Release risk assessment of trace metals in urban soils using in-situ DGT and DIFS model

Science of the Total Environment 694 (2019) 133624

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Release risk assessment of trace metals in urban soils using in-situ DGT and DIFS model Dongyu Xu a,b, Bo Gao a,b,⁎, Song Chen c, Wenqi Peng a,b, Min Zhang b, Xiaodong Qu a,b, Li Gao b, Yanyan Li b a b c

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China Department of Water Environment, China Institute of Water Resources and Hydropower Research, Beijing 100038, China China Construction Water & Environment Company Limited, Beijing 100037, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Igeo and RAC were used to evaluate pollution risk and mobility of trace metals in urban soils. • Influence of Cu in urban soils should not be neglected as per Igeo and RAC assessments. • DGT/DIFS was used to assess the release risk of labile-Cu in urban soils. • Parameters of DIFS model indicated that the release risk of Cu was low in urban soils.

a r t i c l e

i n f o

Article history: Received 2 June 2019 Received in revised form 21 July 2019 Accepted 26 July 2019 Available online 26 July 2019 Editor: Jay Gan Keywords: Trace metals Megacity Urban soils Release risk Diffusive gradients in thin-films (DGT) DIFS model

a b s t r a c t Urbanization and urban construction lead to entensive environmental deterioration. Trace metals in urban soils pose a threat to urban water bodies and local populations. However, the release ability of labile metals and their release risk in urban soils remains unclear. Here, soils were collected from different functional zones in the Pingshan District (PSD) of Shenzhen. Based on results of soil properties, total contents of trace metals, geochemical index (Igeo), and risk assessment code (RAC), diffusive gradients in thin films (DGT) and DGT-induced fluxes in soil (DIFS) model were further used to assess the release risk of trace metals in urban soils. The results showed that the average total concentrations of trace metals (As, Cr, Cu, Pb, and V) were higher than the local soil background values, implying that trace metals accumulated in urban soils. However, the distributions of labile metals determined by DGT were not similar to those of total metal concentrations. Except for As, urban soils from PSD sites exhibited “uncontaminated to moderately contaminated” levels based on the average values of Igeo. Moreover, the pollution and migration of Cu in urban soils are problematic as evidenced by the Igeo and RAC assessments. Release ability of Cu was assessed using parameters of DIFS model (i.e., bioavailability concentrations (CE), resupply ability (R), response time (Tc), desorption rate (k−1), and sorption rate (k1)). Residential areas showed high CE values for Cu, while the resupply ability was low. Furthermore, considering the influences of R, Tc, k−1, and k1, membership function value was used to re-calculate the order of CE in urban soils. The final results

⁎ Corresponding author at: State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China. E-mail address: [email protected] (B. Gao).

https://doi.org/10.1016/j.scitotenv.2019.133624 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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D. Xu et al. / Science of the Total Environment 694 (2019) 133624

suggested that the agricultural zone exhibited the highest release risk among soils from various functional zones. Therefore, DGT and DIFS model should be effective tools to assess the release risk of trace metals in urban soils. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Urban areas have high population densities and intensive anthropogenic activities, such as irrigation, fertilization application, and pesticide use, resulting in large amounts of different toxic pollutants being discharged (Alloway, 2013; Pitt et al., 1995). The pollutants in urban soils are, therefore, a significant environmental issue, and have received considerable attention on a global scale (Alloway, 2013; Jiang et al., 2017). Trace metals are one of these pollutants, and their contamination levels in urban soilshave been the focus of many studies (Chen et al., 2010; Jiang et al., 2017; Li et al., 2017; Wei et al., 2015). Jiang et al. (2017) suggested that Cr, Cu, Cd, Pb, Ni, and Co are accumulated in township soils in Jiangsu Province in China due to anthropogenic inputs; Li et al. (2017) found that the concentrations of heavy metals (Pb, Zn, Cu, Ni, Cd, and Cr) in the urban street dust of Chengdu are higher than the local soil background values, revealing that these heavy metals mainly originate from anthropogenic sources. Similar conclusions were also drawn in studies conducted on other Chinese megacities (Chen et al., 2010; Wei et al., 2015). Thus, trace metals accumulation in urban soils is an important environmental issue as these elements are persistent, non-biodegradable, and highly toxic (Alloway, 2013). Previous studies have mainly focused on the concentrations and distribution, pollution assessment, and ecological or health risks posed by trace metals in urban soils (Zheng et al., 2016; Wei et al., 2015; Wang et al., 2019a; Li et al., 2017). Wei et al. (2015) collected street dusts from different functional zones to study the distribution, accumulation, and health risk of heavy metals in Beijing; Wang et al. (2019) studied the contents of ten heavy metals in 413 topsoil samples to investigate their spatial distribution and source apportionment in urban soils in a typical county-level city Li et al. (2017) investigated pollution characteristics and risk assessments of human exposure to the oral bioaccessibility of heavy metals in urban street dusts from different functional areas in Chengdu. These studies, however, had the following limitations: (1) the experiments and assessments were all based on total metal concentrations determined from ex-situ experiments (e.g., sequential chemical extraction) and (2) total concentrations of trace metals did not reflect the release ability and mobility of trace metals in urban soils (Liang et al., 2014; Roulier et al., 2010). These chemical methods did not consider the bioavailability of trace metals and their concentrations in the soils/sediments. Recently, several studies found that the investigations using the diffusive gradients in thin films (DGT) technique can provide in-situ data to determine the labile metals in soils/sediments (Chen et al., 2018; Gao et al., 2019; Jin et al., 2019; Sun et al., 2018; Xu et al., 2019). The resupply ability of labile metals from the soil could be obtained using DGT-induced fluxes in soil (DIFS) model, and the release ability of metals could also be assessed. In addition, the DIFS model for DGT has been used to (1) reveal the kinetic resupply ability of trace metals from soil solids to porewaterand (2) derive the effective concentration (CE), that is, the hypothetical porewater concentration that required to accumulate the measured amount of an element on DGT resin if there was solely diffusional supply (Williams et al., 2012). Therefore, it can be presumed that combining DGT and DIFS model should be effective for improved assessment of the release risk of trace metals in urban soils. For this study, a typical urbanized study district (Pingshan District, PSD, which is a new development district) was selected in Shenzhen, southern China. The urban soils from four different functional zones (different soil type for urban development) were collected. The main objectives of this study were to: (1) investigate pollution status of

trace metals in urban soils from different functional zones in PSD and (2) evaluate the release risk of trace metals in urban soils with the help of in-situ DGT and DIFS models.

2. Materials and methods 2.1. Sample collection PSD is a new district with both traditional and high-tech industries in Shenzhen. A previous study indicated that the PSD soils exhibited low risk from trace metals (Wu et al., 2016). Therefore, we selected six typical areas in PSD with different functions. Soil samples (0–20 cm) were collected using a stainless-steel shovel in May of 2018; the sampling locations are shown in Fig. 1. Soils weighting 500 g were collected from each sampling site, with three to five replicates of each sample. These sites corresponded to different functional zones in PSD. Sampling sites are expressed as PSD-1 to PSD6. Site PSD-1 has soils from an agricultural demonstration zone, sites PSD-2 and PSD-3 are from residential areas, sites PSD-4 and PSD-5 include farmlands downstream of the river, and PSD-6 is from a green belt along a street. The soil samples were collected from each functional zone using a sampling grid, mixed and stored in labeled polyethylene bags before being transported to the laboratory. All soil samples were freeze-dried, gently crushed and ground in an agate mortar, and passed through a 0.25 mm nylon sieve for further analysis.

2.2. Analytical methods A strong acid digestion method (HNO3+H2O2+HF) was used to analyze the total concentrations of trace metals in the soils. Three replicates of each soil sample were subjected to the treatment. The details of this method have been provided by Wei et al. (2015). Inductively coupled plasma mass spectrometry (ICP-MS DRC-e; PerkinElmer, USA) was used to analyze the total concentrations of trace metals. Quality assurance and control information is given in Table S1, and the certified reference materials for the soils (GSS-15) were produced by the Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. Analytical reagent blanks were prepared for each digestion batch and then analyzed for the same elements. The average recoveries of the different metals were in the range of 98.1% to 100.4% (Table S1). The acid-soluble/exchangeable fraction (F1) of the metals was determined using the first step of the three-stage European Community Bureau of Reference (BCR) sequential extraction method (Pueyo et al., 2001). The ratio of F1 to the total trace metal concentration was applied to assess the mobility of the trace metals in the soils. F1 is the fraction of the available pool of trace metals in the solid phase and reflects the potential metal risk posed. The F1 percentage values were used to investigate the environmental risk posed by the nonstable forms of the metals Based on these values, there are five risk levels: no risk (b 1%), low risk (1–10%), medium risk (11–30%), high risk (31–50%), and very high risk (N 50%) (Maiz et al., 2000). The pH values of the soil samples were measured at a soil/water ratio of 1:5 (g/mL) using a pH meter (Mettler Toledo, Switzerland). The contents of C, H, and N in the soils were measured using an elemental analyzer (Elementar Vario ELIII, Germany). A particle size analyzer was used to measure the contents of clay, silt, and sand.

D. Xu et al. / Science of the Total Environment 694 (2019) 133624

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Fig. 1. Sampling sites in different functional zones in Shenzhen.

the surface of the DGT was rinsed with deionized water. The binding resin gel was eluted with 3 mL of 1 M HNO3 for 24 h. Then, the soil solution obtained from centrifuging the soil sample at 3000 rpm for 15 min, which was then filtered through a 0.45um membrane. The trace metal contents in the eluent and soil solution (Csol) were measured by ICP-MS (Agilent 7700×). The concentrations of labile metals (CDGT) were calculated using the following equations:

2.3. Pollution risk assessment using geoaccumulation index The geoaccumulation index (Igeo) has been used to evaluate metal contamination in soils since the late 1960s (Müller, 1969). The Igeo is calculated using Eq. (1): Igeo ¼ log2

Cn ; 1:5Bn

ð1Þ

where Cn is the measured concentration of trace metal (n) in the soil samples, Bn is the geochemical background values of element n in soils in Shenzhen (CNEMC, 1990), and 1.5 is the background matrix correction factor. The contamination levels corresponding to the obtained values of Igeo are as follows: Igeo b 0 indicates uncontaminated, 0 ≤ Igeo b 1 indicates uncontaminated to moderately contaminated, 1 ≤ Igeo b 2 indicates moderately contaminated, 2 ≤ Igeo b 3 indicates moderately to strongly contaminated, 3 ≤ Igeo b 4 indicates strongly contaminated, 4 ≤ Igeo b 5 indicates strongly to extremely contaminated, and Igeo ≥ 5 indicates extremely contaminated. 2.4. DGT deployment and DIFS simulation 2.4.1. DGT deployment Standard-piston DGT devices were used in this study. The devices were equipped with a 3.14 cm2 exposure window. The pistons included a binding gel layer (including Chelex-100 and ZrO gel), a diffusive gel layer, and a filter membrane. (1) Treatment of soil samples according to Luo et al. (2014): soil samples (60 g) were moistened to 60% of their maximum water holding capacity (MWHC) and equilibrated for 48 h. Then, the soil samples were moistened to 100% of their MWHC for another 24 h. Each soil sample was treated in triplicate. (2) The DGT device deployment: the surfaces of the DGT devices were coated with the treated soils and then pressed carefully onto the soil paste to ensure the soil and the filter membrane of the DGT surface were completely in contact. The DGT deployment was maintained at 25 ± 1 °C for 24 h. (3) Binding resin gel treatment: after the retrieval of the DGT pistons,

M¼


C e V gel þ V acid ; fe

ð2Þ

M  Δg ; DAt

ð3Þ

C DGT ¼

where Ce is the concentration of the metals in the eluent, (mg/L); Vgel is the volume of the binding gel, (L); Vacid is the volume of the eluent, (L); fe is the elution factor for the metal; M is the mass accumulated on the binding gel; Δg is the thickness of the diffusive gel layer and filter membrane; A is the surface area of the gel layer (cm2); t is the deployment time for the DGT devices; and D is the diffusion coefficient of the metals at 25 °C. The ratio (R) of CDGT to Csol (Eq. (4)) of trace metals indicates the potential resupply ability from the soil solid to the soil solution. The equation is as follows: R¼

CDGT Csol

ð4Þ

2.4.2. DIFS simulation DIFS model was used to investigate the kinetic exchange of metals at DGT/soil interface. The effective diffusion coefficient in soil (Ds), soil porosity (φs), diffusion layer porosity (φd), diffusive layer thickness (Δg) and particle concentration (Pc) in soil were measured or derived for DIFS simulation using the methods by reference (Harper et al., 2000). Kdl is the distribution coefficient based on labile-metal solid phase components that can exchange with the solution phase (Csol). Kdl and the

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D. Xu et al. / Science of the Total Environment 694 (2019) 133624

response time (Tc) are used to describe the adsorption (rate constant, k1) and desorption (rate constant, k−1) kinetics. The details of the parameters are listed in Table 3. The relevant equations are as follows (Guan et al., 2017; Xu et al., 2019): Kdl ¼

Tc ¼

Cs 1 k1 ¼ Csol Pc k−1

ð5Þ

1 k1 þ k−1

ð6Þ

2.5. Statistical analysis The descriptive and correlation analyses in this study were performed using the software package SPSS 17.0 and Sigmaplot 10.0. Pearson's correlation tests were performed for the independent variables using SPSS 17.0 at a significance level of p b 0.05 and p b 0.01. 3. Results and discussion 3.1. Soil properties in urban soils The physicochemical properties of the soil samples are presented in Table S2. The pH values of the soils ranged from 6.46 to 8.26. Exception for PSD-1, the soils from the other sampling sites were slightly alkaline. In addition, the soil samples were rich in sand, with the sand content

ranging from 72.26% to 94.30%. There were relatively high contents of carbon (C) at PSD-5 (1.35%) and PSD-4 (1.16%). Additionally, the molar ratios (H/C) of elements were calculated. The high H/C ratios indicate low aromaticity of the soils, while the low ratios indicate high aromaticity (Xu et al., 2014). Among different soil sampling sites (Table S2), the H/C ratios of PSD-4 and PSD-5 were lower than those of the other sampling sites, suggesting that the aromaticity of these two sampling sites should be higher than those of the other sampling sites. The spatial distributions of the six trace metals in PSD soils are presented in Table 1 and Fig. 2. The average concentrations (range) of these trace metals were 16.78 mg/kg (3.30–37.63 mg/kg) for As, 76.08 mg/kg (16.41–120.82 mg/kg) for Cr, 29.67 mg/kg (12.70–44.84 mg/kg) for Cu, 71.55 mg/kg (26.19–95.30 mg/kg) for Pb, and 82.92 mg/kg (21.34–113.91 mg/kg) for V. The mean concentrations of As, Cr, Cu, Pb, and V were 1.8-fold, 2.9-fold, 2.8-fold, 1.8-fold, and 1.8fold higher than their corresponding background values in soils in Shenzhen (CNEMC, 1990), indicating that trace metals were accumulated in the PSD soils. In addition, As and Cu belonged to an intermediate group (3.30–44.84 mg/kg), whereas Pb, V, and Cr comprised a group with much higher concentrations (16.41–342.95 mg/kg). Compared to the Grade II criterion of the National Environmental Quality standards for soils in China (CEPA, 2018) (Table 1), the concentrations of trace metals at all the sampling sites did not exceed this standard (CEPA, 2018). Table 1 also lists the trace metal's concentrations in urban soils from Chinese cities, as well as other countries. Except for the concentrations

Table 1 Concentrations of trace metals and labile metals in urban soils. As

Cr

Cu

Puning (Guangdong urban soil) Dongguan (urban soil) Hangzhou (urban soil) Nanjing (urban soil) Shanghai (urban soil) Hong Kong (urban soil) Shenzhen (urban soil) Tianjin (urban soil) Tyumen, Russia Brightside garden, England Oughtibridge allotments, England Handsworth & Richmond allotments (England) Naples, Italy Moscow, Russia Oslo, Norway Baltimore, USA World-soil average

3.30 7.31 6.80 33.96 37.63 11.66 16.78 3.30 37.63 9.10 ≤40 ≤25 – 8.55 – – – 7.89 13.33 – – – – – 11.00 8.40 23.93 27.57 28.67 12.00 6.30 4.50 – 6.80

16.41 86.64 69.70 79.96 82.99 120.82 76.08 16.41 120.82 27.50 ≤150 ≤250 35.60 60.30 22.40 78.50 – 22.50 74.90 47.50 84.70 107.90 23.10 – 81.00 106.90 123.33 87.33 99.00 11.00 70.00 29.00 38.00 59.50

Concentrations of trace metals 12.70 57.13 21.34 44.84 82.01 93.83 39.04 83.09 85.19 24.31 95.30 91.40 34.31 85.61 91.88 22.81 26.19 113.91 29.67 71.55 82.92 12.70 26.19 21.34 44.84 95.30 113.91 10.70 39.20 45.70 ≤50 ≤70 – ≤100 ≤170 – 23.70 28.60 – 31.30 33.70 – 41.60 65.40 – 176.00 240.00 – 62.57 108.55 – 11.10 42.40 38.90 66.64 160.27 – 41.00 75.70 – 66.10 107.30 – 59.25 70.69 – 23.30 94.60 – 28.33 53.59 – 33.00 45.000 – 40.80 28.30 86.80 64.33 261.33 – 56.00 157.00 – 77.67 199.67 – 74.00 141.00 52.00 40.00 47.00 56.00 24.00 34.00 52.00 35.00 89.00 31.00 38.90 27.00 129.00

Average values (mg/kg) Minimum values (mg/kg) Maximum values (mg/kg)

6.81 0.28 19.97

0.78 0.71 0.82

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6 Average values (mg/kg) Minimum values (mg/kg) Maximum values (mg/kg) Background values (mg/kg) EQSa (5.5 b pH ≤ 6.5)-II grade EQSa (pH N 7.5)-II grade Beijing (urban soil) Guangzhou (urban soil)

a

EQS: Environmental quality standard for soils (GB 15618-2018).

1.10 0.50 2.99

Pb

V

Concentrations of labile metals 0.38 5.30 0.27 3.60 0.53 6.99

References This study This study This study This study This study This study This study This study This study CNEMC, 1990 CEPA, 2018 CEPA, 2018 Zheng et al. (2008) Yuan et al. (2014) Cai et al. (2013) Duzgoren-Aydin et al., 2006 Lu et al. (2007) Wang et al. (2019) Wu et al. (2016) Zhang et al. (2004) Lu et al. (2003) Shi et al. (2008) Li et al. (2004) Shi et al. (2007) Zhao et al. (2013) Konstantinova et al. (2019) Weber et al. (2019) Weber et al. (2019) Weber et al. (2019) Cicchella et al. (2008) Kosheleva et al. (2018) Tijhuis et al. (2002) Yesionis et al. (2008) Kabata-Pendias (2011)

This study This study This study

D. Xu et al. / Science of the Total Environment 694 (2019) 133624

20 15 10 5 0

0

80 60

0.75

4

120

Cu

40 30

2

20 1 10 0

Total concentrations (mg/kg)

Total concentrations CDGT-Cu

0

100

0.55 0.50

60

0.45 0.40

20

0.35 0.30 0.25 PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

500

9 8

100 80

7

60

6

20

5 4

Total concentrations (mg/kg)

V

0

Pb

0

CDGT-V (μg/L)

Total concentrations (mg/kg)

Total concentrations CDGT-V

0.60

Total concentrations CDGT-Pb

80

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

120

0.70 PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

3

140

0.80

20

0

CDGT-Cu (μg/L)

Total concentrations (mg/kg)

50

0.85

100

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

60

0.90

Cr

CDGT-Pb (μg/L)

10

120

Total concentrations CDGT-Cr

50

Total concentrations CDGT-Zn

Zn

400

40

300 30 100

CDGT-Zn (μg/L)

30

Total concentrations (mg/kg)

140

As

CDGT-As (μg/L)

Total concentrations (mg/kg)

Total concentrations CDGT-As

CDGT-Cr (μg/L)

25

40

5

20 0

3

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

Sampling sites Fig. 2. Distributions of total metal concentrations and labile metal concentrations.

of As at PSD-4 (33.96 mg/kg) and PSD-5 (37.63 mg/kg), the As values were lower than other urban soils in China (Table 1). For Cr in PSD soils, the mean value was higher than that in Beijing, Hangzhou, Hong Kong, and other cities in Guangdong Province (including Guangzhou, Puning, and Dongguan), while, lower than that in Nanjing and Shanghai. The Cr concentrations in urban soils of PSD were as followed: PSD-6 N PSD-2 N PSD-5 N PSD-4 N PSD-3 N PSD-1. For Cu in PSD soils, the mean value was closed to the concentrations in several metropolis cities of China (e.g., Shenzhen, Beijing, and Tianjin), and lower than the other cities in Guangdong Province and other counties' soils. The concentrations of Cu in urban soils of PSD were as followed: PSD-2 N PSD-3 N PSD-5 N PSD-4 N PSD-6 N PSD-1. For Pb in PSD soils, the mean value was lower than that in Beijing, Shanghai, Shenzhen, and Tianjin these four metropolis cities of China. The concentrations of Pb in urban soils of PSD were as follows: PSD-4 N PSD-5 N PSD-3 N PSD-2 N PSD-1 N PSD-6. Moreover, the mean values of As, Cr, and Pb in PSD soils were higher than that of world-soil average values, and the mean value of V was lower than that of world-soil average value (Kabata-Pendias, 2011). Distributions of different trace metals in PSD soils were different, which may be attributed to the different anthropogenic activities in urban area. The DGT technique has been identified as an effective method to obtain the information on labile metals in soils. In this study, the levels of labile metals (DGT-metals) at each sampling site are shown in Fig. 2. The

averages (range) of the labile metals (CDGT-metal) were 6.81 μg/L (0.28–19.97 μg/L) for As, 0.78 μg/L (0.71–0.82 μg/L) for Cr; 1.10 μg/L (0.50–2.99 μg/L) for Cu, 0.38 μg/L (0.27–0.53 μg/L) for Pb, and 5.30 μg/L (3.60–6.99 μg/L) for V.

Table 2 Igeo and RAC assessments of trace metals in urban soils. Sampling sites

As

Cr

Cu

Pb

V

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6 Average values

−2.05 −0.90 −1.01 1.32 1.46 −0.23 −0.23

−1.33 1.07 0.76 0.95 1.01 1.55 0.67

Igeo −0.34 1.48 1.28 0.60 1.10 0.51 0.77

−0.04 0.48 0.50 0.70 0.54 −1.17 0.17

−1.68 0.45 0.31 0.41 0.42 0.73 0.11

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6 Average values

4.85 1.73 1.42 2.35 3.16 0.34 2.31

7.41 2.31 2.22 1.47 1.20 0.80 2.57

RAC % 2.24 19.66 11.54 1.43 2.20 2.52 6.60

1.08 7.54 5.05 0.54 0.36 2.10 2.78

0.41 0.05 0.05 0.07 0.11 0.03 0.12

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D. Xu et al. / Science of the Total Environment 694 (2019) 133624

Steady state case 0.3

metal concentration (Jain, 2004; Wang et al., 2019a). The RAC values could also be used to assess the mobility of trace metals in the soils (Gao et al., 2019), which could be represented by the mobility coefficient (the ratio of the F1 fraction by BCR sequence extraction and the total metal concentration) (Maiz et al., 2000) (Table 2). The assessment results of the mobility of trace metals are listed in Table 2. The average RAC values of each metal were 2.31% for As, 2.57% for Cr, 6.60% for Cu, 2.78% for Pb, and 0.12% for V. Excepted for V, other trace metals exhibited low risk in urban soils. The order of the RAC values of different metals was Cu N Pb N Cr N As N V (Table 2 and Fig. 3). This result indicates that the mobility of Cu is the highest among trace metals in this study. In addition, the mobility of Cu using RAC in different sampling sites was as followed: PSD-2 N PSD-3 N PSD-6 N PSD-1 ≈ PSD-5 N PSD-4. Based on the combined the Igeo and RAC values, the release dynamics of Cu from soil solid to soil solution was further investigated.

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

0.4

R

Non steady state case

0.2

0.1

0.0 0

4

8

12

16

20

24

3.4. Release dynamic assessment by DIFS model

Times (h) Fig. 3. Time-dependence of the measured R values for Cu in urban soils and best-fit model lines obtained by DIFS model.

3.2. Pollution assessment of trace metals in urban soils The calculated Igeo value for each trace metal is also presented in Table 2. In general, the average Igeo values were −0.23 for As, 0.67 for Cr, 0.77 for Cu, 0.17 for Pb, and 0.11 for V (Table 2). The average Igeo values thus followed the sequence of Cu N Cr N Pb N V N As. Using the criteria of contamination indicator in urban soils based on Igeo to assess the trace metal contamination of urban soils in PSD, except for As, other trace metals were all at the “uncontaminated to moderately contaminated” level. In addition, it is worth noting that trace metals in the soils from several sampling sites exhibited “moderately contaminated” levels: As at PSD-4 (1.32) and PSD-5 (1.46); Cr at PSD-2 (1.07), PSD-5 (1.01), and PSD-6 (1.55); Cu at PSD-2 (1.48), PSD-3 (1.28), and PSD-5 (1.10). These results revealed that Igeo values of trace metals in the soils from residential areas (PSD-2) and farmlands (PSD-4 and PSD-5) were higher than that in other functional zones. This could be associated with a large number of human activities in these two functional zones. To determine whether or not trace metals in these functional zones could be released and migrated, their release ability and mobility needed further evaluation.

3.3. Migration ability of trace metals in urban soils The risk assessment code (RAC) was used to investigate the environmental risk posed by the nonstable forms of the metals. The F1 fraction of trace metals exhibited the highest mobility in soils/sediments, which represented the fraction of the available pool of trace metals in the solid phase and reflects the potential metal pool. Thus, the RAC values were calculated as the ratio of F1 to the total

3.4.1. Dynamic process simulation by DIFS model According to the Igeo and RAC values, the release ability of Cu was evaluated by combining the DIFS model and DGT technique in urban soils. The parameters simulated from DIFS model are listed in Table 3. The CE value of Cu in urban soils was as follows: PSD-3 N PSD-1 N PSD-5 N PSD-2 ≈ PSD-4 ≈ PSD-6, indicating that Cu from residential area soils exhibited the highest bioavailability (Table 3). In addition, time-dependence of different R values for Cu in urban soils is presented in Fig. 3. These parameters (R, T c , k1, and k−1) were used to assess the kinetic characteristics of Cu release ability in urban soils. From Table 3, it is evident that the k1 values were two to three orders of magnitude higher than those of k−1 in urban soils, indicating that the adsorption rate was significantly higher than desorption rate in the soil solid phase. Additionally, all of R values in urban soils were not close to 1, indicating that the urban soil did not belong to sustained case. Consequently, according to time-dependence of the measured R values for Cu in urban soils in Fig. 3, the resupply mechanism of Cu in PSD soils was attributed to “partial case” (Harper et al., 2000). PSD-6 belonged to the steady state case of “partial case” while the other soils belonged to the non-steady state case (Harper et al., 2000). Moreover, the R values of Cu exhibited a positive correlation with the Mn concentrations in pore water (r = 0.930, p b 0.01) (Fig. S1), indicating that the resupply ability of Cu may be controlled by Mn-oxide in urban soils. Furthermore, the R values of Cu in PSD soils were as followed: PSD-6 N PSD-5 N PSD-2 ≈ PSD-4 N PSD-3 N PSD-1, which was different from the order of CE. Therefore, the membership function method was applied to ascertain the final CE order in PSD soils. 3.4.2. Release risk assessment by membership function method To overcome the influence of the dimensions, and variations in variables, and numeric values, the membership function method was used to assess the release risk based on DGT/DIFS model. The CE can be normalized between 0 and 1, considering the parameters of R, Tc, k1, k−1,

Table 3 Parameters for Cu in urban soils derived from DIFS model. Sampling sites

R

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

0.07 0.11 0.09 0.11 0.24 0.40

Kdl (cm3/g)

Pc (g/cm3)

19.40 2055.52 130.22 70.49 184.93 426.73

1.8975 2.1322 2.2676 1.5773 1.4164 1.3532

ɸd

ɸs

0.95 0.95 0.95 0.95 0.95 0.95

0.5827 0.5541 0.5389 0.6269 0.6517 0.6620

Dd (cm2/s)

Ds (cm2/s)

6.59E-06 6.59E-06 6.59E-06 6.59E-06 6.59E-06 6.59E-06

3.17E-06 3.02E-06 2.95E-06 3.41E-06 3.55E-06 3.61E-06

Tc (s)

k−1 (s−1)

k1 (s−1)

Rdiff

CE (μg/L)

96,810 19,050 28,920 28,150 3891 855.7

2.81E-07 1.20E-08 1.17E-07 3.20E-07 9.81E-07 2.02E-06

1.03E-05 5.25E-05 3.46E-05 3.55E-05 2.57E-04 1.17E-03

0.06 0.05 0.05 0.06 0.06 0.07

19.89 8.95 60.67 8.70 15.33 8.17

D. Xu et al. / Science of the Total Environment 694 (2019) 133624

Acknowledgements

Table 4 The membership function values of five function zones.

PSD-1 PSD-2 PSD-3 PSD-4 PSD-5 PSD-6

7

R

Tc

k−1

k1

Rdiff

Σμ (m)

Order

1.0000 0.8788 0.9394 0.8788 0.4848 0.0000

1.0000 0.1896 0.2925 0.2845 0.0316 0.0000

0.8660 1.0000 0.9477 0.8466 0.5174 0.0000

1.0000 0.9636 0.9790 0.9783 0.7873 0.0000

0.5000 1.0000 1.0000 0.5000 0.5000 0.0000

4.3660 4.0320 4.1586 3.4881 2.3212 0.0000

1 3 2 4 5 6

and Rdiff obtained from DIFS model. The membership function values of each parameter were calculated with the following equations previously reported in Guo et al. (2017): μ ðmi Þ ¼ ðmi −mimin Þ=ðmimax −mimin Þ

ð7Þ

μ ðmi Þ ¼ 1−ðmi −mimin Þ=ðmimax −mimin Þ

ð8Þ

M ¼ Σ μ ðmi Þ

ð9Þ

where mi is the corresponding value of each parameter; mimin and mimax are the minimum value and maximum values, respectively, of Cu's corresponding values for each parameter; M is the sum of the membership function values for each parameter. If CE was positive correlated with other influence factors, Eq. (8) was used to calculate the μ values. If not, Eq. (7) was used to calculate the μ values. The results of the calculations are listed in Table 4. Higher M value indicated a higher the release risk of the element. From Table 4, the order of membership function values in different functional zones was: PSD-1 N PSD-3 N PSD-2 N PSD-4 N PSD-5 N PSD-6. This order suggested that the release risk of Cu in agricultural zone and residential area should not be neglected. This resupply assessment illustrated that although the bioavailability of Cu in urban soils was high, its low resupply ability represents a low release risk.

4. Conclusions In this study, DGT technique and traditional assessment methods were combined to assess the release ability of trace metals in urban soils. In general, most soils in PSD were slightly alkaline, and the elemental compositions of C, H, and N revealed that the aromaticity of soils at PSD-4 and PSD-5 was higher than at other sampling sites due to lower H/C ratios. In addition, the average concentrations of As, Cr, Cu, Pb, and V were higher than their corresponding soil background values for soils in Shenzhen. Except for the total concentration of As in the soils at PSD-4 and PSD-5, the trace metals did not exceed the Grade II criterion of the National Environmental Quality standards for soils in China. In addition, it was noted that trace metals in the soils from several sampling sites exhibited “moderately contaminated” levels using Igeo assessment. Both Igeo and RAC assessments all revealed that the pollution and migration ability of Cu in urban soils should not be neglected. Moreover, the release ability of Cu in urban soils was assessed using parameters of DIFS model. The high CE values were obtained for soils in residential areas, while these soils did not exhibit high resupply ability (according to the R, Tc, k1, and k−1 parameters). Considering the influences of these parameters from DIFS model, membership function value was used to re-calculate the order of CE in urban soils. Based on CE values and parameters from DIFS model, it was determined that the release risk of Cu in agricultural demonstration zone is the highest among soils from different function zone. Therefore, DGT and DIFS model can be used as effective tools to assess the release risk of trace metals in urban soils.

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