An integrated analysis on source-exposure risk of heavy metals in agricultural soils near intense electronic waste recycling activities

Environment International 133 (2019) 105239

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An integrated analysis on source-exposure risk of heavy metals in agricultural soils near intense electronic waste recycling activities

T ⁎

Shiyan Yanga, Mingjiang Hea, Yuyou Zhia, Scott X. Changb, Baojing Gua, Xingmei Liua, , Jianming Xua a College of Environmental and Resource Sciences, Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Zhejiang University, Hangzhou 310058, China b Department of Renewable Resources, University of Alberta, Edmonton, Alberta T6G 2E3, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Exposure E-waste Health risk Heavy metal Source apportionment Source contribution

Conducting integrated analysis of the source, exposure and health risk of heavy metals is critical for developing mitigation strategies of soil contamination. Taking the former electronic waste (e-waste) dismantling center in China as an example this study quantitatively apportioned source contribution of soil heavy metals in this area by statistical analysis and positive matrix factorization (PMF) model. Furthermore, the human health risk of identified sources were quantified by combining source profiles and exposure risk assessment. The seven heavy metals investigated were arsenic (As), cadmium (Cd), copper (Cu), chromium (Cr), nickel (Ni), lead (Pb) and Zinc (Zn). Results indicated that agricultural soils were mainly contaminated with Cd and Cu. Parent material and pesticide, fertilizer application, industrial discharge, and vehicle emission accounted for 46.6, 22.2, and 31.2%, respectively, of the accumulation of metals in the soil. Moreover, these sources contributed 52.9, 19.0, and 28.1%, respectively of the total non-cancer risk. For the total cancer risk, the contribution of these three sources was 39.2, 45.3, and 15.5%, respectively. Despite that industrial discharge contributed the least to the accumulation of metals (22.2%), it contributed the most to the total cancer risk (45.3%). Reducing industrial emission was crucial for minimizing the heavy metal input to agricultural soils and preventing potential health hazard. These findings could provide support for environmental protection authority to improve the management and risk prevention of contaminated farmland.

1. Introduction Heavy metal contamination of agricultural soils can severely impact soil ecosystem function and food security (Tóth et al., 2016). With the rapid urbanization and industrialization, input of heavy metals to agricultural soils through intensive human activities has elevated during the past several decades (Huang et al., 2015). The accumulation, transformation, and uptake of heavy metals via the food chain adversely affect human health (IARC, 2011; Dartan et al., 2015). Chronic low-dose and acute high-dose exposures to heavy metals, such as arsenic (As), cadmium (Cd), and lead (Pb) may reduce organ size/weight, damage neurocognitive ability, and affect the keratosis and vascular complications, even cause cancer (Zhao et al., 2018). Thus, it is necessary to understand the sources of and human exposure risk to metal contamination in agricultural soils to protect human health. Understanding the source apportionment of metals accumulation is important for minimizing heavy metal input to agricultural soils. The

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potential source types of heavy metals could be identified by qualitative models using geo- and multivariate statistics (Davis et al., 2009; Zou et al., 2015). Two different approaches of multivariate statistical analysis, the inner correlation (IC) and principal component analysis (PCA), have been widely applied in identifying the inter-relations of heavy metals in the environment, which could help in distinguishing anthropogenic and natural inputs (Zhi et al., 2016; Dai et al., 2018). Geo-statistical analysis is also an effective tool to visualize the spatial distribution of metal concentrations and it can help to identify the possible source types (Song, et al., 2018). There are also some studies have used the integration method of geo- and multivariate statistical analysis for regional source apportionment (Lin et al., 2016). However, both multivariate statistics (IC and PCA) and geo-statistical analysis can only roughly identify the number and type of sources, but lack the potential to obtain the accurate source contribution (Huang et al., 2015). Therefore, quantitative receptor models are still necessary to obtain the detailed source categories and precise source contribution.

Corresponding author at: 866 Yuhangtang Road, Hangzhou 310058, China. E-mail address: [email protected] (X. Liu).

https://doi.org/10.1016/j.envint.2019.105239 Received 3 June 2019; Received in revised form 27 September 2019; Accepted 2 October 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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et al., 2014), which makes it difficult to apportion the real pollutant sources of metals accumulation in Wen’ling. Although our previous studies have clarified the contaminating history, spatial-temporal variability and health hazard of heavy metals in soil-rice ecosystem in the study area (Zhao et al., 2010; Song et al., 2016; He et al., 2019; Zhao et al., 2019), the specific source categories and their contribution to human health risk remain unclear.

Among these receptor models, positive matrix factorization (PMF) model has been widely implemented to quantify the sources of heavy metals accumulation in the atmosphere (Ivana et al., 2011), sediment (Li et al., 2019), and urban soils (Mehr et al., 2017). PMF has distinct advantage of non-negative constrain condition and utilizes uncertainty to weight each species, so that it can correctly model abnormal data and make sources to be physically reliable (Men et al., 2018a). Nevertheless, this technique is rarely employed to apportion source contribution of metals accumulation in agricultural soils, which may further impact the management and risk control of contaminated farmland. The human health risk of exposure to heavy metals in agriculture soil have merited more attention in recent years, with many approaches being proposed for its estimation (Bi et al., 2018). Health risk assessment model is one of the most common tools in use to quantitatively evaluate the human health hazard of poisonous heavy metals in contaminated media (Yang et al., 2019). Using this approach, a number of studies have successfully assessed the health risk of contaminated soil. It is noteworthy that heavy metals originated from anthropogenic or natural sources displayed greatly differing bioavailability, effective dose, toxicity, and health risk level (Hou et al., 2017; Liu et al., 2018a). Apportioning the source-specific health risk is critical to develop pollution control measures, so that the input and related health risk of key polluted source can effectively be reduced when necessary (Jiang et al., 2017; Ma et al., 2018). However, most previous researches merely been targeted at either the sources or human exposure risk, and few studies expanded to the integration of source apportionment and health risk estimation of heavy metals in agricultural soils. Therefore, a comprehensive analysis that couples pollutant source apportionment and human exposure risk estimation are urgently needed to capture the source-specific health risk. Here, taking a former electronic-waste (e-waste) dismantling center in China as a case study area, we developed a source-exposure risk approach to integrate source apportionment and health risk estimation. Following the determination of metal concentrations in agricultural soils, geo-statistical analysis, multivariate statistical analysis (IC & PCA), and the PMF model were applied, so that they could verify each other and provide more reliable source categories and corresponding contributions to metals accumulation. The health risk value for heavy metals that loaded in each source category were then calculated to quantify the contribution of each pollutant source to human health risk. This study is expected to promote the systematic integration of source apportionment and risk estimation for agricultural soil contamination, thus providing useful implications for better pollutant sources management and human health protection.

2.2. Soil sampling, preparation and chemical analysis A total of 169 topsoil samples (0–20 cm) were collected at each sampling site from Wen’ling in 2016. The spatial distribution of sampling sites were the same as presented in our previous work (He et al., 2019). Each sample was mixture of five subsamples with a total weight of 1 kg. Samples were collected using the plastic spoon, and immediately carried back to the laboratory. Soil samples were then airdried, ground into powder and sieved through 2 mm mesh. A 0.2500 g of homogenized topsoil sample was weighed and digested with HNO3-HF (volume ration 3:1) via a microwave digestion unit (CEM, Mars-X500, USA) at 210 °C for 50 min. Samples were then placed in a hot plate at 140 °C to heat to nearly 1 mL. After that, samples were filtered through a 0.45 μm cellulose acetate filter membrane and diluted to a volume of 50 mL with deionized distilled water. Concentrations of As, Cr, Ni, Cu, and Zn in the digests were then determined by inductively coupled plasma mass spectrometer (ICP-MS, Agilent, 7500a, USA), while the concentration of Cd and Pb were determined with graphite furnace atomic absorption spectroscopy (GFAAS, PerkinElmer, AA800, SG). Methods used to measure soil pH, soil organic matter (SOM), electrical conductivity (EC), and size distribution (silt, sand and clay) for soil particle were the same as presented in Zhao et al. (2010). 2.3. Quality assurance and quality control A certified reference soil material (GBW07443 (GSF-3)) was used for quality assurance and quality control. Procedural blanks, reagent blanks, and duplicate measurements for each set of samples were evaluated. The concentration of As, Cd, Cr, Ni, Cu, Pb, and Zn in reagent blanks was 6.006, 0.153, 3.952, 1.765, 6.896, 0.388, and 12.133 μg/L, while no heavy metals were detected in procedural blanks. Accepted recovery rate ranged from 80 to 120%. The relative deviation of the duplicate samples was < 8% for all batch treatments. The correlation coefficients of calibration curves for the metal concentrations ranged from 0.993 to 0.999. The detection limit for As, Cr, Ni, Cd, Pb, Cu, and Zn was 0.6, 2, 0.1, 0.01, 0.2, 0.065, and 0.025 mg kg−1, respectively.

2. Methods 2.4. The source-exposure risk approach 2.1. Study site This study developed an integrated source-exposure risk approach. This approach is a three-step process that mathematically and statistically links several pollutant sources to human health risk of heavy metals. Fig. 1 provided a schematic of this approach and the details of the process were described below.

This study was conducted in Wen’ling, which is one of the most economically developed city locating in the eastern coastal region of Zhejiang Province, China. In Wen’ling, there are various kinds of industries distributed, such as production of steel wires, electronic machines, textile/clothing, leather, and nonferrous casting. Notably, the study region faced increasing heavy metal contamination due to hundreds of small e-waste dismantling workshops clustered in this city over the past two decades (Liu et al., 2018b). During the past two decades, agrichemicals (fertilizers and pesticides) containing Cd, Cu, and As were also widely applied in Wen’ling to improve rice production. Moreover, the studied area possess developed traffic network with the two arterial highways pass through the whole city. Since 2012, a number of e-waste dismantling workshops had been banned by the Chinese government, while the high level of heavy metals in part of agricultural soils were still detectable (He et al., 2019). Indeed, all human activities could discharge heavy metals in a similar manner (Ha

2.4.1. Source apportionment The approach started with defining the component of source apportionment. This component is used to discern the possible sources and quantify the accurate source contribution of metals accumulation in soils. Herein, the spatial distribution of each metal in soils was mapped to determine the pollution spatial variation. Then, IC and PCA were applied to roughly locate the possible source numbers, and results of IC and PCA were combined with the spatial characteristic of heavy metals to preliminarily identify the natural and anthropogenic input. Finally, PMF was employed for validating the results of above methods, and further quantifying source contribution to metals accumulation in 2

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Fig. 1. Framework of the integrated source–exposure risk approach in this study.

recommended by the USEPA form the basis of estimating the average daily intake dose of metals from each source. The groups in focus were children (aged 1–17) and adults (aged 18+) owing to their physiological differences (Peña-Fernández et al., 2014). Exposure of human to heavy metals in soils mainly has four potential pathways: (1) incidental soil ingestion; (2) direct dermal contact; (3) indirect diet ingestion and (4) soil vapor inhalation (USEPA, 2016). The average daily intake dose of the mth metal through soil ingestion (ADIing, mg kg−1 day−1), dermal contact (ADIder, mg kg−1 day−1), diet ingestion (ADIdiet, mg kg−1 day−1) and inhalation (ADIinh, mg kg−1 day−1) in the nth sample from the kth source can be evaluated by Eqn. 5–8 (USEPA, 2011; 2016):

soils. An original concentration data array X with n by m dimensions can be decomposed into two matrices via the PMF model, including factor contributions (G) and factor profiles (F) with p factors (sources) as shown in Eq. (1) (Paatero et al., 2003; USEPA, 2014): p

xnm =

∑ gnkfkm + enm

(1)

k=1

where xnm is the is the total concentration of the mth metal in the nth sample; gnk is the contribution of the kth source to the nth sample, while fkm is the concentration of the mth metal in the kth source; enm is the residual for each sample and metal. Factor contributions and profiles can be determined by minimizing the objective function as follows (USEPA, 2014): p

i

Q=

j

∑∑ n=1 m=1

k ADInm , ing =

k ADInm , der =

2

⎡ xnm − ∑ gnkfkm ⎤ ⎢ ⎥ k=1 ⎢ ⎥ unm ⎢ ⎥ ⎢ ⎥ ⎣ ⎦

(2)

k ADInm , inh =

(S × X )2 + (0.5×MDL)2

× SA × AF × ABS × EF × ED BW × AT

k Cnm

× IRdiet × EF × ED × 10−6 BW × AT

k Cnm × IRinh × EF × ED PEF × BW × AT

(5)

(6)

(7)

(8)

k Cnm

is the concentration of the mth metal in the nth sample from where the kth source (mg kg−1 day−1); IRing and IRdiet is the ingestion rate of soil and homegrown food, respectively (mg day−1); SA is the exposed surface area of skin (cm2); AF is the adherence factor (kg cm−2 day−1); ABS is the dermal absorption factor (unitless); IRinh is the inhalation rate of soil (m3 day−1); PEF is the emission factor (m3 kg−1); EF is defined as the exposure frequency (day year−1); ED is defined as the exposure duration (year); BW is the body weight of the exposed individual (kg); AT is average time exposure to contaminated soils (day) and 10−6 is a unit conversion factor. Details of parameters applied in the exposure assessment model are given in Table S1.

(3)

If the concentration of one metal outstrips the MDL, the Unc can be calculated as follows (USEPA, 2014):

Un c=

k Cnm

k ADInm , diet =

where unm is the uncertainty of the mth metal in the nth sample. If the concentration of one metal is less than or equal to the detection limit (MDL), the uncertainty (Unc) can be obtained as follows (USEPA, 2014):

Un c= 5 6 × MDL

k Cnm × IRing × EF × ED × 10−6 BW × AT

(4)

where S is relative standard deviation; X is the concentration data of metals. 2.4.2. Exposure assessment The results of the source apportionment were then incorporated into the component of exposure assessment. The methodology

2.4.3. Health risk characterization Once the exposure dose from each source was determined, the 3

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Table 1 Summary statistics for heavy metal concentrations (mg kg−1) in soils.

As Cd Cr Ni Cu Pb Zn

Minimum

Maximum

Median

Mean

SD

CV

Skewness

Kurtosis

BVa

RSVb

Percent (%)

K-S test p value

Transform

After transform p value

3.10 0.03 18.29 7.96 17.77 10.96 50.87

21.47 9.20 164.06 72.06 1122.87 188.74 881.40

9.67 0.21 79.23 37.75 37.04 23.95 127.16

10.25 0.34 74.78 35.03 52.59 33.84 143.74

3.24 0.80 24.86 12.37 88.73 25.02 81.16

0.32 2.33 0.33 0.35 1.69 0.74 0.56

0.69 9.28 −0.46 −0.53 10.70 2.83 5.11

0.97 95.25 0.53 −0.04 127.78 13.34 40.89

5.87 0.129 57.96 36.48 30.54 30.46 107.79

30 0.4 250 70 50 100 200

0 14 0 1 20 1 13

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

Box-Cox Yeo-Johnson Box-Cox Yeo-Johnson Box-Cox Yeo-Johnson Box-Cox

0.476 0.323 0.047 0.102 0.224 0.043 0.089

SD, Standard deviation; CV, Coefficient of variance; BV, Background values for soil heavy metals; RSV, Risk screening values for soil contamination of agriculture Land; Percent, percentage of soil samples exceeded the RSV; K-S test, Kolmogorov-Smirnov test. a CNEMC (China National Environmental Monitoring Center), 1990. Soil Element Background Values in China. China Environmental Science Press, Beijing, China b CEPA (Chinese Environment Protection Administration). 2018. Soil environmental quality-Risk control standard for soil contamination of agriculture land. GB15618-2018.

analysis were implemented by SPSS Statistic 22.0 (IBM, CO, USA). The Box-Cox and Yeo-Johnson methods were applied to transform the nonnormally distributed data. Then, converted data were analyzed by PCA on R 3.4.2 (R core team). The PMF model was run 100 times on PMF 5.0 (USEPA) for further source quantification. Moreover, three methods of error estimation involving bootstrap-base model displacement (BSDISP), base model displacement (DISP) and bootstrap (BS) were employed in PMF 5.0 to detect the error associated with both random and rotational ambiguity. Origin 2019 pro (Origin Lab, Northampton, MA) was used to prepare the graphs.

hazard quotient (HQ) of the mth metal in each sample from the kth source was calculated by adding ratios that divided ADI by the reference exposure dose (RfD) using Eq. (9) (USEPA, 2011): k k k k ADInm , ing ADInm , diet ADInm , der ADInm , inh + + + RfDing RfDdiet RfDder RfDinh

k HQnm =

(9)

Since the sustained paucity of evidences on interactions of multiple heavy metals, the hazard index (HI) posed by multiple non-carcinogenic metals from the kth source in each sample are still determined based on the sum of the HQ of each metal using Eq. (10) (USEPA, 2009): j k HInm =

k ∑ HQnm m=1

3. Results (10) 3.1. Soil properties and heavy metal concentrations in agricultural soils

When HQ and HI are lower than 1, the adverse non-carcinogenic health effect is considered to be not serious, but if they are greater than 1, the significant toxic effects may occur (USEPA, 2009). Additionally, the carcinogenic risk (CR) of the mth carcinogenic element in the nth sample from the kth source can be characterized by the following method:

The soil pH value ranged from 4.82 to 7.81, with a mean value of 5.96 (Table S3). Soil had a mean SOM percentage of 4.7% and mean size distribution was 59.6% silt, 26.6% clay, and 13.8% sand for soil particle. The mean concentration of As, Cr, Cd, Pb, Cu, and Zn (Table 1) all exceeded the background values (BV) of soils in Zhejiang province (CNEMC, 1990) by 1.75, 1.29, 2.67, 1.11, 1.72, and 1.33 folds, respectively. Additionally, the mean concentration of other heavy metals were lower than the risk screening value (RSV) (CEPA, 2018) for agricultural land in China with exception of Cu. Of the 169 soil samples, 20, 14, and 13% of Cu, Cd, and Zn concentration exceeded the corresponding RSV, respectively. However, concentration of Ni and Pb exceeded their RSV only in 1 and 2 sample, respectively. Concentration of As and Cr in all samples were less than the corresponding RSV. Furthermore, the coefficients of variation (CV) for these heavy metals varied considerably, among which Cd and Cu had higher CV values than the others.

k k k k CRnm = ADInm , ing × SFing + ADInm , diet × SFdiet + ADInm , de k r × SFder + EDInm , inh × SFinh

(11)

where SFing, SFdiet, SFder, SFinh is the carcinogenic slope factor (mg kg−1 day−1)−1 for heavy metals through soil ingestion, diet ingestion, dermal contact and inhalation, respectively. Similarly, the total carcinogenic risk (TCR) from the kth source in each sample can be determined by summing the CR of each metal using Eqn. (12) (USEPA, 2011): j k TCRnm =

k ∑ CRnm m=1

(12)

When CR and TCR exceed 1E-04, they are considered as an unacceptable level, whereas if values under 1E-04, there will be no significant carcinogenic effects (USEPA, 2009). Table S2 lists the values of RfD and SF for heavy metals used for the risk characterization.

3.2. Source apportionment 3.2.1. Spatial distribution, inner correlation and PCA of heavy metals The Pb and Zn had a similar distribution with a general decreasing tendency from northwest to southeast (Fig. S1). The distribution of Cd and Cu were also relatively similar and the hotspots with higher concentration of Cu and Cd mainly appeared in northwestern area. Spatial distribution of Cr and Ni concentration were in opposite to that of Pb, Zn, Cd, and Cu. Higher concentrations of Cr and Ni mainly displayed in eastern coastal region. The spatial pattern of As concentration was distinctly different from other metals, middle, southwestern, and eastern costal area appeared higher As concentration. Overall, As, Ni, and Cr were moderately positively correlated with each other (r = 0.560–0.902, p < 0.01) (Fig. S2). Additionally, the moderate positive correlation between Zn, Cu, Pb, and Cd (r = 0.326–0.774, p < 0.01) were observed. Three principal components whose eigenvalues exceeded 1 (noted as PC1, PC2, and PC3) were

2.5. Spatial analysis and data processing The spatial distribution map of metal concentrations and source contribution was depicted using the Ordinary Kriging interpolation technique loaded in Arc GIS 10.2.2 (ESRI, Redlands, CA, USA). In this analysis, the spherical model best fit the experimental semi-variogram. Basic descriptive statistics were calculated for metal concentrations. Kolmogorov-Smirnov (K-S) test was used for normality test of the datasets prior to implementing the PCA. Pearson correlation analysis was applied to assess inner correlations among heavy metals. The geometric mean (GM) HQ (HI) and CR (TCR) values from all samples were used to represent the central tendencies of health risk. The above statistical 4

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Fig. 2. Component loading of the three principal components (PCs) for heavy metal concentrations by Principal Component Analysis with varimax (Note: the number in the brackets indicates the proportion of total variance explained by each PC).

resolved by PCA (Fig. 2 and Table S4), accounting for 84% of the total data variance. PC1 explained 38% of the total variation and it showed high loadings of Cr, Ni, and As. PC2 explained 30% of the total variation with the higher loadings of Pb, Zn, and Cu. Moreover, PC3 explained the lowest percentage (16%) of the total variance and it merely loaded with Cd. 3.2.2. PMF model An optimal three-factor solution with minimum Q value was determined by PMF model. The coefficient between observed and predicted concentration data of seven heavy metals ranged from 0.58 to 0.96 (Table S5). Source profiles from the base run were within the interquartile range (25–75th) of the BS-DISP, DISP, and BS run (Fig. S3), indicating that the factor numbers were reasonable to fully decompose the original dataset. Factor 1 (F1) was predominated by Cr, Ni, and As that accounted for 46.6% of the metal concentrations, while Factor 2 (F2) accounted for 22.2% of the metal concentrations, and predominantly loaded by Cd and Cu (Fig. 3). Factor 3 (F3) accounted for 31.2% of the metal concentrations and it dominantly loaded by Pb and Zn. The spatial distribution of normalized contribution of each factor was shown in Fig. 4. Contribution score of F1 generally increased from west to east, while the contribution score of F3 has a general increasing tendency from southeast to northwest. The distribution of contribution score for F2 was different from that of other factors. Hotspots with higher contribution score of F2 were mainly observed in northwestern, southwestern, and northeastern area.

Fig. 3. Fractional concentrations and percentage contribution of three-factor loading profiles resolved by Positive Matrix Factorization.

decreased following the order as Cd > Cr > As > Pb > Ni. 3.4. Source contribution to human health risk For both children and adults, F1 dominantly contributed to the health risk of As, Cr, and Ni, while F2 contributed the most to the health risk of Cd and Cu (Fig. S4). Additionally, F3 dominantly contributed to the health risk of Pb and Zn. Fig. 5 further summarized the source contribution to total concentrations, total cancer risk and hazard index (total non-cancer risk) of heavy metals. The F1 accounted for the highest percentage (46.6%) of metals concentrations and it contributed the most (52.9%) to hazard index. It is also noteworthy that F2 was only third in the explanation of total metal concentrations (22.2%), whereas it contributed the most to total cancer risk (45.3%). F3 was the second highest contributor to metal concentrations, while the contribution of F3 to both total noncancer (28.1%) and (15.5%) cancer risk were much lower than F1 and F2.

3.3. Sources-oriented risk assessment

4. Discussion

The HI values from total factors for children and adults were 11.8 and 6.7, exceeding the guideline value of 1 by 12 and 7 times (Table 2). The mean HQ value for children from total factors was generally decreased in the order of As > Cu > Zn > Cr > Cd > Ni > Pb, while the mean HQ value for adults decreased following the order of Zn > Cu > As > Ni > Cr > Cd > Pb. For both groups, the HQ value of As, Cu, and Zn from every factor was greater than 1. The TCR values from total factors for adults and children were 3.14E-03 and 1.12 E-02, respectively, which were 8 and 28 times higher than the maximum acceptable value of 1E-04. Additionally, the mean CR value of As, Cr, and Cd from total factors for both groups also exceeded 1E-4, which

4.1. Contamination level and possible sources of heavy metals in agricultural soils Compared to the background values and latest risk screening values of heavy metals in Chinese soils, the agricultural soils in the study area were mainly contaminated by Cd and Cu. The mean concentration of Cd, Cu, Pb, and Zn were much greater than the background values and these elements had the higher CV values (Table 1), suggesting anthropogenic input of these heavy metals (Wei and Yang, 2010). 5

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background value and the elevated As concentration mainly appeared in middle areas with intensive agricultural activities (Fig S1, e). Therefore, As, Cr, and Ni contamination might be originated from natural process combined with agricultural activities. 4.2. Detailed source categories and source contribution to metals accumulation Results from the PMF model further verifies the speculation that PC1 in PCA can be identified as a mixed source due to both PC1 and F1 were predominated by Cr, Ni, and As. It could be found that the concentrations of Cr and Ni loaded in F1 in source profiles were much lower than background values (Fig. 3). Moreover, the higher contribution scores of F1 were mainly found in farming area, where high application rate of pesticides (Fig. 4, a). A certain amount of As and Cd were also observed in commonly used pesticides (Zhao et al., 2010). Therefore, the parent material and pedogenic processes controlled the accumulation of Ni and Cr, while agrochemicals application dominantly contributed to As accumulation in the study area. Previous studies have concluded that Cr and Ni accumulation in soils were mainly from parent material due to the low degree of variability (Rodríguez et al., 2008; Liang et al., 2017). Other researches also reported that the elevated As concentration in agricultural soils over China mainly originated from application of pesticides, livestock manures and inorganic fertilizers (Lin et al., 2017; Zhang et al., 2018), which is consistent with the results from the present research. The Cd, Cu, Pb, and Zn concentrations loaded by F2 and F3 in source profiles were close to or even higher than background value, which indicated an interference of human activities for accumulation of these elements (Fig. 3). The F2 represented the effect of industrial process, which mainly due to that this factor was dominantly loaded by higher concentration of Cd and Cu (Fig. 3). Cu and Cd are the remarkable metals of industrial discharge owing to smelting and metal processing activities (Men et al., 2018b). Particularly, the study area is one of the well-known smelting, metal processing and e-waste dismantling center in China, which had thousands of domestic e-waste recycling workshops since 1990s. These industrial activities discharged a large amount of smoke, dust, and wastewater containing a substantial amount of Cu and Cd (Men et al., 2018b; Schwab et al., 2014), and further lead to severe soil Cd and Cu contamination. In the present study, the spatial distribution of the source contribution of F2 (Fig. 4, b) was similar to that of Cd and Cu concentrations (Fig. S1, a, b). Moreover, northwestern area had the higher contribution score of F2, coinciding with the locations of intensive factories and e-waste recycling courtyards. This indicated that these industrial activities did affect the Cd and Cu contamination in soils. Compared to the PC2 in the PCA results, the PMF model indicates that Pb and Zn dominantly loaded in F3 were more likely originated from traffic emission instead of the industrial activities. Previously, a systematic review also concluded that traffic factors were major contributors of the elevated Pb and Zn concentrations in Chinese soils (Wei and Yang, 2010). Although Pb had been banned as an anti-knocking additive in petrol since 2000. Pb emission from motor brakes and tires still exists and could further accumulate in soils through atmospheric deposition (Chen et al., 2016). A high traffic load could also increase Zn concentration in surface soils (Zhang et al., 2017) by the deposition of Zn-containing dust (Monaci et al., 2000). In this study, the higher Pb and Zn concentrations (Fig. S1, c, d) and F3 contribution score (Fig. 4, c) all displayed in area with denser roads network and intense commercial activities, confirming that traffic emission is the main source of soil Pb and Zn contamination in the study area. The results in this study, to a certain degree, were comparable with previous research. Huang et al. (2018) studied soil in Xiang River New District based on PMF model and found that Pb and Zn accumulation were mainly originated from traffic transportation, while Cd and Cu were highly associated with agriculture activities. Jiang et al. (2017)

Fig. 4. The spatial distribution of normalized contributions of three factors (the average to all sample sites = 1).

Additionally, PC2 and PC3 had the higher loadings in Cu, Pb, Zn, and Cd (Fig. 2), and the hotspots with the higher concentrations of these elements were all located in northwestern area (Fig. S1, a–d), where a number of e-waste dismantling, steel-wire producing and smelting plants, etc. were concentrated (Zhao et al., 2010). Therefore, these industries may be a major source for Cd, Cu, Pb, and Zn that accumulated in agricultural soils. In contrast, the mean concentration of Cr and Ni were relatively close to their corresponding background values and the two elements had small CV values (Table 1), indicating that the two elements were determined by natural processes. In this study, PC1 dominantly loaded the As, Cr, and Ni (Fig. 2) and the higher concentration of As, Cr, and Ni were mainly located in northeastern and eastern coastal region (Fig. S1, e-g), where there was less influence of human activities (He et al., 2019). Moreover, the mean concentration of As was higher than the 6

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Table 2 Estimation of non-cancer (hazard index) and cancer risk (total cancer risk) posed by heavy metals from three different sources for two groups. Children (aged 1–17) Factor 1

Factor 2

Adults (aged 18+) Factor 3

Total Factors

Factor 1

Factor 2

Factor 3

Total Factors

Cd Cr Cu Ni Zn As Pb Hazard index

Hazard quotient of each heavy metal and hazard index 1.38E−01 8.77 E−01 8.61 E−02 1.21E+00 5.27 E−02 4.33 E−01 8.28 E−01 6.35 E−01 5.21 E−01 3.36 E−01 1.61 E−02 9.77 E−02 8.09 E−01 3.66 E−01 8.24 E−01 3.23E+00 8.90 E−02 1.03E+00 1.93 E−02 2.92 E−02 1.25 E−01 6.57E+00 2.07E+00 3.12E+00

1.10E+00 1.70E+00 1.98E+00 4.50 E−01 2.00E+00 4.35E+00 1.74 E−01 1.18E+01

3.69 E−02 3.26 E−01 8.87 E−01 3.60 E−01 8.66 E−01 8.75 E−01 5.76 E−03 3.36E+00

2.35 E−01 1.42 E−02 6.80 E−01 1.72 E−02 3.92 E−01 2.41 E−02 8.74 E−03 1.37E+00

2.31 E−02 1.16 E−01 5.57 E−01 1.05 E−01 8.82 E−01 2.79 E−01 3.75 E−02 2.00E+00

2.95 E−01 4.56 E−01 2.12E+00 4.82 E−01 2.14E+00 1.18E+00 5.20 E−02 6.73E+00

Cd Cr Ni As Pb Total cancer risk

Cancer risk of each heavy metal and total cancer risk 8.40 E−04 5.35 E−03 5.25 E−04 1.82 E−03 4.00 E−05 6.50 E−04 1.20 E−09 5.72 E−11 3.47 E−10 1.45 E−03 7.91 E−05 4.62 E−04 5.70 E−07 8.64 E−07 3.70 E−06 4.11 E−03 5.47 E−03 1.64 E−03

6.72 2.51 1.60 1.99 5.14 1.12

2.25 4.86 2.98 3.94 1.52 1.11

1.43 2.11 1.43 1.09 2.30 1.46

1.40 1.74 8.65 1.26 1.27 5.66

1.80 6.81 3.98 5.30 1.27 3.14

E−03 E−03 E−09 E−03 E−06 E−02

E−04 E−04 E−10 E−04 E−07 E−03

E−03 E−05 E−11 E−05 E−07 E−03

E−04 E−04 E−11 E−04 E−04 E−04

E−03 E−04 E−10 E−04 E−04 E−03

(Zhi et al., 2016). Another challenge on using PMF model to apportion real pollutant source is how to define the appropriate number of factors (Li et al., 2019). Till now, there is no standard rule for determining the appropriate number of factors since PMF factors themselves are in no specific order (Sofowote et al., 2008). PCA outputs could provide the possible range for the PMF factors because that factors in PCA algorithm can be easily decided by the decision rule of eigenvalues greater than or equal to 1(Sundqvist et al., 2009), while the discrepancy deriving from two algorithms could also cause the inaccurate PMF factors. Herein, we merely used different numbers within the possible range indicated by PCA outputs to run PMF model and select the most physically reliable solution, thus the source apportionment might be somewhat uncertain. To better minimize the heavy metal input to agriculture soils, it is necessary to improve the pertaining approaches to obtain more refined source contribution, which is critical in designing and implementing the effective mitigation strategies for contaminated farmland. Fig. 5. Summary of the source contribution (%) to metal concentration, hazard index and total cancer risk.

4.3. Implications of the integrated source-exposure risk analysis On the basis of the health risk results (Table 2), it is clear that the local resident suffered from significant risk of adverse health effects. Previous studies also reported the heightened human health risk from ewaste processing area in comparison with control sites (Fu et al., 2013; Han et al., 2019). Geometric mean of HI for children (11.8) in this study were comparable to HI value (GM:13.8) from a heavily contaminated region in India, where the illegitimate e-waste recycling activities became prosperous recently (Singh et al., 2018). Geometric mean of HI for adults (6.7) were also much higher than that in other e-waste dismantling sites (Isimekhai et al., 2017; Han et al., 2019). Moreover, the TCR for both children and adults were several times higher than the cancer risk level from other mining-impacted areas (Cao et al., 2016; Souza et al., 2017). These reflected that the health risk of residents induced by exposure to soil heavy metals in study area should be of great concern. In addition, parent material and agrochemical application (F1) made the highest contribution to total hazard index (Fig. 5), due to this source being dominantly loaded by As, Cr, and Ni with higher bioavailability and toxicity, especially of As and Cr (Han et al., 2017). Much research have reported the higher non-cancer risk of As and Cr because of its lower RfD (Rovira et al., 2011; Li et al., 2014). Moreover, all available RfD of four exposure pathways were considered in the noncancer risk estimation of As but the other metals only have one or two available RfD in the non-cancer risk estimation (IRIS, 2013), resulting in As has highest total non-cancer risk. Although the amount of

used the same method and concluded that As and Cr in agricultural soils originated from natural source, while Pb, Cd, Cu, and Zn originated from industrial activities. Due to the specific natural and socio-economic condition of different regions, the source characteristic of soil heavy metals may vary among different sites. For instance, Liu et al. (2018a) attributed soil Cr, As and, Zn accumulation in soil of Yu’lin, China to industrial activities, whereas Song et al. (2018) attributed soil Ni and Zn concentrations in Tai’an, China to mixed sources. Other studies also reported the quite different sources of agricultural soil contamination (Guan, et al., 2018; Hu et al., 2018), which indicated the high spatial heterogeneity and uncertainty of the metal emission (Hou et al., 2017). It is worth noting that PMF model has key assumption, that is, the source profiles do not change significantly over the entire transport processes from source to receptor (Huang et al., 2019). In reality, due to the strong spatial variation of soil, the assumption might not be fulfilled. Additionally, PMF model is highly sensitive to outliers (USEPA, 2014). However, metal concentrations in soil usually exhibits a positive skewed distribution and contains abnormal values thus uncertainty produced. The skewness for Cu, Cd, and Zn was 10.70, 9.28, and 5.11 in this study. Correspondingly, the sources of these three metals might be unrealistic. This is due to the fact that PMF model will preferentially fit the outliers of metal concentration to optimize the objective function Q 7

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Ongoing reduction and strict monitoring of agrochemical consumption are also needed to alleviate non-cancer risk. Additionally, this study revealed the research prospective, including incorporating the bioavailability and interactions of heavy metals, optimizing the reasonableness and robustness of PMF output for capturing more accurate and reliable source contribution of health risk.

fertilizer and pesticide consumed in 2016 in the study area has been reduced (Wenling Statistic Bureau, 2016), our result indicated that agrochemical application remains an important contributor to the total non-cancer risk. Ongoing reduction and stricter monitoring of agrochemical application are urgently needed. One unexpected result was that industrial activities (F2) were third in the explanation of total metal concentrations whereas they contributed the most to the total cancer risk (Fig. 5). This might be because that industrial activities in this study dominantly generated Cd with the highest carcinogenic slope factor. Similar results were reported by Liu et al. (2018a) and Huang et al. (2018), showing that industrial-related activities had the highest contribution to the total cancer risk. Despite that the dominant element of industrial activities varied across these researches owing to the regional difference, industrial discharge in most studies generated Cd with the higher carcinogenic effect, thus producing the higher total cancer risk. Furthermore, the lower risk contribution of traffic emission (F3; Fig. 5) may be due to the limited mobility of Pb (Li, 2006) and lower toxicity of Zn (IRIS, 2013). Overall, sources emission contained elements such as As, Cr, and Cd with higher toxicity and bioavailability may produce a higher health hazard. To prevent the health hazard of soil heavy metals from occurring, further reduction of industrial discharge through accelerating technology upgrade and raising the emission standard can greatly improve the risk management. It is also advised that the distance between the site of the high-polluted firms and the human-living region should be extended to reduce the impact of industrial activities on human health. Currently, studies on human health risk were usually conducted without comprehensively considering the source-specific health risk (Sun et al., 2010; Peng, et al., 2016). The integration of source apportionment and exposure risk assessment in this study offers an approach to capture the source contribution to health risk, will be a more effective tool for ensuring the health of human beings and managing pollutant source. Furthermore, the inclusion of source contribution of health risk in environment risk analysis is more meaningful than simply comparing the health risk to the risk threshold defined by the USEPA, delivering the novelty insight into addressing natural and anthropogenic source impacts on human health and interrelationships among components across source, sink and human beings. Even though our study was conducted in a specific region that had intense e-waste recycling activities, the findings could also provide useful policy implications for areas with similar contaminations across the world. The integrated source-exposure risk approach also could be updated and used in heavy metal and organic pollutants co-contaminated investigations. It is noteworthy that the total health risk evaluation merely based upon the sum of the risk of each metal, which might be inaccurate to represent the total health risk of multiple heavy metals (Augustsson et al., 2018). Moreover, the bioavailability of heavy metals were not considered in simple health risk indices, which may bring bias in estimating the effective intake dose of heavy metals in human body (USEPA, 2005). More in-depth source-oriented health risk estimation needs to utilize the bioavailability of specific elements and interactions of multiple metals for capturing the more precise health effects of soil heavy metals from various emission sources.

Declaration of Competing Interest We declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest. Acknowledgements This work was financially supported by the National Natural Science Foundation of China (41721001, 41722111 and 41571477), the National Key Research Program of China (2016YFD0801105), the 111 Project (B17039), China Agricultural Research System (CARS-01-30), and the Fundamental Research Funds for Central Universities. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envint.2019.105239. References Augustsson, A., Uddh-Sã Derberg, T., Filipsson, M., Helmfrid, I., Berglund, M., Karlsson, H., Hogmalm, J., Karlsson, A., Alriksson, S., 2018. Challenges in assessing the health risk of consuming vegetables in metal-contaminated environments. Environ. Int. 113, 269–280. Bi, C., Zhou, Y., Chen, Z., Jia, J., Bao, X., 2018. Heavy metals and lead isotopes in soils, road dust and leafy vegetables and health risks via vegetable consumption in the industrial areas of Shanghai, China. Sci. Total Environ. 619–620, 1349–1357. Cao, S., Duan, X., Zhao, X., Chen, Y., Wang, B., Sun, C., Zheng, B., Wei, F., 2016. Health risks of children's cumulative and aggregative exposure to metals and metalloids in a typical urban environment in China. Chemosphere 147, 404–411. CEPA (Chinese Environment Protection Administration). 2018. Soil environmental quality-Risk control standard for soil contamination of agricultural land. GB156182018. Chinese Environment Protection Administration. http://kjs.mee.gov.cn. (In Chinese). Chen, T., Chang, Q., Liu, J., Clevers, J.G., Kooistra, L., 2016. Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: a case study in northwest China. Sci. Total Environ. 565, 155–164. CNEMC (China National Environmental Monitoring Centre). 1990. The Background Values of Elements in Chinese Soils. China Environmental Science Press, Beijing. (In Chinese). Dai, L., Wang, L., Li, L., Tao, L., Zhang, Y., Ma, C., Xing, B., 2018. Multivariate geostatistical analysis and source identification of heavy metals in the sediment of Poyang Lake in China. Sci. Total Environ. 621, 1433–1444. Dartan, G., Taşpınar, F., Toröz, İ., 2015. Assessment of heavy metals in agricultural soils and their source apportionment: a Turkish district survey. Environ. Monit. Assess. 187 (3), 99–112. Davis, H.T., Aelion, C.M., Mcdermott, S., Lawson, A.B., 2009. Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation. Environ. Pollut. 157 (8), 2378–2385. Fu, J., Zhang, A., Wang, T., Qu, G., Shao, J., Yuan, B., Wang, Y., Jiang, G., 2013. Influence of e-waste dismantling and its regulations: temporal trend, spatial distribution of heavy metals in rice grains, and its potential health risk. Environ. Sci. Technol. 47 (13), 7437–7445. Guan, Q., Wang, F., Xu, C., Pan, N., Lin, J., Zhao, R., Yang, Y., Luo, H., 2018. Source apportionment of heavy metals in agricultural soil based on PMF: a case study in Hexi Corridor, Northwest China. Chemosphere 193, 189–197. Ha, H., Olson, J.R., Ling, B., Rogerson, P.A., 2014. Analysis of heavy metal sources in soil using kriging interpolation on principal components. Environ. Sci. Technol. 48 (9), 4999–5007. Han, Y.H., Liu, X., Rathinasabapathi, B., Li, H.B., Chen, Y., Ma, L.Q., 2017. Mechanisms of efficient As solubilization in soils and As accumulation by As-hyperaccumulator Pteris vittata. Environ. Pollut. 227, 569–577. Han, Y., Tang, Z., Sun, J., Xing, X., Zhang, M., Cheng, J., 2019. Heavy metals in soil contaminated through e-waste processing activities in a recycling area: Implications for risk managemen. Process Saf. Environ. 125, 189–196. He, M., Shen, H., Li, Z., Wang, L., Wang, F., Zhao, K., Liu, X., Wendroth, O., Xu, J., 2019. Ten-year regional monitoring of soil-rice grain contamination by heavy metals with implications for target remediation and food safety. Environ. Pollut. 244, 431–439. Hu, W., Wang, H., Dong, L., Huang, B., Borggaard, O.K., Bruun Hansen, H.C., He, Y.,

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