Heavy Metal Contamination in Wheat Grains: Spatial Analysis and Health Risk Assessment in Southern Iran

Heavy Metal Contamination in Wheat Grains: Spatial Analysis and Health Risk Assessment in Southern Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Heavy Metal Contamination in Wheat Grains: Spatial Analysis and Health Risk Assessment in Southern Iran Roza Aibaghi, Nastaran Talepour, Sahand Jorfi, Neamatollah Jaafarzadeh, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6470086/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 The present study addresses the critical issue of heavy metal contamination in wheat grains, aiming to bridge the existing research gap by examining the spatial distribution of heavy metals and assessing their potential health risks in the southern Iranian oil fields. Employing a quantitative approach, we collected samples from 50 regional wheat cultivation farms and analyzed the concentrations of chromium (Cr), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) using inductively coupled plasma‒mass spectrometry. Our findings revealed concerning levels of heavy metals, with Zn exhibiting the highest concentration (mean: 30.169 mg/kg), while Pb and Hg exceeded the FAO/WHO safety thresholds. Among the studied elements, Hg posed the highest health risk, with health quotient (HQ) values of 1.38 for adults and 2.14 for children. Cr (HQ: 0.000236 for adults; 0.00038 for children), followed by As (HQ: 0.000494 for adults; 0.00076 for children), was identified as the primary carcinogenic heavy metal. Principal component analysis (PCA) revealed that the first factor accounted for 48% of the total variance, primarily attributed to As, Cr, Pb, and Hg, while the second factor explained 27.32%, associated with Cd, Zn, and Cu. Ordinary kriging interpolation indicated elevated heavy metal concentrations in farms located in the eastern, northeastern, and southwestern regions. Based on these findings, we emphasize the urgent need for remediation strategies to reduce heavy metal contamination in wheat grains, highlighting the crucial importance of ensuring food safety and protecting public health. Health risk assessment Heavy metal pollution Kriging Spatial distribution Wheat grains Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 HIGHLIGHTS ThePb and Hg concentrations in wheat exceeded the FAO/WHO MPC values. Hg exhibited the highest health quotient for both adults and children. Cr, followed by As, emerged as the primary carcinogenic heavy metal. accounted for 48% of the total variance (attributed to As, Cr, Pb, and Hg), while the secondary factor explained 27.32% (associated with Cd, Zn, and Cu). The greatest abundance of HMs occurred on farms in the eastern, northeastern, and southwestern regions. 1. Introduction In the contemporary period, chemical pollutants pose risks to environmental and human health, and heavy metals (HMs) play a significant role in contributing to this concern (Huang et al., 2008 ). Heavy metals are metals with high density (atomic mass greater than 8.55 grams per mole or volumetric mass over 5 grams per cubic centimeter) and exhibit metallic properties at room temperature (Li et al., 2014 ). From an ecological perspective, heavy metals are elements with a strong tendency to be absorbed by living tissues, leading to their accumulation and difficulty in being released from those tissues (Lin et al., 2004 ). In nature, heavy metals exist in limited amounts in the Earth's crust. Human exposure to these metals occurs through ingestion, inhalation, and dietary intake. Heavy metals can naturally occur in food or be introduced through human activities (Kumari & Bhattacharya, 2023 ), including mining or industrial operations, along with incorrect application of heavy metal-enriched substances in agriculture, such as chemical fertilizers, pesticides, industrial discharge, sewage sludge, and wastewater irrigation (Kumari & Bhattacharya, 2023 ; Zeng et al., 2011 ). Notably, heavy metals do not undergo metabolic transformation in the human body. However, once inside the body, they accumulate in tissues, leading to various illnesses and adverse effects(Setia et al., 2023 ). Exposure to different heavy metals can lead to a broad spectrum of adverse health consequences. Exposure to lead (Pb) is linked to health issues concerning the central nervous system, cardiovascular system kidneys, and fertility. Cadmium (Cd) poisoning can result in harm to the kidneys, bones, lungs, and liver and may even contribute to the development of cancer. Mercury (Hg) can lead to significant neurological issues in both children and adults. Exposure to chromium (Cr) is associated with nasal irritation, ulceration, skin problems, and eardrum perforation. Prolonged exposure to arsenic (As) is connected to skin lesions, skin cancer, and a range of neurological, respiratory, cardiovascular, and developmental issues (Shi et al., 2023 ). Acute exposure to nickel (Ni) can cause harm to the kidneys, liver, and brain, and continuous exposure can harm the body's tissues. Additionally, Ni predominantly impacts the skin, causing the emergence of pimples, herpes, and erythema. Moreover, it is linked to a greater risk of cancer, neurological disorders, systemic disorders, and reduced fertility. Chronic toxicity from elevated levels of copper (Cu), a red-brown metal, can manifest in various health issues, including neurological weakness, metabolic disorders, cell carcinogenesis, and brain tissue lesions. Furthermore, it can lead to respiratory problems, dizziness, nausea, and diarrhea (Y. Cui et al., 2022 ; Shi et al., 2023 ). Food constitutes the primary source of human exposure to toxic elements, which can potentially induce significant effects on public health, encompassing both carcinogenic and noncarcinogenic impacts(H. Cui et al., 2022 -p. Liu et al., 2020 ). Hence, the Food and Agriculture Organization (FAO), the World Health Organization (WHO), the United States Environmental Protection Agency (US EPA), and other regulatory bodies worldwide closely monitor and enforce strict regulations on toxic heavy metal levels in food to ensure compliance with acceptable concentrations, thus safeguarding public health and safety (Bojago et al., 2023 ; Li et al., 2023 ). Heavy metals are known to present potential health risks, necessitating their evaluation through carcinogenic or noncarcinogenic assessment methods. In 1989, the United States Environmental Protection Agency (US EPA) implemented noncancer risk evaluation techniques, notably the hazard quotient (HQ) and hazard index (HI). Recent use of these methods has substantiated their effectiveness and reliability in gauging the health consequences attributed to heavy metal exposure(Huang et al., 2008 ; Y.-M. Liu et al., 2020 ; Setia et al., 2023 ). Accurate spatial mapping of heavy metal distributions is essential for conducting thorough pollution and health risk assessments(Zhang et al., 2021 ). Whenever conducting locational interpolation of heavy metals, researchers have a range of methods at their disposal, including geostatistical kriging methods, inverse distance weighting (IDW), global polynomials, multiple linear regression, geographically weighted regression (GWR), neural networks, and regression kriging. These techniques provide different ways to estimate heavy metal concentrations at unsampled locations within a study area based on the available data and locational patterns. The selection of the method is influenced by the particular attributes of the dataset and the goals of the research (H. Cui et al., 2022 ; Fu et al., 2022 ; Lv et al., 2022 ). Precise spatial maps play a pivotal role in making well-informed decisions and devising effective policies concerning environmental management and conservation endeavors (Xiang et al., 2019 ). Wheat ( Triticum aestivum L.) plays a significant role as a primary cereal crop, making up 50% of the daily calorie consumption and contributing to more than 20% of the daily intake of zinc (Zn) for people living in developing countries (Y.-M. Liu et al., 2020 ). Despite comprising only approximately 1% of the world's population, Iran consumes approximately 2.5% of the total global wheat production (Hashemi Nejad et al. 2020). Wheat plays a crucial and strategic role in Iran's agriculture due to favorable climatic conditions, leading to widespread cultivation across the country's provinces (Ahmed et al. 2013 ). Ahvaz, located within southern Iranian oil fields, has experienced substantial industrial growth in recent years. This expansion has been marked by the emergence of various industries in the city's vicinity. The discharge of industrial effluents into the Karun River, coupled with the extensive use of fertilizers and pesticides, has raised environmental concerns. The coalescence of industrialization and agricultural practices has resulted in notable heavy metal pollution, necessitating additional scrutiny. This inquiry was prompted by a significant concern regarding the safety of agricultural products intended for human consumption, coupled with the acknowledgment of wheat's central role in providing nourishment to households. The principal aim of this investigation was to perform a detailed examination of the occurrence and concentrations of heavy metal pollutants (including Hg, Cu, Cr, Pb, As, Ni, Zn, and Cd) in wheat grains cultivated in southern Iranian oil fields. Moreover, this study aimed to assess how heavy metals were distributed across wheat grains in terms of their spatial arrangement and to discern plausible sources of contamination. Moreover, comprehensive assessments of potential health risks, encompassing noncancerous and cancer-associated ailments, were conducted to thoroughly understand the potential outcomes of these pollutants. The results of our research, especially in areas abundant in oil, offer valuable knowledge for decision-makers, assisting in the creation of efficient plans to reduce the dangers of heavy metal pollution in agricultural products and ensuring the safety of the environment and public health. By comprehending the geographical spread of heavy metals in these regions, policymakers can enforce specific rules and actions to protect food safety and alleviate the health consequences for nearby societies. 2. Materials and methods 2.1. Study area and sampling During the wheat harvesting season in May 2022, a nonrandom sampling procedure was conducted at the Silo Rajaji laboratory in Ahvaz city, which is located within southern Iranian oil fields. The samples (N = 50) were gathered from wheat harvested in the surrounding areas of Ahvaz, primarily for testing, and then stored in the silo. Ahvaz, the capital of Khuzestan Province, is located at a latitude of 31°20' N and a longitude of 48°40' E, with an elevation of 18 m above sea level in the plains of Khuzestan. Ahlaz Silo, which is situated on the western bank of the Karun River, is a crucial and strategic wheat storage facility for the country. It plays a vital role in wheat exports to neighboring countries, making it highly important for the southwest region of Iran (Fig. 1 ). 2.2. Sampling analysis In the laboratory, the grain samples were washed, dried at 60–65°C, and weighed. Subsequently, the wheat grains were ground using a stainless steel mill. Then, a 2-gram portion of the ground sample was mixed with a 1:4 ratio of a special digestion solution containing 70% nitric acid (HNO3) and 65% hydrochloric acid (HCl) in dedicated digestion tubes. The samples underwent a two-stage digestion process, starting at 40°C for 1 hour and subsequently at 140°C for 3 hours, resulting in a clear solution. After cooling, the samples were sieved through 42-µm Whatman filter paper to achieve smoothness. Next, the samples were brought to a volume of 25 ml using distilled water and then stored in a refrigerator until analysis. The concentrations of zinc, arsenic, mercury, chromium, lead, copper, and cadmium in the samples were established through inductively coupled plasma‒mass spectrometry (ICP-MS). 2.3. Health risk assessment of wheat consumption 2.3.1. Noncancerous health risk assessment of wheat consumption The risk of noncancerous diseases in individuals was assessed using the formula provided by the United States Environmental Protection Agency (USEPA) (Naz et al., 2022 ; Zafarzadeh et al., 2021 ). To do this, the estimated daily intake (EDI, mg/kg. day) was initially calculated using Eq. 1 . $$\:EDI=\:\frac{C\times\:IR\times\:ED\times\:EF\times\:CF}{BW\times\:AT}$$ 1 where C is the concentration of heavy metals in the wheat grains (mg/kg); IR is the wheat grain ingestion rate (Adults: 388, children: 129, g/person/day); ED is the exposure duration (Adult: 24, children: 6, years); EF is the frequency of exposure 365 days/year; CF is a factor (0.01 g/kg); BW is the average body weight (Adult: 70, children: 15, kg); and AT is the average exposure time (Adult: 24, Children: 6, days)(Huang et al., 2022 ; Naz et al., 2022 ). Subsequently, the probability of noncancer risk was calculated using the hazard quotient (HQ) through Eq. 2 . $$\:HQ=\frac{EDI}{RfD}$$ 2 The RfD represents the reference dose, which represents the recommended daily intake of a specific metal measured (mg/kg body weight/day). The value is carefully established to ensure a safe and health-promoting intake range. An HQ value below one signifies that consuming wheat does not cause immediate harmful effects on health. Conversely, if the HQ is equal to or greater than one, there is a likelihood of risk associated with exposure to wheat(EPA, 2006 ; Y.-M. Liu et al., 2020 ; Naz et al., 2022 ). 2.3.2. Assessment of the cancer risk of wheat consumption The computation of carcinogenic risk was performed using Eq. 3. ILCR = EDI×CSF (3) In this equation, ILCR represents the incremental lifetime carcinogenic risk associated with single heavy metals, and CSF represents the cancer slope factor (Y. Cui et al., 2022 ; Rahmani et al., 2018 ; Zafarzadeh et al., 2021 ). If the incremental lifetime cancer risk (ILCR) is less than 10 − 6 , the carcinogenic risk associated with the consumption of water and food is considered to have a negligible impact on human health. However, if the ILCR value exceeds this range, it indicates an elevated probability of risk and an increased likelihood of cancer incidence in the human population. The acceptable levels of carcinogenic risk for hazardous pollutants to human health are categorized as low (1 × 10 − 6 ), moderate (1 × 10 − 5 ), and very hazardous (1 × 10 − 4 ), suggesting that among 10,000 individuals, one person is likely to develop cancer at the respective risk level(Agency, 2007 ; EPA, 2000 ). According to the International Agency for Research on Cancer (IARC) report and similar studies, heavy metals, cadmium, lead, chromium, and arsenic are considered to have carcinogenic effects(R.-p. Liu et al., 2020 ; Y.-M. Liu et al., 2020 ). Therefore, the carcinogenic risk for these elements was calculated. The CSF values utilized in this research are as follows: Cr: 0.5, As: 1.5, Cd: 0.38, and Pb: 0.0085, all expressed in units of (mg/kg/day) −1 (Y. Cui et al., 2022 ; Zafarzadeh et al., 2021 ). 2.3.3. Total health risk assessment of wheat consumption The total hazard index (HI) is calculated by summing the probabilities of exposure to individual elements within each age group (Eq. 4 ). HI values below one indicate no adverse health effects from the consumption of the food substance, while values above one suggest potential health risks (Naz et al., 2022 ; Shokri et al., 2022 ). $$\:\text{H}\text{I}=\sum\:\text{H}\text{Q}=\:{\text{H}\text{Q}}_{\text{P}\text{b}}+\:{\text{H}\text{Q}}_{\text{A}\text{s}}+\:{\text{H}\text{Q}}_{\text{C}\text{d}}+\:{\text{H}\text{Q}}_{\text{H}\text{g}}+{\text{H}\text{Q}}_{\text{C}\text{u}}+\:{\text{H}\text{Q}}_{\text{Z}\text{n}}+\:{\text{H}\text{Q}}_{\text{C}\text{r}}$$ 4 2.4. Statistical analysis Excel 2013 and SPSS version 26 were used for graph plotting and processing the descriptive statistics data, respectively. The Shapiro‒Wilk test (p < 0.05) was used to examine the normality of the data, and the Spearman correlation test was used to investigate the relationships between the average concentrations of elements in the samples. Principal component analysis (PCA) was utilized to study the origin of heavy metals in wheat grains by efficiently pinpointing their source in different agricultural areas with the assistance of MATLAB 2022b (Cai et al., 2019 ). To map the spatial distribution of heavy metal concentrations in wheat, we used the ordinary kriging geostatistical method within ArcGIS Pro 3.0.2 software(Fu et al., 2022 ). This study presents the following technical methods (Fig. 2 ). 3. Results and discussion 3.1. Statistical description of heavy metal contents in wheat samples A box and whisker plot (Fig. 3 ) shows the heavy metal concentrations in the wheat grains. The values and descriptive statistics of the heavy metal concentrations in the wheat grains are shown in Table 1. According to the results obtained, the heavy metal concentrations in the wheat grains decreased in the order Zn > Cu > Pb > Cr > As > Hg > Cd, with mean values of 30.17, 6.2, 0.63, 0.087, 0.06, 0.03 and 0.007 mg/kg, respectively. The Iranian National Standard Organization (INSO) has issued guidelines for HMs in wheat grains, specifically focusing on Pb and Cd. Hence, this study involved comparing HMs in wheat grains against INSO's Pb and Cd standards, as well as the standards established by the Codex Alimentarius Commission (FAO/WHO) for all HMs. Zn (30.17 mg/kg) and Cu (6.20 mg/kg) exhibited the maximum quantity, while Cd (0.007 mg/kg) showed the minimum quantity in the wheat grains. The study revealed that Zn and Cu had significantly higher concentrations in the grain sample. These two elements also exhibited the highest concentrations in the present study. The significant presence of Zn in wheat was attributed to its high concentration in the soil (Jia et al., 2010 ). The concentrations of Zn, Cu, Cr, and Cd in all samples were found to be below the maximum permissible concentrations (MPCs) set by the FAO/WHO (Commission, 2011 ), which are 99.4 mg/kg for Zn, 73.3 mg/kg for Cu, 2.3 mg/kg for Cr, and 0.2 mg/kg for Cd, as well as below the INSO ((INSO), 2021 ) standards for Cd, which is 0.06 mg/kg. However, this study revealed that the levels of Pb in wheat exceeded both the INSO (INSO, 2021) and FAO/WHO (Commission, 2011 ) MPC values, which were 0.2 mg/kg and 0.3 mg/kg, respectively, except for two samples. The Hg concentration in 44 wheat grain samples was found to be equal to or greater than the MPC (0.01 mg/kg) given by the FAO/WHO. Remarkably, while the transfer of Hg from soil to plants is not straightforward, plants can nonetheless uptake Hg from both the soil and airborne particles. This suggests that aerial deposition of Hg might be responsible for the increased Hg levels observed in the grains(Jia et al., 2010 ). Moreover, in 6 samples, the As concentration reached or exceeded the MPC of 0.1 mg/kg given by the FAO/WHO (Ahmad et al., 2019 ; Ahmed et al., 2013 ; Commission, 2011 ; Mengistu, 2021 ). The Shapiro‒Wilk test revealed that the heavy metal distribution was nonnormal (P ≤ 0.05) (Zhou et al., 2014 ). Thus, data normalization through logarithmic transformation was performed before utilizing the kriging method. Skewness and kurtosis were used to assess the asymmetry of the real-valued probability distribution. Positive skewness, indicated by a skewness value greater than zero, signifies that more values are concentrated on the right side of the symmetry axis. However, kurtosis measures the steepness of the distribution pattern. The coefficient of variation (CV) functions as a metric that reveals the extent of scattering. In this research, its use aimed to assess the diversity in heavy metal concentrations and the degree of distinction among the sampled locations. Table 1. Descriptive statistics of heavy metal concentrations in wheat grains. Element Count Min(mg/Kg) Max(mg/Kg) Median(mg/Kg) SD Mean(mg/Kg) C.V % Skewness Kurtosis Cr 50 0.02 0.30 0.07 0.06 0.087 68.37 -0.09 2.40 Cu 50 3.90 10.60 5.80 1.25 6.20 20.12 0.08 3.60 Zn 50 21.30 58.50 29.10 6.30 30.17 21.10 1.22 6.20 As 50 0.01 0.14 0.06 0.03 0.06 53.51 -0.43 2.80 Cd 50 0.001 0.018 0.007 0.003 0.007 45.53 -0.05 3.35 Hg 50 0.004 0.08 0.02 0.02 0.03 70.00 0.09 3.45 Pb 50 0.02 3.60 0.42 0.70 0.63 109.76 -0.27 6.60 Human activities significantly contribute to soil pollution in wheat fields in Ahvaz. The main factors include oil fields, proximity to roads and highways, the extensive use of chemical and animal fertilizers, and the use of pesticides and herbicides (Afkhami et al., 2013 ; Alengebawy et al., 2021 ; Karbassi et al., 2015 ; Radziemska & Fronczyk, 2015 ). Given that the primary water source for wheat fields in the present study, where heavy metal concentrations are high, is the Karun River or its related water channels, which have been contaminated by the discharge of municipal and industrial wastewater, irrigation water might also play a role in the elevated levels of heavy metals in the soil, ultimately resulting in the buildup of these contaminants in wheat grains. (Ali et al., 2012 ; Rizvi et al., 2020 ). 3.2. Potential sources of heavy metal pollution in wheat samples The Spearman correlation coefficient technique and principal component analysis were utilized to evaluate the connections and potential origins of heavy metals (El Behairy et al., 2022 ; Liu et al., 2023 ). For the effective management of soil heavy metal contamination, the qualitative recognition of pollution origins and the allocation of sources are of pivotal significance. Approaches to pinpointing these origins include the use of geostatistical models in conjunction with multivariate statistical methods such as principal component analysis (PCA). PCA, which operates as a linear approach for diminishing dimensionality, reconfigures a range of environmental variables into distinct origins, utilizing a constrained set of principal components (Lv, 2019 ; Tian et al., 2020 ). Figure 4 shows the Spearman correlation matrix for the heavy metal concentrations and the PCA of the heavy metals in the wheat samples. The Spearman correlation coefficients revealed strong correlations between certain metals. Specifically, Cr, Cu, As, Hg, and Pb are closely linked (P < 0.01). Additionally, Cu, Zn, As, Cd, and Pb also had significant associations (P < 0.01). Furthermore, As, Hg, and Pb exhibited strong relationships (P < 0.01). Importantly, Pb had a somewhat weaker correlation with Hg (P < 0.05), and Zn had a weaker correlation with Cd (P < 0.05). These connections between the metals could be due to common sources of pollution. The outcomes of the PCA are presented in Table 2 . These results revealed the presence of two eigenvalues greater than one, which, when combined, explained 75.32% of the total variance (Table 3 ). The primary component accounts for a significant portion, precisely 48%, of the total variance and exhibits strong loadings for As, Cr, Pb, and Hg. Conversely, the secondary factor, characterized by the dominance of Cd, Zn, and Cu, contributed 27.32% of the total variance. The correlation results align with our PCA findings, and they also correspond closely to the outcomes of a study conducted in China (Pan et al., 2016 ). The outcomes of our examination using PCA and Spearman correlations suggest the existence of two main origins for heavy metals. The first source primarily included As, Cr, Pb, and Hg. Pb is largely a result of activities related to vehicles and traffic, as noted in research by Mathur et al. (2016) and Sabin et al. (2006). Furthermore, Cr originates from both vehicular and agricultural practices. The use of fungicides, pesticides, and fertilizers in agriculture is the primary source of As and Hg contamination. Thus, given the widespread application of fertilizers and the proximity of fields to roads in our study area, we can conclude that Pb, Cr, As, and Hg are pollutants originating from vehicle emissions and agricultural activities. The second source mainly consisted of Cd, Cu, and Zn. In our current study, we collected wheat samples with elevated levels of Cd, Cu, and Zn from fields located near oil fields. Previous research has established a strong connection between the increased presence of Cd, Cu, and Zn in soils and the presence of oil fields. Therefore, the second source can be attributed to human activities associated with the oil industry. Liu and colleagues introduced the spatial distribution-principal component analysis (SD-PCA) model, which integrates spatial soil pollution characteristics with linear data transformation using eigenvector-based PCA. Their results emphasized agriculture as the main driver of soil pollution (65.5%), resulting from the cumulative impacts of various heavy metals. Traffic and inherent origins constituted 17.9% and 11.1%, respectively. The model's capacity to monitor heavy metal pollution is valuable for evaluating and managing multisource soil contamination(Liu et al., 2023 ). Another study in Xiangfen County, China, revealed that soil sample levels were slightly greater than regional background levels, with Hg and Cd showing significant enrichment. Principal component and cluster analyses identified three main sources of these heavy metals, including agricultural practices, natural parent materials, and industrial activities (Pan et al., 2016 ). Table 2 Rotated factor structure of heavy metal data in wheat samples. Component Element 1 2 Cr 0.784 0.143 Cu 0.451 0.828 Zn − 0.015 0.897 As 0.798 0.416 Cd − 0.038 0.805 Hg 0.867 − 0.140 Pb 0.845 0.016 Eigenvalue 3.36 1.91 % Of Variance explained 48.00 27.32 % Of cumulative 48.00 75.33 Extraction method: principal component analysis. Rotation method: varimax with Kaiser normalization. Rotation converged in three iterations. Table 3 Outcomes of Principal Component Analysis. Component 1 2 % Of Variance 48.00 27.32 Cr 0.733 − 0.311 Cu 0.832 0.444 Zn 0.481 0.757 As 0.895 − 0.092 Cd 0.411 0.693 Hg 0.648 − 0.594 Pb 0.715 − 0.451 3.3. Spatial distribution of heavy metals in wheat samples The ordinary kriging interpolation method in ArcGIS Pro 3.0.2 software was utilized to analyze the spatial distribution of heavy metals in the wheat samples (Ghong et al., 2023 ). Geostatistical methods, with ordinary kriging being the most favored, are extensively used across different disciplines. The versatility of ordinary kriging, originally intended for mineral reserve estimation, has led to its widespread adoption in various fields (Zhang et al., 2021 ). The kriging interpolation method has broad applications in the disciplines of pedology, environmental studies, and the study of ecosystems, particularly when dealing with regional variables. Its characteristics make it well-suited for estimating and analyzing spatial variations in these fields (Dong et al., 2024 ). By leveraging spatial correlation variance, this method effectively interpolates data and aims to deliver the most accurate and unbiased estimates for unknown values within the study area (Zeng et al., 2023 ; Zhao et al., 2023 ). When certain conditions related to covariance are met, it provides the most accurate linear unbiased estimation for values at unsampled finite positions. This prediction is based on a variogram model established from the spatial arrangement and semivariance of the initial dataset (Peng et al., 2023 ). The kriging method calculates the value at a given point by determining a weighted mean of observed data points in the immediate vicinity. These weights are determined according to the spatial connection of the point to all the collected samples. (Wang et al., 2014 ). This approach utilizes a semivariogram, which is established based on sample characteristics, to assess how the spacing between sample data points influences the estimated attribute values, and the resulting estimator is expressed using Eq. 5. Z ∗ (S 0 ) = \(\:\sum\:_{\text{i}=1}^{n}{\lambda\:}_{i}\left({S}_{0}\right)Z\left({S}_{0i}\right)\:\) (5) Z (S 0i ) refers to the value assigned to the specific point S 0i , which represents a sample taken at location i. In this context, λ i (S 0 ) represents the standard kriging weight for location S 0 , and Z*(S 0 ) represents the approximated value at S 0 . The ideal kriging interpolation model should have a root mean square (RMS) and average squared error (ASE) close to 0 and a root mean square standardized error (RMSS) close to 1. These criteria ensure accurate and precise estimates with minimal bias and variability, making the model reliable for data interpolation in various applications. These principles serve as the key criteria for selecting the most appropriate interpolation model for a given application (Saha et al., 2023 ). At the same time, to distinguish between the dominant effects of inherent factors (natural influences) and stochastic factors (human influences) on spatial regional variation, the ratio of nugget value to sill value has also been regarded as a vital indicator. This index helps assess the level of spatial variability in regionalized variables (Zhao et al., 2023 ). The results of the fitted variogram models using various autocorrelation structures are shown in Table 4 . Figure 5 displays a series of maps depicting the spatial patterns of the estimated values of the heavy metals. Table 4 The results of the fitted variogram models using various auto-correlation structures Element Fitting models Nugget Range Partial sill RMS MS RMSS ASE Cr Exponential 0.09 0.20 0.32 0.06 -0.09 1.00 0.08 Cu Gaussian 0.00 0.13 0.04 1.20 0.00 0.99 1.21 Zn Gaussian 0.00 0.06 0.03 6.77 -0.01 1.01 6.30 As Gaussian 0.02 0.06 0.35 0.05 -0.13 1.01 0.06 Cd Exponential 0.02 0.09 0.27 0.00 -0.08 0.98 0.00 Hg Exponential 0.02 0.41 0.26 0.02 -0.05 1.16 0.02 Pb Gaussian 0.10 0.06 0.29 0.65 -0.18 1.17 0.56 The highest levels of the metals Cd, Zn, and Cu were detected within the northeastern and eastern portions of the study region. A notable point is the presence of the Ramin power plant and the Maroun oil field in these regions. Oil fields have the potential to influence the dispersion of heavy metals in soil due to the process of extracting crude oil, leading to the concurrent pollution of the soil with both petroleum and heavy metals. Prior research suggests that the concentrations of heavy metals such as Cu, Zn, and Cd in soils contaminated by oil tend to increase as oil well operations continue, highlighting that human activities intensify the presence of these metals in oil fields. Among the elements that can detrimentally impact the soil environment in oil-polluted areas, Cd is identified as the most unstable, accessible, and harmful heavy metal, with a lower potential for mobility than Zn and Cu (Fu et al., 2014 ). Industrial emissions also contribute to higher heavy metal concentrations, as exemplified by cadmium levels exceeding permissible limits around the Ramin power plant in Ahvaz (Mostafaii et al., 2021 ). A study in Sehwan Sharif, Jamshoro, analyzed heavy metal concentrations in topsoil samples and grouped elements. Cd, Ni, As, Cr, Pb, and Zn were linked to human activities, while Fe, B, Mn, and Cu were natural spatial maps that indicated metal hotspots, particularly high Zn levels in the city center, attributed to traffic emissions (Bux et al., 2023 ). In our study, the highest levels of Pb were measured in the southeast and west, and the highest levels of Cr were measured in the southeast. In the vicinity of highways, the soil in this study was subjected to the presence of harmful heavy metals originating from vehicle exhaust emissions and a variety of other waste materials associated with transportation. Lead is the predominant heavy metal discharged from road traffic, comprising a minimum of 90% of the total metals in runoff from roads. Additionally, chromium (Cr) is detected in both road runoff and vehicle exhaust emissions (Gao et al., 2022 ; Nawrot et al., 2020 ; Pan et al., 2016 ). The highest levels of Hg were measured in the southwest and the northern, eastern, and southwestern regions. The use of pesticides may play a role in contaminating agricultural soil with heavy metals. Some pesticides include heavy metal components, notably arsenic and lead. Heavy metals such as Hg, As, and Cr can come from various sources in agriculture, including fertilizers, pesticides, livestock manure, and wastewater. Large quantities of fertilizers and pesticides are used in wheat farming within this locality (Ahmadi et al., 2019 ; Alengebawy et al., 2021 ). A study near Pb‒Zn and Au mines assessed heavy metal contamination in soil, revealing elevated levels of As and Pb, exceeding Vietnam's residential soil standards. The pollution indices revealed Pb and As as the main contaminants, while the statistical analysis suggested a combination of human and natural sources (Tran et al., 2022 ). Cereals such as wheat naturally gather heavy metals from a range of sources, including soil, water, and the atmosphere. Nevertheless, the soil primarily plays the leading role in contaminating these plants with heavy metals. In times of drought, substantial amounts of heavy metals stemming from vehicular actions tend to accumulate on roads and intersections. As a result, these contaminants are carried into the soil environment by stormwater runoff, resulting in elevated levels of heavy metals within the soil (Liu et al., 2023 ). Furthermore, the presence of fossil fuel resources and the expansion of the oil and gas industries in Ahvaz and neighboring regions significantly impact atmospheric conditions, affecting the transport of heavy metal pollutants (Darvishi Khatooni, 2017 ). Dust storms in Ahvaz transport heavy metals and other pollutants from both local and foreign sources, becoming a major environmental challenge. Prevailing winds in the region, such as winds from Iraq, transport dust and soil containing heavy metals, contributing to soil and agricultural pollution. The Karun River, which serves as the primary water source in Ahvaz, is crucial for providing water to support agriculture in the area. Current studies indicate that the influx of urban and industrial sewage has led to an increase in pollution in this river, leading to heightened concentrations of heavy metals in crops that are watered with this water. The extensive data analysis, including statistics, correlations, and PCA, revealed congruity among the results. Additionally, these outcomes harmonize effectively with the spatial maps produced. 3.3. Health risk assessment of wheat consumption The mean EDI, HQ, and HI values of the heavy metals related to noncarcinogenic human health risks due to wheat consumption are presented in Table 5 for both adults and children. The results indicate that the mean EDI for adults was less than that for children. According to Table 3 , the order of HQ values in both children and adults is as follows: Hg > As > Pb > Cu > Zn > Cr > Cd. HQ values higher than 1 were found for Hg, As, Pb, and Cu among children. Conversely, in adults, only Hg and As had HQ values greater than 1. The hazard index (HI) was greater than 1 for both adults and children. This implies that prolonged wheat consumption has exposed the inhabitants of the research area to a notable potential noncarcinogenic health hazard. Furthermore, concerning Table 6 , the outcomes of the evaluation regarding cancer risk stemming from the consumption of heavy metal-contaminated wheat indicated that the carcinogenic risk exceeded the acceptable threshold at specific sampling sites. Cr (adults: 0.000236; children: 0.00038), followed by As (adults: 0.000494; children: 0.00076), had the greatest potential carcinogenic risk. The ILCR associated with Pb exceeded 10 − 4 in certain locations (adults: 6 sites; children: 10 sites). Conversely, the ILCR linked to cadmium reached only 10 − 4 in a single instance, exclusively concerning children. Table 5 EDI, HQ, and HI mean values of the heavy metals related to noncarcinogenic human health risks due to wheat consumption Element EDI (mg/kg.day) HQ HI=ƩHQ Children Adults Children Adults Children Adults Cr 0.001 0.000 0.249 0.160 7.702 4.964 Cu 0.082 0.026 1.326 0.855 Zn 0.402 0.129 0.865 0.557 As 0.001 0.003 1.693 1.091 Cd 0.000 0.000 0.062 0.040 Hg 0.000 0.000 2.141 1.380 Pb 0.008 0.003 1.367 0.881 Table 6 Incremental lifetime cancer risk (ILCR) of heavy metals related to carcinogenic human health risks due to wheat consumption Element ILCR Children Adults Cr 0.00038 0.000236 Cd 0.000002 0.000 As 0.00076 0.000494 Pb 0.000032 0.000014 Our findings are consistent with the conclusions reached by Zheng et al., Zafarzadeh et al., Gruszecka-Kosowska et al., Setia et al. and Dehghani et al. (Dehghani et al., 2017 ; Gruszecka-Kosowska, 2020 ; Setia et al., 2023 ; Zheng et al., 2020 ). In contrast, Huang et al. revealed that HQ derived from wheat consumption was less than one across all age categories (Huang et al., 2008 ). A study by Mansouri Moghadam et al. (Mansouri Moghadam et al., 2022 ) examined the health risks associated with heavy metals in wheat samples collected from farms in the northern regions of Ahvaz. The HQ values for cadmium, lead, nickel, chromium, and manganese exceeded 1 for both age groups, while the HQ values for copper, iron, and cobalt were less than 1. Additionally, the ILCRs for toxic metals such as cadmium, lead, nickel, and chromium exceeded 10 − 4 . Shi and colleagues studied the variations in heavy metal levels in Chinese soil over time and across different locations, with a particular focus on pollution assessment and risk analysis. Their investigation revealed that regions where LCR values exceeded 1 × 10 − 4 represented 1.35% for adult females, 1.26% across all the evaluated areas for children, and 0.80% for adult males (Shi et al., 2023 ). A study in Punjab, India, investigated heavy metal (HM) contamination in soil and wheat grain samples near the Sutlej River. High levels of Cd and Pb pose health risks, with Cd having carcinogenic potential (Setia et al., 2023 ). Mercury vapor has a significant impact on sensory, cognitive, and motor functions. Methylmercury, which is more toxic than standard mercury, disrupts the development of the peripheral nervous system in newborns. Overexposure to methylmercury leads to Minamata disease, and skin absorption is also possible. People can come into contact with mercury by inhaling elemental mercury vapor in occupational settings, consuming seafood, engaging in environmental activities, having dental amalgam fillings, and encountering organic mercury compounds (Kumari & Bhattacharya, 2023 ). Both arsenite and arsenate are effectively absorbed via both oral ingestion and inhalation routes. Once arsenic is taken into the body, arsenate is converted to arsenite, leading to the presence of both As(III) and As(V) in the bloodstream(Yüksel et al., 2018 ). Increased exposure to Cr(VI) in children can result in a range of health problems, including periodontitis, stomatitis, peptic ulcers, and nasal bone perforation. The absorption of orally ingested Cr is constrained to below ten percent in the gastrointestinal tract, while more soluble compounds display heightened absorption rates. Cr can partially infiltrate human skin, especially when the integrity of the skin is compromised (Kumari & Bhattacharya, 2023 ). Pb is the primary source of noncarcinogenic risk, and As poses a notable carcinogenic danger. Soil specimens collected from mining and industrial regions, which encompass electronic manufacturing facilities and locations for e-waste disassembly, exhibit substantial pollution. This situation heightens potential hazards to both the environment and human well-being (Shi et al., 2023 ). However, the HI values consistently tended to be greater in children than in adults. This pattern highlights the heightened vulnerability of children to contamination of wheat by heavy metals. This increased susceptibility can be attributed to their lower body weight and reduced physical resilience (Wang et al., 2023 ). Research findings demonstrate that the greatest increases in the levels of heavy metals, such as lead, copper, and cadmium, were detected within the soils of the eastern and northeastern sections of Khuzestan. Human activities are the primary sources responsible for the presence of these pollutants. Heavy metals found in agricultural soils adjacent to roads originate from emissions produced by vehicles (Chen et al., 2010 ; Nduka et al., 2023 ). Investigations indicate that metals such as lead, copper, and cadmium infiltrate the soil through mechanisms such as erosion from vehicle brakes, tire abrasion, oil leaks, and cylinder head washers (Petukhov et al., 2023 ). Moreover, these metals have been applied in various fields, including pigments, fungicides, batteries, and alloys such as bronze and brass. Fertilizers, particularly those derived from animals, contribute to the accumulation of these compounds within soils and the subsequent contamination of plants (He et al., 2023 ). Additionally, the proximity to the Ramin power plant may lead to increased concentrations of these elements in the surrounding region. Overall, the outcomes of evaluating the health risks associated with heavy metals in wheat samples suggested that consuming this product might lead to negative and acute effects for individuals. The unregulated and prolonged use of agricultural inputs, coupled with the establishment of industrial facilities in close vicinity to agricultural lands, the indiscriminate deployment of chemical fertilizers, the utilization of sewage sludge as fertilizers, the cultivation of wheat in proximity to high-traffic roads, and the adoption of urban sewage for irrigation, collectively pose a potential risk for contaminating wheat crops. This subsequent contamination has the potential to exert ramifications on derivative products such as bread. The resulting scenario could have enduring health implications for consumers. Hence, it is prudent to maintain a consistent regimen of monitoring food commodities with respect to their levels of heavy metals and residues of chemical compounds to maintain the integrity of food safety protocols. 4. Conclusions This study focused on the presence of heavy metals (HMs) in wheat grains in southern Iranian oil fields, shedding light on both the distribution patterns of these metals and the potential health hazards they pose. Zinc had the highest average concentration among the metals detected in wheat grains, while cadmium had the lowest concentration. The assessment of health risks revealed that specific regions were at risk due to elevated levels of certain heavy metals. Notably, the lead and mercury concentrations in wheat exceeded the values recommended by the FAO/WHO, with mercury showing the highest health risk quotient for both adults and children. This study emphasized arsenic and chromium as the most significant carcinogenic heavy metals in this particular context. Through the use of ordinary kriging, the study identified the eastern, northeastern, and southwestern parts of the study area as hotspots for elevated heavy metal concentrations, largely attributed to human activities. The contamination of wheat grains in Ahvaz can be attributed to various factors, including oil fields, transportation systems, unregulated use of agricultural inputs, fertilizers, and irrigation with polluted water. Recognizing the paramount role of bread in the Iranian diet, urgent and earnest attention is warranted to address the heightened heavy metal levels in wheat grains. This novel approach to spatial analysis and comprehensive health risk assessment of heavy metals in wheat grains provides valuable insights for policymakers and regulatory authorities to formulate effective strategies aimed at ensuring food safety and public health. Declarations Acknowledgments This article was extracted from the MSc thesis of Roza Aibaghi, and the authors are grateful to Ahvaz Jundishapur University of Medical Sciences for funding and providing the necessary facilities to perform this research (Grant No. ETRC-0017). Funding This work was supported by the Ahvaz Jundishapur University of Medical Sciences under Grant No. ETRC-0017. Data availability The data that support the findings of this study are available from the corresponding author, upon reasonable request. Authors Contributions Y.T.B. and S.J. jointly contributed to conceptualization, methodological design, result validation, and manuscript review. R.A. led the investigation and wrote the primary manuscript draft. N.J. validated results and provided supervisory guidance. E.M. conducted essential statistical analyses for the research. N.T. contributed to improving the study’s spatial aspects using ArcGIS Pro software. 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Environmental Science & Technology . Zheng, S., Wang, Q., Yuan, Y., & Sun, W. (2020). Human health risk assessment of heavy metals in soil and food crops in the Pearl River Delta urban agglomeration of China. Food chemistry , 316 , 126213. Zhou, L., Yang, B., Xue, N., Li, F., Seip, H. M., Cong, X., Yan, Y., Liu, B., Han, B., & Li, H. (2014). Ecological risks and potential sources of heavy metals in agricultural soils from Huanghuai Plain, China. Environmental Science and Pollution Research , 21 , 1360-1369. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png GRAPHICAL ABSTRACT 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. 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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-6470086","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459087612,"identity":"918a375c-d728-4787-8c15-feeb829cd288","order_by":0,"name":"Roza Aibaghi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Roza","middleName":"","lastName":"Aibaghi","suffix":""},{"id":459087613,"identity":"4c2bc173-2550-4b7c-8082-6f1fd61985be","order_by":1,"name":"Nastaran Talepour","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nastaran","middleName":"","lastName":"Talepour","suffix":""},{"id":459087614,"identity":"b56642a2-a9b1-4a3e-864e-5f26e8e86022","order_by":2,"name":"Sahand Jorfi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sahand","middleName":"","lastName":"Jorfi","suffix":""},{"id":459087615,"identity":"527cf227-febb-4e4c-898b-a8f5293a199c","order_by":3,"name":"Neamatollah Jaafarzadeh","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Neamatollah","middleName":"","lastName":"Jaafarzadeh","suffix":""},{"id":459087618,"identity":"7910aec1-8a45-40ba-a21f-46cbfb37bf05","order_by":4,"name":"Elham Maraghi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elham","middleName":"","lastName":"Maraghi","suffix":""},{"id":459087620,"identity":"dc12a157-1547-4aee-9380-71a1a394444a","order_by":5,"name":"Yaser Tahmasebi Birgani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYFACHjCZwAbh2QAxY+MB/BpQtaSBtDQQpwXKPwwm8WqxZz97dMOPPzZ5fBLJjz983HPebm37YaAtNTbROG3hyUu72duWVswmkWYmOePZ7eRtZxKBWo6l5TbgdFiO2Q3ehsOJbRIJZsw8B24nmx0AamFsOIxbC/8bs5t//vwHakn//PnPgXPJZucfEtAikWN2m4ftAFBLjoE0w4EDdmY3CNly443Zbdm25GI2njdlkj0HkhPMbgBtScDjF/b+HLObb/7Y5cm3p2/+8OOAnb3Z+fSHDz7U2ODUggACCWAqEawygaByEOA/AKbsiVI8CkbBKBgFIwoAANCFZ2DvghkOAAAAAElFTkSuQmCC","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yaser","middleName":"Tahmasebi","lastName":"Birgani","suffix":""}],"badges":[],"createdAt":"2025-04-17 09:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6470086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6470086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83292913,"identity":"f209ecde-33eb-4ea8-85fe-5daf8e1ae0c7","added_by":"auto","created_at":"2025-05-22 13:21:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10681355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy area and sampling pointlocations.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/1b09f20db550f241d6671492.png"},{"id":83294153,"identity":"576b3454-af70-48e8-9e73-a4cf944dcd82","added_by":"auto","created_at":"2025-05-22 13:29:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":996362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDemonstration of the research methodology\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/4890f2bb19e40b61a9a2d447.png"},{"id":83292904,"identity":"4a25ed60-d711-4171-9f85-8bf047af2dab","added_by":"auto","created_at":"2025-05-22 13:21:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":239848,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBox and whisker plot of heavy metals concentration in wheat grains\u003c/strong\u003e \u003cstrong\u003eand INSO \u0026amp; FAO/WHO MPC.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/ed9ffad6ff8eb6992854e996.png"},{"id":83292912,"identity":"a17d3b3f-dc29-4a2e-96ff-22c6ffd1bd09","added_by":"auto","created_at":"2025-05-22 13:21:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":170064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Spearman’s correlation matrix for heavy metal concentrations; (B) principal component analysis of heavy metals in wheat samples.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/121949c0c5eda7d690d0aec7.png"},{"id":83292905,"identity":"66f94aaf-6363-4952-8e45-95535eece35d","added_by":"auto","created_at":"2025-05-22 13:21:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1226079,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distributions of HMs in wheat grain.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/efa30a1dfe78b0a191c113e0.png"},{"id":85844866,"identity":"fc17d7cc-adde-4415-9e46-a976f273e600","added_by":"auto","created_at":"2025-07-02 09:32:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14360273,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/b655bffa-1e19-4b88-a130-6c7bdf4cba63.pdf"},{"id":83292902,"identity":"64110fe5-2096-4933-868f-4130e3f2dd01","added_by":"auto","created_at":"2025-05-22 13:21:44","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":219913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGRAPHICAL ABSTRACT\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"GraphicalAbstract.png","url":"https://assets-eu.researchsquare.com/files/rs-6470086/v1/40a492612c61d46fca049eae.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heavy Metal Contamination in Wheat Grains: Spatial Analysis and Health Risk Assessment in Southern Iran","fulltext":[{"header":"HIGHLIGHTS","content":"\u003cul\u003e\n \u003cli\u003eThePb and Hg concentrations in wheat exceeded the FAO/WHO MPC values.\u003c/li\u003e\n \u003cli\u003eHg exhibited the highest health quotient for both adults and children.\u003c/li\u003e\n \u003cli\u003eCr, followed by As, emerged as the primary carcinogenic heavy metal.\u003c/li\u003e\n \u003cli\u003eaccounted for 48% of the total variance (attributed to As, Cr, Pb, and Hg), while the secondary factor explained 27.32% (associated with Cd, Zn, and Cu).\u003c/li\u003e\n \u003cli\u003eThe greatest abundance of HMs occurred on farms in the eastern, northeastern, and southwestern regions.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eIn the contemporary period, chemical pollutants pose risks to environmental and human health, and heavy metals (HMs) play a significant role in contributing to this concern (Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Heavy metals are metals with high density (atomic mass greater than 8.55 grams per mole or volumetric mass over 5 grams per cubic centimeter) and exhibit metallic properties at room temperature (Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). From an ecological perspective, heavy metals are elements with a strong tendency to be absorbed by living tissues, leading to their accumulation and difficulty in being released from those tissues (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In nature, heavy metals exist in limited amounts in the Earth's crust. Human exposure to these metals occurs through ingestion, inhalation, and dietary intake. Heavy metals can naturally occur in food or be introduced through human activities (Kumari \u0026amp; Bhattacharya, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), including mining or industrial operations, along with incorrect application of heavy metal-enriched substances in agriculture, such as chemical fertilizers, pesticides, industrial discharge, sewage sludge, and wastewater irrigation (Kumari \u0026amp; Bhattacharya, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Notably, heavy metals do not undergo metabolic transformation in the human body. However, once inside the body, they accumulate in tissues, leading to various illnesses and adverse effects(Setia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Exposure to different heavy metals can lead to a broad spectrum of adverse health consequences. Exposure to lead (Pb) is linked to health issues concerning the central nervous system, cardiovascular system kidneys, and fertility. Cadmium (Cd) poisoning can result in harm to the kidneys, bones, lungs, and liver and may even contribute to the development of cancer. Mercury (Hg) can lead to significant neurological issues in both children and adults. Exposure to chromium (Cr) is associated with nasal irritation, ulceration, skin problems, and eardrum perforation. Prolonged exposure to arsenic (As) is connected to skin lesions, skin cancer, and a range of neurological, respiratory, cardiovascular, and developmental issues (Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Acute exposure to nickel (Ni) can cause harm to the kidneys, liver, and brain, and continuous exposure can harm the body's tissues. Additionally, Ni predominantly impacts the skin, causing the emergence of pimples, herpes, and erythema. Moreover, it is linked to a greater risk of cancer, neurological disorders, systemic disorders, and reduced fertility. Chronic toxicity from elevated levels of copper (Cu), a red-brown metal, can manifest in various health issues, including neurological weakness, metabolic disorders, cell carcinogenesis, and brain tissue lesions. Furthermore, it can lead to respiratory problems, dizziness, nausea, and diarrhea (Y. Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Food constitutes the primary source of human exposure to toxic elements, which can potentially induce significant effects on public health, encompassing both carcinogenic and noncarcinogenic impacts(H. Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e-p. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hence, the Food and Agriculture Organization (FAO), the World Health Organization (WHO), the United States Environmental Protection Agency (US EPA), and other regulatory bodies worldwide closely monitor and enforce strict regulations on toxic heavy metal levels in food to ensure compliance with acceptable concentrations, thus safeguarding public health and safety (Bojago et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Heavy metals are known to present potential health risks, necessitating their evaluation through carcinogenic or noncarcinogenic assessment methods. In 1989, the United States Environmental Protection Agency (US EPA) implemented noncancer risk evaluation techniques, notably the hazard quotient (HQ) and hazard index (HI). Recent use of these methods has substantiated their effectiveness and reliability in gauging the health consequences attributed to heavy metal exposure(Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Y.-M. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Setia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accurate spatial mapping of heavy metal distributions is essential for conducting thorough pollution and health risk assessments(Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Whenever conducting locational interpolation of heavy metals, researchers have a range of methods at their disposal, including geostatistical kriging methods, inverse distance weighting (IDW), global polynomials, multiple linear regression, geographically weighted regression (GWR), neural networks, and regression kriging. These techniques provide different ways to estimate heavy metal concentrations at unsampled locations within a study area based on the available data and locational patterns. The selection of the method is influenced by the particular attributes of the dataset and the goals of the research (H. Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Precise spatial maps play a pivotal role in making well-informed decisions and devising effective policies concerning environmental management and conservation endeavors (Xiang et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) plays a significant role as a primary cereal crop, making up 50% of the daily calorie consumption and contributing to more than 20% of the daily intake of zinc (Zn) for people living in developing countries (Y.-M. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite comprising only approximately 1% of the world's population, Iran consumes approximately 2.5% of the total global wheat production (Hashemi Nejad et al. 2020). Wheat plays a crucial and strategic role in Iran's agriculture due to favorable climatic conditions, leading to widespread cultivation across the country's provinces (Ahmed et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Ahvaz, located within southern Iranian oil fields, has experienced substantial industrial growth in recent years. This expansion has been marked by the emergence of various industries in the city's vicinity. The discharge of industrial effluents into the Karun River, coupled with the extensive use of fertilizers and pesticides, has raised environmental concerns. The coalescence of industrialization and agricultural practices has resulted in notable heavy metal pollution, necessitating additional scrutiny. This inquiry was prompted by a significant concern regarding the safety of agricultural products intended for human consumption, coupled with the acknowledgment of wheat's central role in providing nourishment to households. The principal aim of this investigation was to perform a detailed examination of the occurrence and concentrations of heavy metal pollutants (including Hg, Cu, Cr, Pb, As, Ni, Zn, and Cd) in wheat grains cultivated in southern Iranian oil fields. Moreover, this study aimed to assess how heavy metals were distributed across wheat grains in terms of their spatial arrangement and to discern plausible sources of contamination. Moreover, comprehensive assessments of potential health risks, encompassing noncancerous and cancer-associated ailments, were conducted to thoroughly understand the potential outcomes of these pollutants. The results of our research, especially in areas abundant in oil, offer valuable knowledge for decision-makers, assisting in the creation of efficient plans to reduce the dangers of heavy metal pollution in agricultural products and ensuring the safety of the environment and public health. By comprehending the geographical spread of heavy metals in these regions, policymakers can enforce specific rules and actions to protect food safety and alleviate the health consequences for nearby societies.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area and sampling\u003c/h2\u003e \u003cp\u003eDuring the wheat harvesting season in May 2022, a nonrandom sampling procedure was conducted at the Silo Rajaji laboratory in Ahvaz city, which is located within southern Iranian oil fields. The samples (N\u0026thinsp;=\u0026thinsp;50) were gathered from wheat harvested in the surrounding areas of Ahvaz, primarily for testing, and then stored in the silo. Ahvaz, the capital of Khuzestan Province, is located at a latitude of 31\u0026deg;20' N and a longitude of 48\u0026deg;40' E, with an elevation of 18 m above sea level in the plains of Khuzestan. Ahlaz Silo, which is situated on the western bank of the Karun River, is a crucial and strategic wheat storage facility for the country. It plays a vital role in wheat exports to neighboring countries, making it highly important for the southwest region of Iran (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Sampling analysis\u003c/h2\u003e \u003cp\u003eIn the laboratory, the grain samples were washed, dried at 60\u0026ndash;65\u0026deg;C, and weighed. Subsequently, the wheat grains were ground using a stainless steel mill. Then, a 2-gram portion of the ground sample was mixed with a 1:4 ratio of a special digestion solution containing 70% nitric acid (HNO3) and 65% hydrochloric acid (HCl) in dedicated digestion tubes. The samples underwent a two-stage digestion process, starting at 40\u0026deg;C for 1 hour and subsequently at 140\u0026deg;C for 3 hours, resulting in a clear solution. After cooling, the samples were sieved through 42-\u0026micro;m Whatman filter paper to achieve smoothness. Next, the samples were brought to a volume of 25 ml using distilled water and then stored in a refrigerator until analysis. The concentrations of zinc, arsenic, mercury, chromium, lead, copper, and cadmium in the samples were established through inductively coupled plasma‒mass spectrometry (ICP-MS).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Health risk assessment of wheat consumption\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Noncancerous health risk assessment of wheat consumption\u003c/h2\u003e \u003cp\u003eThe risk of noncancerous diseases in individuals was assessed using the formula provided by the United States Environmental Protection Agency (USEPA) (Naz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zafarzadeh et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To do this, the estimated daily intake (EDI, mg/kg. day) was initially calculated using Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:EDI=\\:\\frac{C\\times\\:IR\\times\\:ED\\times\\:EF\\times\\:CF}{BW\\times\\:AT}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere C is the concentration of heavy metals in the wheat grains (mg/kg); IR is the wheat grain ingestion rate (Adults: 388, children: 129, g/person/day); ED is the exposure duration (Adult: 24, children: 6, years); EF is the frequency of exposure 365 days/year; CF is a factor (0.01 g/kg); BW is the average body weight (Adult: 70, children: 15, kg); and AT is the average exposure time (Adult: 24, Children: 6, days)(Huang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Naz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subsequently, the probability of noncancer risk was calculated using the hazard quotient (HQ) through Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:HQ=\\frac{EDI}{RfD}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe RfD represents the reference dose, which represents the recommended daily intake of a specific metal measured (mg/kg body weight/day). The value is carefully established to ensure a safe and health-promoting intake range. An HQ value below one signifies that consuming wheat does not cause immediate harmful effects on health. Conversely, if the HQ is equal to or greater than one, there is a likelihood of risk associated with exposure to wheat(EPA, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Y.-M. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Naz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Assessment of the cancer risk of wheat consumption\u003c/h2\u003e \u003cp\u003eThe computation of carcinogenic risk was performed using Eq.\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eILCR\u0026thinsp;=\u0026thinsp;EDI\u0026times;CSF (3)\u003c/p\u003e \u003cp\u003eIn this equation, ILCR represents the incremental lifetime carcinogenic risk associated with single heavy metals, and CSF represents the cancer slope factor (Y. Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rahmani et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zafarzadeh et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If the incremental lifetime cancer risk (ILCR) is less than 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, the carcinogenic risk associated with the consumption of water and food is considered to have a negligible impact on human health. However, if the ILCR value exceeds this range, it indicates an elevated probability of risk and an increased likelihood of cancer incidence in the human population. The acceptable levels of carcinogenic risk for hazardous pollutants to human health are categorized as low (1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), moderate (1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and very hazardous (1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), suggesting that among 10,000 individuals, one person is likely to develop cancer at the respective risk level(Agency, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; EPA, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). According to the International Agency for Research on Cancer (IARC) report and similar studies, heavy metals, cadmium, lead, chromium, and arsenic are considered to have carcinogenic effects(R.-p. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Y.-M. Liu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the carcinogenic risk for these elements was calculated. The CSF values utilized in this research are as follows: Cr: 0.5, As: 1.5, Cd: 0.38, and Pb: 0.0085, all expressed in units of (mg/kg/day) \u003csup\u003e\u0026minus;1\u003c/sup\u003e (Y. Cui et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zafarzadeh et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Total health risk assessment of wheat consumption\u003c/h2\u003e \u003cp\u003eThe total hazard index (HI) is calculated by summing the probabilities of exposure to individual elements within each age group (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). HI values below one indicate no adverse health effects from the consumption of the food substance, while values above one suggest potential health risks (Naz et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shokri et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{H}\\text{I}=\\sum\\:\\text{H}\\text{Q}=\\:{\\text{H}\\text{Q}}_{\\text{P}\\text{b}}+\\:{\\text{H}\\text{Q}}_{\\text{A}\\text{s}}+\\:{\\text{H}\\text{Q}}_{\\text{C}\\text{d}}+\\:{\\text{H}\\text{Q}}_{\\text{H}\\text{g}}+{\\text{H}\\text{Q}}_{\\text{C}\\text{u}}+\\:{\\text{H}\\text{Q}}_{\\text{Z}\\text{n}}+\\:{\\text{H}\\text{Q}}_{\\text{C}\\text{r}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eExcel 2013 and SPSS version 26 were used for graph plotting and processing the descriptive statistics data, respectively. The Shapiro‒Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was used to examine the normality of the data, and the Spearman correlation test was used to investigate the relationships between the average concentrations of elements in the samples. Principal component analysis (PCA) was utilized to study the origin of heavy metals in wheat grains by efficiently pinpointing their source in different agricultural areas with the assistance of MATLAB 2022b (Cai et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To map the spatial distribution of heavy metal concentrations in wheat, we used the ordinary kriging geostatistical method within ArcGIS Pro 3.0.2 software(Fu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study presents the following technical methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Statistical description of heavy metal contents in wheat samples\u003c/h2\u003e \u003cp\u003eA box and whisker plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) shows the heavy metal concentrations in the wheat grains. The values and descriptive statistics of the heavy metal concentrations in the wheat grains are shown in Table\u0026nbsp;1. According to the results obtained, the heavy metal concentrations in the wheat grains decreased in the order Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Hg\u0026thinsp;\u0026gt;\u0026thinsp;Cd, with mean values of 30.17, 6.2, 0.63, 0.087, 0.06, 0.03 and 0.007 mg/kg, respectively. The Iranian National Standard Organization (INSO) has issued guidelines for HMs in wheat grains, specifically focusing on Pb and Cd. Hence, this study involved comparing HMs in wheat grains against INSO's Pb and Cd standards, as well as the standards established by the Codex Alimentarius Commission (FAO/WHO) for all HMs. Zn (30.17 mg/kg) and Cu (6.20 mg/kg) exhibited the maximum quantity, while Cd (0.007 mg/kg) showed the minimum quantity in the wheat grains. The study revealed that Zn and Cu had significantly higher concentrations in the grain sample. These two elements also exhibited the highest concentrations in the present study. The significant presence of Zn in wheat was attributed to its high concentration in the soil (Jia et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The concentrations of Zn, Cu, Cr, and Cd in all samples were found to be below the maximum permissible concentrations (MPCs) set by the FAO/WHO (Commission, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which are 99.4 mg/kg for Zn, 73.3 mg/kg for Cu, 2.3 mg/kg for Cr, and 0.2 mg/kg for Cd, as well as below the INSO ((INSO), \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) standards for Cd, which is 0.06 mg/kg. However, this study revealed that the levels of Pb in wheat exceeded both the INSO (INSO, 2021) and FAO/WHO (Commission, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) MPC values, which were 0.2 mg/kg and 0.3 mg/kg, respectively, except for two samples. The Hg concentration in 44 wheat grain samples was found to be equal to or greater than the MPC (0.01 mg/kg) given by the FAO/WHO. Remarkably, while the transfer of Hg from soil to plants is not straightforward, plants can nonetheless uptake Hg from both the soil and airborne particles. This suggests that aerial deposition of Hg might be responsible for the increased Hg levels observed in the grains(Jia et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, in 6 samples, the As concentration reached or exceeded the MPC of 0.1 mg/kg given by the FAO/WHO (Ahmad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Commission, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mengistu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Shapiro‒Wilk test revealed that the heavy metal distribution was nonnormal (P\u0026thinsp;\u0026le;\u0026thinsp;0.05) (Zhou et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, data normalization through logarithmic transformation was performed before utilizing the kriging method. Skewness and kurtosis were used to assess the asymmetry of the real-valued probability distribution. Positive skewness, indicated by a skewness value greater than zero, signifies that more values are concentrated on the right side of the symmetry axis. However, kurtosis measures the steepness of the distribution pattern. The coefficient of variation (CV) functions as a metric that reveals the extent of scattering. In this research, its use aimed to assess the diversity in heavy metal concentrations and the degree of distinction among the sampled locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cp\u003eTable\u0026nbsp;1. Descriptive statistics of heavy metal concentrations in wheat grains.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCount\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMin(mg/Kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMax(mg/Kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eMedian(mg/Kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMean(mg/Kg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eC.V %\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eSkewness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003eKurtosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e45.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e109.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHuman activities significantly contribute to soil pollution in wheat fields in Ahvaz. The main factors include oil fields, proximity to roads and highways, the extensive use of chemical and animal fertilizers, and the use of pesticides and herbicides (Afkhami et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Alengebawy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karbassi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Radziemska \u0026amp; Fronczyk, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Given that the primary water source for wheat fields in the present study, where heavy metal concentrations are high, is the Karun River or its related water channels, which have been contaminated by the discharge of municipal and industrial wastewater, irrigation water might also play a role in the elevated levels of heavy metals in the soil, ultimately resulting in the buildup of these contaminants in wheat grains. (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rizvi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Potential sources of heavy metal pollution in wheat samples\u003c/h2\u003e \u003cp\u003eThe Spearman correlation coefficient technique and principal component analysis were utilized to evaluate the connections and potential origins of heavy metals (El Behairy et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For the effective management of soil heavy metal contamination, the qualitative recognition of pollution origins and the allocation of sources are of pivotal significance. Approaches to pinpointing these origins include the use of geostatistical models in conjunction with multivariate statistical methods such as principal component analysis (PCA). PCA, which operates as a linear approach for diminishing dimensionality, reconfigures a range of environmental variables into distinct origins, utilizing a constrained set of principal components (Lv, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the Spearman correlation matrix for the heavy metal concentrations and the PCA of the heavy metals in the wheat samples. The Spearman correlation coefficients revealed strong correlations between certain metals. Specifically, Cr, Cu, As, Hg, and Pb are closely linked (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Additionally, Cu, Zn, As, Cd, and Pb also had significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, As, Hg, and Pb exhibited strong relationships (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Importantly, Pb had a somewhat weaker correlation with Hg (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Zn had a weaker correlation with Cd (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These connections between the metals could be due to common sources of pollution. The outcomes of the PCA are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These results revealed the presence of two eigenvalues greater than one, which, when combined, explained 75.32% of the total variance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The primary component accounts for a significant portion, precisely 48%, of the total variance and exhibits strong loadings for As, Cr, Pb, and Hg. Conversely, the secondary factor, characterized by the dominance of Cd, Zn, and Cu, contributed 27.32% of the total variance. The correlation results align with our PCA findings, and they also correspond closely to the outcomes of a study conducted in China (Pan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The outcomes of our examination using PCA and Spearman correlations suggest the existence of two main origins for heavy metals. The first source primarily included As, Cr, Pb, and Hg. Pb is largely a result of activities related to vehicles and traffic, as noted in research by Mathur et al. (2016) and Sabin et al. (2006). Furthermore, Cr originates from both vehicular and agricultural practices. The use of fungicides, pesticides, and fertilizers in agriculture is the primary source of As and Hg contamination. Thus, given the widespread application of fertilizers and the proximity of fields to roads in our study area, we can conclude that Pb, Cr, As, and Hg are pollutants originating from vehicle emissions and agricultural activities. The second source mainly consisted of Cd, Cu, and Zn. In our current study, we collected wheat samples with elevated levels of Cd, Cu, and Zn from fields located near oil fields. Previous research has established a strong connection between the increased presence of Cd, Cu, and Zn in soils and the presence of oil fields. Therefore, the second source can be attributed to human activities associated with the oil industry. Liu and colleagues introduced the spatial distribution-principal component analysis (SD-PCA) model, which integrates spatial soil pollution characteristics with linear data transformation using eigenvector-based PCA. Their results emphasized agriculture as the main driver of soil pollution (65.5%), resulting from the cumulative impacts of various heavy metals. Traffic and inherent origins constituted 17.9% and 11.1%, respectively. The model's capacity to monitor heavy metal pollution is valuable for evaluating and managing multisource soil contamination(Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Another study in Xiangfen County, China, revealed that soil sample levels were slightly greater than regional background levels, with Hg and Cd showing significant enrichment. Principal component and cluster analyses identified three main sources of these heavy metals, including agricultural practices, natural parent materials, and industrial activities (Pan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRotated factor structure of heavy metal data in wheat samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\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\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.143\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\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.828\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\u003e\u0026minus;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.140\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\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Of Variance explained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Of cumulative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eExtraction method: principal component analysis.\u003c/p\u003e \u003cp\u003eRotation method: varimax with Kaiser normalization.\u003c/p\u003e \u003cp\u003eRotation converged in three iterations.\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes of Principal Component Analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Of Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.32\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\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.311\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\u003e0.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.444\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\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.594\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\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.451\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Spatial distribution of heavy metals in wheat samples\u003c/h2\u003e \u003cp\u003eThe ordinary kriging interpolation method in ArcGIS Pro 3.0.2 software was utilized to analyze the spatial distribution of heavy metals in the wheat samples (Ghong et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Geostatistical methods, with ordinary kriging being the most favored, are extensively used across different disciplines. The versatility of ordinary kriging, originally intended for mineral reserve estimation, has led to its widespread adoption in various fields (Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The kriging interpolation method has broad applications in the disciplines of pedology, environmental studies, and the study of ecosystems, particularly when dealing with regional variables. Its characteristics make it well-suited for estimating and analyzing spatial variations in these fields (Dong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By leveraging spatial correlation variance, this method effectively interpolates data and aims to deliver the most accurate and unbiased estimates for unknown values within the study area (Zeng et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). When certain conditions related to covariance are met, it provides the most accurate linear unbiased estimation for values at unsampled finite positions. This prediction is based on a variogram model established from the spatial arrangement and semivariance of the initial dataset (Peng et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The kriging method calculates the value at a given point by determining a weighted mean of observed data points in the immediate vicinity. These weights are determined according to the spatial connection of the point to all the collected samples. (Wang et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This approach utilizes a semivariogram, which is established based on sample characteristics, to assess how the spacing between sample data points influences the estimated attribute values, and the resulting estimator is expressed using Eq.\u0026nbsp;5.\u003c/p\u003e \u003cp\u003eZ\u003csup\u003e\u0026lowast;\u003c/sup\u003e (S\u003csub\u003e0\u003c/sub\u003e) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{\\text{i}=1}^{n}{\\lambda\\:}_{i}\\left({S}_{0}\\right)Z\\left({S}_{0i}\\right)\\:\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003eZ (S\u003csub\u003e0i\u003c/sub\u003e) refers to the value assigned to the specific point S\u003csub\u003e0i\u003c/sub\u003e, which represents a sample taken at location i. In this context, λ\u003csub\u003ei\u003c/sub\u003e (S\u003csub\u003e0\u003c/sub\u003e) represents the standard kriging weight for location S\u003csub\u003e0\u003c/sub\u003e, and Z*(S\u003csub\u003e0\u003c/sub\u003e) represents the approximated value at S\u003csub\u003e0\u003c/sub\u003e. The ideal kriging interpolation model should have a root mean square (RMS) and average squared error (ASE) close to 0 and a root mean square standardized error (RMSS) close to 1. These criteria ensure accurate and precise estimates with minimal bias and variability, making the model reliable for data interpolation in various applications. These principles serve as the key criteria for selecting the most appropriate interpolation model for a given application (Saha et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the same time, to distinguish between the dominant effects of inherent factors (natural influences) and stochastic factors (human influences) on spatial regional variation, the ratio of nugget value to sill value has also been regarded as a vital indicator. This index helps assess the level of spatial variability in regionalized variables (Zhao et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results of the fitted variogram models using various autocorrelation structures are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays a series of maps depicting the spatial patterns of the estimated values of the heavy metals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the fitted variogram models using various auto-correlation structures\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFitting models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNugget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial sill\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRMSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eASE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\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\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.21\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\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExponential\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\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\u003eGaussian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.56\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 highest levels of the metals Cd, Zn, and Cu were detected within the northeastern and eastern portions of the study region. A notable point is the presence of the Ramin power plant and the Maroun oil field in these regions. Oil fields have the potential to influence the dispersion of heavy metals in soil due to the process of extracting crude oil, leading to the concurrent pollution of the soil with both petroleum and heavy metals. Prior research suggests that the concentrations of heavy metals such as Cu, Zn, and Cd in soils contaminated by oil tend to increase as oil well operations continue, highlighting that human activities intensify the presence of these metals in oil fields. Among the elements that can detrimentally impact the soil environment in oil-polluted areas, Cd is identified as the most unstable, accessible, and harmful heavy metal, with a lower potential for mobility than Zn and Cu (Fu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Industrial emissions also contribute to higher heavy metal concentrations, as exemplified by cadmium levels exceeding permissible limits around the Ramin power plant in Ahvaz (Mostafaii et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study in Sehwan Sharif, Jamshoro, analyzed heavy metal concentrations in topsoil samples and grouped elements. Cd, Ni, As, Cr, Pb, and Zn were linked to human activities, while Fe, B, Mn, and Cu were natural spatial maps that indicated metal hotspots, particularly high Zn levels in the city center, attributed to traffic emissions (Bux et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, the highest levels of Pb were measured in the southeast and west, and the highest levels of Cr were measured in the southeast. In the vicinity of highways, the soil in this study was subjected to the presence of harmful heavy metals originating from vehicle exhaust emissions and a variety of other waste materials associated with transportation. Lead is the predominant heavy metal discharged from road traffic, comprising a minimum of 90% of the total metals in runoff from roads. Additionally, chromium (Cr) is detected in both road runoff and vehicle exhaust emissions (Gao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nawrot et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The highest levels of Hg were measured in the southwest and the northern, eastern, and southwestern regions. The use of pesticides may play a role in contaminating agricultural soil with heavy metals. Some pesticides include heavy metal components, notably arsenic and lead. Heavy metals such as Hg, As, and Cr can come from various sources in agriculture, including fertilizers, pesticides, livestock manure, and wastewater. Large quantities of fertilizers and pesticides are used in wheat farming within this locality (Ahmadi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alengebawy et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study near Pb‒Zn and Au mines assessed heavy metal contamination in soil, revealing elevated levels of As and Pb, exceeding Vietnam's residential soil standards. The pollution indices revealed Pb and As as the main contaminants, while the statistical analysis suggested a combination of human and natural sources (Tran et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Cereals such as wheat naturally gather heavy metals from a range of sources, including soil, water, and the atmosphere. Nevertheless, the soil primarily plays the leading role in contaminating these plants with heavy metals. In times of drought, substantial amounts of heavy metals stemming from vehicular actions tend to accumulate on roads and intersections. As a result, these contaminants are carried into the soil environment by stormwater runoff, resulting in elevated levels of heavy metals within the soil (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the presence of fossil fuel resources and the expansion of the oil and gas industries in Ahvaz and neighboring regions significantly impact atmospheric conditions, affecting the transport of heavy metal pollutants (Darvishi Khatooni, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Dust storms in Ahvaz transport heavy metals and other pollutants from both local and foreign sources, becoming a major environmental challenge. Prevailing winds in the region, such as winds from Iraq, transport dust and soil containing heavy metals, contributing to soil and agricultural pollution. The Karun River, which serves as the primary water source in Ahvaz, is crucial for providing water to support agriculture in the area. Current studies indicate that the influx of urban and industrial sewage has led to an increase in pollution in this river, leading to heightened concentrations of heavy metals in crops that are watered with this water. The extensive data analysis, including statistics, correlations, and PCA, revealed congruity among the results. Additionally, these outcomes harmonize effectively with the spatial maps produced.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Health risk assessment of wheat consumption\u003c/h2\u003e \u003cp\u003eThe mean EDI, HQ, and HI values of the heavy metals related to noncarcinogenic human health risks due to wheat consumption are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e for both adults and children. The results indicate that the mean EDI for adults was less than that for children. According to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the order of HQ values in both children and adults is as follows: Hg\u0026thinsp;\u0026gt;\u0026thinsp;As \u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Zn\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Cd. HQ values higher than 1 were found for Hg, As, Pb, and Cu among children. Conversely, in adults, only Hg and As had HQ values greater than 1. The hazard index (HI) was greater than 1 for both adults and children. This implies that prolonged wheat consumption has exposed the inhabitants of the research area to a notable potential noncarcinogenic health hazard. Furthermore, concerning Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the outcomes of the evaluation regarding cancer risk stemming from the consumption of heavy metal-contaminated wheat indicated that the carcinogenic risk exceeded the acceptable threshold at specific sampling sites. Cr (adults: 0.000236; children: 0.00038), followed by As (adults: 0.000494; children: 0.00076), had the greatest potential carcinogenic risk. The ILCR associated with Pb exceeded 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e in certain locations (adults: 6 sites; children: 10 sites). Conversely, the ILCR linked to cadmium reached only 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e in a single instance, exclusively concerning children.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eEDI, HQ, and HI mean values of the heavy metals related to noncarcinogenic human health risks due to wheat consumption\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEDI\u003c/p\u003e \u003cp\u003e (mg/kg.day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHI=ƩHQ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAdults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAdults\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eAdults\u003c/b\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\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e7.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e4.964\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\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.855\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\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.380\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\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.881\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eIncremental lifetime cancer risk (ILCR) of heavy metals related to carcinogenic human health risks due to wheat consumption\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eILCR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAdults\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000494\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\u003e0.000032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000014\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\u003eOur findings are consistent with the conclusions reached by Zheng et al., Zafarzadeh et al., Gruszecka-Kosowska et al., Setia et al. and Dehghani et al. (Dehghani et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gruszecka-Kosowska, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Setia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, Huang et al. revealed that HQ derived from wheat consumption was less than one across all age categories (Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A study by Mansouri Moghadam et al. (Mansouri Moghadam et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the health risks associated with heavy metals in wheat samples collected from farms in the northern regions of Ahvaz. The HQ values for cadmium, lead, nickel, chromium, and manganese exceeded 1 for both age groups, while the HQ values for copper, iron, and cobalt were less than 1. Additionally, the ILCRs for toxic metals such as cadmium, lead, nickel, and chromium exceeded 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e. Shi and colleagues studied the variations in heavy metal levels in Chinese soil over time and across different locations, with a particular focus on pollution assessment and risk analysis. Their investigation revealed that regions where LCR values exceeded 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e represented 1.35% for adult females, 1.26% across all the evaluated areas for children, and 0.80% for adult males (Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A study in Punjab, India, investigated heavy metal (HM) contamination in soil and wheat grain samples near the Sutlej River. High levels of Cd and Pb pose health risks, with Cd having carcinogenic potential (Setia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Mercury vapor has a significant impact on sensory, cognitive, and motor functions. Methylmercury, which is more toxic than standard mercury, disrupts the development of the peripheral nervous system in newborns. Overexposure to methylmercury leads to Minamata disease, and skin absorption is also possible. People can come into contact with mercury by inhaling elemental mercury vapor in occupational settings, consuming seafood, engaging in environmental activities, having dental amalgam fillings, and encountering organic mercury compounds (Kumari \u0026amp; Bhattacharya, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Both arsenite and arsenate are effectively absorbed via both oral ingestion and inhalation routes. Once arsenic is taken into the body, arsenate is converted to arsenite, leading to the presence of both As(III) and As(V) in the bloodstream(Y\u0026uuml;ksel et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Increased exposure to Cr(VI) in children can result in a range of health problems, including periodontitis, stomatitis, peptic ulcers, and nasal bone perforation. The absorption of orally ingested Cr is constrained to below ten percent in the gastrointestinal tract, while more soluble compounds display heightened absorption rates. Cr can partially infiltrate human skin, especially when the integrity of the skin is compromised (Kumari \u0026amp; Bhattacharya, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pb is the primary source of noncarcinogenic risk, and As poses a notable carcinogenic danger. Soil specimens collected from mining and industrial regions, which encompass electronic manufacturing facilities and locations for e-waste disassembly, exhibit substantial pollution. This situation heightens potential hazards to both the environment and human well-being (Shi et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the HI values consistently tended to be greater in children than in adults. This pattern highlights the heightened vulnerability of children to contamination of wheat by heavy metals. This increased susceptibility can be attributed to their lower body weight and reduced physical resilience (Wang et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Research findings demonstrate that the greatest increases in the levels of heavy metals, such as lead, copper, and cadmium, were detected within the soils of the eastern and northeastern sections of Khuzestan. Human activities are the primary sources responsible for the presence of these pollutants. Heavy metals found in agricultural soils adjacent to roads originate from emissions produced by vehicles (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nduka et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Investigations indicate that metals such as lead, copper, and cadmium infiltrate the soil through mechanisms such as erosion from vehicle brakes, tire abrasion, oil leaks, and cylinder head washers (Petukhov et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, these metals have been applied in various fields, including pigments, fungicides, batteries, and alloys such as bronze and brass. Fertilizers, particularly those derived from animals, contribute to the accumulation of these compounds within soils and the subsequent contamination of plants (He et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, the proximity to the Ramin power plant may lead to increased concentrations of these elements in the surrounding region. Overall, the outcomes of evaluating the health risks associated with heavy metals in wheat samples suggested that consuming this product might lead to negative and acute effects for individuals. The unregulated and prolonged use of agricultural inputs, coupled with the establishment of industrial facilities in close vicinity to agricultural lands, the indiscriminate deployment of chemical fertilizers, the utilization of sewage sludge as fertilizers, the cultivation of wheat in proximity to high-traffic roads, and the adoption of urban sewage for irrigation, collectively pose a potential risk for contaminating wheat crops. This subsequent contamination has the potential to exert ramifications on derivative products such as bread. The resulting scenario could have enduring health implications for consumers. Hence, it is prudent to maintain a consistent regimen of monitoring food commodities with respect to their levels of heavy metals and residues of chemical compounds to maintain the integrity of food safety protocols.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study focused on the presence of heavy metals (HMs) in wheat grains in southern Iranian oil fields, shedding light on both the distribution patterns of these metals and the potential health hazards they pose. Zinc had the highest average concentration among the metals detected in wheat grains, while cadmium had the lowest concentration. The assessment of health risks revealed that specific regions were at risk due to elevated levels of certain heavy metals. Notably, the lead and mercury concentrations in wheat exceeded the values recommended by the FAO/WHO, with mercury showing the highest health risk quotient for both adults and children. This study emphasized arsenic and chromium as the most significant carcinogenic heavy metals in this particular context. Through the use of ordinary kriging, the study identified the eastern, northeastern, and southwestern parts of the study area as hotspots for elevated heavy metal concentrations, largely attributed to human activities. The contamination of wheat grains in Ahvaz can be attributed to various factors, including oil fields, transportation systems, unregulated use of agricultural inputs, fertilizers, and irrigation with polluted water. Recognizing the paramount role of bread in the Iranian diet, urgent and earnest attention is warranted to address the heightened heavy metal levels in wheat grains. This novel approach to spatial analysis and comprehensive health risk assessment of heavy metals in wheat grains provides valuable insights for policymakers and regulatory authorities to formulate effective strategies aimed at ensuring food safety and public health.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article was extracted from the MSc thesis of Roza Aibaghi, and the authors are grateful to Ahvaz Jundishapur University of Medical Sciences for funding and providing the necessary facilities to perform this research (Grant No. ETRC-0017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Ahvaz Jundishapur University of Medical Sciences under Grant No. ETRC-0017.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author, upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.T.B. and S.J. jointly contributed to conceptualization, methodological design, result validation, and manuscript review. R.A. led the investigation and wrote the primary manuscript draft. N.J. validated results and provided supervisory guidance. E.M. conducted essential statistical analyses for the research. N.T. contributed to improving the study\u0026rsquo;s spatial aspects using ArcGIS Pro software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The research was ethically approved by the Ethics Committee of the Ahvaz Jundishapur University of Medical Sciences(Approval code: 122, Ethical code: IR.AJUMS.REC.1400.682).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Competing interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e(INSO), I. N. S. O. 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Ecological risks and potential sources of heavy metals in agricultural soils from Huanghuai Plain, China. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e,\u003cem\u003e\u0026nbsp;21\u003c/em\u003e, 1360-1369.\u003cspan dir=\"RTL\"\u003e\u003c/span\u003e\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":"Health risk assessment, Heavy metal pollution, Kriging, Spatial distribution, Wheat grains","lastPublishedDoi":"10.21203/rs.3.rs-6470086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6470086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study addresses the critical issue of heavy metal contamination in wheat grains, aiming to bridge the existing research gap by examining the spatial distribution of heavy metals and assessing their potential health risks in the southern Iranian oil fields. Employing a quantitative approach, we collected samples from 50 regional wheat cultivation farms and analyzed the concentrations of chromium (Cr), copper (Cu), zinc (Zn), arsenic (As), cadmium (Cd), mercury (Hg), and lead (Pb) using inductively coupled plasma‒mass spectrometry. Our findings revealed concerning levels of heavy metals, with Zn exhibiting the highest concentration (mean: 30.169 mg/kg), while Pb and Hg exceeded the FAO/WHO safety thresholds. Among the studied elements, Hg posed the highest health risk, with health quotient (HQ) values of 1.38 for adults and 2.14 for children. Cr (HQ: 0.000236 for adults; 0.00038 for children), followed by As (HQ: 0.000494 for adults; 0.00076 for children), was identified as the primary carcinogenic heavy metal. Principal component analysis (PCA) revealed that the first factor accounted for 48% of the total variance, primarily attributed to As, Cr, Pb, and Hg, while the second factor explained 27.32%, associated with Cd, Zn, and Cu. Ordinary kriging interpolation indicated elevated heavy metal concentrations in farms located in the eastern, northeastern, and southwestern regions. Based on these findings, we emphasize the urgent need for remediation strategies to reduce heavy metal contamination in wheat grains, highlighting the crucial importance of ensuring food safety and protecting public health.\u003c/p\u003e","manuscriptTitle":"Heavy Metal Contamination in Wheat Grains: Spatial Analysis and Health Risk Assessment in Southern Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 13:21:39","doi":"10.21203/rs.3.rs-6470086/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":"56272a78-6480-459f-bf61-8371c1a2ddfe","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-02T09:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 13:21:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6470086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6470086","identity":"rs-6470086","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}