Next Article in Journal
Factors Influencing the Sustained Adoption of Innovative Techniques by Urban Farmers in Lubumbashi, Democratic Republic of Congo
Next Article in Special Issue
Can Nanomaterials Improve the Soil Microbiome and Crop Productivity?
Previous Article in Journal
Modification of Cuticular Wax Composition and Biosynthesis by Epichloë gansuensis in Achnatherum inebrians at Different Growing Periods
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agriculture
Volume 12
Issue 8
10.3390/agriculture12081156
agriculture-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Study on Safe Usage of Agricultural Land in Typical Karst Areas Based on Cd in Soil and Maize: A Case Study of Northwestern Guizhou, China

by
Liyu Yang
1,
Pan Wu
1,2,3 and
Wentao Yang
1,2,3,*
1
College of Resource and Environmental Engineering, Guizhou University, Guiyang 550025, China
2
Key Laboratory of Karst Geological Resources and Environment, Ministry of Education, Guizhou University, Guiyang 550025, China
3
Guizhou Karst Environmental Ecosystems Observation and Research Station, Ministry of Education, Guizhou University, Guiyang 550025, China
*
Author to whom correspondence should be addressed.
Agriculture 2022, 12(8), 1156; https://doi.org/10.3390/agriculture12081156
Submission received: 8 July 2022 / Revised: 1 August 2022 / Accepted: 1 August 2022 / Published: 4 August 2022
(This article belongs to the Special Issue Remediation of Heavy Metals-Contaminated Soils)
Browse Figures
Review Reports Versions Notes

Abstract

:
Cadmium (Cd) is an unnecessary dietary toxin that is harmful to human health. The Cd translocation in soil-crops system varies greatly depending on different soil matrices; therefore, a valuable method that could accurately evaluate soil Cd thresholds needs to be proposed immediately. In the southwestern part of China, a typical karst mountainous area of east Asia, the results of our survey of 492 soil-maize samples in the region showed high Cd accumulation in the soil, with concentrations ranging from 0.07 to 31.95 mg kg−1. The Cd concentrations in maize kernels planted in those fields, however, were quite low, and only 4 samples exceeded the national standard. A comparative study with nonkarst areas revealed a low bioaccumulation factor for soil Cd. This may be interpreted as the weathering soil-forming process of mineral-bearing rock systems, leading to high accumulation as well as low bioavailability of Cd in karst soils. A total of 172 soil-maize samples were evaluated inaccurately by the national standard evaluation procedure, accounting for 34.96% of the total. Therefore, we proposed the species sensitivity distribution model to address this inaccurate assessment. The results show that the hazardous concentrations of 95% and 5% in maize fields were 2.2 and 85.1 mg kg−1 for soil pH ≤ 5.5, 2.5 and 108.5 mg kg−1 for 5.5 < pH ≤ 6.5, and 3.0 and 161.8 mg kg−1 for 6.5 < pH ≤ 7.5, respectively. The total number of unsuitable samples according to the evaluation results decreased from 172 to 2 after modification. Therefore, this result could be considered a more accurate assessment threshold.

1. Introduction

In recent decades, due to the overexploitation of metallurgical and agricultural resources, more than 20 million hectares of arable land in China has been contaminated with varying degrees of heavy metal(loid)s [1,2,3]. The migration and transport of heavy metal(loid)s in agricultural environments has become the focus of governments and scientists [4,5,6]. Cadmium (Cd) is recognized as a nonessential metal element in plants [7,8]. The absorption and translocation of Cd by plants affects the yield and quality of crops to a certain extent, and Cd can accumulate in the human body through the food chain [9,10]. The accumulation of Cd could cause damage to the nervous system and kidneys of the human body, with serious outcomes such as cancer or even death [11,12]. Many studies have proven that Cd has become an important pollutant affecting food safety and human health [13,14,15].
Guizhou Province is located in the center of the karst region in Southwest China, and its ecological environment is very fragile [16,17]. It is generally believed that there are two main sources of soil Cd in karst areas of Guizhou Province, including man-made sources such as those arising from artificial processes of mining, smelting, and pesticide overuse, and natural sources such as the weathering process of ore-bearing rock series [18,19]. In 1990, the China National Environmental Monitoring Center [20] reported that the background value (BGV) of soil Cd in northwestern Guizhou was 0.66 mg kg−1, which was significantly higher than the national background value of 0.097 mg kg−1. This area was considered to be a geochemical anomaly because of the heavy metal(loid)s in the soil [21,22]. Ning et al. [23] found that the content of heavy metal(loid)s in some soils of Guizhou Province exceeded the current Chinese Environmental Quality Standards for Soil (EQSs), but the Cd content of edible parts of crops was low. These results are generally interpreted as indicating the ‘high background, low activity’ characteristics of heavy metal(loid)s in the soil of karst areas in Guizhou Province [24,25]. A number of previous studies have demonstrated that the ability of heavy metal(loid)s in the soil to translocate to crops has no significant relationship with the total amount of heavy metal(loid)s in the soil but is related to the distribution of their fractions in the soil [26,27]. The different distribution fractions of heavy metal(loid)s in soil determine the bioavailability of heavy metals(loid)s in soil [28]. Heavy metal(loid)s with low bioavailability are not easily translocated and accumulate in edible parts through the roots of plants [29].
Northwest Guizhou is the main corn production region in China, and local residents consume corn as a primary food [30]. Our previous investigation (unreported) found that more than half of the maize in Shuicheng District, northwestern Guizhou, China, was planted on land with a Cd level higher than 0.3 mg kg−1. However, studies on the classification of the soil environmental quality of agricultural land in areas with high background values of soil Cd geochemistry are very limited. Most previous studies have focused on man-made pollution areas, including industrial and mining, smelting, and sewage-irrigated areas [4,31,32]. Northwest Guizhou is not only an area with a high Cd background value but also an area with a relatively large maize planting area on the Guizhou–Yunnan Plateau. It is unclear whether there are health risks associated with crops grown in the soil of this high Cd background area.
The EQSs can be used to evaluate the soil contamination level of heavy metal(loid)s well in the Hunan, Jiangsu, and Zhejiang regions of China [33,34,35]. However, the environmental quality of farmland in different regions is quite different due to China’s vast geographic area, the sources of heavy metal(loid)s in Guizhou, China are complex (man-made sources, natural sources), there are many types of crops grown, and the Cd enrichment abilities of different crops are quite different [36,37]. Therefore, the current standards are not fully applicable to soil evaluation in high background Cd value areas, and it is necessary to adjust the corresponding farmland soil safety limit standards based on the Cd concentrations in the edible parts of the crop products. Based on the results of a preliminary investigation of the corn planting area in Shuicheng District [38], a soil geochemical anomaly area in northwest Guizhou, in this research, a collaborative monitoring investigation with 492 pairs of soil-maize samples was carried out. The aims of the study were to (a) systematically investigation the characteristics of the accumulation and distribution of Cd in soil and edible parts of maize in the region, (b) evaluation the suitability of the current Chinese standards for the evaluation of soil Cd levels in this region, and (c) proposition a soil environmental quality benchmark of farmland soil Cd levels for Shuicheng District, Northwest Guizhou Province based on the method of species sensitivity distribution (SSD). These results are expected to be of great significance for the safe use of farmland soil and the protection of human health in high Cd background regions.

2. Materials and Methods

2.1. Investigation Areas

The investigation area was located in the Shuicheng District of Liupanshui city in northwestern Guizhou Province, China (104°33’~105°15’ N, 26°03’~26°55’ E), on the slopes of the first and second terraces on the eastern side of the Yunnan–Guizhou Plateau. Shuicheng District is a typical karst area in southwestern China (Figure 1). The climate type belongs to the subtropical monsoon humid climate. Under the influence of low latitude and high altitude, the climate of this region is warm and humid, with an average annual temperature of approximately 14 °C and the highest temperature in July approximately 22 °C. Maize (Zea may L.) is the main food crop in the whole region, with 56.3% of the staple food being corn. The average concentration of Cd in Liupanshui agricultural soil was previously reported to be 0.26 mg kg−1, which is significantly higher than the national soil background value [21,39].

2.2. Sampling and Sample Analysis

From June 2019 to October 2020, a total of 492 pairs of soil-maize collaborative monitoring samples were collected in the study area by random sampling methods, including 492 soil and 492 crop samples, and three parallel samples were collected at each site. The edible parts of the maize kernels were collected and packed in polyethylene mesh bags, and at the same time, a wooden shovel was used to collect the surface soil (0–20 cm) corresponding to the rhizosphere of the crop. The quarter method was used to collect approximately 500 g of the mixed soil sample, which was put in a clean polyethylene bag and marked. After the soil and crop samples were brought back to the laboratory, the soil samples were naturally dried, rhizomes were removed, and the soil was ground, passed through a 0.149 mm nylon mesh sieve, and stored in aliquots at room temperature for later analysis. The maize kernels were cleaned with tap water and rinsed with deionized water 3~5 times. Then, the samples were placed in a drying oven at 70 °C until the weight of the sample remained constant. The oven-dried samples were ground to a fine powder using a stainless-steel grinder and then passed through a 100-mesh sieve for further analysis. The pH of the soil was determined with a pH meter (pH-3c, INESA Scientific, Shanghai, China), and the water-soil weight ratio was 2.5:1 (w/w). All samples were digested by mixed aqua regia solution [41], and the total Cd concentration of the digestion solution was determined by inductively coupled plasma-mass spectrometry (ICP-MS, Thermo Fisher Scientific, Waltham, MA, USA).

2.3. Evaluation Procedure of the Chinese Soil Environmental Quality Standards (EQSs)

The current Chinese Environmental Quality Standards for Soil (EQSs) (GB15618–2018) issued by the Ministry of Ecology and Environment of China (MEE) in 2018 played a significant role in protecting China’s agricultural soil environment, controlling agricultural soil pollution risk, and ensuring the quality and safety of agricultural products. In the Chinese standard for soil environmental quality, two limits are set for the level of heavy metal(loid)s (Pb, Cd, As, Hg, Cr) in agricultural soil: the Risk Screening Values (RSVs) and the Risk Intervention Values (RIVs) for soil contamination of agricultural land (Table 1) [42]. When the level of heavy metal(loid)s in the soil is less than or equal to the RSVs, the soil is evaluated as a ‘priority protection’ grade, which indicates that the risks posed by heavy metal(loid)s in the soil to farm product quality safety, crop growth or the soil ecological environment are low and can be ignored in general. When the level of heavy metal(loid)s in the soil is between the RSVs and RIVs, the soil is evaluated as ‘safe use’, which indicates that heavy metal(loid)s in the soil may pose risks to farm product quality safety, crop growth, or the soil ecological environment, and the monitoring of the soil environment and collaborative monitoring of farm products need to be strengthened. The soil is evaluated as ‘strictly controlled’ when the heavy metal(loid)s in the soil are greater than the RIV, which indicates that the edible agricultural products grown on the land do not meet the EQSs, and continued cultivation of edible crops is not allowed.

2.4. Quality Control and Statistics

All chemicals were reagent grade, and deionized water was used in all experiments. All glassware and utensils were cleaned, soaked in nitric acid solution (10% v/v) overnight, rinsed with deionized water, and dried before use. National Standard Materials of Soil (GBW07404a (GSS−4a)) and Maize (GBW10012 (GSB−3)) were used during the analysis for quality control. The recovery rates of total Cd were 92.2−106.3% and 90.5−110.4%, respectively.
All data were analyzed by SPSS 19.0 using one-way ANOVA and Duncan’s test (p < 0.05). The data are expressed as the mean ± standard deviation (n = 3). All figures were processed using OriginPro 2019 software (OriginLab Corporation, Northampton, MA, USA).

2.5. Species Sensitivity Distribution (SSD) Method

The species sensitivity distribution (SSD) method was employed to analyze the health risk benchmark value of Cd [43]. The SSD method uses probability distribution functions to extrapolate the toxicological level between different species to realize the risk assessment of pollutants at the levels of biological communities and ecosystems [43]. It is considered to be more accurate in the baseline values used to assess the quality of the soil environment [44]. Specifically, the steps of the method were to (a) calculate the bioaccumulation factor (BCF) of the edible part of maize as the probability distribution index through the analysis of the Cd concentrations in the maize samples and in the corresponding soil collected in the field; (b) fit the SSD curve by a logarithmic logistic distribution model; and (c) calculate the 5% and 95% hazardous concentrations (HC5 and HC95) in maize fields as the health risk benchmark value in the research region according to the limits for Cd in China. HC5 and HC95 indicate that crops grown at this Cd concentration can guarantee that 95% and 5% of the edible parts of the crop contain Cd below the threshold value.
The BCF value was calculated using Equation (1) as follows:
BCF = C i C s
where Ci represents the content of Cd in the edible part of the maize, mg kg−1, and Cs represents the content of Cd in the soil, mg kg−1.
The 1/BCF value of Cd in the soil and the sensitive distribution of crop accumulation effects followed the ‘S’ curve distribution, and the fitted SSD curve was calculated by Equation (2) as follows:
y = a 1 + ( x x 0 ) b
where x represents the value of 1/BCF; y represents the probability of the crop sample Cd cumulative distribution; and a, b, and x0 are constants.
The HC5 and HC95 values in maize fields were employed to estimate the health risk benchmark value in the research region and were calculated using Equations (3) and (4) as follows:
x = 10 lg ( a y − 1 ) b + lg x 0
C e = C f x
Ce represents the value of HC5 or HC95; Cf represents the allowed maximum levels (MLs) of contaminants for foods according to the national standard (0.1 mg kg−1 of Cd for maize).

3. Results

3.1. pH Values and Cd Concentrations in Soil

The frequency distributions of soil pH (a) and the Cd concentrations (b) and scatter plots of soil pH and soil Cd concentration (c) are shown in Figure 2. The soil in the study region was neutral and slightly acidic, and the median and arithmetic average values of soil pH were 5.67 and 5.72, respectively, ranging from 4.03 to 7.97. In terms of the distribution rate, the number of samples with soil pH values less than or equal to 5.5 (pH ≤ 5.5) was 203, accounting for 41.26% of the total. There were 225 sites with pH values greater than 5.5 and less than or equal to 6.5 (5.5 < pH ≤ 6.5), accounting for 45.73% in total. Only 64 of the samples had pH values higher than 6.5 (pH > 6.5) (Figure 2a). In Figure 2b, the average Cd concentration in the soil of the study area was extraordinarily high, ranging from 0.07–31.95 mg kg−1 (dry weight), and the arithmetic average concentration of Cd was 1.71 mg kg−1, which was significantly higher, by approximately 6.58 times, than the background value (BGV) of 0.26 mg kg−1 in Guizhou Province. In terms of the distribution rate, there were 21 samples with Cd concentrations less than or equal to the BGV, accounting for 4.27% of the total; there were 471 sites with Cd concentrations higher than the BGV, accounting for 95.73% of the total, and 64.83% of the soil samples had Cd concentrations more than 2 times higher than the BGV.
Compared with the Risk Screening Values (RSVs) and the Risk Intervention Values (RIVs) in the current Chinese Environmental Quality Standards for Soil (EQSs) (GB15618–2018) issued by the Ministry of Ecology and Environment of China (MEE) in 2018, the Cd concentration of the soil in the study area severely exceeded the standard. More than 95% of the samples had soil Cd concentrations over the RSV (the yellow line) or the RIV (the red line) according to the EQSs (Figure 2c). Specifically, the Cd concentration in 19 (3.86%) soil samples was lower than the RSV (≤RSV), and samples with levels between the RSV and the RIV (RSV<, ≤RIV) and higher than the RIV (≥RIV) accounted for 281 (57.11%) and 192 (39.03%) of the total samples, respectively.

3.2. Cd Concentrations in Maize Kernels

A total of 492 maize samples were collected at the corresponding soil sample points, and the Cd concentrations in the maize kernels are shown in Figure 3. Interestingly, although the concentration of Cd in the soil seriously exceeds the current RSV and RIV standards, our survey results of agricultural products showed quite optimistic results. The Cd concentrations in 492 maize samples were low overall. The median and arithmetic average Cd concentrations in 492 maize samples were 0.071 mg kg‒1 and 0.012 mg kg‒1, respectively, and the range was 0.0012–0.21 mg kg‒1. According to the Cd limit for corn kernels recommended by the national standard (0.1 mg kg‒1), the Cd concentrations of more than 99% of maize kernel samples did not exceed this threshold, and only less than 1% of maize kernel samples had Cd levels exceeding 0.1 mg kg‒1.

3.3. Comparative Analysis of the Study Area and Nonkarst Area Data in China

The data in this study were further compared with data from nonkarst corn growing regions (a maize field in Hunan, Zhejiang, and Hainan provinces, China) at the same latitude in Asia, as reported in the study of Feng et al. [40]. Figure 4a shows that Hunan B was heavily polluted with Cd (Cd = 6.399 ± 0.394 mg kg−1), and the regions of Hunan A, Zhejiang and this study area had moderate Cd pollution levels (Cd = 1. 820 ± 0.029 mg kg−1, 0.906 ± 0.071 mg kg−1, and 1.713 ± 029 mg kg−1), while Hainan was unpolluted (Cd = 0. 053 ± 0.013 mg kg−1). Compared with other regions, the difference in soil Cd concentration in the research region was significant.
As shown in Figure 4b, Hunan B had the highest Cd concentration in maize grains, followed by Hunan A and this research region. The Cd concentration in maize grains in Zhejiang and Hainan did not exceed the standard limit. The probability (P) approximations of empirical cumulative distributions in Hunan A, Hunan B, and the study region that appeared at 0.1 mg kg−1 Cd were 10.99%, 96.10%, and 99.19%, respectively.
The BCF of Cd was calculated by Equation (1) (Figure 4c). The BCF of Hainan was the highest, followed by Hunan A and Hunan B. The BCF value of this study was the lowest among the 5 regions. It is worth noting that the total soil Cd concentrations of Hunan A and this study region were similar, with soil Cd concentrations of 1.82 and 1.71 mg kg−1, respectively. However, the Cd concentration in maize kernels was approximately 3.25 times different between the two regions. The average Cd concentration of maize kernels in Hunan A was 0.039 mg kg−1, while that in the study area was only 0.012 mg kg−1. These results demonstrated the ‘high background, low activity’ characteristics of Cd in the soil of northwestern Guizhou [24,25,45].

3.4. Suitability Analysis of the RSV and RIV for the Classification of Soil Environmental Quality

Referring to the suitability evaluation method proposed by Romkens et al. [46] and Song et al. [47], we verified the suitability of the current Chinese soil Cd standard for the classification of soil environmental safety for maize planting in Guizhou. The results are shown in Table 2. When the soil Cd concentration was less than or equal to the RSV (≤RSV), the appropriate rate of the risk screening value in EQSs was 93%, and the rate of type I errors (false negatives, the soil evaluation result is the ‘safe use’ level, but the Cd concentration in maize kernels exceeds the standard limit) was 7%. When the soil Cd concentration was between RSV and RIV, the rate of safety for maize kernel Cd was 100%. When the soil Cd concentration was higher than the RIV, the type II error rate (false positives, the soil evaluation result was the ‘strict control’ level, but the Cd content in the maize grains did not exceed the standard limit) was as high as 98.28%, and only 1.72% of the samples were suitable under the EQS evaluation. In general, there were 172 pairs of samples in total that were not compatible with evaluation by the EQSs.

3.5. Derivation of Soil Environmental Quality Benchmarks for Maize-Planted Soil

From the above results, it can be concluded that the current Chinese standard was not suitable for evaluating the soil Cd pollution level in the study area (Table 1), and the soil environmental quality benchmarks need to be proposed according to the actual situation. Thus, we utilized the species sensitivity distribution method to calculate the 5% and 95% hazardous concentrations in maize fields to assess the actual Cd pollution level of the research region [43]. Specifically, the logistic distribution model and the standard limit of Cd for maize kernels according to the national standard were used to fit the 1/BCF value and the cumulative probability of maize kernel Cd. Equations (2)–(4) were used to calculate the hazardous concentration based on 95% and 5% crop safety (HC5 and HC95) for soil pH ≤ 5.5, 5.5 < pH ≤ 6.5, and 6.5 < pH ≤ 7.5 (the number of soil points under pH > 7.5 was insufficient). The results are shown in Figure 5.
The HC5 and HC95 values derived using the SSD method are considered to be one of the accurate approaches to define soil environmental quality benchmarks [48,49]. The estimated HC5 and HC95 values of maize-planted soil with different soil pH levels in this study area were 2.2 mg kg−1 and 85.1 mg kg−1 at soil pH ≤ 5.5, 2.5 mg kg−1 and 108.5 mg kg−1 at 5.5 < soil pH ≤ 6.5, and 3.0 mg kg−1 and 161.8 mg kg−1 at 6.5 < soil pH ≤ 7.5, respectively (Table 3). These estimates implied that Cd concentrations in 95% of the maize kernels would not exceed the national food safety standard (≥0.1 mg kg−1) when the soil Cd concentration was below 2.2 mg kg−1 at soil pH ≤ 5.5, below 2.5 mg kg−1 at 5.5 < soil pH ≤ 6.5, and below 3.0 mg kg−1 at 6.5 < soil pH ≤ 7.5. Therefore, when the Cd concentration in the soil is below HC5, although the soil may have a high Cd level, we could consider that only less than 5% of the maize kernels may have a Cd level above the threshold, which means that the soil is Cd uncontaminated [50].
In addition, we compared these derived benchmarks with the RSV and RIV values from the Chinese environmental quality standards (Table 1). The estimated HC5 values of maize-planted soil were 7.3−10 times the RSV for Cd. The estimation shows that the HC95 value of Cd in farmland soils in the study area reached a maximum tolerance value of 85.1−161.8 mg kg−1 (based on different soil pH values), which was significantly higher than the RIV of the EQSs (1−4 mg kg−1). In addition, some previous studies have noted this interesting phenomenon [51]. For example, the HC5 values of heavy metals in maize and rice soils in northwestern Guizhou derived by the SSD model by Xu et al. [44] were higher than the Chinese soil threshold RSV, and HC95 values were higher than RIV; they considered that the current Chinese soil standards were biased toward the evaluation results of this region. Similar results were also disclosed in the investigation by Zhang et al. [22] and Wang et al. [52].

3.6. Suitability Analysis of the Derived Soil Environmental Quality Benchmarks

To evaluate the accuracy of the derived soil environmental quality benchmarks, we validated their adaptability through the EQSs procedure [47]. The results are shown in Table 4. When the soil Cd concentration was less than or equal to the value of HC5 (≤HC5), the appropriate rate was 99.5%, and only 2 samples were type I errors. When the soil Cd concentration was between the HC5 and HC95 values, more than 97.6% of maize kernel Cd concentrations met the national food safety standard, and only 2 samples were found to exceed the standard (≥0.1 mg kg−1). All Cd concentrations in the samples did not exceed the HC95 value, and the rate of type II error rate (false positive rate) was 0%. Overall, 400 of the 492 paired soil-maize samples were suitable for evaluation with the HC5 and HC95 values. Compared with the RSV and RIV in the EQSs, the number of soil points that were evaluated as the ‘priority protection’ level increased from 15 to 402, and the number of unsuitable samples (false negatives and false positives) in the evaluation results decreased from 172 to 2. Therefore, we strongly believe that the HC5 and HC95 values were more suitable for evaluating the safety of the maize fields in the research region.

4. Discussion

In this study, we derived a soil environmental benchmark for the study area by modeling the species-sensitive distribution curve between the soil and maize bioaccumulation factors through a threshold value of Cd content in maize (<0.1 mg kg−1) (Figure 5). It is worth noting that the benchmark values obtained are well above the national thresholds (Table 3). These results should be attributed to the high levels of heavy metals in the soils and the low levels in the maize grains of the study area [38,53,54]. Low availability of Cd in karst soil is one of the key reasons why the Cd concentration in maize was lower in study areas with a high Cd background [55,56]. The low bioavailability of Cd means that Cd is not easily taken up by maize plants and accumulates in the kernel [57,58]. The research area is a karst center in Southwest China and a typical high-Cd geochemical background area, with a background value 6.8 times higher than the national Cd background value [59]. Researchers generally agree that the weathering soil formation process of mineral-bearing rock systems is the main cause of high Cd accumulation in karst soils [60,61]. The soil-forming parent material of geochemically high geological background soils mainly originates from carbonate rocks, which are widely present in Guizhou [62]. This is consistent with the causes of high soil Cd accumulation in many carbonate rock areas [63]. In the soil of Guangxi and Yunan Provinces in China, Cd mainly originates from the weathering and pedogenesis of carbonate rocks, which have high Cd contents [64]. The results of Savignan et al. [65] in southwestern France showed that there are correlations between Cd and the inherent features of calcareous soil (CaCO3, pH, and cation exchange capacity), and Cd is mainly from geogenic causes. Samecka-Cymerman et al. [66] studied heavy metals in the soil of different soil parent materials from southwestern Poland and found that the soil from crystalline limestone has the highest content of Ca and Cd compared with other soil parent rocks. Although the soil Cd fractionation pattern was not measured in this study, a large number of studies have confirmed the “low activity” of heavy metals in karst soils [67,68]. Due to the large amount of calcium carbonate (CaCO3) in the karst system, Ca2+ in CaCO3 is easily exchanged with some metal cations (e.g., Cd2+), which reduces the exchange and effective mass fraction of Cd [69]; in addition, the soil pH in the study area is generally high. In general, the increase in soil pH increases the sorption capacity of negatively charged soil colloids for positively charged heavy metal ions [70,71,72]. The Fe and Mn ions in soils formed from carbonate rocks were higher than those derived from nonkarst soils, and they combine with OH- in the soils to form hydroxy compounds to provide more sorption sites for heavy metal ions [73]. Furthermore, the high salt base saturation and acid–base buffering potential of carbonate rock weathered limestone soils further retard the acidification process of the soil [64,74].
In addition, the validation procedure of the EQSs also showed that this derived soil environmental benchmark can accurately evaluate the true maize Cd exceedance rate in the study area compared to the Chinese soil thresholds (Table 4). With the rapid development of China’s economy, the classification, management, and safe use of heavy metal(loid)-contaminated soil are the key tasks of governments at all levels at present. The current Chinese Environmental Quality Standards revised in 2018 (GB15618—2018) have played a significant role in the protection and administration of the soil environment of agricultural land in China [56,75,76]. However, due to the large differences in the soil environmental quality of farmland in China and the different accumulation factors of soil Cd under anthropogenic pollution or natural activities [18,77,78], there are many differences in the results of soil evaluations and agricultural product evaluations in actual soil quality evaluation processes. Especially in the high geological background areas of northwestern Guizhou, China, there were many situations where the unsuitable evaluation results such as ‘soil heavy metal(loid)s exceed the standard, but heavy metals in agricultural products did not exceed the standard’ or ‘heavy metals in agricultural products exceed the standard, but soil heavy metal(loid)s did not exceed the standard’ [21,39,79]. The results of this study also strongly confirm that the current Chinese Environmental Quality Standards are not fully applicable in northwestern Guizhou Province, where the Cd background value is high (Table 2). According to the fitted results of the SSD model, a soil Cd concentration lower than the HC5 value indicates that the farmland soil has not been significantly polluted by Cd, and the Cd concentration of 95% of the edible parts of the crops does not exceed the national food safety limit. This is in line with China’s 2020–2030 soil management strategy, whose purpose is to determine the pollution of cultivated land nationwide and realize safe use. Therefore, we suggest that standards of classification of soil environmental quality of agricultural land in this area should be adjusted appropriately according to the HC5 and HC95 values to better evaluate the actual pollution levels of local agricultural lands.

5. Conclusions

The results of the survey of 492 paired soil-maize samples illustrated that the soil Cd concentration in the study region showed high Cd accumulation characteristics. More than 96.1% of the soil samples contained Cd concentrations higher than the risk screening value (RSV) in the current Chinese standard for agricultural soil. However, only 4 of the 492 corn samples exceeded the national standard limit threshold (0.1 mg kg−1 for Cd). The adaptability analysis showed that 172 of the 492 paired soil-maize samples were not suitable for the Chinese current standard, accounting for 35.0% of the total. This shows that the evaluation results of the soil in the corn-producing area of Shuicheng, Guizhou, using the current Chinese standard, were inaccurate. The results derived from the species sensitivity distribution model show that the hazardous concentrations of 95% and 5% (HC5 and HC95) in maize fields were 2.2 and 85.1 mg kg−1 for soil pH ≤ 5.5, 2.5 and 108.5 mg kg−1 for 5.5 < pH ≤ 6.5, and 3.0 and 161.8 mg kg−1 for 6.5 < pH ≤ 7.5, respectively. The number of unsuitable samples with these evaluation results decreased from 172 to 2 according to the deduced HC5 and HC95 values. The HC5 and HC95 values could well reflect the health risk of Cd exposure via soil. Therefore, we suggest that the standards of classification of soil environmental quality of agricultural land in this area should be adjusted with reference to the HC5 and HC95 values to evaluate the actual pollution levels of local agricultural lands.

Author Contributions

Conceptualization, P.W.; Methodology, P.W.; Data curation, L.Y. and W.Y.; Writing—original draft, L.Y.; Writing—review & editing, P.W. and W.Y.; Visualization, W.Y.; Project administration, P.W.; Funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Science and Technology Foundation of Guizhou Province (nos. QKHZC [2022]222 and QKHJC [2020]1Y181), the National Natural Science Foundation of China (32101391), the National Key Research and Development Program Projects of China (no. 2018YFC1802600), and the Project of high-level talent training program in Guizhou Province (QKHPTRC [2016] 5664).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Conflicts of Interest

The authors have no relevant financial or non-financial interests to disclose.

References

  1. Lin, Y.B.; Ye, Y.M.; Hu, Y.M.; Shi, H.K. The variation in microbial community structure under different heavy metal contamination levels in paddy soils. Ecotoxicol. Environ. Saf. 2019, 180, 557–564. [Google Scholar] [CrossRef] [PubMed]
  2. Liu, P.; Yang, Y.; Li, M. Responses of soil and earthworm gut bacterial communities to heavy metal contamination. Environ. Pollut. 2020, 265, 114921. [Google Scholar] [CrossRef] [PubMed]
  3. Xu, C.; Pu, J.; Wen, B.; Xia, M. Potential Ecological Risks of Heavy Metals in Agricultural Soil Alongside Highways and Their Relationship with Landscape. Agriculture 2021, 11, 880. [Google Scholar] [CrossRef]
  4. Yang, W.T.; Zhou, H.; Gu, J.F.; Liao, B.H.; Zhang, J.; Wu, P. Application of rapeseed residue increases soil organic matter, microbial biomass, and enzyme activity and mitigates cadmium pollution risk in paddy fields. Environ. Pollut. 2020, 264, 9. [Google Scholar] [CrossRef] [PubMed]
  5. Gupta, N.; Yadav, K.K.; Kumar, V.; Kumar, S.; Chadd, R.P.; Kumar, A. Trace elements in soil-vegetables interface: Translocation, bioaccumulation, toxicity and amelioration—A review. Sci. Total Environ. 2019, 651, 2927–2942. [Google Scholar] [CrossRef]
  6. Yang, W.; Wang, S.; Zhou, H.; Zeng, M.; Zhang, J.; Huang, F.; Shan, S.; Guo, Z.; Yi, H.; Sun, Z.; et al. Combined amendment reduces soil Cd availability and rice Cd accumulation in three consecutive rice planting seasons. J. Environ. Sci. 2022, 111, 141–152. [Google Scholar] [CrossRef]
  7. Ali, H.; Khan, E. Bioaccumulation of non-essential hazardous heavy metals and metalloids in freshwater fish. Risk to human health. Environ. Chem. Lett. 2018, 16, 903–917. [Google Scholar] [CrossRef]
  8. Ma, L.; Huang, L.; Liu, Q.; Xu, S.; Wen, Z.; Qin, S.; Li, T.; Feng, Y. Positive effects of applying endophytic bacteria in eggplant-Sedum intercropping system on Cd phytoremediation and vegetable production in cadmium polluted greenhouse. J. Environ. Sci. 2022, 115, 383–391. [Google Scholar] [CrossRef]
  9. Yang, W.T.; Gu, J.F.; Zou, J.L.; Zhou, H.; Zeng, Q.R.; Liao, B.H. Impacts of rapeseed dregs on Cd availability in contaminated acid soil and Cd translocation and accumulation in rice plants. Environ. Sci. Pollut. Res. 2016, 23, 20853–20861. [Google Scholar] [CrossRef]
  10. Li, Y.; Wang, Y.; Shen, X.; Liu, F. The combinations of sulfur and molybdenum fertilizations improved antioxidant capacity of grazing Guizhou semi-fine wool sheep under copper and cadmium stress. Ecotoxicol. Environ. Saf. 2021, 222, 112520. [Google Scholar] [CrossRef]
  11. Jovanovic, Z. The electrophysiological effects of cadmium on Retzius nerve cells of the leech Haemopis sanguisuga. Comp. Biochem. Physiol. C-Toxicol. Pharmacol. 2021, 247, 109062. [Google Scholar] [CrossRef] [PubMed]
  12. Xu, M.-Y.; Wang, P.; Sun, Y.-J.; Yang, L.; Wu, Y.-J. Joint toxicity of chlorpyrifos and cadmium on the oxidative stress and mitochondrial damage in neuronal cells. Food Chem. Toxicol. 2017, 103, 246–252. [Google Scholar] [CrossRef] [PubMed]
  13. Wen, E.; Yang, X.; Chen, H.; Shaheen, S.M.; Sarkar, B.; Xu, S.; Song, H.; Liang, Y.; Rinklebe, J.; Hou, D.; et al. Iron-modified biochar and water management regime-induced changes in plant growth, enzyme activities, and phytoavailability of arsenic, cadmium and lead in a paddy soil. J. Hazard. Mater. 2021, 407, 124344. [Google Scholar] [CrossRef] [PubMed]
  14. Deng, X.; Yang, Y.; Zeng, H.; Chen, Y.; Zeng, Q. Variations in iron plaque, root morphology and metal bioavailability response to seedling establishment methods and their impacts on Cd and Pb accumulation and translocation in rice (Oryza sativa L.). J. Hazard. Mater. 2020, 384, 121343. [Google Scholar] [CrossRef]
  15. Huang, L.; Li, W.C.; Tam, N.F.Y.; Ye, Z. Effects of root morphology and anatomy on cadmium uptake and translocation in rice (Oryza sativa L.). J. Environ. Sci. 2019, 75, 296–306. [Google Scholar] [CrossRef] [PubMed]
  16. Xia, J.; Wang, J.; Zhang, L.; Wang, X.; Yuan, W.; Anderson, C.W.N.; Chen, C.; Peng, T.; Feng, X. Significant mercury efflux from a Karst region in Southwest China—Results from mass balance studies in two catchments. Sci. Total Environ. 2021, 769, 144892. [Google Scholar] [CrossRef] [PubMed]
  17. Guo, B.; Zang, W.; Luo, W. Spatial-temporal shifts of ecological vulnerability of Karst Mountain ecosystem-impacts of global change and anthropogenic interference. Sci. Total Environ. 2020, 741, 140256. [Google Scholar] [CrossRef]
  18. Chen, F.; Wang, Q.; Meng, F.; Chen, M.; Wang, B. Effects of long-term zinc smelting activities on the distribution and health risk of heavy metals in agricultural soils of Guizhou province, China. Environ. Geochem. Health 2020, 1–16. [Google Scholar] [CrossRef] [PubMed]
  19. He, B.; Yun, Z.; Shi, J.; Jiang, G. Research progress of heavy metal pollution in China: Sources, analytical methods, status, and toxicity. Chin. Sci. Bull. 2013, 58, 134–140. [Google Scholar] [CrossRef] [Green Version]
  20. CNEMC. Chinese Soil Element Background Value; China Environmental Science Press: Beijing, China, 1990. [Google Scholar]
  21. Luo, K.; Liu, H.; Liu, Q.; Tu, Y.; Yu, E.; Xing, D. Cadmium accumulation and migration of 3 peppers varieties in yellow and limestone soils under geochemical anomaly. Environ. Technol. 2020, 43, 10–20. [Google Scholar] [CrossRef]
  22. Zhang, S.; Song, J.; Cheng, Y.; McBride, M.B. Derivation of regional risk screening values and intervention values for cadmium-contaminated agricultural land in the Guizhou Plateau. Land Degrad. Dev. 2018, 29, 2366–2377. [Google Scholar] [CrossRef]
  23. Ning, Z.; Liu, E.; Yao, D.; Xiao, T.; Ma, L.; Liu, Y.; Li, H.; Liu, C. Contamination, oral bioaccessibility and human health risk assessment of thallium and other metal(loid)s in farmland soils around a historic Tl-Hg mining area. Sci. Total Environ. 2021, 758, 143577. [Google Scholar] [CrossRef] [PubMed]
  24. Kong, X.Y.; Liu, T.; Yu, Z.H.; Chen, Z.; Lei, D.; Wang, Z.W.; Zhang, H.; Li, Q.H.; Zhang, S.S. Heavy Metal Bioaccumulation in Rice from a High Geological Background Area in Guizhou Province, China. Int. J. Env. Res. Public Health 2018, 15, 2281. [Google Scholar] [CrossRef] [Green Version]
  25. Wu, H.; Liu, Q.; Ma, J.; Liu, L.; Qu, Y.; Gong, Y.; Yang, S.; Luo, T. Heavy Metal(loids) in typical Chinese tobacco-growing soils: Concentrations, influence factors and potential health risks. Chemosphere 2020, 245, 125591. [Google Scholar] [CrossRef]
  26. Rinklebe, J.; Shaheen, S.M. Miscellaneous additives can enhance plant uptake and affect geochemical fractions of copper in a heavily polluted riparian grassland soil. Ecotoxicol. Environ. Saf. 2015, 119, 58–65. [Google Scholar] [CrossRef] [PubMed]
  27. Gu, J.F.; Zhou, H.; Tang, H.L.; Yang, W.T.; Zeng, M.; Liu, Z.M.; Peng, P.Q.; Liao, B.H. Cadmium and arsenic accumulation during the rice growth period under in situ remediation. Ecotoxicol. Environ. Saf. 2019, 171, 451–459. [Google Scholar] [CrossRef] [PubMed]
  28. Yang, Z.B.; Liu, L.X.; Lv, Y.F.; Cheng, Z.; Xu, X.X.; Xian, J.R.; Zhu, X.M.; Yang, Y.X. Metal availability, soil nutrient, and enzyme activity in response to application of organic amendments in Cd-contaminated soil. Environ. Sci. Pollut. Res. 2018, 25, 2425–2435. [Google Scholar] [CrossRef]
  29. Yang, W.-T.; Zhou, H.; Gu, J.-F.; Liao, B.-H.; Peng, P.-Q.; Zeng, Q.-R. Effects of a Combined Amendment on Pb, Cd, and As Availability and Accumulation in Rice Planted in Contaminated Paddy Soil. Soil Sediment Contam. 2017, 26, 70–83. [Google Scholar] [CrossRef]
  30. Duan, Z.; Luo, Y.; Wu, Y.; Wang, J.; Cai, X.; Wen, J.; Xu, J. Heavy metals accumulation and risk assessment in a soil-maize (Zea mays L.) system around a zinc-smelting area in southwest China. Environ. Geochem. Health 2021, 43, 4875–4889. [Google Scholar] [CrossRef]
  31. Wang, L.; Ma, L.; Yang, Z. Spatial variation and risk assessment of heavy metals in paddy rice from Hunan Province, Southern China. Int. J. Environ. Sci. Technol. 2018, 15, 1561–1572. [Google Scholar] [CrossRef]
  32. Liu, M.; Wang, T.; Skidmore, A.K.; Liu, X. Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images. Sci. Total Environ. 2018, 637, 18–29. [Google Scholar] [CrossRef]
  33. Ye, X.; Xiao, W.; Zhang, Y.; Zhao, S.; Wang, G.; Zhang, Q.; Wang, Q. Assessment of heavy metal pollution in vegetables and relationships with soil heavy metal distribution in Zhejiang province, China. Environ. Monit. Assess. 2015, 187, 378. [Google Scholar] [CrossRef]
  34. Cheng, Y.; Nathanail, C.P.; Ja’afaru, S.W. Generic assessment criteria for human health risk management of agricultural land scenario in Jiangsu Province, China. Sci. Total Environ. 2019, 697, 134071. [Google Scholar] [CrossRef]
  35. He, H.; Shi, L.; Yang, G.; You, M.; Vasseur, L. Ecological Risk Assessment of Soil Heavy Metals and Pesticide Residues in Tea Plantations. Agriculture 2020, 10, 47. [Google Scholar] [CrossRef] [Green Version]
  36. Sun, Y.; Li, H.; Guo, G.; Semple, K.T.; Jones, K.C. Soil contamination in China: Current priorities, defining background levels and standards for heavy metals. J. Environ. Manag. 2019, 251, 109512. [Google Scholar] [CrossRef]
  37. Liu, Y.; Du, Q.; Wang, Q.; Yu, H.; Liu, J.; Tian, Y.; Chang, C.; Lei, J. Causal inference between bioavailability of heavy metals and environmental factors in a large-scale region. Environ. Pollut. 2017, 226, 370–378. [Google Scholar] [CrossRef]
  38. Yang, W.; Chen, Y.; Yang, L.; Xu, M.; Jing, H.; Wu, P.; Wang, P. Spatial distribution, food chain translocation, human health risks, and environmental thresholds of heavy metals in a maize cultivation field in the heart of China’s karst region. J. Soils Sed. 2022, 1–17. [Google Scholar] [CrossRef]
  39. Yang, L.; Yang, W.; Gu, S.; Zhang, J.; Wu, P. Effects of Organic Fertilizers on Cd Activity in Soil and Cd Accumulation in Rice in Three Paddy Soils from Guizhou Province. Bull. Environ. Contam. Toxicol. 2021, 107, 1161–1166. [Google Scholar] [CrossRef]
  40. Feng, L.; Yan, H.; Dai, C.; Xu, W.; Gu, F.; Zhang, F.; Li, T.; Xian, J.; He, X.; Yu, Y.; et al. The systematic exploration of cadmium-accumulation characteristics of maize kernel in acidic soil with different pollution levels in China. Sci. Total Environ. 2020, 729, 138972. [Google Scholar] [CrossRef]
  41. Szymczycha-Madeja, A.; Mulak, W. Comparison of various digestion procedures in chemical analysis of spent hydrodesulfurization catalyst. J. Hazard. Mater. 2009, 164, 776–780. [Google Scholar] [CrossRef]
  42. GB 15618—2018; Soil Environmental Quality, Risk Control Standard for Soil Contamination of Agricultural Land. Ministry of Ecology and Environment: Beijing, China, 2018.
  43. Xu, F.L.; Li, Y.L.; Wang, Y.; He, W.; Kong, X.Z.; Qin, N.; Liu, W.X.; Wu, W.J.; Jorgensen, S.E. Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment. Ecol. Indic. 2015, 54, 227–237. [Google Scholar] [CrossRef]
  44. Xu, M.; Yang, W.; Yang, L.; Chen, Y.; Jing, H.; Wu, P. Health risk assessment and environmental benchmark of heavy metals in cultivated land in mountainous area of northwest Guizhou province. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 3799–3810. [Google Scholar] [CrossRef]
  45. Luo, K.; Liu, H.; Yu, E.; Tu, Y.; Gu, X.; Xu, M. Distribution and release mechanism of heavy metals in sediments of Yelang Lake by DGT. Stoch. Environ. Res. Risk Assess. 2020, 34, 793–805. [Google Scholar] [CrossRef]
  46. Romkens, P.F.A.M.; Guo, H.Y.; Chu, C.L.; Liu, T.S.; Chiang, C.F.; Koopmans, G.F. Prediction of Cadmium uptake by brown rice and derivation of soil-plant transfer models to improve soil protection guidelines. Environ. Pollut. 2009, 157, 2435–2444. [Google Scholar] [CrossRef] [PubMed]
  47. Song, J.; Xu, G.Y.; Luo, Y.M.; Gao, H.; Tang, W. Some thoughts on the classification of soil environmental quality for agricultural land: Taking risk control of Cd in potato producing area of Guizhou as an example. Earth Sci. Front. 2019, 26, 192–198. (In Chinese) [Google Scholar]
  48. Ding, C.; Ma, Y.; Li, X.; Zhang, T.; Wang, X. Derivation of soil thresholds for lead applying species sensitivity distribution: A case study for root vegetables. J. Hazard. Mater. 2016, 303, 21–27. [Google Scholar] [CrossRef]
  49. Gao, J.; Ye, X.; Wang, X.; Jiang, Y.; Li, D.; Ma, Y.; Sun, B. Derivation and validation of thresholds of cadmium, chromium, lead, mercury and arsenic for safe rice production in paddy soil. Ecotoxicol. Environ. Saf. 2021, 220, 112404. [Google Scholar] [CrossRef]
  50. Zhang, Y.; Du, P.; He, Y.; Zhong, X.; Luo, H.; Qin, X.; Xu, G. Derivation of the Thresholds of Available Concentrations of Heavy Metals in Soil Based on Agricultural Product Safety. Huan Jing Ke Xue Huanjing Kexue 2019, 40, 4262–4269. [Google Scholar] [CrossRef]
  51. Mu, D.-M.; Sun, Y.-B. Safety Production Threshold and Land Quality Classification of Vegetable Pb in High Geological Background Area of Southwest China. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 965–974. [Google Scholar] [CrossRef]
  52. Wang, R.; Deng, H.; Jia, Z.-M.; Yan, M.-S.; Zhou, J.; Dong, J.-X.; Wang, J.-B.; Yu, F. Characteristics of Cadmium Enrichment and Pollution Evaluation of a Soil-Crop System in a Typical Karst Area. Huan Jing Ke Xue Huanjing Kexue 2021, 42, 941–951. [Google Scholar] [CrossRef]
  53. Tian, S.Q.; Wang, S.J.; Bai, X.Y.; Zhou, D.Q.; Luo, G.J.; Yang, Y.J.; Hu, Z.Y.; Li, C.J.; Deng, Y.H.; Lu, Q. Ecological security and health risk assessment of soil heavy metals on a village-level scale, based on different land use types. Environ. Geochem. Health 2020, 42, 3393–3413. [Google Scholar] [CrossRef]
  54. Peng, Y.S.; Chen, J.; Wei, H.R.; Li, S.B.; Jin, T.; Yang, R.D. Distribution and transfer of potentially toxic metal(loid)s in Juncus effusus from the indigenous zinc smelting area, northwest region of Guizhou Province, China. Ecotoxicol. Environ. Saf. 2018, 152, 24–32. [Google Scholar] [CrossRef]
  55. Zhang, Q.; Hah, G.-L. Speciation Characteristics and Risk Assessment of Soil Heavy Metals from Puding Karst Critical Zone, Guizhou Province. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 3269–3277. [Google Scholar] [CrossRef]
  56. Yang, Q.; Yang, Z.; Zhang, Q.; Liu, X.; Zhuo, X.; Wu, T.; Wang, L.; Wei, X.; Ji, J. Ecological risk assessment of Cd and other heavy metals in soil-rice system in the karst areas with high geochemical background of Guangxi, China. Sci. China-Earth Sci. 2021, 64, 1126–1139. [Google Scholar] [CrossRef]
  57. Khaliq, M.A.; Tarin, M.W.K.; Guo, J.X.; Chen, Y.H.; Guo, W. Soil liming effects on CH4, N2O emission and Cd, Pb accumulation in upland and paddy rice. Environ. Pollut. 2019, 248, 408–420. [Google Scholar] [CrossRef]
  58. Zhou, H.; Yang, W.T.; Zhou, X.; Liu, L.; Gu, J.F.; Wang, W.L.; Zou, J.L.; Tian, T.; Peng, P.Q.; Liao, B.H. Accumulation of Heavy Metals in Vegetable Species Planted in Contaminated Soils and the Health Risk Assessment. Int. J. Environ. Res. Public Health 2016, 13, 289. [Google Scholar] [CrossRef] [Green Version]
  59. Wang, X.-W.; Liu, H.-Y.; Gu, X.-F.; Tu, Y.; Yu, E.-J.; Wu, P. Distribution Characteristics of Heavy Metals in Soils Affected by Different Land Use Types in a Superimposed Pollution Area with High Geological Background. Huan Jing Ke Xue Huanjing Kexue 2022, 43, 2094–2103. [Google Scholar] [CrossRef]
  60. Quezada-Hinojosa, R.P.; Follmi, K.B.; Verrecchia, E.; Adatte, T.; Matera, V. Speciation and multivariable analyses of geogenic cadmium in soils at Le Gurnigel, Swiss Jura Mountains. Catena 2015, 125, 10–32. [Google Scholar] [CrossRef]
  61. Wen, Y.; Li, W.; Yang, Z.; Zhang, Q.; Ji, J. Enrichment and source identification of Cd and other heavy metals in soils with high geochemical background in the karst region, Southwestern China. Chemosphere 2020, 245, 125620. [Google Scholar] [CrossRef]
  62. Yu, E.; Liu, H.; Tu, Y.; Gu, X.; Ran, X.; Yu, Z.; Wu, P. Superposition Effects of Zinc Smelting Atmospheric Deposition on Soil Heavy Metal Pollution Under Geochemical Anomaly. Front. Environ. Sci. 2022, 10, 62. [Google Scholar] [CrossRef]
  63. Zhang, F.; Wang, C.; Cheng, X.; Ma, H.; He, L. Ecological Assessment, Spatial Analysis, and Potential Sources of Heavy Metals (HMs) in Soils with High Background Values in the Lead-Zinc Mine, Hezhang County, Southwestern China. Water 2022, 14, 783. [Google Scholar] [CrossRef]
  64. Meng, W.; Li, X.-X.; Wu, P. New Insights into Selenium Enrichment in the Soil of Northwestern Guizhou, Southwest China. Bull. Environ. Contam. Toxicol. 2021, 107, 1095–1102. [Google Scholar] [CrossRef] [PubMed]
  65. Savignan, L.; Lee, A.; Coynel, A.; Jalabert, S.; Faucher, S.; Lespes, G.; Chery, P. Spatial distribution of trace elements in the soils of south-western France and identification of natural and anthropogenic sources. Catena 2021, 205, 105000. [Google Scholar] [CrossRef]
  66. Samecka-Cymerman, A.; Kolon, K.; Stankiewicz, A.; Kaszewska, J.; Mroz, L.; Kempers, A.J. Rhizomes and fronds of Athyrium filix-femina as possible bioindicators of chemical elements from soils over different parent materials in southwest Poland. Ecol. Indic. 2011, 11, 1105–1111. [Google Scholar] [CrossRef]
  67. Wen, Y.; Li, W.; Yang, Z.; Zhuo, X.; Guan, D.-X.; Song, Y.; Guo, C.; Ji, J. Evaluation of various approaches to predict cadmium bioavailability to rice grown in soils with high geochemical background in the karst region, Southwestern China. Environ. Pollut. 2020, 258, 113645. [Google Scholar] [CrossRef]
  68. Yang, H.; Lu, M.; Cao, J. Trace elements of the soil-plant systems in subtropical karst and clasolite areas in Guilin, Guangxi, China. Environ. Earth Sci. 2015, 73, 6259–6269. [Google Scholar] [CrossRef]
  69. Zhao, K.L.; Zhang, L.Y.; Dong, J.Q.; Wu, J.S.; Ye, Z.Q.; Zhao, W.M.; Ding, L.Z.; Fu, W.J. Risk assessment, spatial patterns and source apportionment of soil heavy metals in a typical Chinese hickory plantation region of southeastern China. Geoderma 2020, 360, 114011. [Google Scholar] [CrossRef]
  70. Zhao, F.J.; Ma, Y.B.; Zhu, Y.G.; Tang, Z.; McGrath, S.P. Soil Contamination in China: Current Status and Mitigation Strategies. Environ. Sci. Technol. 2015, 49, 750–759. [Google Scholar] [CrossRef]
  71. Zhao, X.-L.; Masaihiko, S. Fractionation and solubility of cadmium in paddy soils amended with porous hydrated calcium silicate. J. Environ. Sci. 2007, 19, 343–347. [Google Scholar] [CrossRef]
  72. Senila, M.; Cadar, O.; Senila, L.; Angyus, B.S. Simulated Bioavailability of Heavy Metals (Cd, Cr, Cu, Pb, Zn) in Contaminated Soil Amended with Natural Zeolite Using Diffusive Gradients in Thin-Films (DGT) Technique. Agriculture 2022, 12, 321. [Google Scholar] [CrossRef]
  73. Suda, A.; Makino, T. Functional effects of manganese and iron oxides on the dynamics of trace elements in soils with a special focus on arsenic and cadmium: A review. Geoderma 2016, 270, 68–75. [Google Scholar] [CrossRef]
  74. Mahar, A.; Wang, P.; Ali, A.; Guo, Z.; Awasthi, M.K.; Lahori, A.H.; Wang, Q.; Shen, F.; Li, R.; Zhang, Z. Impact of CaO, fly ash, sulfur and Na2S on the (im)mobilization and phytoavailability of Cd, Cu and Pb in contaminated soil. Ecotoxicol. Environ. Saf. 2016, 134, 116–123. [Google Scholar] [CrossRef]
  75. Zou, J.; Liu, X.; Dai, W.; Luan, Y. Pollution assessment of heavy metal accumulation in the farmland soils of Beijing’s suburbs. Environ. Sci. Pollut. Res. 2018, 25, 27483–27492. [Google Scholar] [CrossRef]
  76. Liu, Y.; Wang, H.; Li, X.; Li, J. Heavy Metal Contamination of Agricultural Soils in Taiyuan, China. Pedosphere 2015, 25, 901–909. [Google Scholar] [CrossRef]
  77. Briki, M.; Ji, H.; Li, C.; Ding, H.; Gao, Y. Characterization, distribution, and risk assessment of heavy metals in agricultural soil and products around mining and smelting areas of Hezhang, China. Environ. Monit. Assess. 2015, 187, 767. [Google Scholar] [CrossRef]
  78. Bu, Q.; Li, Q.; Zhang, H.; Cao, H.; Gong, W.; Zhang, X.; Ling, K.; Cao, Y. Concentrations, Spatial Distributions, and Sources of Heavy Metals in Surface Soils of the Coal Mining City Wuhai, China. J. Chem. 2020, 2020, 4705954. [Google Scholar] [CrossRef]
  79. Xiao, N.; Wang, F.; Tang, L.; Zhu, L.; Song, B.; Chen, T. Recommended risk screening values for Cd in high geological background area of Guangxi, China. Environ. Monit. Assess. 2022, 194, 202. [Google Scholar] [CrossRef]
Agriculture 12 01156 g001 550
Figure 1. Distribution of sampling sites and locations of the study area and reference area [40].
Figure 1. Distribution of sampling sites and locations of the study area and reference area [40].
Agriculture 12 01156 g001
Agriculture 12 01156 g002 550
Figure 2. Cumulative frequencies of soil pH (a) and soil Cd concentration (b), scatter plots of soil pH and soil Cd concentration (c). RSV (yellow line in Figure 2c) represents the risk screening values for soil contamination of agricultural land in the China RIV (red line in Figure 2c) and represents risk intervention values for soil contamination of agricultural land in China.
Figure 2. Cumulative frequencies of soil pH (a) and soil Cd concentration (b), scatter plots of soil pH and soil Cd concentration (c). RSV (yellow line in Figure 2c) represents the risk screening values for soil contamination of agricultural land in the China RIV (red line in Figure 2c) and represents risk intervention values for soil contamination of agricultural land in China.
Agriculture 12 01156 g002
Agriculture 12 01156 g003 550
Figure 3. The relationship between the Cd concentration in maize kernels and sample numbers of maize. The yellow line represents the limit of light Cd contamination in maize kernels (0.1 mg kg−1) as recommended by national standards. The red line represents the limit of heavy Cd contamination in maize kernels (0.2 mg kg−1) as recommended by national standards.
Figure 3. The relationship between the Cd concentration in maize kernels and sample numbers of maize. The yellow line represents the limit of light Cd contamination in maize kernels (0.1 mg kg−1) as recommended by national standards. The red line represents the limit of heavy Cd contamination in maize kernels (0.2 mg kg−1) as recommended by national standards.
Agriculture 12 01156 g003
Agriculture 12 01156 g004 550
Figure 4. Soil Cd concentration and soil pH value (a), Cd concentration of kernels in the form of a cumulative distribution function (b), bioaccumulation factor of Cd (c) in the study area and a maize field in Hunan, Zhejiang, and Hainan Provinces, China. * indicates a significant difference between the data in the reference area and the data in the study area (Duncan’s test) (p < 0.05).
Figure 4. Soil Cd concentration and soil pH value (a), Cd concentration of kernels in the form of a cumulative distribution function (b), bioaccumulation factor of Cd (c) in the study area and a maize field in Hunan, Zhejiang, and Hainan Provinces, China. * indicates a significant difference between the data in the reference area and the data in the study area (Duncan’s test) (p < 0.05).
Agriculture 12 01156 g004
Agriculture 12 01156 g005 550
Figure 5. Species sensitivity distribution curves for Cd in maize in northwest Guizhou Province under different soil pH values.
Figure 5. Species sensitivity distribution curves for Cd in maize in northwest Guizhou Province under different soil pH values.
Agriculture 12 01156 g005
Table
Table 1. The Risk Screening Values (RSVs) and the Risk Intervention Values (RIVs) of Cd for soil contamination of agricultural land.
Table 1. The Risk Screening Values (RSVs) and the Risk Intervention Values (RIVs) of Cd for soil contamination of agricultural land.
Soil TypeRisk Screening Values (RSVs) of Cd (mg kg−1, Dry Weight)
pH ≤ 5.55.5 < pH ≤ 6.56.5 < pH ≤ 7.5pH > 7.5
Paddy fields0.30.40.60.8
Others0.30.30.30.6
Soil typeRisk Intervention Values (RIVs) of Cd (mg kg−1, dry weight)
pH ≤ 5.55.5 < pH ≤ 6.56.5 < pH ≤ 7.5pH > 7.5
Paddy fields1.52.03.04.0
Others1.52.03.04.0
Table
Table 2. Suitability of the current Chinese Environmental Quality Standards (EQSs) for soil contamination of agricultural land in the classification of soil environmental quality for the research region.
Table 2. Suitability of the current Chinese Environmental Quality Standards (EQSs) for soil contamination of agricultural land in the classification of soil environmental quality for the research region.
ParameterSoil Cd Concentration ≤ RSVRSV < Soil Cd Concentration ≤ RIVSoil Cd Concentration > RIVSubtotal
NESESNESESNESES
Soil pHRSV
(mg kg−1)		RIV
(mg kg−1)		Appropriate StandardsFalse Negatives False PositivesAppropriate Standards
pH ≤ 5.50.31.5311050922203
5.5 < pH ≤ 6.50.32901490670225
6.5 < pH ≤ 7.50.331044012158
pH > 7.50.641050006
Sample numbers14130301713492
Sample numbers in soil concentration range15303174
Proportion of total samples3.05%61.59%35.37%
Proportion of standard adaptability types93%7%100%0.00%98.28%1.72%
NES: Number of maize samples not exceeding the standard, ES: Number of maize samples exceeding the standard.
Table
Table 3. The estimated HC5 and HC95 values of maize-planted soil, the risk screening value (RSV), and the risk intervention value (RIV) of Cd in the Chinese Soil Environmental Quality Standards (EQSs) with different soil pH levels.
Table 3. The estimated HC5 and HC95 values of maize-planted soil, the risk screening value (RSV), and the risk intervention value (RIV) of Cd in the Chinese Soil Environmental Quality Standards (EQSs) with different soil pH levels.
ItemsHC5 Value
(mg kg−1)		HC95 Value
(mg kg−1)		Sample
Numbers		ModelsR2
soil pH ≤ 5.52.285.1203 y = 107.96911 + − 109.915664 1 + ( x 176.78142 ) 1.21581 0.998
5.5 < soil pH ≤ 6.52.5108.5225 y = 102.43199 + − 103.05004 1 + ( x 183.83577 ) 1.45319 0.999
6.5 < soil pH ≤ 7.53.0161.858 y = 108.38357 + − 123.05409 1 + ( x 175.45585 ) 0.94681 0.996
RSVRIV
soil pH ≤ 5.50.31.5
5.5 < soil pH ≤ 6.50.32.0
6.5 < soil pH ≤ 7.50.33.0
The HC5 and HC95 values represent 95% and 5% of the Cd content in the soil where crops were grown, respectively. The ratios of 95% and 5% were described with reference to the current national standard.
Table
Table 4. Suitability analysis of the hazardous concentrations for 95% and 5% crop safety (HC5 and HC95) for evaluating soil contamination of agricultural lands for the classification of soil environmental quality in the research region.
Table 4. Suitability analysis of the hazardous concentrations for 95% and 5% crop safety (HC5 and HC95) for evaluating soil contamination of agricultural lands for the classification of soil environmental quality in the research region.
ParameterSoil Cd Concentration ≤ HC5HC5 < Soil Cd Concentration ≤ HC95Soil Cd Concentration > HC95Subtotal
NESESNESESNESES
Soil pHHC5
(mg kg−1)		HC95
(mg kg−1)		Appropriate StandardsFalse Negatives False PositivesAppropriate Standards
pH ≤ 5.52.285.1175225100203
5.5 < pH ≤ 6.52.5108.5180045000225
6.5 < pH ≤ 7.53.0161.84501210058
pH > 7.5The dataset was too small to be analyzed statistically6
Sample numbers400282200492
Sample numbers in soil concentration range402840
Proportion of total samples82%17%0%
Proportion of standard adaptability types99.5%0.5%97.62%2.38%0.00%0.00%
The 95% and 5% rates were described with reference to the current national standards of China, NES: Number of maize samples not exceeding the standard, ES: Number of maize samples exceeding the standard.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yang, L.; Wu, P.; Yang, W. Study on Safe Usage of Agricultural Land in Typical Karst Areas Based on Cd in Soil and Maize: A Case Study of Northwestern Guizhou, China. Agriculture 2022, 12, 1156. https://doi.org/10.3390/agriculture12081156

AMA Style

Yang L, Wu P, Yang W. Study on Safe Usage of Agricultural Land in Typical Karst Areas Based on Cd in Soil and Maize: A Case Study of Northwestern Guizhou, China. Agriculture. 2022; 12(8):1156. https://doi.org/10.3390/agriculture12081156

Chicago/Turabian Style

Yang, Liyu, Pan Wu, and Wentao Yang. 2022. "Study on Safe Usage of Agricultural Land in Typical Karst Areas Based on Cd in Soil and Maize: A Case Study of Northwestern Guizhou, China" Agriculture 12, no. 8: 1156. https://doi.org/10.3390/agriculture12081156

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop