Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation

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1 This article was downloaded by: [University of Connecticut] On: 27 January 2014, At: 06:59 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Human and Ecological Risk Assessment: An International Journal Publication details, including instructions for authors and subscription information: Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation Mingkai Qu a b, Weidong Li c & Chuanrong Zhang c a Department of Resource and Environmental Information, Huazhong Agricultural University, Wuhan, Hubei, China b Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu, China c Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA Accepted author version posted online: 31 Jan 2013.Published online: 23 Jan To cite this article: Mingkai Qu, Weidong Li & Chuanrong Zhang (2014) Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation, Human and Ecological Risk Assessment: An International Journal, 20:3, , DOI: / To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the Content ) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

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3 Human and Ecological Risk Assessment, 20: , 2014 Copyright C Taylor & Francis Group, LLC ISSN: print / online DOI: / Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation Mingkai Qu, 1,2 Weidong Li, 3 and Chuanrong Zhang 3 1 Department of Resource and Environmental Information, Huazhong Agricultural University, Wuhan, Hubei, China; 2 Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing, Jiangsu, China; 3 Department of Geography and Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA ABSTRACT The objective of this study is to assess the spatial distribution and uncertainty of the potential ecological risks of heavy metals in soil using sequential Gaussian simulation (SGS) and the Hakanson potential ecological risk index (PERI). We collected 130 soil samples in an area of 150 km 2 in the High-Tech Park of Wuhan, China, and measured the concentrations of five heavy metals in soil (i.e., Cd,Cr,Cu, Pb, and Zn). We then simulated the spatial distribution of each heavy metal using SGS, and calculated Hakanson PERIs for individual metals and multiple metals based on the simulated realizations. The spatial uncertainty of the Cd PERI and its occurrence probabilities in different risk grades were further assessed. Results show that the potential ecological risks of Cr, Cu, Pb, and Zn are relatively low in the study area, but Cd indeed reaches a serious level that deserves much attention and essential treatment. The total PERI of multiple heavy metals indicates a moderate grade in most of the study area. In general, combining SGS and the Hakanson PERI appears to be an effective method for evaluating the potential ecological risks of heavy metals in soil and the priority areas for remediation. Key Words: heavy metals in soil, sequential Gaussian simulation, spatial distribution, uncertainty, ecological risk. INTRODUCTION In recent years, soil pollution has become an increasingly serious problem in China with rapid urbanization and industrialization, especially in urban rural transition zones where industrial parks were often established. Unlike many organisms Received 7 September 2012; accepted 10 January Address correspondence to Weidong Li, Department of Geography, University of Connecticut, 215 Glenbrook Road, U-4148, Storrs, CT 06269, USA. weidongwoody@gmail.com 764

4 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil and radionuclides, heavy metals 1 in soils do not decay with time due to their nonbiodegradable nature and long biological half-life (Raghunath et al. 1999). On the contrary, they may gradually accumulate to a toxic concentration level with the discharge of industrial wastes, irrigation with polluted water, and atmospheric deposition from industrial emissions. Once a soil is polluted by heavy metals, they may be further transferred from the soil to other environmental media such as underground water and plants, thus posing a long-term threat to human health, plant growth, and the total environment (Culbard et al. 1988; Folinsbee 1993). Numerous studies have been conducted in recent decades in analyzing the concentrations, spatial distributions and sources of heavy metals in soil as well as their direct impacts on plant growth and indirect impacts on animal and human health (Gay and Korre 2006; Zeng et al. 2009; Qu et al. 2013a). While the concentration levels of heavy metals in soils can be an index to evaluate the status of soil pollution, in many instances evident differences exist in their biological toxicities between different heavy metals. This means that given the same concentration level the hazards of different heavy metals in soils on ecosystems cannot be compared and their joint ecological toxicity is also difficult to evaluate. Therefore, a uniform standardized index is necessary for ecologically evaluating the contamination levels and hazards of heavy metals in soil (Kwon and Lee 1998). Hakanson (1980) introduced the potential ecological risk index (PERI) to assess the degree of heavy metal pollution according to the toxicity of heavy metals and the response of the environment. In recent years, this index has been commonly used for ecological risk assessment (He et al. 1998; Iqbal and Shah 2011; Wang et al. 2012). For example, Cao et al. (2009) studied the potential ecological risk of Cd, Pb, and As in agricultural black soils and also observed their impacts on the morphology of soybean seedlings; Liu et al. (2009) analyzed the ecological risk of seven heavy metals on the Luan River source water quality; and Sun et al. (2010) assessed the source and ecological risk of heavy metals contamination of urban soils in typical regions of Shengyang. More recently, Chen et al. (2012) conducted a comprehensive analysis of heavy metals in soils in Baoshan District of Shanghai, including their potential ecological risk. However, the spatial distribution of heavy metals is typically heterogeneous, and such heterogeneity was seldom accounted for in early studies related to ecological risk assessment. Apparently, the spatial heterogeneity of heavy metals in soil may consequently result in the spatial heterogeneity of ecological risks. Hence, understanding the spatial variability of pollutants and effectively taking it into account in ecological risk assessment are crucial for environmental management and remediation decision-making (Liu et al. 2009). Geostatistical techniques have been often used in modeling the spatial variability of heavy metals in soil and assessing their spatial uncertainty (Goovaerts et al. 1997; Chilès and Delfiner 1999; Cattle et al. 2002; Amini et al. 2005; Qu et al. 2013b). Although relatively rare, basic geostatistical interpolation techniques such as ordinary kriging have been used recently in some studies for mapping the spatial distribution of the PERI of heavy metals in soil 1 Editor s note: There is some dispute as to whether the term heavy metal should be used in scientific literature. The term is used here to be a general term for those metals and semimetals with potential human or environmental toxicity (Medscape 2013; available at Hum. Ecol. Risk Assess. Vol. 20, No. 3,

5 M. Qu et al. (Cao et al. 2009; Chen et al. 2012). However, so far we have not seen any study in geostatistical spatial uncertainty assessment of ecological risks of heavy metals. It is apparent that spatial uncertainty is an intrinsic characteristic in the spatial distribution of heavy metals in soil due to limited observation points, and such uncertainty consequently causes the spatial uncertainty in the spatial estimation of potential ecological risks of heavy metals in soil. The spatial uncertainty information of the PERI should be valuable to the decision-making and risk cost estimation in environmental remediation. Sequential simulation (Johnson 1987) was frequently used in geostatistical modeling of environmental properties. The widely used sequential simulation algorithm for continuous spatial variables in geostatistics is sequential Gaussian simulation (SGS), in which data need to be transformed to follow a Gaussian distribution if they are not and the entire multivariate distribution is then assumed to be Gaussian (Deutsch and Journel 1992; Goovaerts 1997). This critical assumption greatly simplifies the simulation process because every local conditional distribution is Gaussian with parameters given by kriging (Deutsch and Journel 1992). At each unsampled location, the estimation by SGS incorporates all data available within a neighborhood, including original sample data and previous simulated values. Its major purpose is to generate a number of simulated realizations of a target spatial variable in a study area, which can effectively reflect the spatial uncertainty of the target variable resulting from its spatial heterogeneity. Sequential Gaussian simulation has been frequently adopted to simulate the spatial patterns of contaminants in ground water and soils, and delineate their probabilistic risks to surrounding environments (Goovaerts 1997; Zhao et al. 2009; Qu et al. 2013b). In this study, SGS was used to simulate the spatial distributions of heavy metals in soil for evaluating their spatial variability and associated uncertainty in the study area, and the simulated realizations were further used to assess the spatial uncertainty of the PERIs of heavy metals in soil. The objectives of this study are to: (1) explore the spatial distribution patterns of heavy metals in the study area, where we found the soils were apparently polluted by Cd; (2) examine the feasibility of using SGS and the Hakanson PERI to quantitatively assess the spatial uncertainty of the potential ecological risk for each metal, so that an effective uncertainty assessment method in mapping ecological risk of heavy metals in soil can be obtained; (3) map the Hakanson PERIs for single and multiple heavy metals, which may be used for environmental management and remedy decisionmaking of the study area. METHODS AND MATERIALS Study Area and Data An area of approximate 150 km 2 in the urban rural transitional zone of Wuhan City, China, a metropolis in the middle reach of the Yangtze River, was chosen for this study. The study area was previously mostly farmlands with villages, but it was arranged as a high-tech development park in late 1980s and since then some industrial companies were gradually established within the area. No fabrication or smelting industries were ever set up within the area before it became a high-tech 766 Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

6 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil Figure 1. Sampling sites for measuring heavy metals in soil. park. At present, business and resident buildings occupy the northwest part of the study area close to downtown, while electronics factories concentrate in the central part and the left subareas are mainly farmlands and reserved lands for construction. Soil sampling was carried out in the study area in October 2009 and 130 nonrhizosphere topsoil samples (0 20 cm depth) were collected (see sample sites in Figure 1). At each sampling point, 4 6 sub-samples were randomly taken and then mixed to obtain a composite soil sample. All samples were air-dried at room temperature (20 22 C), crushed after stones and debris being removed, and then sieved to soil particles less than 2 mm in diameter. A portion of each soil sample (about 50 g) was ground in an agate grinder and sieved through a mm mesh. The prepared soil samples were then stored in polyethylene bottles for later use. Soil samples were analyzed in a laboratory to determine the concentrations of heavy metals, including Cd, Cr, Cu, Pb, and Zn, which were selected as examples for this study due to their toxicity to the environment, ease to measure and the availability of their toxic-response factor values. About 0.5 g of each prepared soil sample was digested in a Teflon beaker with a mixture of nitric acid (HNO 3 )and perchloric acid (HClO 4 ) using hot plane (Agricultural Chemistry Committee of China 1983). Total concentrations of Cd, Cr, Cu, Pb, and Zn in the digested solution were measured using an inductively-coupled plasma mass spectrometry (X7 ICP-MS, TMO, USA). For quality assurance and quality control, duplicates, method blanks and standard reference materials were also analyzed. Please note that the purpose of the study is to provide an example to demonstrate our suggested method; it does not represent a comprehensive environmental assessment accounting for all Hum. Ecol. Risk Assess. Vol. 20, No. 3,

7 M. Qu et al. pollutants for the study area. For a comprehensive assessment, all toxic metals and toxic organic chemicals in soils should be considered. Sequential Gaussian Simulation Sequential Gaussian simulation (SGS) is based on the multi-gaussian assumption of a random field. Therefore, if sample data apparently deviate from the Gaussian assumption, a prior normal score transformation is usually required to ensure the normality of at least the univariate distribution of data. After a regularly spaced grid covering the region of interest is defined, SGS involves the following steps: (1) Transform the sample data into standard normal data using a normal score transformation if they do not meet a Gaussian distribution. (2) Compute and model the variogram of the normal score transformed data. (3) Establish a random path through all of the grid nodes using the Monte Carlo method (i.e., using a random number generator to generate a series of random numbers uniformly distributed between 0 and 1), in a way that each node is visited only once in each sequence. (4) At each node x 0 : (a) Estimate the parameters (mean and variance) of the Gaussian conditional cumulative distribution function (ccdf) of heavy metals using simple kriging estimator with the normal score variogram model. The conditioning dataset includes a specified number of both the original data and already simulated data within a neighborhood of the location to be simulated. (b) Draw a simulated normal score value from the estimated ccdf and then add it to the conditioning dataset to be used for simulating other nodes. (c) Proceed to the next grid node along the random path and repeat steps (a) and (b) until the entire grid nodes are simulated. (5) Back-transform the simulated normal scores into the simulated values of heavy metals in the original data space. These sequential steps build up only the first realization, {z (1) (x 0j ), j = 1,..., N }, which is only one model of the spatial distribution of the target variable. To generate multiple, say L, realizations, {z (l) (x 0j ), j = 1,...,N, l = 1,..., L}, steps 3 to 5 should be repeated with different random paths passing through all nodes. Although SGS simulates from the kriging distribution at each unsampled location, SGS is much different from kriging and has its unique values: Each of the simulated realizations by SGS may represent a possible spatial pattern of the unknown reality, and these simulated realizations may visually display the spatial uncertainty associated with the kriging distribution and be used as model input to further propagate the uncertainty into related process models (e.g., ecological models). However, kriging interpolation only generates one optimal estimate surface, which usually smoothes out many details. In addition, SGS conditional variance at each location is related with the magnitude of the neighboring sample data because it is estimated from a number of simulated realizations. However, kriging variance at each location only depends on the sample data configuration within the neighborhood because it is estimated from kriging weights and variogram models (Rocha and Yamamoto 768 Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

8 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil Table 1. Summary statistics of concentrations (mg kg 1 ) of heavy metals in topsoil samples. Element Minimum Maximum Mean S.D. C.V. (%) Background value a Ratio b Cd Cr Cu Pb Zn a Natural background values of heavy metals in soil in Hubei Province (Wei et al. 1990). b Ratio = Mean/Background value. 2000). Detailed introduction on kriging and SGS methods can be found in Goovaerts (1997), Deutsch and Journel (1992), and Remy et al. (2009). Ecological Risk Assessment The evaluation of potential ecological risks of heavy metals in soil requires a series of assumptions: (1) abundance effect, that is, the PERI increases with the degree of metals pollution; (2) coordination effect, that is, the ecological hazards of heavy metals are additive and the potential ecological risk of multiple heavy metals is larger than that of a single heavy metal; (3) the toxicities of different heavy metals are different and heavy metals with higher toxicities have larger weights in risk index estimation. Under these prerequisites, the following evaluation indices can be derived: pollution factors, toxic-response factors, pollution degree, PERI for single metals, and PERI for multiple metals (Hakanson 1980). The ecological risk of heavy metals in soils is quantitatively described by the PERI. In this index, both the concentrations and ecological toxicity factors of heavy metals were considered. The PERI for a heavy metal in soil (E i ) is calculated using the equation E i = T i F i = T i C mi C ri, (1) where T i is the ecological toxic-response factor of the heavy metal i, andf i is the pollution factor of the heavy metal, a ratio of the measured concentration C mi of the heavy metal in soil to its background reference value C ri. The preindustrial background values were often adopted as reference values in calculations (Chen and Liu 1992). In this study, related heavy metal background values in soil in the province (Table 1) were adopted as reference values for the study area. According to Hakanson (1980), the values of toxic-response factors for different heavy metals are in the order of Cd = 30 > Cu = Pb = 5 > Cr = 2 > Zn = 1. The toxic-response factors determined by Hakanson (1980) have been used in ecological risk assessment studies in recent years (Cao et al. 2009; Liu et al. 2009; Sun et al. 2010; Iqbal and Shah 2011). So far we have not seen an update on these values. Hum. Ecol. Risk Assess. Vol. 20, No. 3,

9 M. Qu et al. Table 2. Indices and grades of potential ecological risks of heavy metals. Critical range Grade of ecological risk PERI (E i ) of single heavy metal <40 I Low II Moderate III Considerable IV High 320 V Very high Total PERI (RI ) of multiple heavy metals <110 A Low B Moderate C High 440 D Very high The total PERI for multiple heavy metals in soil (RI ) is calculated by summing the PERI values of individual heavy metals in soil RI = n E i, (2) i=1 where E i represents the PERI for the ith individual heavy metal and n is the number of heavy metals in soil studied. RI represents the sensitivity of the biological community to toxic substances (i.e., heavy metals here) in the environment. While E i is a parameter divided into five levels, RI is an integrative value of several contaminants with the division of four grades (Table 2). Uncertainty Evaluation In this study, a number of simulated realizations for the spatial distribution of each heavy metal were generated using SGS. Each of these simulated realizations was then used as input data to the Hakanson PERI computation equation to generate a responding realization of the potential ecological risk caused by the heavy metal; thus the spatial uncertainty in the concentration values of the heavy metal propagated through the Hakanson PERI equation, leading to uncertain responses in potential ecological risk. The spatial uncertainty of the PERI was assessed based on these responding realizations. Here a responding realization means a realization that is propagated from a simulated realization through a process model or an equation, rather than generated directly by a stochastic simulation algorithm. The uncertainty of the ecological risk grade for heavy metal i at a specific location x can be defined as the probability that the PERI of the metal at the location falls within a given specific critical threshold interval. The probability can be calculated using the following equation P [c E i (x) < c + ] = n (x) N, (3) where N is the total number of responding realizations of the PERI for each heavy metal (in this study, we use 500), and n(x) is the number of responding realizations whose PERI response values at the location x were within the interval defined by a pair of thresholds with low value c - and high value c +. Specific thresholds are listed in Table Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

10 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil Table 3. Parameters of the variogram models of normal-score-transformed concentrations of heavy metals in soil in the study area. Model Nugget C 0 Partial sill C C 0 /(C 0 + C) (%) Range (km) R 2 Cd Spherical Cr Exponential Cu Exponential Pb Exponential Zn Exponential In this study, we used the software version 2.1 of SGeMS (Remy et al. 2009) to perform the geostatistical computations. Statistical analyses were conducted in Matlab R2007b, and maps were produced using ArcGIS (version 9.2). All geostatistical analyses were performed on a regular square grid with a cell size of 150 m 150 m. The cell size was mainly determined based on the size of the study area for the purpose of clearly displaying the resulting maps. RESULTS AND DISCUSSION Sample Data Analysis A descriptive statistical summary of sample data for the concentrations of the five heavy metals in soil (i.e., Cd, Cr, Cu, Pb, and Zn) is listed in Table 1. While the average concentrations of Cr, Cu, Pb, and Zn are close to or even less than their background values, the average concentration of Cd, which is 0.74 mg kg 1, reaches 4.36 times of its background value in Hubei Province. This indicates that Cd has apparent enrichment in soils and has caused serious pollution to the environment in the study area. So it is necessary to intensively monitor the concentration status of this heavy metal in soil in the studied region and evaluate its hazard to human beings and the environment. Although four other heavy metals in soil do not indicate enrichment in their average concentrations, their maximum values and coefficients of variation show that they have reached the pollution level at some places or in some subareas in the study area. In addition, background values of heavy metals in soil are related to the soil s parent materials. The province-scale background values of heavy metals in soil may not reflect the local situation of the study area because the province covers a very huge area of 185,900 km 2. The low average soil concentrations of Cr, Cu, Pb, and Zn may imply that the local heavy metal background soil values are also lower than the provincial levels. Unfortunately the local background values are not available. So in this study we simply used the province-scale background values as reference and no adjustment was made. Spatial Distributions of Heavy Metals in Soil Because no apparent anisotropy was found from the sample data, experimental variograms were estimated omni-directly for each metal. The experimental variogram of normal score transformed data of Cd was fitted by a spherical model and Hum. Ecol. Risk Assess. Vol. 20, No. 3,

11 M. Qu et al. Figure 2. The E-type estimates and the 80th realizations generated by SGS for Cd (A and B), Cr (C and D), Cu (E and F), Pb (G and H), and Zn (I and J) in soil, respectively. 772 Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

12 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil Figure 3. SGS conditional variance maps for Cd (A), Cr (B), Cu (C), Pb (D), and Zn (E). the others were all fitted by exponential models with different parameters. Parameters of each variogram model based on corresponding normal score transformed data are presented in Table 3 and they indicate that these heavy metals have different spatial auto-correlation structures. The C 0 /(C 0 + C) ratios of fitted variogram models are all between 25% and 75%, which exhibits moderate spatial auto-correlations for each heavy metal. According to Cambardella et al. (1994), this situation may be attributed to both intrinsic factors such as soil properties and extrinsic factors such as human activities (e.g., land use and industrial impacts). Five hundred simulated realizations for the spatial distribution of each heavy metal were generated using SGS. The E-type estimate averaged from 500 simulated realizations and a randomly selected realization generated by SGS for each of the five heavy metal elements are shown in Figure 2. While every simulated realization Hum. Ecol. Risk Assess. Vol. 20, No. 3,

13 M. Qu et al. may represent a realistic spatial distribution of the corresponding heavy metal without the smoothing effect, the corresponding E-type estimate map does have the smooth effect because it represents an optimal estimation and will approach the kriged surface as the number of simulated realizations increases. These maps show that Cd and Cr have relatively high values in the study area s central and north subareas, which coincide with the locations of industry companies in the High-Tech Development Park of the city; but Cu, Pb, and Zn are apparently distributed more dispersedly, which may imply they mainly inherit from the soil s parental materials. SGS conditional variance maps for the five heavy metals (Figure 3) show both differences and similarities in their spatial distribution patterns. The reason should be that SGS conditional variance at a location is not only related to the spatial configure of surrounding sample data but also related to the magnitude of the sample data Figure 4. The E-type estimates of the ecological risk for Cd (A), Cr (B), Cu (C), Pb (D), and Zn (E). 774 Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

14 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil (Goovaerts 1997). This means that SGS is more reasonable than kriging interpolation in variance estimation because kriging variance depends only on sample data configuration (Rocha and Yamamoto 2000). Ecological Risk of Heavy Metals in Soil The E-type estimate maps of the PERIs for Cd, Cr, Cu, Pb, and Zn (Figure 4) show that the PERIs of the four heavy metals Cr, Cu, Pb, and Zn are all within the low risk grade (i.e., grade I), but the PERI values of Cd range across four different risk grades from the moderate grade (grade II) to the very high grade (grade V) in the study area. Apparently Cd is the major contaminant causing the ecological risk in the study area and should be treated seriously. High PERI values of Cd are mainly distributed in the study area s central and north subareas, while low values are mainly concentrated in the southeast corner of the study area. As the major heavy metal pollutant in the study area, the potential ecological risk of Cd in soil has reached the grades deserving much attention. Shown in Figure 5 are the probability maps of the Cd ecological risk for different pollution grades. One can see that grade III has the largest probability values at most places in the study area, and grade IV has higher probabilities to appear in some subareas in the central region. Although Cd pollution rarely reaches the highest ecological risk grade V (i.e., very high), this grade still has the possibility to occur at many places Figure 5. Probability maps of the Cd ecological risk for different pollution grades: moderate (A), considerable (B), high (C), and very high (D). Hum. Ecol. Risk Assess. Vol. 20, No. 3,

15 M. Qu et al. Figure 6. The E-type estimate (A) of the total PERI and its spatial distribution of corresponding pollution grades (B). in the central and north subareas. Remediation measures for Cd should be first applied to those places with higher occurrence probabilities of higher risk grades. The RI(i.e., total PERI) integrates the contributions of all heavy metal elements in soil to the ecological risk. The E-type estimate of the RI of the five measured heavy metals in soil and its corresponding ecological risk grade map are provided in Figure 6. Because the major contribution comes from Cd, one can see that high RI values mainly appear in the center of the study area (Figure 6a). The total ecological risk grade map based on RI indicates that most of the study area belongs to the moderate grade, mainly because of the low contributions of other heavy metals in soil except for Cd. In general, the potential ecological risk caused by heavy metals in soil, particularly by Cd, in the study area has reached a serious level; therefore, essential measures should be taken to prevent further deterioration, and the top priority area of remediation is the central region, where related pollution sources may be located. CONCLUSION The Hakanson PERI and SGS were combined to characterize the spatial variability and uncertainty of ecological risks of heavy metals in soil in a study area in the urban rural transition zone of the Wuhan City, China. We simulated the spatial distribution of each heavy metal using SGS and fed the simulated realizations into the Hakanson PERI computation equation to obtain the response maps of the PERI for each metal, so that the spatial distribution and uncertainty of the ecological risk caused by each heavy metal and the total ecological risk caused by all studied heavy metals in soil can be assessed and evaluated. The study showed that, based on the province background values, the potential ecological risks for Cr, Cu, Pb, and Zn were all within the low grade but Cd does pose a serious ecological risk in the study area, and thus the total ecological risk still reached a moderate level. With time and more industries being established in the study area, the potential ecological risk may quickly increase if essential measures are not applied. 776 Hum. Ecol. Risk Assess. Vol. 20, No. 3, 2014

16 Spatial Distribution and Uncertainty Assessment of Heavy Metals in Soil Comparing with those evaluation studies purely based on the concentrations of heavy metals in soil, this evaluation should be more realistic because different heavy metals in soil have different ecological toxicities. The uncertainty assessment in mapping the ecological risks of heavy metals may provide valuable information for pollution remedy and control. This study indicates that combining SGS and the Hakanson PERI may be a valuable method for quantitatively assessing the spatial distribution and uncertainty of the potential ecological risk of heavy metals in soil. ACKNOWLEDGMENTS This study was partially supported by the National Natural Science Foundation of China (Grant ), the China postdoctoral Science Foundation (Grant 2013M530273), and was based on part of the contents of the Mingkai Qu s PhD dissertation. We thank the journal s anonymous reviewers and the editor for their constructive comments and suggestions in revising this article. REFERENCES Agricultural Chemistry Committee of China Conventional Methods of Soil and Agricultural Chemistry Analysis (in Chinese), pp Science Press, Beijing, China Amini A, Afyuni M, Khademi H, et al Mapping risk of cadmium and lead contamination to human health in soils of Central Iran. Sci Total Environ 347:64 77 Cambardella CA, Moorman TB, Nocak JM, et al Field-scale variability of soil properties in central Iowa soils. Soil Sci Soc Am J 58: CaoH,LuanZ,WangJ,et al Potential ecological risk of cadmium, lead and arsenic in agricultural black soil in Jilin Province, China. Stoch Environ Res Risk Assess 23:57 64 Cattle JA, McBratney AB, and Minasny B Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. J Environ Qual 31: Chen JS and Liu YJ Research on Heavy Metals in Water Environment in China (in Chinese), pp Chinese Environmental Science Publishing House, Beijing, China Chen YY, Wang J, Gao W, et al Comprehensive analysis of heavy metals in soils from Baoshan District, Shanghai: A heavily industrialized area in China. Environ Earth Sci 67: Chilès JP and Delfiner P Geostatistics Modeling Spatial Uncertainty (Wiley Series in Probability and Statistics). Wiley, New York, NY, USA Culbard EB, Thornton I, Watt J, et al Metal contamination in British urban dusts and soils. J Environ Qual 17: Deutsch CV and Journel AG GSLIB: Geostatistical Software Library and User s Guide. Oxford University Press, New York, USA Folinsbee LJ Human health effects of air pollution. Environ Health Persp 100:45 6 Gay JR and Korre A A spatially-evaluated methodology for assessing risk to a population from contaminated land. Environ Pollut 142(2): Goovaerts P Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, NY, USA Goovaerts P, Webster R, and Dubois JP Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics. Environ Ecol Stat 4:31 48 Hakanson L An ecological risk index for aquatic pollution control: A sedimentological approach. Water Res 14(8): Hum. Ecol. Risk Assess. Vol. 20, No. 3,

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