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

doi:10.1007/s11442-016-1296-x

Abstract

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.

Key words: heavy metals in soils, hazardous risk, multivariate analysis, Indicator Kriging, Rizhao

Yang LIU, Zongwei MA, Jianshu LV, Jun BI. 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.

Figures/Tables 12

Figure 1

Table 1

Table 2

Table 3

Figure 2

Figure 3

Table 5

Figure 4

Figure 5

Figure 6

Table 4

Table 6

References 42

1	Alloway B, 1995. Heavy Metals in Soils. London: Chapman and Hall.
2	Boruvka L, Vacek O, Jehlicka J, 2005. Principal component analysis as a tool to indicate the origin of potentially toxic elements in soils.Geoderma, 128: 289-300. doi: 10.1016/j.geoderma.2005.04.010
3	Cai L M, Xu Z C, Ren M Z.et al., 2012. Source identification of eight hazardous heavy metals in agricultural soils of Huizhou, Guangdong Province, China.Ecotoxicology and Environmental Safety, 78: 2-8. doi: 10.1016/j.ecoenv.2011.07.004 pmid: 22257794
4	Chu H J, Lin Y P, Jang C S.et al., 2010. Delineating the hazard zone of multiple soil pollutants by Multivariate Indicator Kriging and conditioned Latin hypercube sampling.Geoderma, 158: 242-251.
5	Dai J R, Pang X G, Yu C.et al., 2011. Geochemical baselines and background values and element enrichment characteristics in soils in eastern Shandong province.Geochimica, 40: 577-587. (in Chinese) doi: 10.1007/s11629-011-2067-x
6	Davis H T, Aelion C M, McDermott S.et al., 2009. Identifying natural and anthropogenic sources of metals in urban and rural soils using GIS-based data, PCA, and spatial interpolation.Environmental Pollution, 157: 2378-2385.
7	Diodato N, Ceccarelli M, 2004. Multivariate Indicator Kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands.Ecological Indicators, 4: 177-187. doi: 10.1016/j.ecolind.2004.03.002
8	Eldeiry A A, Garcia L A, 2011. Using Indicator Kriging technique for soil salinity and yield management.Journal of Irrigation and Drainage Engineering-ASCE, 137: 82-93. doi: 10.1061/(ASCE)IR.1943-4774.0000280
9	Facchinelli A, Sacchi E, Mallen L, 2001. Multivariate statistical and GIS-based approach to identify heavy metal sources in soils.Environmental Pollution, 114: 313-324. doi: 10.1016/S0269-7491(00)00243-8 pmid: 11584630
10	Franco-Uria A, Lopez-Mateo C, Roca E.et al., 2009. Source identification of heavy metals in pastureland by multivariate analysis in NW Spain.Journal of Hazardous Materials, 165: 1008-1015. doi: 10.1016/j.jhazmat.2008.10.118 pmid: 19070956
11	Fu K, Sun B, He D.et al., 2012. Pollution assessment of heavy metals along the Mekong River and dam effects.Journal of Geograhical Sciences, 22(5): 874-884. doi: 10.1007/s11442-012-0969-3
12	Goovaerts P, 1997. Geostatistics for Natural Resources Evaluation. New York: Oxford University Press.
13	Hakanson L, 1980. An ecological risk index for aquatic pollution-control: A sedimentological approach.Water Research, 14: 975-1001. doi: 10.1016/0043-1354(80)90143-8
14	Halvorson J J, Smith J L, Papendick R I, 1996. Integration of multiple soil parameters to evaluate soil quality: A field example.Biology and Fertility of Soils, 21: 207-214. doi: 10.1007/BF00335937
15	Huang S S, Liao, Q L, Hua M.et al., 2007. Survey of heavy metal pollution and assessment of agricultural soil in Yangzhong district, Jiangsu Province, China.Chemosphere, 67: 2148-2155. doi: 10.1016/j.chemosphere.2006.12.043 pmid: 17275882
16	Imperato M, Adamo P, Naimo D.et al., 2003. Spatial distribution of heavy metals in urban soils of Naples city (Italy).Environmental Pollution, 124: 247-256. doi: 10.1016/S0269-7491(02)00478-5 pmid: 12713924
17	Inigo V, Andrades M, Alonso-Martirena J I.et al., 2011. Multivariate statistical and GIS-based approach for the identification of Mn and Ni concentrations and spatial variability in soils of a humid Mediterranean environment: La Rioja, Spain.Water Air and Soil Pollution, 222: 271-284. doi: 10.1007/s11270-011-0822-9
18	Jang C S, 2013. Use of Multivariate Indicator Kriging methods for assessing groundwater contamination extents for irrigation.Environmental Monitoring and Assessment, 185: 4049-4061. doi: 10.1007/s10661-012-2848-x pmid: 22948288
19	Lark R M.,Ferguson, R B, 2004. Mapping risk of soil nutrient deficiency or excess by disjunctive and Indicator Kriging.Geoderma, 118: 39-53. doi: 10.1016/S0016-7061(03)00168-X
20	Lee C S, Li, X D, Shi W Z.et al., 2006. Metal contamination in urban, suburban, and country park soils of Hong Kong: A study based on GIS and multivariate statistics.Science of the Total Environment, 356: 45-61.
21	Lee J J, Liu C W, Jang C S.et al., 2008. Zonal management of multi-purpose use of water from arsenic-affected aquifers by using a Multi-variable Indicator Kriging approach.Journal of Hydrology, 359: 260-273.
22	Li K, Liang T, Wang L.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. doi: 10.1007/s11442-015-1244-1
23	Lin Y P, Cheng, B Y, Shyu G S.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.
24	Lu A X, Wang J H, Qin X Y.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.
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.
26	Mico C, Recatala L, Peris A.et al., 2006. Assessing heavy metal sources in agricultural soils of an European Mediterranean area by multivariate analysis.Chemosphere, 65: 863-872. doi: 10.1016/j.chemosphere.2006.03.016 pmid: 16635506
27	Nanos N, Rodríguez Martín J A, 2012. Multiscale analysis of heavy metal contents in soils: Spatial variability in the Duero river basin (Spain).Geoderma, 189: 554-562. doi: 10.1016/j.geoderma.2012.06.006
28	Oyedele D J, Amusan A A, Obi A O, 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. doi: 10.1002/(SICI)1097-0010(199610)72:2<250::AID-JSFA651>3.0.CO;2-D
29	Rizhao Municipal Bureau of Statistics (RMBS), 2012. Rizhao Statistical Yearbook in 2012. Beijing: China Statistics Press. (in Chinese)
30	Rodríguez Martín J A, Arias M L, Corbi J M G, 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.
31	Sajn R, Halamic J, Peh Z.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. doi: 10.1016/j.gexplo.2011.06.009
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.
33	Smith J L, Halvorson J J, Papendick R I, 1993. Using Multiple-variable Indicator Kriging for evaluating soil quality.Soil Science Society of America Journal, 57: 743-749. doi: 10.2136/sssaj1993.03615995005700030020x
34	Sollitto D, Romic M, Castrignano A.et al., 2010. Assessing heavy metal contamination in soils of the Zagreb region (Northwest Croatia) using multivariate geostatistics.Catena, 80: 182-194. doi: 10.1016/j.catena.2009.11.005
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.
36	Sun C Y, Liu J S, Wang Y.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. doi: 10.1016/j.chemosphere.2013.02.063
37	Van Meirvenne M, Goovaerts P, 2001. Evaluating the probability of exceeding a site-specific soil cadmium contamination threshold.Geoderma, 102: 75-100. doi: 10.1016/S0016-7061(00)00105-1
38	Wang H Y, Lu, S G, 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. doi: 10.1007/s12665-011-1005-0
39	Yalcin M G, Ilhan S, 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. doi: 10.1007/s00128-008-9461-2
40	Yang P G, Mao R Z, Shao H B.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. doi: 10.1016/j.jhazmat.2009.01.127 pmid: 19282107
41	Zhang C S, 2006. Using multivariate analyses and GIS to identify pollutants and their spatial patterns in urban soils in Galway, Ireland.Environmental Pollution, 142: 501-511. doi: 10.1016/j.envpol.2005.10.028 pmid: 16406233
42	Zhao Y, Wang Z G, Sun W X.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.

	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

	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)

	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

	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

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