Health risk assessment and bioaccessibilities of heavy metals for children in soil and dust from urban parks and schools of Jiaozuo, China

Ecotoxicology and Environmental Safety 191 (2020) 110157

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Health risk assessment and bioaccessibilities of heavy metals for children in soil and dust from urban parks and schools of Jiaozuo, China

T

Qiao Han1, Mingshi Wang∗,2, Jingli Cao3, Chenlu Gui3, Yanping Liu3, Xiangdong He1, Yuchuan He3, Yang Liu1 College of Resource and Environment, Henan Polytechnic University, Jiaozuo, Henan, 454003, China

ARTICLE INFO

ABSTRACT

Keywords: Heavy metals Soil and dust Bioaccessibility Risk assessment Children Jiaozuo China

Urban parks and schools sever as the mainly activity areas for children, but risk assessment posed by heavy metals (HMs) from soil and dust in these area has rarely been investigated. In this study, six urban parks and seven schools in Jiaozuo, China, were taken as research objects to understand the contamination level and bioaccessibility of HMs from soil and dust in urban parks and schools. The results indicated that Zn, Cu, Pb, Cd, As, Ni and Co from soil and dust were above the background values, especially Zn and Cd in dust, and As and Cd in soil. Serious Cd pollution was discovered, and respective Cd concentrations in soil and dust were 17.83 and 7.52 times the background value. Additionally, the average concentration and bioaccessibility of Zn, Mn, Pb, Cd, Cr, Ni and Co in dust were both higher than in soil. High concentration and high bioaccessibility of HMs in dust suggested that HMs contamination were serious and universal in Jiaozuo. The concentrations of most HMs were higher in the gastric phase, except for Cu and Cd which remained higher in the intestinal phase. Both in the gastric phase and intestinal phase, Mn, As and Cd in soil and dust both have high bioaccessibility which all exceed 10%. The carcinogenic and non-carcinogenic risks base on the total HMs for children (soil: 7.93, 1.96E05; dust: 6.44, 3.58E-05) were greater than those for adults (soil: 6.35E-01, 1.32E-05; dust: 5.06E-01, 2.42E-05), and urban parks and schools posed high potential risk for children. Therefore, assessment the risk posed by HMs contamination of soil and dust in urban parks and schools is vital and urgent for children.

1. Introduction High-intensity industrialization and urbanisation were the key factors and integral parts of the economic growth (Jan et al., 2010; Antoci et al., 2018), but numerous heavy metals (HMs) have entered simultaneously the urban environment (Yang et al., 2017; Zhang et al., 2018a, 2018b; Yadav et al., 2019). Due to their non-degradable, urban soil and dust serve gradually as the critical sinks and sources of HMs contaminants in the urban ecosystem (Lu et al., 2010; Gu et al., 2016). It is known that HMs, accumulated in urban soil and dust, are harmful for human health through several pathways including ingestion, inhalation, dermal contact and inadvertent ingestion (Bi et al., 2013; Liao and Zhang, 2016; Han et al., 2016). Additionally, HMs, such as Cd, Hg or Pb, can be acute or severely detrimental to human health and the environment even at low concentrations (Duruibe et al., 2007; Li et al., 2013a; Noli and Tsamos,

2016). HMs are persistent, bio-accumulation and bio-magnification in the body tissues, and remains unclear for homeostasis mechanism of them (Pandey et al., 2003; Han et al., 2016). Any high levels of HMs in soil and dust entering the body tissues will threaten human health and result in severe health risks, such as reduced intelligence for Children, damaging central nervous system and immune systems, affecting internal organs etc. (Christoforidis and Stamatis, 2009; Chen et al., 2015; Eqani et al., 2016). HMs in urban soil and dust are therefore an important indicator of environmental quality in the risk assessment of contaminated sites (Nriagu and Pacyna, 1988; Li et al., 2013c). In the last few decades, investigations concerning the urban soil and dust contamination with HMs were numerous and mainly carried out in the area close to industrial areas, urban street and road (Panagopoulos et al., 2015; Lu et al., 2017; Alsbou and Al Khashman, 2018; Zhang et al., 2019). Although many measures, such as banning leaded gasoline and cleaner production, has been taken, HMs contamination was still

Corresponding author. Henan Polytechnic University, Henan, China. E-mail address: [email protected] (M. Wang). 1 Address: Building 3, Lanyuan, Student Apartment, Henan Polytechnic University, Jiaozuo, Henan, China. 2 Address: Building 14, Jingyuan, Teachers Apartment, Henan Polytechnic University, Jiaozuo, Henan, China. 3 Address: Building 2, Lanyuan, Student Apartment, Henan Polytechnic University, Jiaozuo, Henan, China. ∗

https://doi.org/10.1016/j.ecoenv.2019.110157 Received 26 August 2019; Received in revised form 30 December 2019; Accepted 31 December 2019 0147-6513/ © 2020 Elsevier Inc. All rights reserved.

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very serious in street soil and dust, and HMs in dust posed more risks for human health than that in urban soil (Christoforidis and Stamatis, 2009; Dehghani et al., 2017; Yadav et al., 2019; Maeaba et al., 2019; Khademi et al., 2019). Additionally, due to the behavior of repetitive hand/finger sucking, children are more likely to ingest significant quantities of soil and dust than adults (Bargagli, 1998). However, urban parks and schools, as the mainly activity areas for children, may represent a more probable exposure source than the above area because residents mainly dwell in high-rise buildings in China (Jin et al., 2019). Data concerning the conditions of soils and dusts contaminated by HMs in urban parks and schools is limited. Therefore, it is vital and urgent for children to assess the risk posed by the contamination of soils and dusts HMs in urban parks and schools. At the same time, due to that HMs entering the human body cannot be absorbed completely (Mingot et al., 2011; Luo et al., 2012; Hong et al., 2016), in vitro bioaccessibility of HMs were taken into consideration as well as the concentration of HMs. In recent years, several in vitro digestion models, especially the physiologically-based extraction test (PBET), have been widely applied to evaluate the harm caused by HMs to human health (Sialelli et al., 2010; Zhuang et al., 2016; Gu and Gao, 2018; Li et al., 2018). Moreover, because of high in the absorption rate of HMs from digestion system and hemoglobin sensitivity to HMs for children (Chisolm and O'Hara, 1982), risk assessment results based on the bioaccessibility of HMs would be more comparable and accurate. This study area was located in the region of Jiaozuo (Henan Province, China) which has boomed through coal mining and utilization (Wang et al., 2018a). Six urban parks and severe schools were selected as the research object in this paper. According to the Chinese urban statistical yearly report (2018), the total population of children (0–14 years) in Jiaozuo is approximately 243,900. The main objectives of the present study were to determine total concentrations of HMs in the surface soil and dust of urban parks and schools, discriminate the anthropogenic sources of highly concentrated HMs in the surface soil and dust, explore the bioavailability of HMs and to assess health risk of HMs for children and adults via different exposure pathways. The results of this work can supply a more accurate assessment of the environmental quality in urban parks and schools on children and are helpful for pollution control and urban planning.

area, most regions, except for the concrete structure, are covered by vegetation basically. Dust samples were collected mainly from the edges around the flower beds, followed by windowsills and staircases, and the structures were near the soil samples. The edges are about 0.3–0.8 m above the ground, 20–40 cm wide, and the windowsills and staircases are about 1–1.5 m above the ground, and 10–30 cm wide. The number of top-soil (0–10 cm depth) and dust samples collected for each park or school were determined according to their area. Each soil sample was a composite of five sub-samples taken from an area of 1 m2. Each dust sample was carefully collected by brush and dustpan at least five days after raining incidents so that enough dust was deposited, and sampling tools were cleaned between sampling sites to avoid cross contamination. Before the experiment, extraneous materials, such as rhizomes, hair, sand and gravel, were removed from samples. Then all samples were dried in an oven at 40 °C until constant weight. Soil samples and dust samples were sieved through 200 μm mesh nylon sieves, in which the soil sample was grounded firstly with an agate pestle and mortar. Finally, samples were put into self-sealing polyethylene bags, labeled and taken to the laboratory. 2.3. Physiologically-based extraction test (PBET) analysis PBET analytical methods were modified from previously described studies (Ruby et al., 1996; Intawongse and Dean, 2006). PBET was widely used to estimate uptake and oral HMs Bioaccessibility in gastric and intestinal physiological compartment (Gu and Gao, 2018). The simulated gastric juice contained 2.5 g pepsin (Shanghai Yuanye Bio-Technology Co.), 1.0 g citric acid (Tianjin Hongyan Chemical Reagent Factory), 1.0 g malic acid (Shanghai Yuanye BioTechnology Co.), 0.84 mL lactic acid (Shanghai Yuanye Bio-Technology Co.), 1 ml glacial acetic acid (Guangdong Xilong Science Co.), and 0.15 mol L−1 NaCl (Tianjin Kemiou Chemical Reagent Co.). pH was adjusted consistently to 1.5 with concentrated HCl (Zhengzhou Paini Chemical Reagent Factory). The simulated gastric juice was poured into the cone bottles containing 2 g samples, and each 200 mL was put into the constant temperature concussion box. Argon was injected into the simulated liquid at 1 L/min. After shaking at 100 r·min−1 in a thermostatic bath (maintained at 37 °C) for 1 h, the supernatant 30 mL was extracted and filtered through a 0.45 μm filter disk for analysis. In the intestinal stage, saturated NaHCO3 (Tianjin Hongyan Chemical Reagent Factory) solution was added until the pH of the solution was 8. Then, 0.12 g trypsin (Shanghai Yuanye Bio-Technology Co.) and 0.4 g bile salt (Shanghai Yuanye Bio-Technology Co.) were added into the constant temperature concussion box, for 4 h in the same condition. The supernatant 30 mL was then collected and filtered. Gastric and intestinal extracts were stored at 4 °C in the refrigerator for analysis. Bioaccessibility in the gastric or intestinal stages could be calculated in the following form (Ai et al., 2018; Tang et al., 2004).

2. Materials and methods 2.1. Study area Jiaozuo (35°10′-35°21′N, 113°4′-113°26′E), as it is shown in Fig. 1, is located at the northwest of Henan Province, and the south of the Yellow River. The climate of the study area is warm temperate continental monsoon with average temperatures between 12.8 and 14.9 °C, annual precipitation of 603.5 mm, frost-free period of 231days, and average relative humidity of 62% (Wang et al., 2018b). The prevailing wind directions reveal northeastern and southwestern fluctuations (Wang et al., 2019), and the average wind speed 2.9 m/s. Jiaozuo covers an area of 424,000 square kilometers, including Jiefang district, Zhongzhan district, Macun district and Shanyang district (Li et al., 2014). Several industrial enterprises of different sizes are located in Jiaozuo, among which, WanFang Aluminium Plant and WanFang Power Plant were around urban area. Aeolus Tire Company, China Resources Power Plant, Shanxi Cooking Coal Power Plant, Gaoxin Thermal Power Plant and Power Plant existed in the urban area.

BA =

Ci Vi × 100% Cs ms

where BA is the Bioaccessibility of HMs in soil (%); Ci is the soluble state content (mg·L−1) of HMs in the reaction phase of PBET experiment; Vi (L) is the reaction liquid in each reactor; Cs (mg/kg) is the total amount of HMs in samples; ms (kg) is the mass of the sample. 2.4. Sample analysis and quality control For soil sample and dust sample, electro-thermal plate digestion and inductively coupled plasma mass spectroscopy (ICP-MS, PE NEXION 300) was used for the determination of HMs with the analysis details as described in Wang et al. (2019). Reagent blanks and parallel samples were carried out throughout the analysis to correct the analytical results. National standard soil samples (GBW07401, Geophysical Standard Reference Sample Soil) were used and the deviation between the

2.2. Sampling and sample preparation A total of 41 soil samples and 41 dust samples were collected from seven schools and six parks in December 2016 in Jiaozuo, Henan Province in this work (Fig. 1). During this period, precipitation was relatively scarce and heating has been started in Jiaozuo. In sampling 2

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Fig. 1. Sampling points and map of the study area in Jiaozuo, Henan province, China.

measured and standard values was kept to < 15% for quality control.

CDIing =

2.5. Data analysis

CDIinh =

Processing of the data were carried out by SPSS (IBM, USA). Correlation analysis and principal component analysis (PCA) have been widely used in the study of HMs in dust and soil by domestic and foreign scholars (Borůvka et al., 2005; Wang et al., 2015; Zhang et al., 2018a; Peng et al., 2019). High correlations between specific HMs in sediments may reflect similar levels of contamination and/or release from the same sources of pollution (Håkanson and Jansson, 1983; Li et al., 2009a). Combined with other information, PCA was conducted with quartimax rotation to identify latent factors (Hou et al., 2014).

CDIderm = CDIinh =

c×CF × SA × AF × ABS × EF × ED BW × AT

c × IRh × CF × EF × ED × FSPO × PLAF × PM10 BW × AT

c × EF × FSPO × CF × PLAF × PM10 AT IRhadult EDadult IRhchild EDchild × + BWadult BWchild

(5)

c × CF × EF × AF × ABS AT SAadult EDadult SAchild EDchild × + BWadult BWchild

(6)

The potential carcinogenic (CRs) and non-carcinogenic risks (NCRs; Hazard Quotient, HQ) for metals were estimated using the following equations (USEPA, 2007; Gu et al., 2016):

Total carcinogenic risk (TCR) =

Human health risk model could quantify the daily metal intake from contaminated soil and dust, carcinogenic risk (CRs) and non-carcinogenic risk (NCRs; hazard quotients; HQ) for both children and adults. CRs and HQ were determined using the cancer slope factor (SF) and reference dose (RfD) values from US EPA, respectively (Gu and Gao, 2018). In this study, Zn, Cu, Mn, As, Pb, Cd, Cr, Ni and Co exerted chronic NCRs on human, and some of them (As, Cd, Co, Cr, Ni) also had CRs. For the exposure assessment model, contamination could occur via three main pathways: (a) direct oral ingestion of substrate particles (CDIing), (b) inhalation of suspended particles through the mouth and nose (CDIinh) and (c) dermal adsorption of trace elements in particles adhered to exposed skin (CDIderm) (Miguel et al., 2007; Na et al., 2010). Based on the exposure factors handbook, chemical daily intake (CDI, mg kg−1day−1) of metals through each of the path from soil and dust ware calculated using the following equations (1)–(6). For adults:

CDIing

(4)

CDIderm =

2.6. Human health risk model based on total heavy metals

c×IRg × CF × EF×ED = BW × AT

IRgadult EDadult c × CF × EF IRchild EDchild × + AT BWadult BWchild

Hazard index (HI ) =

HQ =

CR =

CDI ·RfD

CDI · SF

(7)

1

(8)

When HQ < 1 or HI < 1, no adverse health effects are observed, and the NCR is within the acceptable range. When HQ > 1 or HI > 1, NCR exists in the region. When CR < 10−6, no cancer risk exists. When 10−6 < CR < 10−4, the risk is within the acceptable range. When CRN > 10−4, human tolerance is exceeded. All HMs risk reference SFs and RfDs used in the models are presented in Appendix (Table S1) (USEPA, 1991; USEPA, 2007; USEPA, 2008; USEPA, 2011). 3. Results and discussion 3.1. Total metal concentration The concentration of HMs (Zn, Cu, Mn, As, Pb, Cd, Cr, Ni and Co) in 37 soil and 40 dust samples from the urban parks and schools were determined (Table 1), including minimum, maximum, median, mean, standard deviation, kurtosis, skewness, coefficient of variation (CV), and background values. All HMs (except Mn and Cr) from dust and soil were above the background values. Among the HMs surveyed, dust Zn and Cd concentrations exceeded the background value by 6.05 and 17.83 times respectively; Soil As and Cd concentrations exceeded the background value by 5.29, 7.52 times respectively. The concentrations of Zn, Cu, Pb, Cd, Cr, Ni and Co from dust were higher than that from

(1) (2) (3)

For child: 3

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Table 1 Concentrations of heavy metals (mg/kg) in surface soil and dust. Element

Sample type

Minimum

Maximum

Median

Mean ± SD

Kurtosis

Skewness

C.V. (%)

Background valuea

Zn

Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust

28.24 16.84 12.00 3.36 238.22 285.63 17.61 15.51 7.13 12.89 0.12 0.39 12.51 41.66 26.13 32.03 10.41 3.58

365.00 1717.26 38.00 82.88 1302.81 891.79 258.47 36.57 104.70 203.97 2.35 6.66 113.62 551.61 62.28 191.23 39.99 107.54

84.00 303.34 20.19 52.59 485.20 441.11 40.92 21.67 30.83 29.70 0.37 0.87 60.40 98.71 35.63 44.56 15.32 15.45

99.51 ± 65.01 374.30 ± 324.90 20.88 ± 5.19 49.85 ± 20.92 487.57 ± 182.86 473.77 ± 159.92 57.68 ± 46.92 23.08 ± 4.50 30.87 ± 18.30 55.26 ± 48.46 0.53 ± 0.44 1.25 ± 1.11 58.53 ± 27.18 112.07 ± 82.99 35.79 ± 7.54 51.70 ± 25.80 17.88 ± 7.61 25.27 ± 22.04

2.63 2.49 0.99 −0.35 2.39 0.89 2.79 0.86 1.97 1.28 2.59 3.35 0.05 3.94 1.21 4.29 1.54 1.74

8.23 7.49 1.90 −0.71 10.16 0.07 9.02 0.86 6.51 0.97 8.04 14.44 −0.94 20.46 2.68 22.51 1.50 3.50

64 86 24 41 37 33 80 19 58 87 82 88 46 73 21 49 42 86

61.9

Cu Mn As Pb Cd Cr Ni Co a

19.9 570 10.9 25.4 0.07 65.7 30 10.8

The background values of the heavy metals in the table are selected from the background values of soil in Henan Province; SD: standard deviation.

by anthropogenic and anthropogenic pollution. The correlations among Cr, Zn and Mn are statistically significant (**p < 0.01), which suggested that they might share a common source, such as traffic emission, parts and tyre wear (Wang et al., 2007). As, Cu and Co did not show significant correlations with other metals in soil or dust (Table 2), which indicated that the pollution sources of As, Co and Cu differ from those of the other metals (Wang et al., 2019).

soil, especially Zn and Cd, which indicated that the degree of dust contamination was greater than that of soil in terms of HMs concentration. According to our analysis, As concentrations in soil and dust generally decreased from southwest to northeast (Table 1), consistent with the prevailing wind directions in Jiaozuo and in agreement with Mingshi Wang and T.J. Keegan's study results (Keegan et al., 2006; Wang et al., 2019). As is a proven human carcinogen and could potentially damage ecological communities (Sadiq et al., 2003). Coal-fired power plants were the main pollutant (Meng et al., 2016; Huang et al., 2017), which was in keeping with the fact that the variance of As in dust was much smaller than that in soil. The coefficient of variation (CV) reflects the average variation of HM concentrations, with CV > 35% reflecting high variation, 15% < CV < 35% reflecting moderate variation and CV ≤ 15% reflecting low variation. Table 1 showed that the CVs of Zn, Pb, Cd, Cr and Co in dust and soil samples all show a high level of spatial variation (CV > 35%) and a relatively no-uniform spatial distribution, which reveals that the concentration of HMs is obviously disturbed by human activities (Wang et al., 2019). Additionally, the CVs of HMs (expect Mn and Zn), in dust samples were higher than that in soil samples. This result suggests that considerable variability exists in the HMs data, and that the spatial distribution of HMs in this area is not homogeneous (Xu et al., 2014). Besides the high concentrations and high CVs, the Kurtosis and Skewness of Zn and Cd from dust and soil samples were still relatively high, which might been caused by distinct anthropogenic sources (Jin et al., 2019).

3.2.2. Principal component analysis (PCA) Soil and dust data are conducted firstly by KMO and Bartlett tests because these tests (KMO > 0.05; Sig < 0.05) can indicate whether PCA is useful for dimensionality reduction (Wang et al., 2015). The KMO of sampling adequacy for the above variables were 0.68 and 0.53, and Bartlett tests meet the requirements, which suggested that there are compact correlations and the PCA should yield distinct and reliable factors (Field, 2009). The PCA factor loadings (varimax rotation mode) are presented in Table 3. For HMs in soil, three principal components accounted 74.62% of the variance. As, Pb, Cd, and Ni had relatively high first component (PC1a) values, which result was consistent with the results of Pearson's correlation analysis (Table 2) and indicated that the sources of these elements were attributed to anthropogenic pollution. Zn and Pb pollutants in soil originated particularly from leaded gasoline, wear of parts and tyre, and other traffic pollution (Zhang, 2006). On PC2a, which explained 20.3% of the total variance, Co and Cu showed positive loadings of 65.0% and 34.7% respectively. PC3a explained 11.83% of the total variance, and the loadings of Mn, As and Cr were 60.3%, 88.3% and 38.0%, respectively. The distribution of As was greatly affected by coal-fired power plants (Keegan et al., 2006) and smelting (Duan and Tan, 2013). For HMs in dust, there are also three principal components accounting 61.96% of the variance, with the first, second, and third components explaining 32.18%, 16.16%, and 12.99%, respectively. On PC1b, As and Ni showed strongly positive loadings of 41.4% and 44.3%, respectively; On PC2b, Zn, Mn and Cr showed strongly positive loadings of 65.4%, 55.0% and 66.9%; On PC3b, Cu, Pb, and Cd showed strongly positive loadings of 45.5%, 72.0% and 81.2%, respectively. Three principal components suggested that the pollution sources of the dust are composite sources, including transportation, coal-firing, mining, industry and paint (Chen et al., 1997; Zhang et al., 1998; Xu et al., 2001; Liu et al., 2006; Li et al., 2009b). Additionally, whether on PC2a or PC3b, Co all showed weak or negative loadings with other HMs. It furtherly suggested that the pollution sources of Co differ from those of the other metals and could be the chemical plant and electroplating

3.2. Sources of heavy metals 3.2.1. Correlation analysis Table 2 shows the results of the Pearson correlation analysis. It is obvious that the correlations between the metals in soil are stronger than that in dust (Table 2). For soil HMs, significant correlations (p < 0.05) were found between Zn, Pb, Ni and Cd, between Mn, Pb, Cr and Ni, and between Pb, Cd, Cr and Ni. Strong correlations between the concentrations of elements in samples might reflect that the elements have similar contamination levels and they may originate from common sources (Wang et al., 2016). Zn, Mn, Pb, Cr and Ni in soil were grouped together, thus indicating that the anthropogenic sources of these HMs are closely related in the sediments of the study area (Facchinelli et al., 2001; Ali et al., 2016). On the other hand, correlations among the HMs in dust were generally weaker than that in soil, which was in agreement with Jin's study results (Jin et al., 2019). That suggested that the dust HMs were more likely to have been influenced 4

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Table 2 Results of correlation analysis of heavy metal concentrations in soil and dust, Jiaozuo. Type Soil

Dust

Zn Zn Cu Mn As Pb Cd Cr Ni Co Zn Cu Mn As Pb Cd Cr Ni Co

1 0.378* 0.093 −0.034 0.664** 0.482** 0.19 0.548** −0.224 1 −0.219 0.256 0.141 −0.038 0.076 0.450** 0.04 −0.06

Cu

Mn

As

Pb

Cd

Cr

Ni

Co

1 −0.12 0.033 −0.016 0.19 −0.283 0.22 0.274

1 0.339* 0.421** 0.183 0.697** 0.535** −0.272

1 0.091 −0.023 0.198 0.101 −0.324

1 0.761** 0.628** 0.772** −0.456

1 0.347* 0.645** −0.137

1 0.609** −0.406

1 −0.212

1

1 −0.525** −0.354* 0.312* 0.391* −0.336* −0.353* 0.311

1 0.334* −0.237 0.051 0.578** 0.195 −0.313*

1 −0.114 −0.215 0.239 0.326* −0.334*

1 0.446** −0.228 −0.021 0.179

1 −0.097 0.066 0.03

1 0.164 −0.251

1 −0.146

1

*p < 0.05; **p < 0.01; coefficients (**) are in bold.

results (Andrew and Ip, 2007; Li, 2013d). PH in gastric stage was lower than that in intestinal stage, which could increase the activity of enzymes. HMs could be more likely to release in the condition of acidic, which leaded to the high bioaccessibility in the gastric stage. However, after entering the intestinal stage, HMs were easily precipitated, and absorbed, so that HMs would be fixed and passivate again (Aurélie et al., 2011; Zheng et al., 2013). Due to the complexation with digestive enzymes (pepsin, bile salt and pancreatin), the bioaccessibility of Cu in gastric phase could be less than that in corresponding intestinal phase (Williams et al., 1998; Sialelli et al., 2010; Luo et al., 2012). All HMs in the gastric phase and intestinal phase of dust samples were higher bioaccessibility than that in soil samples. For example, the average bioaccessibility of Zn, Pb, Cr, Ni and Co in the dust samples was about 3.2, 1.8, 1.9, 1.9 and 2.6 times that in soil samples in gastric phase and 2.6, 14.9, 2.7, 2.3 and 4.1 times that in soil sample in intestinal phase. High concentrations and high bioaccessibilities of HMs in dust suggested the universal and serious pollution of these HMs in Jiaozuo. Additionally, with low concentration, some HMs (Mn, Pb, Zn and Ni) had high bioaccessibility (Tables 1 and 4), which indicated that bioaccessibility might be related to the metal speciation and soil characteristics such as conductivity, pH, texture and organic matter content may influence metal bioavailability (Wu et al., 2015; Zhao, 2018; Xing et al., 2019). It suggested that bioaccessibilities of HMs based on the PBET was closer to the actual harm of HMs than other method based on the concentration of HMs.

Table 3 Principal components (PC) of dust and soil samples. Element

Zn Cu Mn As Pb Cd Cr Ni Co Eigenvalue Cumulative of variation (%)

Soil

Dust

PC1a

PC2a

PC3a

PC1b

PC2b

PC3b

0.405 0.154 0.162 −0.051 0.458 0.428 0.251 0.435 −0.115 3.824 42.49

0.181 0.650 −0.299 0.062 −0.220 −0.015 −0.468 −0.067 0.347 1.827 62.79

0.011 0.105 0.603 0.883 0.151 −0.056 0.380 0.257 −0.403 1.064 74.62

−0.033 −0.337 0.260 0.414 −0.033 −0.031 0.120 0.443 −0.323 2.953 32.81

0.654 −0.264 0.550 0.086 −0.142 0.106 0.669 −0.032 −0.147 1.455 48.97

0.075 0.425 −0.091 −0.142 0.720 0.812 −0.142 0.161 0.103 1.169 61.96

industry (Suh et al., 2019). 3.3. Bioaccessibility of heavy metails in soil and dust Percent bioaccessibilities (gastric and intestinal phases) of Zn, Cu, Mn, As, Pb, Cd, Cr, Ni and Co are presented in Fig. 2. Both bioaccessible and residual concentrations of HMs in the soil and dust are summarized in Table 4. The difference between the bioaccessible concentrations of HMs in soil and dust samples was much less than the total concentrations (Tables 1 and 4), which indicated that the total amount of HMs had no significant effect on its bioaccessibility. For soil samples, average of metal bioaccessibilities were as follows: Mn > Pb > Cd > As > Zn > Ni > Cu > Co > Cr for gastric phase and Cd > Mn > As > Cu > Zn > Ni > Co > Cr > Pb for intestinal phase. For dust samples, the average of metal bioaccessibilities were as follows: Zn > Mn > Pb > As > Cd > Ni > Cu > Co > Cr for gastric phase and Pb > Cd > Mn > As > Zn > Ni > Cu > Co > Cr for intestinal phase (Table 4). The elements (Zn, Mn, As and Cd) showed higher bioaccessibility than other metals, and Zn demonstrated the highest bioaccessibility (36.77%) in the gastric phase of dust samples. Cr demonstrated the lowest bioaccessibility (2.87%) in the intestinal phase of soil samples. In gastric and intestinal phase, Cr was mainly in the form of residue and not easily digested by human gastrointestinal fluid (Li et al., 2013b; Piggio et al., 2009). For soil and dust samples, the bioaccessibility of Zn, Mn, As, Ni and Co (except for Cu) in the gastric stage were higher than that in the intestinal stage, which was in agreement with Li yi and Andraw's study

3.4. Health risk assessment of heavy metals based on total heavy metals Results of NCR and CR human health risk assessment for children and adults of HMs from the soil and dust through possible exposure pathways (ingestion, inhalation, and dermal contact) were shown in Table 5. For NCR effects of soil and dust, As, Cr and Mn posed the greatest NCR to adults and children, followed by Pb and Co, and then Ni, Cd, Zn and Cu. The HQ values of all HMs from soil and dust for adults were lower than 1, indicating that the NCRs of HMs in urban parks and schools of Jiazouo were acceptable. However, the NCRs of Mn, As (soil and dust) and Cr (dust) for children exceeded the threshold of 1, which suggested that these HMs posed NCR heath risk to children, especially As in soil (3.83) and dust (1.53). Thought the concentration of Cd and Zn were above the background values, the NCR and CR of them for adults and children were under the safety threshold. For the CR effects of soil and dust, the order of CR of HMs for both children and adults were Cr > Co > As > Ni > Cd in soil, while in dust were Cr > Co > Ni > As > Cd (Table 5). The CR probabilities for all 5

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Fig. 2. Spatial variations of heavy metals in total concentrations and percentage bioaccessibility of heavy metals in soil and dust from schools and parks of Jiaozuo, China.

6

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Fig. 2. (continued)

7

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in dust were higher than that in soil, indicating that the potential risk posed by dust still deserve public attention. As the greening rate of urban soil and the hardening rate of cement keep increasing, HMs in dust may represent a more probable exposure source for people than that in soil. Additionally, ingestion of soil and dust appeared to be the main exposure pathway for HMs to children and adult, followed by dermal contact and inhalation routes, respectively. This result is consistent with other studies (Yeganeh et al., 2013; Chabukdhara and Nema, 2013; Qing et al., 2015; Shahab A et al., 2018). Therefore, it is necessary for children to keep the hand and mouth clean and avoiding “hand eating” when they went out to play in urban parks and schools.

Table 4 Bioaccessiblity of heavy metals in the gastric phase and intestinal phase of soil and dust. HMs

Zn Cu Mn As Pb Cd Cr Ni Co

Type

Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust Soil dust

Gastric (%)

Intestinal (%)

Residual (%)

Range

Mean

Range

Mean

Range

Mean

5.07–18.71 22.41–47.84 2.43–27.55 1.42–14.93 16.46–40.80 17.79–44.95 5.97–27.77 12.62–27.37 7.49–41.48 18.02–78.04 0.98–11.84 12.02–35.81 0.90–11.84 3.09–10.69 4.93–13.40 10.20–26.09 3.38–10.85 3.46–38.64

11.35 36.17 8.69 5.61 26.31 29.63 15.78 21.12 20.48 35.76 13.26 20.57 3.55 6.57 8.69 16.82 6.74 17.39

1.96–10.82 5.77–22.87 3.13–32.88 1.83–29.15 10.50–30.31 14.13–39.89 5.59–21.87 14.20–26.93 0.30–3.69 1.85–36.79 1.19–36.64 7.78–36.54 1.15–5.68 3.72–15.71 4.04–10.92 10.61–23.23 1.77–5.47 2.90–29.02

5.95 15.34 10.76 8.43 18.16 24.34 13.19 19.17 1.23 18.32 15.30 21.00 2.89 7.84 6.73 15.44 3.63 14.66

73.39–89.24 30.79–71.82 39.57–94.44 55.92–96.56 28.89–72.23 15.17–66.08 50.85–88.45 47.75–68.47 56.17–91.55 5.11–77.30 37.45–89.58 35.83–74.89 83.15–97.95 76.46–93.19 76.69–91.02 50.68–79.18 83.68–94.84 35.13–93.32

82.70 48.50 80.55 85.96 55.54 46.04 71.04 59.71 78.29 45.92 71.43 58.43 93.56 85.59 84.58 67.74 89.63 67.95

4. Conclusion The concentration, bioaccessibilities and health risk assessment of Zn, Cu, Mn, As, Pb, Cd, Cr, Ni and Co from soil and dust in six urban parks and seven schools in Jiaozuo were investigated in this study. However, the results indicated that: (1) High concentration and high coefficient of variation suggested that HMs contamination from soil and dust were very serious in urban parks and schools of Jiaozuo and obviously disturbed by human activities. Among the HMs surveyed, Cd concentrations in soil and dust exceeded the background value by 17.83, 7.52 times respectively. (2) Subsequently, Pearson's correlation analysis and principal component analysis were conducted to identify the potential sources of HMs in soil and dust. HMs in soil with strong correlations reflected that the elements might have originate from common sources, which HMs in dust with weak correlations and PC1b, PC2b and PC3b both suggested that the pollution sources of the dust are

HMs to children and adults were under the acceptable level (< 1 × 10−4). Hazard quotients and hazard indices indicated no significant risk of CR effects to children and adults exposed to soil and dust in the urban parks and schools studied. Additionally, the NCR and CR effects of HMs (expect for Mn and As)

Table 5 Health risk assessment of heavy metals based on total heavy metals in soil and dust for adults and children. Sample type

Soil

Dust

Sample type

Soil Dust

Heavy metals

Zn Cu Mn As-non-cancer As-cancer Pb Cd-non-cancer Cd-cancer Cr-non-cancer Cr-cancer Ni-non-cancer Ni-cancer Co-non-cancer Co-cancer Zn Cu Mn As-non-cancer As-cancer Pb Cd-non-cancer Cd-cancer Cr-non-cancer Cr-cancer Ni-non-cancer Ni-cancer Co-non-cancer Co-cancer Type

Non-cancer Cancer Non-cancer Cancer

Adults

Children

HQing

HQinh

HQdermal

HI

5.69E-04 8.96E-04 1.82E-02 3.30E-01

2.32E-05 2.44E-05 3.71E-03 6.56E-03 4.15E-07 8.23E-05 7.37E-04 1.78E-08 1.36E-02 1.18E-05 9.27E-05 1.44E-07 1.56E-05 8.40E-07 8.73E-05 5.81E-05 3.60E-03 2.63E-03 1.66E-07 1.47E-04 1.75E-03 4.22E-08 2.61E-02 2.26E-05 1.34E-04 2.08E-07 2.21E-05 1.19E-06

1.73E-06 2.73E-06 1.78E-01 1.00E-03

5.94E-04 9.23E-04 2.00E-01 3.37E-01

4.58E-05 2.75E-06

1.53E-02 1.64E-03

1.07E-02

5.78E-02

9.07E-06

3.17E-03

1.64E-02

1.79E-02

6.52E-06 6.51E-06 1.73E-01 4.01E-04

2.23E-03 2.20E-03 1.94E-01 1.35E-01

8.20E-05 6.52E-06

2.73E-02 3.89E-03

2.05E-02

1.11E-01

1.31E-05

4.58E-03

2.31E-02

2.53E-02

1.51E-02 9.03E-04 3.35E-02 3.07E-03 1.53E-03 2.14E-03 2.14E-03 1.77E-02 1.32E-01 2.71E-02 2.14E-03 6.41E-02 4.43E-03 2.17E-03

Risk

4.15E-07 1.78E-08 1.18E-05 1.44E-07 8.40E-07

1.66E-07 4.22E-08 2.26E-05 2.08E-07 1.19E-06

Adults

HQing

HQinh

HQdermal

HI

6.52E-03 1.03E-02 2.08E-01 3.78E + 00

1.38E-04 1.44E-04 2.20E-02 3.89E-02 6.15E-07 4.88E-04 4.37E-03 2.64E-08 8.09E-02 1.75E-05 5.50E-04 2.14E-07 9.27E-05 1.25E-06 5.17E-04 3.45E-04 2.14E-02 1.56E-02 2.46E-07 8.73E-04 1.03E-02 6.25E-08 1.55E-01 3.35E-05 7.94E-04 3.09E-07 1.31E-04 1.76E-06

2.66E-05 4.18E-05 2.73E + 00 1.54E-02

6.68E-03 1.04E-02 2.96E + 00 3.83E + 00

7.03E-04 4.22E-05

1.74E-01 1.48E-02

1.64E-01

6.28E-01

1.39E-04

3.58E-02

2.51E-01

2.69E-01

1.00E-04 9.99E-05 2.66E + 00 6.15E-03

2.51E-02 2.49E-02 2.88E + 00 1.53E + 00

1.26E-03 1.00E-04

3.12E-01 3.50E-02

3.14E-01

1.20E + 00

2.01E-04

5.18E-02

3.55E-01

3.80E-01

1.73E-01 1.03E-02 3.83E-01 3.52E-02 1.76E-02 2.45E-02 2.45E-02 2.02E-01 1.51E + 00 3.10E-01 2.45E-02 7.34E-01 5.08E-02 2.48E-02

Risk

6.15E-07 2.64E-08 1.75E-05 2.14E-07 1.25E-06

2.46E-07 6.25E-08 3.35E-05 3.09E-07 1.76E-06

Children

HQing

HQinh

HQdermal

HI

4.04E-01

2.49E-02

2.06E-01

6.35E-01

2.54E-01

3.46E-02

2.17E-01

5.06E-01

Risk 1.32E-05 2.42E-05

8

HQing

HQinh

HQdermal

HI

4.62 E+00

1.48E-01

3.16 E+00

7.93 E+00

2.91 E+00

2.05E-01

3.33 E+00

6.44 E+00

Risk 1.96E-05 3.58E-05

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CRediT authorship contribution statement Qiao Han: Investigation, Conceptualization, Writing - original draft, Formal analysis, Data curation, Writing - review & editing. Mingshi Wang: Conceptualization, Methodology, Supervision, Project administration, Writing - review & editing. Jingli Cao: Investigation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Chenlu Gui: Investigation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing. Yanping Liu: Formal analysis, Data curation. Xiangdong He: Formal analysis, Data curation. Yuchuan He: Formal analysis, Data curation. Yang Liu: Investigation, Formal analysis. Declaration of competing interest None. Acknowledgments This work was supported by the Foundation of Henan Educational Committee (16A170001), the Excellent Youth Foundation of Henan Polytechnic University, the Fundamental Research Funds for the Universities of Henan Province (NSFRF1631) and Foundation of Key scientific research project of colleges and universities in Henan province (20A170009). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.110157. References Ai, Y., Li, X., Gao, Y., Zhang, M., Zhang, Y., Zhang, X., 2018. In vitro bioaccessibility of potentially toxic metals (PBET) in baoji urban soil (NW China) from different functional areas and its implication for health risk assessment. Environ. Geochem. Health 1–19. Ali, M.H., Mustafa, A.-R.A., El-Sheikh, A.A., 2016. Geochemistry and spatial distribution of selected heavy metals in surface soil of Sohag, Egypt: a multivariate statistical and GIS approach. Environ. Earth Sci. 75, 1257. Alsbou, E., Al-Khashman, O., 2018. Heavy metal concentrations in roadside soil and street dust from Petra region, Jordan. Environ. Monit. Assess. 190 (1), 48. Andrew, T., Ip, K., 2007. Bioaccessibility of metals in dust from the indoor environment: application of a physiologically based extraction test. Environ. Sci. Technol. 41, 7851–7856 (in Chinese). Antoci, A., Galeotti, M., Sordi, S., 2018. Environmental pollution as engine of industrialization. Commun. Nonlinear Sci. Numer. Simul. 58, 262–273. Bargagli, R., 1998. Trace Elements in Terrestrial Plants: an Ecophysiological Approach to Biomonitoring and Biorecovery. Springer-Verlag, Berlin, Germany. Bi, X., Liang, S., Li, X., 2013. A novel in situ method for sampling urban soil dust: particle size distribution, trace metal concentrations, and stable lead isotopes. Environ. Pollut. 177, 48–57. Borůvka, L., Vacek, O., Jehlička, J., 2005. Principal component analysis as a tool to indicate the origin of potentially toxic elements in soils. Geoderam 128, 289–300. Chabukdhara, M., Nema, A.K., 2013. Heavy metals assessment in urban soil around industrial clusters in Ghaziabad, India: probabilistic health risk approach. Ecotoxicol. Environ. Saf. 87, 57–64. Chen, M., Chen, J., Li, D., 1997. Sources Apportionment of High Concentrated Atmospheric Particulates in Shanghai Urban. Shanghai Academy of Environment Sciences, pp. 15–17. Chen, H., Teng, Y., Lu, S., Wang, Y., Wang, J., 2015. Contamination features and health risk of soil heavy metals in China. Sci. Total Environ. 512–513, 143–153.

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