Source Identification of Cd and Pb in Typical Farmland Topsoil In

sustainability

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

Junji Zhang 1 , Zeming Shi 1,2,*, Shijun Ni 1,2, Xinyu Wang 1,2,*, Chao Liao 1 and Fei Wei 1

1 College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; [email protected] (J.Z.); [email protected] (S.N.); [email protected] (C.L.); [email protected] (F.W.) 2 Sichuan Province Key Laboratory of Nuclear Techniques in Geosciences, Chengdu 610059, China * Correspondence: [email protected] (Z.S.); [email protected] (X.W.)

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) Pb 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. Keywords: farmland topsoil; Cd and Pb; Pb isotope; multivariate statistical analysis; source of pollution Citation: 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 1. Introduction China: A Case Study. Sustainability The toxicity and bioavailability of potential toxic metals (such as Cd and Pb) in soils 2021, 13, 3729. https://doi.org/ have recently attracted increasing levels of public attention [1–3]. Cadmium (Cd) is a 10.3390/su13073729 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 Academic Editor: Adriano Sofo U.S. Environmental Protection Agency [5]. Generally, Pb enters the natural environment via industrial production (mining and smelting), the use of lead-containing products, Received: 3 March 2021 the combustion of fossil fuels (coal, leaded gasoline), and the use of mineral fertilizers and Accepted: 25 March 2021 Published: 26 March 2021 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

Publisher’s Note: MDPI stays neutral the mining and smelting of metalliferous materials, electroplating, gas exhaust emissions, with regard to jurisdictional claims in energy and fuel production, and fertilizer and pesticide application are the main ways that published maps and institutional afﬁl- human activities cause Cd pollution [11]. Nowadays, anthropogenic factors are playing an iations. 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 Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. rates [14]. Multivariate analysis (principal component analysis) and correlation analysis This article is an open access article has been widely used to assist the interpretation of environmental data and sources of distributed under the terms and pollution [15]. conditions of the Creative Commons The study area, located in the farm belt of Chengdu Plain in the southwest of China, Attribution (CC BY) license (https:// is suffering from severe Cd and Pb pollution due to the development of the social economy. creativecommons.org/licenses/by/ In terms of the environmental quality standards for soils in China (GB15618-1995), the Cd 4.0/).

Sustainability 2021, 13, 3729. https://doi.org/10.3390/su13073729 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 3729 2 of 11

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 [7,20–22]. 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. Vari- ous methods are used to identify the sources of Cd and Pb and to calculate contribution rates. Pb isotopes were used to calculate the contribution rate of Pb sources. Multivariate statistical analysis and Pb 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 Figure1. 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 Pb 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. Sustainability 2021, 13, 3729 3 of 11 Sustainability 2021, 13, x FOR PEER REVIEW 3 of 12

FigureFigure 1.1. LocationsLocations ofof thethe studystudy areaarea andand thethe samplingsampling sitesite plots.plots.

2.2.2.2. ChemicalChemical AnalysisAnalysis TheThe treatmenttreatment and and analysis analysis of the of elements the elements in the soilin samplesthe soil followedsamples the followed GBW07404 the andGBW07404 GBW07405. and GBW07405. The soil and The pollution soil and sourcepollution samples source were samples dried were and dried crushed and usingcrushed a 0.071using mm a 0.071 sieve. mm According sieve. According to the United to the States United Environmental States Environmental Protection Protection Agency (USEPA) Agency procedure,(USEPA) procedure, the samples the weresamples digested were digested with HNO with3, HClOHNO34, HClOand HF4 and (USEPA-3052 HF (USEPA-3052 1996; USEPA-3050B 1996) [23]. Soil samples of 0.1 g were mixed with 5 mL of HNO in a Teflon 1996; USEPA-3050B 1996) [23]. Soil samples of 0.1 g were mixed with 5 mL of3 HNO3 in a vial and heated to dryness at 80 ◦C. Then, 2.5 mL of HF, 2 mL of HNO and 1 mL of HClO Teflon vial and heated to dryness at 80 °C. Then, 2.5 mL of HF, 2 mL3 of HNO3 and 1 mL4 were added and the mixture was heated to 160 ◦C for digestion and concentration. Totals of HClO4 were added and the mixture was heated to 160 °C for digestion and concentra- of 1.25 mL of deionized water, 1.25 mL of HNO and 2.5 mL of H O were added to the dry tion. Totals of 1.25 mL of deionized water, 1.253 mL of HNO3 2and2 2.5 mL of H2O2 were residue for further digestion, and then, 5% (v/v) HNO was added at a constant volume added to the dry residue for further digestion, and then,3 5% (v/v) HNO3 was added at a upconstant to 25 mLvolume for preservation.up to 25 mL for The preservation blank sample. The was blank used sample to ensure was theused correctness to ensure the of the detection. The As concentration was determined by atomic fluorescence spectrometry correctness of the detection. The As concentration was determined by atomic fluorescence (AFS), and the concentrations of other elements were determined by inductively coupled spectrometry (AFS), and the concentrations of other elements were determined by induc- plasma–optical emission spectroscopy (ICP-OES). Pb isotope analysis was performed using tively coupled plasma–optical emission spectroscopy (ICP-OES). 𝑃𝑏 isotope analysis was a high-precision inductively coupled plasma mass spectrometer. The ratios of 208Pb , 208Pb performed using a high-precision inductively coupled plasma mass spectrometer.207Pb The206 ra-Pb 206 and Pbwere obtained, controlling the precision and accuracy with a reference material tios of207 Pb , and were obtained, controlling the precision and accuracy with of Pb isotope and internal standard. The standard error of the isotope measurement was a reference material of 𝑃𝑏 isotope and internal standard. The standard error of the iso- less than 0.5%. tope measurement was less than 0.5%. 2.3. Multivariate Analysis 2.3. Multivariate Analysis Descriptive analysis, Pearson correlation coefficient analysis and principal component analysisDescriptive are broadly analysis, used toPearson identify correlation the relationship coefficient between analysis potential and principal toxic metals compo- and possiblenent analysis pollution are broadly sources used in the to environmentidentify the relationship [24–26]. In thisbetween study, potential SPSS for toxic Windows, metals versionand possible 19 (SPSS pollution Inc, USA), sources was in utilized the environment in the multivariate [24–26]. statistical In this study, analysis. SPSS for Win- dows,Descriptive version 19 analysis, (SPSS Inc, including USA), was the utilized mean, standard in the multivariate deviation (SD),statistical skewness, analysis. coeffi- cientDescriptive of variation analysis, (CV), etc., including can be utilized the mean, to reflectstandard the deviation dispersion (SD), degree skewness, for potential coeffi- toxiccient metalsof variation in the (CV), soil, and etc., indirectly can be utilized indicate to the reflect sources the ofdispersion said potential degree toxic for metals.potential toxicThe metals enrichment in the soil, factor and (EF) indirectly can be indica used totedetermine the sources the of enrichment said potential of potential toxic metals. toxic metalsThe in enrichment the soil and factor to differentiate (EF) can be between used to determine metals originating the enrichment from human of potential activities toxic andmetals those in originatingthe soil and from to differentiate natural processes. between When metals using originating enrichment from factors, human a referenceactivities elementand those should originating be introduced from natural to standardize processes. theWhen tested using elements. enrichment The factors, reference a reference element thatelement is used should is often be aintroduced conservative to standardiz one, such ase the Ni, tested Mn, Sc, elements. etc. [27]. The enrichmentreference element factor (EF)that valueis used can is often be calculated a conservative via the one, formula such proposed as Ni, Mn, by Sc, Buat-Menard etc. [27]. The and enrichment Chesselet factor [28]: (EF) value can be calculated via the formula proposed by Buat-Menard and Chesselet [28]: h i h i EF = Cn(sample)/Cre f (sample) / Bn(baseline)/Bre f (baseline) (1) EF=[𝐶(𝑠𝑎𝑚𝑝𝑙𝑒)⁄𝐶(𝑠𝑎𝑚𝑝𝑙𝑒)][𝐵⁄ (𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒)⁄ 𝐵 (𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒)] (1) Sustainability 2021, 13, 3729 4 of 11

where Cn (sample) is the concentration of the examined element in the farmland topsoil, Cre f (sample) is the concentration of the reference element in the farmland topsoil, Bn (baseline) is the content of the examined element in Chinese soils (here, Chinese background values were selected as the baseline), and Bre 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 vari- able 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 Table1. 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.

Table 1. Potential toxic metal concentrations of farmland topsoil around Mianyuan River (mg·kg−1).

Element Range Mean Median SD CV 1 Skewness Reference Value 2 EFs Cd 0.21–2.2 0.81 0.72 0.40 0.49 1.62 0.08 9.16 Pb 14.3–139 33.32 31.35 18.38 0.55 5.35 30.90 0.97 Cu 21–61.2 39.24 39.90 8.38 0.21 −0.09 31.10 1.13 Zn 67.4–391 125.84 118.50 53.07 0.42 3.46 86.50 1.31 Cr 62.7–122 91.40 88.80 12.67 0.14 0.28 79.00 1.04 Ni 21.2–50.2 36.25 36.65 6.79 0.19 −0.04 32.60 1.00 As 4.89–40.8 10.11 9.22 5.60 0.55 4.62 10.40 0.87 Hg 0.087–0.41 0.21 0.19 0.09 0.42 0.91 0.06 3.04 Se 0.22–1.39 0.70 0.68 0.23 0.33 0.43 0.10 6.58 P 662–3030 1537.03 1405.00 606.95 0.39 0.46 713.53 1.94 1 Coefﬁcient of variation (CV)—standard deviation (SD)/mean. 2 Background values of soil elements in China (1990).

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 Sustainability 2021, 13, x FOR PEER REVIEW 5 of 12

Sustainability 2021, 13, 3729 5 of 11 are large, and the EF of Cd is close to 10, suggesting the influence of human activities. Com- bined with their characteristics of high SD and CV, Cd, P and Se are also widely enriched in thein thestudy study area. area. The The EFs EFs of ofPb, Pb, Cu, Cu, Cr Cr and and Zn Zn ar aree small, small, indicating indicating characteristics characteristics of of local local enrichment,enrichment, and and they they may may be be determined determined by by both both natural natural and and human human activities activities [38,39]. [38,39].

3.2.3.2. Spatial Spatial Distribution Distribution Characteristics Characteristics of of Cd Cd and and Pb Pb TheThe spatial spatial patterns patterns of of the the Pb Pb and and Cd Cd processed processed by by Kriging Kriging interpolation interpolation are are shown shown inin Figure Figure 2.2. The The contents contents of ofPb Pb and and Cd Cdin th ine thestudy study area areaare mostly are mostly higher higher than the than back- the groundbackground concentration concentration values, values, which which are 30.90 are 30.90mg/kg mg/kg for Pb for and Pb 0.08 and mg/kg 0.08 mg/kg for Cd. for Spa- Cd. tially,Spatially, Pb and Pband Cd Cdare arehighly highly enriched enriched in inthe the northwest northwest region, region, and and this this value value gradually gradually decreasesdecreases towards towards the the southeast southeast region. region. The Thecontents contents of Pb of and Pb Cd and in the Cd northwest in the northwest region areregion relatively are relatively high, with high, values with of over values 33 ofmg/kg over and 33 mg/kg1.0 mg/kg, and respectively. 1.0 mg/kg, Correspond- respectively. ingly,Correspondingly, the contents the of contentsPb and Cd of Pb in andthe Cdsoutheastern in the southeastern region are region lower are than lower those than in those the northwest,in the northwest, with values with valuesof 30 mg/kg of 30 mg/kgand 0.7 andmg/kg, 0.7 mg/kg,respectively. respectively. This indicates This indicates that topog- that raphytopography affects affectsthe spatial the spatialdistribution distribution of Pb and of PbCd and in farmland Cd in farmland topsoil to topsoil a certain to a extent, certain whichextent, may which relate may to relate the rapid to the decline rapid declinein water in flow water velocity ﬂow velocity from the from mountains the mountains to the plains.to the Pb plains. and Cd Pb andare precipitated Cd are precipitated in the form in theof complexes form of complexes [40–43]. Moreover, [40–43]. Moreover, the con- tentsthe contents of Pb and of Cd Pb andtend Cd to decrease tend to decrease as one moves as one further moves furtheraway from away the from Mianyuan the Mianyuan River. River. Accordingly, the content of Pb decrease from over 31 mg/kg to 25 mg/kg, and the Accordingly, the content of Pb decrease from over 31 mg/kg to 25 mg/kg, and the content content of Cd decreases from over 0.58 mg/kg to below 0.4 mg/kg. This distribution of Cd decreases from over 0.58 mg/kg to below 0.4 mg/kg. This distribution also proves also proves that the Cd and Pb concentrations in the cultivated soil are controlled by the that the Cd and Pb concentrations in the cultivated soil are controlled by the transporta- transportation of the Mianyuan River and its tributaries. In the northwest of the study area, tion of the Mianyuan River and its tributaries. In the northwest of the study area, there there are phosphate mines and phosphating plants, both of which are likely to be major are phosphate mines and phosphating plants, both of which are likely to be major sources sources of cadmium and lead. Deng et al. found that the frequency human activities in of cadmium and lead. Deng et al. found that the frequency human activities in developed developed areas is also an important factor leading to potential toxic metals enrichment [44]. areas is also an important factor leading to potential toxic metals enrichment [44]. The The high concentrations of Cd and Pb in the towns of Mianzhu and Fuxin indicate that 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 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 the study area. The Cd and Pb were brought into the study area from the northwestern of Mianyuan River, and they then migrated into the topsoil through irrigation, and became partenriched of Mianyuan in it. At theRiver, same and time, they potential then migrated toxic metals into arethe releasedtopsoil through from urban irrigation, living areasand becameinto the enriched topsoil. in it. At the same time, potential toxic metals are released from urban living areas into the topsoil.

(a) (b)

FigureFigure 2. 2. SpatialSpatial distribution distribution of of soil soil Pb Pb and and Cd Cd co concentrationsncentrations around around Mianyuan Mianyuan River. River. (a ()a Spatial) Spatial distributiondistribution of of soil soil Cd; Cd; ( (bb)) spatial spatial distribution distribution of of soil soil Pb. Pb. Sustainability 2021, 13, 3729 6 of 11

3.3. Pb Source Identiﬁcation Based on Isotopic Ratio A mixed linear model is established from the Pb 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 Pb isotope. The following formula shows the ternary linear model.  206Pb 206Pb 206Pb 206Pb = f1 × + f2 × + f3 ×  207Pb 207Pb 207Pb 207Pb  s 1 2 3 208Pb 208Pb 208Pb 208Pb (2) = f1 × + f2 × + f3 ×  206Pb s 206Pb 1 206Pb 2 206Pb 3  f1 + f2 + f3 = 1

In these equations, 206Pb represents the ratio of 206 to 207 , 208Pb represents the ratio 207Pb Pb Pb 206Pb of 208Pb to 206Pb, and f1, f2 and f3 represent the contribution rates of the three pollution sources to the Pb present in the samples, respectively. Table2 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.

Table 2. Isotope ratios and elemental contents of lead sources.

Pb Cd Sample Description 206Pb 208Pb mg·kg−1 207Pb 206Pb S1 Agricultural Soil 36.1 0.64 1.190 2.059 S2 Agricultural Soil 14.3 0.73 1.202 2.050 S3 Agricultural Soil 27.9 0.49 1.206 2.062 S4 Agricultural Soil 21.5 0.63 1.213 2.041 S5 Agricultural Soil 23.8 0.57 1.204 2.038 W01 Phosphate Ore 24.3 1.13 1.396 1.743 W02 Coal Ore 22.1 9.94 1.271 1.966 W03 Dustfall 138 4.38 1.163 2.114 W04 Ardealite 35.3 0.46 1.358 1.785

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 ( 206Pb ) and between 207Pb 2.106 and 2.115 ( 208Pb )[46]. The value range of the Pb isotopes of the dustfall in the study 206Pb area ( 206Pb = 1.163, 208Pb = 2.114) is close to the value range of automobile exhaust, but far 207Pb 206Pb 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. Figure3 shows that there is a linear relationship between the 206Pb and 208Pb of the four 207Pb 206Pb pollution sources and five agricultural soils. The Pb 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 Pb contents in the agricultural soils of the study area. In Table2, the 206Pb and 208Pb values of 207Pb 206Pb the agricultural soils, dustfall, coal mine and phosphogypsum are substituted into equation Sustainability 2021, 13, x FOR PEER REVIEW 7 of 12

and phosphogypsum; that is to say, these three layers play a major role in controlling the 𝑃𝑏 contents in the agricultural soils of the study area. In Table 2, the and values Sustainability 2021, 13, 3729 of the agricultural soils, dustfall, coal mine and phosphogypsum are substituted into 7equa- of 11 tion 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%. groupThe (1). 𝑃𝑏 The isotope contribution analysis of indicates Pb from that dustfall the Pb to agriculturalin the topsoils soils of the is 74.94%, study area that is from pri- coalmarily mines contributed is 14.87%, by and vehicle that fromexhaust. phosphogypsum Coal mining activities is 10.19%. and the production of phosphorus products also contribute to the Pb in topsoils.

FigureFigure 3.3. SourcesSources ofof leadlead pollutionpollution in in farmland farmland topsoils topsoils as as revealed revealed by by206 Pb and and208 Pb compositions. composi- 207Pb 206Pb tions. The Pb isotope analysis indicates that the Pb in the topsoils of the study area is primarily3.4. Multivariate contributed Analysis by Results vehicle exhaust. Coal mining activities and the production of phosphorus products also contribute to the Pb in topsoils. 3.4.1. Correlation Analysis 3.4. MultivariateA correlation Analysis analysis Results of 10 elements in the agricultural soils of the study area was carried 3.4.1.out. As Correlation shown in Table Analysis 3, Pb was strongly correlated with Zn, Cd, As and Se, and the Cd was in correlationA correlation with Pb, analysis Zn, Cu, of As, 10 Se elements and P. The in thecorrelation agricultural coefficients soils of are the all studyabove area0.5, which was carriedindicates out. good As correlation, shown in and Table that3, Pbthe waspolluti stronglyon sources correlated of these withelements Zn, are Cd, roughly As and simi- Se, andlar. However, the Cd was the in correlation correlation coe withfficients Pb, Zn,of each Cu, element As, Se andare diff P. Theerent, correlation indicating coefficientsthat the lev- areels of all influence above 0.5, of the which different indicates pollution good sources correlation, are different. and that Moreover, the pollution the correlation sources coef- of theseficient elements between areCd roughlyand P is 0.856, similar. which However, indicate thes that correlation the Cd may coefficients be affected of eachby phosphate element aremining different, or processing. indicating Excluding that the Cu, levels the oflinear influence correlation of the between different Ni pollutionand the other sources elements are different.is not significant. Moreover, This the indicates correlation that coefficientCu is affected between by natural Cd and processe P is 0.856,s. However, which indicatesthere is a thatsignificant the Cd linear may becorrelation affected bybetween phosphate Cu and mining Cd, Se or and processing. P. This indicates Excluding that Cu, Cu theis affected linear correlationby human activities, between related Ni and to the phosphate other elements mining is or not processing. significant. This indicates that Cu is affected by natural processes. However, there is a significant linear correlation between CuTable and 3. Cd,Pearson Se and correlation P. This matrix indicates for the that metal Cu concentrations. is affected by human activities, related to Correlation Cd phosphatePb miningCu or processing.Zn Cr Ni As Hg Se P Cd 1 0.000 0.000 0.000 0.010 0.006 0.000 0.244 0.000 0.000 Pb 0.624 2 Table 1 3. Pearson 0.010 correlation0.000 matrix0.930 forthe metal0.798 concentrations. 0.000 0.581 0.000 0.045 Cu 0.796 2 0.415 2 1 0.000 0.000 0.000 0.178 0.001 0.000 0.000 CorrelationZn Cd0.881 2 Pb0.873 2 Cu0.727 2 Zn 1 Cr 0.093 Ni 0.069 As0.000 0.323 Hg 0.000 Se 0.000 P CdCr 0.413 1 2 0.0000.015 0.0000.594 2 0.0000.276 0.0101 0.0060.001 0.0000.393 0.2440.276 0.265 0.000 0.0000.014 2 2 2 PbNi 0.6240.4412 −10.043 0.0100.642 0.0000.298 0.9300.503 0.798 1 0.000 0.534 0.5810.043 0.004 0.000 0.045 0.002 2 2 2 CuAs 0.7960.6032 0.4150.8812 0.2231 0.0000.756 0.000−0.143 0.000−0.104 0.1781 0.0010.493 0.006 0.000 0.0000.040 Hg 0.194 0.093 0.529 2 0.165 0.181 0.331 1 −0.115 1 0.189 0.102 Zn 0.881 2 0.873 2 0.727 2 1 0.093 0.069 0.000 0.323 0.000 0.000 Se 0.646 2 0.583 2 0.645 2 0.729 2 0.186 0.454 2 0.441 2 0.218 1 0.000 Cr 0.413 2 0.015 0.594 2 0.276 1 0.001 0.393 0.276 0.265 0.014 P 0.856 2 0.328 1 0.779 2 0.696 2 0.395 1 0.483 2 0.335 1 0.270 0.628 2 1 Ni 0.441 2 −0.043 0.642 2 0.298 0.503 2 1 0.534 0.043 0.004 0.002 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 As 0.603 2 0.881 2 0.223 0.756 2 −0.143 −0.104 1 0.493 0.006 0.040 <0.1 (2-tailed). Hg 0.194 0.093 0.529 2 0.165 0.181 0.331 1 −0.115 1 0.189 0.102 Se 0.646 2 0.583 2 0.645 2 0.729 2 0.186 0.454 2 0.441 2 0.218 1 0.000 P 0.856 2 0.328 1 0.779 2 0.696 2 0.395 1 0.483 2 0.335 1 0.270 0.628 2 1 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). Sustainability 2021, 13, 3729 8 of 11

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 Pb 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 Table4. 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.

Table 4. Total variance explained and component matrices illustrated (four factors selected).

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Component % of Cumulative % of Cumulative % of Cumulative Total Total Total Variance (%) Variance (%) Variance (%) 1 4.89 69.80 69.80 4.89 69.80 69.80 2.57 36.74 36.74 2 1.25 17.83 87.63 1.25 17.83 87.63 1.99 28.43 65.17 3 0.44 6.29 93.93 0.44 6.29 93.93 1.14 16.21 81.39 4 0.28 3.97 97.89 0.28 3.97 97.89 1.08 15.40 96.79 5 0.07 0.99 98.89 6 0.06 0.86 99.75 7 0.02 0.25 100.00 Note: Extraction method: principal component analysis.

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 Table5.

Table 5. Rotated component matrix for the data of the topsoil.

Component Element 1 2 3 4 Pb 0.915 0.082 0.259 0.227 Cd 0.458 0.718 0.210 0.370 Zn 0.697 0.455 0.320 0.383 Cu 0.123 0.526 0.293 0.788 As 0.956 0.204 0.113 −0.041 Se 0.301 0.321 0.866 0.234 P 0.136 0.916 0.270 0.257 Eigenvalue Percent of Variance 36.744 28.427 16.213 15.404 Cumulative Percent 36.744 65.171 81.385 96.788 Note: Extraction method: principal component analysis. Rotation method: Varimax with Kaiser normalization. Rotation converged after ﬁxed iterations.

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 Pb 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 Sustainability 2021, 13, 3729 9 of 11

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 Pb 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 Pb 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. Sustainability 2021, 13, 3729 10 of 11

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. [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. [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.[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. [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. [CrossRef] 6. Komárek, M.; Ettler, V.; Chrastný, V.; Mihaljeviˇc,M. Lead isotopes in environmental sciences: A review. Environ. Int. 2008, 34, 562–577. [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. [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. [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. [CrossRef] 10. Filippelli, G.M.; Morrison, D.; Cicchella, D. Urban geochemistry and human health. Elements 2012, 8, 439–444. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. [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. 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. [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. [CrossRef] 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. [CrossRef] Sustainability 2021, 13, 3729 11 of 11

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. [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. [CrossRef] 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. [CrossRef] 28. Sutherland, R.A. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environ. Geol. 2000, 39, 611–627. [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. [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. [CrossRef] 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. [CrossRef] 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. [CrossRef] 33. Mallen, L. Multivariate Statistical and GIS-based Approach to Identify Heavy Metal Sources in Soils. Environ. Pollut. 2016, 114. [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. [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. [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. [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. [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. [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. [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. [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. [CrossRef][PubMed] 42. Erel, Y. Mechanisms and velocities of anthropogenic Pb migration in mediterranean soils. Environ. Res. 1998, 78, 112–117. [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. [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. [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. [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. [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. [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. [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. [CrossRef][PubMed] 50. Mileusnić,M.; Mapani, B.S.; Kamona, A.F.; Ružiˇcić,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. [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. [CrossRef] 52. Komínková, D.; Fabbricino, M.; Gurung, B.; Race, M.; Tritto, C.; Ponzo, A. Sequential application of soil washing and phytoreme- diation in the land of fires. J. Environ. Manag. 2018, 206, 1081–1089. [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. [CrossRef][PubMed]