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Article

Heavy Metal Contamination in Soils from a Major Planting Base of Winter Jujube in the Yellow River Delta, China

1
Shandong Engineering and Technology Research Center for Ecological Fragile Belt of Yellow River Delta, School of Biological & Environmental Engineering, Binzhou University, Binzhou 256600, China
2
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
3
Shandong Anhe Safety Technology Research Institute Company Limited, Binzhou 256600, China
*
Authors to whom correspondence should be addressed.
Processes 2022, 10(9), 1777; https://doi.org/10.3390/pr10091777
Submission received: 20 July 2022 / Revised: 28 August 2022 / Accepted: 1 September 2022 / Published: 5 September 2022
(This article belongs to the Special Issue Advances in Remediation of Contaminated Sites: Volume I)

Abstract

:
Surface soils form a major planting base of winter jujube in China were collected and detected for six heavy metals including Co, Ni, Cu, Zn, Cd, and Pb. The concentrations of Co, Ni, Cu, Zn, Cd, and Pb were 27.6 ± 6.0, 57.9 ± 12.8, 67.1 ± 10.3, 102.6 ± 23.4, 0.24 ± 0.07, and 25.1 ±5.9 mg/kg, respectively, showing an order of Zn > Cu > Ni > Co > Pb > Cd. The contents of the investigated metals were frequently observed higher than their related background values, suggesting that extra metal inputs occurred. Levels of all elements were below the associated risk screening values of agricultural soil in China, indicating healthy planting conditions for the winter jujube cultivation. Nemerow comprehensive pollution indexes of the metals in all the sampling stations were lower than 0.7, revealing a non-pollution status of the soils. Geo-accumulation indexes suggested that Zn and Pb caused no pollution, and Co, Ni, Cu, and Cd seemed to result in slight pollution. Co, Ni, Zn, Cd, and Pb had similar sources, which might be related to some natural processes and the use of fertilizers. Extra Cu might be mainly from the use of copper-containing pesticides. Based on our observations, the soils from the planting base of winter jujube in the Yellow River Delta were safe for the cultivation of winter jujube, and the rational utilization of pesticide and fertilizer were proposed to control the new inputs of heavy metals.

1. Introduction

Soil is an important resource for human survival, whose quality determines the quality of agricultural products and directly affects people’s health [1]. During the past several decades, the overall situation of the soil environment in China was not optimistic [2]. Soil pollution in China is mainly caused by a high background value of soil environment, industrial and mining activities, and agricultural production [3]. In the long run, considering the current agricultural production mode, heavy metals tend to be the key threats affecting the quality of farmland soils in China [4].
Heavy metals, known as a type of harmful substances, will accumulate in the soil after entering this environmental matrix and will be absorbed by varieties of crops through the root system, thus leading to some human health problems [5,6,7]. Discharge of industrial wastes and traffic exhaust, unreasonable use of agricultural fertilizers and pesticides, and placing of solid wastes aggravate the heavy metal pollution in soil [8,9,10,11]. The monitoring and source analysis of heavy metal pollution in soils, especially in agricultural land, is of great significance for people to master the current situation, assess the potential risks, and carry out the prevention and control of heavy metal pollution. Evaluation and traceability work on heavy metal pollution in soil have been frequently conducted around the world [12,13,14,15]. During the tree fruit production, heavy metals might be released into the soil through several processes, such as the application of fertilizer and pesticide [8].
Winter jujube (Zizyphus jujuba Mill. cv. Dongzao), a local specialty fruit in China, is delicious and nutritious [16]. The fruit of winter jujube contains about 20 amino acids, such as aspartic acid, threonine, and serine, with a total content of 9.8 mg/kg. The contents of protein, total flavonoids, niacin, dietary fiber, total sugar, riboflavin, carotene, and thiamine in the fruit could reach 1.65%, 0.26%, 8.7 mg/kg, 2.3%, 17.3%, 2.2 mg/kg, 1.1 mg/kg, and 0.1 mg/kg, respectively, and the content of ascorbic acid is 70-times that of apple and 100-times that of pear [17]. Binzhou City in Shandong Province is one of the most important planting bases of winter jujube, with planting areas of about 70,000 hm2 [18]. Several investigations have reported the heavy metal residues in winter jujube fruits [19,20,21]. However, information on the heavy metal pollution in the soil planting winter jujube are scarce. Under a background of pursuing ecological protection and high-quality development in the Yellow River Delta region, it is of importance to reveal the pollution status, sources, and potential ecological risks of heavy metals in the soils of the winter jujube planting base.
The objectives of the present study were to (1) determine the levels of heavy metals in the soils from orchards planting winter jujube in Binzhou City; (2) evaluate the pollution status and potential ecological risks of metals in the soil; and (3) analyze the possible sources of these trace elements. Our observations will provide a scientific basis for the safe planting of winter jujube and the prevention and control of soil environmental risk.

2. Materials and Methods

2.1. Study Area and Sampling

The study area is located in Binzhou City, Shandong Province, in China (Figure 1). Geographically, it was in the hinterland of the Yellow River Delta, with a continental monsoon climate. In October 2020, 17 surface soil samples from different orchards were collected using a soil auger. Each sample was mixed by five random sub-samples in a 50 m × 50 m area. The samples were stored in a clean PE package and stored at −20 °C.

2.2. Heavy Metal Determination

In the lab, the soil samples were dried in a vacuum freeze dryer for about 72 h. The dried samples were ground and passed through a 0.5-mm sieve and then microwave digested using an HNO3-HCl-HF system. After finishing the digestion procedure, samples were heated on an electric hot plate to remove the acid. Afterwards, the sample was diluted by adding 5% HNO3. Finally, the concentration of heavy metals was measured by an inductively coupled plasma mass spectrometer (ICP-MS). For each batch of experimental samples, 1 program blank and 3 blank samples were set. The recoveries of the reference material (ERM-S-510204) were above 75%.

2.3. Evaluation of the Metal Pollution

The contents of heavy metals were compared with the soil geochemical background values of Binzhou City [22] and risk screening values of agricultural land soil in China (GB 15618-2018) to analyze the pollution status of heavy metals. The pollution degree and potential ecological hazard of heavy metals in the soil were evaluated by the Nemerow comprehensive pollution index [23], geoaccumulation index [24], and potential ecological hazard index [25]. A correlation analysis and principal component analysis of heavy metal content were conducted to analyze the sources of heavy metals in the soil.

2.3.1. Nemerow Comprehensive Pollution Index

The Nemerow comprehensive index method can obtain a comprehensive pollution index of various pollutants through a single factor pollution index (Pi), so as to comprehensively evaluate the pollution degree of all pollutants. Pi could be described as Pij = Cij/Cis, where Pij is the single pollution index of element i in sampling point j; Cij is the concentration of element i in sampling point j, and Cis is the risk screening value of element i for agricultural land soil in China. The formula for the Nemerow comprehensive index is as follows:
P j = ( P j m a x 2 + P j a v e 2 ) / 2
where Pj is the Nemerow comprehensive pollution index at point j; Pjmax is the maximum value of the single pollution index of all elements at point j; Pjave is the average value of the single pollution index of all elements at point j. The evaluation criterion of the Nemerow comprehensive index is shown in Table 1.

2.3.2. Geo-Accumulation Index

The geo-accumulation index(Igeo) has been widely used to evaluate the impact of heavy metals on the soil [26,27]. The formula is as follows:
I g e o = log 2 [ C i 1.5 × B i ]
where Ci is the measured concentration of element i; Bi is reference value of element i. According to this parameter, soil pollution of heavy metals could be divided into 7 grades (Table 1).

2.3.3. Potential Ecological Risk Index

The potential ecological risk index of a single metal (Eri) was calculated according to the following formula:
Eri = Tri × (Csi/Cni)
where Csi is the detected concentration of element i; Cni is the reference concentration of element i. Tri is the toxic response factor of the element i. The values of Tri for Ni, Cu, Zn, Cd, and Pb were 5, 5, 1, 30, and 5, respectively [28]. The combined hazard of metal elements (RI) is the sum of Eri of each trace element. The degrees of Eri and RI are shown in Figure 2.

3. Results and Discussion

3.1. Analysis of Heavy Metal Content in the Soil

Concentrations of heavy metals in the soils from the winter jujube planting base in Binzhou City are shown in Table 2. The concentrations (mean ± SD) of Co, Ni, Cu, Zn, Cd, and Pb were 27.6 ± 6.0, 57.9 ± 12.8, 67.1 ± 10.3, 102.6 ± 23.4, 0.24 ± 0.07, and 25.1 ±5.9 mg/kg, respectively. Zn was the most abundant element, followed by Cu and Ni. Cd concentrations were 2–3 orders of magnitude lower than those of the other elements. In general, the magnitude orders of metal concentrations were in line with those of the background values for metals in soils of Binzhou City [22]. It could be observed that the average concentrations of the six metals all exceeded the soil background values, suggesting that extra input of heavy metals may occur in this area. Actually, concentrations of Co, Ni, and Cu were higher than related background values [22] at all the sampling points, and Zn, Cd, and Pb were observed with concentrations higher than associated background values at 16, 15, and 10 sampling points, respectively.
Our results were higher than those reported in the topsoil planting winter jujube in Binzhou City obtained about 10 years ago (the average values of Co, Ni, Cu, Zn, Cd, and Pb were 12.17, 30.28, 23.31, 68.36, 0.16, and 22.79 mg/kg, respectively), showing an increasing trend of metal levels [20]. Besides, concentrations of Cu, Zn, Cd, and Pb in the present study were higher than those of farmland soil in Binzhou City in 2011 (average values of Cu, Zn, Cd, and Pb were 23.51, 54.35, 0.057, and 22.28 mg/kg, respectively) [29]. Compared with the metal levels (the average values of Ni, Zn, Cd, and Pb in topsoil in 2014 were 33.4, 97.4, 0.21, and 23.3 mg/kg, respectively) in soils of another planting area of winter jujube in Dagang, Tianjin city [30], our results were observed with higher concentrations of Ni and similar levels of Zn, Cd, and Pb. Therefore, the long-term cultivation of fruit trees might be the major reasons for the enrichment of heavy metals in the soils of the Yellow River Delta.
In addition, the concentrations of Ni, Cu, Zn, Cd, and Pb in the soils were all lower than related risk screening values (Co was not involved) for agricultural soils in China (GB 15618-2018), indicating healthy planting conditions of the soils. The mean values of Co, Ni, Cu, Zn, Cd, and Pb were 1.1–2.8-times those of the related background values. This showed that the management practices, such as the selection, application amount, and frequency of fertilizer/pesticide, by the farmers in this region might be different.

3.2. Evaluation of Heavy Pollution in the Soils

The Nemerow comprehensive pollution index is a widely-used method to evaluate the pollution status of heavy metals in soils [31]. Values of this parameter in the 17 sampling stations ranged from 0.27 to 0.53, with an average value of 0.37, obviously lower than 0.7, suggesting a non-pollution status of heavy metals in the soils (Table 3).
The values of Igeo for different metal elements are shown in Figure 3. In detail, the Igeo values for Co, Ni, Cu, Zn, Cd, and Pb ranged from 0.11–1.09, −0.37–0.91, 0.49–1.43, −0.62–0.46, −0.76–0.80, and −1.04–0.08, respectively. In general, the average values of Igeo of Zn and Pb were less than 0, showing that the soils might have been barely affected by Zn and Pb. The average values of Igeo of Co, Ni, Cu, and Cd were between 0 and 1, showing a slight pollution of these above metals. In detail, the proportions of those stations without pollution of Ni, Zn, Cd, and Pb were 11.8%, 58.8%, 41.2%, and 94.1%, respectively, as well as those with slight pollution for Co, Ni, Cu, Zn, Cd, and Pb, which were 88.2%, 88.2%, 76.5%, 41.2%, 58.8% and 5.9%, respectively. It should be noted that the Igeo values of Co and Cu were observed to be greater than 1 at two stations and four stations, respectively, indicating moderate pollution.
Besides, we also calculated the potential ecological hazard index (Eri) based on the risk screening values for soil pollution of agricultural land in China (Table 2.), to judge the pollution effects (ecological risk) of heavy metals in the soils. The results were shown in Table 4. The Eri values of the Ni, Cu, Zn, Cd, and Pb ranged from 0.90–2.19, 1.25–2.41, 0.23–0.48, 6.52–19.13, and 0.48–1.03, respectively, with a generally decrease as Cd > Cu > Ni > Pb > Zn. All the values of Eri of a single element in the soil samples were far below 40, demonstrating mild ecological hazards of these metals. As for the ecological hazards of multiple metals, the RI values far below 150 indicated extremely low risks in all the sampling stations.

3.3. Analyisis of Sources of Heavy Metals in the Soils

The occurrences of heavy metals in soil are mainly affected by natural and human factors, and the similarity of the sources could lead to certain correlations between different elements [32,33]. Correlation analysis between heavy metals is an important basis for inferring the source of heavy metals. The results of a Pearson correlation analysis among heavy metals in the soils are shown in Table 5. A significant positive correlation among Co, Ni, Zn, Cd, and Pb could be observed, and this might suggest that these metals have same sources or behaviors. Conversely, the correlation between Cu and other heavy metals were not significant, indicating that the source or environmental behavior of Cu was different from the other metals.
To further analyze the sources of the six heavy metals, a principal component analysis was performed. Firstly, Kaiser–Meyer–Olkin (KMO) and Bartlett’s tests were conducted to test the concentration values of heavy metals. The KMO value was 0.687, and the associated probability of Bartlett’s sphericity test was 0.000, meeting the requirements of a principal component analysis. The eigenvalues were greater than 1, and two principal components were screened out. The two principal components could explain 68.4% and 18.5% of the total variance of the variables, reflecting most of the information of the original data.
The factor load distributions of heavy metals in the soils are shown in Figure 4. Co, Ni, Zn, Cd, and Pb had a higher load in the first principal component, and the loads were 0.950, 0.916, 0.766, 0.924, and 0.935, respectively. In addition, as mentioned above, significant positive correlations among Co, Ni, Zn, Cd, and Pb were observed. These findings suggested that the five metals might have the same sources or behaviors. On the one hand, it was related to the natural soil parent material. On the other hand, the contents of the five heavy metals exceeded the background values, and this indicated that they were also greatly affected by human activities. Winter jujube planting could be described as the major sources of these metals. Irrigation could be ignored, because the water for occasional artificial irrigation comes from the Yellow River, whose quality is quite high. Fertilizers were commonly applied during the cultivation of winter jujube, and a local standard (T/ZHDZ 009-2019) was used to provide guidance for the management practices of the fertilizer. The proposed amounts of base fertilizer for decomposed manure, microbial fertilizer, medium and trace element fertilizer, and nitrogen phosphorus potassium compound fertilizer were 45,000–72,000 kg/ha, 2700 kg/ha, 225–450 kg/ha, and 900 kg/ha, respectively. Of course, in reality, the type, frequency, and amount of the fertilizer might vary at different orchards. A great deal of literature reported that different types of fertilizers (especially organic fertilizer) had a high content of heavy metals, for example, Cd and As enrichment in P fertilizers and Zn fertilizers [8,34,35]. Therefore, it is inferred that the extra inputs of the five heavy metals were related to the long-term application of fertilizers containing heavy metals.
Cu had a higher load in the second principal component, with a load of 0.926, which is consistent with the result of the correlation analysis, too. It is concluded that the source of Cu was different from the that of the other heavy metals. Cu is known as the main active ingredient of some pesticides, such as Bordeaux mixture, which is widely used in orchards, leading to the continuous accumulation of copper in the orchard soil [36,37]. Actually, during the cultivation processes of winter jujube, Cu-containing pesticides/fertilizers were generally sprayed in July or August, according to our talking with the farmers. Those Cu in the trees would enter the soils through rainfall. Therefore, Cu in the soils of the study area was mainly related to the use of Cu-containing pesticides. In the process of planting winter jujube, fertilizers and pesticides with a low content of heavy metals should be used, and the dosage and frequency of the fertilizers and pesticides can be adjusted to control the further input of heavy metals.

4. Conclusions

In the present study, the occurrence of six heavy metals, including Co, Ni, Cu, Zn, Cd, and Pb, in soils of the winter jujube planting base were measured. The metal contents were observed to be exceeding the related soil background values of Binzhou City, showing an enrichment phenomenon. Besides, the metal levels in our study were higher than those in soils planting winter jujube in Binzhou City 10 years ago, presenting an increasing trend. On the other hand, these values were lower than the national risk screening values of metals in agricultural soil, revealing that the healthy/ecological risks caused by these metals were extremely low. The pollution degree and potential ecological risks were comprehensively discussed by calculating several indexes. The Nemerow comprehensive pollution indexes suggested no pollution of these metals. The Igeo values of metals revealed a non to slight pollution of Ni, Zn, Cd, and Pb, as well as a slight to moderate pollution of Co and Cu. Both the Eri and RI values indicated that the ecological risks caused by these metals were extremely low. Co, Ni, Zn, Cd, and Pb might come from the long-term fertilization, and Cu tended to come mainly from the pesticide use during the cultivation process. In general, the soils from the planting base of winter jujube in the Yellow River Delta were safe for the cultivation of jujube. The rational utilization of pesticide and fertilizer could be efficient measurements proposed to control the fresh inputs of heavy metals.

Author Contributions

Z.Z., W.X. and J.L.: conceptualization, obtaining research funding, and editing; C.S., D.Z. and Q.Y.: software, data analysis, and writing—original draft preparation; W.H. and J.J.: Writingreview and editing; M.Z., X.W. and Y.Z.: investigation and validation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (ZR2018PD002), Science and Technology Support Plan for Youth Innovation of Colleges and Universities in Shandong Province (2020KJD005), and the Experimental Technology Project of Binzhou University (BZXYSYXM202101).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Alengebawy, A.; Abdelkhalek, S.T.; Qureshi, S.R.; Wang, M.Q. Heavy metals and pesticides toxicity in agricultural soil and plants: Ecological risks and human health implications. Toxics 2021, 9, 42. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, N.; Zheng, Y.; He, X.; Li, X.; Zhang, X. Analysis of the report on the national general survey of soil contamination. J. Agro-Environ. Sci. 2017, 36, 1689–1692. (In Chinese) [Google Scholar]
  3. Hu, W.; Tao, T.; Tian, K.; Zhao, Y.; Huang, B.; Luo, Y. Status and prospect of farmland soil environmental quality management in China. Acta. Ped. Sin. 2021, 58, 1094–1109. (In Chinese) [Google Scholar]
  4. Yuan, X.; Xue, N.; Han, Z. A meta-analysis of heavy metals pollution in farmland and urban soils in China over the past 20 years. J. Environ. Sci. 2021, 101, 217–226. (In Chinese) [Google Scholar] [CrossRef] [PubMed]
  5. Su, Y.H.; Mcgrath, S.P.; Zhao, F.J. Rice is more efficient in arsenite uptake and translocation than wheat and barley. Plant Soil 2010, 328, 27–34. [Google Scholar] [CrossRef]
  6. Römkens, 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]
  7. Xiang, M.T.; Li, Y.; Yang, J.Y.; Lei, K.G.; Li, Y.; Li, F.; Zheng, D.F.; Fang, X.Q.; Cao, Y. Heavy metal contamination risk assessment and correlation analysis of heavy metal contents in soil and crops. Environ. Pollut. 2021, 278, 116911. [Google Scholar] [CrossRef]
  8. Peryea, F.J. Heavy metal contamination in deciduous tree fruit orchards: Implications for mineral nutrient management. Acta Hortic. 2001, 564, 31–39. [Google Scholar] [CrossRef]
  9. Guney, M.; Onay, T.T.; Copty, N.K. Impact of overland traffic on heavy metal levels in highway dust and soils of Istanbul, Turkey. Environ. Monit. Assess. 2010, 164, 101–110. [Google Scholar] [CrossRef]
  10. Akoto, R.; Anning, A.K. Heavy Metal enrichment and potential ecological risks from different solid mine wastes at a mine site in Ghana. Environ. Adv. 2021, 3, 100028. [Google Scholar] [CrossRef]
  11. Qin, G.W.; Niu, Z.D.; Yu, J.D.; Li, Z.H.; Ma, J.Y.; Xiang, P. Soil heavy metal pollution and food safety in China: Effects, sources and removing technology. Chemosphere 2021, 267, 129205. [Google Scholar] [CrossRef] [PubMed]
  12. Singh, S.; Kumar, M. Heavy metal load of soil, water and vegetables in peri-urban Delhi. Environ. Monit. Assess. 2006, 120, 79–91. [Google Scholar] [PubMed]
  13. Kachenko, A.G.; Singh, B. Heavy metals contamination in vegetables grown in urban and metal smelter contaminated sites in Australia. Water Air Soil Pollut. 2006, 169, 101–123. [Google Scholar] [CrossRef]
  14. Hossain, M.A.; Ali, N.M.; Islam, M.S.; Hossain, H.M.Z. Spatial distribution and source apportionment of heavy metals in soils of Gebeng industrial city, Malaysia. Environ. Earth Sci. 2015, 73, 115–126. [Google Scholar] [CrossRef]
  15. Hu, Y.; He, K.; Sun, Z.; Chen, G.; Cheng, H. Quantitative source apportionment of heavy metal(loid)s in the agricultural soils of an industrializing region and associated model uncertainty. J. Hazard. Mater. 2020, 391, 122244. [Google Scholar]
  16. Chen, H.; Ma, G.; Hou, Y.; Yang, J.; Shen, Z.; Li, Y. Principal component analysis and evaluation of fruit quality of winter jujube under different cultivation modes. North. Hortic. 2022, 10, 41–48. (In Chinese) [Google Scholar]
  17. Zhou, S.; Dong, X.; Guo, W.; Sun, Y.; Liu, J.; Li, Y. Introduction of nutrition and variety of winter jujube. Spec. Econo. Anim. Plant. 2019, 2, 45–48. (In Chinese) [Google Scholar]
  18. You, X.S. The analysis model of winter jujube orchard jujube intercropping vegetables in Binzhou Zone. China Fruit Veg. 2016, 36, 58–59; 61. (In Chinese) [Google Scholar]
  19. Rui, Y.; Shen, L.; Sheng, J. Content of trace elements and heavy metals in Chinese winter jujube fruit. Spectroscopy Spectr. Anal. 2008, 28, 1928–1930. (In Chinese) [Google Scholar]
  20. Gao, Z.; Wang, M.; Pang, X.; Liu, Z. Influence of soil geochemistry to the qualtiy of winter jujube in Zhanhua. J. Anhui Agri. Sci. 2011, 39, 2711–2714. (In Chinese) [Google Scholar]
  21. Wang, C.; Liu, H.; Xia, X.; Liu, Z.; Wang, H.; Wang, H.; Zheng, W. The distribution of soil elements as well as its influence on the quality of Dongzao (winter jujube) in Zhanhua Dongzao growing area. Geophys Geochem Explor. 2012, 36, 641–650. [Google Scholar]
  22. Pang, X.; Dai, J.; Chen, L.; Liu, H.; Yu, C.; Han, L.; Ren, T.; Hu, X.; Wang, H.; Wang, Z.; et al. Soil geochemical background value of 17 cities in Shandong Province. Shandong. Land. Resour. 2019, 35, 46–56. (In Chinese) [Google Scholar]
  23. Nie, J.; Kuang, L.; Li, Z.; Xu, W.; Wang, C.; Chen, Q.; Li, A.; Zhao, X.; Xie, H.; Zhao, D.; et al. Assessing the mass fraction and potential health risk of heavy metals in China’s main deciduous fruits. J. Integr. Agric. 2016, 15, 1645–1655. [Google Scholar] [CrossRef]
  24. Ackah, M. Soil elemental concentrations, geoaccumulation index, non-carcinogenic and carcinogenic risks in functional areas of an informal e-waste recycling area in Accra, Ghana. Chemosphere 2019, 235, 908–917. [Google Scholar] [CrossRef] [PubMed]
  25. Hakanson, L. An ecological risk index for aquatic pollution control: A sedimentological approach. Water. Res. 1980, 14, 975–1001. [Google Scholar] [CrossRef]
  26. Kahangwa, C.A. Application of principal component analysis, cluster analysis, pollution index and geoaccumulation index in pollution assessment with heavy metals from gold mining operations, Tanzania. J. Geosci. Environ. Prot. 2022, 10, 303–317. [Google Scholar] [CrossRef]
  27. Ren, S.Y.; Song, C.Q.; Ye, S.J.; Cheng, C.X.; Gao, P.C. The spatiotemporal variation in heavy metals in China’s farmland soil over the past 20years: A meta-analysis. Sci. Total Environ. 2022, 806, 150322. [Google Scholar] [CrossRef]
  28. Xu, Z.; Ni, S.; Tuo, X.; Zhang, C. Calculation of heavy metals’ toxicity coefficient in the evaluation of potential ecological risk index. Environ. Sci. Technol. 2008, 31, 112–115. (In Chinese) [Google Scholar]
  29. Yong, M.; Zhang, M.; Wang, S.W.; Liu, G. Farmland soil heavy metal source analysis and evaluation in Bincheng County of Shandong Province, China. J. China Agric. Univ. 2014, 19, 119–125. (In Chinese) [Google Scholar]
  30. Wang, W.X.; Li, G.K.; Cao, S.P. Study on the quality analysis and site geological background of Dagang Winter Jujube in Tianjin. J. Shanxi Agric. Sci. 2019, 47, 1230–1234. (In Chinese) [Google Scholar]
  31. Yang, Y.; Shi, X.; Zhang, C. Spatial distribution and evaluation of heavy metal pollution of reclaiming village based on Nemerow integrated pollution index method. Res. Soil. Water. Conserv. 2016, 23, 338–343. (In Chinese) [Google Scholar]
  32. Xu, L.; Dai, H.P.; Skuza, L.; Xu, J.M.; Shi, J.C.; Wang, Y.J.; Shentu, J.; Wei, S.H. Integrated survey on the heavy metal distribution, sources and risk assessment of soil in a commonly developed industrial area. Ecotox. Environ. Safe 2022, 236, 113462. [Google Scholar]
  33. Liu, S.Q.; Peng, B.; Li, J.F. Ecological risk evaluation and source identification of heavy metal pollution in urban village soil based on XRF technique. Sustainability 2022, 14, 5030. [Google Scholar]
  34. Salami, H.A.; Matemilola, S.; Fasasi, S.A.; Ahmed, M.O.; Adigun, I.A.; Adeleke, A.A.; Fashina, S.M.; Olowosokedile, O. Towards achieving food security in Nigeria: Afuzzy comprehensive assessment of heavy metals contamination in organic fertilizers. Curr. Res. Agric. Sci. 2021, 8, 110–127. [Google Scholar]
  35. Lindvall, E.; Gustavsson, A.M.; Samuelsson, R.; Magnusson, T.; Palmborg, C. Ash as a phosphorus fertilizer to reed canary grass: Effects of nutrient and heavy metal composition on plant and soil. Glob. Chang. Biol. Bioenergy 2015, 7, 553–564. [Google Scholar]
  36. Tegenaw, A.; Sorial, G.A.; Demessie, E.S.; Han, C. Influence of water chemistry on colloid-size Cu-based pesticides particles: A case of Cu(OH)2 commercial fungicide/bactericide. Chemosphere 2020, 239, 124699. [Google Scholar] [PubMed]
  37. Blotevogel, S.; Oliva, P.; Sobanska, S.; Viers, J.; Schreck, E. The fate of Cu pesticides in vineyard soils: A case study using δ 65 Cu isotope ratios and EPR analysis. Chem. Geol. 2018, 477, 35–46. [Google Scholar]
Figure 1. Sampling area in the Yellow River Delta.
Figure 1. Sampling area in the Yellow River Delta.
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Figure 2. The degrees of Eri and RI of heavy metals.
Figure 2. The degrees of Eri and RI of heavy metals.
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Figure 3. Geoaccumulation indexes for heavy metals in the sampling stations.
Figure 3. Geoaccumulation indexes for heavy metals in the sampling stations.
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Figure 4. Distribution of heavy metal load in the soils.
Figure 4. Distribution of heavy metal load in the soils.
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Table 1. Classification of metal pollution degree based on the Nemerow comprehensive pollution index and geo-accumulation index.
Table 1. Classification of metal pollution degree based on the Nemerow comprehensive pollution index and geo-accumulation index.
GradepPollution DegreeGradeIgeoPollution Degree
I≤0.7No pollutionI≤0No pollution
II0.7–1Slight pollution II0–1Slight pollution
III1–2Moderate pollution III1–2Moderate pollution
IV2–3Severe pollution IV2–3Moderate severepollution
V>3Extremely Severe pollution V3–4Severe pollution
VI4–5Relatively severe pollution
VII>5Extremely severe pollution
Table 2. Heavy metal concentrations in soils from winter jujube farms of Binzhou City (mg/kg).
Table 2. Heavy metal concentrations in soils from winter jujube farms of Binzhou City (mg/kg).
Sample PointsCoNiCuZnCdPb
J131.166.471.7106.90.2729.8
J220.434.256.273.90.1817.7
J335.565.269.1126.10.3831.6
J438.681.170.0130.60.3332.5
J539.183.476.2139.40.3435.0
J630.062.453.696.40.2730.5
J729.663.471.297.40.2728.8
J827.457.850.285.10.2326.4
J928.357.968.7111.60.2328.4
J1026.562.459.799.60.1820.7
J1126.656.762.983.70.1319.7
J1222.650.096.4101.10.2220.2
J1320.545.872.5143.00.2519.5
J1419.744.368.169.90.1316.2
J1526.449.962.3120.40.2122.2
J1625.960.565.690.80.2327.4
J1721.243.366.767.50.1619.8
Background value12.229.523.869.10.14722.1
Risk screening value/1902003000.6170
Exceeding standard rate (%)-0.00.00.00.00.0
Table 3. Nemerow comprehensive indexes of soil pollution from heavy metals.
Table 3. Nemerow comprehensive indexes of soil pollution from heavy metals.
Sample PointspPollution DegreeSample PointspPollution Degree
J10.40No pollutionJ100.31No pollution
J20.27No pollutionJ110.28No pollution
J30.53No pollutionJ120.41No pollution
J40.48No pollutionJ130.41No pollution
J50.49No pollutionJ140.29No pollution
J60.39No pollutionJ150.35No pollution
J70.39No pollutionJ160.35No pollution
J80.34No pollutionJ170.29No pollution
J90.36No pollution
Table 4. Potential ecological hazard index of a single element and comprehensive potential ecological hazard index of heavy metals in the soil.
Table 4. Potential ecological hazard index of a single element and comprehensive potential ecological hazard index of heavy metals in the soil.
EirRI
NiCuZnCdPb
Minimum0.901.250.236.520.4810.10
Maximum2.192.410.4819.131.0323.92
Average1.521.680.3411.840.7416.12
Standard deviation0.340.260.083.540.174.06
Potential ecological hazardSlightSlightSlightSlightSlightSlight
Table 5. Pearson correlation analysis results of heavy metals in the soil.
Table 5. Pearson correlation analysis results of heavy metals in the soil.
CoNiCuZnCdPb
Co1.00
Ni0.941 **1.00
Cu0.0600.1311.00
Zn0.600 *0.572 *0.3521.00
Cd0.819 **0.720 **0.2200.749 **1.00
Pb0.917 **0.875 **0.0400.550 *0.866 **1.00
Note: * means significant correlation at p < 0.05 level; ** means extremely significant correlation at p < 0.01 level.
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Shan, C.; Zhang, Z.; Zhao, D.; Zou, M.; Huang, W.; Wang, X.; Jin, J.; Zhang, Y.; Yang, Q.; Xie, W.; et al. Heavy Metal Contamination in Soils from a Major Planting Base of Winter Jujube in the Yellow River Delta, China. Processes 2022, 10, 1777. https://doi.org/10.3390/pr10091777

AMA Style

Shan C, Zhang Z, Zhao D, Zou M, Huang W, Wang X, Jin J, Zhang Y, Yang Q, Xie W, et al. Heavy Metal Contamination in Soils from a Major Planting Base of Winter Jujube in the Yellow River Delta, China. Processes. 2022; 10(9):1777. https://doi.org/10.3390/pr10091777

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Shan, Changqing, Zaiwang Zhang, Deyong Zhao, Meiling Zou, Wenwen Huang, Xiangrui Wang, Jianchao Jin, Yanpeng Zhang, Qian Yang, Wenjun Xie, and et al. 2022. "Heavy Metal Contamination in Soils from a Major Planting Base of Winter Jujube in the Yellow River Delta, China" Processes 10, no. 9: 1777. https://doi.org/10.3390/pr10091777

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