Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China

Environmental Pollution 248 (2019) 349e357

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Accumulation, ecological-health risks assessment, and source apportionment of heavy metals in paddy soils: A case study in Hanzhong, Shaanxi, China* Ran Xiao, Di Guo, Amjad Ali, Shenshen Mi, Tao Liu, Chunyan Ren, Ronghua Li, Zengqiang Zhang* College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 November 2018 Received in revised form 22 January 2019 Accepted 15 February 2019 Available online 19 February 2019

Contamination of agricultural soil by heavy metals has become a global issue concerning food security and human health risk. In this study, a soil investigation was conducted to evaluate metals accumulation, potential ecological and health risks as well as to identify sources of metals in paddy soils in Hanzhong City, which is located in a sedimentary basin. Ninety-two (92) surface soil samples (bulk soil) and their corresponding rice samples, 21 irrigation water samples, and 18 fertilizer samples were collected from two typical counties and quantified for the heavy metals (i.e., As, Cd, Cu, Hg, Pb, and Zn) concentrations. The results showed that As, Cd, and Zn were the main contaminants in soils in the studied area. Additionally, elevated Hg content in soils might also pose risks to the local ecosystem. Cadmium and As demonstrated high mobility, and their average contents in rice grains were slightly higher than the permissible threshold (0.20 mg kg
1). Moreover, Pb, As, and Cd intake via rice consumption might result in potential risks to local residents. Metal distribution revealed that pollution in the studied area is nonhomogeneous, and agricultural activities (As, Cu, and Cd), transportation emission (Cu and Pb), coal combustion (Hg and As), and smelting activities (Zn, Pb, and Cu) were ascertained as the potential sources based on the Positive matrix factorization (PMF) analysis results. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Heavy metals Risk assessment Source apportionment Positive matrix factorization Paddy soils

1. Introduction Soil, the skin of the earth, is essential to living organisms by providing nutrients for plant growth and serving as habitat for microflora and fauna (Bezdicek et al., 1996). Moreover, soil serves as a filter system for detoxification by absorbing or degrading different types of contaminants (Hillel, 2007; Wu et al., 2018). Despite that, excessive accumulation of pollutants in the soil would significantly reduce its quality as well as productivity (Yi et al., 2018). Nowadays, soil contamination by heavy metals (including both metal and metalloid), especially among agricultural soils, has become the focus of attention, owing to the high toxicity, long persistence, and bio-magnification traits of metals (Jiang et al., 2019). Accordingly, soil pollution prevention and remediation have become priority

*

This paper has been recommended for acceptance by Dr. Yong Sik Ok. * Corresponding author. College of Natural Resources and Environment, Northwest A&F University, Yangling, Shanxi Province, 712100, PR China. E-mail address: [email protected] (Z. Zhang). https://doi.org/10.1016/j.envpol.2019.02.045 0269-7491/© 2019 Elsevier Ltd. All rights reserved.

tasks for authorities worldwide (Khalid et al., 2017). China is now facing serious agricultural soil contamination due to the remarkable industrialization, urbanization, and development of intensified agriculture in the past few decades (Zhang et al., 2015; Yang et al., 2018; Huang et al., 2019). According to the first national survey of soil contamination, 19.4% of the agricultural soil samples were contaminated mainly with heavy metals such as cadmium (Cd), nickel (Ni), copper (Cu), arsenic (As), mercury (Hg), and lead (Pb) (MEPRC, 2014a). Consequently, approximately 13.9% reduction in grain production, due to the elevated metal contents in soils, was reported by Zhang et al. (2015). Meanwhile, escalating food safety problems have become one of the major public concerns (Huang et al., 2007; Zhang et al., 2015). For example, studies found Cd contents in about 10e20% of commercial rice samples in southern China (Hunan, Guangdong, and Fujian Province, specifically) were above the maximum allowable level (0.20 mg kg
1) (Xie et al., 2008; Zhen et al., 2008; Zhao et al., 2014). Moreover, Huang et al. (2007) found metal (i.e., Cd, Hg, and Pb) contents in a certain proportion of commercial vegetable samples in Jiangsu

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Province exceeded their respective maximum allowable threshold levels. Therefore, a 10-Chapter ‘Soil Contamination Prevention and Control Action Plan’ was promulgated by the Chinese government in 2016 to guide soil pollution prevention and remediation works in the following 30 years (CSC, 2016; Hou et al., 2017a). Assessing contamination status, evaluating potential risks to local ecology, and identifying potential sources of contamination are the prerequisites for the successful implementation of this Action Plan (CSC, 2016). So far, numerous researches have been conducted to investigate the contamination of agricultural soil in Shaanxi (Shen et al., 2017; Xiao et al., 2017), Hunan (Yi et al., 2018; Zhang et al., 2018b), and Guangdong province (Zhao et al., 2012) etc. However, most of them only focused on pollution rating or ecological/health risks assessment without conducting source identification and apportionment (Jiang et al., 2019; Huang et al., 2018; Wu et al., 2018). Since metals in soils may originate from natural and various anthropogenic sources. Apportion source contribution is necessary for understanding contamination characteristics and contributions of different sources before taking effective measurements to improve soil quality (Liang et al., 2017). Till now, numerous analytical methods/models, including geographic information systems, multivariate statistical analysis, and receptor models, have been developed for source identification qualitatively or quantitatively (Jiang et al., 2019). Among them, the Positive Matrix Factorization (PMF) has been widely applied for pollutant source apportionment in various media including atmosphere, hydrosphere, and pedosphere (USEPA, 2004; Liang et al., 2017; Hou et al., 2017b). Thus, in this research, the PMF model was applied for a better understanding of heavy metal pollution of paddy soils in Hanzhong city. Hanzhong city (32 080 5400
33 5301600 N, 105 300 5000 -108 160 4500 E), which covered an area of 27246 square kilometers, is located in the southwest Shaanxi. Geographically, it lies in the center of the Hanzhong Basin (a sedimentary basin), with the Daba Mountains and the Qin Mountains in the south and north, respectively. Hanjiang River, the biggest tributary of the Yangtze River, flow from the west to the east in this area. Due to heavy rainfall and moist/humid climate, Hanzhong is a crucial grain production base. Rice is the main staple crop cultivated in this area with an annual production of 0.7 million tons year
1, which accounted for over 70% of rice production in Shaanxi Province. Meanwhile, major industries in Hanzhong city are equipment manufacturing, non-ferrous smelting, energy, and chemical industry. However, the scale of industry in the

studied area was significantly lower compared with that in the coastal region or in eastern China. Nevertheless, recently a routine investigation found part of market rice samples (~40%) produced in this area were contaminated with metals, Cd specifically. However, no studies related to metal accumulation in soils were conducted in this area. Therefore, in this study, a soil investigation was launched in two typical counties in the area. The three main objectives of this study were: 1) to understand the contamination level of agricultural soils in the studied area; 2) to evaluate metal accumulation in rice grain and to explore influence factors; 3) to assess the health risks of metals to residents via rice consumption; 4) to apportion potential sources of metals using the PMF model. This research is expected to help in designing soil remediation/protection in the studied area, and also provide a reference for policies making associated with soil heavy metal pollution and protection researches in other basin areas. 2. Materials and methods 2.1. Study area The study area, including Chenggu and Mianxian, is located in Hanzhong city, Shaanxi Province, China. This area has a temperate and humid climate, with cool, damp winter and hot, humid summer. The average temperature is 14.33  C with annual precipitation of 853 mm. Soils in the studied area are classified as paddy soil, yellow-brown earth, and yellow-cinnamon soil according to the classification and codes for Chinese soil (GB/T 17296e2009; SAPRC, 2009). 2.2. 2.2. Sampling and characterization Sample collection was conducted in September 2017 right before rice harvesting. In total, 92 surface soil samples (0e20 cm) and the corresponding rice samples, 21 irrigation water samples, and 18 fertilizer samples were collected in the study area. A map showing the sampling points is presented in Fig. 1. 2.2.1. Soil and rice samples Surface soil (0e20 cm, bulk soil) samples were collected at sites following the standard sampling procedure according to MEPRC (2004). In brief, five samples were collected following an “S-shaped path” method and thereafter mixed thoroughly to form an

Fig. 1. Map showing the research area and sampling points. Note: Yellow nails represent soil/plant sampling points, and blue nails represent water sampling points. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

R. Xiao et al. / Environmental Pollution 248 (2019) 349e357

individual composite sample. Simultaneously, rice samples, including straw and grain, were collected from the same sampling point. After collection, samples were stored in polythene bags and transported back to the lab as soon as possible. Once reached to the lab, plant samples were thoroughly washed with distilled water (DI) and oven dried at 60  C. Rice grains were separated, pulverized and then stored in polyethylene bags for further analysis. Meanwhile, soil samples were air-dried, grounded to pass through 2-mm and 0.147-mm sieves, and kept in polyethylene bags until analysis. 2.2.2. Irrigation water Water samples were randomly collected from the irrigation canals following HJ/T 91e2002 (MEPRC, 2002). Samples were stored in a cooler filled with ice to minimize biodegradation and volatilization prior to analysis. 2.2.3. Fertilizer Eighteen samples of commonly used fertilizer, including multiple phosphatic fertilizers, compound fertilizers, and organic fertilizers, were collected from the local market and stored in polyethylene bags until analysis.

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2.4. Evaluation of soil metal pollution status Geoaccumulation index (Igeo) was adopted to evaluate soil metal pollution by comparing metal concentrations in received soils and preindustrial concentrations, which can be defined by the following equation (Zhang et al., 2018a).

Igeo ¼ log2

C ix

! (1)

1:5  C ib

Additionally, an index to characterize pollution status, the pollution load index (PLI) was determined as follows:

PLI ¼

C 1x

C 2x

C 6x

Cb

Cb

C 6b

 1

…  2

!1 6

(2)

where C ix means the measured concentration of heavy metal i (i ¼ 6) in the sample x, and C ib values were based on its background concentration in soils, of which the values were 11.1, 0.094, 21.4, 0.03, 21.4, and 69.4 mg kg
1 for As, Cd, Cu, Hg, Pb, and Zn, respectively (CNEMC, 1990). 2.5. Ecological risks of soil heavy metals

2.2.4. Analytical methods Soil pH and electrical conductivity (EC) were determined with a 1:2.5 soil-water extracts using a pH meter (PHSJ-3E, INESA, China) and a conductometer (DDSJ-308 A, SDSTI, China), respectively. Soil organic matter content (SOC) was measured by a titration method as described by Shen et al. (2017). Meanwhile, the dissolved organic carbon (DOC) content in soil samples was analyzed by extracting 1.0 g of soil with 10 mL DI water in 25 mL centrifuge tubes at 30 rpm for 0.5 h. Extracts were then centrifuged and filtered through a 0.45 mm cellulose nylon membrane filter and analyzed with a total carbon analyzer (TOC-VCSH, Shimadzu, USA). To determine the total metal (As, Cd, Cu, Hg, Pb, and Zn) content, soil samples (<0.147 mm) were digested with HNO3-HCl mixture (3:1) using MARS microwave system according to USEPA Method 3051 A (USEPA, 2007). Rice samples were digested with a mixture of HNO3-H2O2 at a volume ratio of 5:1 at 120  C for 10 h. Concentrations of Cd, Cu, Pb, and Zn were determined using an atomic absorption spectrophotometer (FAAS Z-5000, Hitachi, Japan). Mercury and As contents were determined using atomic fluorescence spectrometry (AFS-230 E, Haiguang Analytical Instrument Co., China). Additionally, the available metals in soils were extracted with DTPA-TEA (pH ¼ 7.30) at a ratio of 1:2.5 (w/v). Moreover, metal contents in water samples were determined following the procedure described by Yi et al. (2018). Concentrations of metals in fertilizers were analyzed after dissolving 1 g of various fertilizers in 25 mL of 1% HNO3 (for chemical fertilizer) or total digestion with HNO3-HCl mixture (3:1) (Li et al., 2012).

The potential ecological risk index (PERI), which is based on both metal contents and its toxicity, was used to assess the ecological risks posed by heavy metals in soil (Wu et al., 2018; Zhang et al., 2018a). The PERI of a signal metal (Eir ) and the accumulative risks (RI) of sampling sites were calculated using the following equation:

Eir

¼

RI ¼

T ir n X

Chemicals used in this study were guaranteed reagent grade. Plastic containers and glassware were kept in 20% HNO3 overnight and subsequently cleaned with DI water. All digestions and metal analysis were performed in triplicates. Reagent blanks were used to correct analytical values. Additionally, standard reference materials (i.e, GBW07405 (soil) and GBW10011 (wheat)) were used to validate the accuracy of analytical processes. The recovery rates of the target metals in the standard references ranged from 89% to 110%. Moreover, two continual calibration verification (CCV) standards were analyzed after every 20 samples for quality control during the analytical process.

!

C ib

T ir 

i

C ix C ib

(3)

(4)

where T ir is the biological toxicity of an individual metal (i), which was determined as As ¼ 10, Cu ¼ Pb ¼ 1, Cd ¼ 30, Hg ¼ 40 and Zn ¼ 5 (Hakanson, 1980). 2.6. Biological accumulation coefficient Biological accumulation coefficient (BAC), which is helpful to understand the bioavailability of metals in soils (Xiao et al., 2017), was employed to characterize the quantitative transfer of a given metal from soil to rice grain. The BAC values can be obtained following the equation listed below.

BAC ¼ 2.3. Quality assurance and quality control



C ix

C irice C ix

(5)

Where C irice is the metal (i) content in rice, and C ix is the metal (i) concentration in the corresponding soil. 2.7. Health risk of soil heavy metal Health risks, including both carcinogenic and non-carcinogenic effects to adults, via the consumption of rice, was evaluated according to the USEPA Health Risk Handbook and the Technical Specification for Soil Monitoring (USEPA, 2001; MEPRC, 2014b). The estimated daily intake (EDI) of different metals via rice consumption was calculated using the following equation:

Descriptive statistics of soil properties (pH, EC, and SOM content) and the concentrations of metals in soils samples are shown in Table 1. As can be seen, the average soil pH was 6.65 (5.42e8.65) with 68.5% of the soil samples below pH 7.0. The EC values of soil were relatively low (71.4 mS cm
1), indicating soils in the studied area were non-saline but abundant in dissolved ions. Moreover, the SOM contents also exhibited significant spatial variation with the values ranged from 10.3 to 34.6 g kg
1 and averaged at 20.8 g kg
1, which was consistent with the nationally and locally average SOM contents in agricultural soils (19.8 and 21.5 g kg
1, respectively) (Ban et al., 2015). Additionally, the average DOC content was 314 mg kg
1 (92.7e693 mg kg
1) for soils in the studied area. As for metals, the average metal contents for As, Cd, Cu, Hg, Pb,

25 (50.0%) 0.6 (23.1%) 100 (0.0%) 0.6 (3.8%) 140 (0.0%) 250 (30.8%) 30 (26.2%) 0.4 (50%) 50 (4.8%) 0.5 (9.5%) 100 (0.0%) 200 (57.1%) 30 (20.0%) 0.3 (0.0%) 50 (40.0%) 0.5 (0.0%) 80 (0.0%) 200 (0.0%) 11.1 0.09 21.4 0.03 21.4 69.4 28.2 0.52 26.2 0.35 43.7 258 Background values for soils in Shaanxi Province (CNEMC, 1990). Risk screen values of pollutants for agricultural soils in China (GB15618-2018; MEPRC, 2018).

3.1. Soil properties and metal accumulation in soils

a

3. Results and discussion

b

The descriptive statistical analysis of soil properties and heavy meal contents was performed using SPSS 22.0, and Pearson tests for correlation were used to reveal relationships among the soil properties, total/available metal contents, and metal accumulation in rice grains. Additionally, linear regression analysis was used to determine the relationship between the extractable metal contents and soil properties (i.e., pH and SOM) using Originpro 2016, with a significance defined at p < 0.05. Moreover, the potential sources of heavy metals in soils were analyzed using EPA PMF software (Version 5.0).

Table 1 Statistical results of soil properties and heavy metal concentrations in the studied area (n ¼ 92).

2.8. Statistical analysis

g kg 10.3 mg kg
1 92.7 10.4 0.05 22.6 0.05 6.36 53.5

Where C irice is the metal (i) content in rice, IR is the ingestion rate (0.3 kg day
1), EF is the exposure frequency (350 days year
1), ED is the exposure duration (24 years), BW is the body weight (60 kg), AT is the average time period (d). For carcinogenic and noncarcinogenic effects, AT ¼ 72 years  365 days and AT ¼ ED  365 days, respectively. SF refers to cancer slope factor (mg/kg/d)
1, and RfDi is the daily accumulation via food consumption that would not cause deleterious effects during the lifetime. Generally, the calculated cancer risk is acceptable or tolerable when the values fit in the range from 1  10
6 to 1  10
4, a value < 1  10
6 means no risk, but a value over 1  10
4 indicates metal accumulation would pose significantly carcinogenic risk to people (Li et al., 2014; Xiao et al., 2017). Additionally, HQ and HI values over 1 means there is a potential health risk associated with metal accumulation. The values of SF and RfDi are listed in Table S1.

13 50 25 44 33 200 24 89 50 74

(9)

0.47 1.48 0.78 0.54 0.41 3.21 1.81 3.5 1.56 1.63

HQi

i

0.73 1253 29 18553 60.4 7.52 61 0.06 332.7 25806

6 X

0.85 35.4 5.38 136 7.77 2.74 7.81 0.25 18.2 161

HI ¼

(8)

6.5 64.3 18.9 302 22.2 0.38 31 0.22 31.7 151

. HQ i ¼ EDIirice RfDi

6.65 71.4 20.9 314 23.2 1.37 32.9 0.28 36.4 217

For non-carcinogenic risk assessment, the target hazard quotient (HQ) was calculated according to Eq. (8). Additionally, the total hazard index (HI) was calculated by adding the HQ of each element, as described by Eq. (9):

8.65 78.8 34.6 693 43.1 15.8 68.9 1.6 122 742

(7)

5.42

Carcinogenic risk ¼ EDIirice  SF


1

The carcinogenic risk of As was calculated using Eq. (7):

mS cm
1 64.1

(6)

pH EC SOM DOC As Cd Cu Hg Pb Zn

C irice  IR  EF  ED BW  AT

Minimum value Maximum value Mean value Median Standard Variation Skewness CV (%) 75% Value Background Risk Screen Valuesb/Exceedance rates (%) Deviation Valuea pH  5.5 5.5 < pH  6.5 6.5 < pH  7.5 pH  7.5

EDIirice ¼

20 (52.6%) 0.8 (10.5) 100 (0.0%) 1 (0.0%) 240 (0.0%) 300 (0.0%)

R. Xiao et al. / Environmental Pollution 248 (2019) 349e357

Soil properties/Heavy metals Units

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R. Xiao et al. / Environmental Pollution 248 (2019) 349e357

and Zn were 23.2, 1.37, 32.9, 0.28, 36.4, and 217 mg kg
1soil, while their maximum concentrations reached to 43.1, 15.8, 68.9, 1.60, 122, and 742 mg kg
1, respectively. The coefficient of variation (CV) was 33%, 200%, 24%, 89%, 50%, and 74% for As, Cd, Cu, Hg, Pb, and Zn, respectively, indicating high degrees of variations among sampling sites. Additionally, the high CV values suggested that spatial distributions of metals in the studied area are non-homogeneous (Zhang et al., 2018a). Furthermore, Kolmogorov-Smirnov and Shapiro-Wilk tests of normality revealed that soil As content was normally distributed. However, concentrations of other metals in soils were not normally distributed. The mean contents of target metals in soils were higher than their respective background values for soils in the Shaanxi Province according to CNEMC (1990), indicating soils in the studied area were influenced by anthropogenic activities (Wu et al., 2018; Zhang et al., 2018a). Compared with the risk screening values (RSVs) for agricultural soils at different pH values, the overall exceedance rates for As, Cd, Hg, and Zn were 33.7%, 29.3%, 6.5%, and 34.8%, respectively. More specifically, the occurrence of As contamination (50%) was mostly among soils with pH higher than 6.5, while the over-standard rates for Cd (34%) and Zn (54.5%) were higher among neutral-acidic (6.5 < pH  7.5) and acidic (5.5 < pH  6.5) soil samples, respectively. Based on this analysis, As was the primary pollutant in soils in this area, followed by Zn and Cd. To quantitatively evaluate pollution status in the studied area, Igeo index and the accumulative PLI values were calculated, and the results are presented in Fig. S1 in the supplementary materials. The average Igeo values decreased in the order of Hg (2.28) > Cd (1.78) > Zn (0.75) > As (0.39) > Cu and Pb (<0.10), indicating higher accumulation of Hg, Cd, and Zn in soils than other metals. More specifically, over 90%, 40%, and 30% of the soil samples were contaminated with Hg, Cd, and Zn from the moderate (>1) to the extremely contaminated level (>5) (Table S2). Additionally, 57.6% and 43.4% of soils samples were at the moderate (1 < PLI  2) and the high contaminated (2 < PLI  5) levels, respectively, according to the calculated PLI values and the evaluation criterion (Table S2, Zhang et al., 2018b). Furthermore, the cumulative ecological risk indexes of target metals in soils ranged from 166 to 5777 (Fig. S1b), indicating the accumulated metals in soils would pose risks to the local ecosystem (Shen et al., 2017; Wu et al., 2018; Zhang et al., 2018a). More specifically, approximate 6.5% of the sampling sites exhibited moderate potential ecological risk, and proportion of soil samples in the strong potential, very strong potential, and highly-strong potential ecological risk categories were 40.2%, 34.8%, and 18.5%, respectively. Additionally, the individual index values for different metals decreased in the order of Hg > Cd > As > Zn, Pb, and Cu, which followed the similar sequence of the Igeo values. Mercury and Cd were the main contributors to the total ecological risks with the contribution at 52.3% and 44.5%, respectively. Accordingly, Cd and Hg were the main toxic elements in soils, which would exert potential risks to the local environment. 3.2. Bioavailability of metal in soils Apart from the total concentration of metals, metal speciation is also significant when evaluating pollution status, because metal accumulation in plants is generally positively related to the available metal content in soils (Zhang et al., 2018a). The concentration of DTPA-extractable metal in soils are presented in Fig. S2a. The average contents for extractable-As, Cd, Cu, Pb, and Zn in soils were 9.17, 0.63, 7.27, 10.5, and 14.8 mg kg
1, respectively. Additionally, the content of available Hg was below detection level. Approximate 42.5%, 46.3%, 22.1%, 29.0%, and 6.8% of the total As, Cd, Cu, Pb, and Zn in soils were extractable with DTPA, respectively. The high

353

proportion of available As, Cd, Cu, and Pb implied the high mobility of such metals (Wang et al., 2018a). Moreover, a positive correlation between the total and available metal content was found for Cd, Cu, Pb, and Zn with p  0.05 (Fig. S2b). However, no significant correlation was detected between the available and total As contents, which was probably related to the properties of DTPA (i.e, high affinity to metal cations other than anions). Similarly, significant relationships between DTPA-extractable and the total concentration of Cu, Pb, Zn, and Cd in paddy soils, rather than As and Hg, were reported by Hang et al. (2009). Furthermore, the available metal contents were found to be negatively correlated to SOM contents (p < 0.05) (Fig. S2c), which was consistent with previous studies (Kashem and Singh, 2001; Khan et al., 2017). However, a positive correlation between the logarithmic value of EDTA-extractable metal (i.e., Cr, Cu, Fe, Mn, Pb, or Zn) was reported by Zeng et al. (2011). Different SOM constitution and properties might be the reason to the dissimilar results. SOM can facilitate metal immobilization by adsorption or forming complexation with humic substances (Khan et al., 2017; Zeng et al., 2011). However, the availability of metals would be enhanced once SOM serves as chelates (Zeng et al., 2011). Moreover, soil pH was positively correlated to the available As content but negatively correlated to the concentration of available Cu, Cd, Pb, and Zn although the influences were non-significant (p > 0.05). The lower bioavailability of As associated with reduced soil pH can be explained by the stronger binding effects between As ionic and positively charged soil minerals under acidic conditions (Park et al., 2016). By comparison, the lower availability of other metals with rising soil pH was due to the formation of insoluble precipitates (Bolan et al., 2014). 3.3. Accumulation of metals in rice The concentrations of metals in rice grains collected from the studied area are presented in Fig. 2a. The average As, Cd, Cu, Hg, Pb, and Zn contents were 0.21, 0.22, 0.40, 0.01, 0.13, and 22.5 mg kg
1, respectively. Levels of As and Cd in rice grains were slightly higher than their respective maximum allowable level i.e., 0.20 mg kg
1 (MHPRC, 2017). Additionally, the average contents of Cu, Hg, and Pb in rice grains were below their regulated threshold levels. More specifically, As content in 45.6% of the rice samples exceeded its acceptable limited, and the over-standard rates for Cd, Hg, and Pb were 22.8%, 16.3%, and 35.9%, respectively. Overall, As and Cd were the dominant metal pollutants in grains in the studied area. These findings were similar but not identical to previous studies by Chen et al. (2018). They found Cd concentration in 10% of rice samples (n ¼ 180) across China over the allowable level, but As contents in all rice samples were below the limits (Chen et al., 2018). The Cd exceedance in 22.8% of rice samples can be explained by the elevated contents in soils (Table 1). Additionally, As contamination in over 40% of the rice samples was probably related to the increased As mobility in soils due to the unusually rainy season (>20 days) before rice harvesting. Studies have found flooding condition can promote As mobility through reducing soil redox potential, and hence aggravated As accumulation in grains, especially when the flood occurred at the filling and harvesting stages (Zeng et al., 2011; Kumarathilaka et al., 2018). Furthermore, the Pearson correlation between the concentrations of metals in rice and soils as well as other soil properties is presented in Table S3. No significant correlation was observed between the accumulation of metals in rice and its corresponding metal contents in soils. Similar results were previously reported by Sungur et al. (2014) and Zhang et al. (2018a). By comparison, a significantly positive relationship was identified between the extractable Cd/Zn contents and the accumulated metal contents in

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R. Xiao et al. / Environmental Pollution 248 (2019) 349e357

Fig. 2. (A) Metal accumulation in rice samples and (b) biological accumulation efficiencies (BAC) of different metals.

rice (p < 0.05). Accordingly, soil metal contamination assessment should also consider the metal availability instead of just relying on the total soil metal contents (Zhang et al., 2018a). Moreover, soil properties also influenced metal accumulation in rice grains. In this study, SOM content is negatively correlated with the accumulation of Cd and Hg but positively related to the contents of Cu and Zn in rice grains with p < 0.05. The reduced Cd and Hg accumulation in rice grown in soils with high SOM contents can be explained with the reduced metal availability as presented in Fig. 2c. However, the elevated Cu and Zn accumulation in rice grain might be related to the application of compost fertilizer, which generally contained high contents of both metals. With the decomposition of organic compounds, Cu and Zn that bound to the labile carbon in the compost would gradually release and thereafter be absorbed by rice plants (Zhou et al., 2005). However, not all Cu and Zn are accessible to rice plants, and the residual stable carbon compounds (e.g. humic substance) in the introduced compost further lead to the lower availability of metals through adsorption or forming complexation as described in Section 3.2. Moreover, atmospheric deposition of the metal enriched-particles on rice leaves might also lead to the accumulation of metals since foliar uptake is an important pathway of heavy metal accumulation by plants (Shahid et al. 2012). Biological accumulation coefficient (BAC) is helpful to evaluate the distribution of metals in the plant-soil system (Fig. 2b). Being slightly different from the sequence of bioavailable metal content, the BAC values for different metals decreased in the order of Cd > Zn > Pb > As, Cu, and Hg with average values at 40.7%, 15.2%, 6.0% and lower than 1.0%, respectively. Cd demonstrated the highest BAC, although its available metal content in soils was not the highest. The dissimilarity between the BAC and bioavailability of metals can be explained with the different uptake, translocation, and accumulation mechanisms that rice developed to enrich Cd in grains compared with other metals (Li et al., 2017; Khaliq et al., 2019). 3.4. Daily metal accumulation and health risk assessment The daily intake of metals among adults via rice consumption decreased in the order of Zn » Cu > Cd, As > Pb > Hg from over 100 mg day
1 (Zn) to 1.05 mg day
1 (Cd) and to less than 0.02 mg day
1 (Hg) (Fig. S3a). The levels of daily metals accumulation were below the tolerable daily intake recommended by the WHO/FAO (2007), which were 120, 47, 6500, 40, 200 and 33000 mg day
1 for As, Cd, Cu, Pb, and Zn, respectively. Despite that, the hazard quotients for Pb, As, and Cd was higher than 1.0, indicating that such metal accumulated in rice might have some detrimental effects on local residents after consumption (MEPRC, 2014b; Xiao

et al., 2017). Additionally, Pb and As were the main contributors to the potential chronic diseases among local residents, which accounted for 46% and 24% of the HI value, respectively (Fig. S3b). Despite that, the calculated cancer risk value was 1.52 E-9, suggesting As intake would not pose a significant carcinogenic risk to local residents. 3.5. Source identification of metals in the paddy soils After analysis with PMF model, five factors were identified as possible sources of heavy metal in agricultural soils (Fig. 3). The number “5” is a reasonable quantity of factors to explain the information contained in the original data based on the minimum Q value, low residuals, and high r2 index obtained from multiple trails. The first factor (F1) was dominated by Cd and accounted for over 85% of the loading values. Anthropogenic Cd in agricultural soils may originate from the application of industrial or municipal effluents, sewage sludge, phosphate fertilizer, and atmospheric deposition (Rehman et al., 2018). According to the field investigation, there was no history of industrial or municipal effluents and sewage sludge utilization in the studied area. Additionally, metal contents in irrigation water were all below the maximum allowable metal content in irrigation water (Table S4) (GB5084-2005, MEPRC, 2005). Therefore, we analyzed the Cd contents in commercial fertilizers. Results showed that Cd contents in chemical fertilizers ranged from 0.28 mg kg
1 to 10.8 mg kg
1, and an even higher Cd content (10.5 mg kg
1) was found among organic fertilizers. Cd contents in the majority of the chemical fertilizers (14/16) were lower than the maximum allowable limit (<10.0 mg kg
1) for fertilizer, but Cd concentration in all organic fertilizer was above the threshold level (Table S5). Considering the application rate (40e50 kg ha
1 a
1), the annual introduction of Cd in surface soils via fertilizer application can be as high as 2.74 mg kg
1 soil a
1. Similarly, Rao et al. (2018) found the long-term application of fertilizer, especially inorganic fertilizer, led to an enrichment of Cd in surface soil, which resulted in Cd accumulation in rice. Despite that, many scientists believe atmospheric deposition rather than phosphorus fertilizer application as the source of Cd accumulation in soils (Shi et al., 2018; Yi et al., 2018). For example, Yi et al. (2018) found atmospheric deposition as the primary source of Cd, As, and Zn in soils with contributions at over 90% based on a study in Hunan Province. However, no simultaneous high contribution of Factor 1 on the accumulation of Pb, Zn, or Hg, which were general contaminants in wet/dry precipitation from industrial activities or combustion of fossil fuels, was identified in this study. Similarly, Shi et al. (2019) found the input fluxes of heavy metals via atmospheric deposition varied greatly among different regions, and related to

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Fig. 3. (A) Factor profiles from PMF model and (b) contribution of different factors on heavy metal accumulation in the studied area.

the difference in industrial and agricultural production. Accordingly, we attributed Factor 1 as fertilizer application. Moreover, Cd in fertilizers generally had high bioavailability than it in the soils (Zhou et al., 2005), which partly explained the higher BAC value of Cd than other metals even at the relatively low total soil Cd content. The second factor (F2) was characterized by Cu and Pb (40.2% and 56.6% respectively) and can be classified as traffic emission sources. Lead is the major pollutants in transportation due to the combustion of leaded gasoline (Zhang et al., 2016). Even though the utilization of leaded gasoline has been banned in China since 2000, elevated Pb contents in soils along the roadside were still widely reported recently (Yan et al., 2018). Additionally, the wear and tear of pavements, tires, and brake linings were regarded as main sources of Cu along the roadside (Zhang et al., 2016). Moreover, traffic exhaust and surface runoff from the road are believed as the main factors of Hg in paddy soils that were distant from industrial chimneys (Jiang et al., 2019). The third factor (F3) was associated with the accumulation of As and Cu in soils with the contribution at 68.6% and 37.6%, respectively. This factor may be attributed to the utilization of agrochemicals (i.e., pesticides/herbicides) since As and Cu are the main functional elements. A previous study by Liang et al. (2017) also attributed the accumulation of As and Cu to the application of pesticide/fungicides. Wang et al. (2019) estimated that the annual import of As in rice field via the application of arsenic-based herbicides were 0.28e3.84 mg ha
1. Similarly, the extensive utilization of Cu-based pesticides/fungicide like Bordeaux mixture also leads to Cu accumulation in agricultural soils (Wang et al., 2018b). Luo et al. (2009) reported that the annual input of Cu via the application of agrochemical products can be over 5000 tons. The fourth factor (F4) that had high relevance to Hg than other elements can be interpreted as the combustion of fossil fuels, especially coal, which is also regarded as an important source of Hg emission worldwide (Wang and Luo, 2017). Coal is the dominant energy source and accounted for about 60% of Chinese energy structure (Hu et al., 2018). Due to the impurities in coal, hazardous

metals will evaporate to the atmosphere as gases or metal-enriched particles (Hu et al., 2018). Wang and Luo (2017) estimated that Hg emission in China in 2014 was 292 tons, and the main sources were power and heat generation, industrial boilers, domestic coal-stoves. According to the local statistical data, coal consumption in Hanzhong City was over 4 million tons in 2017. Considering the average Hg contents (0.02e0.61 mg kg
1) in coals, annual emission of Hg in the studied area can be up to 2.4 tons. Even worse, Hg emissions were difficult to diffuse but gradually precipitated in the studied area due to the sedimentary basin. As such, As emission from coal combustion might also attribute to the elevated As content in soils. For example, Tian et al. (2015) estimated the total emission of As from coal combustion over China was about 406.4 tons. Lastly, the fifth factor (F5) defined with Zn, Pb, and Cu with the contribution of 72.7%, 27.3%, and 17.0%. Numerous studies have demonstrated the accumulation of Zn and Pb around Zn smelters due to the dry/wet precipitation of metal-containing particles (Shen et al., 2017; Xiao et al., 2017; Yang et al., 2018). In the studied area, a Zn smelter has been established since 1968. In 2015, the annual production of Zn and Pb were 60 and 20 thousand tons. Additionally, the accumulation of Cu in soils is related to the previously Cu smelting activities. Accordingly, industrial sources can be identified as the main source of Zn, Pb, and Cu in this area. 4. Conclusions Anthropogenic activities lead to the accumulation of metals (i.e., As, Cd, Cu, Hg, Pb, and Zn) in paddy soils in Hanzhong city, and the spatial distribution of metals demonstrated a high degree of variations. Arsenic, Cd, and Zn were the main contaminants, and approximately 30% of the sampling sites were contaminated with such elements. Additionally, the accumulation of Cd and Hg in soils would pose toxic effects to the local ecosystems. Metals, especially As and Cd, demonstrated high availability in soils, and the level of As and Cd in rice grains were slightly higher than their respective maximum allowable level (0.2 mg kg
1). Daily intake of metals

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decreased in the order of Zn » Cu > Cd, As > Pb > Hg, and the accumulation of As, Cd, and Pb might lead to detrimental effects on local residents. Further source apportionment based on the PMF model showed that five different sources were responsible for the accumulation of metals in soils. Cd in soils was derived from the application of chemical fertilizer; Cu and Pb were assigned for traffic emission sources; As and Cu was attributed to the utilization of agrochemicals; Hg was from the combustion of fossil fuels; and the influx of Zn, Pb, and Cu were from smelting activities. Accordingly, effective regulation should be enforced including the utilization of safe fertilizers and agrochemicals as well as the installment of dust extraction facilities in order to guarantee the safety of agricultural products in the studied area. Acknowledgments We gratefully acknowledge the National Key Research and Development Program of China in the 13th Five-Year Plan (No. SQ2017YFNC060064-01) and the Fundamental Research Funds for the Central Universities (No.2452016159) for the financial support. The authors thank all the supporters of this project and the referees for their constructive comments. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.envpol.2019.02.045. References Bezdicek, D.F., Papendick, R.I., Lal, R., 1996. Introduction: importance of soil quality to health and sustainable land management. Methods Assess. Soil Qual. (methodsforasses) 1e8. Ban, S.T., Chang, Q.R., Zhao, Y.T., Yue, Jiang, 2015. Spatial characteristics of soil organic matter in cultivated land in Hanzhong Basin. J. Northwest A&F Univ. (Natural Sci. Ed.) 43 (2), 159e165 (in Chinese). Bolan, N., Kunhikrishnan, A., Thangarajan, R., Kumpiene, J., Park, J., Makino, T., Kirkham, M.B., Scheckel, K., 2014. Remediation of heavy metal(loid)s contaminated soils e to mobilize or to immobilize? J. Hazard Mater. 266, 141e166. Chen, H., Tang, Z., Wang, P., Zhao, F.-J., 2018. Geographical variations of cadmium and arsenic concentrations and arsenic speciation in Chinese rice. Environ. Pollut. 238, 482e490. CNEMC (China National Environmental Monitor Center), 1990. The Soil Background Values in China. Environmental Science Press, Beijing (in Chinese). CSC (China State Council), 2016. The action plan for soil pollution prevention and control. Available at: http://www.gov.cn/zhengce/content/2016-05/31/content_ 5078377.htm (accessed in November) (in Chinese). FAO/WHO, 2007. Summary of evaluations performed by the joint FAO/WHO expert committee on food additives (JECFA 1956-2007) (first through 68th meetings). In: Food and Agriculture Organization of the United Nations and the World Health Organization. ILSI Press International Life Sciences Institute, Washington, DC., USA. Hakanson, L., 1980. An ecological risk index for aquatic pollution control. Sedimentol. Approaches Water Res. 14 (8), 975e1001. Hang, X.S., Wang, H.Y., Zhou, J.M., Ma, C.L., Du, C.W., Chen, X.Q., 2009. Risk assessment of portentially toxic element pollution in soils and rice (Oryza sativa) in a typical area of Yangtze River Delta. Environ. Pollut. 157, 2542e2549. Hillel, D., 2007. Soil in the Environment: Crucible of Terrestrial Life. Elsevier. Hou, D.Y., Li, F.S., 2017a. Comp;exities surrounding China's soil action plan. Land Degrad. Dev. 28 (7), 2315e2320. Hou, D.Y., O'Connor, D., Nathanail, P., Tian, L., Ma, Y., 2017b. Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: a critical review. Environ. Pollut. 231, 1188e1200. Hu, G., Liu, G., Wu, D., Fu, B., 2018. Geochemical behavior of hazardous volatile elements in coals with different geological origin during combustion. Fuel 233, 361e376. Huang, S., Liao, Q., Hua, M., Wu, X., Bi, K., Yan, C., Chen, B., Zhang, X., 2007. Survey of heavy metal pollution and assessment of agricultural soil in Yangzhong district, Jiangsu Province, China. Chemosphere 67 (11), 2148e2155. Huang, Y., Deng, M., Wu, S., Japenga, J., Li, T., Yang, X., He, Z., 2018. A modified receptor model for source apportionment of heavy metal pollution in soil. J. Hazard Mater. 354, 161e169. Huang, Y., Wang, L., Wang, W., Li, T., He, Z., Yang, X., 2019. Current status of agricultural soil pollution by heavy metals in China: a meta-analysis. Sci. Total Environ. 651, 3034e3042.

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