The Geochemical Characteristics and Exploitation Threshold of Copper in the Cultivated Soils of Guanzhong Plain, Shaanxi Province

Abstract

Moderate copper (Cu) intake is beneficial for human health; however, China has not established recommended Cu levels in major food crops and their cultivation lands. This study focuses on the Guanzhong Plain in Shaanxi Province, where we collected geochemical survey data at a scale of 1:250,000 and gathered 77 sets of wheat seed and root soil samples. We identified the Cu content and distribution characteristics within both soil and wheat grains in the area. Key factors influencing the bioaccumulation factor (BAF) of Cu in wheat were selected to establish a predictive model using artificial neural networks (ANN). Additionally, we determined recommended thresholds for Cu content in both the wheat and soil. The findings indicated, as follows, that: (1) the soil Cu content ranged from 13.00 to 98.00 mg/kg, with an average concentration of 32.21 mg/kg. Higher levels were found near alluvial deposits along the Qinling Mountains, showing a pattern of higher concentrations in the south than in the north; (2) the concentration of Cu in wheat grains varied from 2.94 to 6.34 mg/kg, with an average of 4.56 mg/kg; importantly, none exceeded the NY861-2004 permissible contamination limits; and (3) we recommend optimal ranges for Cu content for wheat grains of 3.16–10.00 mg/kg and establishing thresholds for cu-rich agricultural lands suitable for growing wheat of 20.80–50.00 mg/kg.

1. Introduction

Cu is an essential trace element in the human body, necessary for normal bone development and to prevent anemia. However, excessive intake can lead to Cu poisoning and increases the risk of gastrointestinal, cardiovascular and cerebrovascular diseases [1,2,3,4,5]. Therefore, it is important to consume appropriate amounts of Cu for good health.

There are numerous pathways for trace elements to enter the human body; however, the vast majority of trace elements are ingested through food, meaning that the trace element contents in soil and agricultural products are closely associated with human health. Soil is the most significant environmental medium in the terrestrial system and the main component of land resources. In recent years, as a heavy metal element, the geochemical characteristics and ecological effects of Cu in soil have become a research focus [6,7,8,9]. Nevertheless, although Cu is a beneficial element, unlike Se, a development threshold in land and agricultural products has not been established. Therefore, it is of considerable significance to investigate the content, migration patterns, and enrichment rules of Cu in cultivated soil and major crops.

In theory, there should be a positive correlation between the element content in crop grains and the bioaccumulation factor (BAF) of soil elements; however, the corresponding relationship is not obvious in existing studies [10,11]. This is not only related to complex soil systems (including chemical composition, mineral composition, texture, SOM, pH etc.), but also closely linked to the geochemical behavior of different elements [12]. Identifying key influencing factors will help to create a better understanding of both Cu migration laws within soil–crop systems and the geochemical cycle process of Cu.

The Guanzhong area of Shaanxi Province contains large areas rich in Cu resources. The biggest challenge lies in developing these land resources rationally while protecting human health due to a lack of development thresholds for land and agricultural products. Dietary structures vary among residents in different regions. For instance, European and American countries primarily consume beef, chicken, eggs, and bread, while China’s diet is mainly based on various cereal staples, which can be roughly categorized as “rice in the south and noodles in the north”. Wang Yizheng et al. developed a BAF prediction model for Cu content in rice grains and determined the threshold value by considering the dietary structure of residents [13]. They concluded that the optimal range of Cu content in rice is 0.615–8.204 mg/kg, and that the ideal range of Cu content in safe rice-planting soil is 6–84 mg/kg in Qintang District, Guigang City, Guangxi Province. Therefore, it is worthwhile to consider formulating development standards for Cu-rich land and wheat that are specific to each area by combining the results of dietary structure surveys with RNI (recommended intake of trace elements) and UL (maximum intake tolerable by human body).

Previous crop BAF prediction models were mostly constructed using multiple linear regression [14,15,16]; however, this modeling method did not adequately account for nonlinear relationships within complex environments such as soil and crops [17,18,19]. Artificial neural networks (ANNs), which simulates the working mode of human brain neurons, have been widely utilized due to their ability to learn and recognize complex patterns across various fields such as landslide disaster prediction and photovoltaic pollution prediction [20,21]. Guo et al. established a BAF prediction model for Cd and Se in rice–soil samples from Ziyang County, Ankang City, Shaanxi Province using an ANN methodology [22]. Upon comparison with other models, such as multiple linear regression (MLR), they found that ANN had better predictive capabilities, which is consistent with the research results of many scholars [23,24].

In this paper, 77 sets of wheat and its root soil samples were collected in a typical cultivated area of Guanzhong Plain. The geochemical characteristics and spatial distribution of Cu in the soil and wheat in the study area were investigated. The controlling factors of Cu’s migration and transformation in the soil–wheat system were investigated. Based on this, a BAF model of Cu in soil–wheat system was established using ANN. Finally, according to the dietary structure survey of 300 local residents, combined with RNI and UL, we proposed the development threshold of characteristic land and characteristic crop wheat in the study area. In this study, human health, crops, and soil were linked by Cu, and the threshold value of wheat and land development was proposed according to the actual diets of local residents, which provides new information for regions that want to determine the threshold value of a certain element of land or agricultural products and rationally develop characteristic land and agricultural products.

2. Materials and Methods

2.1. Overview of the Study Area

The research area is located in the west of Xi’an City, Shaanxi Province, including Xingping City and Wugong County in the north of Weihe River, and Mei County, Zhouzhi County and Huyi district in the south, with a longitude of 107°38’–108°43’ and a latitude of 33°36’–34°23’. The location of the study area is shown in Figure 1.

Guanzhong Plain is located in the mid-latitude region and has a temperate semi-arid, semi-humid, monsoon climate. The average precipitation is 600–750 mm and the precipitation is mostly concentrated from July to September. The distribution is very uneven in this region. Atmospheric precipitation is the main recharge water source in the study area; however, the recharge capacity is relatively weak due to drought.

The study area has many geomorphic types and complex structures. The overall terrain here is low in the north and high in the south (Figure 2), which mainly includes mountains, alluvial plains, flood plains, and loess tableland. Influenced by the type of parent material, landform and geomorphology, the main soil in the Guanzhong area is brown soil, accounting for 32.17% of the area’s soil. Cultivated land and grassland are the main land types in the study area. The cultivated land is mainly distributed in Xingping City, Wugong County and Meixian County, while the woodland and grassland are mainly distributed in the southern Qinling Mountains of Zhouzhi County and Huyi District. The research area belongs to the Guanzhong Plain city cluster, a key area of the national western development and is an important node of the “Belt and Road” with convenient transportation. Guanzhong Plain has always been one of the main grain-producing areas in China and mainly grows wheat and corn but also fruits and vegetables. The winter wheat–summer maize double-cropping rotation per year is a common farming method in the local area and the wheat yield is considerable. For example, in 2022, the annual output of wheat in Xingping City and Wugong County were 106,500 t and 214,300 t. The annual output of wheat in Zhouzhi County, Huyi District and Meixian County were 52,400 t, 104,800 t and 78,600 t.

2.2. Sample Collection and Processing Methods

The samples collected in this study were wheat (Xinong 3517, Xinong 1018 and Xinong 511, from Yangling, China) and its root soil. Grains and corresponding root soil samples were collected at the wheat maturity stage with a sampling density of 1 point /4–6 km². Pests, diseases and other special plants were avoided when sampling. The wheat was divided into 5 sub-sampling sites, each of which was 1 m² to 2 m²; 5–10 plants were evenly selected. The wheat grains were cut off and put into a bag. Then, the 5 sub-samples were mixed into one in equal parts weighing 300 g–500 g and numbered.

At each wheat collection site, the collected wheat plants were uprooted and the soil from the crop roots was shaken off onto a disposable plastic sheet. All the soil shaken off from the sample points was mixed evenly and put into a clean cloth bag and numbered. When the soil was too wet, a plastic bag was put on the outside of the cloth bag. Each sample weighed more than 2000 g. Finally, a GPS was used to record the coordinates of the sampling points and the details were recorded in a questionnaire.

A dietary structure survey was conducted in rural areas of the study area. Resident adults (18–60 years old) were the main respondents. Questions concerning the consumption and sources of rice, wheat, vegetables, meat, fruit, milk, and other foods were asked and answers were recorded in detail. A total of 300 questionnaires were completed.

The sample collection, processing, and other field work were carried out in accordance with relevant standards. This ensured the high quality of the field work.

2.3. Sample Analysis Method

After all the samples were collected and processed, they were sent to Hubei Geological Experimental Test Center for sample analysis and testing.

The analytical qualities, such as the detection limits, precision and accuracy of all samples, were performed in accordance with the relevant standards. The detection limits and qualified conditions of each test index are shown in

Table 1. Methods and detection limits of Cu, MnO, TFe₂O₃ and pH in wheat and soil.

Sample Type	Test Indexes	Units	Test Methods	Standard Detection Limit	Actual Detection Limit	Pass Rate of Accuracy	Precision Pass Rate
Wheat	Cu	mg/kg	Inductively coupled plasma mass spectrometry [25]	0.050	0.025	100%	100%
Soil	MnO	mg/kg	Inductively coupled plasma atomic emission spectrometry [26]	10	5	100%	100%
	TFe2O3	%	X-ray fluorescence spectrometry [26]	0.05	0.02	100%	100%
	Cu	mg/kg	Inductively coupled plasma mass spectrometry [26]	1	0.1	100%	100%
	pH	/	The ion selective electrode method [27]	0.10	0.01	100%	100%

.

2.4. Data Processing Methods

Microsoft Excel 2010 and SPSS 25.0 were used for the data processing and basic statistical analysis. Using the ANN toolbox in Rstudio 4.4.1, the nonlinear fitting operation of Cu content in soil–wheat in the study area was carried out, and the Cu enrichment coefficient in wheat was predicted by the model. ArcGIS 10.7 was used to compile the geochemical maps of elements.

3. Results

3.1. Characteristics of Soil Cu Content

The statistics of Cu content in soil in the study area are shown in

Table 2. Comparison of soil Cu content characteristics in typical cultivated areas of Guanzhong Plain with others (mg/kg).

Element	Number of Samples	Maximum	Minimum	Median	Mean	Shaanxi Province a	Northwest China a	China b
Cu	716	98.00	13.00	31.00	32.21	27	24	22.60

^a From soil geochemical parameters in China [28]; ^b from background values of soil elements in China [29].

. It can be seen that the content of Cu in the soil ranges from 13.00–98.00 mg/kg, with an average value of 32.21 mg/kg, which is significantly higher than the background value of soil Cu content in China, and also higher than the average content of soil Cu in Shaanxi Province and northwest China.

The spatial distribution characteristic of soil Cu in the study area is shown in Figure 3; the overall performance is “high in the south and low in the north”. The high-value Cu area is mainly distributed in the alluvial and diluvial areas in the foremountains of the Qinling Mountains. The average Cu content in the soil of the three counties in this area is higher than 30 mg/kg, among which Huyi District is the most enriched, with an average content of 35.12 mg/kg. The low-value Cu area is mainly consistent with loess; the average Cu content in the soil of Wugong County and Xingping City in this area is lower than 30 mg/kg, but it is still higher than the average Cu content in the soil of northwest China.

3.2. Characteristics of Copper Content in Wheat Seed

The content of Cu in wheat grains in the study area ranged from 2.94 to 6.34 mg/kg, with an average value of 4.56 mg/kg and a median value of 4.49 mg/kg. The Limits of Eight Elements in Cereals, Legumes, Tubes and Its Products Standard (NY 861-2004) [30] stipulates that the limit of Cu in grains is 10.0 mg/kg. The Cu content of wheat grains collected in the study area did not exceed the limit value and was slightly higher than the national average Cu content of wheat (4.3 mg/kg) in the China Food Composition Tables Standard Edition [31], and has good safety and development potential.

Figure 4 shows the scatterplot of Cu content in wheat seeds and root soil. It can be seen that there is no obvious correlation between the Cu content in the wheat seeds and root soil. These results indicate that the Cu content in the wheat seeds was influenced by many other factors such as soil physicochemical properties and Cu bioavailability.

3.3. Choice of Modeling Factor

In theory, the levels of element contents in soil absorbed by crops is positively correlated with the bioavailability of the elements in the soil; in fact, the factors affecting the uptake of elements by crops are very complex. Therefore, the BAF of soil Cu was used as the expression of the Cu absorption capacity of wheat grains. The calculation formula is as follows:

BAF = C_seed/C_soil

(1)

BAF is the bioaccumulation factor; C_seed is the content of elements in crop grains; C_soil is the content of elements in root soil.

The correlation coefficient between the BAF_Cu in wheat seed and the physical and chemical properties of soil was analyzed by SPSS using 77 sets of wheat seed and root soil data. Fifty-nine sets of samples were randomly selected as prediction samples and 18 sets of samples were used as test samples. The Pearson correlation coefficients between the BAF_Cu in wheat and several soil physicochemical properties were analyzed by SPSS. The results showed that Cu, MnO, TFe₂O₃ and BAF in soil had higher correlation coefficients (

Table 3. Pearson correlation between main factors of soil and BAF_Cu.

	Cu	Fe2O3	MnO	pH	SOM	SiO2	Al2O3
BAFCu	−0.65	−0.610	−0.507	−0.151	−0.03	0.25	−0.01

). In addition, pH is generally considered to be the main controlling factor of heavy metal elements in soil; thus, Cu, MnO, TFe₂O₃ and pH were taken as the independent variable factors of the Cu prediction model and BAF_Cu was taken as the dependent variable.

In the study area, soil MnO, TFe₂O₃, and pH were identified as primary determinants affecting the bioavailability of Cu. The correlation analysis revealed a negative relationship between these factors and the BAF_Cu.

Soil pH is a crucial physicochemical property that significantly influences the transformation and mobility of elements in soil [32,33,34]. The bioavailability of Cu in wheat is impacted by the soil pH (Figure 5). As the soil pH decreases and the H⁺ ion concentration increases, competition for adsorption between Ca²⁺ and Mg²⁺ ions in the soil solution intensifies, leading to the enhanced desorption and migration ability for weakly bound Cu²⁺ ions, thereby improving their biological availability [35]. Conversely, an increase in the pH value results in the decreased bioavailability of Cu.

Iron and manganese oxides, as integral components of the soil structure, tend to form nodules that adsorb and immobilize Cu within the soil matrix, hindering crop uptake and reducing overall Cu availability (Figure 6 and Figure 7). Iron and manganese oxides possess substantial surface charge, a large specific surface area, rich structural characteristics, and exhibit a high affinity for heavy metals such as Cu. Previous research has demonstrated that heavy metals, like Cu, can be sequestered within soils through mechanisms including isomorphous substitution, isogeny-driven processes, and co-precipitation with iron and manganese oxides [36,37,38].

3.4. Soil-Wheat Seed Copper Prediction Model

Using the artificial neural network toolbox in Rstudio, a neural network prediction model for Cu content in soil–wheat seeds was established (Figure 8), and the optimal prediction model was obtained when a hidden layer and four neurons were set up. The correlation coefficient between the BAF predicted by this model and the BAF of the actual sample is 0.78 (

Table 4. The selection of independent variables of the ANN model and the prediction effect of the model.

Dependent Variable	Independent Variable	Number of Model Samples	Number of Validated Samples	Correlation Coefficient
BAFCu	Cu, MnO, TFe2O3, pH	59	18

).

To evaluate the performance of the network model, a corresponding multiple-regression (MLR) model was developed as follows:

lg(BAF_Cu) = 0.785 − 0.16 × lg(pH) − 0.038 × lg(MnO) − 0.359 × lg(TFe₂O₃) − 0.752 × lg(Cu)

The normalized mean error (NME) and normalized root mean squared error (NRMSE) were used to judge the accuracy and precision of the model prediction results. The NME and NRMSE of the ANN prediction equation were −0.0018 and 0.1514. The NME and NRMSE of the MLR prediction equation were −0.0155 and 0.1532. In general, The NME and NRMSE of the two prediction models were much less than 1, indicating that the accuracy and precision of the models are good, and that the established models can better predict the Cu content in wheat grains. The NME is negative; that is, the BAF predicted by the model is lower than the actual value.

After comparing the predicted results of the ANN model and MLR model with the actual values, it can be found (Figure 9 and Figure 10) that most of the sample points fall near the fitting line, and that only a few decimal points deviate from the fitting line. The predicted results of the two models are similar; R² is about 0.6, indicating a good fitting effect. After comparing the two models, we can see that the ANN prediction model can be effectively used to predict to a certain extent, but there is a large deviation between the predicted value and the actual value of individual sample points. It is necessary to increase the number of samples in the future to optimize the model and reduce the deviation between the predicted value and the actual value.

3.5. Optimum Range of Cu in Soil and Wheat

The average daily intake of different foods of residents in the study area and Chinese adults are shown in

Table 5. Comparison of the average daily intake of various foods between the study area and Chinese adults.

Study Area			China
Food Type	Content (g/d)	Proportion	Food Type	Content (g/d)	Proportion
Wheat and its products	229.63	23.88%	Wheat and its products	117.3	12.75%
Rice	80.28	8.35%	Rice	131.6	14.31%
Corn	5.31	0.55%	Corn	15	1.63%
Vegetables	204.57	21.28%	Vegetables	286.5	31.15%
Fruits	133.26	13.86%	Fruits	55.7	6.06%
Potatoes	2.05	0.21%	Potatoes	35.6	3.87%
Meat	107.05	11.13%	Meat	158.1	17.19%
Pulses	13.54	1.41%	Pulses	15.5	1.69%
Nut	1.90	0.20%	Nut	4.4	0.48%
Milk	105.84	11.01%	Milk	42.2	4.59%
Oils	50.60	5.26%	Oils	42	4.57%
Condiments	27.50	2.86%	Condiments	15.9	1.73%
Total	961.53	100%	Total	919.8	100%

. Data from the study area were derived from the questionnaire survey of local residents’ dietary structure; national data were derived from the average daily average intake of various types of food during 2015–2017 in the China Health Statistical Yearbook 2022 [39]. As can be seen from the table, the daily intake of wheat and its products for adults in the study area is about three times that of rice, and the intake of wheat and its products accounts for about 23.88% of the total intake. The intake of wheat products and rice products is close to that of urban residents, accounting for about 12.75% and 14.31% of the total intake, respectively. In general, the main energy sources in the dietary structure of the residents in the study area were wheat and its products.

Since the external intake of Cu in human body is mainly through food, the dietary intake is calculated according to the dietary exposure assessment method, as follows:

$$\mathrm{E}\mathrm{D}\mathrm{I}=\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{C}}_{\mathrm{j}}\times {\mathrm{I}\mathrm{R}}_{\mathrm{j}}$$

(2)

In the formula, EDI (estimated daily intake) represents the daily Cu intake (mg/d), j represents a certain type of food, and n is the total number of food intake per day. C_j represents the content of Cu in j foods (mg/kg) and IR_j represents the daily intake of j foods (g).

D_j(%) represents the contribution rate of Cu intake through j food to the total food intake, calculated as follows:

$${\mathrm{D}}_{\mathrm{j}}=\left({\displaystyle \frac{{\mathrm{C}}_{\mathrm{j}}\times {\mathrm{I}\mathrm{R}}_{\mathrm{j}}}{\mathrm{E}\mathrm{D}\mathrm{I}}}\right)\times \mathrm{100\%}$$

(3)

In the formula, EDI (estimated daily intake) represents the daily Cu intake (mg/d), C_j represents the Cu content in j food (mg/kg), and IR_j represents the daily intake of j food (g).

The RNI value (0.8 mg/d) and UL value (8 mg/d) of Cu given in the Chinese Dietary Reference Intakes (WS/T578-2017) [40] were taken as the daily Cu intake range for adults. It is concluded that the development threshold of Cu-rich agricultural products suitable for local residents in the study area is 3.08–30.80 mg/kg, while the development threshold of Cu-rich agricultural products suitable for urban residents in the country is 3.24–32.40 mg/kg (

Table 6. Cu threshold of soil and wheat grain in typical cultivated areas of Guanzhong Plain and China (mg/kg).

		Minimal	Maximum
Study area	Wheat	3.08	30.80
	Soil	20.28	202.76
China	Wheat	3.24	32.40
	Soil	21.33	213.30

).

According to the ANN model of soil–wheat Cu, the average values of several independent variables were substituted into the model; the optimal range of soil Cu content in the study area was 20.28–202.76 mg/kg. For the wheat to be sold nationwide, the optimal range of Cu content in soil in the study area is 21.33–213.30 mg/kg (Table 6).

4. Discussion

The thresholds of wheat and land development calculated in Section 3.4 of this paper, determined whether wheat was suitable for local residents in the study area or suitable for national sale. The maximum value of Cu exceeded the limit value stipulated in the Limits of Eight Elements in Cereals, Legumes, Tubes and Its Products Standard (NY 861-2004) [30] (the limit value of Cu in grains was 10.0 mg/kg). Therefore, the development threshold of Cu-rich wheat for consumption by local residents in the study area was determined to be 3.08–10 mg/kg, while the development threshold of Cu-rich wheat for sale across the country was determined to be 3.24–10 mg/kg. Similarly, the calculated land threshold also exceeded the soil Cu limit (50 mg/kg) stipulated in the Soil Environmental Quality Risk Control Standard for the Soil Contamination of Agricultural Land (GB 15618-2018) [41]. Therefore, the Cu-rich land development threshold for wheat cultivation applicable to local residents in the study area is 20.28–50 mg/kg, while the Cu-rich land development threshold for wheat cultivation sold across the country is 21.33–50 mg/kg. Wheat sold throughout the country has a higher threshold for grains and planting lands, because residents in other urban areas have a lower intake of wheat products than Guanzhong, which makes their intake of Cu lower, thus, they have higher requirements for Cu content in wheat and land.

According to the survey results of the dietary structure in the study area, local residents consume wheat and its products as their main food source; therefore, it is more reliable to calculate the soil Cu threshold in the study area through the Cu threshold value of wheat. However, the Cu content of uncollected samples except for wheat may be different to the actual situation in the local area. If the Cu content of meat, vegetables, fruits, eggs, milk, nuts, and other foods is systematically collected and analyzed, the Cu development thresholds of soil and wheat in the study area can be defined more accurately.

The threshold values of Cu content in wheat grains and land in the study area were obtained based on the questionnaire survey of adults (18–60 years old) in the study area and did not distinguish between genders. Therefore, this range value may be less applicable for areas where the sex ratio is unbalanced or where the elderly and children are concentrated. In addition, this threshold range is only applicable to wheat varieties planted in the study area; different wheat varieties have different Cu absorption capacities [42,43]. Therefore, for different wheat varieties, the development thresholds of wheat grains and land should be calculated by re-modeling based on the actual local conditions.

5. Conclusions

The average content of Cu in the topsoil of the study area was 32.21 mg/kg, which was significantly higher than the average content of soil in Shaanxi Province and northwest China. In terms of spatial distribution, the Cu content in soils distributed in the alluvial and diluvial areas of the Qinling Mountains is relatively high, which is obviously affected by the bedrock. The distribution of the low-value area is mainly consistent with that of loess. The content of Cu in wheat grains ranged from 2.94 to 6.34 mg/kg, with an average value of 4.56 mg/kg, which was slightly higher than the national average Cu content; no samples exceeded the standard.

An ANN prediction model of the BAF value of Cu and important soil indexes was constructed. According to the prediction model and the survey results of the dietary structure of residents in the field, the optimal range of Cu content in wheat grains in the study area was 3.08–10 mg/kg. The soil Cu content corresponding to the development of Cu-rich wheat ranged from 20.28–50 mg/kg. When the whole country is considered, the optimal range of Cu content in wheat grains in the study area is 3.24–10 mg/kg and the corresponding threshold range of Cu development in the land is 21.33–50 mg/kg.