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Establishing a health risk assessment for metal speciation in soil—A case study in an industrial area in China
Ecotoxicology and Environmental Safety 166 (2018) 488–497
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Establishing a health risk assessment for metal speciation in soil—A case study in an industrial area in China
T
⁎
Yimei Zhanga,c, , Jie Chenb,c, Liqun Wanga,c, Yalong Zhaoa,c, Ping Oub, WeiLin Shib a
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China Suzhou University of Science and Technology, Suzhou 215009, China c Laboratory of Environment Remediation and Function Material, Suzhou Research Academy of North China Electric Power University, Suzhou, Jiangsu 215213, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Metal-speciation Human health Soil Simulation
An improved method was proposed which integrates the distribution of metal speciation simulated by chemical equilibrium model, different exposure models and average daily intake dose modified by analytic hierarchy process for human health risk assessment of metal species (MS). With the rapid development of economic and urbanization, the metals pollution had become more serious in industrial areas. Adverse effects of soil contaminants on human health in typical industrial area should be assessed to evaluate the risks of soils in these areas. The method was applied to study nickel (Ni) species health risks in soil of industrial areas. The pH possessed significant impact to determine distribution/existence and solubility of Ni species, followed by DOC. The non-carcinogenic risk (HQ) of Ni species were less than 1 in each sampling points, except Ni2+. In addition, the carcinogenic risk (CR) of different Ni species were less than 10−6, except for FANi and Ni2+.
1. Introduction Soil pollution from metals has accelerated in China in recent decades due to te high-intensity development of land resources and rapid economic development in recent decades (Lu et al., 2015; Lin et al., 2017). Metals and their compounds are frequently used as catalysts and chemical additives in industrial processes, and as such, soils can be polluted by industrial emissions (Salmani-Ghabeshi et al., 2016; Huang et al., 2017; Li et al., 2015). In addition, industrial areas are often in regions of dense human activity (Guan et al., 2017). Metal and metalloid contaminated soil is a major environmental problem, resulting in increased human exposure to these contaminants (Peña-Fernández et al., 2014). Human safety is a primary consideration in risk assessment of soil contamination. Industrial areas have been included as key governance plots in China's soil management and protection. Thus, the adverse effects of soil contaminants on human health in typical industrial areas should be assessed. Increasing evidence shows that the hazards of metals to organisms health are determined by metal species, rather than the total concentration (TC) of metals (Gu et al., 2016; Mashal et al., 2015; Reimann et al., 2005; Reis et al., 2014; Zhang et al., 2017), because toxicity/ bioavailability strongly depends on their species in natural environment (Castlehouse et al., 2010; Song and Ma, 2016). Furthermore, researches
have shown that the health risks from metals from their species, determined through speciation experimental extraction (such as RBA, Tessier and etc.) with a combination of health risk assessment models in soil (Yang et al., 2015; Dehghani et al., 2017; Li and Ji, 2017). Previous studies have shown the adverse effects of metals in soil and ignored the effects in soil solutions. However, it is widely accepted that the toxicity and bioavailability of metals in soils are conditional on their speciation in soil solution, especially on the concentration of free metal ions (Ge et al., 2000; Schneider et al., 2016) and on the solid/solution distribution of total metals (Zhang et al., 2015). Empirical and mechanistic models (such as WHAM VI, Visual MINTEQ, PHREEQC) have been widely employed for metal species (MS) in soil systems (Rooney et al., 2007; Ge et al., 2000; Schneider et al., 2016; Zhang et al., 2015), Compared with speciation experimental extraction approaches, these models detail speciation, and are time-saving and cost- saving. On the other hand, functionally these models meet the qualification that simulating the distribution of metal speciation with variation in environmental conditions, in virtue of metals speciation formation are closely relevant to soil properties such as pH, organic matter, Mn and/or Fe content and oxidation-reduction potential (Ukowski et al., 2013; Barman et al., 2015). The empirical and mechanistic models are crucial tools in the analysis of MS in soil. The absorption dose of metals is not equal to the actual
Abbreviations: TC, Total concentration; MS, Metal species ⁎ Corresponding author at: College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China E-mail address: [email protected] (Y. Zhang). https://doi.org/10.1016/j.ecoenv.2018.09.046 Received 3 February 2018; Received in revised form 8 August 2018; Accepted 9 September 2018 0147-6513/ © 2018 Published by Elsevier Inc.
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based on detailed MS health risk values and their characteristics. (3) It identifies the priority species ingested by organisms and provides detailed information on the study of bioavailability; (4) It can be conducive to identifying the contribution/ priority of MS risks caused by variation in soil environmental factors associated with the probability of risk in excess of the accepted level.
concentration of pollutants (Zhuang et al., 2009; Machida et al., 2004), and traditional health risk assessments neglect the relationship between concentration and uptake of pollutants by organisms. In addition, many lines of evidence distinctly show that uptake and metal toxicity are mainly dependent on the dissolution concentration and free ion activity (Steenbergen et al., 2005; Van Gestel and Koolhaas, 2004; Zhang et al., 2013). For these reasons, it is regarded clearly and definitely that to modify the average daily dose is equivalent demanded. The analytic hierarchy process (AHP) is a multi-criteria decision making method that helps to solve a complex problem with multiple conflicting and subjective criteria (Ishizaka and Labib, 2011). This mechanism can make it possible to effectively, accurately and conveniently modify the average daily intake with influences from multiple factors. The major objectives of this study were as follows: (1) to propose an improvement method that can quantify and distinguish the predominant MS in soil that are known to pose health risks; (2) to identify the dominant MS absorbed by organism based on an analytic hierarchy process; (3) to apply the method to an industry assessing the carcinogenic and non-carcinogenic risks of Ni speciation in soil; (4) to evaluate and compare the health risks between TC level and each species concentration; (5) to confirm the distribution of MS risks along with variation soil environmental factors at each sampling points.
2.1. Modeling Visual MINTEQ. 3.1 (Gustafsson, 2012) was used to simulate MS in soil solutions with the Stockholm Humic Model (SHM) (Gustafsson, 2001) and Hydrous Ferric Oxide (HFO) (Butler et al., 2005). The SHM has been used for speciation of many trace metals. HFO plays a significant role in MS because hydrous ferric are important ligands for metals, and have a high specific surface area and strong affinity for many elements (Schneider et al., 2016). The determined concentrations in the soil solution of cations (K, Ca, Na, Mg, Al, Fe and Ni) and anions (F-, Cl-, NO2-, NO3-, PO43- and SO42-), as well as the pH, Eh and the DOC content, were used to perform the calculation. The values of MS concentration (Ci, mol/L,i = 1, 2, 3, …, n ) and activity ( Ai ,i = 1, 2, 3, …, n ) (i is the number of MS) were obtained from the simulation. 2.2. Exposure assessment
2. Framework for modeling human health risks from metal species
Human health risks resulting from soil contaminants were evaluated based on the US EPA site-specific risk assessment method (US EPA OOSW, 2009; Us Epa, 2011, 1996). Classically, there are three central exposure pathways of metals to human body, either through oral ingestion, inhalation and dermal contact. The average daily dose of MS was modified through the analytic hierarchy process (AHP) (Cheng et al., 2002). In this study, we use the following algorithm to select the greater average daily dose for MS through the AHP technique:
A method for assessing health risks from MS was developed to quantify and distinguish the contribution of MS risks to human in soil (Fig. 1). Four main aspects make this approach innovative. First, a chemical equilibrium model (Visual MINTEQ) was used to provide information of MS concentration and activity. Second, an analytic hierarchy process was employed that modified the average daily dose of MS to determine the sequence of dominant species that could be intake by the organism. Third, the method compared the health risks between MS and the TC level through exposure pathways. Finally, it input different environmental conditions for simulation and then helped to understand the impact of MS risks on factors variation. Some advantages of this method can be summarized as follows: (1) It can assess the health risks of metals more accurately; (2) It offers available information to propose a hierarchical risk management strategy that provides reference for flexible and cost-efficient risk management policy-making. For example, specific treatment measures for remediation can be proposed
Step 1: Structure the problem as a hierarchy:
Fig. 1. Flow chart of the steps for assessing human health risk from MS in soil. 489
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Table 1 Physicochemical properties of soil solution. Parameters
pH
DOC (mg/L)
F-(mg/L)
Cl-(mg/L)
NO2-(mg/L)
NO3-(mg/L)
PO43-(mg/L)
SO42-(mg/L)
Min Max Mean SD Median 95% 50% Parameters Min Max Mean SD Median 95% 50%
3.51 9.14 7.13 1.56 7.57 7.72 7.33 HCO3-(mg/L) 367.80 2100.11 838.38 286.04 805.20 947.01 874.66
39.80 267.30 149.82 64.03 166.40 174.13 157.94 K (mg/L) 1.05 7.99 4.53 1.63 4.39 5.15 4.74
1.14 11.04 4.93 2.46 4.41 5.87 5.25 Ca (mg/L) 4.37 50.55 17.24 10.70 12.98 21.30 18.59
2.22 809.81 53.82 154.36 13.64 112.44 73.40 Na (mg/L) 56.00 1800.00 528.58 370.41 450.95 669.24 575.56
0.14 2.05 0.90 0.57 0.81 1.11 0.97 Mg (mg/L) 0.34 4.34 1.80 1.07 1.30 2.21 1.94
1.38 9.07 3.48 2.03 2.44 4.25 3.74 Al (mg/L) 0.98 89.30 15.76 16.97 11.04 22.21 17.92
2.13 17.02 4.57 3.51 3.16 5.90 5.02 Fe (g/L) 22.90 55.60 40.41 60.62 40.10 42.71 41.18
11.82 206.77 62.16 51.38 42.56 81.67 68.68 Ni (mg/L) 34.69 2300.00 324.38 459.15 112.50 498.74 382.61
calculate the judgment matrix (Hurley, 2001; Hurley, 2001; Ishizaka and Labib, 2011; Srdjevic, 2005) include the additive normalization method (AN), eigenvector method (EV), weighted least-squares method (WLS) and so on. In this study, the eigenvector method (EV) (Hurley, 2001) was employed, as shown in the following: Assuming a judgment matrix: P = (pij )n × n .
p p ⎡ 11 12 p p ⎢ P = ⎢ 21 22 ⋮ ⋮ ⎢p p ⎣ n1 n2
The judgement is a relative value or a quotient a / b of two quantities, a and b, with the same units (intensity, meters, utility, etc). The alternatives are compared based on the criteria. These can be numerical, verbal (table) or graphical. It makes pairwise comparisons by means of a matrix, n, and the comparison judgment matrix shows the following:
X1 b11 b21 b31 ⋮ bn1
X2 b12 b22 b32 ⋮ bn2
X3 b13 b23 b33 ⋮ bn3
⋯ ⋯ ⋯ ⋯ ⋱ ⋯
Among: bij =
xij xji
Step 4: Build pairwise comparison of gold A: Establishing the judgment matrix of two indexes for the concentration and activity in the gold A level. In this paper, the e 0/4 ~e 8/4 scale structure (Table 1) is employed to determine the matrix, with the concentration and activity as qualitative criteria. It identified that the index of activity compared with concentration is “Moderate plus”, and the pairwise comparison matrix (A) is as follows:
Xn b1n b2n b3n ⋮ bnn
1 e3/4 ⎤ A = ⎡ 3/4 ⎢1/ e 1 ⎥ ⎦ ⎣
,(i, j = 1, 2, ⋯n) . The judgement meets the fol-
lowing conditions: (a). bij > 0 ; (b). bij =
1 ; bji
(c). bii = 1(i = 1, 2, ⋯n) .
Concentration (Ci ) and Activity ( Ai ) were as the evaluation criteria (B), respectively: We can see the pairwise comparison matrix (B) in below:
⎡ 1 ⋯ b1n ⎤ B = ⎢ ⋮ bij ⋮ ⎥ ⎢ ⎥ ⎣ bn1 ⋯ 1 ⎦ bij =
Cij Cji
orbij =
(4)
Calculating the relative weights of the pairwise comparison matrix of gold A according to step 3, the results were that: w0, B1 = 0.321; w0, B2 = 0.679. Step 5: Assess the consistency of the comparison matrix of criterion B and gold A:
(1) As priorities make sense only if derived from consistent or near consistent matrices, a consistency check must be applied (Ishizaka and Labib, 2011). Saaty (1977) has proposed a consistency index (CI), which is related to the eigenvalue method.
Aij Aji
(3)
Calculating the maximum eigenvalue of matrix P, λ max using eigenvector method (EV). In summary, computing the relative weights of the pairwise comparison matrix of criterion B, the results were that: Weight value: BC → w1i, BA → w 2i (i = 1, 2, …, n) ; 2 λ max value: BC → λ1max , BA → λ max .
Step 2: Build pairwise comparison of criterion B:
B X1 X2 X3 ⋮ Xn
⋯ p1n ⎤ ⋯ p2n ⎥ ⋱ ⋮ ⎥ ⋯ pnn ⎥ ⎦
(2)
To obtain the pairwise comparison matrix BC and BA for Concentration (Ci ) and Activity ( Ai ), respectively.
CI =
Step 3: Compute the relative weights of the pairwise comparison matrix of criterion B:
λmax − n n−1
(5)
The consistency ratio, the ratio of CI and RI, is given by:
CR = CI/RI where:
According to a broad literature review, some methods used to 490
(6)
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n dimension of the matrix (or the number of species for metal) λmax maximal eigenvalue RI the random index (the average CI of 500 randomly filled matrices) Furthermore, the random index (RI) in Eq. (12) is a constant value (Ishizaka and Labib, 2011), and the values of RI can be calculated by MATLAB software.
HQd, i =
whereRfDd = RfDO × ABSGI HQinh, i =
w11 w 21 w 22 ⎤ ((w0,B1, w0,B2)) ⋮ ⎥ w 2n ⎥
⎦
(7)
Step 6: Calculate health risk In consideration of the Exposure Factors Handbook (USEPA, 2011), the modified average daily dose (mg/kg/day) of a MS via different exposure pathways was calculated using the following Eqs. (8)–(12)
MCi × EF × ED × IRO × CF1 × RBA CIi = ingestion exposure BW × AT ADi =
PEi =
MCi ×EF × ED × SA × AF × ABSd × CF1 dermal exposure BW × AT MCn × EF × ED × ET ×
1 PEF
AT
inhalation exposure
(14) (15)
(16) (17)
The non-cancer risks from the total concentration (C ) of metal were calculated by Eq. (13) - (15) with corresponding average daily dose. The total non-cancer risk of the total concentration (HITC ) was the sum of HQ from three exposure pathwayswhere: HQO, i oral hazard quotient of the i metal species (unitless), HQd, i dermal hazard quotient of the i metal species (unitless), HQinh, i inhalation hazard quotient of the i metal species (unitless), RfDO oral reference dose of metal (mg/kg/day)., RfDd dermally adjusted reference dose of metal (mg/kg/day), ABSGI gastrointestinal absorption factor (unitless), RfC inhalation reference concentration of metal (mg/m3). For cancer risks, the following linear low-dose cancer risk equations were applied (Wcisło et al., 2016), depending on the exposure pathway:
Calculating the synthetic weight of project X to gold A in below:
⎡ w1 =⎢ 2 ⋮ ⎢ ⎣ w1n
PEi inhalation exposure RfC
HI = HQO, i + HQd, i + HQinh, i
Step 6: Calculate the synthetic weight
Ws
ADi dermal exposure RfDd
(8)
CRO, i = CIi × CSFO oral exposure
(18)
CRd, i = ADi × CSFd dermal exposure
(19)
CSFO ABSGI
(20)
WhereCSFd =
(9)
(10)
CRinh, i = PEi× IURinhalation exposure
(21)
TR = CRO, i + CRd, i + CRinh, i
(22)
n
MCi =
∑ SMi × wis
SMi = 1000×Ci × M × ni × V / M
The cancer risks from the TC (C ) of metal were calculated by Eq. (18) - (20) , with the corresponding average daily dose. The total cancer risk from metal TC (TRTC ) was the sum of HQ from three exposure pathways.where: CRO, i oral cancer risk of the i metal species (unitless), CSFO oral cancer slope factor (mg/kg/day)−1 of metal, CRd, i dermal cancer risk of the i metal species (unitless), CSFd dermally adjusted carcinogenic slope factor of metal (mg/kg/day)−1, CRinh, i inhalation cancer risk of the i metal species (unitless), PEi pulmonary exposure (mg/m3) of the i metal species, IUR inhalation unit risk (mg/m3)−1 of metal.
(11)
i=1
(i = 1,
2, …, n)
(12)
The average daily dose of metal total concentration (C ) was calculated by Eqs. (8)–(10) with TC instead of MC .where: CIi contaminant ingestion intake of the i metal species (mg/kg/day), ADi absorbed dose of the i metal species (mg/kg/day), PEi pulmonary exposure of the i metal species (mg/m3), MCi modified concentration of the i metal species from soil (mg/kg), Ci molar concentration of the i metal species in soil (mol/L), SMi concentration of the i metal species in soil (mg/kg), M relative atomic mass of metal, ni the number of target metal from the i metal species (i.e. Cr2O72-, ni = 2), V / M ratio of volume to mass (L/kg, assumed value is 1), EF exposure frequency (days/year), ED exposure duration (years), IRO ingestion rate (mg/day), BW body weight (kg), RBA relative bioavailability factor of metal (unitless), AT averaging time (equal to exposure duration for non-carcinogens and 70 years for carcinogens (days)), CF1 conversion factor of metal (10−6 kg/ mg), SA skin surface area exposed (cm2), AF soil-to-skin adherence factor (mg/cm2/day), ABSd dermal absorption fraction (unitless), EF exposure time (h/h), PEF particulate emission factor (m3/kg)., TC total concentration of metal (mg/kg)
2.4. Illustrating example An illustrative example is given in the Supplementary material to facilitate the readers to understand the calculation process for the proposed health risk assessment. 3. Case study 3.1. Study area The study area is located at Suzhou which lies to the southeast of Jiangsu province in eastern China. The region has a subtropical monsoon ocean climate with an average annual temperature of 15.7 °C and a mean annual precipitation of about 1100 mm. The prevailing summer and winter wind directions are southeast and northeast, respectively. The annual average wind speed is 3.4 m/s. The study area belongs to the well-developed regions in China. The area has been undergoing rapid and intense industrialization and urbanization over the past two decades. The study site was a legacy electroplating site that ordered to be shut down by the government. The electroplating factory was built in 1968 and covers an area of 26,666.67 m2 which mainly operated in electroplating and metal stamping parts in the past. Metal waste and residues have resulted in a level of metals in soil that exceed the standard.
2.3. Risk characterization Risk characterization was conducted according to the US EPA health risk assessment model which was characterized separately for cancer and non-cancer effects. Chemicals which produce both non-cancer and cancer effects were evaluated with regard to both types of effects. For non-carcinogenic risks, expressed by the hazard quotients, the following equations were employed (Wcisło et al., 2016), depending on the exposure pathway:
HQO, i =
CIi oral exposure RfDO
(13) 491
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Although sampling points 20# and 21# were also in the weak alkali environment, Ni2+ was not the dominant species because of the low Ni contents. In strong acid conditions, Ni2+ is the most predominant species followed by FehONi+. However, FeONi+ is the most predominant species, followed by Ni2+ in weak acid environment (8# and 16#). In addition, NiNO2+, NiHCO3+, NiF+ and NiCl+ were possible dominant species in acid conditions, due to relatively large Ni dissolution rates in this context. The results obtained from Fig. 2(b) are as follows: the dissolved and absorbed probability of Ni were obviously distinct at different sampling sites, which was described by variation in the concentration of factors in the soil solution, especially the pH. Approximately 100% dissolved Ni at points 3# and 13#, with the absorbed rates close to zero, was a result of a strong acid environment. However, irrespective of strong or weak alkali conditions, the probabilities of absorbed for Ni were all close to 100% with approximately 0% dissolved. The results associated with Fig. 2(a), it can be clearly obtained when the proportion of FehONi+ and FeONi+ as dominant species is greater, the adsorption rates of Ni were all close to 100%, indicating FehONi+ and FeONi+ were insoluble. At different sampling points, a variety of Ni dissolved inorganic and dissolved organic matter (DOM) were completely consistent, which seriously affected the dissolution rate of Ni (Fig. 2(c)). The concentration of DOC was also an important factor affecting the dissolved inorganic and organic compounds of Ni in soil solution, especially in for certain samples (2#, 3#, 4#, 13#, 14# and 22#). The results indirectly demonstrated that pH is the most important factor in determining the formation of MS in soil solution.
3.2. Sample collection and analysis A total of 30 sampling points were selected according to the distribution of the main functional zones (Fig. S1). The handheld GPS was used to determine the sampling points. Each site was sampled bottomup by a columnar sampler according to 0–20 cm, 20–40 cm, 40–60 cm, 60 –90 cm, 90–110 cm and 110–150 cm soil depth, and then mixed together to composite one sample (about 1.2–2.5 kg) in a polyethylene zip-bag, which was immediately transported to the laboratory. The soil samples were air-dried at room temperature and passed through a 2 mm nylon sieve to remove plant roots, debris, glasses and other materials. Soil solutions were extracted by centrifuge for 45 min at a speed of 3500 r/min and then another 45 min at a higher speed of 15000 r/min after incubation overnight at 50 cm water tension and 20 °C. The extracted soil pore water was passed through 0.45 µm filters. The TC of metals (K、Ca、Na、Mg、Fe、Ni and Al) in soil solution were measured by inductively coupled plasma mass spectrometry (ICP – MS, Agilent 7900). The concentrations of F-, Cl-, NO2-, SO42-, NO3- and PO43were detected by Solid-phase Extraction Coupled with Ion Chromatography (Dionex, ICS-1600), whereas HCO3- was determined by acidbase titration. Soil solution pH was determined by pH Meter (PHS-3C, LEICI) at 25 °C. Dissolved organic (DOC) was determined by combustion catalytic oxidation and using the NDIR (non-dispersive infrared gas analysis) detection method (Shimadzu TOC-V). To ensure analysis quality, standard materials of soil (GSS – 2) and cabbage (GBW10014) were used. 3.3. Speciation modeling
3.4. Health risk
The chemical parameters of each sample of soil solution are presented in Table 1. These component values were put into Visual MINTEQ to obtain the species of target metal (Ni). The values at different sampling sites ranged from 3.51 to 9.14, showing that the pH differed significantly at the sampling sites. The soil solutions show a wide range of DOC values (39.80 ~ 267.30 mg/L) with a mean of 149.82 mg/L. In addition, the median, 95% and 50% values of DOC were about 150 mg/L. The contents of Fe, at the sampling sites was generally higher with the mean, median, 95% and 50% values of above 40 g/L. The concentrations of Al in soil solutions was in a broad range (between 0.98 and 89.30 mg/L) with mean of 15.76 mg/L. Conventional ions (F-, Cl-, NO2-, SO42-, NO3-, PO43-, K、Ca、Na and Mg) in soil solutions at the study area are presented. The concentration of the target metal (Ni) in different sampling soil solutions ranged from 34.69 to 2300 mg/L, indicating the distribution of Ni has significant variation. Previous studies indicated that pH plays a key role in the formation of chemical species to metals (Tye et al., 2004; Zhang et al., 2013). pH not only determines the distribution of MS but also DOC as well as Fe2+ which significantly affected the formation MS in soil solutions (Z et al., 2012; Sébastien et al., 2000). Therefore, the effects of the distribution and dissolved rate in the soil solutions, caused by variation in soil solution environmental factors at each sampling points is discussed. Percentages of the total concentration for predominant species in the soil solutions from each sampling sites are shown in Fig. 2(a). The change of % dissolved and % absorbed for Ni concentration caused by pH at each sampling points is presented in Fig. 2(b). The influence of % dissolved on Ni dissolved inorganic and bound to DOM at each sampling sites is shown in Fig. 2(c). As shown in Fig. 2(a), the percentages of Ni species showed significant differences in various sampling points, indicating that different soil solution environments affect the distribution of MS. As described by previous studies, pH significantly impacts the determination of the present MS. In the alkaline environment (1#, 5#, 6#, 12#, 17#, 18#, 19#, 24#, 25#, 27# and 29#), only FehONi+, FeONi+ and FANi+ (Fulvic acid and Ni bound) as dominant species were present. Under the condition of weak alkali, the superiority Ni species was Ni2+, FehONi+, FeONi+ and FANi+ at sites (7#, 9#, 11#, 15#, 26# and 30#).
In order to obtain a more accurate health risk assessment for metals, the MS were employed in estimates instead of total concentration. The health risk assessment focused on adults because the study area was an industrial site. Thus, the exposure and toxicological parameters values (Table 2) for risks evaluation were selected according to the healthbased risk assessment of contaminated sites (Wcisło et al., 2016; Us Epa, 2009, 2011). The species of Ni were Ni2+, NiNO2+, NiHCO3+, FehONi+, FeONi+, FANi+, FA-Ni2+, NiF+ and NiCl+, but FehONi+ and FeONi+ were undissolved, as obtained from Fig. 2. In addition, dissolved Ni concentration is very important for determining Ni toxicity in soil (Zhang et al., 2015). Therefore, FehONi+ and FeONi+ were not considered in the following health risk assessment because these cannot be taken in by organisms. The results of non-carcinogenic and carcinogenic risks of Ni species in each sampling site are presented in Fig. 3 and Fig. 4, respectively. The comparison result of health risks evaluation for metal species and the total concentration is shown in Fig. 5. The HQ values both modified and non-modified for Ni species, were less than 1 in each sampling site, (except Ni2+) at several individual sampling points in this study area (Fig. 3). In the study area the total of the modified and non-modified HQ values of Ni2+ exceeded or were equal to the non-cancer risk value of 1 in three sampling points (2#, 3# and 14#), demonstrating that the public may experience Ni2+ noncarcinogenic effects, and that these sites were significant polluted areas. The modified and non-modified CR values of different Ni species were less than 10−6 besides FANi and Ni2+ at some points (Fig. 4) Modified and non-modified Ni2+ CR values were almost the same as those above 10−6 at points 2#, 3#, 4#, 8#, 13#, 14#, 22# and 28# with acid environment, and points 15# was also more than 10−6 because of the highest Ni TC (2300 mg/kg) in the weak alkali environment (pH = 7.44), indicating pH and metal TC were the important factors in determining risks to human. Modified and non-modified CR of FANi as predominant species for Ni exceeded acceptable levels of cancer risk at points 2#, 3#, 8#, 14#, 15#, 16# and 28#. The above conclusions show that Ni2+ and FANi were the most dominant species in intake. The HQ and CR values for different Ni species varied with 492
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Fig. 2. (a). The % of total concentration for the predominant species in the soil solution at each sampling sites (pH in parentheses); (b). The change of % dissolved and % absorbed for Ni concentration caused by pH at each sampling points; (c). The influence of % dissolved on Ni dissolved inorganic and bound to DOM at each sampling sites (DOC in parentheses, mg/L).
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Fig. 3. Results for modified and non-modified non- carcinogenic risk.
environmental conditions. Non-modified HQ and CR of Ni2+ and Ni (SO4)22- were greater than the modified values that illustrated that organism intake of Ni2+ and Ni(SO4)22- affected concentration more critically than activity. The opposite phenomenon of risks for another
Ni species compared with Ni2+ and Ni(SO4)22-, showed that activity was more important than concentration for Ni species ingested by living things. The maximum risks (HQ and CR) values of NiCl2, NiHCO3+ and FA-Ni2+ were more than 1010 times the minimum, followed by NiCl+, 494
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Fig. 4. Results for modified and non-modified carcinogenic risk.
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Fig. 5. Health risks of comparison between TC and MS.
value of 10−6 in a total of 30 sampling points. However, the TRm varied from 3.33E-11 to 2.71E-05 and exceeded acceptable non-carcinogenic risk level were only in 10 points (2#, 3#, 4#, 8#, 13#, 14#, 15#, 16#, 22# and 28#). The HIm and TRm at 2#, 3# and 14# were all above the acceptable level of risks, so these points were heavily polluted areas and required close attention. These results shown that considering metal species for health risks estimates could achieve more accurate results reduce the cost of soil remediation and provide reference information.
NiNO3+ and NiNO2+ (> 108), which clarified that environmental factors play an important role in the formation of these Ni species. NiHCO3+, NiOH+, NiSO4, Ni(NO2)2 and NiCl2 had analogous changes regulation of risks at these sampling sites, Ni(OH)3-, Ni(OH)2 and NiCO3 also varied consistently suggesting that the environmental conditions needed for their formation were similar. The sum of species risks both HIm and TRm were less than or were equal to the total concentration risks at each sampling points (Fig. 5). It was proved that the health risks were determined by metal species, but not by the total metal concentration. At different sampling points, even if the total Ni concentration risks were similar, there was a significant difference in the sum of the Ni species risks, such as at 2# and 5#, 17# and 18# or 25# and 26#, etc., showing that environmental factors seriously affected the distribution of metal species risks. Connecting with Fig. 2, studying the influence of environmental considerations on health risks, it could be concluded that pH played a key role in the species risks, followed by DOC. The HITC varied from 0.21 to 13.08 and exceeded or was equal to the non-cancer risk value of 1 in 11 points (2#, 3#, 5#, 8#, 10#, 12#,14#, 15#,16#, 27# and 30#). However, the HIm varied from 2.49E-06 to 2.03 and exceeded acceptable noncarcinogenic risk level were only in 2#, 3# and 14#. The TRTC varied from 2.64E-06–1.75E-04 and exceeded or were equal to the cancer risk
4. Conclusions Quantifying the effects on metal species can provide specific health risks estimate for metals to achieve effective and accuracy risks. The simulated species of Ni by Visual MINTEQ were Ni2+, NiNO2+, NiHCO3+, FehONi+, FeONi+, FANi+, FA-Ni2+, NiF+ and NiCl+, but FehONi+ and FeONi+ were insoluble. Distribution and dissolution of Ni speciation were changed along with the environment conditions variation, and pH is the most important factor to determine the formation of metal species in soil solution. The carcinogenesis and noncarcinogenesis risks of Ni species and Ni total concentration were calculated at 30 sampling points. The sum of species risks both HIm and TRm less than or were equal to the total concentration risks at each 496
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sampling points. At different sampling points, even if the total Ni concentration risks were similar, there was a significant difference in the sum of the Ni species risks. The result indicated that metals species play an important role in determining health effects, rather than the total level concentration. Meanwhile, it illustrated that the concentrations of metal species were employed to assess health risk was necessary, and which should be paid serious attention.
and water pollution on food safety and health risks in China. Environ. Int. 77, 5–15. Machida, M., Kayama, F., Horiguchi, H., Oguma, E., Sasaki, S., Miyamoto, K., et al., 2004. Dietary exposure to cadmium at close to the current provisional tolerable weekly intake does not affect renal function among female Japanese farmers. Environ. Res. 95, 20–31. Mashal, K., Salahat, M., Al-Qinna, M., Al-Degs, Y., 2015. Spatial distribution of cadmium concentrations in street dust in an arid environment. Arab. J. Geosci. 8, 3171–3182. Peña-Fernández, A., González-Muñoz, M.J., Lobo-Bedmar, M.C., 2014. Establishing the importance of human health risk assessment for metals and metalloids in urban environments. Environ. Int. 72, 176–185. Reimann, C., Filzmoser, P., Garrett, R.G., 2005. Background and threshold: critical comparison of methods of determination. Sci. Total Environ. 346, 1–16. Reis, A.P., Patinha, C., Noack, Y., Robert, S., Dias, A.C., Silva, E.F.D., 2014. Assessing the human health risk for aluminium, zinc and lead in outdoor dusts collected in recreational sites used by children at an industrial area in the western part of the Bassin Minier de Provence, France. J. Afr. Earth Sci. 99, 724–734. Rooney, C.P., Zhao, F.J., Zhao, F.J., Mcgrath, S.P., 2007. Phytotoxicity of nickel in a range of European soils: influence of soil properties, Ni solubility and speciation. Environ. Pollut. 145, 596–605. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281. Salmani-Ghabeshi, S., Palomo-Marín, M.R., Bernalte, E., Rueda-Holgado, F., MiróRodríguez, C., Cereceda-Balic, F., et al., 2016. Spatial gradient of human health risk from exposure to trace elements and radioactive pollutants in soils at the PuchuncavíVentanas industrial complex, Chile ☆. Environ. Pollut. 218, 322–330. Schneider, A.R., Ponthieu, M., Cancès, B., Conreux, A., Morvan, X., Gommeaux, M., et al., 2016. Influence of dissolved organic matter and manganese oxides on metal speciation in soil solution: a modelling approach. Goldschmidt 618–627. Sébastien, Sauvé, William Hendershot, A., Allen, H.E., 2000. Solid-solution partitioning of metals in contaminated soils: dependence on pH, total metal burden, and organic matter. Environ. Sci. Technol. 34, 1125–1131. Song, N., Ma, Y., 2016. The toxicity of HCrO4- and CrO42- to barley root elongation in solution culture: ph effect and modelling. Chemosphere 171, 537. Srdjevic, B., 2005. Combining different prioritization methods in the analytic hierarchy process synthesis. Comput. Oper. Res. 32, 1897–1919. Steenbergen, N.T.T.M., Iaccino, F., Winkel, M.D., Reijnders, L., Peijnenburg, W.J.G.M., 2005. Development of a biotic ligand model and a regression model predicting acute copper toxicity to the earthworm Aporrectodea caliginosa. Environ. Sci. Technol. 39, 5694. Tye, A.M., Young, S., Crout, N.M.J., Zhang, H., Preston, S., Zhao, F.J., et al., 2004. Speciation and solubility of Cu, Ni and Pb in contaminated soils. Eur. J. Soil Sci. 55, 579–590. Ukowski, A., Wiater, J., Le Niewska, B., 2013. Content and fractional composition of nickel in arable soils depending on physicochemical properties. Ecol. Chem. Eng. A 20, 129. US EPA OOSW. 2009. Risk assessment guidance for superfund (RAGS), Volume I: Human health evaluation manual (Part F, Supplemental guidance for inhalation risk assessment). S. Us Epa, 1996. Soil Screening Guidance: technical Background Document, US EPA Superfund. USEPA, 2011. Exposure Factors Handbook: 2011 Edition. Van Gestel, C.A., Koolhaas, J.E., 2004. Water-extractability, free ion activity, and pH explain cadmium sorption and toxicity to Folsomia candida (Collembola) in seven soil-pH combinations. Environ. Toxicol. Chem. 23, 1822–1833. Wcisło, E., Bronder, J., Bubak, A., Rodríguez-Valdés, E., Gallego, J.L., 2016. Human health risk assessment in restoring safe and productive use of abandoned contaminated sites. Environ. Int. 94, 436. Yang, K., Im, J., Jeong, S., Nam, K., 2015. Determination of human health risk incorporating experimentally derived site-specific bioaccessibility of arsenic at an old abandoned smelter site. Environ. Res. 137, 78–84. Z, S., E, P., DL, S., 2012. Kinetics of Ni sorption in soils: roles of soil organic matter and Ni precipitation. Environ. Sci. Technol. 46, 2212. Zhang, X., Li, J., Wei, D., Li, B., Ma, Y., 2015. Predicting soluble nickel in soils using soil properties and total nickel. PLos One 10, e0133920. Zhang, X.Q., Li, J.M., Li, B., Ma, Y.B., Huang, Z.B., 2013. Evaluation the influence of soil solution chemistry on soluble nickel toxicity to Bok Choy. Adv. Mater. Res. 750–752, 1441–1447. Zhang, Y., Chen, J., Shi, W., Zhang, D., Zhu, T., Li, X., 2017. Establishing a human health risk assessment methodology for metal species and its application of Cr6+ in groundwater environments. Chemosphere 189, 525–537. Zhuang, P., Mcbride, M.B., Xia, H., Li, N., Li, Z., 2009. Health risk from metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Sci. Total Environ. 407, 1551–1561.
Acknowledgments This work was financially supported by the National Natural Science Foundation of China (Grant No. 51878272, 51508187), the National Natural Science Foundation of Jiangsu Province (Grant No. BK20150390), National Natural Science Foundation of China (Grant No. NSFC31570515), and the Fundamental Research Funds for the Central Universities (JB 2016016). The authors are particularly grateful to Suzhou Research Academy of North China Electric Power University. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2018.09.046. References Barman, M., Datta, S.P., Rattan, R.K., Meena, M.C., 2015. Chemical fractions and bioavailability of nickel in alluvial soils. Plant Soil Environ. 61. Butler, B.A., Ranville, J.F., Ross, P.E., 2005. Comparison between observed and model predicted particulate metal transport in a mining-impacted stream. (NORTH FORK CLEAR CREEK, Colo.) 1. Castlehouse, H., Osborn, A.M., Cripps, J., Gilkes, R.J., Prakongkep, N., 2010. Modelling the chemical influences on bioavailability of geogenic arsenic in soils. Wcss 70–73. Cheng, E.W.L., Li, H., Ho, D.C.K., 2002. Analytic hierarchy process (AHP) A defective tool when used improperly. Meas Bus Excell. 6, 33–37. Dehghani, S., Moore, F., Keshavarzi, B., Hale, B.A., 2017. Health risk implications of potentially toxic metals in street dust and surface soil of Tehran, Iran. Ecotoxicol. Environ. Saf. 136, 92. Ge, Y., Murray, P., Hendershot, W.H., 2000. Trace metal speciation and bioavailability in urban soils. Environ. Pollut. 107, 137–144. Gu, Y.G., Lin, Q., Gao, Y.P., 2016. Metals in exposed-lawn soils from 18 urban parks and its human health implications in southern China's largest city, Guangzhou. J. Clean. Prod. 115, 122–129. Guan, Y., Chu, C., Shao, C., Ju, M., Dai, E., 2017. Study of integrated risk regionalisation method for soil contamination in industrial and mining area. Ecol. Indic. 83, 260–270. Gustafsson, J.P., 2001. Modeling the acid–base properties and metal complexation of humic substances with the Stockholm humic model. J. Colloid Interface Sci. 244, 102–112. Gustafsson, J.P., 2012. Visual MINTEQ. 3.0 user guide. Dep. Land Water Resour. Eng. Huang, Y., Chen, Q., Deng, M., Japenga, J., Li, T., 2017. metal pollution and health risk assessment of agricultural soils in a typical peri-urban area in southeast China. J. Environ. Manag. 207, 159–168. Hurley, W.J., 2001. The analytic hierarchy process: a note on an approach to sensitivity which preserves rank order. Comput. Oper. Res. 28, 185–188. Ishizaka, A., Labib, A., 2011. Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 38, 14336–14345. Li, H., Ji, H., 2017. Chemical speciation, vertical profile and human health risk assessment of metals in soils from coal-mine brownfield, Beijing, China. J. Geochem. Explor. Li, J., Pu, L., Liao, Q., Ming, Z., Dai, X., Yan, X., et al., 2015. How anthropogenic activities affect soil metal concentration on a broad scale: a geochemistry survey in Yangtze River Delta, Eastern China. Environ. Earth Sci. 73, 1823–1835. Lin, Y., Ma, J., Zhang, Z., Zhu, Y., Hou, H., Zhao, L., et al., 2017. Linkage between human population and trace elements in soils of the Pearl River Delta: implications for source identification and risk assessment. Sci. Total Environ. 944, 610–611. Lu, Y., Song, S., Wang, R., Liu, Z., Meng, J., Sweetman, A.J., et al., 2015. Impacts of soil
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Establishing a health risk assessment for metal speciation in soil—A case study in an industrial area in China
T
⁎
Yimei Zhanga,c, , Jie Chenb,c, Liqun Wanga,c, Yalong Zhaoa,c, Ping Oub, WeiLin Shib a
College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China Suzhou University of Science and Technology, Suzhou 215009, China c Laboratory of Environment Remediation and Function Material, Suzhou Research Academy of North China Electric Power University, Suzhou, Jiangsu 215213, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Metal-speciation Human health Soil Simulation
An improved method was proposed which integrates the distribution of metal speciation simulated by chemical equilibrium model, different exposure models and average daily intake dose modified by analytic hierarchy process for human health risk assessment of metal species (MS). With the rapid development of economic and urbanization, the metals pollution had become more serious in industrial areas. Adverse effects of soil contaminants on human health in typical industrial area should be assessed to evaluate the risks of soils in these areas. The method was applied to study nickel (Ni) species health risks in soil of industrial areas. The pH possessed significant impact to determine distribution/existence and solubility of Ni species, followed by DOC. The non-carcinogenic risk (HQ) of Ni species were less than 1 in each sampling points, except Ni2+. In addition, the carcinogenic risk (CR) of different Ni species were less than 10−6, except for FANi and Ni2+.
1. Introduction Soil pollution from metals has accelerated in China in recent decades due to te high-intensity development of land resources and rapid economic development in recent decades (Lu et al., 2015; Lin et al., 2017). Metals and their compounds are frequently used as catalysts and chemical additives in industrial processes, and as such, soils can be polluted by industrial emissions (Salmani-Ghabeshi et al., 2016; Huang et al., 2017; Li et al., 2015). In addition, industrial areas are often in regions of dense human activity (Guan et al., 2017). Metal and metalloid contaminated soil is a major environmental problem, resulting in increased human exposure to these contaminants (Peña-Fernández et al., 2014). Human safety is a primary consideration in risk assessment of soil contamination. Industrial areas have been included as key governance plots in China's soil management and protection. Thus, the adverse effects of soil contaminants on human health in typical industrial areas should be assessed. Increasing evidence shows that the hazards of metals to organisms health are determined by metal species, rather than the total concentration (TC) of metals (Gu et al., 2016; Mashal et al., 2015; Reimann et al., 2005; Reis et al., 2014; Zhang et al., 2017), because toxicity/ bioavailability strongly depends on their species in natural environment (Castlehouse et al., 2010; Song and Ma, 2016). Furthermore, researches
have shown that the health risks from metals from their species, determined through speciation experimental extraction (such as RBA, Tessier and etc.) with a combination of health risk assessment models in soil (Yang et al., 2015; Dehghani et al., 2017; Li and Ji, 2017). Previous studies have shown the adverse effects of metals in soil and ignored the effects in soil solutions. However, it is widely accepted that the toxicity and bioavailability of metals in soils are conditional on their speciation in soil solution, especially on the concentration of free metal ions (Ge et al., 2000; Schneider et al., 2016) and on the solid/solution distribution of total metals (Zhang et al., 2015). Empirical and mechanistic models (such as WHAM VI, Visual MINTEQ, PHREEQC) have been widely employed for metal species (MS) in soil systems (Rooney et al., 2007; Ge et al., 2000; Schneider et al., 2016; Zhang et al., 2015), Compared with speciation experimental extraction approaches, these models detail speciation, and are time-saving and cost- saving. On the other hand, functionally these models meet the qualification that simulating the distribution of metal speciation with variation in environmental conditions, in virtue of metals speciation formation are closely relevant to soil properties such as pH, organic matter, Mn and/or Fe content and oxidation-reduction potential (Ukowski et al., 2013; Barman et al., 2015). The empirical and mechanistic models are crucial tools in the analysis of MS in soil. The absorption dose of metals is not equal to the actual
Abbreviations: TC, Total concentration; MS, Metal species ⁎ Corresponding author at: College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China E-mail address: [email protected] (Y. Zhang). https://doi.org/10.1016/j.ecoenv.2018.09.046 Received 3 February 2018; Received in revised form 8 August 2018; Accepted 9 September 2018 0147-6513/ © 2018 Published by Elsevier Inc.
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based on detailed MS health risk values and their characteristics. (3) It identifies the priority species ingested by organisms and provides detailed information on the study of bioavailability; (4) It can be conducive to identifying the contribution/ priority of MS risks caused by variation in soil environmental factors associated with the probability of risk in excess of the accepted level.
concentration of pollutants (Zhuang et al., 2009; Machida et al., 2004), and traditional health risk assessments neglect the relationship between concentration and uptake of pollutants by organisms. In addition, many lines of evidence distinctly show that uptake and metal toxicity are mainly dependent on the dissolution concentration and free ion activity (Steenbergen et al., 2005; Van Gestel and Koolhaas, 2004; Zhang et al., 2013). For these reasons, it is regarded clearly and definitely that to modify the average daily dose is equivalent demanded. The analytic hierarchy process (AHP) is a multi-criteria decision making method that helps to solve a complex problem with multiple conflicting and subjective criteria (Ishizaka and Labib, 2011). This mechanism can make it possible to effectively, accurately and conveniently modify the average daily intake with influences from multiple factors. The major objectives of this study were as follows: (1) to propose an improvement method that can quantify and distinguish the predominant MS in soil that are known to pose health risks; (2) to identify the dominant MS absorbed by organism based on an analytic hierarchy process; (3) to apply the method to an industry assessing the carcinogenic and non-carcinogenic risks of Ni speciation in soil; (4) to evaluate and compare the health risks between TC level and each species concentration; (5) to confirm the distribution of MS risks along with variation soil environmental factors at each sampling points.
2.1. Modeling Visual MINTEQ. 3.1 (Gustafsson, 2012) was used to simulate MS in soil solutions with the Stockholm Humic Model (SHM) (Gustafsson, 2001) and Hydrous Ferric Oxide (HFO) (Butler et al., 2005). The SHM has been used for speciation of many trace metals. HFO plays a significant role in MS because hydrous ferric are important ligands for metals, and have a high specific surface area and strong affinity for many elements (Schneider et al., 2016). The determined concentrations in the soil solution of cations (K, Ca, Na, Mg, Al, Fe and Ni) and anions (F-, Cl-, NO2-, NO3-, PO43- and SO42-), as well as the pH, Eh and the DOC content, were used to perform the calculation. The values of MS concentration (Ci, mol/L,i = 1, 2, 3, …, n ) and activity ( Ai ,i = 1, 2, 3, …, n ) (i is the number of MS) were obtained from the simulation. 2.2. Exposure assessment
2. Framework for modeling human health risks from metal species
Human health risks resulting from soil contaminants were evaluated based on the US EPA site-specific risk assessment method (US EPA OOSW, 2009; Us Epa, 2011, 1996). Classically, there are three central exposure pathways of metals to human body, either through oral ingestion, inhalation and dermal contact. The average daily dose of MS was modified through the analytic hierarchy process (AHP) (Cheng et al., 2002). In this study, we use the following algorithm to select the greater average daily dose for MS through the AHP technique:
A method for assessing health risks from MS was developed to quantify and distinguish the contribution of MS risks to human in soil (Fig. 1). Four main aspects make this approach innovative. First, a chemical equilibrium model (Visual MINTEQ) was used to provide information of MS concentration and activity. Second, an analytic hierarchy process was employed that modified the average daily dose of MS to determine the sequence of dominant species that could be intake by the organism. Third, the method compared the health risks between MS and the TC level through exposure pathways. Finally, it input different environmental conditions for simulation and then helped to understand the impact of MS risks on factors variation. Some advantages of this method can be summarized as follows: (1) It can assess the health risks of metals more accurately; (2) It offers available information to propose a hierarchical risk management strategy that provides reference for flexible and cost-efficient risk management policy-making. For example, specific treatment measures for remediation can be proposed
Step 1: Structure the problem as a hierarchy:
Fig. 1. Flow chart of the steps for assessing human health risk from MS in soil. 489
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Table 1 Physicochemical properties of soil solution. Parameters
pH
DOC (mg/L)
F-(mg/L)
Cl-(mg/L)
NO2-(mg/L)
NO3-(mg/L)
PO43-(mg/L)
SO42-(mg/L)
Min Max Mean SD Median 95% 50% Parameters Min Max Mean SD Median 95% 50%
3.51 9.14 7.13 1.56 7.57 7.72 7.33 HCO3-(mg/L) 367.80 2100.11 838.38 286.04 805.20 947.01 874.66
39.80 267.30 149.82 64.03 166.40 174.13 157.94 K (mg/L) 1.05 7.99 4.53 1.63 4.39 5.15 4.74
1.14 11.04 4.93 2.46 4.41 5.87 5.25 Ca (mg/L) 4.37 50.55 17.24 10.70 12.98 21.30 18.59
2.22 809.81 53.82 154.36 13.64 112.44 73.40 Na (mg/L) 56.00 1800.00 528.58 370.41 450.95 669.24 575.56
0.14 2.05 0.90 0.57 0.81 1.11 0.97 Mg (mg/L) 0.34 4.34 1.80 1.07 1.30 2.21 1.94
1.38 9.07 3.48 2.03 2.44 4.25 3.74 Al (mg/L) 0.98 89.30 15.76 16.97 11.04 22.21 17.92
2.13 17.02 4.57 3.51 3.16 5.90 5.02 Fe (g/L) 22.90 55.60 40.41 60.62 40.10 42.71 41.18
11.82 206.77 62.16 51.38 42.56 81.67 68.68 Ni (mg/L) 34.69 2300.00 324.38 459.15 112.50 498.74 382.61
calculate the judgment matrix (Hurley, 2001; Hurley, 2001; Ishizaka and Labib, 2011; Srdjevic, 2005) include the additive normalization method (AN), eigenvector method (EV), weighted least-squares method (WLS) and so on. In this study, the eigenvector method (EV) (Hurley, 2001) was employed, as shown in the following: Assuming a judgment matrix: P = (pij )n × n .
p p ⎡ 11 12 p p ⎢ P = ⎢ 21 22 ⋮ ⋮ ⎢p p ⎣ n1 n2
The judgement is a relative value or a quotient a / b of two quantities, a and b, with the same units (intensity, meters, utility, etc). The alternatives are compared based on the criteria. These can be numerical, verbal (table) or graphical. It makes pairwise comparisons by means of a matrix, n, and the comparison judgment matrix shows the following:
X1 b11 b21 b31 ⋮ bn1
X2 b12 b22 b32 ⋮ bn2
X3 b13 b23 b33 ⋮ bn3
⋯ ⋯ ⋯ ⋯ ⋱ ⋯
Among: bij =
xij xji
Step 4: Build pairwise comparison of gold A: Establishing the judgment matrix of two indexes for the concentration and activity in the gold A level. In this paper, the e 0/4 ~e 8/4 scale structure (Table 1) is employed to determine the matrix, with the concentration and activity as qualitative criteria. It identified that the index of activity compared with concentration is “Moderate plus”, and the pairwise comparison matrix (A) is as follows:
Xn b1n b2n b3n ⋮ bnn
1 e3/4 ⎤ A = ⎡ 3/4 ⎢1/ e 1 ⎥ ⎦ ⎣
,(i, j = 1, 2, ⋯n) . The judgement meets the fol-
lowing conditions: (a). bij > 0 ; (b). bij =
1 ; bji
(c). bii = 1(i = 1, 2, ⋯n) .
Concentration (Ci ) and Activity ( Ai ) were as the evaluation criteria (B), respectively: We can see the pairwise comparison matrix (B) in below:
⎡ 1 ⋯ b1n ⎤ B = ⎢ ⋮ bij ⋮ ⎥ ⎢ ⎥ ⎣ bn1 ⋯ 1 ⎦ bij =
Cij Cji
orbij =
(4)
Calculating the relative weights of the pairwise comparison matrix of gold A according to step 3, the results were that: w0, B1 = 0.321; w0, B2 = 0.679. Step 5: Assess the consistency of the comparison matrix of criterion B and gold A:
(1) As priorities make sense only if derived from consistent or near consistent matrices, a consistency check must be applied (Ishizaka and Labib, 2011). Saaty (1977) has proposed a consistency index (CI), which is related to the eigenvalue method.
Aij Aji
(3)
Calculating the maximum eigenvalue of matrix P, λ max using eigenvector method (EV). In summary, computing the relative weights of the pairwise comparison matrix of criterion B, the results were that: Weight value: BC → w1i, BA → w 2i (i = 1, 2, …, n) ; 2 λ max value: BC → λ1max , BA → λ max .
Step 2: Build pairwise comparison of criterion B:
B X1 X2 X3 ⋮ Xn
⋯ p1n ⎤ ⋯ p2n ⎥ ⋱ ⋮ ⎥ ⋯ pnn ⎥ ⎦
(2)
To obtain the pairwise comparison matrix BC and BA for Concentration (Ci ) and Activity ( Ai ), respectively.
CI =
Step 3: Compute the relative weights of the pairwise comparison matrix of criterion B:
λmax − n n−1
(5)
The consistency ratio, the ratio of CI and RI, is given by:
CR = CI/RI where:
According to a broad literature review, some methods used to 490
(6)
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n dimension of the matrix (or the number of species for metal) λmax maximal eigenvalue RI the random index (the average CI of 500 randomly filled matrices) Furthermore, the random index (RI) in Eq. (12) is a constant value (Ishizaka and Labib, 2011), and the values of RI can be calculated by MATLAB software.
HQd, i =
whereRfDd = RfDO × ABSGI HQinh, i =
w11 w 21 w 22 ⎤ ((w0,B1, w0,B2)) ⋮ ⎥ w 2n ⎥
⎦
(7)
Step 6: Calculate health risk In consideration of the Exposure Factors Handbook (USEPA, 2011), the modified average daily dose (mg/kg/day) of a MS via different exposure pathways was calculated using the following Eqs. (8)–(12)
MCi × EF × ED × IRO × CF1 × RBA CIi = ingestion exposure BW × AT ADi =
PEi =
MCi ×EF × ED × SA × AF × ABSd × CF1 dermal exposure BW × AT MCn × EF × ED × ET ×
1 PEF
AT
inhalation exposure
(14) (15)
(16) (17)
The non-cancer risks from the total concentration (C ) of metal were calculated by Eq. (13) - (15) with corresponding average daily dose. The total non-cancer risk of the total concentration (HITC ) was the sum of HQ from three exposure pathwayswhere: HQO, i oral hazard quotient of the i metal species (unitless), HQd, i dermal hazard quotient of the i metal species (unitless), HQinh, i inhalation hazard quotient of the i metal species (unitless), RfDO oral reference dose of metal (mg/kg/day)., RfDd dermally adjusted reference dose of metal (mg/kg/day), ABSGI gastrointestinal absorption factor (unitless), RfC inhalation reference concentration of metal (mg/m3). For cancer risks, the following linear low-dose cancer risk equations were applied (Wcisło et al., 2016), depending on the exposure pathway:
Calculating the synthetic weight of project X to gold A in below:
⎡ w1 =⎢ 2 ⋮ ⎢ ⎣ w1n
PEi inhalation exposure RfC
HI = HQO, i + HQd, i + HQinh, i
Step 6: Calculate the synthetic weight
Ws
ADi dermal exposure RfDd
(8)
CRO, i = CIi × CSFO oral exposure
(18)
CRd, i = ADi × CSFd dermal exposure
(19)
CSFO ABSGI
(20)
WhereCSFd =
(9)
(10)
CRinh, i = PEi× IURinhalation exposure
(21)
TR = CRO, i + CRd, i + CRinh, i
(22)
n
MCi =
∑ SMi × wis
SMi = 1000×Ci × M × ni × V / M
The cancer risks from the TC (C ) of metal were calculated by Eq. (18) - (20) , with the corresponding average daily dose. The total cancer risk from metal TC (TRTC ) was the sum of HQ from three exposure pathways.where: CRO, i oral cancer risk of the i metal species (unitless), CSFO oral cancer slope factor (mg/kg/day)−1 of metal, CRd, i dermal cancer risk of the i metal species (unitless), CSFd dermally adjusted carcinogenic slope factor of metal (mg/kg/day)−1, CRinh, i inhalation cancer risk of the i metal species (unitless), PEi pulmonary exposure (mg/m3) of the i metal species, IUR inhalation unit risk (mg/m3)−1 of metal.
(11)
i=1
(i = 1,
2, …, n)
(12)
The average daily dose of metal total concentration (C ) was calculated by Eqs. (8)–(10) with TC instead of MC .where: CIi contaminant ingestion intake of the i metal species (mg/kg/day), ADi absorbed dose of the i metal species (mg/kg/day), PEi pulmonary exposure of the i metal species (mg/m3), MCi modified concentration of the i metal species from soil (mg/kg), Ci molar concentration of the i metal species in soil (mol/L), SMi concentration of the i metal species in soil (mg/kg), M relative atomic mass of metal, ni the number of target metal from the i metal species (i.e. Cr2O72-, ni = 2), V / M ratio of volume to mass (L/kg, assumed value is 1), EF exposure frequency (days/year), ED exposure duration (years), IRO ingestion rate (mg/day), BW body weight (kg), RBA relative bioavailability factor of metal (unitless), AT averaging time (equal to exposure duration for non-carcinogens and 70 years for carcinogens (days)), CF1 conversion factor of metal (10−6 kg/ mg), SA skin surface area exposed (cm2), AF soil-to-skin adherence factor (mg/cm2/day), ABSd dermal absorption fraction (unitless), EF exposure time (h/h), PEF particulate emission factor (m3/kg)., TC total concentration of metal (mg/kg)
2.4. Illustrating example An illustrative example is given in the Supplementary material to facilitate the readers to understand the calculation process for the proposed health risk assessment. 3. Case study 3.1. Study area The study area is located at Suzhou which lies to the southeast of Jiangsu province in eastern China. The region has a subtropical monsoon ocean climate with an average annual temperature of 15.7 °C and a mean annual precipitation of about 1100 mm. The prevailing summer and winter wind directions are southeast and northeast, respectively. The annual average wind speed is 3.4 m/s. The study area belongs to the well-developed regions in China. The area has been undergoing rapid and intense industrialization and urbanization over the past two decades. The study site was a legacy electroplating site that ordered to be shut down by the government. The electroplating factory was built in 1968 and covers an area of 26,666.67 m2 which mainly operated in electroplating and metal stamping parts in the past. Metal waste and residues have resulted in a level of metals in soil that exceed the standard.
2.3. Risk characterization Risk characterization was conducted according to the US EPA health risk assessment model which was characterized separately for cancer and non-cancer effects. Chemicals which produce both non-cancer and cancer effects were evaluated with regard to both types of effects. For non-carcinogenic risks, expressed by the hazard quotients, the following equations were employed (Wcisło et al., 2016), depending on the exposure pathway:
HQO, i =
CIi oral exposure RfDO
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Although sampling points 20# and 21# were also in the weak alkali environment, Ni2+ was not the dominant species because of the low Ni contents. In strong acid conditions, Ni2+ is the most predominant species followed by FehONi+. However, FeONi+ is the most predominant species, followed by Ni2+ in weak acid environment (8# and 16#). In addition, NiNO2+, NiHCO3+, NiF+ and NiCl+ were possible dominant species in acid conditions, due to relatively large Ni dissolution rates in this context. The results obtained from Fig. 2(b) are as follows: the dissolved and absorbed probability of Ni were obviously distinct at different sampling sites, which was described by variation in the concentration of factors in the soil solution, especially the pH. Approximately 100% dissolved Ni at points 3# and 13#, with the absorbed rates close to zero, was a result of a strong acid environment. However, irrespective of strong or weak alkali conditions, the probabilities of absorbed for Ni were all close to 100% with approximately 0% dissolved. The results associated with Fig. 2(a), it can be clearly obtained when the proportion of FehONi+ and FeONi+ as dominant species is greater, the adsorption rates of Ni were all close to 100%, indicating FehONi+ and FeONi+ were insoluble. At different sampling points, a variety of Ni dissolved inorganic and dissolved organic matter (DOM) were completely consistent, which seriously affected the dissolution rate of Ni (Fig. 2(c)). The concentration of DOC was also an important factor affecting the dissolved inorganic and organic compounds of Ni in soil solution, especially in for certain samples (2#, 3#, 4#, 13#, 14# and 22#). The results indirectly demonstrated that pH is the most important factor in determining the formation of MS in soil solution.
3.2. Sample collection and analysis A total of 30 sampling points were selected according to the distribution of the main functional zones (Fig. S1). The handheld GPS was used to determine the sampling points. Each site was sampled bottomup by a columnar sampler according to 0–20 cm, 20–40 cm, 40–60 cm, 60 –90 cm, 90–110 cm and 110–150 cm soil depth, and then mixed together to composite one sample (about 1.2–2.5 kg) in a polyethylene zip-bag, which was immediately transported to the laboratory. The soil samples were air-dried at room temperature and passed through a 2 mm nylon sieve to remove plant roots, debris, glasses and other materials. Soil solutions were extracted by centrifuge for 45 min at a speed of 3500 r/min and then another 45 min at a higher speed of 15000 r/min after incubation overnight at 50 cm water tension and 20 °C. The extracted soil pore water was passed through 0.45 µm filters. The TC of metals (K、Ca、Na、Mg、Fe、Ni and Al) in soil solution were measured by inductively coupled plasma mass spectrometry (ICP – MS, Agilent 7900). The concentrations of F-, Cl-, NO2-, SO42-, NO3- and PO43were detected by Solid-phase Extraction Coupled with Ion Chromatography (Dionex, ICS-1600), whereas HCO3- was determined by acidbase titration. Soil solution pH was determined by pH Meter (PHS-3C, LEICI) at 25 °C. Dissolved organic (DOC) was determined by combustion catalytic oxidation and using the NDIR (non-dispersive infrared gas analysis) detection method (Shimadzu TOC-V). To ensure analysis quality, standard materials of soil (GSS – 2) and cabbage (GBW10014) were used. 3.3. Speciation modeling
3.4. Health risk
The chemical parameters of each sample of soil solution are presented in Table 1. These component values were put into Visual MINTEQ to obtain the species of target metal (Ni). The values at different sampling sites ranged from 3.51 to 9.14, showing that the pH differed significantly at the sampling sites. The soil solutions show a wide range of DOC values (39.80 ~ 267.30 mg/L) with a mean of 149.82 mg/L. In addition, the median, 95% and 50% values of DOC were about 150 mg/L. The contents of Fe, at the sampling sites was generally higher with the mean, median, 95% and 50% values of above 40 g/L. The concentrations of Al in soil solutions was in a broad range (between 0.98 and 89.30 mg/L) with mean of 15.76 mg/L. Conventional ions (F-, Cl-, NO2-, SO42-, NO3-, PO43-, K、Ca、Na and Mg) in soil solutions at the study area are presented. The concentration of the target metal (Ni) in different sampling soil solutions ranged from 34.69 to 2300 mg/L, indicating the distribution of Ni has significant variation. Previous studies indicated that pH plays a key role in the formation of chemical species to metals (Tye et al., 2004; Zhang et al., 2013). pH not only determines the distribution of MS but also DOC as well as Fe2+ which significantly affected the formation MS in soil solutions (Z et al., 2012; Sébastien et al., 2000). Therefore, the effects of the distribution and dissolved rate in the soil solutions, caused by variation in soil solution environmental factors at each sampling points is discussed. Percentages of the total concentration for predominant species in the soil solutions from each sampling sites are shown in Fig. 2(a). The change of % dissolved and % absorbed for Ni concentration caused by pH at each sampling points is presented in Fig. 2(b). The influence of % dissolved on Ni dissolved inorganic and bound to DOM at each sampling sites is shown in Fig. 2(c). As shown in Fig. 2(a), the percentages of Ni species showed significant differences in various sampling points, indicating that different soil solution environments affect the distribution of MS. As described by previous studies, pH significantly impacts the determination of the present MS. In the alkaline environment (1#, 5#, 6#, 12#, 17#, 18#, 19#, 24#, 25#, 27# and 29#), only FehONi+, FeONi+ and FANi+ (Fulvic acid and Ni bound) as dominant species were present. Under the condition of weak alkali, the superiority Ni species was Ni2+, FehONi+, FeONi+ and FANi+ at sites (7#, 9#, 11#, 15#, 26# and 30#).
In order to obtain a more accurate health risk assessment for metals, the MS were employed in estimates instead of total concentration. The health risk assessment focused on adults because the study area was an industrial site. Thus, the exposure and toxicological parameters values (Table 2) for risks evaluation were selected according to the healthbased risk assessment of contaminated sites (Wcisło et al., 2016; Us Epa, 2009, 2011). The species of Ni were Ni2+, NiNO2+, NiHCO3+, FehONi+, FeONi+, FANi+, FA-Ni2+, NiF+ and NiCl+, but FehONi+ and FeONi+ were undissolved, as obtained from Fig. 2. In addition, dissolved Ni concentration is very important for determining Ni toxicity in soil (Zhang et al., 2015). Therefore, FehONi+ and FeONi+ were not considered in the following health risk assessment because these cannot be taken in by organisms. The results of non-carcinogenic and carcinogenic risks of Ni species in each sampling site are presented in Fig. 3 and Fig. 4, respectively. The comparison result of health risks evaluation for metal species and the total concentration is shown in Fig. 5. The HQ values both modified and non-modified for Ni species, were less than 1 in each sampling site, (except Ni2+) at several individual sampling points in this study area (Fig. 3). In the study area the total of the modified and non-modified HQ values of Ni2+ exceeded or were equal to the non-cancer risk value of 1 in three sampling points (2#, 3# and 14#), demonstrating that the public may experience Ni2+ noncarcinogenic effects, and that these sites were significant polluted areas. The modified and non-modified CR values of different Ni species were less than 10−6 besides FANi and Ni2+ at some points (Fig. 4) Modified and non-modified Ni2+ CR values were almost the same as those above 10−6 at points 2#, 3#, 4#, 8#, 13#, 14#, 22# and 28# with acid environment, and points 15# was also more than 10−6 because of the highest Ni TC (2300 mg/kg) in the weak alkali environment (pH = 7.44), indicating pH and metal TC were the important factors in determining risks to human. Modified and non-modified CR of FANi as predominant species for Ni exceeded acceptable levels of cancer risk at points 2#, 3#, 8#, 14#, 15#, 16# and 28#. The above conclusions show that Ni2+ and FANi were the most dominant species in intake. The HQ and CR values for different Ni species varied with 492
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Fig. 2. (a). The % of total concentration for the predominant species in the soil solution at each sampling sites (pH in parentheses); (b). The change of % dissolved and % absorbed for Ni concentration caused by pH at each sampling points; (c). The influence of % dissolved on Ni dissolved inorganic and bound to DOM at each sampling sites (DOC in parentheses, mg/L).
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Fig. 3. Results for modified and non-modified non- carcinogenic risk.
environmental conditions. Non-modified HQ and CR of Ni2+ and Ni (SO4)22- were greater than the modified values that illustrated that organism intake of Ni2+ and Ni(SO4)22- affected concentration more critically than activity. The opposite phenomenon of risks for another
Ni species compared with Ni2+ and Ni(SO4)22-, showed that activity was more important than concentration for Ni species ingested by living things. The maximum risks (HQ and CR) values of NiCl2, NiHCO3+ and FA-Ni2+ were more than 1010 times the minimum, followed by NiCl+, 494
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Fig. 4. Results for modified and non-modified carcinogenic risk.
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Fig. 5. Health risks of comparison between TC and MS.
value of 10−6 in a total of 30 sampling points. However, the TRm varied from 3.33E-11 to 2.71E-05 and exceeded acceptable non-carcinogenic risk level were only in 10 points (2#, 3#, 4#, 8#, 13#, 14#, 15#, 16#, 22# and 28#). The HIm and TRm at 2#, 3# and 14# were all above the acceptable level of risks, so these points were heavily polluted areas and required close attention. These results shown that considering metal species for health risks estimates could achieve more accurate results reduce the cost of soil remediation and provide reference information.
NiNO3+ and NiNO2+ (> 108), which clarified that environmental factors play an important role in the formation of these Ni species. NiHCO3+, NiOH+, NiSO4, Ni(NO2)2 and NiCl2 had analogous changes regulation of risks at these sampling sites, Ni(OH)3-, Ni(OH)2 and NiCO3 also varied consistently suggesting that the environmental conditions needed for their formation were similar. The sum of species risks both HIm and TRm were less than or were equal to the total concentration risks at each sampling points (Fig. 5). It was proved that the health risks were determined by metal species, but not by the total metal concentration. At different sampling points, even if the total Ni concentration risks were similar, there was a significant difference in the sum of the Ni species risks, such as at 2# and 5#, 17# and 18# or 25# and 26#, etc., showing that environmental factors seriously affected the distribution of metal species risks. Connecting with Fig. 2, studying the influence of environmental considerations on health risks, it could be concluded that pH played a key role in the species risks, followed by DOC. The HITC varied from 0.21 to 13.08 and exceeded or was equal to the non-cancer risk value of 1 in 11 points (2#, 3#, 5#, 8#, 10#, 12#,14#, 15#,16#, 27# and 30#). However, the HIm varied from 2.49E-06 to 2.03 and exceeded acceptable noncarcinogenic risk level were only in 2#, 3# and 14#. The TRTC varied from 2.64E-06–1.75E-04 and exceeded or were equal to the cancer risk
4. Conclusions Quantifying the effects on metal species can provide specific health risks estimate for metals to achieve effective and accuracy risks. The simulated species of Ni by Visual MINTEQ were Ni2+, NiNO2+, NiHCO3+, FehONi+, FeONi+, FANi+, FA-Ni2+, NiF+ and NiCl+, but FehONi+ and FeONi+ were insoluble. Distribution and dissolution of Ni speciation were changed along with the environment conditions variation, and pH is the most important factor to determine the formation of metal species in soil solution. The carcinogenesis and noncarcinogenesis risks of Ni species and Ni total concentration were calculated at 30 sampling points. The sum of species risks both HIm and TRm less than or were equal to the total concentration risks at each 496
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sampling points. At different sampling points, even if the total Ni concentration risks were similar, there was a significant difference in the sum of the Ni species risks. The result indicated that metals species play an important role in determining health effects, rather than the total level concentration. Meanwhile, it illustrated that the concentrations of metal species were employed to assess health risk was necessary, and which should be paid serious attention.
and water pollution on food safety and health risks in China. Environ. Int. 77, 5–15. Machida, M., Kayama, F., Horiguchi, H., Oguma, E., Sasaki, S., Miyamoto, K., et al., 2004. Dietary exposure to cadmium at close to the current provisional tolerable weekly intake does not affect renal function among female Japanese farmers. Environ. Res. 95, 20–31. Mashal, K., Salahat, M., Al-Qinna, M., Al-Degs, Y., 2015. Spatial distribution of cadmium concentrations in street dust in an arid environment. Arab. J. Geosci. 8, 3171–3182. Peña-Fernández, A., González-Muñoz, M.J., Lobo-Bedmar, M.C., 2014. Establishing the importance of human health risk assessment for metals and metalloids in urban environments. Environ. Int. 72, 176–185. Reimann, C., Filzmoser, P., Garrett, R.G., 2005. Background and threshold: critical comparison of methods of determination. Sci. Total Environ. 346, 1–16. Reis, A.P., Patinha, C., Noack, Y., Robert, S., Dias, A.C., Silva, E.F.D., 2014. Assessing the human health risk for aluminium, zinc and lead in outdoor dusts collected in recreational sites used by children at an industrial area in the western part of the Bassin Minier de Provence, France. J. Afr. Earth Sci. 99, 724–734. Rooney, C.P., Zhao, F.J., Zhao, F.J., Mcgrath, S.P., 2007. Phytotoxicity of nickel in a range of European soils: influence of soil properties, Ni solubility and speciation. Environ. Pollut. 145, 596–605. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281. Salmani-Ghabeshi, S., Palomo-Marín, M.R., Bernalte, E., Rueda-Holgado, F., MiróRodríguez, C., Cereceda-Balic, F., et al., 2016. Spatial gradient of human health risk from exposure to trace elements and radioactive pollutants in soils at the PuchuncavíVentanas industrial complex, Chile ☆. Environ. Pollut. 218, 322–330. Schneider, A.R., Ponthieu, M., Cancès, B., Conreux, A., Morvan, X., Gommeaux, M., et al., 2016. Influence of dissolved organic matter and manganese oxides on metal speciation in soil solution: a modelling approach. Goldschmidt 618–627. Sébastien, Sauvé, William Hendershot, A., Allen, H.E., 2000. Solid-solution partitioning of metals in contaminated soils: dependence on pH, total metal burden, and organic matter. Environ. Sci. Technol. 34, 1125–1131. Song, N., Ma, Y., 2016. The toxicity of HCrO4- and CrO42- to barley root elongation in solution culture: ph effect and modelling. Chemosphere 171, 537. Srdjevic, B., 2005. Combining different prioritization methods in the analytic hierarchy process synthesis. Comput. Oper. Res. 32, 1897–1919. Steenbergen, N.T.T.M., Iaccino, F., Winkel, M.D., Reijnders, L., Peijnenburg, W.J.G.M., 2005. Development of a biotic ligand model and a regression model predicting acute copper toxicity to the earthworm Aporrectodea caliginosa. Environ. Sci. Technol. 39, 5694. Tye, A.M., Young, S., Crout, N.M.J., Zhang, H., Preston, S., Zhao, F.J., et al., 2004. Speciation and solubility of Cu, Ni and Pb in contaminated soils. Eur. J. Soil Sci. 55, 579–590. Ukowski, A., Wiater, J., Le Niewska, B., 2013. Content and fractional composition of nickel in arable soils depending on physicochemical properties. Ecol. Chem. Eng. A 20, 129. US EPA OOSW. 2009. Risk assessment guidance for superfund (RAGS), Volume I: Human health evaluation manual (Part F, Supplemental guidance for inhalation risk assessment). S. Us Epa, 1996. Soil Screening Guidance: technical Background Document, US EPA Superfund. USEPA, 2011. Exposure Factors Handbook: 2011 Edition. Van Gestel, C.A., Koolhaas, J.E., 2004. Water-extractability, free ion activity, and pH explain cadmium sorption and toxicity to Folsomia candida (Collembola) in seven soil-pH combinations. Environ. Toxicol. Chem. 23, 1822–1833. Wcisło, E., Bronder, J., Bubak, A., Rodríguez-Valdés, E., Gallego, J.L., 2016. Human health risk assessment in restoring safe and productive use of abandoned contaminated sites. Environ. Int. 94, 436. Yang, K., Im, J., Jeong, S., Nam, K., 2015. Determination of human health risk incorporating experimentally derived site-specific bioaccessibility of arsenic at an old abandoned smelter site. Environ. Res. 137, 78–84. Z, S., E, P., DL, S., 2012. Kinetics of Ni sorption in soils: roles of soil organic matter and Ni precipitation. Environ. Sci. Technol. 46, 2212. Zhang, X., Li, J., Wei, D., Li, B., Ma, Y., 2015. Predicting soluble nickel in soils using soil properties and total nickel. PLos One 10, e0133920. Zhang, X.Q., Li, J.M., Li, B., Ma, Y.B., Huang, Z.B., 2013. Evaluation the influence of soil solution chemistry on soluble nickel toxicity to Bok Choy. Adv. Mater. Res. 750–752, 1441–1447. Zhang, Y., Chen, J., Shi, W., Zhang, D., Zhu, T., Li, X., 2017. Establishing a human health risk assessment methodology for metal species and its application of Cr6+ in groundwater environments. Chemosphere 189, 525–537. Zhuang, P., Mcbride, M.B., Xia, H., Li, N., Li, Z., 2009. Health risk from metals via consumption of food crops in the vicinity of Dabaoshan mine, South China. Sci. Total Environ. 407, 1551–1561.
Acknowledgments This work was financially supported by the National Natural Science Foundation of China (Grant No. 51878272, 51508187), the National Natural Science Foundation of Jiangsu Province (Grant No. BK20150390), National Natural Science Foundation of China (Grant No. NSFC31570515), and the Fundamental Research Funds for the Central Universities (JB 2016016). The authors are particularly grateful to Suzhou Research Academy of North China Electric Power University. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ecoenv.2018.09.046. References Barman, M., Datta, S.P., Rattan, R.K., Meena, M.C., 2015. Chemical fractions and bioavailability of nickel in alluvial soils. Plant Soil Environ. 61. Butler, B.A., Ranville, J.F., Ross, P.E., 2005. Comparison between observed and model predicted particulate metal transport in a mining-impacted stream. (NORTH FORK CLEAR CREEK, Colo.) 1. Castlehouse, H., Osborn, A.M., Cripps, J., Gilkes, R.J., Prakongkep, N., 2010. Modelling the chemical influences on bioavailability of geogenic arsenic in soils. Wcss 70–73. Cheng, E.W.L., Li, H., Ho, D.C.K., 2002. Analytic hierarchy process (AHP) A defective tool when used improperly. Meas Bus Excell. 6, 33–37. Dehghani, S., Moore, F., Keshavarzi, B., Hale, B.A., 2017. Health risk implications of potentially toxic metals in street dust and surface soil of Tehran, Iran. Ecotoxicol. Environ. Saf. 136, 92. Ge, Y., Murray, P., Hendershot, W.H., 2000. Trace metal speciation and bioavailability in urban soils. Environ. Pollut. 107, 137–144. Gu, Y.G., Lin, Q., Gao, Y.P., 2016. Metals in exposed-lawn soils from 18 urban parks and its human health implications in southern China's largest city, Guangzhou. J. Clean. Prod. 115, 122–129. Guan, Y., Chu, C., Shao, C., Ju, M., Dai, E., 2017. Study of integrated risk regionalisation method for soil contamination in industrial and mining area. Ecol. Indic. 83, 260–270. Gustafsson, J.P., 2001. Modeling the acid–base properties and metal complexation of humic substances with the Stockholm humic model. J. Colloid Interface Sci. 244, 102–112. Gustafsson, J.P., 2012. Visual MINTEQ. 3.0 user guide. Dep. Land Water Resour. Eng. Huang, Y., Chen, Q., Deng, M., Japenga, J., Li, T., 2017. metal pollution and health risk assessment of agricultural soils in a typical peri-urban area in southeast China. J. Environ. Manag. 207, 159–168. Hurley, W.J., 2001. The analytic hierarchy process: a note on an approach to sensitivity which preserves rank order. Comput. Oper. Res. 28, 185–188. Ishizaka, A., Labib, A., 2011. Review of the main developments in the analytic hierarchy process. Expert Syst. Appl. 38, 14336–14345. Li, H., Ji, H., 2017. Chemical speciation, vertical profile and human health risk assessment of metals in soils from coal-mine brownfield, Beijing, China. J. Geochem. Explor. Li, J., Pu, L., Liao, Q., Ming, Z., Dai, X., Yan, X., et al., 2015. How anthropogenic activities affect soil metal concentration on a broad scale: a geochemistry survey in Yangtze River Delta, Eastern China. Environ. Earth Sci. 73, 1823–1835. Lin, Y., Ma, J., Zhang, Z., Zhu, Y., Hou, H., Zhao, L., et al., 2017. Linkage between human population and trace elements in soils of the Pearl River Delta: implications for source identification and risk assessment. Sci. Total Environ. 944, 610–611. Lu, Y., Song, S., Wang, R., Liu, Z., Meng, J., Sweetman, A.J., et al., 2015. Impacts of soil
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