LIU Yang; MA Zongwei; LV Jianshu; BI Jun

doi:10.1007/s11442-016-1296-x

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地理学报(英文版) ›› 2016, Vol. 26 ›› Issue (6) : 735-749. DOI: 10.1007/s11442-016-1296-x

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Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China

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Abstract

With rapid economic and social development, soil contamination arising from heavy metals has become a serious problem in many parts of China. We collected a total of 445 samples (0-20 cm) at the nodes of a 2 km×2 km grid in surface soils of Rizhao city, and analyzed sources and risk pattern of 10 heavy metals (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn). The combination of Multivariate statistics analysis and Geostatistical methods was applied to identify the sources and hazardous risk of heavy metals in soils. The result indicated that Cr, Ni, Co, Mn, Cu, and As were mainly controlled by parent materials and came from natural sources. Cd and Hg originated from anthropogenic sources. Pb and Zn, belonging to different groups in multivariate analysis, were associated with joint effect of parent materials and human inputs. Ordinary Kriging and Indicator Kriging suggested that single element and elements association from the same principal components had similar spatial distribution. Through comprehensive assessment on all elements, we also found the high risk areas were located in the populated urban areas and western study area, which could be attributed to the higher geological background in the western part and strong human interference in the eastern part.

导出引用

LIU Yang, MA Zongwei, LV Jianshu, BI Jun. [J]. Journal of Geographical Sciences, 2016, 26(6): 735-749 https://doi.org/10.1007/s11442-016-1296-x

LIU Yang, MA Zongwei, LV Jianshu, BI Jun. Identifying sources and hazardous risks of heavy metals in topsoils of rapidly urbanizing East China[J]. Journal of Geographical Sciences, 2016, 26(6): 735-749 https://doi.org/10.1007/s11442-016-1296-x

1 Introduction

With the rapid industrialization and urbanization, the heavy metals contamination in soils has become an important hazard to the ecosystem health (Alloway, 1995; Fu et al., 2012; Li et al., 2015). Heavy metals are the essential indicators to evaluate the environmental quality, which has been a hotspot in geographical, soil and environmental sciences. Soil properties are mainly influenced by natural factor and anthropogenic activities. In particular, human activities, including vehicle emissions, industrial waste, application of pesticide and fertilizer, atmospheric deposition resulting from dust and aerosol, are commonly the main driving forces of heavy metals pollution in soils (Alloway, 1995; Cai et al., 2012; Sebai et al., 2007). The raised heavy metals exceeding some thresholds could influence soil physical and chemical properties, restrain the micro-organism activity, and block nutrients supplies. Furthermore, heavy metals pose a risk to human health through food intake, direct ingestion and dermal contact. Heavy metals are not removed by natural degradation processes; they accumulated in soils over time. Therefore, sources identification and hazardous risk delineation of heavy metals contamination is essential because it can provide references for soil remediation and effective management recommendations.

Multivariate analysis and Geostatistical method have been widely used to the study on modeling spatial variability of soil properties and mapping the spatial distribution and hazardous risk (Lee et al., 2006; Facchinelli et al., 2001). Commonly, correlation analysis, principal component analysis and cluster analysis were applied to identify the anthropogenic and natural sources of elements. These methods could substitute the comprehensive indicator for the one variables association, and reflect the relationship of various variables. Furthermore, the results derived from these methods can verify each other (Yalcin and Ilhan, 2008). Recently, there were many examples of multivariate analysis in sources identification of heavy metals (Facchinelli et al., 2001; Inigo et al., 2011; Boruvka et al., 2005; Zhang, 2006; Imperato et al., 2003; Sajn et al., 2011).

Indicator Kriging (IK) is a non-parametric geostatistical method, which is commonly used to solve the highly skewed and soil pollution data. Indicator Kriging makes no assumptions about the underlying invariant distribution, and 0-1 indicator transformations of the data make the predictor robust to outliers (Goovaerts, 1997). The estimated value in the unsampling sites by Indicator Kriging indicates the probability that the values are more or less than a specified cut-off. Indicator Kriging has been used in soil science, which included the quality of soils (Smith et al., 1993; Halvorson et al., 1996; Oyedele et al.; 1996), soil degradation (Diodato and Ceccarelli, 2004; Eldeiry and Garcia, 2011), as well as environmental risk of heavy metals (Goovaerts, 1997; Van Meirvenne and Goovaerts, 2001; Chu et al., 2010). Furthermore, the application of IK also spread into hydrology and water resource science, ecology, including groundwater pollution risk, potentiality of water resources (Lee et al., 2008; Jang, 2013).

Since the 1980s, Rizhao experienced spreading economic and social development. The gross domestic product has reached 10.25 billion Yuan, and there are 1072 industrial enterprises and 0.61 billion vehicles. The rapid development poses strong pressure on the soil environment, and has resulted in the environmental pollution. In this paper, we started with a systematic sampling and chemical analysis of 445 topsoil samples from Rizhao city. The concentrations of 10 heavy metals (As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn) were presented and compared with other regions. Multivariate statistical procedures were used to identify heavy metal sources, while Indicator Kriging was used to delineate the hazardous risk of heavy metals exceeding the background values of the study area.

2 Materials and methods

2.1 Study area

Rizhao city (119°0′43″E-119°39′22″E and 35°4′55″N-35°36′25″N) is located in the southeastern part of Shandong province, East China (Figure 1), and extends for about 1800 km². The study area consists of an alluvial plain in the eastern part and mountainous area in the western part. The elevation presents a decreasing trend from southwest to northeast with a range of 1.3-656.9 m. Skeleton and brown soils are located in the western mountainous area, which are commonly originated from granite and metamorphic rocks. Alluviation and marine deposition are the main parent materials as the origin of moisture soil and cinnamon soil. The area has a temperate continental climate with a mean annual temperature of 13℃. The average annual precipitation in the area is 870 mm, and the annual average relative humidity is 83%. With the rapid urbanization and industrialization, currently the urban area has increased to 116 km² from 13 km² in the early 1980. The industry concentrates on the coastal area in the eastern part, while the agriculture is mainly located in the western part of the study area.

Figure 1 The location and sampling sites in Rizhao, East China

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2.2 Soil samples collection and chemical analysis

The study area was divided into 2 km×2 km cells, and then the 445 sites are selected at the centre of each cell (without sites in the water body). At each sampling site, four to six subsamples of topsoil (0-20 cm) were taken and mixed thoroughly to obtain a composite sample. In the case of soil being unavailable at the centre of the cell (i.e. urban area, road, etc.), an alternative location is selected as close as possible to the centre of the cell to find the natural soils. Locations of sampling sites are shown in Figure 1.

Samples were air-dried at room temperature (25℃), and were conducted to remove stones, coarse materials, and other debris. The samples with 10g weight were ground to a fine 0.15 mm powder. The samples for analyzing Cd, Cr, Cu, Mn, Ni, Pb, and Zn were digested using H₂SO₄-HNO₃-HF, the levels of Cr, Cu, Mn, Ni, Pb, and Zn concentrations in the samples were determined by flame atomic absorption spectrophotometer, while Cd was determined by graphite furnace atomic absorption spectrophotometer. The samples for Hg determination were digested by H₂SO₄-HNO₃-KMnO₄, and were determined with an atomic fluorescence spectrometer. The recovery of chemical measure was 10±100%, indicating that the accuracy was satisfactory for this study.

2.3 Descriptive and multivariate analysis

Descriptive statistical indicators including range, mean, median, standard deviation (SD), variation of coefficient (CV), skewness and kurtosis were computed to examine the data structures. Correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were used to evaluate the data to acquire the sources of heavy metals, and were performed using SPSS 16.0 software.

2.4 Indicator Kriging

Indicator Kriging (IK) is a non-parametric geostatistical method for estimating the probability of exceeding a specific threshold value at a given location. IK includes Univariate Indicator Kriging and Multiple-variable Indicator Kriging.

In Univariate Indicator Kriging, the original value I(u; z_k) was transformed into indicator variables (Lark and Ferguson, 2004; Lin et al., 2010), which is written as follows:

(1)

The following calculation step is concomitant with Ordinary Kriging.

A linear combination of indicator variables can be used to estimate the probability in unsampling location, according to The Indicator Kriging system can be solved using where I^*(u₀; z_k) represents the values of the indicator variable at the measured locations; η is the Lagrange multiplier; γ_j is the variogram of the indicator variable at the respective lag distance, λ_i is a weighting factor in estimation.

(2)

(3)

Multiple-variable Indicator Kriging can combine indicator variables resulted from respective cut-off together into one integrative variable. Based on the results of various variables calculated in Eq. 1, the multiple-variables integration is obtained by Eq. 4, and the following calculation steps are the same to Univariate Indicator Kriging.

(4)

where the weights of various heavy metals W_k are determined by the contribution of respective toxic response factors (Hakanson 1980) according to Eq. 5. The toxic response factors of Hg, Cd, As, Pb, Co, Cu, Ni, Cr, Zn, and Mn are 40, 30, 10, 5, 5, 5, 5, 2, 1, and 1, respectively, which can be used to assess the comprehensive risk from the combination of elements.

(5)

where W_k represents the weight of each heavy metal, R_k is toxic response factors; m is the number of elements in the comprehensive evaluation on potential risk.

In this study, the potential hazardous risk derived from Indicator Kriging was conducted using Geostatistical analyst in ArcGIS software (Esri inc., USA).

3 Results and discussion

3.1 Descriptive statistics of heavy metals in soils

The descriptive statistics of 445 heavy metals samples are summarized in Table 1. The average contents of As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb and Zn were 5.04, 0.20, 10.87, 54.09, 17.57, 0.043, 597.08, 23.52, 27.78 and 63.10 mg/kg, respectively. The mean concentrations of As, Co, Cr and Cu were lower than the background values of eastern Shandong province (Dai et al., 2011), but their maximum concentrations were much higher than background values. The mean concentrations of Pb, Zn, Hg, Cd, Ni and Mn exceeded background values; in particular, the mean contents of Hg and Cd almost reached twice time their responding background values. The coefficient of variation (CV) is an independent measure of relative dispersion, and it is a useful tool to demonstrate the degree of discrete distribution of different elements. Commonly, the lithogenic element has relatively low CV, while the CV of the elements affected by anthropogenic sources tends to be quite high. Based on CV classification, As, Co, Mn, Pb and Zn (26.5%, 32.2%, 21.7%, 21.8% and 31.8%) had moderate variability (15%Cd, Cr, Cu, Hg and Ni (196.9%, 50.6%, 50.6%, 385.0% and 54.6%) had high variability (CVs>36%). It could be suggested that the CVs of Hg and Cd were higher than other elements, and the two elements may be dominated by anthropogenic sources. The skewness values of heavy metal contents showed larger variability of heavy metal contents, with positively skewed frequency distribution. This is common for heavy metals as a result of its common presence of a point source contamination. The skewness of various elements followed a decreasing trend of Cd > Hg > Ni > Cu > Cr > Pb > Zn > Co > As> Mn, the skewness of Cd and Hg were quiet higher than others, indicating that the distribution of Cd and Hg in the study area was highly asymmetric.

Table 1 The descriptive statistics contents of heavy metals in Rizhao, East China (mg/kg)

	Range	Min	Max	Median	Mean	SD	CV (%)	Skewness	Kurtosis	Background values of eastern Shandong province (Dai et al., 2011)
As	9.90	1.90	11.80	4.90	5.04	1.335	26.5	0.755	1.323	6.30
Cd	4.78	0.026	4.81	0.095	0.20	0.227	196.9	19.922	413.056	0.108
Co	22.80	3.00	25.80	10.30	10.87	3.497	32.2	0.975	1.248	11.00
Cr	265.40	12.20	277.60	46.00	54.09	27.387	50.6	2.934	14.994	56.20
Cu	94.90	4.90	99.80	15.60	17.57	8.896	50.6	3.699	22.382	19.60
Hg	3.27	0.008	3.28	0.025	0.043	0.165	385.0	17.628	338.390	0.029
Mn	920.00	292.00	1212.00	595.50	597.08	129.84	21.7	0.542	1.383	552.00
Ni	159.20	7.10	166.30	20.20	23.52	12.829	54.6	4.346	37.054	23.50
Pb	54.10	18.20	72.30	26.30	27.78	6.045	21.8	2.340	9.511	25.40
Zn	152.30	18.30	170.60	60.30	63.10	20.087	31.8	1.077	2.633	56.10

3.2 Comparison of the contents between Rizhao and other regions

Table 2 illustrates the heavy metals levels in Rizhao compared with other regions in the world. The mean content of As in the study area was the lowest among various regions mentioned above. The average content of Cd was lower than Alicante, Ebro, Zagreb, Galicia, Kavadarci, Ireland, and Yangzhong, and higher than other regions. For Co, the mean content was lower than other regions expect for Alicante and Ireland. The mean content of Cr was close to Luhe, Zagreb and Galicia, and lower than Ju, Zhengding and Yangzhong. The level of Cu was relatively low, merely higher than Huizhou, Ebro, Ireland and Duero. The mean value of Hg contents sampled in Rizhao was close to Ebro and Juxian county, and lower than other regions. Mn showed the average content close to Juxian county and Zagreb. The mean content of Ni, similar to Galicia, was lower than Juxian county, Piemonte, Luhe, Zagreb, Zhengding, Kavadarci, and Yangzhong. The mean value of Pb contents, except lower than Juxian county, Huizhou, and Yangzhong, was higher than other regions. The average content of Zn in the study area was close to Piemonte and Ireland, but lower than Juxian county, Luhe, Zagreb, Galicia, Shunyi, Zhengding and Yangzhong. Overall, in the presented study, As, Cu and Hg exhibited relatively lower level compared with other regions, Cd, Co, Ni, Mn, and Zn showed moderate position in all the regions, while Cr and Pb have an upper position among various regions.

Table 2 The mean contents of Rizhao compared with typical regions in the world (mg/kg)

	As	Cd	Co	Cr	Cu	Hg	Mn	Ni	Pb	Zn	References
Rizhao	5.04	0.20	10.87	54.09	17.57	0.04	597.08	23.52	27.78	63.10	This work
Juxian county	-	0.13	13.26	68.28	22.97	0.037	598.65	29.36	28.4	65.81	(Lv et al., 2014)
Huizhou	10.19	0.1	-	27.61	16.74	0.22	-	14.89	44.66	57.21	(Cai et al., 2012)
Alicante	-	0.34	7.1	26.5	22.5	-	295	20.9	22.8	52.8	(Mico et al., 2006)
Ebro	-	0.415	-	20.27	17.33	0.0356	-	20.5	17.54	57.53	(Rodríguez Martín et al., 2006)
Piemonte	-	-	19.001	46.157	58.309	-	-	83.163	16.101	62.683	(Facchinelli et al., 2001)
Luhe	-	0.046	-	55.01	23.94	0.07	-	29.37	27.37	65.12	(Zhao et al., 2010)
Zagreb	-	0.4	10.9	54.6	56.1	-	579	35.2	23.2	77.9	(Sollitto et al., 2010)
Galicia	-	0.31	14	54.1	20.5	-	659.9	23.5	11.7	98.7	(Franco-Uria et al., 2009)
Shunyi	7.85	0.136	-	-	22.4	0.073	-	-	20.4	69.8	(Lu et al., 2012)
Zhengding	6.16	0.15	-	57.77	21.22	0.08	-	25.04	18.8	69.96	(Yang et al., 2009)
Kavadarci	8.5	0.32	15	50	30	-	780	74	21	56	(Stafilov et al., 2013)
Ireland	10.2	0.326	6.2	42.6	16.2	0.086	462	17.5	24.8	62.6	(Zhang, 2006)
Yangzhong	-	0.3	-	77.2	33.9	0.2	-	38.5	35.7	98.1	(Huang et al., 2007)
Duero	-	0.159	-	20.53	11.01	0.0421	-	-	15.08	42.42	(Nanos and Rodríguez Martín, 2012)

“-” represents unavailable

3.3 Multivariate analysis of heavy metals in soils

3.3.1 Correlation analysis results

Correlation analysis can provide the interesting information on the relationship among various elements, and commonly high correlation between elements could indicate the common source. Pearson’s correlation coefficients of heavy metals in soils are summarized in Table 3. Correlation coefficients of Co-Cr, Mn-Co, Ni-Co, Mn-Cr, Ni-Cr and Ni-Mn were 0.739, 0.719, 0.700, 0.403, 0.947 and 0.367, respectively, and have passed the 0.01 level significance tests. The strongly positive correlation among Co, Cr, Mn and Ni commonly indicates their natural sources. As, Cd, Cu, Hg, Pb and Zn had little correlation with other elements (r<0.25), and these relationships among these heavy metals should be further assessed by multivariate analysis. The correlation analysis results suggested that there existed some original relationship between heavy metals, and indicated different possible sources of elements.

Table 3 Correlation coefficient matrix of heavy metals in soils

	As	Cd	Co	Cr	Cu	Hg	Mn	Ni	Pb	Zn
As	1	0.029	-0.009	-0.044	0.208**	0.031	0.044	-0.060	-0.066	-0.105*
Cd	0.029	1	0.036	0.020	0.084*	0.030	0.071	0.025	0.199**	-0.047
Co	-0.009	0.036	1	0.739**	0.367**	0.012	0.719**	0.700**	0.164**	0.115**
Cr	-0.044	0.020	0.739**	1	0.256**	-0.010	0.403**	0.947**	0.004	0.022
Cu	0.208**	0.084*	0.367**	0.256**	1	-0.016	0.284**	0.249**	0.142**	-0.055
Hg	0.031	0.030	0.012	-0.010	-0.016	1	0.029	-0.020	-0.025	-0.016
Mn	0.044	0.071	0.719**	0.403**	0.284**	0.029	1	0.367**	0.251**	0.232**
Ni	-0.060	0.025	0.700**	0.947**	0.249**	-0.020	0.367**	1	0.030	-0.035
Pb	-0.066	0.199**	0.164**	0.004	0.142**	-0.025	0.251**	0.030	1	-0.066
Zn	-0.105**	-0.047	0.115**	0.022	-0.055	-0.016	0.232**	-0.035	-0.066	1

** represents significant at the 0.01 level* represents significant at the 0.05 level

3.3.2 PCA analysis results

Principal component analysis is a powerful tool to identify effective sources of heavy metals. The results of PCA in the topsoil are shown in Table 4 and Figure 2. Four principal components (PC1, PC2, PC3 and PC4) were obtained with an Eigen value higher than 1, together explaining 73.7% variance of the total data. The 36.1% variance was explained by PC1, showing elevated loadings of Co, Cr, Mn, Ni, Zn, and moderate loading of Cu and Pb. PC2 amounted on 15.4% variance of total data, and showed significant loadings of Cd (0.881) and Pb (0.726). PC3 with the 12.1% variance was dominated by the loading As and Cu. PC4 with a variance loading of 10.1% was merely dominated by Hg.

3.3.3 Cluster analysis results

Although CA is not notably different from PCA, this method is an alternative to confirm the results. The cluster analysis results for the heavy metal contents in the studied soils are illustrated in Figure 3. It can be seen that cluster analysis coincided with the PCA. The total data can be divided in to four clusters. Cluster 1 included Cr, Ni, Co, Mn and Zn, which was consistent with the PC1. Cluster 2 contained As and Cu, which were grouped in the PC 3. Pb, Cd and Hg were contained in the cluster 3, which could reflect the PC2 and PC4. It can be demonstrated that the relationship of heavy metals was consistent with PCA.

3.3.4 Sources identification of heavy metals in soils

Based on correlation analysis, PCA and CA results, four elements groups could be identified. As, Cr, Co, Cu, Ni, Mn, partially Pb and partially Zn associated with PC1 were mainly controlled by natural sources. Cd and partially Pb originated from human activities. Hg, as an isolated element, was dominated by human inputs.

Figure 2 The loadings of the first four principal components

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Figure 3 The result of cluster analysis

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The heavy metals in Group 1, Cr, Ni, Co and Mn, with strongly positive correlations in the correlation analysis, had highly positive loading in PC1 and were classified together in CA. The mean contents of Co, Cr, Mn, Ni and Zn were close to or lower than the background value of eastern Shandong province. Sajn et al. (2011), Facchinelli et al. (2001), and Boruvka et al. (2005) suggested a lithogenic origin of Co, Cr, Mn, Ni and Zn in soils, and basic and ultrabasic rocks tend to present high contents than other parent materials. Therefore, it seemed reasonable to infer that PC1 represented the natural origin.

Pb and Cd are commonly influenced by human activity. Pb mainly resulted from the vehicle exhaust and coal combustion. Cd came from the application of fertilizers and pesticides; meanwhile waste water discharge from the electroplating and metallurgy also contribute to the Cd contents (Mico et al., 2006; Rodríguez Martín et al., 2006). Furthermore, the mean contents of Cd and Pb in the study area were much larger than the background values. Therefore, Cd and Pb were seemed to result from the anthropogenic sources.

As and Cu could also be considered lithogenic elements. The sources of As and Cu depend on the human activities in different areas (Sun et al., 2013). Some previous works found that As and Cu were affected by human inputs, such as coal combustion and the application of fertilizers. In the present study, the mean content of As and Cu were lower than background values, and they were negatively correlated with human elements. Therefore, based on the multivariate analysis of topsoil samples, we concluded that the natural sources dominated the As and Cu contents.

In this study, Hg is an isolated anthropogenic element in multivariate analysis, which also has been demonstrated in the works of Cai et al. (2012) and Davis et al. (2009). If an element has equivalent loads in two PCs, this element has the common properties of both PCs (Wang and Lu, 2011). Zn and Pb had the high load in both PC1 and PC2, and it could be concluded that the parent materials and human activities determined the contents of these elements. Cu with the loads in the two natural PCs could be considered as the natural sources.

3.4 Geostatistical analysis

Geostatistics were commonly employed to examine the spatial structure and variability of heavy metals. In the present study, Ordinary Kriging was applied to map the spatial patterns of four PCs derived from principal component analysis. The potential hazardous risk of elements is defined here as probability of exceeding the respective background value in eastern Shandong province (Dai et al., 2011), which could be delineated by Indicator Kriging.

3.4.1 Variograms fitting

The variogram could depict the variance of the sample values at various distances of separation, and provides the input parameters for the spatial interpolation of Kriging. The types of variograms mainly include Spherical, Exponential and Gaussian models, with parameters including nugget (Co), sill (Co+C), range values, coefficients of determination (R²). Nugget represents the experimental error and field variation within the minimum sampling spacing. The ratio of nugget to sill can be used to reflect the extent of spatial autocorrelations of the factors. If the ratio is less than 25%, the variable has strong spatial dependence; between 25% and 75%, the variable has moderate spatial dependence; and greater than 75%, the variable shows only weak spatial dependence. Strong spatial dependence of soil properties can be contributed to intrinsic factors (soil formation factors), and weak spatial dependence can be contributed to extrinsic factors. Coefficients of determination (R²) reflect the precision that elements are fitted by variogram.

The attributes of the variogram fitting are summarized in Table 5. The variograms of PC1, PC4, Cd, Co, Cr, Cu, Hg, Mn, Ni, Co-Cr-Mn-Ni-Zn, Cd-Pb, As-Cu and all elements integration variables were adjusted best to an exponential model and their ranges were between 1660m to 45900m. PC2, PC3, As and Pb variables could be fitted with spherical model, and Gaussian model was used to fit Zn variables. The values of R² were greater than 0.553, showing that the variogram models gave good descriptions of spatial structure. The nugget/sill ratios of PC1, PC3, As, Co, Cr, Cu, Mn, Ni, Co-Cr-Mn-Ni-Zn and As-Cu were 0.192, 0.234, 0.197, 0.145, 0.191, 0.123, 0.125, 0.124, 0.212 and 0.125 respectively, indicating high spatial dependence due to the effects of natural factors such as parent materials and topography. The nugget/sill ratios of PC2, PC4, Cd, Hg, Pb, Zn, Cd-Pb and all elements integration ranged from 0.409 to 0.818, suggesting low spatial dependence related to anthropogenic factors.

Table 5 The variograms fitting of heavy metals in soils

	Variable	Model	Co	Co+C	Co/Co+C	R/m	Rss	R²
Ordinary Kriging	PC1	Exponential	3.588	18.67	0.192	45900	7.42E-03	0.988
	PC2	Spherical	1.297	2.195	0.591	13730	4.05E-03	0.964
	PC3	Spherical	0.297	1.271	0.234	8240	1.45E-04	0.943
	PC4	Exponential	0.137	0.167	0.818	3330	3.61E-03	0.553
Univariate Indicator Kriging	As	Spherical	0.046	0.233	0.197	8110	9.01E-04	0.875
	Cd	Exponential	0.108	0.235	0.460	28890	3.83E-04	0.975
	Co	Exponential	0.032	0.221	0.145	28680	3.55E-04	0.975
	Cr	Exponential	0.026	0.136	0.191	7770	4.67E-04	0.949
	Cu	Exponential	0.024	0.195	0.123	4620	3.51E-04	0.847
	Hg	Exponential	0.084	0.109	0.771	4500	8.50E-04	0.670
	Mn	Exponential	0.031	0.248	0.125	5760	2.86E-03	0.625
	Ni	Exponential	0.025	0.201	0.124	8460	3.70E-04	0.966
	Pb	Spherical	0.119	0.221	0.538	9480	9.46E-04	0.879
	Zn	Gaussian	0.130	0.318	0.409	32510	3.43E-04	0.994
Multiple Variable Indicator Kriging	Co-Cr-Mn-Ni-Zn	Exponential	0.033	0.156	0.212	19530	1.54E-04	0.981
	Cd-Pb	Exponential	0.115	0.172	0.669	24540	1.48E-04	0.979
	As-Cu	Exponential	0.016	0.128	0.125	4740	2.04E-04	0.812
	All elements	Exponential	0.028	0.056	0.500	1660	7.49E-06	0.986

3.4.2 Spatial patterns of PCs

Figure 4 indicates the spatial patterns of various principal components. The spatial pattern of PC1 was characterized by high contents in western mountainous region and low content along the coastal areas, which was similar to the pattern of parent material. The soils from granite and metamorphic rock have higher geological background than alluviation and marine deposit along the coastal area. Therefore, the distribution of PC1 was controlled by the parent materials, which is consistent with the results of variogram analysis.

The high values of PC2 were located in western agricultural area and eastern urban area (Figure 4b). The developed economy and intensive industry resulted in the high values in eastern urban area. There is serious soil intrusion in the western part; however the soil fertility in the western part is higher than that in the eastern part, which could be attributed to the excessive application of chemical fertilizer in the western part. The application of chemical fertilizer in the western part is 2.39 times higher than the mean level in the whole study area (RMBS, 2012), which contributed to the high PC2 scores in the western part.

The higher region of PC3 is located in the coastal area (Figure 4c). The As content in urban area was higher than others, but it always was lower than the background value. The high geological background in alluviation and marine deposit was the dominating factor on the content of As, which is consistent with the tendency of the whole Shandong province (Dai et al., 2011). The spatial pattern of PC4 followed the decreasing trend from the eastern to the western part, and the high scores were consistent with the location of urban area, indicating its human source (Figure 4d).

Figure 4 The spatial patterns of the principal components in Rizhao, East China

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3.4.3 Delineating high risk areas by Indicator Kriging

Figures 5 and 6 illustrate the spatial distribution of hazardous risk of various elements and elements association in the topsoils. The kriged maps showed that the single element, elements association and their corresponding PCs had some similar features, indicating that it is reasonable to apply Indicator Kriging to delineate hazardous risk of heavy metals.

Figure 5 Risk probability maps of single heavy metals in Rizhao, East China

Full size|PPT slide

The spatial distributions of Co, Cr, Mn, Ni, Zn and Co-Cr-Mn-Ni-Zn were characterized by an overall tendency downward from western to eastern part, and the areas with high risk probabilities (Prob>0.8) were mainly located in the western part originated from granite and metamorphic rocks (Figures 5c, 5d, 5g, 5h 5j and 6a). For Cd-Pb, the high risk probability accounting for 5.5% of the total area was associated with the eastern urban part and the western study area (Figure 6b and Table 6). The map of As-Cu had the highest proportion of non-risk level and showed no area of high risk level (Figure 6c and Table 6). The probability map of Hg showed that high risk probability of Hg was evidenced in the eastern urban area, and covered 18503 hm² of the total area (Figure 5f and Table 6).

For the comprehensive risk of all elements integration, no area was found to be in the highest risk level, and low and moderate risk level covered more than 90% of the study area (Table 6). The considerable risk was mainly located in western and eastern parts, which could be attributed to the high geological background in the western part and strong human activity in the eastern part.

Figure 6 Comprehensive risk probability of heavy metals in Rizhao, East China

Full size|PPT slide

Table 4 Results of factors matrix

Element	PC1	PC2	PC3	PC4
As	-0.065	-0.179	0.883	-0.091
Cd	0.160	0.881	0.173	0.140
Co	0.910	-0.144	0.014	0.023
Cr	0.810	-0.471	-0.118	0.029
Cu	0.477	0.065	0.551	-0.182
Hg	-0.005	0.001	0.162	0.968
Mn	0.756	0.170	0.107	0.037
Ni	0.792	-0.460	-0.137	0.018
Pb	0.333	0.726	-0.025	-0.077
Zn	0.748	0.518	-0.151	0.004

Table 6 Results of environmental risk assessment of Rizhao, East China

Probability range	Co-Cr-Mn-Ni-Zn		Cd-Pb		As-Cu		Hg		All elements
Probability range	Area (hm²)	Percentage (%)	Area (hm²)	Percent- age( %)	Area (hm²)	Percentage (%)	Area (hm²)	Percentage (%)	Area (hm²)	Percentage (%)
Non-risk (0-0.2)	55804	30.2	45431	24.6	106320	57.6	3165	1.7	2197	1.2
Low risk (0.2-0.4)	49423	26.8	58031	31.4	61429	33.3	32820	17.8	68712	37.2
Moderate (0.4-0.6)	42652	23.	32384	17.5	16397	8.9	79396	43.0	102097	55.3
Considerable risk (0.6-0.8)	28841	15.6	38734	21.0	573	0.3	50835	27.5	11713	6.3
High risk (0.8-1.0)	7999	4.3	10139	5.5	0	0	18503	10.0	0	0

4 Conclusions

Based on 445 samples of a 2 km × 2 km grid soil data, the sources and hazardous risk of heavy metals were identified. The contents of As, Co, Cr and Cu were lower than the background values of eastern Shandong province, while Cd, Hg, Mn, Ni, Pb and Zn were higher than the background values. Cd and Hg were dominated by anthropogenic inputs. As, Co, Cr, Cu, Mn and Ni were controlled by parent materials. Pb and Zn came from the combination of human inputs and natural sources.

The spatial distribution maps of heavy metal contents were created based on geostatistical technique. The single element, elements association and its corresponding PCs have some similar features, indicating that it is reasonable to apply Indicator Kriging in the delineating risk of heavy metals. The relatively high level of comprehensive risk was found to be located in the western and eastern parts, and furthermore more measures should be taken to prevent soil environmental deterioration in these areas.

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

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This paper presents a practical method to manage soil salinity and yield in order to obtain maximum economic benefits. The method was applied to a study area located in the southeastern part of the Arkansas River Basin in Colorado where soil salinity is a problem in some areas. The following were the objectives of this study: (1) generate classified maps and the corresponding zones of uncertainty of expected yield potential for the main crops grown in the study area; (2) compare the expected potential productivity of different crops based on the soil salinity conditions; and (3) assess the expected net revenue of multiple crops under different soil salinity conditions. Four crops were selected to represent the dominant crops grown in the study area: alfalfa, corn, sorghum, and wheat. Six fields were selected to represent the range of soil salinity levels in the area. Soil salinity data were collected in the fields using an EM-38 and the location of each soil salinity sample point was determined using a global position system unit. Different scenarios of crops and salinity levels were evaluated. Indicator variograms were constructed for each scenario to represent the different classes of percent yield potential based on soil salinity thresholds of each crop. Indicator kriging (IK) was applied to each scenario to generate maps that show the expected percent yield potential areas and the corresponding zones of uncertainty for each of the different classes. Expected crop net revenue for each scenario was calculated and all the results were compared to determine the best scenarios. The results of this study show that IK can be used to generate guidance maps that divide each field into areas of expected percent yield potential based on soil salinity thresholds for different crops. Zones of uncertainty can be quantified by IK and used for risk assessment of the percent yield potential. Wheat and sorghum show the highest expected yield potential based on the different soil salinity conditions that were evaluated. Expected net revenue for alfalfa and corn are the highest under the different soil salinity conditions that were evaluated.

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Arable layer of pastureland in Galicia (NW Spain) was monitored for total content of heavy metals, and analysed by multivariate statistical techniques, in order to investigate the different origin that metals may have in pasture soils. Principal component analysis (), cluster analysis (CA) and correlation matrix (CM) were applied to 65 samples in which the total concentrations of Mn, Fe, Zn, Cu, Cr, Co, Ni, Cd and Pb were measured. Four significant components were extracted by , explaining 78.830% of total variance. Mn, Co and Ni (and partially Cu), and Fe and Cr, were associated in two lithogenic components, respectively, while an anthropogenic origin was identified for Cd, Pb and Zn. Zn (and Cu) were mainly associated with soil fertilisation by slurry or with other activities regarding management. Although the origin of Cd and Pb was also attributed to slurry application, other sources like commercial fertilisers, vehicle exhaust or aerial deposition were not discarded as possible contributors. CA confirmed and completed the results obtained by , classifying the data in four groups representing different areas. Group 1 represented samples corresponding to areas were the application of manure was moderate, while Group 2 included samples of lithogenic origin. Highest contents of anthropogenic metals were included in Group 3, although soils in this cluster were not considered as polluted. The last cluster grouped the samples with the lowest content of all the metals analysed, representing areas correctly managed and not affected by other external sources. Finally, the results obtained by CM agreed with and CA, also helping in elucidating individual relationships between metals.

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https://doi.org/10.1016/j.chemosphere.2006.12.043

https://www.ncbi.nlm.nih.gov/pubmed/17275882

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM17275882

Imperato

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Concentrations of surface and sub-surface soil Cu, Cr, Pb and Zn in the Naples city urban area were measured in 1999. Contourmaps were constructed to describe the metals spatial distribution. In the most contaminated soil samples, metals were speciated by means of the European Commission sequential extraction procedure. At twelve sites, Cu, Pb and Zn levels in soil were compared with those from a 1974 sampling. Many surface soils from the urban area as well as from the eastern industrial district contained levels of Cu, Pb and Zn that largely exceeded the limits (120, 100 and 150 mg kg(-l) for Cu, Pb and Zn, respectively) set for soils of public, residential and private areas by the Italian Ministry of Environment. values were never above regulatory limits(120 mg kg(-1)). Copper apparently accumulates in soils contiguous to railway lines and tramway. Cu and Cr existed in soil mainly inorganic forms (-68%), whereas Pb occurs essentially as residual mineral phases (77%). The considerable presence of Zn in the soluble, exchangeable and bound fraction (23%) suggests this element has high potential bioavailability and leachability through the soil. Concentrations of Cu, Pb and Zn have greatly increased since the 1974 sampling, with higher accumulation in soils from roadside fields.

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https://www.ncbi.nlm.nih.gov/pubmed/12713924

http://www.sciencedirect.com/science/article/pii/S0269749102004785

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The goal of the present work was to increase our knowledge on the behavior of manganese and nickel in soil within a Mediterranean environment. The study assessed the concentration levels of Mn and Ni (heavy metals selected for their essential role in the development of plants) in 250 soil horizon samples within 125 soil profiles of undisturbed soils in La Rioja (Spain). The study was undertaken to investigate and predict Mn and Ni concentrations on a regional scale. The analysis of spatial distribution of the elements was found to be affected by the nature of bedrock and, to a lesser extent, the anthropogenic origin. The variation of vertical distributions can be related, first, to natural sources-mainly the bed rocks-and, second, to soil processes. The geographical distribution of soil Mn is important to agriculture, nutrition, and health. Soil Mn and Ni maps of the area were elaborated, using geostatistics and geographic information systems. Mapping of geographical distributions will be useful in future research to determine regional patterns of Mn and Ni bioavailability, Mn and Ni deficiencies, and the possible consequences of land disposal of Mn- and Ni-laden wastes.

https://doi.org/10.1007/s11270-011-0822-9

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https://doi.org/10.1007/s10661-012-2848-x

https://www.ncbi.nlm.nih.gov/pubmed/22948288

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM22948288

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Lark R

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The objective of this study was to compare DK and IK empirically. This was done using a large data set on available phosphorus in the topsoil of a field in Nebraska. A prediction subset of the data (247 points) was extracted and DK and IK were used to estimate, from these data, for each of the remaining (1622) validation data points, the conditional probabilities that available phosphorus was less than or equal to three threshold values. The two techniques were then compared by computing the proportion of the validation sites incorrectly classified, with respect to each threshold, by reference to the conditional probabilities. In fact IK and DK gave very similar results by this criterion. It was concluded that neither of the techniques could be generally recommended in preference to the other. There are practical considerations that could determine the best method to use in any given circumstance and these are summarized in a decision tree.

https://doi.org/10.1016/S0016-7061(03)00168-X

http://www.sciencedirect.com/science/article/pii/S001670610300168X

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, Wang

.et al., 2015. Contamination and health risk assessment of heavy metals in road dust in Bayan Obo Mining Region in Inner Mongolia, North China.Journal of Geographical Sciences, 25(12): 1439-1451.

The objective of this study was to investigate the concentration and spatial distribution patterns of 9 potentially toxic heavy metal elements (As, Cd, Co, Cr, Pb, Cu, Zn, Mn, and Ni) in road dust in the Bayan Obo Mining Region in Inner Mongolia, China. Contamination levels were evaluated using the geoaccumulation index and the enrichment factor. Human health risks for each heavy metal element were assessed using a human exposure model. Results showed that the dust contained significantly elevated heavy metal elements concentrations compared with the background soil. The spatial distribution pattern of all tested metals except for As coincided with the locations of industrial areas while the spatial distribution of As was associated with domestic sources. The contamination evaluation indicated that Cd, Pb, and Mn in road dust mainly originated from anthropogenic sources with a rating of “heavily polluted” to “extremely polluted,” whereas the remaining metals originated from both natural and anthropogenic sources with a level of “moderately polluted”. The non-cancer health risk assessment showed that ingestion was the primary exposure route for all metals in the road dust and that Mn, Cr, Pb, and As were the main contributors to non-cancer risks in both children and adults. Higher HI values were calculated for children (HI=1.89), indicating that children will likely experience higher health risks compared with adults (HI=0.23). The cancer risk assessment showed that Cr was the main contributor, with cancer risks which were 2–3 orders of magnitude higher than those for other metals. Taken in concert, the non-cancer risks posed by all studied heavy metal elements and the cancer risks posed by As, Co, Cr, Cd, and Ni to both children and adults in Bayan Obo Mining Region fell within the acceptable range.

https://doi.org/10.1007/s11442-015-1244-1

http://d.wanfangdata.com.cn/Periodical_dlxb-e201512003.aspx

本文引用 [1]

Lin Y

, Cheng, B

, Shyu G

.et al., 2010. Combining a finite mixture distribution model with Indicator Kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan.Environmental Pollution, 158: 235-244.

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本文引用 [1]

Lu A

, Wang J

, Qin X

.et al., 2012. Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China.Science of the Total Environment, 425: 66-74.

/s?wd=paperuri%3A%28d18719c0b09c97083c6afb73bebb5fe7%29&filter=sc_long_sign&sc_ks_para=q%3DMultivariate%20and%20geostatistical%20analyses%20of%20the%20spatial%20distribution%20and%20origin%20of%20heavy%20metals%20in%20the%20agricultural%20soils%20in%20Shunyi%2C%20Beijing%2C%20China&sc_us=9529741075607607414&tn=SE_baiduxueshu_c1gjeupa&ie=utf-8

25	Lv J, Zhang Z, Liu Y.et al., 2015. Identifying the origins and spatial distributions of heavy metals in soils of Ju county (Eastern China) using multivariate and geostatistical approach.Journal of Soils and Sediments, 15: 163-178.

Mico

, Recatala

, Peris

.et al., 2006. Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis.Chemosphere, 65: 863-872.

<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">According to the European Thematic Strategy for Soil Protection, the characterization of the content and source of heavy metals in soils are necessary to establish quality standards on a regional level that allow the detection of sampling sites affected by pollution. In relation to this, the surface horizons of 54 agricultural soils under vegetable crops in the Alicante province (Spain), a representative area of the European Mediterranean region, were sampled to determine the content of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn. Analytical determinations were performed by atomic absorption spectroscopy after microwave sample digestion in acid solution. Results indicated that heavy metal levels were similar to those reported by authors working on agricultural soils from other parts of the Mediterranean region, with the exception of Cu and Pb in some samples. Multivariate analysis (principal component analysis and cluster analysis) was performed to identify a common source for heavy metals. Moreover, soil properties were determined in order to characterize agricultural soils and to analyze relationships between heavy metal contents and soil properties. The content of Co, Cr, Fe, Mn, Ni and Zn were associated with parent rocks and corresponded to the first principal component called the lithogenic component. A significant correlation was found between lithogenic metals and some soil properties such as soil organic matter, clay content, and carbonates, indicating an important interaction among them. On the other hand, elements such as Cd, Cu and Pb were related to anthropic activities and comprised the second (Cu and Pb) and third principal components (Cd), designated the anthropogenic components. Generally, Cd, Cu and Pb showed a lower correlation with soil properties due to the fact that they remain in available forms in these agricultural soils. Taking into account these results and other achieved in other parts of the European Mediterranean region, it can be concluded that soil quality standards are highly needed to declare soils affected by human induced pollution. This is particularly relevant for anthropogenic metals (Cd, Cu and Pb, and in some areas also Zn). Further research in other agricultural areas of the region would improve the basis for proposing such soil quality standards.</p>

https://doi.org/10.1016/j.chemosphere.2006.03.016

https://www.ncbi.nlm.nih.gov/pubmed/16635506

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM16635506

本文引用 [1]

Nanos

, Rodríguez Martín J

, 2012. Multiscale analysis of heavy metal contents in soils: Spatial variability in the Duero river basin (Spain).Geoderma, 189: 554-562.

Source identification of heavy metals in soil is not straightforward since several inputs of either an anthropogenic or natural origin contribute to their total content. Here we explore the spatial variation and covariation of seven heavy metals (Cd, Cr, Ni, Pb, Zn, Cu and Hg) in the agricultural soils of the Duero river basin (one of the largest in Spain) where both anthropogenic activities (mainly agriculture and industry) and natural factors may be responsible for their total concentration. Factorial kriging and principal components analysis were used on a data set that comprised 721 soil samples. We found that the concentrations of heavy metals in the analysed samples do not exceed the limits set by Spanish legislation excepting mercury that presents high values in a limited number of samples (maximum 104102μg/kg). The linear model of coregionalization–the basic model for factorial kriging analysis–was composed of two structures (representing two scales of variation) with ranges of 2002km (local scale) and 13002km (regional scale). Six of seven elements (Cd, Cr, Ni, Pb, Zn and Cu) were found to be strongly correlated regardless of the spatial scale considered. In contrast, correlations of Hg with other elements were small at the local spatial scale but augmented substantially at the regional scale. We conclude that agricultural practices in the Duero basin have not altered yet the natural content for Cd, Cr, Ni, Pb, Zn and Cu. On the other hand, Hg inputs from human origin, most probably related to airborne emission and deposition from industrial plants, are observable at the local spatial scale. Finally, no human-induced correlations among heavy metals were detected at the regional spatial scale. Based on the results of this study and in accordance with the results obtained in the nearby Ebro river basin (Rodríguez et al., 2008) we conclude that anthropogenic heavy metals in soil are visible only at local spatial scales. In contrast, natural factors maximize their influence on the distribution of heavy metals when considering larger spatial scales.

https://doi.org/10.1016/j.geoderma.2012.06.006

http://www.sciencedirect.com/science/article/pii/S0016706112002418

Oyedele D

, Amusan A

, Obi A

, 1996. The use of Multiple-variable Indicator Kriging technique for the assessment of the suitability of an acid soil for maize.Tropical Agriculture, 73: 259-263.

For adequate quantification of soil quality, specific indicators need to be measured spatially. The choice of indicators, however, depends on the use to which the quality assessment is to be put. The Multiple-Variable Indicator Kriging (MVIK) method provides a novel means of integrating these factors into a single index. The quality of virgin farmland for maize (Zea mays L.) farming was evaluated on a 250 m x 250 m plot. Soil samples were collected from 100-cm-deep auger holes dug on 30 m x 30 m grids in the field. Soil pH, cation exchange capacity, exchangeable Al++, organic C, and soil depth to plinthite were used as indicators for the assessment. The MVIK method was used to integrate these factors into a single index. Based on the variogram of the index, additional points were interpolated by kriging and a suitability probability contour map produced. Areas of low probability for meeting the criteria required for maize farming are indicated on the map. Soil conditions responsible for the failure to meet the criteria were identified, and ameliorative measures suggested. The usefulness of this technique for land evaluation for soil management and for environmental purposes was also discussed.

https://doi.org/10.1002/(SICI)1097-0010(199610)72:2<250::AID-JSFA651>3.0.CO;2-D

http://europepmc.org/abstract/AGR/IND20596555

Rizhao Municipal Bureau of Statistics (RMBS), 2012. Rizhao Statistical Yearbook in 2012. Beijing: China Statistics Press. (in Chinese)

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Rodríguez Martín J

, Arias M

, Corbi J M

, 2006. Heavy metals contents in agricultural topsoils in the Ebro basin (Spain). Application of the multivariate geoestatistical methods to study spatial variations.Environmental Pollution, 144: 1001-1012.

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本文引用 [1]

Sajn

, Halamic

, Peh

.et al., 2011. Assessment of the natural and anthropogenic sources of chemical elements in alluvial soils from the Drava River using multivariate statistical methods.Journal of Geochemical Exploration, 110: 278-289.

The objectives of this study are as follows: (a) an assessment of the geochemical background signature of the Drava Valley before the industrial revolution; (b) an evaluation of anthropogenic geochemical influences on the alluvial plains and river terraces in the valley; and (c) a determination of the spatial distribution of trace elements in the alluvial soils of the Drava River downstream of the Austrian-Slovenian border to the confluence of Mura and Drava Rivers.<br/>Samples of topsoil (depth of 0-5 cm) and subsoil (depth of 20-30 cm) were collected from 134 sampling sites on alluvial plains and river terraces. Analysis for 41 chemical elements was performed. Based on a comparison of statistical parameters, the spatial distribution of particular elements and the results of factor analysis, one anthropogenic and three natural geochemical associations were identified. The anthropogenic association (As-Ba-Cd-Mo-Pb-Sb-Zn) is mostly a result of historical zinc and lead mining and smelting in the Drava River watershed. The natural geochemical associations (Al-Fe-K-Co-Cr-Cu-Li-Ni-Rb-Sc-Th, Ti-Ce-La-Nb-Ta and Ca-Mg-Sr) were mainly influenced by lithology. The entire assessed area of about 130 km(2) is, according to Slovenian and Croatian legislation, critically polluted with trace elements, especially zinc. (C) 2011 Elsevier B.V. All rights reserved.

https://doi.org/10.1016/j.gexplo.2011.06.009

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本文引用 [1]

32	Sebai T, Lagacherie B, Soulas G.et al., 2007. Spatial variability of isoproturon mineralizing activity within an agricultural field: Geostatistical analysis of simple physicochemical and microbiological soil parameters.Environmental Pollution, 145: 680-690. 本文引用 [1]

Smith J

, Halvorson J

, Papendick R

, 1993. Using Multiple-variable Indicator Kriging for evaluating soil quality.Soil Science Society of America Journal, 57: 743-749.

Soil quality is the most important factor for sustaining the global biosphere. Soil quality may be defined in several different ways including productivity, sustainability, environmental quality, and effects on human nutrition. To quantify soil quality, specific soil indicators need to be measured spatially. These indicators are mainly soil properties whose values relate directly to soil quality but may also include policy, economic, or environmental considerations. Because assessing soil quality is complex, the individual soil quality indicators need to be integrated to form a soil quality index. This integration needs to be flexible enough to evaluate soil quality at spatial scales ranging from the farm to the regional level, be applicable to all types of agricultural land use, and be able to incorporate all types of soil quality information. We have developed a multiple-variable indicator ranging (MVIK) procedure that may provide a means to integrate soil quality parameters into an index to produce soil quality maps on a landscape basis. These maps would indicate the areas on a landscape that have a high probability of having good soil quality according to predetermined criteria. This procedure can provide probability maps based on any range of chosen criteria and thus is universally applicable. In addition, it allows the identification of the indicator parameter(s) responsible for zones of low soil quality, thus allowing specific management plans or land use policies to be developed.

https://doi.org/10.2136/sssaj1993.03615995005700030020x

http://dl.sciencesocieties.org/publications/sssaj/abstracts/57/3/SS0570030743

Sollitto

, Romic

, Castrignano

.et al., 2010. Assessing heavy metal contamination in soils of the Zagreb region (Northwest Croatia) using multivariate geostatistics.Catena, 80: 182-194.

<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">The assessment of soil contamination and location of pollution sources represent a crucial issue in soil remediation. Topsoil samples were collected in the Zagreb area (Northwest Croatia) and the total contents of trace and major elements were determined. A multivariate geostatistical analysis was used to estimate soil chemical composition variability. Factorial Kriging Analysis (FKA) was used to investigate the scale-dependent correlation structure of some variables by modelling co-regionalization of ten chemical variables, co-kriging specific factors and mapping them. The FKA provided two regionalized factors at different spatial scales of variability: the first factor at shorter range for Zn, Pb, Cd, Cu and Ni indicated different sources of anthropogenic contamination, whereas Ca (mainly loading on the longer range factor) was related to the lithology and parent material composition. The methodology used has proved to be a useful tool to separate geological and anthropogenic causes of variation in soil heavy metal content and to identify common pollution sources.</p>

https://doi.org/10.1016/j.catena.2009.11.005

http://www.sciencedirect.com/science/article/pii/S0341816209002148

35	Stafilov T, Sajn R, Alijagic J, 2013. Distribution of arsenic, antimony, and thallium in soil in Kavadarci and its surroundings, Republic of Macedonia.Soil & Sediment Contamination, 22: 105-118. 本文引用 [1]

Sun C

, Liu J

, Wang

.et al., 2013. Multivariate and geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, Northeast China.Chemosphere, 92: 517-523.

The characterization of the content and source of heavy metals in soils are necessary to establish quality standards on a regional level and to assess the potential threat of metals to food safety and human health. The surface horizons of 114 agricultural soils in Dehui, a representative agricultural area in the black soil region, Northeast China, were collected and the concentrations of Cr, Ni, Cu, Zn, and Pb were analyzed. The mean values of the heavy metals were 49.7 +/- 7.04, 20.8 +/- 3.06, 18.9 +/- 8.51, 58.9 +/- 7.16, and 35.4 +/- 9.18 mg kg(-1) for Cr, Ni, Cu, Zn, and Pb, respectively. Anthropic activities caused an enrichment of Cu and Pb in soils. However, metal concentrations in all samples did not exceed the guideline values of Chinese Environmental Quality Standard for Soils. Multivariate and geostatistical analyses suggested that soil Cr, Ni, and Zn had a lithogenic origin. Whereas, the elevated Cu concentrations in the study area were associated with industrial and agronomic practices, and the main sources of Pb were industrial fume, coal burning exhausts, and domestic waste. Source identification of heavy metals in agricultural soil is a basis for undertaking appropriate action to reduce metal inputs. (C) 2013 Elsevier Ltd. All rights reserved.

https://doi.org/10.1016/j.chemosphere.2013.02.063

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本文引用 [1]

Van Meirvenne

, Goovaerts

, 2001. Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold.Geoderma, 102: 75-100.

A non-parametric approach for assessing the probability that heavy metal concentrations in soil exceed a location-specific environmental threshold is presented. The methodology is illustrated for an airborne Cd-contaminated area in Belgium. Non-stationary simple indicator kriging, using a soft indicator coding to account for analytical uncertainty, was used in combination with declustering weights to construct the local conditional cumulative distribution function (ccdf) of Cd. The regulatory Cd contamination threshold (CT) depends on soil organic matter and clay content, which entails that its value is not constant across the study area and also is uncertain. Therefore, soft indicator kriging was used to construct the ccdfs of organic matter and clay. Latin hypercube sampling of the ccdfs of Cd, soil organic matter and clay yielded a map of the probability that Cd concentrations exceed the site-specific CT. Cross-validation showed that the ccdfs provide accurate models of the uncertainty about these variables. At a probability level of 80% we found that the CT was exceeded at 27.3% of the interpolated locations, covering 3192 ha of the study area, illustrating the extent of the pollution. Additionally, a new methodology is proposed to sample preferentially the locations where the uncertainty about the probability of exceeding the CT, instead of the uncertainty about the pollutant itself, is at a maximum. This methodology was applied in a two-stage sampling campaign to identify locations where additional Cd samples should be collected in order to improve the classification into safe and contaminated locations.

https://doi.org/10.1016/S0016-7061(00)00105-1

http://www.sciencedirect.com/science/article/pii/S0016706100001051

Wang H

, Lu, S

, 2011. Spatial distribution, source identification and affecting factors of heavy metals contamination in urban-suburban soils of Lishui city, China.Environmental Earth Sciences, 64: 1921-1929.

An investigation on spatial distribution, possible pollution sources, and affecting factors of heavy metals in the urban-suburban soils of Lishui city (China) was conducted using geographic information system (GIS) technique and multivariate statistics. The results indicated that the topsoils in urban and suburban areas were enriched with metals, such as Cd, Cu, Pb, and Zn. Spatial distribution maps of heavy metal contents, based on geostatistical analysis and GIS mapping, indicated that Cd, Cr, Cu, Mn, Ni, Pb, and Zn had similar patterns of spatial distribution. Their hot-spot areas were mainly concentrated in the densely populated old urban area of the city. Multivariate statistical analysis (correlation analysis, principal component analysis, and clustering analysis) showed distinctly different associations among the studied metals, suggesting that Cr, Cu, Ni, Pb, Cd, and Zn had anthropogenic sources, whereas Co and V were associated with parent materials and therefore had natural sources. The Cd, Cr, Ni, Pb, and Zn contents were positively correlated with soil organic matter, pH, and sand content (p < 0.01). It is concluded that GIS and multivariate statistical methods can be used to identify hot-spot areas and potential sources of heavy metals, and assess soil environment quality in urban-suburban areas.

https://doi.org/10.1007/s12665-011-1005-0

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本文引用 [1]

Yalcin M

, Ilhan

, 2008. Multivariate analyses to determine the origin of potentially harmful heavy metals in beach and dune sediments from KizKalesi Coast (Mersin), Turkey.Bulletin of Environmental Contamination and Toxicology, 81: 57-68.

<a name="Abs1"></a>The aim of this study was to investigate variations in heavy metal concentrations and natural and artificial sources of heavy minerals in beach and dune sediments along Kizkalesi (Mersin) coast in Turkey. To this aim, sand sediment samples were collected from 20 locations throughout Kizkalesi coast and concentrations of Zn, Ni, Cu, Co, V, Mo, Ag, Sb, Sn, Cd, W, Hg, Pb, As, Si, Al, Fe, Ca, Mg, S, K, Na, Cl, Ti, Mn and Cr were determined. Simple analyses (frequency histogram), multivariate analyses (Coefficient correlation, Cluster Analysis), Principal Component Analysis, Model Summary and ANOVA were used to analyze the concentration values. Al, Fe, Mg, Cl, Ti, Mn, Cr and Ni were dominant heavy metals. Principal Component Analysis revealed six principal components. It was confirmed by Cluster Analysis. Based on the Hierarchical Cluster analyses, three different general groups were formed at a 50% arbitrary similarity of Q-type level. The frequency histogram indicated that W, Ag, Co, V, Cu, As, Sn, Ni, Zn, Pb, Cr, Cl and Mg concentrations originated from the nearby area, while Mn, Ti, Al and Fe Mg concentrations came from either the nearby area or moderately remote sources. Data from the study area showed that the Model Summary (based on <i>R</i> <sup>2</sup> = 100%) was sufficient for the statistical data and that the Model ANOVA (variations of Pb) had a high explanatory power. The region lying on Miocene carbonate rocks of the Tauride belt were affected by the contaminants of anthropogenic origin that included coastal deposits, coastal erosion, the Kizkalesi settlement area, urban wastes, Mersin-Antalya road extending parallel to the shoreline and disposal sites of hotels.

https://doi.org/10.1007/s00128-008-9461-2

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Yang P

, Mao R

, Shao H

.et al., 2009. An investigation on the distribution of eight hazardous heavy metals in the suburban farmland of China.Journal of Hazardous Materials, 167: 1246-1251.

Understanding spatial variability of hazardous soil heavy metals is an important precondition for suitably monitoring and evaluating eco-environment quality in the primary agricultural production zone. One hundred topsoils were sampled from the urban-rural transition zone in Taihang Piedmont Plain, China. The contents of eight heavy metals Cu, Zn, Cr, Ni, Pb, Cd, Hg and As were tested for each soil sample, and their spatial patterns were analyzed by the semivariogram approach of geostatistics and geographical information system (GIS) technology. Results showed that Cd concentration exceeded its background level. The local pollution from Cd attributed to the anthropogenic influence. The concentrations of eight hazardous heavy metals are relatively lower than the critical values of the national soil quality standard. The correlation distance of soil heavy metals ranged from 3.28 to 11.63 km, with the eight heavy metals having moderate spatial dependence. Cu, Cr, Ni, Pb and As are associated with and controlled by parent material. The results are helpful for improving agricultural and forest ecosystem in the and and semiarid region. (C) 2009 Elsevier B.V. All rights reserved.

https://doi.org/10.1016/j.jhazmat.2009.01.127

https://www.ncbi.nlm.nih.gov/pubmed/19282107

http://www.sciencedirect.com/science/article/pii/S0304389409001599

本文引用 [1]

Zhang C

, 2006. Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland.Environmental Pollution, 142: 501-511.

Galway is a small but rapidly growing tourism city in western Ireland. To evaluate its environmental quality, a total of 166 surface soil samples (0-10 cm depth) were collected from parks and grasslands at the density of 1 sample per 0.25 km2 at the end of 2004. All samples were analysed using ICP-AES for the near-total concentrations of 26 chemical elements. Multivariate statistics and GIS techniques were applied to classify the elements and to identify elements influenced by human activities. Cluster analysis (CA) and principal component analysis (PCA) classified the elements into two groups: the first group predominantly derived from natural sources, the second being influenced by human activities. GIS mapping is a powerful tool in identifying the possible sources of pollutants. Relatively high concentrations of Cu, Pb and Zn were found in the city centre, old residential areas, and along major traffic routes, showing significant effects of traffic pollution. The element As is enriched in soils of the old built-up areas, which can be attributed to coal and peat combustion for home heating. Such significant spatial patterns of pollutants displayed by urban soils may imply potential health threat to residents of the contaminated areas of the city.

https://doi.org/10.1016/j.envpol.2005.10.028

https://www.ncbi.nlm.nih.gov/pubmed/16406233

http://med.wanfangdata.com.cn/Paper/Detail/PeriodicalPaper_PM16406233

Zhao

, Wang Z

, Sun W

.et al., 2010. Spatial interrelations and multi-scale sources of soil heavy metal variability in a typical urban-rural transition area in Yangtze River Delta region of China.Geoderma, 156: 216-227.

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脚注

The authors have declared that no competing interests exist.

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Abstract
关键词
Key words
引用本文
1 Introduction
2 Materials and methods
2.1 Study area
Figure 1 The location and sampling sites in Rizhao, East China
2.2 Soil samples collection and chemical analysis
2.3 Descriptive and multivariate analysis
2.4 Indicator Kriging
3 Results and discussion
3.1 Descriptive statistics of heavy metals in soils
Table 1 The descriptive statistics contents of heavy metals in Rizhao, East China (mg/kg)
3.2 Comparison of the contents between Rizhao and other regions
Table 2 The mean contents of Rizhao compared with typical regions in the world (mg/kg)
3.3 Multivariate analysis of heavy metals in soils
Table 3 Correlation coefficient matrix of heavy metals in soils
Figure 2 The loadings of the first four principal components
Figure 3 The result of cluster analysis
3.4 Geostatistical analysis
Table 5 The variograms fitting of heavy metals in soils
Figure 4 The spatial patterns of the principal components in Rizhao, East China
Figure 5 Risk probability maps of single heavy metals in Rizhao, East China
Figure 6 Comprehensive risk probability of heavy metals in Rizhao, East China
Table 4 Results of factors matrix
Table 6 Results of environmental risk assessment of Rizhao, East China
4 Conclusions
参考文献
脚注

收稿日期	接受日期	出版日期
2015-07-20	2015-12-28	2016-06-15
发布日期
2016-06-15

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Abstract

关键词

Key words

引用本文

1 Introduction

2 Materials and methods

2.1 Study area

Figure 1 The location and sampling sites in Rizhao, East China

2.2 Soil samples collection and chemical analysis

2.3 Descriptive and multivariate analysis

2.4 Indicator Kriging

3 Results and discussion

3.1 Descriptive statistics of heavy metals in soils

Table 1 The descriptive statistics contents of heavy metals in Rizhao, East China (mg/kg)

3.2 Comparison of the contents between Rizhao and other regions

Table 2 The mean contents of Rizhao compared with typical regions in the world (mg/kg)

3.3 Multivariate analysis of heavy metals in soils

Table 3 Correlation coefficient matrix of heavy metals in soils

Figure 2 The loadings of the first four principal components

Figure 3 The result of cluster analysis

3.4 Geostatistical analysis

Table 5 The variograms fitting of heavy metals in soils

Figure 4 The spatial patterns of the principal components in Rizhao, East China

Figure 5 Risk probability maps of single heavy metals in Rizhao, East China

Figure 6 Comprehensive risk probability of heavy metals in Rizhao, East China

Table 4 Results of factors matrix

Table 6 Results of environmental risk assessment of Rizhao, East China

4 Conclusions

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1 Introduction

2 Materials and methods

2.1 Study area

Figure 1 The location and sampling sites in Rizhao, East China

2.2 Soil samples collection and chemical analysis

2.3 Descriptive and multivariate analysis

2.4 Indicator Kriging

3 Results and discussion

3.1 Descriptive statistics of heavy metals in soils

Table 1 The descriptive statistics contents of heavy metals in Rizhao, East China (mg/kg)

3.2 Comparison of the contents between Rizhao and other regions

Table 2 The mean contents of Rizhao compared with typical regions in the world (mg/kg)

3.3 Multivariate analysis of heavy metals in soils

Table 3 Correlation coefficient matrix of heavy metals in soils

Figure 2 The loadings of the first four principal components

Figure 3 The result of cluster analysis

3.4 Geostatistical analysis

Table 5 The variograms fitting of heavy metals in soils

Figure 4 The spatial patterns of the principal components in Rizhao, East China

Figure 5 Risk probability maps of single heavy metals in Rizhao, East China

Figure 6 Comprehensive risk probability of heavy metals in Rizhao, East China

Table 4 Results of factors matrix

Table 6 Results of environmental risk assessment of Rizhao, East China

4 Conclusions

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