Next Article in Journal
Improvement of a Truss-Reinforced, Half-Concrete Slab Floor System for Construction Sustainability
Next Article in Special Issue
The Nexus between Tourism Activities and Environmental Degradation: Romanian Tourists’ Opinions
Previous Article in Journal
Towards a Sustainable News Business: Understanding Readers’ Perceptions of Algorithm-Generated News Based on Cultural Conditioning
Previous Article in Special Issue
The Unsustainable Use of Sand: Reporting on a Global Problem
 
 
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Source Identification of Cd and Pb in Typical Farmland Topsoil in the Southwest of China: A Case Study

by 1, 1,2,*, 1,2, 1,2,*, 1 and 1
1
College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
2
Sichuan Province Key Laboratory of Nuclear Techniques in Geosciences, Chengdu 610059, China
*
Authors to whom correspondence should be addressed.
Sustainability 2021, 13(7), 3729; https://doi.org/10.3390/su13073729
Received: 3 March 2021 / Revised: 22 March 2021 / Accepted: 25 March 2021 / Published: 26 March 2021
(This article belongs to the Special Issue Environmental Impact and Nature Conservation)

Abstract

:
Cd and Pb in farmland topsoil are controlled by many factors. To identify the source of potential toxic metals in the farmland topsoil around Mianyuan River, the chemical analysis and multivariate statistical analysis are performed in this study. The results indicate the following: (1) The concentration of Cd and Pb in soil exceed the background value of Chinese soil elements. (2) Cd is significantly enriched in the whole region and Pb is locally enriched, both of them are more or less influenced by human activities. (3) The contents of Cd and Pb increase significantly following the flow direction of river. (4) P b isotope analysis indicates that the main source of Pb in the soil include the air dust, coal and phosphate plant, and the contribution of them decreases successively. (5) Linear correlation analysis and principal component analysis show that the main sources of Cd in the soil are mining phosphate rock, air dust, phosphate plant and coal mining.

1. Introduction

The toxicity and bioavailability of potential toxic metals (such as Cd and Pb) in soils have recently attracted increasing levels of public attention [1,2,3]. Cadmium (Cd) is a harmful heavy metal that ranks seventh among all heavy metals in terms of harm to animals and plants [4]. Lead is a priority heavy metal pollutant, as designated by the U.S. Environmental Protection Agency [5]. Generally, Pb enters the natural environment via industrial production (mining and smelting), the use of lead-containing products, the combustion of fossil fuels (coal, leaded gasoline), and the use of mineral fertilizers and sewage sludge [6]. Cd pollution is mainly caused by the soil parent material, soil type, water system [7], dustfall [8,9] and human activities [10]. Previous studies have found that the mining and smelting of metalliferous materials, electroplating, gas exhaust emissions, energy and fuel production, and fertilizer and pesticide application are the main ways that human activities cause Cd pollution [11]. Nowadays, anthropogenic factors are playing an important role in potential toxic metals contamination [12].
Isotope ratios are commonly used in the field of environmental science to determine composition. The mixture of lead from different sources determines the variation in the lead isotope ratio of the soil [13]. Thus, lead isotope ratios combined with concentration data have been successfully used to trace pollution sources and estimate source contribution rates [14]. Multivariate analysis (principal component analysis) and correlation analysis has been widely used to assist the interpretation of environmental data and sources of pollution [15].
The study area, located in the farm belt of Chengdu Plain in the southwest of China, is suffering from severe Cd and Pb pollution due to the development of the social economy. In terms of the environmental quality standards for soils in China (GB15618-1995), the Cd concentrations here generally exceed the Level 2 threshold (0.3 mg/kg) and Pb concentrations generally exceed the Level 1 threshold (35 mg/kg) [16,17]. Level 1 is typical for natural reserves, and Level 2 is used to indicate the threshold value for safe agricultural production and for protecting human health [18]. Cadmium rice seriously endangers the health of local residents and the development of the regional economy. The terrain slopes slowly from northwest to southeast. Holocene alluvial and pluvial deposits cover the surface in the region. Mines (distributed in the Longmen Mountains) and industrial and residential areas (distributed between the study area and the Longmen Mountains) constitute the potential sources of potential toxic metals (Pb, Cd) contamination in the area [19]. Studies have shown that potential toxic metals pollution around Mianyuan River has not changed in recent years [7,20,21,22].
Previous studies on the Mianyuan River Basin have mainly focused on the contamination of stream sediments near mines. The contribution of sources of Cd and Pb pollution have not been made clear because of the complexity of the potential pollution sources. Therefore, an investigation into the sources and topsoils in this region, in order to further clarify the sources of pollution and their proportional contributions, was performed. Various methods are used to identify the sources of Cd and Pb and to calculate contribution rates. P b isotopes were used to calculate the contribution rate of Pb sources. Multivariate statistical analysis and P b isotopes were used to calculate the contribution rate of Cd sources. It is expected that This study is helpful for understanding the mechanism of potential toxic metal pollution in the study area and providing a basis for the regional prevention and control of potential toxic metal pollution in the plain.

2. Materials and Methods

2.1. Sampling and Analysis

To investigate the factors influencing Cd and Pb pollution in the Mianzhu region’s farmland topsoil, the study area was divided into several blocks, from which samples were collected. A field investigation was conducted to individually investigate the pollution sources in the vicinity of the study area. Samples of pollution sources were collected from mines, residential areas and industrial areas in the upstream region of the study area.
A series of investigations into farmland topsoil and pollution sources in the area were conducted during 2015. The sampling grid is shown in Figure 1. According to the grid standard of 2 × 2 km, 38 topsoil samples were collected for the whole area. Each soil sample was collected in the 0–20 cm region. In order to reduce differences in soil composition, five subsamples were integrated. The collected samples were air-dried and stored in polypropylene bottles. The reference materials were HJ/T 166-2004. In total, 5 samples from the 38 samples were analyzed for P b isotopes. Kriging interpolation was used to treat the Pb and Cd contents. Four kinds of pollution source sample were collected. Phosphate ore (W01) was collected at the Qingping Phosphate Mine in the upper region of the Mianyuan River. The coal mine sample (W02) was collected at the Tianchi coal mine near Meizigou, a tributary in the upper region of Mianyuan River. A dustfall sample (W03) was collected from residential gathering areas, and the phosphogypsum sample (W04) was collected from the phosphating plant. In total, 5 samples were randomly taken from 38 kinds of topsoil to test the element content again in 2018. The results, though, showed that there was no significant difference in potential toxic metals concentration between the two sampling periods. Therefore, it can be concluded that the results from the 2015 investigation can guide the current stage of environmental governance.

2.2. Chemical Analysis

The treatment and analysis of the elements in the soil samples followed the GBW07404 and GBW07405. The soil and pollution source samples were dried and crushed using a 0.071 mm sieve. According to the United States Environmental Protection Agency (USEPA) procedure, the samples were digested with HNO3, HClO4 and HF (USEPA-3052 1996; USEPA-3050B 1996) [23]. Soil samples of 0.1 g were mixed with 5 mL of HNO3 in a Teflon vial and heated to dryness at 80 °C. Then, 2.5 mL of HF, 2 mL of HNO3 and 1 mL of HClO4 were added and the mixture was heated to 160 °C for digestion and concentration. Totals of 1.25 mL of deionized water, 1.25 mL of HNO3 and 2.5 mL of H2O2 were added to the dry residue for further digestion, and then, 5% (v/v) HNO3 was added at a constant volume up to 25 mL for preservation. The blank sample was used to ensure the correctness of the detection. The As concentration was determined by atomic fluorescence spectrometry (AFS), and the concentrations of other elements were determined by inductively coupled plasma–optical emission spectroscopy (ICP-OES). P b isotope analysis was performed using a high-precision inductively coupled plasma mass spectrometer. The ratios of   208 P b 207 P b , 208 P b 206 P b and 206 P b 207 P b were obtained, controlling the precision and accuracy with a reference material of P b isotope and internal standard. The standard error of the isotope measurement was less than 0.5%.

2.3. Multivariate Analysis

Descriptive analysis, Pearson correlation coefficient analysis and principal component analysis are broadly used to identify the relationship between potential toxic metals and possible pollution sources in the environment [24,25,26]. In this study, SPSS for Windows, version 19 (SPSS Inc, USA), was utilized in the multivariate statistical analysis.
Descriptive analysis, including the mean, standard deviation (SD), skewness, coefficient of variation (CV), etc., can be utilized to reflect the dispersion degree for potential toxic metals in the soil, and indirectly indicate the sources of said potential toxic metals.
The enrichment factor (EF) can be used to determine the enrichment of potential toxic metals in the soil and to differentiate between metals originating from human activities and those originating from natural processes. When using enrichment factors, a reference element should be introduced to standardize the tested elements. The reference element that is used is often a conservative one, such as Ni, Mn, Sc, etc. [27]. The enrichment factor (EF) value can be calculated via the formula proposed by Buat-Menard and Chesselet [28]:
EF = [ C n ( s a m p l e ) / C r e f ( s a m p l e ) ] / [ B n ( b a s e l i n e ) / B r e f ( b a s e l i n e ) ]
where C n (sample) is the concentration of the examined element in the farmland topsoil, C r e f (sample) is the concentration of the reference element in the farmland topsoil, B n (baseline) is the content of the examined element in Chinese soils (here, Chinese background values were selected as the baseline), and B r e f (baseline) is the content of the reference element in Chinese soils.
Based on the linear correlation efficient, the relationship among the potential toxic metals was explored [29,30]. Principal component analysis (PCA) is widely used in the examination of soil environmental pollution [31,32] by extracting common factors from variable groups to seek implicit relations [33]. KMO and the Bartlett spherical test are usually used to test the suitability of PCA. A KMO > 0.9 indicates perfect usability; 0.9 > KMO > 0.7 means suitable for use; 0.7 > KMO > 0.6 means generally effective; KMO < 0.5 means not suitable for use [34].

3. Results and Discussion

3.1. Potential Toxic Metals Concentrations

The statistical descriptions are shown in Table 1. The average contents of Pb, Cd, Cu, Zn, Cr, Ni, Hg, Se, and P exceeded the reference value (background value of Chinese soil elements), and the level of Cd exceeded the reference value more than 10-fold, which indicates that the potential toxic metals in the farmland topsoil of the study area were enriched. In addition, the CV results of these 10 elements can be split into two categories, that is, the CV values of most of the elements (Cd, Pb, As, Hg, Zn, Se, P) are greater than 0.3, and those of Cu, Ni and Cr are less than 0.2. Potential toxic metals contributed primarily by natural sources have low CV values, while those affected by human activities generally have high CV values [35]. This indicates that the concentrations of Cd, Pb, Zn, Hg and P have significant spatial heterogeneity, and their production may be dominated by human activities [36]. The presence of Ni may be primarily caused by natural factors [37]. The skewness values of Cd, Pb, Zn and As are all greater than 1, which indicates that the concentrations of Cd, Pb, Zn and As are generally high. The presence of Cu and Cr with a large SD and high mean exhibit characteristics of human activities. However, the Cu and Cr have very low CV and skewness values, which are characteristics indications of a natural source.
The content range of Ni is narrower, and the average is closer to the reference value. These factors indicate that Ni is not, or is only slightly, enriched in the farmland topsoil. The CV of Ni is very small, and the skewness of Ni is close to 0. These factors indicate that the distribution characteristics of the Ni in the study area are much more normal, that is, they are the characteristics of natural processes. Combined with the above conclusions, Ni can be selected as the reference element. The results show that the EFs of Cd, P and Se are large, and the EF of Cd is close to 10, suggesting the influence of human activities. Combined with their characteristics of high SD and CV, Cd, P and Se are also widely enriched in the study area. The EFs of Pb, Cu, Cr and Zn are small, indicating characteristics of local enrichment, and they may be determined by both natural and human activities [38,39].

3.2. Spatial Distribution Characteristics of Cd and Pb

The spatial patterns of the Pb and Cd processed by Kriging interpolation are shown in Figure 2. The contents of Pb and Cd in the study area are mostly higher than the background concentration values, which are 30.90 mg/kg for Pb and 0.08 mg/kg for Cd. Spatially, Pb and Cd are highly enriched in the northwest region, and this value gradually decreases towards the southeast region. The contents of Pb and Cd in the northwest region are relatively high, with values of over 33 mg/kg and 1.0 mg/kg, respectively. Correspondingly, the contents of Pb and Cd in the southeastern region are lower than those in the northwest, with values of 30 mg/kg and 0.7 mg/kg, respectively. This indicates that topography affects the spatial distribution of Pb and Cd in farmland topsoil to a certain extent, which may relate to the rapid decline in water flow velocity from the mountains to the plains. Pb and Cd are precipitated in the form of complexes [40,41,42,43]. Moreover, the contents of Pb and Cd tend to decrease as one moves further away from the Mianyuan River. Accordingly, the content of Pb decrease from over 31 mg/kg to 25 mg/kg, and the content of Cd decreases from over 0.58 mg/kg to below 0.4 mg/kg. This distribution also proves that the Cd and Pb concentrations in the cultivated soil are controlled by the transportation of the Mianyuan River and its tributaries. In the northwest of the study area, there are phosphate mines and phosphating plants, both of which are likely to be major sources of cadmium and lead. Deng et al. found that the frequency human activities in developed areas is also an important factor leading to potential toxic metals enrichment [44]. The high concentrations of Cd and Pb in the towns of Mianzhu and Fuxin indicate that the population concentration is also a factor contributing to the accumulation of Cd and Pb in the study area. The Cd and Pb were brought into the study area from the northwestern part of Mianyuan River, and they then migrated into the topsoil through irrigation, and became enriched in it. At the same time, potential toxic metals are released from urban living areas into the topsoil.

3.3. Pb Source Identification Based on Isotopic Ratio

A mixed linear model is established from the P b isotope ratio in order to trace the sources of Pb pollution and determine their relative contributions [18,45]. According to the results of the principal component analysis (see below), Pb in farmland topsoils can be assessed as having three components, which can be further explored by using the ternary linear model of the P b isotope. The following formula shows the ternary linear model.
( 206 P b 207 P b ) s = f 1 × ( 206 P b 207 P b ) 1 + f 2 × ( 206 P b 207 P b ) 2 + f 3 × ( 206 P b 207 P b ) 3 ( 208 P b 206 P b ) s = f 1 × ( 208 P b 206 P b ) 1 + f 2 × ( 208 P b 206 P b ) 2 + f 3 × ( 208 P b 206 P b ) 3 f 1 + f 2 + f 3 = 1
In these equations, 206 P b 207 P b represents the ratio of 206 P b to 207 P b , 208 P b 206 P b represents the ratio of 208 P b to 206 P b , and f 1 , f 2 and f 3 represent the contribution rates of the three pollution sources to the P b present in the samples, respectively.
Table 2 shows that the Cd contents of the four pollution sources range from 0.22 mg/kg to 9.94 mg/kg, which is higher than the natural background value of 0.2 mg/kg. The Pb content primarily ranges from 20 to 35.3 mg/kg, which is close to the natural background value of 35 mg/kg. This shows that these sources have the potential to cause potential toxic metals (Pb and Cd) pollution. The contents of Pb (14.3 to 36.1 mg/kg) and Cd (0.49 to 0.73 mg/kg) have values higher than the natural background value, indicating that the agricultural soils in the study area were polluted.
Although phosphate and coal mining activities, as well as phosphating plants, are likely to be the main sources of lead in the study area, atmospheric coal burning, and automobile exhaust emissions also need to be considered. The emission of automobile exhausts and the burning of coal cause atmospheric depositions [18]. Zhao et al. found that the isotopic composition of lead in automobile exhausts was between 1.150 and 1.162 ( 206 P b 207 P b ) and between 2.106 and 2.115 ( 208 P b 206 P b ) [46]. The value range of the P b isotopes of the dustfall in the study area ( 206 P b 207 P b = 1.163, 208 P b 206 P b = 2.114) is close to the value range of automobile exhaust, but far from that of the coal mine, phosphate ore and phosphogypsum. In addition, the study area has a well-developed transportation infrastructure, with a large volume of traffic and automobile exhaust emissions. This indicates that the source of the dustfall in the study area is dominated by automobile exhaust deposition.
Figure 3 shows that there is a linear relationship between the 206 P b 207 P b and 208 P b 206 P b of the four pollution sources and five agricultural soils. The P b isotope values of the five agricultural soils are located in the triangular end element region, composed of dustfall, coal ore and phosphogypsum; that is to say, these three layers play a major role in controlling the P b contents in the agricultural soils of the study area. In Table 2, the 206 P b 207 P b and 208 P b 206 P b values of the agricultural soils, dustfall, coal mine and phosphogypsum are substituted into equation group (1). The contribution of Pb from dustfall to agricultural soils is 74.94%, that from coal mines is 14.87%, and that from phosphogypsum is 10.19%.
The P b isotope analysis indicates that the Pb in the topsoils of the study area is primarily contributed by vehicle exhaust. Coal mining activities and the production of phosphorus products also contribute to the Pb in topsoils.

3.4. Multivariate Analysis Results

3.4.1. Correlation Analysis

A correlation analysis of 10 elements in the agricultural soils of the study area was carried out. As shown in Table 3, Pb was strongly correlated with Zn, Cd, As and Se, and the Cd was in correlation with Pb, Zn, Cu, As, Se and P. The correlation coefficients are all above 0.5, which indicates good correlation, and that the pollution sources of these elements are roughly similar. However, the correlation coefficients of each element are different, indicating that the levels of influence of the different pollution sources are different. Moreover, the correlation coefficient between Cd and P is 0.856, which indicates that the Cd may be affected by phosphate mining or processing. Excluding Cu, the linear correlation between Ni and the other elements is not significant. This indicates that Cu is affected by natural processes. However, there is a significant linear correlation between Cu and Cd, Se and P. This indicates that Cu is affected by human activities, related to phosphate mining or processing.

3.4.2. Principal Component Analysis

Given the good correlation of Cd, Pb, Zn, Cu, As, Se and P in the soil of the study area, common factors can be extracted. To further explore the sources of Pb and Cd, the principal component analysis method was used. The results show that the KMO value of the seven elements was 0.763, which means they are suitable for principal component analysis. The Sig index of the Bartlett spherical test was 0, while the approximate chi-square (331.76) and df index (21) were larger, indicating that the Bartlett spherical test achieved significance. There were an obvious linear correlation between the soil samples, and principal component analysis could be carried out.
Based on the results of the P b isotope analysis, all seven initial eigenvalues extracted from the potential toxic metals concentration values are processed by the maximum variance method. The analysis results are shown in Table 4. The results show that seven elements were extracted from the agricultural soils, including Pb, Zn, Cd, Cu, As, Se and P. The eigenvalues of the first four were all greater than 1, and the cumulative variance contribution rate was over 95%. The variance contribution rates of the fifth, sixth and seventh factors were very small, and their influences can be neglected.
According to the analysis results, the main characteristics of the elements (variables) were calculated via the criteria of eigenvalue > 1 and cumulative variance contribution > 85%. The results are shown in Table 5.
Combined with the factor load matrix results, the Pb content can be assessed to be mainly controlled by the first main factor, and the contributions of the third factor and the fourth factor decrease. Pb and As showed high similarity in terms of the first major factor, indicating that they have similar provenances. Further, this factor represents the main source of Pb in the agricultural soils of the study area. Combined with the results of the P b isotope, the first factor can be determined to be dustfall. Previous studies have found that the deposition of vehicle exhaust can lead to an increase in the Pb and Cd concentrations in dustfall [47,48], and that dustfall is an important factor leading to the enrichment of Cd and Pb in the soil of the study area. The Cd is mainly affected by the second main factor. The second main factor is very similar to P. Field investigation shows that the phosphorite ores produced upstream of the study area contain svanbergite, which is the main useful mineral produced in this process. The P in the mineral forces the Cd from the ores to migrate into the water system in large quantities, and from there into the study area. Therefore, the second main factor is determined to be the phosphate mine. Se and the third factor show very high similarity. By comparing the Se contents of several pollution sources, we see that stone from the coal mine is rich in Se. Combined with the results for the P b isotope, this factor is determined to be the coal mine. Cu shows very high similarity with the fourth factor. From the geochemical affinity of the elements, we can infer Cu’s sulfur-affinity characteristic [49]. According to field observations, phosphate ore production and processing enterprises are present upstream of the study area. A number of sulfur-affinity elements are contained in phosphate ore. Cu mainly migrates through processes of hydrolysis, precipitation, ion exchange and adsorption [50]. In the process of phosphate ore treatment, the Cu moved into the Mianyuan River and migrated via the co-precipitation or adsorption of colloidal Al and Fe sediments. Finally, it entered the agricultural soils of the study area through irrigation channels. Combined with the results for the P b isotope, it can be determined that the fourth main factor is the phosphogypsum produced by the phosphate plant. The sources of Cd can be ranked, in terms of contribution, as follows: phosphate mine > dustfall > phosphate plant > coal mine.
Our analysis of source contribution indicates that the concentrations and pollution degrees of Cd and Pb in farmland topsoils are related to industrial production, mineral mining activities and land use patterns. The Pb in the farmland topsoils of the study area is primarily contributed by vehicle exhaust. Coal mining activities and the production of phosphorus products also contribute to the Pb levels in the topsoils. Cd is mainly contributed by phosphate mining, dustfall, phosphate plant activities and coal mining, and phosphate mining contributes the most to the Cd levels in the soil. The most severe metallic pollution of the farmland topsoils was found in the northwest of the study area and the residential area, which are priority areas for pollution control. Pb has low chemical mobility during soil weathering, and easily forms stable complexes in topsoils [51]. Cd has greater chemical mobility than Pb, but it can be adsorbed by clay minerals. A series of physical and chemical measures should be taken to immobilize the Cd and Pb in topsoils [52,53].

4. Conclusions

The agricultural topsoil in the study area is polluted by Pb and Cd. Cd displays clear regional enrichment characteristics, while Pb shows local enrichment characteristics. The presence of both of these is mainly determined by human activities. The contents of Pb and Cd decrease from NE to SW and tend to decline as one moves away from the Mianyuan River. The factors influencing topsoil Pb pollution can be ranked as follows: dustfall > coal mine > phosphogypsum. The factors influencing topsoil Cd pollution can be ranked as follows phosphate pine > dust fall > phosphate plant > coal mine.

Author Contributions

Conceptualization, J.Z.; methodology, J.Z. and X.W.; validation, Z.S. and S.N.; investigation, C.L. and F.W.; writing—original draft preparation, J.Z.; writing—review and editing, Z.S. and S.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the financial support of Opening Fund of Provincial Key Lab of Applied Nuclear Techniques in Geosciences (GNZDS 2018002), Science and Technology Department of Sichuan Province (18YYJC1029), Department of Natural Resources of Sichuan Province (KJ20193).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data which also form part of an ongoing study.

Acknowledgments

The authors would like to thank all the participants in the research activities as well as other participants within the ISSUE program for their dedication.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gurung, B.; Race, M.; Fabbricino, M.; Komínková, D.; Libralato, G.; Siciliano, A.; Guida, M. Assessment of metal pollution in the Lambro Creek (Italy). Ecotoxicol. Environ. Saf. 2018, 148, 754–762. [Google Scholar] [CrossRef]
  2. Ahmad, K.; Wajid, K.; Khan, Z.I.; Ugulu, I.; Memoona, H.; Sana, M.; Nawaz, K.; Malik, I.S.; Bashir, H.; Sher, M. Evaluation of Potential Toxic Metals Accumulation in Wheat Irrigated with Wastewater. Bull. Environ. Contam. Toxicol. 2019, 102, 822–828. [Google Scholar] [CrossRef]
  3. Sarret, G.; Blommaert, H.; Wiggenhauser, M. Comment on “Speciation and fate of toxic cadmium in contaminated paddy soils and rice using XANES/EXAFS spectroscopy”. J. Hazard. Mater. 2021, 401. [Google Scholar] [CrossRef]
  4. Tang, L.; Luo, W.; Tian, S.; He, Z.; Stoffella, P.J.; Yang, X. Genotypic differences in cadmium and nitrate co-accumulation among the Chinese cabbage genotypes under field conditions. Sci. Hortic. 2016, 201, 92–100. [Google Scholar] [CrossRef]
  5. Yang, Q.; Li, Z.; Lu, X.; Duan, Q.; Huang, L.; Bi, J. A review of soil heavy metal pollution from industrial and agricultural regions in China: Pollution and risk assessment. Sci. Total Environ. 2018, 642, 690–700. [Google Scholar] [CrossRef]
  6. Komárek, M.; Ettler, V.; Chrastný, V.; Mihaljevič, M. Lead isotopes in environmental sciences: A review. Environ. Int. 2008, 34, 562–577. [Google Scholar] [CrossRef]
  7. Li, B.; Xiao, R.; Wang, C.; Cao, L.; Zhang, Y.; Zheng, S.; Yang, L.; Guo, Y. Spatial distribution ofsoil cadmium and its influencing factors in peri-urban farmland: A case study in the Jingyang District, Sichuan, China. Environ. Monit. Assess. 2017, 189, 1–16. [Google Scholar] [CrossRef]
  8. Gallon, C.; Ranville, M.A.; Conaway, C.H.; Landing, W.M.; Buck, C.S.; Morton, P.L.; Flegal, A.R. Asian industrial lead inputs to the north pacific evidenced by lead concentrations and isotopic compositions in surface waters and aerosols. Environ. Sci. Technol. 2011, 45, 9874–9882. [Google Scholar] [CrossRef]
  9. Okin, G.S.; Parsons, A.J.; Wainwright, J.; Herrick, J.E.; Bestelmeyer, B.T.; Peters, D.C.; Fredrickson, E.L. Do Changes in Connectivity Explain Desertification? Bioscience 2009, 59, 237–244. [Google Scholar] [CrossRef]
  10. Filippelli, G.M.; Morrison, D.; Cicchella, D. Urban geochemistry and human health. Elements 2012, 8, 439–444. [Google Scholar] [CrossRef]
  11. Six, L.; Smolders, E. Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils. Sci. Total Environ. 2014, 485–486, 319–328. [Google Scholar] [CrossRef]
  12. Creuzer, J.; Hargiss, C.L.M.; Norland, J.E.; DeSutter, T.; Casey, F.X.; DeKeyser, E.S.; Ell, M. Does Increased Road Dust Due to Energy Development Impact Wetlands in the Bakken Region? Water. Air. Soil Pollut. 2016, 227, 1–15. [Google Scholar] [CrossRef]
  13. Sun, J.; Hu, G.; Yu, R.; Lin, C.; Wang, X.; Huang, Y. Human health risk assessment and source analysis of metals in soils along the G324 Roadside, China, by Pb and Sr isotopic tracing. Geoderma 2017, 305, 293–304. [Google Scholar] [CrossRef]
  14. Walraven, N.; van Os, B.J.H.; Klaver, G.T.; Middelburg, J.J.; Davies, G.R. The lead (Pb) isotope signature, behaviour and fate of traffic-related lead pollution in roadside soils in The Netherlands. Sci. Total Environ. 2014, 472, 888–900. [Google Scholar] [CrossRef]
  15. Jiang, Y.; Guo, X. Multivariate and geostatistical analyses of heavy metal pollution from different sources among farmlands in the Poyang Lake region, China. J. Soils Sediments 2019, 19, 2472–2484. [Google Scholar] [CrossRef]
  16. Fang, F.; Yang, Y.; Guo, J.; Zhou, H.; Fu, C.; Li, Z. Three-dimensional fluorescence spectral characterization of soil dissolved organic matters in the fluctuating water-level zone of Kai County, Three Gorges Reservoir. Front. Environ. Sci. Eng. China 2011, 5, 426–434. [Google Scholar] [CrossRef]
  17. Peng, S.; Fu, G.Y.Z.; Zhao, X.H. Integration of USEPA WASP model in a GIS platform. J. Zhejiang Univ. Sci. A 2010, 11, 1015–1024. [Google Scholar] [CrossRef]
  18. Kong, J.; Guo, Q.; Wei, R.; Strauss, H.; Zhu, G.; Li, S.; Song, Z.; Chen, T.; Song, B.; Zhou, T.; et al. Contamination of heavy metals and isotopic tracing of Pb in surface and profile soils in a polluted farmland from a typical karst area in southern China. Sci. Total Environ. 2018, 637–638, 1035–1045. [Google Scholar] [CrossRef]
  19. Shi, Z.; Wang, X.; Ni, S. Metal Contamination in Sediment of One of the Upper Reaches of the Yangtze River: Mianyuan River in Longmenshan Region, Southwest of China. Soil Sediment Contam. 2015, 24, 368–385. [Google Scholar] [CrossRef]
  20. Luo, J.; Zhang, J.; Huang, X.; Liu, Q.; Luo, B.; Zhang, W.; Rao, Z.; Yu, Y. Characteristics, evolution, and regional differences of biomass burning particles in the Sichuan Basin, China. J. Environ. Sci. 2020, 89, 35–46. [Google Scholar] [CrossRef]
  21. Li, B.; Fu, Y.J.; Wang, C.Q.; Yang, Y. Speciation distribution characteristics of heavy metals and its relationships with soil acid chemical properties in the Chengdu plain. Nat. Environ. Pollut. Technol. 2015, 14, 349–354. [Google Scholar]
  22. Wang, X.; Bai, J.; Wang, J.; Le, S.; Wang, M.; Zhao, Y. Variations in cadmium accumulation and distribution among different oilseed rape cultivars in Chengdu Plain in China. Environ. Sci. Pollut. Res. 2019, 26, 3415–3427. [Google Scholar] [CrossRef] [PubMed]
  23. Chenery, S.R.; Izquierdo, M.; Marzouk, E.; Klinck, B.; Palumbo-roe, B.; Tye, A.M. Science of the Total Environment Soil—Plant interactions and the uptake of Pb at abandoned mining sites in the Rookhope catchment of the N. Pennines, UK—A Pb isotope study. Sci. Total Environ. 2012, 433, 547–560. [Google Scholar] [CrossRef][Green Version]
  24. Cai, L.; Xu, Z.; Ren, M.; Guo, Q.; Hu, X.; Hu, G.; Wan, H.; Peng, P. Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China. Ecotoxicol. Environ. Saf. 2012, 78, 2–8. [Google Scholar] [CrossRef]
  25. Yongming, H.; Peixuan, D.; Junji, C.; Posmentier, E.S. Multivariate analysis of heavy metal contamination in urban dusts of Xi’an, Central China. Sci. Total Environ. 2006, 355, 176–186. [Google Scholar] [CrossRef]
  26. Zheng, Y.; Gao, Q.; Wen, X.; Yang, M.; Chen, H.; Wu, Z.; Lin, X. Multivariate statistical analysis of heavy metals in foliage dust near pedestrian bridges in Guangzhou, South China in 2009. Environ. Earth Sci. 2013, 70, 107–113. [Google Scholar] [CrossRef][Green Version]
  27. Asia, A.E.I.; Mishra, V.K.; Kim, K.; Kang, C.; Chan, K. Wintertime sources and distribution of airborne lead in Korea. Atmos. Environ. 2004, 38, 2653–2664. [Google Scholar] [CrossRef]
  28. Sutherland, R.A. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environ. Geol. 2000, 39, 611–627. [Google Scholar] [CrossRef]
  29. Sakan, S.M.; Dević, G.J.; Relić, D.J.; Andelković, I.B.; Sakan, N.M.; Dordević, D.S. Environmental Assessment of Heavy Metal Pollution in Freshwater Sediment, Serbia. Clean Soil Air Water 2015, 43, 838–845. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Xu, M.; Li, X.; Qi, J.; Zhang, Q.; Guo, J.; Yu, L.; Zhao, R. Hydrochemical characteristics and multivariate statistical analysis of natural water system: A case study in Kangding County, Southwestern China. Water 2018, 10, 80. [Google Scholar] [CrossRef][Green Version]
  31. Xia, F.; Qu, L.; Wang, T.; Luo, L.; Chen, H.; Dahlgren, R.A.; Zhang, M.; Mei, K.; Huang, H. Distribution and source analysis of heavy metal pollutants in sediments of a rapid developing urban river system. Chemosphere 2018, 207, 218–228. [Google Scholar] [CrossRef][Green Version]
  32. Thuong, N.T.; Yoneda, M.; Ikegami, M.; Takakura, M. Source discrimination of heavy metals in sediment and water of to Lich River in Hanoi City using multivariate statistical approaches. Environ. Monit. Assess. 2013, 185, 8065–8075. [Google Scholar] [CrossRef]
  33. Mallen, L. Multivariate Statistical and GIS-based Approach to Identify Heavy Metal Sources in Soils. Environ. Pollut. 2016, 114. [Google Scholar] [CrossRef]
  34. Jiang, C.; Jun, Z.; Gao, L. Sources and Ecological Risk Assessment of Heavy Metal(loid)s in Agricultural Soils of Huzhou, China. Soil Sediment Contam. 2015, 24, 437–453. [Google Scholar] [CrossRef]
  35. Yang, Z.; Lu, W.; Long, Y.; Bao, X.; Yang, Q. Assessment of heavy metals contamination in urban topsoil from Changchun City, China. J. Geochem. Explor. 2011, 108, 27–38. [Google Scholar] [CrossRef]
  36. Wang, L.F.; Yang, L.Y.; Kong, L.H.; Li, S.; Zhu, J.R.; Wang, Y.Q. Spatial distribution, source identification and pollution assessment of metal content in the surface sediments of Nansi Lake, China. J. Geochem. Explor. 2014, 140, 87–95. [Google Scholar] [CrossRef]
  37. Zhang, L.; Qin, X.; Tang, J.; Liu, W.; Yang, H. Review of arsenic geochemical characteristics and its significance on arsenic pollution studies in karst groundwater, Southwest China. Appl. Geochem. 2017, 77, 80–88. [Google Scholar] [CrossRef]
  38. Kim, B.S.M.; Salaroli, A.B.; de Ferreira, P.A.L.; Sartoretto, J.R.; de Mahiques, M.M.; Figueira, R.C.L. Spatial distribution and enrichment assessment of heavy metals in surface sediments from Baixada Santista, Southeastern Brazil. Mar. Pollut. Bull. 2016, 103, 333–338. [Google Scholar] [CrossRef]
  39. González-Acevedo, Z.I.; García-Zarate, M.A.; Núñez-Zarco, E.A.; Anda-Martín, B.I. Heavy metal sources and anthropogenic enrichment in the environment around the Cerro Prieto Geothermal Field, Mexico. Geothermics 2018, 72, 170–181. [Google Scholar] [CrossRef]
  40. Chae, J.S.; Choi, M.S.; Song, Y.H.; Um, I.K.; Kim, J.G. Source identification of heavy metal contamination using metal association and Pb isotopes in Ulsan Bay sediments, East Sea, Korea. Mar. Pollut. Bull. 2014, 88, 373–382. [Google Scholar] [CrossRef]
  41. Cheng, H.; Hu, Y. Lead (Pb) isotopic fingerprinting and its applications in lead pollution studies in China: A review. Environ. Pollut. 2010, 158, 1134–1146. [Google Scholar] [CrossRef] [PubMed]
  42. Erel, Y. Mechanisms and velocities of anthropogenic Pb migration in mediterranean soils. Environ. Res. 1998, 78, 112–117. [Google Scholar] [CrossRef] [PubMed]
  43. Kelepertzis, E.; Komárek, M.; Argyraki, A.; Šillerová, H. Metal(loid) distribution and Pb isotopic signatures in the urban environment of Athens, Greece. Environ. Pollut. 2016, 213, 420–431. [Google Scholar] [CrossRef]
  44. Deng, J.; Zhang, J.; Yin, H.; Hu, W.; Zhu, J.; Wang, X. Ecological risk assessment and source apportionment of metals in the surface sediments of river systems in Lake Taihu Basin, China. Environ. Sci. Pollut. Res. 2020, 27, 25943–25955. [Google Scholar] [CrossRef] [PubMed]
  45. Yu, R.; Hu, G.; Yang, Q.; He, H.; Lin, C. Identification of Pb sources using Pb isotopic compositions in the core sediments from Western Xiamen Bay, China. Mar. Pollut. Bull. 2016, 113, 247–252. [Google Scholar] [CrossRef] [PubMed]
  46. Zhao, Z.Q.; Zhang, W.; Li, X.D.; Yang, Z.; Zheng, H.Y.; Ding, H.; Wang, Q.L.; Xiao, J.; Fu, P.Q. Atmospheric lead in urban Guiyang, Southwest China: Isotopic source signatures. Atmos. Environ. 2015, 115, 163–169. [Google Scholar] [CrossRef]
  47. Madany, I.M.; Salim Akhter, M.; Al Jowder, O.A. The correlations between heavy metals in residential indoor dust and outdoor street dust in Bahrain. Environ. Int. 1994, 20, 483–492. [Google Scholar] [CrossRef]
  48. Zhang, X.; Yan, Y.; Wadood, S.A.; Sun, Q.; Guo, B. Source apportionment of cadmium pollution in agricultural soil based on cadmium isotope ratio analysis. Appl. Geochem. 2020, 123, 104776. [Google Scholar] [CrossRef]
  49. Punia, A.; Siddaiah, N.S.; Singh, S.K. Source and Assessment of Metal Pollution at Khetri Copper Mine Tailings and Neighboring Soils, Rajasthan, India. Bull. Environ. Contam. Toxicol. 2017, 99, 633–641. [Google Scholar] [CrossRef] [PubMed]
  50. Mileusnić, M.; Mapani, B.S.; Kamona, A.F.; Ružičić, S.; Mapaure, I.; Chimwamurombe, P.M. Assessment of agricultural soil contamination by potentially toxic metals dispersed from improperly disposed tailings, Kombat mine, Namibia. J. Geochem. Explor. 2014, 144, 409–420. [Google Scholar] [CrossRef]
  51. Ma, L.; Konter, J.; Herndon, E.; Jin, L.; Steinhoefel, G.; Sanchez, D.; Brantley, S. Quantifying an early signature of the industrial revolution from lead concentrations and isotopes in soils of Pennsylvania, USA. Anthropocene 2014, 7, 16–29. [Google Scholar] [CrossRef]
  52. Komínková, D.; Fabbricino, M.; Gurung, B.; Race, M.; Tritto, C.; Ponzo, A. Sequential application of soil washing and phytoremediation in the land of fires. J. Environ. Manag. 2018, 206, 1081–1089. [Google Scholar] [CrossRef] [PubMed]
  53. Sobik-Szołtysek, J.; Wystalska, K.; Grobelak, A. Effect of addition of sewage sludge and coal sludge on bioavailability of selected metals in the waste from the zinc and lead industry. Environ. Res. 2017, 156, 588–596. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Locations of the study area and the sampling site plots.
Figure 1. Locations of the study area and the sampling site plots.
Sustainability 13 03729 g001
Figure 2. Spatial distribution of soil Pb and Cd concentrations around Mianyuan River. (a) Spatial distribution of soil Cd; (b) spatial distribution of soil Pb.
Figure 2. Spatial distribution of soil Pb and Cd concentrations around Mianyuan River. (a) Spatial distribution of soil Cd; (b) spatial distribution of soil Pb.
Sustainability 13 03729 g002
Figure 3. Sources of lead pollution in farmland topsoils as revealed by 206 P b 207 P b and 208 P b 206 P b compositions.
Figure 3. Sources of lead pollution in farmland topsoils as revealed by 206 P b 207 P b and 208 P b 206 P b compositions.
Sustainability 13 03729 g003
Table 1. Potential toxic metal concentrations of farmland topsoil around Mianyuan River (mg·kg−1).
Table 1. Potential toxic metal concentrations of farmland topsoil around Mianyuan River (mg·kg−1).
ElementRangeMeanMedianSDCV 1SkewnessReference Value 2EFs
Cd0.21–2.20.810.720.400.491.620.089.16
Pb14.3–13933.3231.3518.380.555.3530.900.97
Cu21–61.239.2439.908.380.21−0.0931.101.13
Zn67.4–391125.84118.5053.070.423.4686.501.31
Cr62.7–12291.4088.8012.670.140.2879.001.04
Ni21.2–50.236.2536.656.790.19−0.0432.601.00
As4.89–40.810.119.225.600.554.6210.400.87
Hg0.087–0.410.210.190.090.420.910.063.04
Se0.22–1.390.700.680.230.330.430.106.58
P662–30301537.031405.00606.950.390.46713.531.94
1 Coefficient of variation (CV)—standard deviation (SD)/mean. 2 Background values of soil elements in China (1990).
Table 2. Isotope ratios and elemental contents of lead sources.
Table 2. Isotope ratios and elemental contents of lead sources.
SampleDescriptionPbCd 206 P b 207 P b 208 P b 206 P b
mg·kg−1
S1Agricultural Soil36.10.641.1902.059
S2Agricultural Soil14.30.731.2022.050
S3Agricultural Soil27.90.491.2062.062
S4Agricultural Soil21.50.631.2132.041
S5Agricultural Soil23.80.571.2042.038
W01Phosphate Ore24.31.131.3961.743
W02Coal Ore22.19.941.2711.966
W03Dustfall1384.381.1632.114
W04Ardealite35.30.461.3581.785
Table 3. Pearson correlation matrix for the metal concentrations.
Table 3. Pearson correlation matrix for the metal concentrations.
CorrelationCdPbCuZnCrNiAsHgSeP
Cd10.0000.0000.0000.0100.0060.0000.2440.0000.000
Pb0.624 210.0100.0000.9300.7980.0000.5810.0000.045
Cu0.796 20.415 210.0000.0000.0000.1780.0010.0000.000
Zn0.881 20.873 20.727 210.0930.0690.0000.3230.0000.000
Cr0.413 20.0150.594 20.27610.0010.3930.2760.2650.014
Ni0.441 2−0.0430.642 20.2980.503 210.5340.0430.0040.002
As0.603 20.881 20.2230.756 2−0.143−0.10410.4930.0060.040
Hg0.1940.0930.529 20.1650.1810.331 1−0.11510.1890.102
Se0.646 20.583 20.645 20.729 20.1860.454 20.441 20.21810.000
P0.856 20.328 10.779 20.696 20.395 10.483 20.335 10.2700.628 21
Note: The left lower part is the correlation coefficient; the right upper part is the significance level. 1 p < 0.05 (2-tailed). 2 p <0.1 (2-tailed).
Table 4. Total variance explained and component matrices illustrated (four factors selected).
Table 4. Total variance explained and component matrices illustrated (four factors selected).
ComponentInitial EigenvaluesExtraction Sums of Squared LoadingsRotation Sums of Squared Loadings
Total% of
Variance
Cumulative
(%)
Total% of
Variance
Cumulative
(%)
Total% of
Variance
Cumulative
(%)
14.8969.8069.804.8969.8069.802.5736.7436.74
21.2517.8387.631.2517.8387.631.9928.4365.17
30.446.2993.930.446.2993.931.1416.2181.39
40.283.9797.890.283.9797.891.0815.4096.79
50.070.9998.89
60.060.8699.75
70.020.25100.00
Note: Extraction method: principal component analysis.
Table 5. Rotated component matrix for the data of the topsoil.
Table 5. Rotated component matrix for the data of the topsoil.
ElementComponent
1234
Pb0.9150.0820.2590.227
Cd0.4580.7180.2100.370
Zn0.6970.4550.3200.383
Cu0.1230.5260.2930.788
As0.9560.2040.113−0.041
Se0.3010.3210.8660.234
P0.1360.9160.2700.257
Eigenvalue Percent of Variance36.74428.42716.21315.404
Cumulative Percent36.74465.17181.38596.788
Note: Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization. Rotation converged after fixed iterations.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhang, J.; Shi, Z.; Ni, S.; Wang, X.; Liao, C.; Wei, F. Source Identification of Cd and Pb in Typical Farmland Topsoil in the Southwest of China: A Case Study. Sustainability 2021, 13, 3729. https://doi.org/10.3390/su13073729

AMA Style

Zhang J, Shi Z, Ni S, Wang X, Liao C, Wei F. Source Identification of Cd and Pb in Typical Farmland Topsoil in the Southwest of China: A Case Study. Sustainability. 2021; 13(7):3729. https://doi.org/10.3390/su13073729

Chicago/Turabian Style

Zhang, Junji, Zeming Shi, Shijun Ni, Xinyu Wang, Chao Liao, and Fei Wei. 2021. "Source Identification of Cd and Pb in Typical Farmland Topsoil in the Southwest of China: A Case Study" Sustainability 13, no. 7: 3729. https://doi.org/10.3390/su13073729

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