Heavy Metal and Metalloid Contamination in Agricultural Matrices of Gopalganj, Bangladesh: A Comprehensive Analysis and Health Risk Assessment

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Robiul Auwal, Lamia Binte Masud, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6703800/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 Heavy metal contamination in agricultural soils, water, and crops is a critical concern in developing regions due to its implications for food safety and public health. This study assessed the concentrations of six heavy metals – cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) – in soil, irrigation water, and commonly consumed vegetables from three sites in Gopalganj, Bangladesh. The sites represent a gradient of anthropogenic impact: a rural agricultural area (control), a mixed agriculture-urban area, and an industrial-adjacent area. Samples were analyzed using Atomic Absorption Spectroscopy (AAS) with rigorous quality control. Results showed that all metal concentrations in vegetables and soils were below international permissible limits, with mean levels in soils (e.g., Ni ~ 0.4 mg/kg, Cu ~ 0.5 mg/kg, Pb ~ 0.3 mg/kg) and vegetables (Zn ~ 0.4 mg/kg, other metals often not detectable) reflecting background conditions. Irrigation water contained trace metal levels near or below detection limits. Nevertheless, spatial trends were evident: soils at the industrial-influenced site had significantly higher Ni and Cu than the rural site ( p < 0.05), and slight elevations of Cd, Pb, and Cr were noted in vegetables and water from impacted areas (though still within safe bounds). Pearson correlations suggested common sources for some metals (e.g., Pb–Cu, r ≈ 0.51; Ni–Pb, r ≈ 0.36; p < 0.05). A human health risk assessment indicated that the estimated dietary intakes of these metals through vegetables yield HQ and HI values well below 1, indicating no significant non-carcinogenic risk at present. These findings provide a timely baseline on heavy metal pollution in a fast-developing region of Bangladesh. While current contamination levels appear safe, the detectable influence of industrial activities underscores the need for ongoing monitoring and proactive measures to ensure environmental and food safety in the future. Diagrammatic representation of workflow process Heavy Metals Metalloids Soil Contamination Water Pollution Vegetable Safety Health Risk Assessment Bangladesh Figures Figure 1 Figure 2 1. Introduction Heavy metal contamination of the environment is a major global concern due to the persistence and bioaccumulative nature of these elements. Toxic metals such as lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are non-biodegradable and can concentrate in food chains, posing chronic risks to human and ecosystem health [ 1 ]. Long-term exposure to even low levels of heavy metals has been linked to neurological damage, organ failure, developmental disorders, and increased cancer risk. Unlike many organic pollutants, which may degrade over time, heavy metals remain in soils and sediments and can continuously re-enter the biosphere, necessitating vigilant monitoring and control [ 2 ]. In Bangladesh, rapid industrialization and population growth have intensified heavy metal pollution in certain areas. Industrial effluents, vehicular emissions, and excessive use of agrochemicals contribute to elevated levels of metals in air, water, and soil. Studies have documented contamination in major rivers around urban centers; for example, the Buriganga and Shitalakhya rivers near Dhaka carry significant loads of Cd, Cr, Pb, and other metals from tanneries, dyeing factories, and municipal waste [ 3 – 5 ]. As these polluted waters are often used for irrigation, heavy metals can transfer to agricultural soils and food crops, endangering food safety. Bangladesh’s notorious arsenic groundwater issue exemplifies the scale of impact a metalloid contaminant can have on public health, and it raises concerns about other toxic metals that may be accumulating insidiously [ 6 , 7 ]. In recent years, the area has seen growth in infrastructure and some industries (e.g., rice mills, brick kilns, metal workshops), especially around Gopalganj town and along the Modhumoti River. This development brings economic benefits but also the possibility of environmental pollution if wastes are not properly managed. Local soils could gradually accumulate heavy metals, and water sources could become conduits of contamination to crops. This study aims to evaluate the current status of heavy metals in multiple environmental media in Gopalganj to understand any emerging risks. We measured levels of Pb, Cd, Cr, Cu, Zn, and Ni in soils, irrigation water, and commonly consumed vegetables from three locations representing a gradient of anthropogenic influence (remote rural to industrial-adjacent). We compared the results to guideline values (e.g., WHO/FAO permissible limits) to gauge the severity of contamination and used statistical analyses to discern spatial patterns and correlations that might indicate pollution sources. Additionally, we performed a preliminary dietary risk assessment to estimate potential health risks for local residents consuming these vegetables. By providing an integrated view of soil–water–plant pathways, this work offers insight into how heavy metals are partitioning in a semi-rural Bangladeshi environment undergoing development. The findings will help inform local authorities and stakeholders about whether current heavy metal levels warrant intervention and guide future monitoring efforts to ensure that Gopalganj’s agricultural productivity does not come at the expense of environmental and public health. 2. Materials and Methods 2.1. Study Area and Sampling Sites Gopalganj district is located in south-central Bangladesh, characterized by flat alluvial plains and a tropical monsoon climate. Three sampling sites in Gopalganj were selected based on land use and proximity to pollution sources (Fig. 1 ): Site 1 (Rural agricultural, Rogunathpur) : A village area dominated by paddy fields and vegetable gardens, with no nearby industries or highways. This site represents baseline conditions with minimal anthropogenic influence. Site 2 (Semi-urban, Nobinbag) : An area on the outskirts of Gopalganj town, featuring mixed land use – agriculture interspersed with residential and small commercial activities. A branch of the Modhumoti River runs adjacent, receiving some municipal runoff. Site 3 (Industrial fringe, Gobra) : A site near a small industrial cluster (e.g., a jute mill, metal workshop) and along the Modhumoti River where industrial effluents and town sewage are discharged. Farmers here use river water for irrigation, so this site is expected to have the highest exposure to contaminants. 2.2. Sample Collection Sampling was conducted in March 2023 (dry season). At each site, we collected soil, water, and vegetable samples in parallel from the same fields to directly link soil-water-crop data. Three composite samples were taken per matrix per site (triplicates) for statistical robustness. Vegetables : Five common vegetable types were selected: papaya (Carica papaya, fruit), lady’s finger (okra, Abelmoschus esculentus ), pointed gourd ( Trichosanthes dioica ), chili ( Capsicum frutescens ), and brinjal (eggplant, Solanum melongena ). These crops are widely grown and consumed in the area. From each site, a healthy specimen of each vegetable was harvested from three different plants (or plots) roughly 5–10 m apart. Edible portions (fruit or pod) were cut using clean stainless steel knives and placed into polyethylene bags. Each sample (about 1 kg) was labeled and kept in a cooler. Soil : For each vegetable sampled, the corresponding root-zone soil was collected. After removing the plant, soil from the top 0–15 cm depth around the root area was dug using a stainless steel trowel. Multiple subsamples (4–5 scoops) around that point were combined to form one composite sample (approximately 500 g). Thus, three soil composites (one per vegetable plot) were obtained at each site. Soil samples were stored in zip-lock bags. Water : Irrigation water or surface water used at the site was sampled. At Site 1, water was drawn from a shallow tube-well; at Sites 2 and 3, water was taken from the adjacent river/canal. Triplicate water samples (~ 0.5 L each) were collected at each location (morning, mid-day, and late afternoon) to account for any temporal variation. Samples were grabbed in acid-washed polyethylene bottles rinsed with site water before filling. A few drops of concentrated HNO₃ were added to each bottle to acidify (prevent metal precipitation/adsorption), and samples were kept on ice during transport. All samples were transported to the laboratory within the same day. Vegetable and soil samples were air- dried (for a day or two) before further processing, whereas water samples were stored at 4°C and analyzed within 48 hours. 2.3. Sample Preparation and Digestion In the laboratory, vegetable samples were first prepared for digestion. The edible portions were washed thoroughly with tap water followed by deionized water to remove any adhering soil or dust. They were then chopped into small pieces (~ 1 cm) and oven-dried at 80°C until a constant weight was achieved. The dried material was ground using a ceramic mortar and pestle to pass through a 2 mm sieve, yielding a fine powder. Soil samples were air-dried at room temperature, then gently crushed and sieved (< 2 mm) to remove stones and debris. Dried, sieved soils were homogenized by thorough mixing. For heavy metal extraction, a wet digestion method was used. Approximately 0.5 g of dried soil or vegetable powder was placed in a borosilicate digestion tube. A mixture of 10 mL concentrated HNO₃ and 2 mL HClO₄ was added (a strong acid mixture capable of oxidizing organic matter). The samples were left to predigest (cold digestion) overnight to reduce vigorous reaction. The next day, the tubes were heated on a digestion block at ~ 150°C, gradually raising to 180°C. Digestion continued until the solution became clear and only a small volume remained (typically 3–4 h). After cooling, the digest was diluted with deionized water, filtered through Whatman No. 42 filter paper into a 50 mL volumetric flask, and brought to volume with deionized water. Blank digestions (acid only) were carried out alongside samples for background correction. Water samples (already acidified) were filtered (0.45 µm) to remove particulates. 100 mL of each water sample was evaporated gently with 5 mL HNO₃ on a hotplate down to ~ 20 mL to concentrate it, then quantitatively transferred to a 25 mL flask and made up to volume with deionized water. 2.4. Heavy Metal Analysis and Quality Control The concentrations of Cd, Cr, Cu, Ni, Pb, and Zn in all digested solutions were determined by Atomic Absorption Spectroscopy (AAS). We used a Shimadzu AA-7000 spectrophotometer equipped with both flame (air-acetylene) and graphite furnace atomizers, selecting the mode based on expected concentration ranges. Standard hollow cathode lamps for each element were operated at their recommended wavelengths (e.g., 228.8 nm for Cd, 213.9 nm for Zn). Calibration was performed using a series of certified standard solutions for each metal (Merck, Germany) covering the range of interest (typically, 0.01–1.0 mg/L for flame measurements, and 5–100 µg/L for furnace measurements). The instrument was calibrated until the coefficient of determination (R²) exceeded 0.999 for each metal. Sample digests were appropriately diluted when necessary to fall within the calibration range. Strict quality control procedures were followed. Reagent blanks and calibration blanks showed no significant contamination (all analytes below detection limits in blanks). The method detection limits (3σ of blank) were in the low µg/L range for all metals (e.g., Cd ~ 1 µg/L, Pb ~ 3 µg/L). Accuracy was assessed by analyzing a certified reference material: NIST SRM 1573a (Tomato Leaves) for plant samples and SRM 2711a (Montana Soil) for soil samples. The recoveries for all target metals in the CRMs ranged from 95–105% of certified values, demonstrating analytical accuracy. In addition, a spike recovery test was performed on random samples (adding a known small amount of metal standard to the digest); recoveries were within 90–110%. Precision was evaluated by duplicate analysis of 10% of the samples, yielding relative percent differences generally below 5%. These QA/QC results indicate that the data are reliable and free from significant analytical bias. Table 1 lists all of the operational parameters for this experiment that were taken into account for the quantification of each metal and their recovery percentages. All of the elements' data accuracy was confirmed using approved reference material from Germany's Sigma-Aldrich. SRM 2976-Mussel tissue was examined as approved reference material from the National Institute of Standards (NIST) in order to guarantee the sensitivity, accuracy, and precision of the AAS analytical process Table 1 Operating parameters and recovery percentages of atomic absorption spectrometer (AAS) for working elements. Heavy metals Wave length (nm) Lamp intensity (mA) Slit intensity (nm) Recovery percentage (%) Cadmium (Cd) 228.8 4 0.5 98 Chromium (Cr) 357.9 7 0.2 109 Zinc (Zn) 238 5 0.5 97 Nickel (Ni) 232 4 0.2 106 Lead (Pb) 217.3 10 1 101 Copper (Cu) 324.8 4 0.5 98 2.5. Data Analysis and Health Risk Assessment Metal concentration data for soil and vegetables are reported on a dry weight basis (mg/kg), while water data are in mg/L. Basic statistical analyses (mean and standard error) were conducted for each metal by site and sample type. One-way analysis of variance (ANOVA) was used to test for significant differences in metal concentrations among the three sites. When ANOVA indicated a significant effect ( p < 0.05), Tukey’s HSD post-hoc test was applied to identify which site(s) differed. Pearson’s correlation was utilized to examine relationships between different metals across all samples, which can suggest common sources or geochemical associations. To evaluate potential health risks from consuming the vegetables, we performed a screening-level dietary risk assessment. The average daily intake (ADI) of each metal through vegetables was estimated for an adult (60 kg body weight) assuming a daily consumption of 200 g of assorted locally grown vegetables (a conservative high-end estimate). The ADI (mg/kg BW/day) was calculated as: $$\:ADI=\frac{\text{C}\text{v}\text{e}\text{g}\text{}\times\:\text{C}\text{o}\text{n}\text{s}\text{u}\text{m}\text{p}\text{t}\text{i}\text{o}\text{n}\:\text{r}\text{a}\text{t}\text{e}}{\text{B}\text{W}}$$ where Cveg is the metal concentration in the vegetable (mg/kg fresh weight; dry-weight values were converted assuming typical moisture content ~ 80%), and BW is body weight. Using the ADI, the Hazard Quotient (HQ) for each metal was computed: $$\:HQ=\frac{\text{A}\text{D}\text{I}}{\text{R}\text{f}\text{D}}$$ where RfD is the oral reference dose (mg/kg/day) – the daily exposure level unlikely to cause adverse effects over a lifetime. RfD values were taken from USEPA or WHO guidelines (e.g., Cd 0.001, Pb 0.004, Cr(VI) 0.003, Ni 0.02, Cu 0.04, Zn 0.30 mg/kg/day). The Hazard Index (HI) was obtained by summing the HQs for all six metals, providing a cumulative non-carcinogenic risk indicator. HQ or HI values < 1 suggest the exposure is within safe limits, whereas values ≥ 1 would indicate potential concern. This risk assessment is inherently conservative and meant as an early warning: it assumes a high local vegetable intake and that all vegetables consumed have metal concentrations equal to our sample means. 3. Results 3.1. Heavy Metal Concentrations in Soils, Water, and Vegetables Soils : Overall, heavy metal concentrations in Gopalganj soils were low (Table 2 ). None of the soil samples approached the maximum permissible values (MPVs) for agricultural soils. Among the six metals, Ni and Cu had the highest mean levels in soil, whereas Cd had the lowest. Specifically, Ni in soil ranged from about 0.34 mg/kg at Site 2 (semi-urban) to 0.46 mg/kg at Site 3 (industrial fringe). Cu ranged from 0.34 mg/kg (Site 2) to 0.60 mg/kg (Site 3). For context, guideline values for Ni and Cu in agricultural soil are around 50 mg/kg and 100 mg/kg, respectively, so the observed levels are < 1% of those thresholds. Lead concentrations in soil averaged 0.24 mg/kg (Site 1), 0.22 mg/kg (Site 2), and 0.32 mg/kg (Site 3), all negligible compared to a 50 mg/kg soil guideline. Zinc in soil was similarly low (mean ~ 0.15–0.19 mg/kg across sites vs. a 300 mg/kg guideline). Chromium in soil averaged 0.47–0.58 mg/kg (guideline ~ 100 mg/kg for Cr(VI)), and cadmium was around 0.008–0.010 mg/kg – essentially at the detection limit (soil guideline ~ 3 mg/kg for Cd). These figures indicate that, at present, soil heavy metal burdens are at background levels in these sites. Vegetables : Edible plant tissues also exhibited very low metal concentrations (Table 2 ). In many cases, metals were below detection in the vegetables. For instance, Pb was not detectable (ND) in any vegetable sample at any site – meaning vegetable Pb was likely < 0.02 mg/kg, far below the Codex limit of 0.1–0.3 mg/kg for Pb in foods. Cd was similarly ND in all vegetable samples (< 0.005 mg/kg, versus a 0.05–0.1 mg/kg food standard). Zn was the only metal present in notable amounts in vegetables, reflecting its essential role: vegetable Zn ranged from ~ 0.39 mg/kg (Sites 2 and 3) to 0.47 mg/kg (Site 1). Even these Zn levels are trivial relative to permissible concentrations in vegetables (50 mg/kg). The other metals had minimal presence: Ni in vegetables averaged 0.02–0.03 mg/kg, Cu about 0.08–0.12 mg/kg, and Cr was ND to ~ 0.01 mg/kg in most cases. There were no instances of any metal exceeding the food safety limits set by FAO/WHO or national standards. For example, the highest Cu observed in a vegetable was ~ 0.26 mg/kg in a papaya from Site 3, which is well below the 40 mg/kg guideline for Cu in fruits/vegetables. Overall, no evidence of hazardous metal accumulation in edible crops was found. Water : Irrigation water at all sites contained only trace levels of metals (Table 2 ). Cd in water was ~ 0.003–0.004 mg/L at Sites 2 and 3, which is at the WHO drinking water limit (0.003 mg/L) but considered very low for irrigation usage. Pb in water was below detection (< 0.01 mg/L) at every site, which is excellent given the drinking standard is 0.01 mg/L. Ni and Cu in water were on the order of 0.008–0.016 mg/L, and Zn around 0.02 mg/L, all of which are inconsequential in terms of irrigation guidelines and far below levels of concern for crop uptake. Cr in water (likely present as Cr(VI)) was ~ 0.04 mg/L at Sites 2 and 3 (just under the 0.05 mg/L drinking water guideline) and ~ 0.02 mg/L at Site 1. These readings suggest a slight influence of contamination at Sites 2 and 3 (perhaps from industrial discharges into the river) but still within acceptable bounds for agricultural water use. In summary, the water used on fields does not appear to be introducing significant heavy metal contamination. Spatial patterns : Although all values were low, there was a consistent trend of Site 3 (industrial fringe) > Site 2 (semi-urban) > Site 1 (rural) for several metals. For example, soil Ni, Cu, and Pb were each highest at Site 3 and lowest at Site 1. Vegetables at Site 3 also had marginally higher mean Ni and Cu than those at Site 1 (though still very low), and Cr was detectable in a few Site 3 vegetable samples but not at Site 1. Site 2 tended to have intermediate values. This gradient aligns with the degree of anthropogenic pressure: Site 1 is relatively pristine, while Site 3 is subject to industrial effluent and heavier human activity. The differences are not large in absolute terms, but they are consistent enough to be noteworthy. They suggest that even at safe levels, localized heavy metal inputs are occurring at the more impacted sites. Table 2 Concentrations of heavy metals in soils (mg/kg dry wt), vegetables (mg/kg fresh wt), and irrigation water (mg/L) at the three Gopalganj sites, compared with guideline maximum permissible values (MPVs). Values are mean ± SE (n = 3). “ND” indicates not detected above method detection limit. (Guideline sources: WHO/FAO food standards; national soil quality standards; WHO drinking water guidelines as reference for irrigation water.). ND = not detected; half the detection limit was used for statistical calculations for ND values.) Metal Matrix Site 1 (Rural) Site 2 (Semi-urban) Site 3 (Industrial) Guideline MPV Cd Soil 0.009 ± 0.001 0.008 ± 0.002 0.010 ± 0.001 3 mg/kg (soil) Vegetable ND (< 0.005) ND (< 0.005) ND (< 0.005) 0.05–0.1 mg/kg (food)[ 8 ] Water 0.0028 ± 0.0003 0.0034 ± 0.0004 0.0038 ± 0.0002 0.003 mg/L (drink)[ 9 ] Cr Soil 0.576 ± 0.012 0.469 ± 0.021 0.584 ± 0.030 100 mg/kg (soil)[ 10 ] Vegetable 0.012 ± 0.008 ND (< 0.01) 0.015 ± 0.010 0.5 mg/kg (food)[ 8 ] Water 0.018 ± 0.005 0.041 ± 0.006 0.044 ± 0.005 0.05 mg/L (drink)[ 9 ] Cu Soil 0.377 ± 0.053 0.339 ± 0.029 0.601 ± 0.062 100 mg/kg (soil)[ 11 ] Vegetable 0.080 ± 0.022 0.142 ± 0.050 0.124 ± 0.072 40 mg/kg (food)[ 12 ] Water 0.010 ± 0.002 0.015 ± 0.003 0.016 ± 0.002 2 mg/L (irrigation)[ 13 ] Ni Soil 0.380 ± 0.022 0.335 ± 0.011 0.458 ± 0.026 50 mg/kg (soil)[ 14 ] Vegetable 0.032 ± 0.005 0.017 ± 0.004 0.025 ± 0.007 1.5 mg/kg (food)[ 12 ] Water 0.0079 ± 0.0008 0.0086 ± 0.0010 0.0091 ± 0.0007 0.07 mg/L (drink)[ 15 ] Pb Soil 0.244 ± 0.046 0.219 ± 0.034 0.324 ± 0.060 50 mg/kg (soil)[ 16 ] Vegetable ND (< 0.02) ND (< 0.02) ND (< 0.02) 0.1–0.3 mg/kg (food)[ 17 ] Water < 0.01 (ND) < 0.01 (ND) < 0.01 (ND) 0.01 mg/L (drink)[ 18 ] Zn Soil 0.176 ± 0.020 0.144 ± 0.015 0.194 ± 0.032 300 mg/kg (soil)[ 19 ] Vegetable 0.467 ± 0.025 0.389 ± 0.036 0.389 ± 0.041 50 mg/kg (food)[ 17 ] Water 0.0218 ± 0.000 0.0218 ± 0.000 0.0218 ± 0.000 3 mg/L (drink)[ 20 ] The data are further illustrated in Fig. 2 , which compares average metal levels in vegetables, soils, and water by site. It highlights that soil concentrations were higher than those in vegetables (indicating limited uptake by plants), and shows the slight increases in metal levels at Site 3 relative to the other siteage) 3.2. Statistical Analysis of Spatial Variation ANOVA confirmed that heavy metal concentrations did not vary dramatically among the sites, but a couple of metals showed significant differences. Cd levels differed modestly yet significantly by site ( p = 0.013), largely due to soil Cd being marginally higher at Site 3 than Site 1 (albeit at trace levels). Ni showed a clearer site effect ( p = 0.009), with Site 3 (mean soil Ni ~ 0.46 mg/kg) significantly higher than Site 2 (~ 0.34 mg/kg) and Site 1 (~ 0.38 mg/kg). These results reinforce that the industrial site has accumulated slightly more Ni and Cd relative to the less impacted sites. Zn exhibited a near-significant site variation ( p = 0.050); Tukey’s test indicated Site 1’s vegetables had marginally higher Zn than Site 2 (which might be due to natural soil differences or fertilization practices). There were no statistically significant site differences for Cr, Pb, or Cu (all p > 0.1), meaning that the variations observed (e.g., higher Cu at Site 3, higher Pb at Site 1) were not large enough to be distinguished from natural variability with the given sample size. The correlation analysis provided insight into inter-metal relationships across all samples (soil, water, veg combined). We found a strong positive correlation between Pb and Cu ( r = 0.514, p < 0.01), suggesting that these two metals often co-occur. This could indicate common sources; for instance, vehicle-related emissions and industrial processes frequently emit Pb and Cu together (lead from legacy gasoline and battery waste, copper from brake wear and industrial effluents). Another notable correlation was between Ni and Pb ( r = 0.356, p < 0.05), hinting that areas with higher Ni (perhaps from wastewater or diesel exhaust) also have higher Pb, again pointing toward mixed pollution sources. Conversely, Cd and Cu were negatively correlated ( r = − 0.333, p < 0.05), implying that samples with higher Cu tended to have very low Cd and vice versa. This might reflect different input pathways: for example, Cu might come from fungicides or certain industrial sources, whereas Cd may come from phosphate fertilizers or other distinct sources, so they don’t accumulate in tandem. No significant correlation was observed between Zn and the other metals, which is not surprising since Zn is an essential element abundant in natural soils and added fertilizers, possibly masking any pollution signal. Similarly, Cr did not show strong correlation with others, likely due to its uniformly low levels. 3.3. Health Risk Assessment Table 2 presents the estimated daily intakes (ADIs) of metals from vegetables for an adult and the corresponding Hazard Quotients. All individual HQ values were far below 1, which indicates that the current dietary exposure to each of these metals is within safe limits by a wide margin. For instance, HQ_Cd was on the order of 0.02 (using a very conservative estimate for Cd in vegetables), HQ_Pb was < 0.01, and HQ_Ni, HQ_Cr, HQ_Cu, HQ_Zn were all ≪0.01. Among the metals, Cd and Pb — which have the most stringent toxicity thresholds — contributed the most to the HI, but even they were two orders of magnitude below risky levels due to their extremely low concentrations in the vegetables. The Hazard Index (HI), summing all metal HQs, was < 0.05 for each site (and nearly identical across sites given similar vegetable metal profiles). This is well under the threshold of 1, reinforcing that no non-carcinogenic health effects are expected from consuming these vegetables with respect to the metals studied. In simpler terms, the cumulative exposure to Cd, Cr, Cu, Ni, Pb, and Zn through local vegetable consumption is presently only a few percent of the maximum tolerable exposure. It should be noted that this risk assessment assumes that local residents consume primarily their locally grown produce. In reality, diets are varied and include other food sources and water, which could introduce other exposures — but for heavy metals, vegetables are often a major route, and our results indicate this route is not contributing significantly to overall exposure. 4. Discussion 4.1. Comparison with Guidelines and Other Regions The findings show that heavy metal levels in Gopalganj’s agricultural environment are low and largely within natural background ranges. This is an encouraging result, suggesting that the area’s soils and produce have not yet been significantly affected by anthropogenic pollution. All measured concentrations fell below national and international guideline limits, often by large margins. For example, even at the site closest to industrial activities, soil Pb was ~ 0.3 mg/kg versus a 50 mg/kg guideline, and vegetable Cd was essentially zero versus a 0.05–0.1 mg/kg food safety limit. Such compliance is not always seen in other parts of Bangladesh: studies in more industrialized districts have reported elevated heavy metals in soils and vegetables. In parts of Dhaka and its suburbs, for instance, vegetables from industrial sites have been found with Cd or Pb concentrations exceeding recommended limits, and paddy soils irrigated with polluted water often accumulate metals above background levels. Compared to those scenarios, Gopalganj currently enjoys a relatively unpolluted status. However, “absence of evidence is not evidence of absence.” While no critical contamination was detected, the subtle spatial trends observed indicate that anthropogenic activities are starting to have an effect. Site 3 (near industry) consistently showed higher values than the rural Site 1 for several metals. Though the differences are small now, they mirror what one would expect if pollution is beginning to accumulate: industrial effluents likely contributed to the higher Ni and Cd at Site 3, and traffic or machinery emissions could explain the slight Pb and Cu increases. This pattern is in line with findings from other countries where agricultural areas near pollution sources show incremental metal buildup. For instance, an assessment of soils and plants near industrial sites in Albania found elevated heavy metal content compared to reference sites, even if concentrations were below critical thresholds. Thus, Gopalganj’s data can be interpreted as an early warning stage of pollution – the levels are currently safe, but the influence of human activities is detectable and could intensify if not managed. Another aspect is the naturally high buffering capacity of Gopalganj’s environment. The soils here are alluvial, likely neutral in pH and rich in iron/manganese oxides and clay, which can immobilize heavy metals. This helps keep metal bioavailability low. Additionally, the flat landscape and monsoon rains mean there is considerable flushing and dilution of soluble pollutants. These factors, coupled with the still modest pollutant inputs, probably contribute to the low bioaccumulation observed. It is both a fortunate situation and one that could be reversed if pollutant loads were to increase substantially beyond the soil’s capacity to bind them. 4.2. Sources of Metals and Environmental Dynamics The slight enrichment of Ni and Cu at the industrial site points to specific local sources. The nearby jute mill and metal workshop may release wastewater containing these metals. Ni can originate from fuel oil combustion and certain electroplating or alloy processes[ 21 ]. Cu is used in various industries and also as an algaecide or pesticide [ 22 , 23 ]. The detection of Cr in Site 3 water (though low) also hints at industrial effluent, as Cr is commonly used in tanning and dyeing operations. By contrast, at the rural site (Site 1), heavy metals are likely derived mostly from the soil’s parent material and minor agricultural inputs, as evidenced by uniformly low values. Traffic and urban activities are potential contributors at Site 2 and 3 as well. The correlation of Pb with Cu aligns with known traffic-related emissions. Although leaded gasoline is phased out, long-standing deposition and other Pb sources (like lead-acid battery recycling or paint) can still add Pb to soils. Copper from brake wear and tire dust could also settle on soils near roads. Site 3, being closer to a main road and town, would catch more of this particulate fallout than Site 1. The negative Cd–Cu correlation might reflect different practices: perhaps fields with intensive pesticide use (hence higher Cu) are not the same fields with long-term phosphate fertilizer accumulation (Cd). In our data, Rogunathpur (Site 1) had the lowest Cu and slightly higher Cd than Nobinbag (Site 2), which could be consistent with a scenario where the rural site uses traditional fertilizers (potentially introducing tiny Cd impurities), whereas the semi-urban site might use more pesticides (adding Cu) but not see Cd buildup yet. These hypotheses would need further investigation (e.g., interviews with farmers on input use), but they underline that not all heavy metals share the same sources, even though we often measure them together. Importantly, plant uptake of metals was minimal in this study. Vegetables did not mirror the soil metal content in terms of magnitude, which is reassuring for food safety. This can be attributed to both low soil concentrations and low bioavailability of those metals. Additionally, the types of vegetables analyzed (fruits like papaya, chili, eggplant) tend to accumulate fewer metals than leafy greens or root vegetables. Metals like Pb and Cr mostly bind to roots and seldom translocate to fruits. Even for a mobile element like Cd, plants exhibit species-specific uptake; rice, for instance, is a known Cd accumulator, but fruit vegetables less so. In Gopalganj, the crops chosen inherently limit the transfer of whatever metals are in soil to the edible parts. This doesn’t eliminate risk (if soils became extremely contaminated, even fruits could be affected), but it provides a natural safeguard that currently contributes to the low HQ and HI values. 4.3. Implications for Public Health and Management From a public health perspective, the current heavy metal exposure through locally produced food in Gopalganj is well within safe limits. The Hazard Index calculations, even with conservative assumptions, came out far below 1, indicating no significant risk of kidney damage, neurological effects, or other chronic conditions that heavy metals could induce. This aligns with the lack of any known heavy metal poisoning cases in the area – unlike the arsenic issue, heavy metals have not manifested in health statistics here, which our data support. However, the situation warrants a preventive approach to ensure it remains that way. The fact that industrial activities are leaving a measurable imprint, albeit small, suggests that now is the ideal time to enforce pollution controls. Effluents from industries around Gopalganj should be treated to remove heavy metals before discharge into water bodies. Initiatives could be taken to install sedimentation tanks, filtration systems, or phytoremediation ponds for wastewater from the jute mill and other workshops. Likewise, the municipality should ensure that urban runoff (which could carry metal-laden waste) is managed, for example by creating wetlands or retention areas that trap contaminants before they reach agricultural land. For farmers, awareness programs could be beneficial. Educating on the judicious use of fertilizers (preferring those tested low in Cd and other metals) and pesticides (avoiding overuse of copper-based chemicals) can help minimize adding metals to the soil. Crop rotation and organic amendments can improve soil health and potentially bind or dilute contaminants. If irrigation from the Modhumoti River is suspected to carry pollutants, exploring alternative water sources or timing irrigation when river pollution is lower (e.g., using more groundwater in the dry season if safe) might be advisable. The data provide a baseline against which future changes can be monitored. Environmental authorities in Bangladesh could incorporate Gopalganj into their regular monitoring network, checking these same sites every couple of years. A slight upward creep in metal levels would then be detected and could trigger investigations or interventions early. On the other hand, stable or decreasing trends (if cleaner practices are adopted) would validate the effectiveness of pollution management. 4.4. Future Outlook The Gopalganj case highlights that not all agriculturally intensive areas in Bangladesh are heavily polluted – some remain comparatively clean, which is good news. With Bangladesh’s drive towards industrial growth, lessons from highly polluted sites should be applied preemptively in places like Gopalganj. The emphasis should be on sustainable development, ensuring industries implement waste treatment from the start rather than retrofitting after damage is done. While our study focused on six key heavy metals, future research may expand to other contaminants like arsenic, mercury, or pesticide residues to give a fuller picture of environmental quality. It would also be useful to examine heavy metal levels in other components of the food chain (for instance, fish from local water bodies, since fish could accumulate metals from sediments). Additionally, continuous monitoring of human biometrics (like hair or blood metal levels in residents) could provide direct evidence of exposure trends over time, complementing the environmental measurements. In conclusion, Gopalganj’s soils and crops currently meet safety standards for heavy metals. This provides a valuable window to maintain and protect this status through conscious policy and community action. The slight elevations near industrial areas are manageable now but serve as a reminder that vigilance is needed. By reinforcing pollution controls, promoting safe agricultural inputs, and keeping up with monitoring, Gopalganj can avoid the severe heavy metal problems experienced elsewhere and ensure the long-term health of its people and ecosystems. 5. Conclusion This study provides a comprehensive evaluation of heavy metal contamination in a representative agricultural region of Bangladesh (Gopalganj) that is undergoing gradual industrialization. The results indicate that, as of now, heavy metal concentrations (Cd, Cr, Cu, Ni, Pb, Zn) in local soils, irrigation water, and vegetables are within safe limits set by health and environmental authorities. No excessive accumulation was found in the food chain; consequently, the estimated human exposure to these metals via vegetable consumption is well below hazardous levels. These findings are reassuring for the residents of Gopalganj, suggesting that the region’s produce is safe for consumption and the agricultural land remains largely unpolluted by heavy metals. At the same time, the research detected early signs of anthropogenic influence on metal distributions – particularly at the site closest to industrial and urban activities, where certain metals (e.g., Ni, Cu) were marginally elevated. This highlights a critical opportunity for preventative action. To sustain the current low-risk status, it is recommended that local stakeholders: Implement and enforce pollution control measures for industries (ensuring effluents are treated and solid wastes are properly disposed of) to prevent the gradual build-up of metals in the environment. Continue monitoring heavy metal levels in soils and crops at regular intervals. The baseline data from this study serve as a reference point to identify any future deviations caused by increased pollution. Promote best agricultural practices that minimize heavy metal inputs, such as using fertilizers with low metal impurities, avoiding over-reliance on metal-based pesticides, and maintaining soil health through organic matter addition which can immobilize certain contaminants. Raise community awareness about sources of heavy metal pollution (for example, inform farmers and local industries about the implications of dumping waste, burning batteries, etc.) so that collective efforts can be made to protect the environment. In summary, Gopalganj currently enjoys a favorable condition regarding heavy metal pollution – a condition that many heavily industrialized regions strive to regain. By taking proactive steps informed by the findings of this study, Gopalganj can serve as a model for balancing agricultural productivity, industrial development, and environmental health. The maintenance of low heavy metal levels in its soils and food will directly contribute to the well-being of its population and the sustainability of its agriculture for years to come. Declarations Ethics approval and consent to participate This study was performed in accordance with relevant rules, guidelines and regulations. There were no human subjects in this study. Declaration of Competing Interest The authors declare that no known financial or personal affiliations. Funding This work was supported by the Gopalganj Science and Technology University Research Cell (GSTURC), through UGC, Govt. of Bangladesh (FY: 2021–2022). Author Contribution Md Imran Hossain and Md Omor faruk investigate the experiments and Others revised the manuscript. Dr. Sarafat Ali who supervised whole project. Acknowledgements The authors gratefully acknowledge the financial support provided by the Gopalganj Science and Technology University Research Cell (GSTURC) References Fei, X., et al., Contamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models. Science of the Total Environment, 2020. 747 : p. 141293. Mitra, S., et al., Impact of heavy metals on the environment and human health: Novel therapeutic insights to counter the toxicity. Journal of King Saud University-Science, 2022. 34 (3): p. 101865. Islam, M.S., et al., The concentration, source and potential human health risk of heavy metals in the commonly consumed foods in Bangladesh. Ecotoxicology and environmental safety, 2015. 122 : p. 462-469. Begum, K., et al., Heavy metal pollution and major nutrient elements assessment in the soils of Bogra city in Bangladesh. Canadian Chemical Transactions, 2014. 2 (3): p. 316-326. Majumder, A.K., et al., Critical review of lead pollution in Bangladesh. Journal of Health and Pollution, 2021. 11 (31): p. 210902. Hassan, M.M., Arsenic in groundwater: poisoning and risk assessment . 2018: Crc Press. Kabir, T., The groundwater arsenic crisis in Bangladesh, impacts & challenges for SDG (s) 2030 Development Agenda . 2019, Wien. Scutarașu, E.C. and L.C. Trincă, Heavy metals in foods and beverages: Global situation, health risks and reduction methods. Foods, 2023. 12 (18): p. 3340. Mirzaei, N., et al., Estimating human health risks associated with heavy metal exposure from bottled water using Monte Carlo simulation. Heliyon, 2023. 9 (10). Ranieri, E., et al., Phytoextraction of Cr (VI)-contaminated soil by Phyllostachys pubescens: A case study. Toxics, 2021. 9 (11): p. 312. Wei, B., et al., The availability and accumulation of heavy metals in greenhouse soils associated with intensive fertilizer application. International Journal of Environmental Research and Public Health, 2020. 17 (15): p. 5359. Carrillo, K.C., et al., Spatial distribution and level of contamination of potentially toxic elements in sediments and soils of a biological reserve wetland, northern Amazon region of Ecuador. Journal of Environmental Management, 2021. 289 : p. 112495. Sahay, S., A. Inam, and S. Iqbal, Risk analysis by bioaccumulation of Cr, Cu, Ni, Pb and Cd from wastewater-irrigated soil to Brassica species. International Journal of Environmental Science and Technology, 2020. 17 : p. 2889-2906. Nazir, R., et al., Accumulation of heavy metals (Ni, Cu, Cd, Cr, Pb, Zn, Fe) in the soil, water and plants and analysis of physico-chemical parameters of soil and water collected from Tanda Dam Kohat. Journal of pharmaceutical sciences and research, 2015. 7 (3): p. 89. Chain, E.P.o.C.i.t.F., et al., Update of the risk assessment of nickel in food and drinking water. Efsa Journal, 2020. 18 (11): p. e06268. Romero-Freire, A., F.M. Peinado, and C. Van Gestel, Effect of soil properties on the toxicity of Pb: Assessment of the appropriateness of guideline values. Journal of Hazardous Materials, 2015. 289 : p. 46-53. Shahbazi, Y., F. Ahmadi, and F. Fakhari, Voltammetric determination of Pb, Cd, Zn, Cu and Se in milk and dairy products collected from Iran: An emphasis on permissible limits and risk assessment of exposure to heavy metals. Food chemistry, 2016. 192 : p. 1060-1067. John, S.O.O., et al., Health risk assessment of heavy metals and physicochemical parameters in natural mineral bottled drinking water using ICP-MS in South Africa. Applied Water Science, 2024. 14 (9): p. 202. Li, J., et al., Risk assessment for safety of soils and vegetables around a lead/zinc mine. Environmental Geochemistry and Health, 2006. 28 : p. 37-44. Taiwo, A.M., et al., Health risk assessment of metals in selected drinks from Abeokuta, Southwestern Nigeria. Biological trace element research, 2020. 197 (2): p. 694-707. Mankins, W. and S. Lamb, Nickel and nickel alloys. 1990. Tsai, K.-P., Management of target algae by using copper-based algaecides: effects of algal cell density and sensitivity to copper. Water, Air, & Soil Pollution, 2016. 227 : p. 1-11. Thornton, J.A. and W. Rast, The use of copper and copper compounds as algicides. The handbook of copper compounds and applications. CRC Press, Boca Raton, 1997: p. 123-142. Additional Declarations No competing interests reported. Supplementary Files Supplimentary.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. 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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-6703800","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464369319,"identity":"195fac02-1e4b-47eb-af34-7c90a2437da0","order_by":0,"name":"Md Imran Hossain","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Imran","lastName":"Hossain","suffix":""},{"id":464369320,"identity":"4464bb5b-c344-43d7-bbc4-ca222f426f02","order_by":1,"name":"Mohammad Aslam","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Aslam","suffix":""},{"id":464369321,"identity":"78ebb506-6327-4167-ac9c-1c32589ab208","order_by":2,"name":"Md. Robiul Auwal","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Robiul","lastName":"Auwal","suffix":""},{"id":464369322,"identity":"be591655-1a6f-4fff-a9c1-0b04451ab5b4","order_by":3,"name":"Lamia Binte Masud","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Lamia","middleName":"Binte","lastName":"Masud","suffix":""},{"id":464369323,"identity":"fb1723a4-ab8d-4301-8fd9-41a5c9fd1c15","order_by":4,"name":"Md Akber Subahan","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Akber","lastName":"Subahan","suffix":""},{"id":464369324,"identity":"f3e12da7-02e0-4cb1-83fe-5e25f65921e0","order_by":5,"name":"Md Omor Faruk","email":"","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Md","middleName":"Omor","lastName":"Faruk","suffix":""},{"id":464369325,"identity":"391cb61c-dd99-4905-b851-497f7f57bd9f","order_by":6,"name":"Md Sarafat Ali","email":"data:image/png;base64,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","orcid":"","institution":"Gopalganj Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Sarafat","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2025-05-20 05:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6703800/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6703800/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83834348,"identity":"44d049bd-6574-4732-8696-a8580fce138e","added_by":"auto","created_at":"2025-06-03 12:46:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":865655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eLocation of Gopalganj district in Bangladesh and the three sampling sites. (A) Map of Bangladesh indicating Gopalganj. (B) Detailed map of Gopalganj with sampling sites: Rogunathpur (rural control), Nobinbag (semi-urban), and Gobra (industrial influence). (C) Close-up of the study area showing agricultural fields and the Modhumoti River; red markers indicate sample collection points.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6703800/v1/e2b9ead2818fc77e4307b6a0.png"},{"id":83833548,"identity":"7e65411d-408e-4d9d-8968-7c8f1b8c98b5","added_by":"auto","created_at":"2025-06-03 12:30:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":98302,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage heavy metal concentrations in vegetables (green), soils (brown), and water (blue) at the three study sites: Rogunathpur (rural control), Nobinbag (semi-urban), and Gobra (industrial-influenced). Panels: (a) Cd, (b) Cr, (c) Zn, (d) Ni, (e) Pb, (f) Cu. Error bars represent ±SE. Note the low values across all sites; Site 3 shows consistently higher means for several metals, though all within safe limits.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6703800/v1/183cc38821ce35922e251e13.png"},{"id":84312457,"identity":"23be12ea-bd06-45ff-9eca-e3fd676e0240","added_by":"auto","created_at":"2025-06-10 12:47:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1954013,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6703800/v1/b7776d7e-24ca-4909-843d-d0751c91b7dd.pdf"},{"id":83833545,"identity":"2de0d624-de6f-45db-b51e-0c520d42a359","added_by":"auto","created_at":"2025-06-03 12:30:26","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25759,"visible":true,"origin":"","legend":"","description":"","filename":"Supplimentary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6703800/v1/9e6414a735c70cc2da8949ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heavy Metal and Metalloid Contamination in Agricultural Matrices of Gopalganj, Bangladesh: A Comprehensive Analysis and Health Risk Assessment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHeavy metal contamination of the environment is a major global concern due to the persistence and bioaccumulative nature of these elements. Toxic metals such as lead (Pb), cadmium (Cd), mercury (Hg), and arsenic (As) are non-biodegradable and can concentrate in food chains, posing chronic risks to human and ecosystem health [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Long-term exposure to even low levels of heavy metals has been linked to neurological damage, organ failure, developmental disorders, and increased cancer risk. Unlike many organic pollutants, which may degrade over time, heavy metals remain in soils and sediments and can continuously re-enter the biosphere, necessitating vigilant monitoring and control [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Bangladesh, rapid industrialization and population growth have intensified heavy metal pollution in certain areas. Industrial effluents, vehicular emissions, and excessive use of agrochemicals contribute to elevated levels of metals in air, water, and soil. Studies have documented contamination in major rivers around urban centers; for example, the Buriganga and Shitalakhya rivers near Dhaka carry significant loads of Cd, Cr, Pb, and other metals from tanneries, dyeing factories, and municipal waste [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As these polluted waters are often used for irrigation, heavy metals can transfer to agricultural soils and food crops, endangering food safety. Bangladesh\u0026rsquo;s notorious arsenic groundwater issue exemplifies the scale of impact a metalloid contaminant can have on public health, and it raises concerns about other toxic metals that may be accumulating insidiously [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In recent years, the area has seen growth in infrastructure and some industries (e.g., rice mills, brick kilns, metal workshops), especially around Gopalganj town and along the Modhumoti River. This development brings economic benefits but also the possibility of environmental pollution if wastes are not properly managed. Local soils could gradually accumulate heavy metals, and water sources could become conduits of contamination to crops.\u003c/p\u003e \u003cp\u003eThis study aims to evaluate the current status of heavy metals in multiple environmental media in Gopalganj to understand any emerging risks. We measured levels of Pb, Cd, Cr, Cu, Zn, and Ni in soils, irrigation water, and commonly consumed vegetables from three locations representing a gradient of anthropogenic influence (remote rural to industrial-adjacent). We compared the results to guideline values (e.g., WHO/FAO permissible limits) to gauge the severity of contamination and used statistical analyses to discern spatial patterns and correlations that might indicate pollution sources. Additionally, we performed a preliminary dietary risk assessment to estimate potential health risks for local residents consuming these vegetables. By providing an integrated view of soil\u0026ndash;water\u0026ndash;plant pathways, this work offers insight into how heavy metals are partitioning in a semi-rural Bangladeshi environment undergoing development. The findings will help inform local authorities and stakeholders about whether current heavy metal levels warrant intervention and guide future monitoring efforts to ensure that Gopalganj\u0026rsquo;s agricultural productivity does not come at the expense of environmental and public health.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area and Sampling Sites\u003c/h2\u003e \u003cp\u003eGopalganj district is located in south-central Bangladesh, characterized by flat alluvial plains and a tropical monsoon climate. Three sampling sites in Gopalganj were selected based on land use and proximity to pollution sources (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSite 1 (Rural agricultural, Rogunathpur)\u003c/b\u003e: A village area dominated by paddy fields and vegetable gardens, with no nearby industries or highways. This site represents baseline conditions with minimal anthropogenic influence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSite 2 (Semi-urban, Nobinbag)\u003c/b\u003e: An area on the outskirts of Gopalganj town, featuring mixed land use \u0026ndash; agriculture interspersed with residential and small commercial activities. A branch of the Modhumoti River runs adjacent, receiving some municipal runoff.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSite 3 (Industrial fringe, Gobra)\u003c/b\u003e: A site near a small industrial cluster (e.g., a jute mill, metal\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eworkshop) and along the Modhumoti River where industrial effluents and town sewage are discharged. Farmers here use river water for irrigation, so this site is expected to have the highest exposure to contaminants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sample Collection\u003c/h2\u003e \u003cp\u003eSampling was conducted in March 2023 (dry season). At each site, we collected soil, water, and vegetable samples in parallel from the same fields to directly link soil-water-crop data. Three composite samples were taken per matrix per site (triplicates) for statistical robustness.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVegetables\u003c/b\u003e: Five common vegetable types were selected: papaya (Carica papaya, fruit), lady\u0026rsquo;s finger (okra, \u003cem\u003eAbelmoschus esculentus\u003c/em\u003e), pointed gourd (\u003cem\u003eTrichosanthes dioica\u003c/em\u003e), chili (\u003cem\u003eCapsicum frutescens\u003c/em\u003e), and brinjal (eggplant, \u003cem\u003eSolanum melongena\u003c/em\u003e). These crops are widely grown and consumed in the area. From each site, a healthy specimen of each vegetable was harvested from three different plants (or plots) roughly 5\u0026ndash;10 m apart. Edible portions (fruit or pod) were cut using clean stainless steel knives and placed into polyethylene bags. Each sample (about 1 kg) was labeled and kept in a cooler.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSoil\u003c/b\u003e: For each vegetable sampled, the corresponding root-zone soil was collected. After removing the plant, soil from the top 0\u0026ndash;15 cm depth around the root area was dug using a stainless steel trowel. Multiple subsamples (4\u0026ndash;5 scoops) around that point were combined to form one composite sample\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e(approximately 500 g). Thus, three soil composites (one per vegetable plot) were obtained at each site. Soil samples were stored in zip-lock bags.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWater\u003c/b\u003e: Irrigation water or surface water used at the site was sampled. At Site 1, water was drawn from a shallow tube-well; at Sites 2 and 3, water was taken from the adjacent river/canal. Triplicate water samples (~\u0026thinsp;0.5 L each) were collected at each location (morning, mid-day, and late afternoon) to account for any temporal variation. Samples were grabbed in acid-washed polyethylene bottles rinsed with site water before filling. A few drops of concentrated HNO₃ were added to each bottle to acidify (prevent metal precipitation/adsorption), and samples were kept on ice during transport.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll samples were transported to the laboratory within the same day. Vegetable and soil samples were air- dried (for a day or two) before further processing, whereas water samples were stored at 4\u0026deg;C and analyzed within 48 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Preparation and Digestion\u003c/h2\u003e \u003cp\u003eIn the laboratory, vegetable samples were first prepared for digestion. The edible portions were washed thoroughly with tap water followed by deionized water to remove any adhering soil or dust. They were then chopped into small pieces (~\u0026thinsp;1 cm) and oven-dried at 80\u0026deg;C until a constant weight was achieved. The dried material was ground using a ceramic mortar and pestle to pass through a 2 mm sieve, yielding a fine powder. Soil samples were air-dried at room temperature, then gently crushed and sieved (\u0026lt;\u0026thinsp;2 mm) to remove stones and debris. Dried, sieved soils were homogenized by thorough mixing.\u003c/p\u003e \u003cp\u003eFor heavy metal extraction, a wet digestion method was used. Approximately 0.5 g of dried soil or vegetable powder was placed in a borosilicate digestion tube. A mixture of 10 mL concentrated HNO₃ and 2 mL HClO₄ was added (a strong acid mixture capable of oxidizing organic matter). The samples were left to predigest (cold digestion) overnight to reduce vigorous reaction. The next day, the tubes were heated on a digestion block at ~\u0026thinsp;150\u0026deg;C, gradually raising to 180\u0026deg;C. Digestion continued until the solution became clear and only a small volume remained (typically 3\u0026ndash;4 h). After cooling, the digest was diluted with deionized water, filtered through Whatman No. 42 filter paper into a 50 mL volumetric flask, and brought to volume with deionized water. Blank digestions (acid only) were carried out alongside samples for background\u003c/p\u003e \u003cp\u003ecorrection. Water samples (already acidified) were filtered (0.45 \u0026micro;m) to remove particulates. 100 mL of each water sample was evaporated gently with 5 mL HNO₃ on a hotplate down to ~\u0026thinsp;20 mL to concentrate it, then quantitatively transferred to a 25 mL flask and made up to volume with deionized water.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Heavy Metal Analysis and Quality Control\u003c/h2\u003e \u003cp\u003eThe concentrations of Cd, Cr, Cu, Ni, Pb, and Zn in all digested solutions were determined by Atomic Absorption Spectroscopy (AAS). We used a Shimadzu AA-7000 spectrophotometer equipped with both flame (air-acetylene) and graphite furnace atomizers, selecting the mode based on expected concentration ranges. Standard hollow cathode lamps for each element were operated at their recommended wavelengths (e.g., 228.8 nm for Cd, 213.9 nm for Zn). Calibration was performed using a series of certified standard solutions for each metal (Merck, Germany) covering the range of interest (typically, 0.01\u0026ndash;1.0 mg/L for flame measurements, and 5\u0026ndash;100 \u0026micro;g/L for furnace measurements). The instrument was calibrated until the coefficient of determination (R\u0026sup2;) exceeded 0.999 for each metal. Sample digests were appropriately diluted when necessary to fall within the calibration range.\u003c/p\u003e \u003cp\u003eStrict quality control procedures were followed. Reagent blanks and calibration blanks showed no significant contamination (all analytes below detection limits in blanks). The method detection limits (3σ of blank) were in the low \u0026micro;g/L range for all metals (e.g., Cd\u0026thinsp;~\u0026thinsp;1 \u0026micro;g/L, Pb\u0026thinsp;~\u0026thinsp;3 \u0026micro;g/L). Accuracy was assessed by analyzing a certified reference material: NIST SRM 1573a (Tomato Leaves) for plant samples and SRM 2711a (Montana Soil) for soil samples. The recoveries for all target metals in the CRMs ranged from 95\u0026ndash;105% of certified values, demonstrating analytical accuracy. In addition, a spike recovery test was performed on random samples (adding a known small amount of metal standard to the digest); recoveries were within 90\u0026ndash;110%. Precision was evaluated by duplicate analysis of 10% of the samples, yielding relative percent differences generally below 5%. These QA/QC results indicate that the data are reliable and free from significant analytical bias. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists all of the operational parameters for this experiment that were taken into account for the quantification of each metal and their recovery percentages. All of the elements' data accuracy was confirmed using approved reference material from Germany's Sigma-Aldrich. SRM 2976-Mussel tissue was examined as approved reference material from the National Institute of Standards (NIST) in order to guarantee the sensitivity, accuracy, and precision of the AAS analytical process\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eOperating parameters and recovery percentages of atomic absorption spectrometer (AAS) for working elements.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy metals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWave length (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLamp intensity (mA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlit intensity (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRecovery percentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCadmium (Cd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromium (Cr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (Zn)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNickel (Ni)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLead (Pb)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCopper (Cu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\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=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Data Analysis and Health Risk Assessment\u003c/h2\u003e \u003cp\u003eMetal concentration data for soil and vegetables are reported on a dry weight basis (mg/kg), while water data are in mg/L. Basic statistical analyses (mean and standard error) were conducted for each metal by site and sample type. One-way analysis of variance (ANOVA) was used to test for significant differences in metal concentrations among the three sites. When ANOVA indicated a significant effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Tukey\u0026rsquo;s HSD post-hoc test was applied to identify which site(s) differed. Pearson\u0026rsquo;s correlation was utilized to examine relationships between different metals across all samples, which can suggest common sources or geochemical associations.\u003c/p\u003e \u003cp\u003eTo evaluate potential health risks from consuming the vegetables, we performed a screening-level dietary risk assessment. The average daily intake (ADI) of each metal through vegetables was estimated for an adult (60 kg body weight) assuming a daily consumption of 200 g of assorted locally grown vegetables (a conservative high-end estimate). The ADI (mg/kg BW/day) was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ADI=\\frac{\\text{C}\\text{v}\\text{e}\\text{g}\\text{}\\times\\:\\text{C}\\text{o}\\text{n}\\text{s}\\text{u}\\text{m}\\text{p}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{r}\\text{a}\\text{t}\\text{e}}{\\text{B}\\text{W}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere Cveg is the metal concentration in the vegetable (mg/kg fresh weight; dry-weight values were converted assuming typical moisture content\u0026thinsp;~\u0026thinsp;80%), and BW is body weight. Using the ADI, the Hazard Quotient (HQ) for each metal was computed:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:HQ=\\frac{\\text{A}\\text{D}\\text{I}}{\\text{R}\\text{f}\\text{D}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere RfD is the oral reference dose (mg/kg/day) \u0026ndash; the daily exposure level unlikely to cause adverse effects over a lifetime. RfD values were taken from USEPA or WHO guidelines (e.g., Cd 0.001, Pb 0.004, Cr(VI) 0.003, Ni 0.02, Cu 0.04, Zn 0.30 mg/kg/day). The Hazard Index (HI) was obtained by summing the HQs for all six metals, providing a cumulative non-carcinogenic risk indicator. HQ or HI values\u0026thinsp;\u0026lt;\u0026thinsp;1 suggest the exposure is within safe limits, whereas values\u0026thinsp;\u0026ge;\u0026thinsp;1 would indicate potential concern.\u003c/p\u003e \u003cp\u003eThis risk assessment is inherently conservative and meant as an early warning: it assumes a high local vegetable intake and that all vegetables consumed have metal concentrations equal to our sample means.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003e3.1. Heavy Metal Concentrations in Soils, Water, and Vegetables\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSoils\u003c/b\u003e: Overall, heavy metal concentrations in Gopalganj soils were low (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). None of the soil samples approached the maximum permissible values (MPVs) for agricultural soils. Among the six metals, Ni and Cu had the highest mean levels in soil, whereas Cd had the lowest. Specifically, Ni in soil ranged from about 0.34 mg/kg at Site 2 (semi-urban) to 0.46 mg/kg at Site 3 (industrial fringe). Cu ranged from 0.34 mg/kg (Site 2) to 0.60 mg/kg (Site 3). For context, guideline values for Ni and Cu in agricultural soil are around 50 mg/kg and 100 mg/kg, respectively, so the observed levels are \u0026lt;\u0026thinsp;1% of those thresholds. Lead concentrations in soil averaged 0.24 mg/kg (Site 1), 0.22 mg/kg (Site 2), and 0.32 mg/kg (Site 3), all negligible compared to a 50 mg/kg soil guideline. Zinc in soil was similarly low (mean\u0026thinsp;~\u0026thinsp;0.15\u0026ndash;0.19 mg/kg across sites vs. a 300 mg/kg guideline). Chromium in soil averaged 0.47\u0026ndash;0.58 mg/kg (guideline\u0026thinsp;~\u0026thinsp;100 mg/kg for Cr(VI)), and cadmium was around 0.008\u0026ndash;0.010 mg/kg \u0026ndash; essentially at the detection limit (soil guideline\u0026thinsp;~\u0026thinsp;3 mg/kg for Cd). These figures indicate that, at present, soil heavy metal burdens are at background levels in these sites.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eVegetables\u003c/b\u003e: Edible plant tissues also exhibited very low metal concentrations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In many cases, metals were below detection in the vegetables. For instance, Pb was not detectable (ND) in any vegetable sample at any site \u0026ndash; meaning vegetable Pb was likely\u0026thinsp;\u0026lt;\u0026thinsp;0.02 mg/kg, far below the Codex limit of 0.1\u0026ndash;0.3 mg/kg for Pb in foods. Cd was similarly ND in all vegetable samples (\u0026lt;\u0026thinsp;0.005 mg/kg, versus a 0.05\u0026ndash;0.1 mg/kg food standard). Zn was the only metal present in notable amounts in vegetables, reflecting its essential role: vegetable Zn ranged from ~\u0026thinsp;0.39 mg/kg (Sites 2 and 3) to 0.47 mg/kg (Site 1). Even these Zn levels are trivial relative to permissible concentrations in vegetables (50 mg/kg). The other metals had minimal presence: Ni in vegetables averaged 0.02\u0026ndash;0.03 mg/kg, Cu about 0.08\u0026ndash;0.12 mg/kg, and Cr was ND to ~\u0026thinsp;0.01 mg/kg in most cases. There were no instances of any metal exceeding the food safety limits set by FAO/WHO or national standards. For example, the highest Cu observed in a vegetable was ~\u0026thinsp;0.26 mg/kg in a papaya from Site 3, which is well below the 40 mg/kg guideline for Cu in fruits/vegetables. Overall, no evidence of hazardous metal accumulation in edible crops was found.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWater\u003c/b\u003e: Irrigation water at all sites contained only trace levels of metals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cd in water was ~\u0026thinsp;0.003\u0026ndash;0.004 mg/L at Sites 2 and 3, which is at the WHO drinking water limit (0.003 mg/L) but considered very low for irrigation usage. Pb in water was below detection (\u0026lt;\u0026thinsp;0.01 mg/L) at every site, which is excellent given the drinking standard is 0.01 mg/L. Ni and Cu in water were on the order of 0.008\u0026ndash;0.016 mg/L, and Zn around 0.02 mg/L, all of which are inconsequential in terms of irrigation guidelines and far below levels of concern for crop uptake. Cr in water (likely present as Cr(VI)) was ~\u0026thinsp;0.04 mg/L at Sites 2 and 3 (just under the 0.05 mg/L drinking water guideline) and ~\u0026thinsp;0.02 mg/L at Site 1. These readings suggest a slight influence of contamination at Sites 2 and 3 (perhaps from industrial discharges into the river) but still within acceptable bounds for agricultural water use. In summary, the water used on fields does not appear to be introducing significant heavy metal contamination.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial patterns\u003c/b\u003e: Although all values were low, there was a consistent trend of Site 3 (industrial fringe)\u0026thinsp;\u0026gt;\u0026thinsp;Site 2 (semi-urban)\u0026thinsp;\u0026gt;\u0026thinsp;Site 1 (rural) for several metals. For example, soil Ni, Cu, and Pb were each highest at Site 3 and lowest at Site 1. Vegetables at Site 3 also had marginally higher mean Ni and Cu than those at Site 1 (though still very low), and Cr was detectable in a few Site 3 vegetable samples but not at Site 1. Site 2 tended to have intermediate values. This gradient aligns with the degree of anthropogenic pressure: Site 1 is relatively pristine, while Site 3 is subject to industrial effluent and heavier human activity. The differences are not large in absolute terms, but they are consistent enough to be noteworthy. They suggest that even at safe levels, localized heavy metal inputs are occurring at the more impacted sites.\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\u003e\u003cem\u003eConcentrations of heavy metals in soils (mg/kg dry wt), vegetables (mg/kg fresh wt), and irrigation water (mg/L) at the three Gopalganj sites, compared with guideline maximum permissible values (MPVs). Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (n\u0026thinsp;=\u0026thinsp;3). \u0026ldquo;ND\u0026rdquo; indicates not detected above method detection limit. (Guideline sources: WHO/FAO food standards; national soil quality standards; WHO drinking water guidelines as reference for irrigation water.). ND\u0026thinsp;=\u0026thinsp;not detected; half the detection limit was used for statistical calculations for ND values.)\u003c/em\u003e\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\u003eMetal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatrix\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSite\u0026nbsp;1 (Rural)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSite\u0026nbsp;2 (Semi-urban)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSite\u0026nbsp;3 (Industrial)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGuideline MPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026nbsp;mg/kg (soil)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026ndash;0.1\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0034\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0038\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u0026nbsp;mg/L (drink)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.576\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.469\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.584\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026nbsp;mg/kg (soil)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026nbsp;mg/L (drink)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.377\u0026thinsp;\u0026plusmn;\u0026thinsp;0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.601\u0026thinsp;\u0026plusmn;\u0026thinsp;0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u0026nbsp;mg/kg (soil)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124\u0026thinsp;\u0026plusmn;\u0026thinsp;0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026nbsp;mg/L (irrigation)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.380\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.335\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026nbsp;mg/kg (soil)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0079\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0091\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u0026nbsp;mg/L (drink)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.244\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.219\u0026thinsp;\u0026plusmn;\u0026thinsp;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u0026thinsp;\u0026plusmn;\u0026thinsp;0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026nbsp;mg/kg (soil)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND (\u0026lt;\u0026thinsp;0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u0026ndash;0.3\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 (ND)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 (ND)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01 (ND)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u0026nbsp;mg/L (drink)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u0026nbsp;mg/kg (soil)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.467\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u0026nbsp;mg/kg (food)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0218\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0218\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0218\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026nbsp;mg/L (drink)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data are further illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which compares average metal levels in vegetables, soils, and water by site. It highlights that soil concentrations were higher than those in vegetables (indicating limited uptake by plants), and shows the slight increases in metal levels at Site 3 relative to the other siteage)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Statistical Analysis of Spatial Variation\u003c/h2\u003e \u003cp\u003eANOVA confirmed that heavy metal concentrations did not vary dramatically among the sites, but a couple of metals showed significant differences. Cd levels differed modestly yet significantly by site (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), largely due to soil Cd being marginally higher at Site 3 than Site 1 (albeit at trace levels). Ni showed a clearer site effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), with Site 3 (mean soil Ni\u0026thinsp;~\u0026thinsp;0.46 mg/kg) significantly higher than Site 2 (~\u0026thinsp;0.34 mg/kg) and Site 1 (~\u0026thinsp;0.38 mg/kg). These results reinforce that the industrial site has accumulated slightly more Ni and Cd relative to the less impacted sites. Zn exhibited a near-significant site variation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050); Tukey\u0026rsquo;s test indicated Site 1\u0026rsquo;s vegetables had marginally higher Zn than Site 2 (which might be due to natural soil differences or fertilization practices). There were no statistically significant site differences for Cr, Pb, or Cu (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1), meaning that the variations observed (e.g., higher Cu at Site 3, higher Pb at Site 1) were not large enough to be distinguished from natural variability with the given sample size.\u003c/p\u003e \u003cp\u003eThe correlation analysis provided insight into inter-metal relationships across all samples (soil, water, veg combined). We found a strong positive correlation between Pb and Cu (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.514, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that these two metals often co-occur. This could indicate common sources; for instance, vehicle-related emissions and industrial processes frequently emit Pb and Cu together (lead from legacy gasoline and battery waste, copper from brake wear and industrial effluents). Another notable correlation was between Ni and Pb (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.356, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), hinting that areas with higher Ni (perhaps from wastewater or diesel exhaust) also have higher Pb, again pointing toward mixed pollution sources. Conversely, Cd and Cu were negatively correlated (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.333, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), implying that samples with higher Cu tended to have very low Cd and vice versa. This might reflect different input pathways: for example, Cu might come from fungicides or certain industrial sources, whereas Cd may come from phosphate fertilizers or other distinct sources, so they don\u0026rsquo;t accumulate in tandem. No significant correlation was observed between Zn and the other metals, which is not surprising since Zn is an essential element abundant in natural soils and added fertilizers, possibly masking any pollution signal. Similarly, Cr did not show strong correlation with others, likely due to its uniformly low levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Health Risk Assessment\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the estimated daily intakes (ADIs) of metals from vegetables for an adult and the corresponding Hazard Quotients. All individual HQ values were far below 1, which indicates that the current dietary exposure to each of these metals is within safe limits by a wide margin. For instance, HQ_Cd was on the order of 0.02 (using a very conservative estimate for Cd in vegetables), HQ_Pb was \u0026lt;\u0026thinsp;0.01, and HQ_Ni, HQ_Cr, HQ_Cu, HQ_Zn were all ≪0.01. Among the metals, Cd and Pb \u0026mdash; which have the most stringent toxicity thresholds \u0026mdash; contributed the most to the HI, but even they were two orders of magnitude below risky levels due to their extremely low concentrations in the vegetables.\u003c/p\u003e \u003cp\u003eThe Hazard Index (HI), summing all metal HQs, was \u0026lt;\u0026thinsp;0.05 for each site (and nearly identical across sites given similar vegetable metal profiles). This is well under the threshold of 1, reinforcing that no non-carcinogenic health effects are expected from consuming these vegetables with respect to the metals studied. In simpler terms, the cumulative exposure to Cd, Cr, Cu, Ni, Pb, and Zn through local vegetable consumption is presently only a few percent of the maximum tolerable exposure.\u003c/p\u003e \u003cp\u003eIt should be noted that this risk assessment assumes that local residents consume primarily their locally grown produce. In reality, diets are varied and include other food sources and water, which could introduce other exposures \u0026mdash; but for heavy metals, vegetables are often a major route, and our results indicate this route is not contributing significantly to overall exposure.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Comparison with Guidelines and Other Regions\u003c/h2\u003e \u003cp\u003eThe findings show that heavy metal levels in Gopalganj\u0026rsquo;s agricultural environment are low and largely within natural background ranges. This is an encouraging result, suggesting that the area\u0026rsquo;s soils and produce have not yet been significantly affected by anthropogenic pollution. All measured concentrations fell below national and international guideline limits, often by large margins. For example, even at the site closest to industrial activities, soil Pb was ~\u0026thinsp;0.3 mg/kg versus a 50 mg/kg guideline, and vegetable Cd was essentially zero versus a 0.05\u0026ndash;0.1 mg/kg food safety limit. Such compliance is not always seen in other parts of Bangladesh: studies in more industrialized districts have reported elevated heavy metals in soils and vegetables. In parts of Dhaka and its suburbs, for instance, vegetables from industrial sites have been found with Cd or Pb concentrations exceeding recommended limits, and paddy soils irrigated with polluted water often accumulate metals above background levels. Compared to those scenarios, Gopalganj currently enjoys a relatively unpolluted status.\u003c/p\u003e \u003cp\u003eHowever, \u0026ldquo;absence of evidence is not evidence of absence.\u0026rdquo; While no critical contamination was detected, the subtle spatial trends observed indicate that anthropogenic activities are starting to have an effect. Site 3 (near industry) consistently showed higher values than the rural Site 1 for several metals. Though the differences are small now, they mirror what one would expect if pollution is beginning to accumulate: industrial effluents likely contributed to the higher Ni and Cd at Site 3, and traffic or machinery emissions could explain the slight Pb and Cu increases. This pattern is in line with findings from other countries where agricultural areas near pollution sources show incremental metal buildup. For instance, an assessment of soils and plants near industrial sites in Albania found elevated heavy metal content compared to reference sites, even if concentrations were below critical thresholds. Thus, Gopalganj\u0026rsquo;s data can be interpreted as an early warning stage of pollution \u0026ndash; the levels are currently safe, but the influence of human activities is detectable and could intensify if not managed.\u003c/p\u003e \u003cp\u003eAnother aspect is the naturally high buffering capacity of Gopalganj\u0026rsquo;s environment. The soils here are alluvial, likely neutral in pH and rich in iron/manganese oxides and clay, which can immobilize heavy metals. This helps keep metal bioavailability low. Additionally, the flat landscape and monsoon rains mean there is considerable flushing and dilution of soluble pollutants. These factors, coupled with the still modest pollutant inputs, probably contribute to the low bioaccumulation observed. It is both a fortunate situation and one that could be reversed if pollutant loads were to increase substantially beyond the soil\u0026rsquo;s capacity to bind them.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Sources of Metals and Environmental Dynamics\u003c/h2\u003e \u003cp\u003eThe slight enrichment of Ni and Cu at the industrial site points to specific local sources. The nearby jute mill and metal workshop may release wastewater containing these metals. Ni can originate from fuel oil combustion and certain electroplating or alloy processes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cu is used in various industries and also as an algaecide or pesticide [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The detection of Cr in Site 3 water (though low) also hints at industrial effluent, as Cr is commonly used in tanning and dyeing operations. By contrast, at the rural site (Site 1), heavy metals are likely derived mostly from the soil\u0026rsquo;s parent material and minor agricultural inputs, as evidenced by uniformly low values.\u003c/p\u003e \u003cp\u003eTraffic and urban activities are potential contributors at Site 2 and 3 as well. The correlation of Pb with Cu aligns with known traffic-related emissions. Although leaded gasoline is phased out, long-standing deposition and other Pb sources (like lead-acid battery recycling or paint) can still add Pb to soils. Copper from brake wear and tire dust could also settle on soils near roads. Site 3, being closer to a main road and town, would catch more of this particulate fallout than Site 1.\u003c/p\u003e \u003cp\u003eThe negative Cd\u0026ndash;Cu correlation might reflect different practices: perhaps fields with intensive pesticide use (hence higher Cu) are not the same fields with long-term phosphate fertilizer accumulation (Cd). In our data, Rogunathpur (Site 1) had the lowest Cu and slightly higher Cd than Nobinbag (Site 2), which could be consistent with a scenario where the rural site uses traditional fertilizers (potentially introducing tiny Cd impurities), whereas the semi-urban site might use more pesticides (adding Cu) but not see Cd buildup yet. These hypotheses would need further investigation (e.g., interviews with farmers on input use), but they underline that not all heavy metals share the same sources, even though we often measure them together.\u003c/p\u003e \u003cp\u003eImportantly, plant uptake of metals was minimal in this study. Vegetables did not mirror the soil metal content in terms of magnitude, which is reassuring for food safety. This can be attributed to both low soil concentrations and low bioavailability of those metals. Additionally, the types of vegetables analyzed (fruits like papaya, chili, eggplant) tend to accumulate fewer metals than leafy greens or root vegetables. Metals like Pb and Cr mostly bind to roots and seldom translocate to fruits. Even for a mobile element like Cd, plants exhibit species-specific uptake; rice, for instance, is a known Cd accumulator, but fruit vegetables less so. In Gopalganj, the crops chosen inherently limit the transfer of whatever metals are in soil to the edible parts. This doesn\u0026rsquo;t eliminate risk (if soils became extremely contaminated, even fruits could be affected), but it provides a natural safeguard that currently contributes to the low HQ and HI values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Implications for Public Health and Management\u003c/h2\u003e \u003cp\u003eFrom a public health perspective, the current heavy metal exposure through locally produced food in Gopalganj is well within safe limits. The Hazard Index calculations, even with conservative assumptions, came out far below 1, indicating no significant risk of kidney damage, neurological effects, or other chronic conditions that heavy metals could induce. This aligns with the lack of any known heavy metal poisoning cases in the area \u0026ndash; unlike the arsenic issue, heavy metals have not manifested in health statistics here, which our data support.\u003c/p\u003e \u003cp\u003eHowever, the situation warrants a preventive approach to ensure it remains that way. The fact that industrial activities are leaving a measurable imprint, albeit small, suggests that now is the ideal time to enforce pollution controls. Effluents from industries around Gopalganj should be treated to remove heavy metals before discharge into water bodies. Initiatives could be taken to install sedimentation tanks, filtration systems, or phytoremediation ponds for wastewater from the jute mill and other workshops. Likewise, the municipality should ensure that urban runoff (which could carry metal-laden waste) is managed, for example by creating wetlands or retention areas that trap contaminants before they reach agricultural land.\u003c/p\u003e \u003cp\u003eFor farmers, awareness programs could be beneficial. Educating on the judicious use of fertilizers (preferring those tested low in Cd and other metals) and pesticides (avoiding overuse of copper-based chemicals) can help minimize adding metals to the soil. Crop rotation and organic amendments can improve soil health and potentially bind or dilute contaminants. If irrigation from the Modhumoti River is suspected to carry pollutants, exploring alternative water sources or timing irrigation when river pollution is lower (e.g., using more groundwater in the dry season if safe) might be advisable.\u003c/p\u003e \u003cp\u003eThe data provide a baseline against which future changes can be monitored. Environmental authorities in Bangladesh could incorporate Gopalganj into their regular monitoring network, checking these same sites every couple of years. A slight upward creep in metal levels would then be detected and could trigger investigations or interventions early. On the other hand, stable or decreasing trends (if cleaner practices are adopted) would validate the effectiveness of pollution management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Future Outlook\u003c/h2\u003e \u003cp\u003eThe Gopalganj case highlights that not all agriculturally intensive areas in Bangladesh are heavily polluted \u0026ndash; some remain comparatively clean, which is good news. With Bangladesh\u0026rsquo;s drive towards industrial growth, lessons from highly polluted sites should be applied preemptively in places like Gopalganj. The emphasis should be on sustainable development, ensuring industries implement waste treatment from the start rather than retrofitting after damage is done.\u003c/p\u003e \u003cp\u003eWhile our study focused on six key heavy metals, future research may expand to other contaminants like arsenic, mercury, or pesticide residues to give a fuller picture of environmental quality. It would also be useful to examine heavy metal levels in other components of the food chain (for instance, fish from local water bodies, since fish could accumulate metals from sediments). Additionally, continuous monitoring of human biometrics (like hair or blood metal levels in residents) could provide direct evidence of exposure trends over time, complementing the environmental measurements.\u003c/p\u003e \u003cp\u003eIn conclusion, Gopalganj\u0026rsquo;s soils and crops currently meet safety standards for heavy metals. This provides a valuable window to maintain and protect this status through conscious policy and community action. The slight elevations near industrial areas are manageable now but serve as a reminder that vigilance is needed. By reinforcing pollution controls, promoting safe agricultural inputs, and keeping up with monitoring, Gopalganj can avoid the severe heavy metal problems experienced elsewhere and ensure the long-term health of its people and ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides a comprehensive evaluation of heavy metal contamination in a representative agricultural region of Bangladesh (Gopalganj) that is undergoing gradual industrialization. The results indicate that, as of now, heavy metal concentrations (Cd, Cr, Cu, Ni, Pb, Zn) in local soils, irrigation water, and vegetables are within safe limits set by health and environmental authorities. No excessive accumulation was found in the food chain; consequently, the estimated human exposure to these metals via vegetable consumption is well below hazardous levels. These findings are reassuring for the residents of Gopalganj, suggesting that the region\u0026rsquo;s produce is safe for consumption and the agricultural land remains largely unpolluted by heavy metals.\u003c/p\u003e \u003cp\u003eAt the same time, the research detected early signs of anthropogenic influence on metal distributions \u0026ndash; particularly at the site closest to industrial and urban activities, where certain metals (e.g., Ni, Cu) were marginally elevated. This highlights a critical opportunity for preventative action. To sustain the current low-risk status, it is recommended that local stakeholders:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eImplement and enforce pollution control measures\u003c/b\u003e for industries (ensuring effluents are treated and solid wastes are properly disposed of) to prevent the gradual build-up of metals in the environment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eContinue monitoring\u003c/b\u003e heavy metal levels in soils and crops at regular intervals. The baseline data from this study serve as a reference point to identify any future deviations caused by increased pollution.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePromote best agricultural practices\u003c/b\u003e that minimize heavy metal inputs, such as using fertilizers with low metal impurities, avoiding over-reliance on metal-based pesticides, and maintaining soil health through organic matter addition which can immobilize certain contaminants.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRaise community awareness\u003c/b\u003e about sources of heavy metal pollution (for example, inform farmers and local industries about the implications of dumping waste, burning batteries, etc.) so that collective efforts can be made to protect the environment.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn summary, Gopalganj currently enjoys a favorable condition regarding heavy metal pollution \u0026ndash; a condition that many heavily industrialized regions strive to regain. By taking proactive steps informed by the findings of this study, Gopalganj can serve as a model for balancing agricultural productivity, industrial development, and environmental health. The maintenance of low heavy metal levels in its soils and food will directly contribute to the well-being of its population and the sustainability of its agriculture for years to come.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThis study was performed in accordance with relevant rules, guidelines and regulations. There were no human subjects in this study.\u003c/p\u003e\n\u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no known financial or personal affiliations.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by the Gopalganj Science and Technology University Research Cell\u003c/p\u003e\n\u003cp\u003e(GSTURC), through UGC, Govt. of Bangladesh (FY: 2021\u0026ndash;2022).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eMd Imran Hossain and Md Omor faruk investigate the experiments and Others revised the manuscript. Dr. Sarafat Ali who supervised whole project.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support provided by the Gopalganj Science and Technology University Research Cell (GSTURC)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFei, X., et al., \u003cem\u003eContamination assessment and source apportionment of heavy metals in agricultural soil through the synthesis of PMF and GeogDetector models.\u003c/em\u003e Science of the Total Environment, 2020. \u003cstrong\u003e747\u003c/strong\u003e: p. 141293.\u003c/li\u003e\n\u003cli\u003eMitra, S., et al., \u003cem\u003eImpact of heavy metals on the environment and human health: Novel therapeutic insights to counter the toxicity.\u003c/em\u003e Journal of King Saud University-Science, 2022. \u003cstrong\u003e34\u003c/strong\u003e(3): p. 101865.\u003c/li\u003e\n\u003cli\u003eIslam, M.S., et al., \u003cem\u003eThe concentration, source and potential human health risk of heavy metals in the commonly consumed foods in Bangladesh.\u003c/em\u003e Ecotoxicology and environmental safety, 2015. \u003cstrong\u003e122\u003c/strong\u003e: p. 462-469.\u003c/li\u003e\n\u003cli\u003eBegum, K., et al., \u003cem\u003eHeavy metal pollution and major nutrient elements assessment in the soils of Bogra city in Bangladesh.\u003c/em\u003e Canadian Chemical Transactions, 2014. \u003cstrong\u003e2\u003c/strong\u003e(3): p. 316-326.\u003c/li\u003e\n\u003cli\u003eMajumder, A.K., et al., \u003cem\u003eCritical review of lead pollution in Bangladesh.\u003c/em\u003e Journal of Health and Pollution, 2021. \u003cstrong\u003e11\u003c/strong\u003e(31): p. 210902.\u003c/li\u003e\n\u003cli\u003eHassan, M.M., \u003cem\u003eArsenic in groundwater: poisoning and risk assessment\u003c/em\u003e. 2018: Crc Press.\u003c/li\u003e\n\u003cli\u003eKabir, T., \u003cem\u003eThe groundwater arsenic crisis in Bangladesh, impacts \u0026amp; challenges for SDG (s) 2030 Development Agenda\u003c/em\u003e. 2019, Wien.\u003c/li\u003e\n\u003cli\u003eScutarașu, E.C. and L.C. 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Inam, and S. Iqbal, \u003cem\u003eRisk analysis by bioaccumulation of Cr, Cu, Ni, Pb and Cd from wastewater-irrigated soil to Brassica species.\u003c/em\u003e International Journal of Environmental Science and Technology, 2020. \u003cstrong\u003e17\u003c/strong\u003e: p. 2889-2906.\u003c/li\u003e\n\u003cli\u003eNazir, R., et al., \u003cem\u003eAccumulation of heavy metals (Ni, Cu, Cd, Cr, Pb, Zn, Fe) in the soil, water and plants and analysis of physico-chemical parameters of soil and water collected from Tanda Dam Kohat.\u003c/em\u003e Journal of pharmaceutical sciences and research, 2015. \u003cstrong\u003e7\u003c/strong\u003e(3): p. 89.\u003c/li\u003e\n\u003cli\u003eChain, E.P.o.C.i.t.F., et al., \u003cem\u003eUpdate of the risk assessment of nickel in food and drinking water.\u003c/em\u003e Efsa Journal, 2020. \u003cstrong\u003e18\u003c/strong\u003e(11): p. e06268.\u003c/li\u003e\n\u003cli\u003eRomero-Freire, A., F.M. Peinado, and C. Van Gestel, \u003cem\u003eEffect of soil properties on the toxicity of Pb: Assessment of the appropriateness of guideline values.\u003c/em\u003e Journal of Hazardous Materials, 2015. \u003cstrong\u003e289\u003c/strong\u003e: p. 46-53.\u003c/li\u003e\n\u003cli\u003eShahbazi, Y., F. Ahmadi, and F. Fakhari, \u003cem\u003eVoltammetric determination of Pb, Cd, Zn, Cu and Se in milk and dairy products collected from Iran: An emphasis on permissible limits and risk assessment of exposure to heavy metals.\u003c/em\u003e Food chemistry, 2016. \u003cstrong\u003e192\u003c/strong\u003e: p. 1060-1067.\u003c/li\u003e\n\u003cli\u003eJohn, S.O.O., et al., \u003cem\u003eHealth risk assessment of heavy metals and physicochemical parameters in natural mineral bottled drinking water using ICP-MS in South Africa.\u003c/em\u003e Applied Water Science, 2024. \u003cstrong\u003e14\u003c/strong\u003e(9): p. 202.\u003c/li\u003e\n\u003cli\u003eLi, J., et al., \u003cem\u003eRisk assessment for safety of soils and vegetables around a lead/zinc mine.\u003c/em\u003e Environmental Geochemistry and Health, 2006. \u003cstrong\u003e28\u003c/strong\u003e: p. 37-44.\u003c/li\u003e\n\u003cli\u003eTaiwo, A.M., et al., \u003cem\u003eHealth risk assessment of metals in selected drinks from Abeokuta, Southwestern Nigeria.\u003c/em\u003e Biological trace element research, 2020. \u003cstrong\u003e197\u003c/strong\u003e(2): p. 694-707.\u003c/li\u003e\n\u003cli\u003eMankins, W. and S. Lamb, \u003cem\u003eNickel and nickel alloys.\u003c/em\u003e 1990.\u003c/li\u003e\n\u003cli\u003eTsai, K.-P., \u003cem\u003eManagement of target algae by using copper-based algaecides: effects of algal cell density and sensitivity to copper.\u003c/em\u003e Water, Air, \u0026amp; Soil Pollution, 2016. \u003cstrong\u003e227\u003c/strong\u003e: p. 1-11.\u003c/li\u003e\n\u003cli\u003eThornton, J.A. and W. Rast, \u003cem\u003eThe use of copper and copper compounds as algicides.\u003c/em\u003e The handbook of copper compounds and applications. CRC Press, Boca Raton, 1997: p. 123-142.\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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, Metalloids, Soil Contamination, Water Pollution, Vegetable Safety, Health Risk Assessment, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-6703800/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6703800/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeavy metal contamination in agricultural soils, water, and crops is a critical concern in developing regions due to its implications for food safety and public health. This study assessed the concentrations of six heavy metals \u0026ndash; cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) \u0026ndash; in soil, irrigation water, and commonly consumed vegetables from three sites in Gopalganj, Bangladesh. The sites represent a gradient of anthropogenic impact: a rural agricultural area (control), a mixed agriculture-urban area, and an industrial-adjacent area. Samples were analyzed using Atomic Absorption Spectroscopy (AAS) with rigorous quality control. Results showed that all metal concentrations in vegetables and soils were below international permissible limits, with mean levels in soils (e.g., Ni\u0026thinsp;~\u0026thinsp;0.4 mg/kg, Cu\u0026thinsp;~\u0026thinsp;0.5 mg/kg, Pb\u003c/p\u003e \u003cp\u003e~\u0026thinsp;0.3 mg/kg) and vegetables (Zn\u0026thinsp;~\u0026thinsp;0.4 mg/kg, other metals often not detectable) reflecting background conditions. Irrigation water contained trace metal levels near or below detection limits. Nevertheless, spatial trends were evident: soils at the industrial-influenced site had significantly higher Ni and Cu than the rural site (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and slight elevations of Cd, Pb, and Cr were noted in vegetables and water from impacted areas (though still within safe bounds). Pearson correlations suggested common sources for some metals (e.g., Pb\u0026ndash;Cu, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.51; Ni\u0026ndash;Pb, \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.36; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A human health risk assessment indicated that the estimated dietary intakes of these metals through vegetables yield HQ and HI values well below 1, indicating no significant non-carcinogenic risk at present. These findings provide a timely baseline on heavy metal pollution in a fast-developing region of Bangladesh. While current contamination levels appear safe, the detectable influence of industrial activities underscores the need for ongoing monitoring and proactive measures to ensure environmental and food safety in the future.\u003c/p\u003e \u003cp\u003eDiagrammatic representation of workflow process\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Heavy Metal and Metalloid Contamination in Agricultural Matrices of Gopalganj, Bangladesh: A Comprehensive Analysis and Health Risk Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 12:30:21","doi":"10.21203/rs.3.rs-6703800/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"2d69af3b-5f26-43ca-910a-265d7414628d","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T12:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 12:30:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6703800","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6703800","identity":"rs-6703800","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}