Authors: Qinghai Deng, Zhenzhou Sun, Liping Zhang, Yongshuai Zhang, Liangyu Zhou, Jingjing Yang, Guizong Sun, Chang Lu
Categories: Article, Atmospheric deposition, Ecology, Environmental impact, Environmental sciences, Heavy metals, Predictive model, Soil, Transfer
Source: Scientific Reports
Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area.
Keywords: Heavy metals, Transfer, Soil, Atmospheric deposition, Predictive model
**Subject ** Ecology, Environmental sciences, Environmental impact
Over the past few decades, rapid economic development has led to the proliferation of diverse industrial and commercial activities such as non-ferrous metal mining, boiler coal burning, and ferrous metal smelting. These activities emit a large amount of toxic heavy metals, waste gas, and other pollutants, causing severe air pollution^1–5^. Non-essential trace elements such as Pb, Cd, Hg, As, and Cr can pose significant risks to human health when they accumulate excessively^6,7^. These heavy metals can enter the human body through inhalation, dermal contact, and dietary intake, with food consumption being the predominant pathway^8^. Heavy metal contamination in agricultural products has caused environmental pollution and even instances of foodborne illnesses, posing a significant threat to human health^9,10^. Crops primarily accumulate heavy metals through root absorption from soil, followed by translocation to edible parts. Prior research has focused on the uptake, transfer, compartmentation, and accumulation mechanisms of heavy metals in the soil-crop system^11,12^, as well as evaluated heavy metal contamination, identified sources of heavy metals, and assessed the associated health risks^13–17^. Current research is still focusing on characterizing the contamination of soil and crops and the health risks associated with heavy metals^12,18–25^.
Recent studies have demonstrated that plants possess the ability to transport heavy metals not only through their root systems but also through their aerial organs including leaves, fruits, and flowers^26,27^. Leaves can absorb heavy metals from the atmosphere through cuticle adsorption and stomatal penetration^28^. This significant finding has prompted research on the investigation of the migration pathways of pollutants within different plant components, encompassing roots, stems, leaves, flowers, and fruits. For instance, Zhang et al. discovered a 153% increase in the concentration of Cd in the aboveground parts of Chinese cabbage cultivated under open-air conditions compared with those grown under film-covered conditions^29^. Agricultural products grown near industrial or mining areas also had higher heavy metal content than those grown in other areas. Leafy vegetables were more prone to accumulating heavy metals than root and fruit vegetables. Wang et al. conducted open-air and indoor potting simulation experiments and found that atmospheric deposition significantly affected the accumulation of Pb, Cd, Hg, and As in the aboveground parts of radish^30^. Studies on wheat have demonstrated that, in addition to roots and leaves, wheat ears possess the ability to absorb and transport heavy metals. The contribution rate of heavy metals absorbed and transported by wheat ears to the accumulation of Pb in wheat grains is higher than that of roots^29,31^. These findings suggest that atmospheric heavy metals can be highly bioaccumulative in crops and highlight the importance of studying the effects of pollutants on various parts of plants.
The concentration of heavy metals in atmospheric deposition is significantly elevated in China, and it is more active than heavy metals in soil. Plants can easily accumulate them. In some areas, it has become an important source of heavy metal accumulation in agricultural products^32^. Therefore, it is crucial to consider the contribution of atmospheric deposition to the accumulation of heavy metals in crops, particularly when there is a relatively high concentration of heavy metals in atmospheric deposition. However, there have been limited studies on the migration of heavy metals from atmospheric deposition to fruits. To address this knowledge gap, we conducted a study in the wheat planting area lying north of Linshu County, China. Samples of wheat rhizosphere soil, atmospheric deposition, leaves, and fruits were collected and analyzed for heavy metal content while considering various environmental factors. Additionally, a predictive model was established to estimate the heavy metal content in wheat under the dual influence of soil and atmospheric deposition. Our research findings could provide theoretical and practical guidance for understanding the impact of soil-atmosphere deposition on heavy metals in agricultural products. They might also help explore effective measures to prevent and control the absorption of heavy metals in leaves, thereby ensuring the safety of agricultural products.
The study area was situated in the northern part of Linshu County, Linyi City, Shandong Province, China. Its geographic coordinates range from 118° 31′ 00″ E to 118° 51′ 00″ E with a latitude from 34° 50′ 00″ 'N to 35° 06′ 00″ N, as shown in Fig. 1. The total area is approximately 600 km^2^, with cultivated land occupying around 376 km^2^, approximately 62.8% of the total land area. The primary crop rotation method used was a winter wheat-summer maize rotation system. The population in this region is approximately 670,000 and includes two streets (Linshu Street and Zhengshan Street) and three towns (Jiaolong, Qingyun, and Yushan). The area has a warm temperate monsoon climate with an average temperature of 12.7 °C and an annual sunshine duration of 2459 h. It experiences an accumulated frost-free period of approximately 4238 °C annually, with an annual average rainfall reaching 852 mm and an annual average water surface evaporation of 1036 mm throughout the county. The study area belongs to the Shuhe River system within the Huaihe gentle slope hilly areas where the terrain elevations are higher toward the northeast but lower toward the west and south, resulting in a fan-shaped slope. The predominant landforms within this territory consist of gently sloping hills, which can be broadly classified into three low mountains, hills, and plains.
Figure 1 The geographical coordinates of the sampling sites.
In June 2021, we gathered wheat samples based on regional distribution characteristics. We used a sampling unit size of 0.1–0.2 hm^2^ and distributed our sampling points accordingly (Fig. 1). Within each sampling unit, we selected over 20 plants and mixed their wheat ears to form composite samples. We collected 37 wheat grains and corresponding roots, stems, leaves, and supporting rhizosphere soil samples. The weight of each sample was 500 g (fresh weight). To prevent cross-contamination, we dried the samples under sunlight with ventilation at a minimum distance of 1 m from each other before sending them to the laboratory. We also peeled and dried the samples before transportation.
We rinsed the wheat grain samples using deionized water and placed them on a sanitized tray for uncontaminated drying. Subsequently, we peeled and ground the samples. For roots, stems, and leaves, the samples were cut into 0.5–1 cm strips with a stainless steel knife or scissor, and then dried at 75 °C in an oven until they reached a constant weight. Afterward, we pulverized the air-dried samples using an agate mortar and pestle before passing them through a nylon plastic sieve with mesh numbers ranging from 40 to 60 to ensure uniform mixing before to testing.
To determine the properties of the collected soil samples, they were initially sieved through a 2 mm mesh to eliminate any coarse particles. Subsequently, the resulting soil samples underwent pH level analysis. Following this step, the soil samples that passed through the 2 mm sieve were extracted using the quartering method. Some of these samples were then milled using agate cans until they passed through a 0.25 mm sieve, which was necessary for soil organic matter (OM) detection. The soil samples that passed through the 0.25 mm sieve were then further ground using agate balls until they passed through a 0.074 mm sieve. This allowed for the measurement of various elements such as Cd, Cr, Hg, Ni, Pb, As, Cu, Zn.
Atmospheric deposition was collected using a rectangular dust-catching cylinder measuring 100 cm in length, 60 cm in width, and 60 cm in height (Fig. 2). A total of 15 sampling points were strategically placed across the entire region at a density of 1 per 64 km^2^. The locations of the sampling points are depicted in Fig. 1, primarily situated on residential rooftops adjacent to the main crop cultivation areas in the northern region of Linshu County. We made sure to avoid areas with obvious impacts, such as dusty roads, restaurants, and industrial and mining enterprises' emissions.
Figure 2 Dust cylinder.
The deposition receiving cylinder was prepared by soaking it in 10% hydrochloric acid for 24 h before use. Afterward, it was rinsed multiple times with deionized water. Once the cylinder had completely dried, its opening was sealed with a plastic film. We then installed and secured the cylinder at the designated sampling point. The atmospheric dry and wet deposition samples were collected for 1 year starting from the installation time, which was September 2020. The cylinder was retrieved in October 2021.
To retrieve the sample, we first placed the settling tank at the sampling point until the upper solution became clear. Subsequently, the supernatant was decanted into a clean and dried plastic bucket filled with deionized water. We recorded the total volume of the upper supernatant layer (V1) before collecting two bottles of this layer after stirring. Nitric acid solution (1:1) was added to the supernatant to detect several heavy metal elements and 5% potassium dichromate to detect elemental mercury. After removing the upper clear liquid layer, the remaining precipitate and suspension were loaded into a plastic bucket, and the total volume (V2) was recorded. Finally, we dispatched the sample to undergo testing and analysis in a laboratory.
The soil and atmospheric deposition samples were analyzed using the methods outlined in Table 1. The pH level was determined using the ion-selective electrode method, while OM was detected using the potassium dichromate volumetric method. X-ray fluorescence spectrometry was used to detect the presence of Pb, Ni, Cr, Zn, and Cu, while graphite furnace atomic absorption spectrometry was used for Cd detection. Hydride generation-atomic fluorescence spectrometry was used to detect As and Hg.
We conducted an analysis on wheat grains and their various components, including a composite of roots, stems, and leaves. The determination of As and Hg was carried out through closed acid dissolution-atomic fluorescence spectrometry, while Cd, Pb, Ni, Cr, Zn, and Cu were quantified using inductively coupled plasma mass spectrometry. Table 1 presents the detection techniques employed along with the corresponding limits for each element.
Quality control was conducted using the nationally certified reference materials of soil (GBW07427) and plants (GBW10011), and blank samples were analyzed. The recovery rate of elements in standard soil and plant samples reached 85.0%-115.0%, and both recoveries met international standards. Each sample was analyzed in triplicate for all analyses.
Statistical analyses were performed on soil, atmospheric deposition, and crop heavy metals using SPSS 27 (IBM Corporation, Armonk, NY, USA). ArcGIS (Esri, Redlands, CA, USA) was used to analyze the distribution of heavy metals in atmospheric deposition. We performed Pearson correlation analyses and constructed correlation heat maps using RStudio to assess the relationship between heavy metals in wheat grains, roots, stems, and leaves; soil properties; soil heavy metal content; and atmospheric deposition of heavy metals.
Redundancy analysis (RDA) was performed in Canoco 5.0 (Wageningen University, TIEL, NLD) to investigate the relationship between community composition and quantitative and categorical environmental variables. The method combines multiple linear regression (MLR) and principal component analysis (PCA) to achieve this outcome. Comparative PCA results of wheat seeds demonstrated that RDA can be considered a constrained version of PCA. Both methods are used to identify new variables to replace the original variables, but the main difference is that the coordinates of the samples in the RDA ordination plot are linear combinations of environmental factors.
We performed stepwise multiple linear regression analysis using RStudio to model the linear correlation between a dependent variable and one or more independent variables. We aimed to establish a predictive model for heavy metal contamination of the soil-atmosphere-wheat system. The heavy metal content of wheat grains was selected as the dependent variable, while the heavy metal content of soil and atmospheric deposition and soil physicochemical properties were the independent variables.
All the authors abide by the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. We confirm that the current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant materials. Our wheat sampling is done under license.
Statistical information of soil parameters and heavy metal concentrations of the wheat rhizosphere soil in the study area is shown in Tables 2 and 3. Among these, Zn exhibited the highest average content, while Hg displayed the lowest. The variation coefficient of heavy metals in descending order was Hg > As > Zn > Ni > Cd > Cu > Pb > Cr, ranging from 30 to 50%, indicating some differences in the distribution of soil heavy metal in the region, Fig. 3 further supports this observation. The soil pH in the study area exhibited a range of 4.75 to 7.46, with higher values observed in the western and central regions (Fig. 4). The average pH was determined to be 5.63, accompanied by a coefficient of variation of 13.02%. These findings collectively indicate that the soil within the investigated area predominantly displayed acidic characteristics. The OM content ranged between 6.00 × 10^4^ mg/kg and 36.5 × 10^4^ mg/kg, with an average of 16.9 × 10^4^ mg/kg, and the coefficient of variation was 37.9%, indicating that OM distribution in the region varied to a certain extent (Table 3). The highest concentration of soil OM is observed in the western region of the study area, while the lowest concentration is found in the eastern part near the center (Fig. 4). The variation coefficient of soil parameters in descending order was MgO > CaO > N > OM > P > Fe2O3 > K2O > Na2O > pH > Al2O3 > SiO2. Combined with the distribution map of soil parameters (Fig. 4), we can generally see that there are some differences in the spatial distribution of soil parameters.
Figure 3 Distribution of heavy metals in the rhizosphere soil.
Figure 4 Distribution of parameters in wheat rhizosphere soil.
Table 2 displays the statistical analysis results of the heavy metal concentrations in wheat grain in the study area. Overall, Cu and Zn exhibited higher levels compared to other heavy metals, with concentrations of 5.33 mg/kg and 33.1 mg/kg, respectively. The content of Cu and Zn in wheat grains were similar in spatial distribution, which were higher in the middle and northeast, and lower in the northwest and southeast (Fig. 5). The average levels for Ni, Cr, As, Pb, Cd, and Hg were 1.06 mg/kg, 0.19 mg/kg, 0.09 mg/kg, 0.06 mg/kg, 0.042 mg/kg, and 0.004 mg/kg, respectively. In terms of spatial distribution patterns observed for these As and Pb demonstrated a comparable trend with high values observed in the northwest region but low values found in the southeast; similarly, Hg and Cr showed a consistent pattern with high values detected in the southeast region but low values identified in the northwest region. Ni was lower in the middle and higher in the northwest and southeast. Ca is lower in the east and west, higher in the middle and north and south.
Figure 5 Distribution of heavy metals in the wheat grain.
Additionally, Fig. 6 displays the distribution of the heavy metal concentrations in wheat roots, stems, leaves in the study area, except for Hg and Pb, the contents of heavy metals in wheat roots, stems and leaves showed similar distribution characteristics with those in wheat grains. Conversely, there is an inverse spatial distribution pattern for Hg and Pb between wheat roots, stems, leaves, and grains. The average Hg, As, Cr, Ni, Cu, Zn, Cd, and Pb content in wheat roots, stems, and leaves were 0.202 mg/kg, 0.176 mg/kg, 12.0 mg/kg, 2.21 mg/kg, 2.89 mg/kg, 13.7 mg/kg, 0.109 mg/kg, and 0.371 mg/kg (Table 2). These results indicate significant differences in the distribution of heavy metals in roots, stems, leaves, and wheat grain, which can limit the internal transport of wheat^27^.
Figure 6 Distribution of heavy metals in the wheat roots, stems, and leaves.
Atmospheric dry and wet deposition were collected from 15 samples in the study area. The dates were then interpolated using the Kriging interpolation method to determine the values of atmospheric dry and wet deposition at the 37 sampling points. Table 2 show the content characteristics of the atmospheric dry and wet deposition, respectively. The atmospheric dry deposition generally contained higher heavy metals levels than the wet deposition, with differences in their coefficient of variation, which might be attributed to the nature of the heavy metal elements and climatic factors^33^. The coefficient of variation of atmospheric dry deposition was 91.7%, while that of atmospheric the wet deposition was 249%. Overall, the coefficient of variation exceeded 30.0% for both dry and wet deposition, except for As and Hg in the dry deposition. This result suggests significant differences in the distribution of atmospheric dry and wet deposition in the region. Similarly, according to the distribution of atmospheric dry and wet deposition (Figs. 7 and 8), the same conclusion can also be drawn.
Figure 7 Distribution of heavy metals in dry atmospheric deposition.
Figure 8 Distribution of heavy metals in wet atmospheric deposition.
The distribution of heavy metals in atmospheric deposition is illustrated in Figs. 7 and 8. The concentration of As in atmospheric dry deposition exhibits a higher level in the southwest and a lower level in the northeast, primarily attributed to coal-fired dust emissions. On the other hand, the As in atmospheric wet deposition was higher on the east and west sides, lower in the middle, and peaked in the west. Regarding Cr concentration in atmospheric dry deposition, it is notably elevated in the northeast due to rock differentiation debris and weathering dust from rocks with high background values. However, no significant variation was observed for Cr concentration across the study area. The distribution of Cd content in atmospheric deposition exhibited consistency between dry and wet deposition, with higher concentrations observed in the northern regions. This was mainly due to industrial production activities such as smelting and calcination of non-ferrous metals, sintering of ores, and treatment of Cd-containing wastes. The geological lithology of the region is primarily monzonitic granitic gneiss, which may also be related to the high concentration of Cd. The distribution of Hg content in atmospheric deposition was consistent in dry and wet deposition, with higher values in the south and lower in the north, with a well-defined north–south boundary. The concentration of Hg in atmospheric deposition primarily stems from the extensive utilization of fossil fuels. Moreover, the elevated levels of Cu in atmospheric dry deposition are observed in both southeastern and northwestern regions, which coincide with proximity to major highways and primary sources of atmospheric deposition. The concentration of Cu in atmospheric wet deposition exhibited a higher level in the northern region, while other areas demonstrated a more uniform distribution pattern. In terms of atmospheric dry deposition, the concentration of Zn was found to be elevated in the southeast due to various anthropogenic activities such as roasting zinc sulfide ore, smelting and processing impurity metals containing Zn, steel production, waste incineration, rubber tire wear, and coal combustion. Notably, there were no significant variations observed in the distribution of Zn in atmospheric wet deposition. The concentration of Ni in atmospheric dry deposition was higher in the central-eastern region and lower in the surrounding areas, and there was no significant difference in the distribution of Ni in atmospheric wet deposition. The concentration of Pb was found to be higher in the central and southwestern regions, which can be attributed to industrial pollution and automobile exhaust resulting from the relatively developed industry and a high number of transport vehicles in these areas. Moreover, there was an elevated content of Pb observed in atmospheric wet deposition specifically in the eastern region.
In order to investigate the relationship between soil, atmospheric deposition (DAD: dry atmospheric deposition of heavy metal content; WAD: wet atmospheric deposition of heavy metals), and heavy metal content in wheat grains, we initially constructed a box plot illustrating the levels of heavy metals in soil, atmospheric deposition, and wheat grains (Fig. 9). The overall trend reveals the common characteristics shared by all elements. The concentration of heavy metals in atmospheric wet deposition is relatively low, whereas the concentration of heavy metals in atmospheric dry deposition is comparatively high. With the exception of As, the concentrations of other elements in atmospheric drying are significantly higher than those found in soil. In terms of concentration alone, the migration of heavy metals through atmospheric deposition exhibits greater advantages compared to that observed in soil.
Figure 9 Heavy metal content box plot.
Pearson's correlation analyses were conducted to investigate the associations between heavy metal concentrations in wheat rhizosphere soil, wheat roots, stems, and leaves, wheat grains; atmospheric dry and wet deposition; soil OM; and soil pH (Fig. 10).
Figure 10 Pearson correlation diagram depicting the relationship between heavy metal concentrations in wheat rhizosphere soil, roots/stems/leaves, wheat grains, etc.
We observed no significant correlation between the Hg concentration in wheat grains and the concentration of heavy metals in soil. However, a significant correlation was found between the Hg concentration in wheat grains and both dry deposition (R^2^ = 0.351, P < 0.05) and wet deposition (R^2^ = 0.667, P < 0.01) of atmospheric Hg. The Cd content in wheat grains showed a significant positive correlation with atmospheric dry deposition (R^2^ = 0.562, P < 0.01) and wet deposition of Cd (R^2^ = 0.549, P < 0.01). Similarly, the Cd content in roots, stems, and leaves exhibited a significant positive correlation with atmospheric dry deposition (R^2^ = 0.402, P < 0.05) and wet deposition of Cd (R^2^ = 0.414, P < 0.05). However, a significant negative correlation exists between the Cd content in wheat and the levels of OM and pH.
The Pb content in wheat grains showed a significant positive correlation with the atmospheric dry deposition of Pb (R^2^ = 0.605, P < 0.01), while the Pb content in roots, stems, and leaves exhibited a significant negative correlation with soil Pb (R^2^ = − 0.494, P < 0.01). The As content in wheat grains was significantly correlated with atmospheric dry deposition of As (R^2^ = 0.356, P < 0.05); however, the correlations with other plant parts were weak. The Zn content in wheat grains was significantly correlated with both atmospheric dry deposition of Zn (R^2^ = 0.387, P < 0.05) and atmospheric wet deposition of Zn (R^2^ = 0.370, P < 0.05), as well as with the content in roots, stems, and leaves (R^2^ = 0.583, P < 0.01). The Zn content in roots, stems, and leaves exhibited a significant negative correlation with OM (R^2^ = − 0.334, P < 0.05). Cu in wheat grains demonstrated a significant positive correlation with atmospheric dry deposition of Cu (R^2^ = 0.359, P < 0.05), as well as the content in roots, stems, and leaves (R^2^ = 0.583, P < 0.01). Furthermore, the Cu content in roots, stems, and leaves displayed a significant positive correlation with wheat grains (R^2^ = 0.422, P < 0.05).
The Ni content in wheat grains showed no significant correlation with atmospheric deposition of Ni, but exhibited significant correlations with OM (R^2^ = − 0.654, P < 0.01), pH (R^2^ = − 0.623, P < 0.01), and the content in roots, stems, and leaves (R^2^ = 0.319, P < 0.05). Additionally, the Ni content in roots, stems, and leaves was significantly correlated with OM (R^2^ = − 0.351, P < 0.05). Notably, no significant correlation was observed between the Cr content in grains.
Niu et al. conducted a study using open top chambers and soil Hg enrichment experiments to investigate the concentration of Hg in crop leaves^34^. They found that the air Hg concentration was significantly correlated with the Hg concentration in crop leaves, while the soil Hg concentration did not show a significant correlation. It is well known that vegetation uptake of Hg from the atmosphere and its subsequent transfer to the soil is a major source of Hg in biological communities, accounting for 60–90% of global terrestrial Hg deposition^25^. Consistent with the results of previous studies and the findings of this analysis, a significant correlation was observed between Hg levels in wheat grains and atmospheric deposition of Hg in this study, while the association with soil Hg was not pronounced. These results further indicate that for Hg elements, both atmospheric deposition of Hg and gaseous Hg in the atmosphere follow similar migration pathways, ultimately leading to their accumulation in crops through aboveground plant parts such as leaves.
The empirical model of Pb in wheat grains was established by Wu et al., incorporating soil Pb content and soil properties as key factors. Notably, the influential variables in this model are identified as soil pH and soil Pb concentration^35^. Ma et al. revealed that wheat ears have the capacity to uptake Pb from atmospheric deposition during the grain-filling stage, contributing up to 26% of the Pb content in wheat grains^27^. According to our research, a significant correlation existed between atmospheric deposition and the Pb concentration in wheat grains. Conversely, soil Pb was not found to be a statistically significant factor. Therefore, we conclude that atmospheric deposition is the primary source of Pb in wheat grains, which aligns with the findings reported by Qiao et al. ^36^.
Recent research has indicated that wheat leaves are capable of directly absorbing Cd from the atmosphere through their stomata. Among all the leaves, flag leaves tend to have a higher Cd concentration. Although leaves contribute up to 34.44% of grain Cd accumulation during the grain-filling stage, this decreases gradually to 14.48%, highlighting the importance of the early grain-filling stage of leaf Cd contribution^37^. The analysis conducted in this study revealed a significant correlation between wheat grains and atmospheric Cd deposition, which is consistent with the findings of previous research. Additionally, Cd level in wheat grains also showed significant correlations with soil OM and soil pH. Interestingly, the analysis also revealed that heavy metals from soil and atmospheric deposition, such as As and Cr, were not significantly related.
This study also discovered a weak connection between Cu, Zn, and Ni levels in wheat grains and atmospheric deposition. This shows that the contribution rate of Cu, Zn and Ni from atmospheric deposition to wheat grain is small, but the impact on the accumulation of heavy metals in crops cannot be ignored^38^.
To gain a better understanding of the impact of soil characteristics on the transportation of heavy metals, we performed RDA using the heavy metal content and soil characteristics of wheat grains (Fig. 11). The red and blue vectors represent the soil characteristics and grain-heavy metal content, respectively. The cosine value of the angle between the heavy metals and the soil characteristic arrow indicates their correlation. The length and direction of the arrow express the dependence of heavy metals in wheat grains on soil characteristics. The results show shorter arrows corresponding to As, Cr, Hg, and Pb in grains, indicating that soil characteristics had a minimal effect on the heavy metal content in grains.
Figure 11 RDA ordination plot of the impact of soil characteristics on grains heavy metal content.
We observed a small angle between the arrows representing Cd, Zn, and Ni and those for Na2O and K2O. Additionally, the cosine value was greater than zero. The angle between the arrows indicating pH and OM was close to 180°. These results suggest that Na2O and K2O positively impacted the Cd, Zn, and Ni content in grains. Conversely, pH and OM had a negative impact on the Cd content in grains. Plants need two essential macronutrients, K^+^ and Ca^2+^. These are generally transported into plants through passive transport systems, such as ion channels. The presence of K^+^ and Ca^2+^ channels can facilitate the migration of heavy metals from soil to crop roots in a synergistic manner. Some studies suggest that H^+^ coupled transport is beneficial for the absorption of heavy metals by rice plants^39,40^. In accordance with the results presented herein, we can theorize that a decrease in soil pH increased the release of H^+^ into the soil solution, significantly improving the transfer ability of heavy metals from soil to rice roots.
We observed a small angle between the arrows for grain Cu and Fe2O3, MgO, Al2O3, and CaO, with a positive cosine value. By contrast, the grain Cu arrow was in the opposite direction of SiO2, with an angle close to 180°. This suggests that Fe2O3, MgO, Al2O3, and CaO had a beneficial impact on the grain Cu content, while SiO2 had an adverse effect on the grain Cd content.
Altogether, it is clear that there is a strong correlation between Cd, Zn, Cu, and Ni concentration in grains and soil properties, indicating that the soil significantly impacts these heavy metal levels in the grains. Therefore, we can conclude that these heavy metals in the grain are partially derived from the soil and greatly influenced by soil properties.
This study aimed to demonstrate the impact of heavy metals from different sources on the heavy metal content in wheat grains. To accomplish this, we used a stepwise MLR method with data on soil heavy metals, soil properties, atmospheric deposition, and wheat heavy metal content. The resulting MLR model for heavy metal content in the soil-atmosphere-grain system is presented in Table 4 (P < 0.1; P < 0.05*; P < 0.01**; P < 0.001***). The model was effective in predicting Hg (R^2^ = 0.587), Pb (R^2^ = 0.588), Cd (R^2^ = 0.634), As (R^2^ = 0.540), Zn (R^2^ = 0.492), and Ni (R^2^ = 0.735), but not Cu (R^2^ = 0.346) and Cr (R^2^ = 0.271). Chen et al. previously established a model for Zn and Cd but not for Hg and Pb, suggesting that atmospheric sources may have hindered model establishment. However, the model presented here, which incorporated atmospheric deposition and soil heavy metals, successfully fitted Hg and Pb, indicating the significant impact of atmospheric deposition on grain-heavy metal content. The strong correlation observed in Fig. 12 between the measured and predicted values provides empirical evidence for the model's reliability.
Figure 12 Correlations between the measured heavy metal levels in wheat seeds and the predicted model values.
The model showed significance (P < 0.001) between Hg content in wheat grains and atmospheric wet deposition of Hg, indicating a substantial impact of atmospheric Hg deposition on wheat grains contamination. Additionally, we observed a significant association (P < 0.05) with soil pH, exhibiting a negative coefficient, thereby reinforcing the notion that pH exerts an influential adverse effect on heavy metal accumulation in wheat grains. Thus, the greater the soil acidity, the more the Hg was enriched in wheat grains. The difference in soil characteristics was not significant on their own, but when added, they optimized the model; therefore they were retained. When comparing our model to the Hg transport model established by Hussain et al., the Hg content in wheat grains of both models showed a negative correlation with soil pH. However, it should be noted that the Hg in wheat grains is more biased toward atmospheric deposition sources^41^.
According to studies conducted by Ma et al., atmospheric deposition is the primary source of Pb in wheat tissues, while the latter study revealed that wheat spikes play a significant role in the presence of Pb in wheat grains^27,42^. Study's predictive model aligns with these previous findings, indicating that atmospheric Pb deposition significantly impacted (P < 0.01) Hg content in wheat grains. As mentioned above, we found that soil pH had a significant negative effect (P < 0.05) on heavy metals in wheat grains, affecting the acidity of soil. Overall, the migration characteristics of Hg and Pb were similar, with a strong contribution from atmospheric deposition sources and a negative relationship with soil pH.
A predictive model for soil-wheat Cd was developed by Bai et al., demonstrating that soil Cd and pH levels are important factors in Cd accumulation in wheat grains^43^. The model in this study is optimized after adding the heavy metal components of atmospheric deposition on the basis of soil heavy metal content. Our results showed that soil Cd significantly (P < 0.05) impacted wheat grains Cd. Additionally, the soil characteristics of CaO, SiO2, Al2O3, and Fe2O3 also significantly (CaO, SiO2, Al2O3: P < 0.05; Fe2O3: P < 0.1) impacted wheat grains Cd.
Our model showed a significant correlation between wheat grains As content, soil N (P < 0.001), and Fe2O3 (P < 0.01). However, no significant correlation was found between wheat grains As content and either soil As or atmospheric deposition As. Based on Shen et al.’s study that N and Fe have a regulating effect on As concentration in soil and crops^44^, we speculate that N and Fe were the main sources regulating As in wheat grains.
According to the findings of this study, the predictive model revealed that the Ni content of wheat grains had a significant correlation with soil pH (P < 0.001), soil OM, Na2O (P < 0.01), and atmospheric Si2O, Al2O3, and MgO deposition (P < 0.05). In addition, the Zn content in wheat grains was also significantly correlated with soil OM (P < 0.01) and atmospheric deposition (P < 0.05). The results suggest that the relationship between the Ni and Zn content in wheat grains and the soil characteristics was more significant than that with atmospheric deposition. This indicates that the Ni and Zn content had a greater proportion of soil sources, which had a greater influence on their transport.
Based on the detection and analysis of heavy metals in soil, atmospheric deposition, wheat grains, and wheat roots, stems, and leaves, we found significant spatial variability in heavy metal content across different media in the study area. Our research revealed distinct patterns in the transfer and accumulation characteristics of various heavy metals from soil and atmospheric deposition into wheat. For instance, the concentrations of Hg and Pb in wheat grains were significantly correlated with the corresponding atmospheric deposition levels, while their correlation with soil heavy metal content and soil properties was not significant. This indicates that atmospheric deposition is the primary factor influencing the Hg and Pb content in wheat grains. Conversely, the concentrations of Cd, Cu, Zn, and Ni in wheat grains were significantly correlated with their levels in soil and soil properties, suggesting that soil heavy metal content and soil properties are the main factors affecting the concentrations of Cd, Cu, Zn, and Ni in wheat grains. Compared to previous studies, our model has been optimized by including atmospheric deposition as a factor, resulting in a multiple linear regression model that effectively predicts the concentrations of Hg, Pb, Cd, Ni, Zn, and As in wheat grains. These findings provide important insights for managing heavy metal contamination in wheat and offer valuable references for future research and practical applications. Future studies should aim to further optimize the model, expand the study area to enhance the model's applicability, and explore additional factors that may influence heavy metal content in wheat, thereby aiding in the development of more effective agricultural management and environmental protection strategies.
We would like to express our gratitude to No. 7 Geological Brigade, Shandong Provincial Bureau of Geology & Mineral Resources for inventory data. Funding for this project was provided by the Natural Science Foundation of Shandong Province, China [ZR2022MD032].
Conceptualization, Q.D., L.Z.; Methodology, Q.D., Z.S.; Investigation, Y.Z., L.Z.; Data curation, J.Y., G.S., and C.L.; Writing—original & draft, Z.S., Writing—review & editing, Q.D., L.Z.; Funding acquisition, Q.D.; All authors have read and agreed to the published version of the manuscript.
Funding for this project was provided by the Natural Science Foundation of Shandong Province, China [ZR2022MD032].
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
The authors declare no competing interests.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.