 0.14 ± 0.002 RA (Mohammadpur) L. siceraria Leaves 0.95 ± 0.17 0.81 ± 0.005 0.26 ± 0.08 0.57 ± 0.001 L. siceraria Stems 1.69 ± 0.21 0.84 ± 0.01 0.87 ± 0.22 0.39 ± 0.005 L. siceraria Roots 4.05 ± 0.14 1.1 ± 0.04 13.76 1.36 0.11 ± 0.001 Our previous study (leaves and stems of B. alba grown in IA-2) 201.63 ± 30.43 1.60 ± 0.10 12.17 ± 3.59 - Safe Limit for Vegetables 5 0.2 0.3 2.3 IA-1 (Hazaribagh) soil 608.53 ± 5.59 2.89 ± 0.01 3.59 ± 0.3 1.81 ± 0.2 IA-2 (Hemayetpur) soil 745.93 ± 4.21 2.93 ± 0.02 35.47 ± 1.67 3.23 ± 0.05 RA (Mohammadpur) soil 6.96 ± 0.62 1.22 ± 0.04 46.85 ± 2.22 2.3 ± 0.14 Our previous study (IA-2 soil) 10573.02 ± 586 4.02 ± 0.06 19.67 ± 2.11 - USPA, WHO Limits (mg/kg) for soil 150 1 300 12 Environmental Pollution Assessment To determine the degree of individual heavy metal pollution in the analyzed soils, the single pollution index (PI) of the investigated four metals in IA soils was calculated and presented in Fig. 2 a. It was observed that Cr had PI value of more than 5 in both IA soils indicating very strong contamination, PICr > PICd. IA-2 soil had slightly higher contamination of Cr than that of IA-1 soil. PI values of Pb and Mn were found to be within 1 ~ 2 in IA-2 soils, indicating still contamination but low. The decreasing order of metal pollution in IA soils was Cr > Cd > Mn > Pb. As a result, the bigger variations in the PI values of the examined soils suggested that metal pollution of soils came from a variety of sources. Next, the overall pollution level of the studied soils was calculated from PINemerow values, which were 45.16 (IA-1) and 55.37 (IA-2) indicating greater pollution according to Table 1 . Figure 2 b reflects the overall contamination status of the studied areas with respect to the studied metals from the calculated MPI values. In terms of MPI values of the studied soils and vegetables, soils have higher MPI than vegetables. Both IAs were more polluted than RA, while IA-2 was more polluted than the other two areas due to having higher PI values of Cr, and Cd (refer to Fig. 2 a). The MPI trend decreases as follows: IA-2 > IA-1 > RA. Although the pollution in the industrial areas (IA-1 and IA-2) can be attributed primarily to the discharge of untreated solid waste from tannery operations, the relatively elevated MPI values observed in RA samples suggest the influence of other diffuse pollution sources—potentially including atmospheric deposition, vehicular emissions, and urban runoff (Sultan et al. 2022 ). Therefore, while RA is not directly impacted by industrial effluents, it cannot be considered entirely unpolluted. The higher MPI values of both soils and vegetables suggested the correlation between soil and vegetables in terms of heavy metal uptake mechanism. In addition, both the IAs were identified as priority areas for environmental management and remediation efforts from the perspective of heavy metal pollution. In identifying the sources and extent of contamination for each metal of concern, calculating the enrichment factor (EF) and geo-accumulation index (Igeo) of the studied soils was performed and presented in Fig. 2 c and Fig. 2 d respectively. Given that the RA, designated as the control site, exhibited a relatively high MPI (refer to Fig. 2 b), the use of RA as a baseline for EF calculation would not adequately reflect true natural background levels. Therefore, EF values were calculated using average upper continental crust (UCC) (Turekian and Wedepohl 1961 ; Taylor and McLennan 1995 ) concentrations as the reference baseline, to provide a standardized assessment of anthropogenic enrichment. The Igeo was calculated using metal concentrations from RA soil as the local background to evaluate site-specific pollution levels relative to nearby reference conditions. According to the established categories of EF mentioned in Table 1 , Cr and Cd showed high and very high enrichment respectively in the IA areas indicating the influence of anthropogenic sources (Fig. 2 c). The enrichment of Cd in RA was also high, which may be from other source of atmospheric pollution (Sultan et al. 2022 ). Pb had minor while Mn had no enrichment in soils. The descent order of EF is as follows: Cd > Cr > Pb > Mn. As presented in Fig. 2 d, the highest Igeo was obtained for Cr in IA-I soil and both Cr and Cd in IA-2 soil. The enrichment of Cr in IAs and Cd in IA-2 had Igeo more than 1 suggesting moderate contamination relative to the background levels, possibly influenced by mainly anthropogenic sources. The Igeo of Cd in IA-I soil between 0 and 1 suggesting unpolluted or moderately polluted, which could stem from a combination of natural geological processes and anthropogenic activities. Pb only in soils of IA-2 had Igeo between 0 and 1 offering insights into both natural and anthropogenic influences. Mn had natural geochemical variations and showed no pollution at all. To evaluate the phytoremediation ability of the studied plants, the bioconcentration factor (BCF) was calculated for the entire plants and their different parts and presented in Figure S2 . For the entire plants, the BCF > 1 was obtained for Cd and Pb in both plants in all the studied areas, denoting them as hyperaccumulators. A higher accumulation ability of Cd and Pb than Cr was also observed in our previous study (Hossain et al. 2021 ) due to the higher retention ability of Cr in soil. However, the phytoremediation ability depends on various factors, including the type of metal, the plant species, and the specific conditions of the soil, as noted for Cr showing slightly higher BCF than 1 in RA ( Figure S2 ). Therefore, Cr was also considered as a hyperaccumulator for RA. The order of phytoremediation ability of the studied plants for the metals was as follows: Cd > Pb > Cr > Mn. Several studies also reported the metals Cr, Cd, and Pb as bioaccumulators (Ashraf et al. 2019 ). Figure S2b shows that different parts of the plants can have varying abilities to accumulate different metals. For example, the roots of both plants showed a higher ability to absorb Cr and Pb than the other parts. It is reported that roots are the main accumulators of metal (Golestanifard et al. 2020 ). All the parts of both plants showed almost equal ability to absorb Cd. Therefore, it is a matter of great warning that Cd can transfer to not only the roots but also all the edible parts of the plant. Although the bioaccumulation and phytoremediation ability of the studied plants (edible and non-edible parts) for the metals alerts us not to consume such plants, it shows the alternative application of those plants in the affected areas to minimize specific contaminant risks as well. Therefore, the studied plants can be cultivated in soils contaminated with heavy metals like Cr, Cd, and Pb to reduce the concentration of those metals from soils. Ecological risk assessment To provide crucial insights into the potential ecological risks posed by contaminants to the environment, particularly in soil samples, the Er (Ecological Risk) and RI (Risk Index) values were calculated. The Er values presented in Fig. 3 a show the ecological risk posed by specific contaminants such as Cr, Cd, and Pb in different soil samples studied. Both IA soils have a significant ecological risk associated with Cd, followed by a low risk with Cr, while Pb poses a relatively low risk. The RA soil has low ecological risks associated with both Cr and Cd, but a notably high risk linked to Pb. Comparing the Er values, it was observed that both IA soils exhibit similar levels of ecological risk for Cd and Cr, with Hazaribagh showing slightly higher values. The RA soil consistently shows lower ecological risks for Cd and Cr compared to the other two locations. As Cr and Cd are well known for their toxicity to various organisms, including plants, animals, and microorganisms, when present in elevated concentrations in the soil (López-Luna et al. 2009 ), it is expected that they both would accumulate in the food chain, leading to adverse effects on ecosystems, including decreased biodiversity, impaired growth and reproduction, and even mortality in sensitive species. Therefore, considering the high Er values for both Cr and Cd in Hazaribagh and Hemayetpur soil, there is a cumulative ecological risk posed by these contaminants. The combined impact of these contaminants can exacerbate their individual effects, leading to a higher overall ecological risk in those areas. The RI values presented in Fig. 3 b show the overall assessment of the ecological risk in the studied areas based on the contaminants (Cr, Cd, and Pb) present. Comparing RI Values, it was observed that IA-2 has the highest RI value, indicating the highest overall ecological risk among the three locations. The IA-1 follows with a slightly lower RI value, suggesting a lower but still considerable overall ecological risk. The RA shows the lowest RI value, signifying the lowest overall ecological risk. Overall, the calculated Er and RI values in this study suggest that Mohammadpur soil has the lowest overall ecological risk among the three locations, while Hazaribagh and Hemayetpur soils exhibit higher ecological risks, especially due to elevated levels of Cd and Cr, posing potential threats to ecosystems. Therefore, Hazaribagh and Hemayetpur are considered as the priority areas for remediation due to their higher overall ecological risks, which may require immediate remediation efforts to mitigate risks associated with Cd and Cr. However, Mohammadpur's high Pb content poses a localized risk, highlighting the importance of targeted remediation efforts. Human Health Risk Assessment Non-carcinogenic risk The non-carcinogenic (HQ) and carcinogenic risks (CR) of the studied four metals in the IA and RA soil-grown vegetable systems presented in Table S4 and Table S5 respectively were determined for nearby residents through different exposure routes, i.e., ingestion, dermal absorption, inhalation, and diet. The results indicated significant differences in the magnitude of non-carcinogenic and carcinogenic risks associated with different routes. The most contributing exposure route of metals from the studied soil vegetable system to consumers was diet, which contributed from 92.47 to 99.95% of the non-carcinogenic risks and from 95.63 to 99.94% of carcinogenic risks. Upon observation, the carcinogenic risk associated with dietary intake ranged from 7.83 × 10 − 6 to 0.000799, inhalation ranged from 6.47 × 10 − 15 to 1.58 × 10 − 7, dermal intake ranged from 7.14 × 10 − 6 to 4.38 × 10 − 6, and ingestion intake ranged from 8.60 × 10 − 9 to 1.42 × 10 − 5. The magnitude of the carcinogenic risk associated with dietary intake is over 5000 and 180 times higher than that associated with inhalation and dermal intake respectively. This suggests that consuming vegetables cultivated in specific soils (IA soils) poses a substantially higher risk of carcinogenic exposure compared to inhaling and absorbing (through the skin) any potential contaminants from the same source. Although ingestion of soil has a lower magnitude of risk compared to dietary intake but is higher than inhalation and dermal intake. Therefore, the ingestion of soil emerged as the second most significant exposure pathway of metals among consumers. The order of HQ and CR values of four exposures of soil vegetable systems of the studied areas was as follows: HQdiet > HQing > HQderm > HQinh. The results were in line with findings from previous research (Hossain et al. 2021 ; Chowdhury et al. 2024 ; Liu et al. 2013 ). Figure 4 a shows a comparative risk assessment of non-carcinogenic (HQ) health effects associated with exposure to the studied four metals (Cr, Cd, Pb, and Mn) of the studied vegetables grown in soils of three different areas (IA-1, IA-2, and RA). The results suggest varying levels of risk across the different metals and areas, with Cr posing the highest risk followed by Cd, while Pb and Mn pose lower risks within the IA areas (Cr > Cd > Pb > Mn). In addition, Cr showed a higher potential risk of non-carcinogenic health effects in IA-2 compared to IA-1 while the potential risk of non-carcinogenic health effects associated with Cd exposure is similar in both areas. Carcinogenic risk The carcinogenic risk assessment for four metals in the studied soil-grown vegetables through the diet pathway is given in Fig. 4 b. Among the four studied metals, the calculated carcinogenic risk values for Cr in all areas exceed the acceptable limit (> 1E-06). However, Cr measured in the studied vegetables grown in IA soils had the highest cancer risks in this study while Cr in IA-2 had a higher cancer risk than that of IA-1. Therefore, Cr was assumed to be the main pollutant source for producing cancer-like diseases after human exposure, which was also considered as carcinogenic in previous studies (Cao et al. 2014 ; Hossain et al. 2021 ). The risks associated with Cd, Pb, and Mn contamination in all soil types were slightly higher than the acceptable range. Moreover, all the analyzed metals may have the potential risk of developing cancer in humans on long-term exposure to those metals. Figure 4 c and Fig. 4 d display the HI and CR of vegetables grown in IA and RA soils, along with the analyzed soils. The HI for all examined vegetables in IA soil was notably higher than 1 (according to Table 1 ), indicating a clear non-carcinogenic risk for dietary exposure. It was revealed that RA soil posed the lowest scores for both non-cancer and cancer risk while the IA soil-grown vegetables were 102 times more carcinogenic than the safe limits 10 − 6 – 10 − 4 . The total cancer risk of IA-1 soil-grown B. alba and L. siceraria showed that 486 consumers and 470 consumers per 1 million people respectively (Fig. 4 d), are potentially at cancer risk after consuming these two vegetables, where 427 consumers in 1 million people will affect for Cr only (Fig. 4 b). The serious cancer risk of Cr in old tannery estate in Hazaribagh Dhaka was also found in the literature (Juel et al. 2020 ). Although the threshold limit is 1 chance in 10,00,000 people of developing cancer risk (Caravanos et al. 2006 ), the total cancer risk of IA-1 and IA-2 soil indicates 18 and 23 residents per 100 people are at cancer risk through soil exposure routes. As a result, these analyzed vegetables are deemed to pose a significant hazard to consumers. Additionally, the potential health risks are generally higher in IA-2 soil (HI: 1.16) and vegetables compared to IA-1, suggesting a greater need for remediation measures in IA-2 to mitigate these risks. Furthermore, the outcomes of the assessments for both non-cancer and cancer risks revealed that chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) were the primary heavy metals contributing to both types of risks. Among these, chromium (Cr) posed the greatest cancer risk. Consequently, it is suggested that the studied soil in the investigated industrial area is unsuitable for cultivating the analyzed vegetables due to the heightened risk of heavy metal exposure, which significantly compromises food safety for residents. It should be noted that the leaves and stems of the studied vegetables are cooked and eaten for their laxative properties. The roots of Basella alba are astringent and its cooked roots can be used in the treatment of diarrhoea (Ashraf et al. 2019 ). It's crucial to acknowledge that the risk assessment models used in previous studies may yield overestimations or underestimations in this research. Nevertheless, these findings provide valuable insights in implementing effective measures to mitigate the significant environmental challenges posed by ongoing metal contamination (Liu et al. 2013 ). Statistical analysis Correlation analysis: Statistical studies such as correlation analysis can aid in targeted pollution control measures and environmental management strategies to mitigate the risks associated with heavy metal contamination. Statistical studies were conducted to reveal the association among metals in soil-vegetable systems. The Pearson product-moment correlation coefficients of the studied four heavy metals in IA and RA are shown in Table 3 , which highlights varying degrees of correlation between different heavy metal concentrations. For IA-1, the concentration of Cr had a strong correlation with that of Mn while the concentration of Cd showed a strong correlation with that of other heavy metals (Cr, Pb, and Mn). These findings suggest potential common sources or environmental behaviors influencing the concentrations of these heavy metals in IA-1. For IA-2, Cr showed a strong positive correlation with Pb (r = 0.99, p < 0.01) and Mn (r = 0.88, p < 0.01), while Cd exhibited a moderately strong positive correlation with Mn (r = 0.84, p < 0.05). The strong correlations between chromium (Cr) and both lead (Pb) and manganese (Mn) suggest the possibility of shared contamination sources or similar environmental pathways in IA-2. The correlations among Pb, Cr, and Cd are consistent with our experimental findings as well as previous studies (Hossain et al. 2021 ; Islam et al. 2014 ). Interestingly, Pb measured in RA exhibited the strongest positive correlation (r = 0.92**, p < 0.01) with Cr and a second strong positive correlation (r = 0.86*, p < 0.05), with Mn, indicating a significant association between them. This could be due to similar industrial activities or environmental conditions favoring their presence. As observed in the literature, RA is highly affected by Pb (Sultan et al. 2022 ). Table 3 Correlation matrix between the studied metal concentrations in IA and RA Soils. Studied areas Studied metals Cr Cd Pb Mn IA-1 Cr 1 0.86* 0.67 0.98** Cd 1 0.76* 0.76* Pb 1 0.55 Mn 1 IA-2 Cr 1 0.76* 0.99** 0.88** Cd 1 0.72 0.84* Pb 1 0.87* Mn 1 RA Cr 1 0.39 0.92** 0.62 Cd 1 0.38 0.18 Pb 1 0.86* Mn 1 Principle component analysis (PCA): While the correlations provided valuable insights, PCA was employed as an exploratory tool to identify underlying patterns in heavy metal concentrations and to infer possible contamination sources in the soil samples from both industrial and residential areas. The calculated factor loadings of PCA data, together with cumulative percentages, and percentages of variance explained by each factor are shown in Table S6 . In all three sampling areas—IA-1, IA-2, and RA—three principal components were extracted, each having Eigenvalues greater than 1. In the residential area (RA), Pb exhibited strong loading on the first component, which accounted for 69.55% of the total variance. Mn showed moderate associations with the second and third components, which explained 21.77% and 8.54% of the variance, respectively. Cr and Cd did not exhibit high loadings in the first component in RA, suggesting that these elements may have originated from diffuse, possibly natural sources or minor anthropogenic inputs in that area. In contrast, in IA-1, both Cr and Cd displayed strong negative loadings on the first principal component, which accounted for 82.49% of the total variance. Similarly, in IA-2, Cr, Cd, and Pb were all highly associated with the first component, which explained 88.57% of the variance. These strong associations imply a common source, likely linked to industrial activities. The second component in both industrial areas showed weaker contributions to total variance (13.04% in IA-1 and 8.52% in IA-2) and contained moderate loadings of Pb and Mn, potentially reflecting background contributions or secondary pollution pathways. It is important to note that as PCA was conducted using only four heavy metals (Cr, Cd, Pb, Mn), the analysis should be viewed as indicative rather than conclusive. Nevertheless, the PCA patterns, supported by the concentration profiles shown in Fig. 5 , suggest that Cr and Cd are the principal contaminants in the industrial areas. Their elevated levels are likely attributable to untreated waste discharge and chemical usage in leather processing operations, as reported by previous studies (NCSU 2025 ; Kashem and Singh 1999 ). Given the small number of variables and limited sampling locations, further investigations involving a broader set of contaminants, a larger dataset, and additional advanced statistical tools are warranted to validate source apportionment and assess potential human health risks. Conclusions This study provides a comprehensive analysis of heavy metal contamination (Cr, Cd, Pb, and Mn) in soils and vegetables from industrial areas of Hazaribagh and Hemayetpur, Dhaka, Bangladesh, highlighting a pronounced disparity in pollution levels and associated risks posed by elevated levels of Cr, Cd, and Pb. The industrial soils, particularly those in Hemayetpur, showed significantly higher contamination due to ongoing industrial activities of tannery estates and insufficient waste treatment. Cr and Cd concentrations were notably higher than safe limits, posing severe ecological risks and potential toxicity to aquatic life and human health. Vegetables grown in contaminated soils accumulated these metals, with roots showing the highest levels, indicating a low translocation rate to edible parts. The elevated bioconcentration factors for Cd across all plant parts highlight the potential for widespread human exposure, challenging current agricultural practices in polluted zones. Statistical analyses confirmed the understanding that Cr, Cd, and Pb share common anthropogenic origins, reinforcing the imperative for source control and pollution abatement strategies. Ecological risk assessments further identify IA soils as priority sites for remediation and identified Cd and Cr as the main contributors to environmental hazards to mitigate adverse environmental and health outcomes. Human health risk assessments highlighted dietary intake as the most significant exposure route, posing substantial non-carcinogenic and carcinogenic risks. Ultimately, this study advocates for stringent regulatory oversight, comprehensive soil and crop monitoring, and the adoption of phytoremediation approaches to address the dual challenges of environmental degradation and food safety in heavily industrialized regions. As this study is cross-sectional, it does not reflect temporal variations in heavy metal concentrations. Future research should focus on larger sample sizes including a broader range of metals and extended monitoring to better understand the long-term impacts of heavy metal exposure and the effectiveness of current and proposed remediation efforts. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The authors are grateful to the people of Katasur Road, Mohammadpur, Hazaribagh, and Savar tannery state for their kind cooperation in collecting soil samples. They are also grateful to the Material Science Division, Materials Science Division, Atomic Energy Centre Dhaka-1000, Bangladesh for providing the necessary facilities to carry out this research work. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Ahmed S, Mahdi MM, Nurnabi M, Alam MZ, Choudhury TR (2022) Health risk assessment for heavy metal accumulation in leafy vegetables grown on tannery effluent contaminated soil. Toxicology Reports 9:346-355. doi:https://doi.org/10.1016/j.toxrep.2022.03.009 Alam MS, Han B, Pichtel J (2020) Assessment of soil and groundwater contamination at a former Tannery district in Dhaka, Bangladesh. 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Geological society of America bulletin 72 (2):175-192. doi:https://doi.org/10.1130/0016-7606(1961)72[175:DOTEIS]2.0.CO;2 Usero J, Gonzalez-Regalado E, Gracia I (1997) Trace metals in the bivalve molluscs Ruditapes decussatus and Ruditapes philippinarum from the Atlantic Coast of Southern Spain. Environment International 23 (3):291-298. doi:https://doi.org/10.1016/S0160-4120(97)00030-5 Wang Z, Luo P, Zha X, Xu C, Kang S, Zhou M, Nover D, Wang Y (2022) Overview assessment of risk evaluation and treatment technologies for heavy metal pollution of water and soil. Journal of Cleaner Production 379:134043. doi:https://doi.org/10.1016/j.jclepro.2022.134043 Wardad Y (2024) Govt goes for CETP, SWM upgradation under PPP. The Financial Express, February 18, 2024, Whitehead P, Bussi G, Peters R, Hossain M, Softley L, Shawal S, Jin L, Rampley C, Holdship P, Hope R (2019) Modelling heavy metals in the Buriganga River System, Dhaka, Bangladesh: Impacts of tannery pollution control. Science of the Total Environment 697:134090. doi:https://doi.org/10.1016/j.scitotenv.2019.134090 Wickham H, Grolemund G (2017) R for Data Science. O'Reilly Media, Xiang M, Li Y, Yang J, Lei K, Li Y, Li F, Zheng D, Fang X, Cao Y (2021) Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops. Environmental Pollution 278:116911. doi:https://doi.org/10.1016/j.envpol.2021.116911 Yang L, Ren Q, Zheng K, Jiao Z, Ruan X, Wang Y (2022) Migration of heavy metals in the soil-grape system and potential health risk assessment. Science of the Total Environment 806:150646. doi:https://doi.org/10.1016/j.scitotenv.2021.150646 Zheng S, Wang Q, Yuan Y, Sun W (2020) Human health risk assessment of heavy metals in soil and food crops in the Pearl River Delta urban agglomeration of China. Food chemistry 316:126213. doi:https://doi.org/10.1016/j.foodchem.2020.126213 Zulfiqar U, Farooq M, Hussain S, Maqsood M, Hussain M, Ishfaq M, Ahmad M, Anjum MZ (2019) Lead toxicity in plants: Impacts and remediation. Journal of environmental management 250:109557. doi:https://doi.org/10.1016/j.jenvman.2019.109557 Supplementary Files Table1.docx SupportingInformationHMTanneriesFinal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7387758\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":501985544,\"identity\":\"ae997bbc-f458-4b56-865a-d3140e538af8\",\"order_by\":0,\"name\":\"Thamina Acter\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"East West University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Thamina\",\"middleName\":\"\",\"lastName\":\"Acter\",\"suffix\":\"\"},{\"id\":501985545,\"identity\":\"f032f436-2b86-4ac3-b642-d19c987a7406\",\"order_by\":1,\"name\":\"Tony Thomas Nokrek\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Daffodil International University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tony\",\"middleName\":\"Thomas\",\"lastName\":\"Nokrek\",\"suffix\":\"\"},{\"id\":501985546,\"identity\":\"93e7c8fb-0747-4dfe-a7d6-4217b2f62095\",\"order_by\":2,\"name\":\"Md. Masud Rana\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Daffodil International University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md.\",\"middleName\":\"Masud\",\"lastName\":\"Rana\",\"suffix\":\"\"},{\"id\":501985547,\"identity\":\"f2d9019c-939c-45e8-83e7-8c3b57ce579c\",\"order_by\":3,\"name\":\"Durjoy Chakraborty\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Daffodil International University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Durjoy\",\"middleName\":\"\",\"lastName\":\"Chakraborty\",\"suffix\":\"\"},{\"id\":501985548,\"identity\":\"cf33df36-07ec-4181-840d-7f53819a0c3e\",\"order_by\":4,\"name\":\"M H M Imrul Kabir\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"East West University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"M\",\"middleName\":\"H M Imrul\",\"lastName\":\"Kabir\",\"suffix\":\"\"},{\"id\":501985549,\"identity\":\"238ec562-3b56-4ab1-b3b4-8c5839820dc3\",\"order_by\":5,\"name\":\"Md Al-Mamun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Atomic Energy Center Dhaka: Atomic Energy Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Md\",\"middleName\":\"\",\"lastName\":\"Al-Mamun\",\"suffix\":\"\"},{\"id\":501985550,\"identity\":\"87c07e7b-6fa1-41d8-8379-792dd75e8ce9\",\"order_by\":6,\"name\":\"Md Shahedul Islam\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"https://orcid.org/0000-0002-9931-2965\",\"institution\":\"Daffodil International University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Md\",\"middleName\":\"Shahedul\",\"lastName\":\"Islam\",\"suffix\":\"\"},{\"id\":501985551,\"identity\":\"7da4a13e-589f-40da-afe0-f183c1aca097\",\"order_by\":7,\"name\":\"Kamarun Monira Mow\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"East West University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kamarun\",\"middleName\":\"Monira\",\"lastName\":\"Mow\",\"suffix\":\"\"},{\"id\":501985552,\"identity\":\"71899d24-490a-4ee2-b199-39927e339428\",\"order_by\":8,\"name\":\"Nizam Uddin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Daffodil International University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nizam\",\"middleName\":\"\",\"lastName\":\"Uddin\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-08-16 13:36:34\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-7387758/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7387758/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":89925847,\"identity\":\"b9e5c78a-ff5e-4f95-aed4-4585de2321be\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:29:23\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":554127,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSampling location of the soil-vegetable system in tannery zones (IA) and residential zone (RA), Dhaka, Bangladesh.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/f4c51044f2313c12907664b1.png\"},{\"id\":89924522,\"identity\":\"5753544e-d010-42d5-9ea5-97a0b2cfd3c2\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:29\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":83815,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssessment of four heavy metal pollution in the studied areas using pollution indices, i.e., a) Single pollution index, b) Enrichment factor (EF), c) Metal Pollution index (MPI), and d) Geo-accumulation index (I\\u003csub\\u003egeo\\u003c/sub\\u003e) for the soil-vegetable systems in three studied areas.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/2e04ce4c760a0831cc148616.png\"},{\"id\":89924481,\"identity\":\"a59d6d7b-8e95-444c-b32d-54a86ce48077\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:23\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":41610,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEcological risk by four heavy metal contamination in soil-vegetable systems of the studied areas.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/9e357bf4ceacabcee4696cbf.png\"},{\"id\":89924476,\"identity\":\"006cc8e1-0081-470f-be12-978f88658609\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:23\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57765,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eHealth hazard assessment by four heavy metal contamination in soil-vegetable systems of the three studied areas.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/80162ccb273275c92e2d3647.png\"},{\"id\":89924536,\"identity\":\"8922ae84-55f4-483d-9ce3-29200a9af00d\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:38\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":71692,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePrinciple component analysis of four heavy metals in the three studied areas.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/f3b6f997e8402f276e2f8574.png\"},{\"id\":90038172,\"identity\":\"060760e4-f203-4fa0-840d-99f28fe57cdf\",\"added_by\":\"auto\",\"created_at\":\"2025-08-27 16:15:08\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1966456,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/990fa52f-d12e-41dc-ad2e-a4e2496fee59.pdf\"},{\"id\":89924473,\"identity\":\"ee19f620-ae91-4026-ba16-fa718984e3b2\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:22\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":24869,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/dab92f6272e25528055673e1.docx\"},{\"id\":89924498,\"identity\":\"f09a9ba4-0c48-4a19-b5dd-c3eca50e87fa\",\"added_by\":\"auto\",\"created_at\":\"2025-08-26 13:21:25\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":380239,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupportingInformationHMTanneriesFinal.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7387758/v1/1bd766a025cef287053b53bc.docx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Comparative Environmental and Human Health Risk Assessment of Heavy Metal Contamination near Dhaka’s Tannery Estates\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eAs technology advances rapidly, environmental pollution, including heavy metal pollution, has simultaneously become a worldwide concern in the twenty-first century (Han et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Every sector of the environment\\u0026mdash;soil (Ruan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Lima et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Xiang et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Deng et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Yang et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Palansooriya et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), water (Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), sediments, air, and food (Ruan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Lima et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Xiang et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Deng et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Yang et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Hassan et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Kumar et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Rai et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) is affected by heavy metal pollution. In this chain of events, heavy metal contamination in soil-vegetable systems presents significant environmental and health concerns globally (Ruan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Lima et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Hassan et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Kumar et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Rai et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), especially in regions with high industrial activity (Ruan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Lima et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Hassan et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Among the various industries contributing to heavy metal contamination, the tannery sector holds particular significance due to its use of metal-based chemicals in leather processing (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Dhaka, Bangladesh, a densely populated urban center, is no exception, with its tannery industry contributing to heavy metal pollution in the surrounding environment (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Rahman et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mizan et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Tanneries are known to release a variety of heavy metals such as chromium, lead, and cadmium, which can accumulate in soil and subsequently be taken up by plants, potentially entering the food chain and posing risks to human health (Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWithin the urban landscape of Dhaka, two prominent tannery estates, Savar and Hazaribagh, stand out as significant industrial hubs, housing numerous tanneries engaged in leather production and thus have garnered attention for their potential impact on environmental quality and public health (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Rahman et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mizan et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These estates, characterized by intensive industrial activities, inadequate waste management practices, and proximity to residential areas, present a critical environmental challenge in terms of heavy metal pollution (Mizan et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Savar and Hazaribagh are both located within the urban landscape of Dhaka, but they differ significantly in terms of tannery operations, waste management practices, and proximity to residential areas. These variations can potentially result in differing levels of heavy metal pollution in the soil-vegetable systems surrounding these estates (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Ahmed et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Rahman et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Mizan et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHeavy metals such as Cr, Cd, Pb, and Mn, pertain to known toxicity, can be prevalent in industrial effluents, and can accumulate in soil and plant tissues (Ruan et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Yang et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Haque et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Chowdhury and Alam \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Haque et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The tannery sector extensively employs Cr-based chemicals in leather processing. Chromium, primarily present in the form of hexavalent chromium [Cr (VI)], is a known carcinogen and poses significant health risks when ingested through contaminated food and water (Kerger et al. \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e; Kerger et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e). Cadmium, a byproduct of various industrial processes, is highly toxic and can accumulate in the human body, leading to severe health effects, including kidney damage and bone diseases (Alissa and Ferns \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Lead, a ubiquitous environmental pollutant, is associated with neurological impairments and developmental disorders, especially in children (Brochin et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Joint et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e1972\\u003c/span\\u003e), while manganese, although an essential micronutrient, can exert toxic effects at elevated concentrations, particularly on the nervous system. Excessive exposure to these metals can result in a range of adverse health effects including neurological disorders, respiratory issues, cardiovascular problems, reproductive issues, developmental abnormalities, and damage to the liver and kidneys (Joint et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e1972\\u003c/span\\u003e; Jaishankar et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eAlthough the previous toxicological studies in the old Tannery Estate in Hazaribagh, Dhaka helped the government to relocate most of the unplanned tanneries in various parts of the country, including those at Hazaribagh under Dhaka metropolis to the new Tannery Estate in Hemayetpur, Savar, Dhaka for eradicating the heavy metal pollution from the urban areas near the river Buriganga and its aquatic life (Islam et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Whitehead et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), dozens of small rawhide processing units are still unlawfully in operation in Hazaribagh, Dhaka (Sarker \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Hasan \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Besides, the new estate equipped with modern Common Effluent Treatment Plant (CETP) is being operated by the Dhaka Tannery Industrial Estate Wastage Treatment Plant Company Ltd from June 2021. However, numerous electromechanical components of CETP have sustained damage over time due to lack of pre-treatment of liquid waste and the excessive use of water, resulting in diminishing the efficiency of key units significantly, rendering it impractical to adequately treat effluents to the required standards (Wardad \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). As a result, both the area has the probability to bring similar risks to the inhabitants, aquatic life of the Dhaleswari and Buriganga Rivers as well as the total environment of that region. Therefore, it is of utmost importance to conduct consecutive toxicological studies of the soil and vegetables of both areas regularly.\\u003c/p\\u003e\\u003cp\\u003eA study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) first systematically assessed heavy metal pollution (Cr, Pb, Ni, Cu, Cd) in soil and vegetables in the relocated tannery estate in Hemayetpur, Savar, Dhaka. The current study expands to include both Hemayetpur and Hazaribagh to understand pollution dynamics of heavy metals commonly associated with tannery waste over time, through systematic sampling and analysis of soil and vegetable samples. Therefore, this study was designed to systematically conduct a comparative assessment of heavy metal contamination in soil\\u0026ndash;vegetable systems in two prominent tannery estates of Dhaka, Bangladesh\\u0026mdash;Hazaribagh and Hemayetpur (Savar). First, to systematically evaluate the concentrations of selected heavy metals\\u0026mdash;chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn)\\u0026mdash;in soil and vegetable samples collected from the two tannery-affected sites. Second, to assess the level of heavy metal contamination in soil and vegetable tissues using established pollution indices. Third, to evaluate both the potential ecological risks posed by metal accumulation in the soil\\u0026ndash;plant system and the associated human health risks.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eSelection of study area and samples:\\u003c/h2\\u003e\\u003cp\\u003eThis study was conducted in two tannery-impacted locations in Dhaka, Bangladesh, selected based on the intensity of surrounding anthropogenic activities and historical relevance to leather processing. The first sampling site, designated as Industrial Area 1 (IA-1), is situated in Hazaribagh, which historically functioned as the principal center for tannery operations in Dhaka. Although official tannery activities were discontinued in this location in 2017 following government directives, the area continues to pose environmental concerns due to residual contamination associated with decades of industrial usage. The second site, identified as Industrial Area 2 (IA-2), encompasses the relocated tannery estate in Hemayetpur, Savar. This facility has been operational since its establishment in 2017 and currently accommodates more than 150 active tanning units. The estate is estimated to produce approximately 40,000 cubic meters of industrial effluent per day. However, the Central Effluent Treatment Plant (CETP) servicing the site has a maximum treatment capacity of only 25,000 cubic meters per day, indicating the potential for substantial volumes of untreated or partially treated effluent being discharged into the surrounding environment.\\u003c/p\\u003e\\u003cp\\u003eAs a comparative control site for evaluating pollution levels in the industrial study areas, a non-industrial location situated along Katasur Road in the Mohammadpur area of Dhaka was selected. This site, referred to as the Residential Area (RA), is geographically distant from both leather-processing zones and other major industrial operations and thus presumed to be minimally influenced by tannery-related pollution.\\u003c/p\\u003e\\u003cp\\u003eTwo edible vegetable species were selected from all three areas: Malabar Spinach (\\u003cem\\u003eBasella alba\\u003c/em\\u003e) and Bottle Gourd (\\u003cem\\u003eLagenaria siceraria\\u003c/em\\u003e). These were selected based on their widespread local cultivation, fast-growing nature, and known potential to bioaccumulate heavy metals. Additionally, both species are frequently consumed as part of the local diet, thereby offering relevance for evaluating potential risks to human health arising from environmental contamination.\\u003c/p\\u003e\\u003cp\\u003eSoil and vegetable samples were collected from IA-1 and IA-2 within the coordinates of 22\\u0026deg;47\\u0026prime;17.0\\u0026Prime;N to 22\\u0026deg;46\\u0026prime;20.7\\u0026Prime;N and 90\\u0026deg;14\\u0026prime;39.4\\u0026Prime;E to 90\\u0026deg;14\\u0026prime;31.2\\u0026Prime;E. RA samples were collected between 23\\u0026deg;44\\u0026prime;11.7\\u0026Prime;N and 23\\u0026deg;45\\u0026prime;10.5\\u0026Prime;N and 91\\u0026deg;22\\u0026prime;40\\u0026Prime;E to 90\\u0026deg;21\\u0026prime;44\\u0026Prime;E (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eSample collection and pretreatment:\\u003c/h3\\u003e\\n\\u003cp\\u003eThree surface soil samples were collected from solid waste dumping areas adjacent to tanneries in IA-1 and IA-2, and from cultivated fields in RA. All samples were collected at a uniform depth of 10 cm using a stainless-steel auger.\\u003c/p\\u003e\\u003cp\\u003eEach plant species was collected from three areas, making a total of 6 vegetable samples. After a growth period of two and a half months, vegetables were harvested. Plant parts (leaves, stems, roots) were separated, thoroughly washed with deionized water, chopped, and oven-dried at 70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5\\u0026deg;C for 48 hours to constant weight, following standard protocols for minimizing thermal decomposition. The dried samples were then ground using a stainless-steel grinder and sieved through a 2 mm mesh. All processed materials were stored in zip-lock polyethylene bags and preserved at \\u0026minus;\\u0026thinsp;20\\u0026deg;C until further digestion.\\u003c/p\\u003e\\n\\u003ch3\\u003eSample Digestion\\u003c/h3\\u003e\\n\\u003cp\\u003eSample digestion procedures were followed according to Hossain et. Al\\u0026rsquo;s study, 14 which was adopted from previous studies (Hseu et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). For the digestion of soil, 1 g of each soil sample was soaked in 10 mL HNO\\u003csub\\u003e3\\u003c/sub\\u003e for 24 h. Then the mixture was allowed to be heated for 2h from 80\\u0026deg;C to 100\\u0026deg;C. After heating, 5 mL HClO\\u003csub\\u003e4\\u003c/sub\\u003e was added and heated at 150\\u0026deg;C for 1 h using a hot plate and then at 180\\u0026deg;C for the next 1 h. Finally, the solution was diluted to 50 mL and filtered. For the digestion of vegetable samples, 0.5 g of vegetable from each sample was soaked in 5 mL HNO\\u003csub\\u003e3\\u003c/sub\\u003e and 3 mL H\\u003csub\\u003e2\\u003c/sub\\u003eO\\u003csub\\u003e2\\u003c/sub\\u003e for 24 h. The mixture was then heated for 4 h with a gradual increase in temperature from 80\\u0026deg;C to 180\\u0026deg;C. Finally, the solution was diluted to 50 mL and filtered. The filtrate of digested soil and vegetable samples was collected and stored in vial freezing condition (20\\u0026deg;C) for the AAS analysis.\\u003c/p\\u003e\\n\\u003ch3\\u003eAnalysis of samples:\\u003c/h3\\u003e\\n\\u003cp\\u003eThe digested soil and vegetable samples were analyzed using an Atomic Absorption Spectrophotometer (AAS) (Model: ZA3300, Hitachi High Technologies Corporation, Japan) to determine the concentrations of four heavy metals: chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn). AAS was selected for the analysis of heavy metals due to its high sensitivity, accuracy, and reliability in detecting trace metal concentrations in environmental samples (Chowdhury et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Noli and Tsamos \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Furthermore, the method is cost-effective and suitable for routine monitoring of heavy metal pollution in developing-country contexts such as Bangladesh (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Kashem and Singh \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e), where resources and access to advanced instrumentation may be limited. The analytical wavelengths used for the respective hollow cathode lamps were 359.3 nm for Cr, 228.8 nm for Cd, 283.3 nm for Pb, and 279.5 nm for Mn. The slit width was maintained at 0.7 nm for all elements. Lamp current settings were 2 mA for Cr, Cd, and Pb, and 2.5 mA for Mn, optimized to enhance analytical sensitivity and signal stability. The detection limits (LOD) of the instrument for the analyzed metals were as follows: Cr \\u0026ndash; 0.01 mg/L, Cd \\u0026ndash; 0.001 mg/L, Pb \\u0026ndash; 0.005 mg/L, and Mn \\u0026ndash; 0.01 mg/L.\\u003c/p\\u003e\\n\\u003ch3\\u003eQuality assurance, precision and accuracy\\u003c/h3\\u003e\\n\\u003cp\\u003eAll samples were analyzed in triplicate, and results were expressed as mean values. Calibration curves were established using certified reference standards. For lead (Pb), calibration was performed in the range of 0.5 to 2.5 mg/L, and for chromium (Cr), from 0.25 to 2.0 mg/L. The method detection limits (MDL) were calculated as follows: Pb \\u0026ndash; 0.27 mg/L; Cr \\u0026ndash; 0.15 mg/L. The limit of quantifications (LOQ) was estimated as three times the MDL values: Pb \\u0026ndash; 0.81 mg/L; Cr \\u0026ndash; 0.45 mg/L. Spike recovery tests were conducted by adding known concentrations of metals to both soil and plant matrix samples. The average recovery rates exceeded 90% for both analytes in both matrices. Analytical precision was assessed through repeated measurements and expressed as relative standard deviation (RSD). The standard deviations (SD) were calculated to be within 6%. For Pb, RSD values were 3.62%, and for Cr, 3.71%. Accuracy was further confirmed using procedural blanks and standard additions. No significant contamination was detected in blank samples.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eAssessment criteria:\\u003c/h2\\u003e\\u003cp\\u003eAfter obtaining the analyzed metal concentrations from the studied soils and vegetables, different indices as described in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e were used for evaluation of heavy metal pollution for the respective IA areas.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eHeavy metals pollution indices:\\u003c/h3\\u003e\\n\\u003cp\\u003eThe heavy metals pollution in the studied soils and vegetables was assessed for the level of metal pollution using standard pollution indices such as Contamination Factor (CF) and Pollution Load Index (PLI). The permitted limits in soil and vegetables suggested by FAO/WHO,25 and the established pollution indices are described in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\n\\u003ch3\\u003eEcological risk indices:\\u003c/h3\\u003e\\n\\u003cp\\u003eThe exposure possibility of the studied metals from the environment (e.g., soil) to the two specific plant tissues was evaluated by observing the bioaccumulation of the studied metals in plant tissues. Then the potential ecological risk of the studied metals on the plant species was assessed with equitation suggested by H\\u0026aring;kanson (Hakanson \\u003cspan class=\\\"CitationRef\\\"\\u003e1980\\u003c/span\\u003e) (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eHuman health hazard indices:\\u003c/h2\\u003e\\n \\u003cp\\u003eAs the older tannery estate was located near the residential areas and the present tannery estate is surrounded with by paddy and vegetable fields, it is highly desirable that the residents in those areas could be chronically exposed to heavy metal pollution from the studied soil vegetable system via the following four major metal exposure pathways: (Liu et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) (1) direct oral ingestion of soil particles, (2) dermal absorption of trace metals in particles adhered to exposed skin (3) diet through the vegetables grown in pollutant soils, and (4) inhalation suspended particulates emitted from the soil by air through mouth and nose. In particular, Cd, Cr, and Pb are known to have both non-carcinogenic and carcinogenic risks (Oni et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Zheng et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), but Mn has a low carcinogenic effect (Assem et al. \\u003cspan class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Therefore, the analyzed metal concentrations from the studied soils and vegetables were also utilized to evaluate the non-carcinogenic and carcinogenic risks of human health with exposure parameters for dietary intake (e.g., daily vegetable consumption, body weight, and exposure frequency) using \\u003cstrong\\u003eTable \\u003cspan class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eMultivariate statistical analysis\\u003c/h2\\u003e\\n \\u003cp\\u003eMultivariate statistical analysis for the studied samples was performed to figure out the correlation among the sources of the studied metals and identify the potential metals in the respective areas. Pearson correlation tests were performed using SPSS software.40 The significance of differences between metal concentrations in RA and IA soil as well as the accumulation of metals in two different varieties of vegetables grown in those soils were studied by two-way ANOVA41 and Tukey HSD at 0.05% level using R-studio (Wickham and Grolemund \\u003cspan class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Due to the limited number of heavy metals (n\\u0026thinsp;=\\u0026thinsp;4) and constrained sample size, the factor analysis, i.e., Principal component analysis (PCA) (Jolliffe and Cadima \\u003cspan class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) was applied in an exploratory capacity to assess potential grouping patterns and source indications using both of the software where Varimax rotation was used for maximizing the sum of variance of the factor coefficients.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eHeavy metals quantification in soil and plants\\u003c/h2\\u003e\\u003cp\\u003e\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e shows the concentrations of the four studied metals (Cr, Cd, Pb, and Mn) in three different types of soils collected from three different areas, i.e., industrial area-1 (IA-1), industrial area-2 (IA-2), residential area (RA) and their permitted limits in soil suggested by FAO/WHO. The raw data to draw the \\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e is tabulated in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. The pH of the industrial soils is slightly basic, where the range of pH for IA-1 and IA-2 is 7.4\\u0026ndash;7.67 and 7.5\\u0026ndash;7.7 respectively. The pH of the residential soil is almost neutral, and its pH range is 6.5\\u0026ndash;6.7. Among all the studied metals, the concentration of the two metals (Cr and Cd) in two types of IA soils was higher than that of the RA soil and their recommended levels suggested by WHO/FAO (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e). Therefore, the IA-soils were more contaminated than RA soil in terms of the concentration of Cr and Cd. It was noticeable that the concentration of Cr in both IA-1 and IA-2 soils respectively (608.53 and 745.93 mg/kg), were 4\\u0026ndash;5 times greater than that of the safe limit (150 mg/kg). As the so-called CETP set up in the Dhaka Tannery Industrial Estate at Hemayetpur in Savar has been showing poor performance in processing solid waste since its inception in 2021 (Rahaman \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), it is expected to have higher Cr concentration in IA-2 soils disposed of with tanning agents such as basic chromium sulphate [Cr\\u003csub\\u003e2\\u003c/sub\\u003e(SO4)\\u003csub\\u003e3\\u003c/sub\\u003e] from leather-processing industries (Hasan et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, the concentration of Cr in the IA-2 soil was significantly lower than in the previous studies, i.e., from 200 mg/kg (Hasan et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) or 10573 mg/kg (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) to 745 mg/kg (this study). The reason behind this decreasing trend in Cr contamination in the Hemayetpur tannery estate in this study may be the continuous flashing out of Cr from the soil surface due to heavy rains in monsoon and removal of some Cr by solid waste processing using poor functioned CETP. It is a matter of warning that IA-1 soil has still higher Cr loadings although the tannery industries were removed from Hazaribagh in 2017. A wide range of Cr (113.7\\u0026ndash;37,000 mg/kg) in the old tannery estate Hazaribagh Dhaka Bangladesh has also been reported in other studies (Alam et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Juel et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The higher Cr concentration in IA-1 soil indicates the presence of rawhide processing units running in that area.\\u003c/p\\u003e\\u003cp\\u003eThe order of concentration of all the metals in the three studied areas decreased as follows: IA-1 \\u0026amp; IA-2 soil: Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn and RA soil: Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn. The second most concentrated metal in IA soils was Cd. Although it is noticed that Cd in IA-2 soil was less than that of Hossain et. al\\u0026rsquo;s study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), its concentration was still two times higher than the safe limit (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The concentration of Pb was observed as two times higher in IA-2 soil (\\u003cb\\u003eFigure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e\\u003c/b\\u003e) than that of Hossain et. al\\u0026rsquo;s study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In this study, the highest Pb concentration was noticed for RA soil. The higher Pb concentration in soil and dust of the Mohammadpur area (RA in this study) was also reported in the literature (Sultan et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), which may be the contributions of heavy traffic activities and exhaust and non-exhaust parts of vehicles (Caravanos et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Ashraf et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Although the RA soil has a higher Mn concentration than the IA soils, the concentration of Mn in both RA and IA soils was lower than the safe limit.\\u003c/p\\u003e\\u003cp\\u003eThe quantification results in this study indicated that the studied soils were contaminated with the studied metals, which can ultimately result in the growth of plants containing elevated levels of those metals. Therefore, the quantification of the studied heavy metals in two types of plants (\\u003cem\\u003eB. alba\\u003c/em\\u003e and \\u003cem\\u003eL. siceraria\\u003c/em\\u003e) was performed by analyzing all the portions of the vegetables, i.e., leaves, stems, and roots using AAS and the results were presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe three metals, Cr, Cd, and Pb exceeded the safe limit concentration for vegetables (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), which indicates their toxicity in plants. The highest concentrated metal was Cr in both plants. The second most concentrated metals were Cd and Pb. The order of concentration of all the metals in the two studied vegetables decreased as follows: Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn. It is found in the literature that the Cr availability to plants is facilitated by Cr complexation with them.51 The Cr and Pb toxicity in plants was also found in previous study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). More specifically, the edible parts (leaves and stems) of the \\u003cem\\u003eB. alba\\u003c/em\\u003e in the IA-2 soil of this study had comparatively higher Cd but lower Cr and Pb than that of Hossain et al\\u0026rsquo;s study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). By comparing the concentration of different parts of the vegetables, it was observed that most of the studied metals (except Cd) were highly loaded in roots, followed by the stems and leaves of both vegetables in all types of soil. The metal concentration in the parts of the vegetable decreased in the following order: root\\u0026thinsp;\\u0026gt;\\u0026thinsp;stems\\u0026thinsp;\\u0026gt;\\u0026thinsp;leaves. The roots of both the two vegetables accumulated 3 times more Cr and 10 times more Pb than its leaves in all types of soils signifying a low rate of translocation of these metals from the roots to leaves. These findings agreed with previous studies where roots were considered as the primary site for Cr and Pb accumulation in most aquatic plants, rather than shoots (Gil-Cardeza et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Zulfiqar et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tiwari et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Higher Cr and Pb accumulation can cause toxic effects on the plant's metabolic activity and translocation of nutrients by reducing the uptake of essential elements, i.e., Fe, K, Mg, Mn, P, and Ca (Zulfiqar et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tiwari et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). It should be mentioned that the amount of Mn found in this study was low.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eConcentration of the metals in two types of vegetables and soils of the three studied locations measured using AAS.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eStudied Area\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u003cp\\u003eAnalyzed parts\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e\\u003cp\\u003eConcentration of Studied heavy metals (mg/kg)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eCr\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCd\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ePb\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMn\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eIA-1 (Hazaribagh)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e36.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.25\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e44.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.008\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e139.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.82\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eIA-2 (Hemayetpur)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e64.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.29\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e104.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.75\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.5 6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.007\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e222.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.13\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.88\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.43\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.029\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eIA-1 (Hazaribagh)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.98\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.06\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e43.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.96\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.49\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.23\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.015\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0005\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e141.03\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.41\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eIA-2 (Hemayetpur)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e59.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.006\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e86.65\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.51\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.19\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.004\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e212.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.45\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.76\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.73\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.16\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.011\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eRA (Mohammadpur)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0006\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2 8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.03\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.064\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.0017\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eB. alba Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.11\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.32\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.002\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003eRA (Mohammadpur)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Leaves\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.95\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.17\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.08\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Stems\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.69\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.005\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cem\\u003eL. siceraria Roots\\u003c/em\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.05\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.76 1.36\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.001\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eOur previous study (leaves and stems of B. alba grown in IA-2)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e201.63\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30.43\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e1.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.10\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e12.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.59\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eSafe Limit for Vegetables\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eIA-1 (Hazaribagh) soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e608.53\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.59\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.01\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.59\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.81\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eIA-2 (Hemayetpur) soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e745.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.21\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e35.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.23\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.05\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eRA (Mohammadpur) soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.22\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e46.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.22\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.14\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eOur previous study (IA-2 soil)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e10573.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;586\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e4.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.06\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003e19.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.11\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e-\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u003cp\\u003eUSPA, WHO Limits (mg/kg) for soil\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e150\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e300\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e12\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEnvironmental Pollution Assessment\\u003c/h2\\u003e\\u003cp\\u003eTo determine the degree of individual heavy metal pollution in the analyzed soils, the single pollution index (PI) of the investigated four metals in IA soils was calculated and presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea. It was observed that Cr had PI value of more than 5 in both IA soils indicating very strong contamination, PICr\\u0026thinsp;\\u0026gt;\\u0026thinsp;PICd. IA-2 soil had slightly higher contamination of Cr than that of IA-1 soil. PI values of Pb and Mn were found to be within 1\\u0026thinsp;~\\u0026thinsp;2 in IA-2 soils, indicating still contamination but low. The decreasing order of metal pollution in IA soils was Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb. As a result, the bigger variations in the PI values of the examined soils suggested that metal pollution of soils came from a variety of sources. Next, the overall pollution level of the studied soils was calculated from PINemerow values, which were 45.16 (IA-1) and 55.37 (IA-2) indicating greater pollution according to Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb reflects the overall contamination status of the studied areas with respect to the studied metals from the calculated MPI values. In terms of MPI values of the studied soils and vegetables, soils have higher MPI than vegetables. Both IAs were more polluted than RA, while IA-2 was more polluted than the other two areas due to having higher PI values of Cr, and Cd (refer to Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea). The MPI trend decreases as follows: IA-2\\u0026thinsp;\\u0026gt;\\u0026thinsp;IA-1\\u0026thinsp;\\u0026gt;\\u0026thinsp;RA. Although the pollution in the industrial areas (IA-1 and IA-2) can be attributed primarily to the discharge of untreated solid waste from tannery operations, the relatively elevated MPI values observed in RA samples suggest the influence of other diffuse pollution sources\\u0026mdash;potentially including atmospheric deposition, vehicular emissions, and urban runoff (Sultan et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Therefore, while RA is not directly impacted by industrial effluents, it cannot be considered entirely unpolluted. The higher MPI values of both soils and vegetables suggested the correlation between soil and vegetables in terms of heavy metal uptake mechanism. In addition, both the IAs were identified as priority areas for environmental management and remediation efforts from the perspective of heavy metal pollution.\\u003c/p\\u003e\\u003cp\\u003eIn identifying the sources and extent of contamination for each metal of concern, calculating the enrichment factor (EF) and geo-accumulation index (Igeo) of the studied soils was performed and presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed respectively. Given that the RA, designated as the control site, exhibited a relatively high MPI (refer to Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb), the use of RA as a baseline for EF calculation would not adequately reflect true natural background levels. Therefore, EF values were calculated using average upper continental crust (UCC) (Turekian and Wedepohl \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e1961\\u003c/span\\u003e; Taylor and McLennan \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e) concentrations as the reference baseline, to provide a standardized assessment of anthropogenic enrichment. The Igeo was calculated using metal concentrations from RA soil as the local background to evaluate site-specific pollution levels relative to nearby reference conditions. According to the established categories of EF mentioned in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Cr and Cd showed high and very high enrichment respectively in the IA areas indicating the influence of anthropogenic sources (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). The enrichment of Cd in RA was also high, which may be from other source of atmospheric pollution (Sultan et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Pb had minor while Mn had no enrichment in soils. The descent order of EF is as follows: Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn. As presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed, the highest Igeo was obtained for Cr in IA-I soil and both Cr and Cd in IA-2 soil. The enrichment of Cr in IAs and Cd in IA-2 had Igeo more than 1 suggesting moderate contamination relative to the background levels, possibly influenced by mainly anthropogenic sources. The Igeo of Cd in IA-I soil between 0 and 1 suggesting unpolluted or moderately polluted, which could stem from a combination of natural geological processes and anthropogenic activities. Pb only in soils of IA-2 had Igeo between 0 and 1 offering insights into both natural and anthropogenic influences. Mn had natural geochemical variations and showed no pollution at all.\\u003c/p\\u003e\\u003cp\\u003eTo evaluate the phytoremediation ability of the studied plants, the bioconcentration factor (BCF) was calculated for the entire plants and their different parts and presented in \\u003cb\\u003eFigure S2\\u003c/b\\u003e. For the entire plants, the BCF\\u0026thinsp;\\u0026gt;\\u0026thinsp;1 was obtained for Cd and Pb in both plants in all the studied areas, denoting them as hyperaccumulators. A higher accumulation ability of Cd and Pb than Cr was also observed in our previous study (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) due to the higher retention ability of Cr in soil. However, the phytoremediation ability depends on various factors, including the type of metal, the plant species, and the specific conditions of the soil, as noted for Cr showing slightly higher BCF than 1 in RA (\\u003cb\\u003eFigure S2\\u003c/b\\u003e). Therefore, Cr was also considered as a hyperaccumulator for RA. The order of phytoremediation ability of the studied plants for the metals was as follows: Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn. Several studies also reported the metals Cr, Cd, and Pb as bioaccumulators (Ashraf et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). \\u003cb\\u003eFigure S2b\\u003c/b\\u003e shows that different parts of the plants can have varying abilities to accumulate different metals. For example, the roots of both plants showed a higher ability to absorb Cr and Pb than the other parts. It is reported that roots are the main accumulators of metal (Golestanifard et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). All the parts of both plants showed almost equal ability to absorb Cd. Therefore, it is a matter of great warning that Cd can transfer to not only the roots but also all the edible parts of the plant. Although the bioaccumulation and phytoremediation ability of the studied plants (edible and non-edible parts) for the metals alerts us not to consume such plants, it shows the alternative application of those plants in the affected areas to minimize specific contaminant risks as well. Therefore, the studied plants can be cultivated in soils contaminated with heavy metals like Cr, Cd, and Pb to reduce the concentration of those metals from soils.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eEcological risk assessment\\u003c/h2\\u003e\\u003cp\\u003eTo provide crucial insights into the potential ecological risks posed by contaminants to the environment, particularly in soil samples, the Er (Ecological Risk) and RI (Risk Index) values were calculated. The Er values presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea show the ecological risk posed by specific contaminants such as Cr, Cd, and Pb in different soil samples studied. Both IA soils have a significant ecological risk associated with Cd, followed by a low risk with Cr, while Pb poses a relatively low risk. The RA soil has low ecological risks associated with both Cr and Cd, but a notably high risk linked to Pb. Comparing the Er values, it was observed that both IA soils exhibit similar levels of ecological risk for Cd and Cr, with Hazaribagh showing slightly higher values. The RA soil consistently shows lower ecological risks for Cd and Cr compared to the other two locations. As Cr and Cd are well known for their toxicity to various organisms, including plants, animals, and microorganisms, when present in elevated concentrations in the soil (L\\u0026oacute;pez-Luna et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), it is expected that they both would accumulate in the food chain, leading to adverse effects on ecosystems, including decreased biodiversity, impaired growth and reproduction, and even mortality in sensitive species. Therefore, considering the high Er values for both Cr and Cd in Hazaribagh and Hemayetpur soil, there is a cumulative ecological risk posed by these contaminants. The combined impact of these contaminants can exacerbate their individual effects, leading to a higher overall ecological risk in those areas.\\u003c/p\\u003e\\u003cp\\u003eThe RI values presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb show the overall assessment of the ecological risk in the studied areas based on the contaminants (Cr, Cd, and Pb) present. Comparing RI Values, it was observed that IA-2 has the highest RI value, indicating the highest overall ecological risk among the three locations. The IA-1 follows with a slightly lower RI value, suggesting a lower but still considerable overall ecological risk. The RA shows the lowest RI value, signifying the lowest overall ecological risk. Overall, the calculated Er and RI values in this study suggest that Mohammadpur soil has the lowest overall ecological risk among the three locations, while Hazaribagh and Hemayetpur soils exhibit higher ecological risks, especially due to elevated levels of Cd and Cr, posing potential threats to ecosystems. Therefore, Hazaribagh and Hemayetpur are considered as the priority areas for remediation due to their higher overall ecological risks, which may require immediate remediation efforts to mitigate risks associated with Cd and Cr. However, Mohammadpur's high Pb content poses a localized risk, highlighting the importance of targeted remediation efforts.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eHuman Health Risk Assessment\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eNon-carcinogenic risk\\u003c/h2\\u003e\\u003cp\\u003eThe non-carcinogenic (HQ) and carcinogenic risks (CR) of the studied four metals in the IA and RA soil-grown vegetable systems presented in \\u003cb\\u003eTable S4\\u003c/b\\u003e and \\u003cb\\u003eTable S5\\u003c/b\\u003e respectively were determined for nearby residents through different exposure routes, i.e., ingestion, dermal absorption, inhalation, and diet. The results indicated significant differences in the magnitude of non-carcinogenic and carcinogenic risks associated with different routes. The most contributing exposure route of metals from the studied soil vegetable system to consumers was diet, which contributed from 92.47 to 99.95% of the non-carcinogenic risks and from 95.63 to 99.94% of carcinogenic risks. Upon observation, the carcinogenic risk associated with dietary intake ranged from 7.83 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;6 to 0.000799, inhalation ranged from 6.47 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;15 to 1.58 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;7, dermal intake ranged from 7.14 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;6 to 4.38 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;6, and ingestion intake ranged from 8.60 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;9 to 1.42 \\u0026times; 10\\u0026thinsp;\\u0026minus;\\u0026thinsp;5. The magnitude of the carcinogenic risk associated with dietary intake is over 5000 and 180 times higher than that associated with inhalation and dermal intake respectively. This suggests that consuming vegetables cultivated in specific soils (IA soils) poses a substantially higher risk of carcinogenic exposure compared to inhaling and absorbing (through the skin) any potential contaminants from the same source. Although ingestion of soil has a lower magnitude of risk compared to dietary intake but is higher than inhalation and dermal intake. Therefore, the ingestion of soil emerged as the second most significant exposure pathway of metals among consumers. The order of HQ and CR values of four exposures of soil vegetable systems of the studied areas was as follows: HQdiet\\u0026thinsp;\\u0026gt;\\u0026thinsp;HQing\\u0026thinsp;\\u0026gt;\\u0026thinsp;HQderm\\u0026thinsp;\\u0026gt;\\u0026thinsp;HQinh. The results were in line with findings from previous research (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Chowdhury et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea shows a comparative risk assessment of non-carcinogenic (HQ) health effects associated with exposure to the studied four metals (Cr, Cd, Pb, and Mn) of the studied vegetables grown in soils of three different areas (IA-1, IA-2, and RA). The results suggest varying levels of risk across the different metals and areas, with Cr posing the highest risk followed by Cd, while Pb and Mn pose lower risks within the IA areas (Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb\\u0026thinsp;\\u0026gt;\\u0026thinsp;Mn). In addition, Cr showed a higher potential risk of non-carcinogenic health effects in IA-2 compared to IA-1 while the potential risk of non-carcinogenic health effects associated with Cd exposure is similar in both areas.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCarcinogenic risk\\u003c/h2\\u003e\\u003cp\\u003eThe carcinogenic risk assessment for four metals in the studied soil-grown vegetables through the diet pathway is given in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb. Among the four studied metals, the calculated carcinogenic risk values for Cr in all areas exceed the acceptable limit (\\u0026gt;\\u0026thinsp;1E-06). However, Cr measured in the studied vegetables grown in IA soils had the highest cancer risks in this study while Cr in IA-2 had a higher cancer risk than that of IA-1. Therefore, Cr was assumed to be the main pollutant source for producing cancer-like diseases after human exposure, which was also considered as carcinogenic in previous studies (Cao et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The risks associated with Cd, Pb, and Mn contamination in all soil types were slightly higher than the acceptable range. Moreover, all the analyzed metals may have the potential risk of developing cancer in humans on long-term exposure to those metals.\\u003c/p\\u003e\\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed display the HI and CR of vegetables grown in IA and RA soils, along with the analyzed soils. The HI for all examined vegetables in IA soil was notably higher than 1 (according to Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), indicating a clear non-carcinogenic risk for dietary exposure. It was revealed that RA soil posed the lowest scores for both non-cancer and cancer risk while the IA soil-grown vegetables were 102 times more carcinogenic than the safe limits 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;6\\u003c/sup\\u003e \\u0026ndash; 10\\u003csup\\u003e\\u0026minus;\\u0026thinsp;4\\u003c/sup\\u003e. The total cancer risk of IA-1 soil-grown \\u003cem\\u003eB. alba\\u003c/em\\u003e and \\u003cem\\u003eL. siceraria\\u003c/em\\u003e showed that 486 consumers and 470 consumers per 1\\u0026nbsp;million people respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ed), are potentially at cancer risk after consuming these two vegetables, where 427 consumers in 1\\u0026nbsp;million people will affect for Cr only (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). The serious cancer risk of Cr in old tannery estate in Hazaribagh Dhaka was also found in the literature (Juel et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Although the threshold limit is 1 chance in 10,00,000 people of developing cancer risk (Caravanos et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e), the total cancer risk of IA-1 and IA-2 soil indicates 18 and 23 residents per 100 people are at cancer risk through soil exposure routes. As a result, these analyzed vegetables are deemed to pose a significant hazard to consumers. Additionally, the potential health risks are generally higher in IA-2 soil (HI: 1.16) and vegetables compared to IA-1, suggesting a greater need for remediation measures in IA-2 to mitigate these risks. Furthermore, the outcomes of the assessments for both non-cancer and cancer risks revealed that chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn) were the primary heavy metals contributing to both types of risks. Among these, chromium (Cr) posed the greatest cancer risk. Consequently, it is suggested that the studied soil in the investigated industrial area is unsuitable for cultivating the analyzed vegetables due to the heightened risk of heavy metal exposure, which significantly compromises food safety for residents. It should be noted that the leaves and stems of the studied vegetables are cooked and eaten for their laxative properties. The roots of Basella alba are astringent and its cooked roots can be used in the treatment of diarrhoea (Ashraf et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). It's crucial to acknowledge that the risk assessment models used in previous studies may yield overestimations or underestimations in this research. Nevertheless, these findings provide valuable insights in implementing effective measures to mitigate the significant environmental challenges posed by ongoing metal contamination (Liu et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section3\\\"\\u003e\\u003ch2\\u003eCorrelation analysis:\\u003c/h2\\u003e\\u003cp\\u003eStatistical studies such as correlation analysis can aid in targeted pollution control measures and environmental management strategies to mitigate the risks associated with heavy metal contamination. Statistical studies were conducted to reveal the association among metals in soil-vegetable systems.\\u003c/p\\u003e\\u003cp\\u003eThe Pearson product-moment correlation coefficients of the studied four heavy metals in IA and RA are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, which highlights varying degrees of correlation between different heavy metal concentrations. For IA-1, the concentration of Cr had a strong correlation with that of Mn while the concentration of Cd showed a strong correlation with that of other heavy metals (Cr, Pb, and Mn). These findings suggest potential common sources or environmental behaviors influencing the concentrations of these heavy metals in IA-1. For IA-2, Cr showed a strong positive correlation with Pb (r\\u0026thinsp;=\\u0026thinsp;0.99, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) and Mn (r\\u0026thinsp;=\\u0026thinsp;0.88, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01), while Cd exhibited a moderately strong positive correlation with Mn (r\\u0026thinsp;=\\u0026thinsp;0.84, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). The strong correlations between chromium (Cr) and both lead (Pb) and manganese (Mn) suggest the possibility of shared contamination sources or similar environmental pathways in IA-2. The correlations among Pb, Cr, and Cd are consistent with our experimental findings as well as previous studies (Hossain et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Islam et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Interestingly, Pb measured in RA exhibited the strongest positive correlation (r\\u0026thinsp;=\\u0026thinsp;0.92**, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01) with Cr and a second strong positive correlation (r\\u0026thinsp;=\\u0026thinsp;0.86*, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), with Mn, indicating a significant association between them. This could be due to similar industrial activities or environmental conditions favoring their presence. As observed in the literature, RA is highly affected by Pb (Sultan et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eCorrelation matrix between the studied metal concentrations in IA and RA Soils.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eStudied areas\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eStudied metals\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eCr\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eCd\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003ePb\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eMn\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003eIA-1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCr\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.86*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.67\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.98**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCd\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.76*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.76*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePb\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.55\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMn\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003eIA-2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCr\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.76*\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.99**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.88**\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCd\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.72\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.84*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePb\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.87*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMn\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e\\u003cp\\u003eRA\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCr\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.39\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.92**\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.62\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCd\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.38\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.18\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003ePb\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e0.86*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eMn\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003ePrinciple component analysis (PCA):\\u003c/h2\\u003e\\u003cp\\u003eWhile the correlations provided valuable insights, PCA was employed as an exploratory tool to identify underlying patterns in heavy metal concentrations and to infer possible contamination sources in the soil samples from both industrial and residential areas. The calculated factor loadings of PCA data, together with cumulative percentages, and percentages of variance explained by each factor are shown in \\u003cb\\u003eTable S6\\u003c/b\\u003e. In all three sampling areas\\u0026mdash;IA-1, IA-2, and RA\\u0026mdash;three principal components were extracted, each having Eigenvalues greater than 1. In the residential area (RA), Pb exhibited strong loading on the first component, which accounted for 69.55% of the total variance. Mn showed moderate associations with the second and third components, which explained 21.77% and 8.54% of the variance, respectively. Cr and Cd did not exhibit high loadings in the first component in RA, suggesting that these elements may have originated from diffuse, possibly natural sources or minor anthropogenic inputs in that area. In contrast, in IA-1, both Cr and Cd displayed strong negative loadings on the first principal component, which accounted for 82.49% of the total variance. Similarly, in IA-2, Cr, Cd, and Pb were all highly associated with the first component, which explained 88.57% of the variance. These strong associations imply a common source, likely linked to industrial activities. The second component in both industrial areas showed weaker contributions to total variance (13.04% in IA-1 and 8.52% in IA-2) and contained moderate loadings of Pb and Mn, potentially reflecting background contributions or secondary pollution pathways. It is important to note that as PCA was conducted using only four heavy metals (Cr, Cd, Pb, Mn), the analysis should be viewed as indicative rather than conclusive. Nevertheless, the PCA patterns, supported by the concentration profiles shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, suggest that Cr and Cd are the principal contaminants in the industrial areas. Their elevated levels are likely attributable to untreated waste discharge and chemical usage in leather processing operations, as reported by previous studies (NCSU \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Kashem and Singh \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e). Given the small number of variables and limited sampling locations, further investigations involving a broader set of contaminants, a larger dataset, and additional advanced statistical tools are warranted to validate source apportionment and assess potential human health risks.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study provides a comprehensive analysis of heavy metal contamination (Cr, Cd, Pb, and Mn) in soils and vegetables from industrial areas of Hazaribagh and Hemayetpur, Dhaka, Bangladesh, highlighting a pronounced disparity in pollution levels and associated risks posed by elevated levels of Cr, Cd, and Pb. The industrial soils, particularly those in Hemayetpur, showed significantly higher contamination due to ongoing industrial activities of tannery estates and insufficient waste treatment. Cr and Cd concentrations were notably higher than safe limits, posing severe ecological risks and potential toxicity to aquatic life and human health. Vegetables grown in contaminated soils accumulated these metals, with roots showing the highest levels, indicating a low translocation rate to edible parts. The elevated bioconcentration factors for Cd across all plant parts highlight the potential for widespread human exposure, challenging current agricultural practices in polluted zones. Statistical analyses confirmed the understanding that Cr, Cd, and Pb share common anthropogenic origins, reinforcing the imperative for source control and pollution abatement strategies. Ecological risk assessments further identify IA soils as priority sites for remediation and identified Cd and Cr as the main contributors to environmental hazards to mitigate adverse environmental and health outcomes. Human health risk assessments highlighted dietary intake as the most significant exposure route, posing substantial non-carcinogenic and carcinogenic risks. Ultimately, this study advocates for stringent regulatory oversight, comprehensive soil and crop monitoring, and the adoption of phytoremediation approaches to address the dual challenges of environmental degradation and food safety in heavily industrialized regions. As this study is cross-sectional, it does not reflect temporal variations in heavy metal concentrations. Future research should focus on larger sample sizes including a broader range of metals and extended monitoring to better understand the long-term impacts of heavy metal exposure and the effectiveness of current and proposed remediation efforts.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of Competing Interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgement\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors are grateful to the people of Katasur Road, Mohammadpur, Hazaribagh, and Savar tannery state for their kind cooperation in collecting soil samples. They are also grateful to the Material Science Division, Materials Science Division, Atomic Energy Centre Dhaka-1000, Bangladesh for providing the necessary facilities to carry out this research work.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAhmed S, Mahdi MM, Nurnabi M, Alam MZ, Choudhury TR (2022) Health risk assessment for heavy metal accumulation in leafy vegetables grown on tannery effluent contaminated soil. Toxicology Reports 9:346-355. doi:https://doi.org/10.1016/j.toxrep.2022.03.009\\u003c/li\\u003e\\n\\u003cli\\u003eAlam MS, Han B, Pichtel J (2020) Assessment of soil and groundwater contamination at a former Tannery district in Dhaka, Bangladesh. 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Journal of environmental management 250:109557. doi:https://doi.org/10.1016/j.jenvman.2019.109557\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Heavy Metals, Soil Contamination, Tannery Estate, Environmental Monitoring, Food Safety\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7387758/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7387758/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis study studies contamination outlines, pollution causes, and allied health influences in two tannery estate locations in Dhaka, Bangladesh: Hemayetpur, Savar (new) and Hazaribagh (old). Soil and vegetable samples were collected from industrial areas (IA) and residential/agricultural areas (RA). Four heavy metals\\u0026mdash;chromium (Cr), cadmium (Cd), lead (Pb), and manganese (Mn)\\u0026mdash;were enumerated using flame-polarized atomic absorption spectrophotometry (AAS). Statistical tools, including pollution indices, bioconcentration factors (BCF), correlation analysis, and principal component analysis (PCA), were applied to assess health risks. Non-carcinogenic and carcinogenic health risks were evaluated according to WHO/FAO guidelines. Heavy metal concentrations in soils (mg/kg) were: Hazaribagh \\u0026ndash; Cr: 608.53, Cd: 2.89, Pb: 3.59, Mn: 1.81; Hemayetpur \\u0026ndash; Cr: 745.93, Cd: 2.93, Pb: 35.47, Mn: 3.23. In vegetables, \\u003cem\\u003eB. alba\\u003c/em\\u003e contained Cr: 219.86, Cd: 7.73, Pb: 5.23, Mn: 0.56; \\u003cem\\u003eL. siceraria\\u003c/em\\u003e contained Cr: 391.37, Cd: 8.11, Pb: 5.49, Mn: 2.67. Industrial soils exceeded WHO/FAO permissible limits for Cr and Cd by up to 42 and 14 times, respectively. Cr was the dominant pollutant, followed by Cd, Pb, and Mn. Pollution indices indicated severe contamination, particularly in IA-2 (Hemayetpur). Vegetables revealed high Cd BCFs, with root concentrations exceeding safety threshold levels. Dietary exposure was the primary health risk pathway, with Cd posturing the uppermost ecological risk and Cr the highest carcinogenic risk. Industrial activities, primarily tanning and insufficient effluent treatment via the Common Effluent Treatment Plant (CETP), are the major contamination sources (Cr\\u0026thinsp;\\u0026gt;\\u0026thinsp;Cd\\u0026thinsp;\\u0026gt;\\u0026thinsp;Pb). While RA soils exhibited lower contamination, diffuse pollution was apparent.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Comparative Environmental and Human Health Risk Assessment of Heavy Metal Contamination near Dhaka’s Tannery Estates\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-08-26 13:21:18\",\"doi\":\"10.21203/rs.3.rs-7387758/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a64c57bb-b1da-46ce-913b-4fb6c9edbbf2\",\"owner\":[],\"postedDate\":\"August 26th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-08-27T16:07:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-08-26 13:21:18\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7387758\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7387758\",\"identity\":\"rs-7387758\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}