A geochemical survey of trace elements in agricultural and non-agricultural topsoil in Dexing area, China

Journal of Geochemical Exploration 104 (2010) 118–127

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A geochemical survey of trace elements in agricultural and non-agricultural topsoil in Dexing area, China Yanguo Teng a,b,⁎, Shijun Ni c, Jinsheng Wang a,b, Rui Zuo a,b, Jie Yang a,b a b c

College of Water Sciences, Beijing Normal University, Beijing 100875, China Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, Beijing 100875, China Department of Geochemistry, Chengdu University of Technology, Chengdu 610059, China

a r t i c l e

i n f o

Article history: Received 11 June 2009 Accepted 20 January 2010 Available online 29 January 2010 Keywords: Geochemical survey Trace element Geochemical background Element assemblage Topsoil pollution

a b s t r a c t In China, soil pollution is very serious, which has jeopardized the ecology, food safety, the people's health, and even the sustainable development of agriculture. In order to investigate the soil pollution situation, a total of 874 agricultural and non-agricultural topsoil samples were collected from Dexing area, northeast of Jiangxi Province, China. The total elemental concentrations of 17 elements (As, Hg, Mo, Cd, Cr, Zn, Cu, Mn, Ti, Pb, Fe, Ca, K, Si, Al, Mg, and Na) were determined. The geochemical background and threshold was predicted with the method of the median ± median absolute deviation (MAD). The agricultural soil median concentration of trace elements was similar to that of the non-agricultural soil. In contrast to Jiangxi soil background of trace elements, the geochemical background of the study area was obviously higher. The maps of the pollution indices for As, Cd, Cr, Cu, Hg, Mn, Mo, Pb, Ti and Zn of non-agricultural soil and agricultural soils in the study area, showed that the highest level of pollution is distributed near and along the Lean River, especially in the neighboring and surrounding Dexing and Leping mining area. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Soil forms a thin layer over the surface of the earth that performs many processes essential to life (Wienhold et al., 2004). Soil has always been important to humans and their health, providing a resource that can be used for shelter and food production (Abrahams, 2002). In recent years a number of anthropogenic sources such as dumping of waste, smelter stacks, waste incineration, vehicle exhaust, fertilizers, agricultural waste, and sewage sludge have contributed notably to the increase of environmental metal concentrations (Markus and McBratney, 1996; Kwolek, 1999; Bilos et al., 2001; Hlavay et al., 2001; Koch and Rotard, 2001). Trace element input into soils by anthropogenic activities and implications for human health was reviewed by Senesi et al. (1999). Metal-polluted soils constitute a major environmental problem. Consequently, they are subject to detailed risk assessment and management studies (Peters et al., 1986; Schuhmacher et al., 1997; Prasad and Nazareth, 2000; Zayed, 2001; Granero and Domingo, 2002). Accurate estimates of the natural, background concentration of major and trace elements in topsoil are important for several applications, including the assessment of soil fertility, mineral exploration, contamination assessment, the application of sewage sludge to agricultural land, and estimations of diffuse pollution through atmospheric deposition (Rawlins et al., 2003; Li et al., 2008). ⁎ Corresponding author. College of Water Sciences, Beijing Normal University, Beijing 100875, China. Tel.: +86 10 58800399; fax: +86 10 58802738. E-mail address: [email protected] (Y. Teng). 0375-6742/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.gexplo.2010.01.006

The natural occurrence of elements varies between different rock types, and certain bedrocks can provide exceptionally high metal concentrations to the overlying soils (Lottermoser, 2002). The comparative analyses of rocks, soils and plants show the influence of soil-forming processes on the migration of elements (Motuzova and Van, 1999). The monitoring of chemical properties, including heavy metals, in soils is necessary if better management and remediation practices are to be established for polluted soils (Jo and Koh, 2004). The State Environment Protect Agency (SEPA) of China (2006) soil pollution assessment concluded that: China faces “serious” soil pollution that jeopardizes the ecology, food safety, people's health and the sustainable development of agriculture. It is estimated that nationwide 12 million tons of grain are polluted each year by heavy metals that have found their way into soil. According to incomplete statistics, about 150 million mu (10 million hectares) of arable land in China have been polluted. In 2006, SEPA and the Ministry of Land and Resources jointly launched China's first soil pollution survey backed by a budget of 1 billion yuan (125 million U.S. dollars). The program aims to assess soil quality across the country by analyzing the amount of heavy metals, pesticide residue and organic pollutants in the soil. This study was to carry out a comprehensive environmental geochemical research by the analytical data of 17 elements; for the environmentally geochemical survey, Cu, Pb, Zn, Hg, As, Cd, Cr, Mo, Ti and Mn were focused on. The topsoil layer is of particular interest as degradation may occur due to atmospheric deposition, anthropogenic activities and/or natural geochemical processes (US EPA, 1992; Aelion

Y. Teng et al. / Journal of Geochemical Exploration 104 (2010) 118–127

et al., 2008; Sultan and Shazili, 2009), therefore the authors selected the topsoil as investigated media for the following objects: (1) established geochemical background of trace elements in non-agricultural and agricultural topsoil in the study area; (2) assessed the degree of pollution; and (3) classified geochemical associations of the trace elements.

2. Area description The study area (Dexing area) is located between 28°50′–29°20′ north latitude and 117°00′–118°00′ east longitude, in northeast of Jiangxi province (Fig. 1). The elevation was about 500–1000 m in the eastern portion of the study area, while it was about 200 m in the western portion of the study area. The climate of the study area was in general described as subtropical monsoon. The mean annual temperature is 17 °C, and the mean annual rainfall is 1900 mm.

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The lithologic description of the area is illustrated in Fig. 1 and Table 1. The stratum is full-fledged except Silurian, Devonian and Tertiary, and spread all over the study area (Jiangxi Geological Survey, 1980). The space patterns of sedimentary rocks, magmatic rocks and Quaternary sediment and alluvium are shown in Fig. 1. In mineral resources, Jiangxi province is the famous foundation of nonferrous mineral resources in China. The main mineral resources are deposited in Dexing City and Leping City in the study area. In Dexing City, there are some vast deposits such as Tongkuangshan Cu– Mo deposit and Fujiawu Cu–Mo deposit (also called Dexing copper mine, is the biggest open store of copper in Asia), Jinshan Ag–Au deposit, Yinshan Ag–Pb–Zn deposit, and so on. In Leping City, the Zhongbu and Shiligang Mn mine is one of the four greatest Mn sources in China. In addition, the coal reserves at the mine are among the greatest coal reserves in Jiangxi province. The main stream in the study area was the Lean River with some branches such as the Jishui River, the Dawu River, and the Changle

Fig. 1. Geology and topsoil samples location map.

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Table 1 Lithologic description. Lithologic classification Sedimentary rocks Quaternary

Cretaceous Jurassic

Triassic Permian Carboniferous

Ordovic

Cambrian Sinian

Magmatic rock Late Jurassic period Middle Jurassic period Permian period: Late Cretaceous period Devonian period

Description Lianxu formation and Boyanghu formation: gravel, clay, sandy soil Jinxian formation and Wangchenggang formation: gravel, sand, laterite Liantang formation: sand, clay Shangshu formation, Dengshan formation: volcanic breccia, tuffite,basalt, rhyolite Shixi formation and Lengshuiwu formation: sandstone, siltstone, conglomerate, schalstein Maodian formation and Hekou formation: conglomerate, siltstone, basalt, schalstein Ehuling formation, rhyolite, dacite, schalstein, granoclastic Mengao formation: sandstone, siltstone, shale, conglomerate Zhangping formation: quartzose arkose, siltstone, mudstone Shuibei formation: quartzose arkose, sandstone, carbonaceous shale, coal-bearing sand-shale Lelin formation, Zhoutan formation, Jilin formation, Xiushui formation, and Hanyuan formation: mud-limestone, sandy slate, siltstone, coal-bearing, sand-shale Zijiachong formation and Sanqiutian formation: sandstone, coal-bearing shale, coal-bearing sandstone Qinglong formation: sandy shale, mud-limestone Leping formation, Changxin formation, and Qixia formation: siltstone, quartzose sandstone, mudstone, coal-bearing limestone Qixia formation and Gufeng formation: siliceous mudstone,limestone, carbonaceous shale Laolongdong formation, Huanglong formation, and Chuanshan formation: dolomite and limestone Zishan formation: sandstone, siltstone, coal-bearing shale Hengyong formation: tuffaceous packsand and siltstone, carbonaceous slate Changwu formation, Huangnigang formation, Hule formation,and Yanwashan formation: limestone, siliceous rock, shale and siltstone, coal-nearing limestone Yinzhubu formation and Ningguo formation: carbonaceous shale, calcareous shale, limestone Guanyintang formation, Yangliugang, Huayansi formation, and Xizushan formation: shale, siltstone, siliceous rocks, siliceous shale, limestone Yangliugang formation: limestone, calcareous and carbonaceous shale Piyuancun formation and Lantian formation: silt shale, limestone, tuffaceous siliceous rocks, dolomitic limestone Xiuning formation and Nantuo formation: siliceous limestone, tuffaceous mudstone, sandstone, glutenite

Quartz-diorite,granodiorite porphyry Monzonitic granite, feldspar granite Peridotite, oliver pyroxenite, pyroxenite, gabbro, pyroxenes amphibolite Granodiorite, feldspar granite, granite-porphyry Biotite-hornblende diorite, biotite-hornblende quartz-diorite

River. The Dawu River receives a large amount of the acidic mine drainage (pH 2–3) and the waste effluents containing Cu, Mo and As, which were discharged from the neighboring Dexing Cu Mine or from many smelters and mining/panning activities. The Jishui River receives a large amount of waste effluent containing Cd, Pb and Zn, which are discharged from Yinshan Pb and Zn extraction facility (Teng et al., 2004, 2009). Paddy soil, yellow soil and red soil are the main soil types in the study area. Paddy soil is found in the plains; whereas, yellow soil and red soil are distributed in the hilly areas. Some studies have revealed that the soil was polluted by the agricultural and mining activities (He et al., 1998; Liu et al., 2003).

3. Materials and methods 3.1. Sample collection and preparation for the analysis The sampling guideline was according to FOREGS Geochemical Mapping Field Manual (Salminen et al., 1998). A total of 874 topsoil samples (including 437 agricultural topsoil samples) were collected from the study area (approximately 4800 km2) from December 2003 to April 2004. In the same specific sample site, agricultural topsoil (0– 115 cm) in the farmland (ploughing layer) and non-agricultural topsoil (0–15 cm) in the neighboring abandoned farmland (grassland) was collected, and each sample was controlled at 1–1.5 kg. Sampling sites were far from potential contamination sources (industrial plants, busy roads, houses, etc.), with a nominal density of one sample per 16 km2, and one sample per 4 km2 especially around Dexing copper mine area and Leping coal mine area. Fine grain size material was collected from the centre of the soil, avoiding, wherever possible, the collection of

organic matter. Each sample represents composite material taken from four points over a patch of land with 1 km2. The collected samples have been air-dried at 35–40 °C for a few days. The soil was pretreated by sifting through a plastic net (mesh of 6 mm) and mixed thoroughly; material of >6 mm was discarded. The soil was milled with carnelian mortar pass to 0.015 mm sieve for chemical analysis.

3.2. Chemical analyses The analysis methods and its detection limits for trace elements were listed in Table 2. Total contents of K, Ca, Na, Mg, Si, Al, Mn, Ti and Fe were determined by wavelength-dispersive X-ray fluorescence spectroscopy (WD-XRF, PW2403). The samples were pressed into uniform pellets of 40 mm diameter using the Herzog semi-automatic press machine under 200 kN with a standing time of 30 s. In the case of soil samples, since the pellets were not easily formed, sample powders were thoroughly mixed with accurately weighted cellulose microcrystalline as binder (Chodos et al., 1960). Details of the sample preparation for the WD-XRF analysis are described in Marques et al. (2004) and the WD-XRF procedures per se are described by Boyd and Mertzman (1987). Before the determination of As, Cd, Cr, Cu, Hg, Mo, Pb, and Zn, the samples were digested with 60% perchloric acid, 40% hydrofluoric acid, concentrated nitric acid and concentrated hydrochloric acid (Page et al., 1986). The total concentrations of Cu, Pb, Zn, and Cr were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES, Perkin-Elmer 3300 DV). Cd was analyzed by atomic absorption spectroscopy (AAS, Hitachi 508), As was analyzed by atomic fluorescence spectroscopy (AFS, AFS-1201 produced by

Y. Teng et al. / Journal of Geochemical Exploration 104 (2010) 118–127 Table 2 Chemical analysis methods, detection limits and instrument type. Elements Determination method

Determination limits

Al As Ca Cd Cr

WD-XRF AFS WD-XRF AAS ICP-AES

0.05 (%) 0.24 (mg kg− 1) 0.05 (%) 0.03 (mg kg− 1) 4.5 (mg kg− 1)

Cu

ICP-AES

Fe Hg K Mg Mn Mo

WD-XRF AFS WD-XRF WD-XRF WD-XRF Polarography

Na Pb

WD-XRF ICP-AES

Si Ti Zn

WD-XRF WD-XRF ICP-AES

Instrument type

PW2403 (Phillips, Netherlands) AFS-1201 (KCHAIGUANG, China) PW2403 (Phillips, Netherlands) Hitachi 508 (Hitachi, Japan) Perkin-Elmer 3300 DV (PerkinElmer, USA) 0.74 (mg kg− 1) Perkin-Elmer 3300 DV (PerkinElmer, USA) 0.05 (%) PW2403 (Phillips, Netherlands) 0.005 (mg kg− 1) XGY-1011A (Langfang, China) 0.05 (%) PW2403 (Phillips, Netherlands) 0.05 (%) PW2403 (Phillips, Netherlands) −1 PW2403 (Phillips, Netherlands) 9.3 (mg kg ) 0.3 (mg kg− 1) JPS-303 (Chengdu Instrument, China) 0.1 (%) PW2403 (Phillips, Netherlands) Perkin-Elmer 3300 DV (Perkin1.9 (mg kg− 1) Elmer, USA) 0.1 (%) PW2403(Phillips, Netherlands) −1 PW2403 (Phillips, Netherlands) 8.5 (mg kg ) Perkin-Elmer 3300 DV (Perkin3.0 (mg kg− 1) Elmer, USA)

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were 100%; Zn was 96.43%; Cd, Hg and Mo were 91.67%; and Cu was 90.48%. The spot-check analysis results showed that the eligibility was respectively: As was 85%; Mo was 90%; Cd and Cr were 95%; and the others were 100%. 3.4. Statistical analysis Cluster analysis was performed by the STATISTICA 6.0 for Windows and cumulative frequency distribution was performed by the SPSS 11.0 for Windows. The Kolmogorov–Smirnov(K–S) test was applied to test the normal distribution for both raw data. The cluster was performed with the method of weighted average linkage between the groups. The 1-pearson correlation coefficient for the cluster intervals and the elements, which showed a close correlation, were identified and grouped for further analysis. The statistical method was used for calculating the geochemical background of trace elements in the soil. The MAD (median ± median absolute deviation) and empirical cumulative distribution functions were better suited for assisting in the estimation of threshold values and the range of background data (Reimann et al., 2005), so MAD method was applied for determining the geochemical background and threshold in the study area. 4. Results and discussion

KCHAIGUANG, China), and Hg was analyzed by atomic fluorescence spectroscopy (AFS, XGY-1011A produced by Langfang, China).

3.3. Accuracy control According to Regional Geochemical Exploration Regulation (1:200,000) (China Ministry of Geology and Mineral Resources, 1995), the accuracy, precision, eligibility, and spot-check were used for quality control in laboratory analysis. The standard reference material (GSS-1, GSS-2, GSS-3, GSS-4 soil purchased from the National Research Center for Geoanalysis of China) were incorporated to control the analysis accuracy. The results showed no sign of contamination, and revealed that the precision and bias of the analysis were generally below 5%. The recovery rates for the elements in the standard reference material ranged from 95 to 105%. The geochemical sample percent of pass for trace elements were respectively: Si, Al, Fe, Ca, Mg, K, Na, Mn, Ti and Pb

4.1. Geochemical background of trace elements In exploration geochemistry, the term ‘geochemical background’ was defined as “the normal abundance of an element in barren earth material” and concluded “it is more realistic to view background as a range rather than an absolute value” (Hawkes and Webb, 1962). The concept of geochemical background was introduced to differentiate between normal element concentrations and anomalies, which might be indicative of an ore occurrence (Reimann and Garrett, 2005). While in environmental geochemistry, Porteous (1996) defined geochemical background as “background concentration of pollutants. If the atmosphere in a particular area is polluted by some substance from a particular local source, then the background level of pollution is that concentration, which would exist without the local source being present. Measurements would then be required to detect how much pollution the local source is responsible for”. In 2005 and 2007, Gałuszka gave a new definition: “Geochemical background is a

Table 3 Geochemical background of non-agricultural and agricultural topsoil. Element

As (mg/kg) Cd (mg/kg) Cr (mg/kg) Cu (mg/kg) Hg (mg/kg) Mn (mg/kg) Mo (mg/kg) Pb (mg/kg) Ti (mg/kg) Zn (mg/kg) Al2O3 (%) CaO (%) Fe2O3 (%) K2O (%) MgO (%) Na2O (%) SiO2 (%) a b

Non-agricultural topsoil

Agricultural topsoil

China soil

Min

Max

Median

MADa

Median±2MAD

Min

Max

Median

MAD

Median±2MAD

Background value

2.2 0.05 14 8 0.04 113 0.29 17 1598 25 7 0.07 1.88 0.5 0.17 0.05 51.14

899 2.8 666 953 4.98 8670 87.6 1094 14000 1610 22.08 10.82 12.6 4.46 3.83 1.49 83.07

11.4 0.16 70 32 0.11 470 0.78 35 5322 78 13.77 0.27 4.76 2.26 0.62 0.29 68.37

3.8 0.05 11 8 0.02 192 0.35 6 499 15 1.5 0.09 0.84 0.4 0.14 0.13 3

3.8–19.0 0.15–0.17 58–92 16–48 0.07–0.15 86–854 0.08–1.48 23–47 4324–6320 48–108 10.77–19.08 0.09–0.45 3.08–6.44 1.46–3.06 0.34–0.90 0.03–0.55 62.37–74.37

1.8 0.04 10 6 0.03 88 0.12 16 1082 27 7.3 0.08 1.57 0.77 0.22 0.06 35.93

182 8.33 659 1825 1.03 1382 51.6 1312 11755 15800 20.65 23.5 9.64 4.8 3.64 3.03 83.41

10 0.2 70 33 0.09 275 0.72 39 5448 81 13.69 0.25 4.26 2.31 0.6 0.31 69.1

3.5 0.06 9 9 0.02 83 0.33 7 403 16 1.28 0.07 0.7 0.36 0.14 0.14 2.84

3.0–17.0 0.08–0.32 52–88 15–51 0.05–0.13 109–441 0.06–1.38 27–51 4642–6254 49–113 11.13–16.25 0.11–0.39 2.86–5.66 1.59–3.03 0.32–0.88 0.03–0.59 63.42–74.78

9.6 0.079 57.3 20.7 0.038 540 1.1 23.5 3810 68 12.56 1.30 4.24 2.27 1.23 1.49 –

MAD, median absolute deviation. Median of China soil background value (Wei et al., 1991).

b

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Table 4 Mainly industrial pollution sources in the study area. Pollution sources

Description

Cm1

Xianqiao coal mining Dumping waste rocks, duns and coal dusts site bearing some trace elements. Jishan coal mining site Dongjiashan coal mining site Yongshan coal mining site Henglu coal mining site Lanqiao coal mining site Guanmuling coal mining site Zhushanli coal mining site Mingshan coal mining site Zhenqiao coal mining site Qiaotouqiu coal mining site Mn mining site High contents of Mn, Fe, and Cr in dumping waste rocks and effluents. Tieshanfeng Mn mining site Zhongbujie Mn mining site Mn mining site Cu and Mo mining site Acid mine drainage with a mount of As, Cd, Cu, Cr, Mo, Pb, and Zn. Cu and Mo mining site Cu and Mo mining site Cu and Mo mining site Longshou Cu and Mo mining site Tongchang Cu and Mo mining site Zhushahong Cu and Mo mining site Xishan Cu and Mo mining site Fujiawu Cu and Mo mining site Longshou Cu and Mo mining site Cu and Mo mining site Bagushan Cu and Mo mining site Shankeng Cu and Mo mining site Dongkenkou Cu and Mo mining site Zhongbujie Zn and Pb Acid mine drainage with a mount of As, Cd, mining site Pb, and Zn. Yinshan Zn and Pb mining site Zn and Pb mining site Zn and Pb mining site Huaqiao Au mining Acid mine drainage with amount of As, Hg, site Pb, and Zn. Zn and Pb smelter High concentrations of Pb and Zn in the effluents. Cu and Mo extracting High concentrations of Cu and Mo in the plant effluents. Electroni-chemistry High concentrations of Cr, Ni, and Pb in the industry effluents.

Cm2 Cm3 Cm4 Cm5 Cm6 Cm7 Cm8 Cm9 Cm10 Cm11 Mm1 Mm2 Mm3 Mm4 Mc1 Mc2 Mc3 Mc4 Mc5 Mc6 Mc7 Mc8 Mc9 Mc10 Mc11 Mc12 Mc13 Mc14 Mz1 Mz2 Mz3 Mz4 Ma1 SM1 Ex1 ECI

Mainly pollutants

theoretical ‘natural’ concentration of a substance in a specific environmental sample (or medium), considering the spatial and temporal variables, which may be determined with direct, indirect, and integrated methods” (Gałuszka, 2005, 2007).

Table 5 Proportion of non-agricultural and agricultural topsoil PI. PI

0 < PI ≤ 1 1 < PI ≤ 2 2 < PI ≤ 3 3 < PI ≤ 4 4 < PI ≤ 5 5 < PI

Non-agricultural 52 topsoil (%) Agricultural topsoil 37 (%)

34

9

2

1

2

51

6

3

1

2

There are two major methods used for assessing background concentrations (Matschullat et al., 2000): direct (geochemical) and indirect (statistical). The geochemical methods of this type of study are often criticized as having subject sample selection criteria, high costs, and heavy laboratory workload (Gałuszka, 2007). In contrast, far more popular statistical methods have been used not only for assessing background concentrations, but also for the separation of geochemical anomalies from geochemical background (Gałuszka, 2007). The calculation of [mean ± 2 standard deviation] to estimate threshold values dividing background data from anomalies, is still used for almost 50 years after its introduction, and delivers arbitrary estimates (Reimann et al., 2005). The [median ± 2 median absolute deviation (MAD)] was better suited for assisting in the estimation of threshold values and the range of background data. The MAD of trace elements in agricultural and non-agricultural soil in the study area was shown in Table 3. Dexing area was the famous foundation of nonferrous mineral resources in China, and there was highly geochemical background of trace elements such as As, Cd, Cu, Cr, Hg, Mo, Pb, and Zn (Zhu et al., 1983). With that in mind, we took the median concentration of trace elements as “regional background value”, and took the upper threshold concentration of trace elements as “local background value” (especially for nonferrous mining area) for distinguishing natural source from mining and smelting source. Therefore, the regional background value of trace elements in non-agricultural topsoil was established: As 11.4 mg/kg, Cd 0.16 mg/kg, Cr 70 mg/kg, Cu 32 mg/kg, Hg 0.11 mg/ kg, Mn 470 mg/kg, Mo 0.78 mg/kg, Pb 35 mg/kg, and Zn 78 mg/kg; while the local background value of trace elements was: As 19.0 mg/kg, Cd 0.17 mg/kg, Cr 92 mg/kg, Cu 48 mg/kg, Hg 0.15 mg/kg, Mn 854 mg/ kg, Mo 1.48 mg/kg, Pb 47 mg/kg, and Zn 108 mg/kg. And the regional background value of trace elements in agricultural topsoil was: As 10 mg/kg, Cd 0.2 mg/kg, Cr 70 mg/kg, Cu 33 mg/kg, Hg 0.09 mg/kg, Mn 275 mg/kg, Mo 0.72 mg/kg, Pb 39 mg/kg, and Zn 81 mg/kg; while the local background value of trace elements was: As 17.0 mg/kg, Cd 0.32 mg/kg, Cr 88 mg/kg, Cu 51 mg/kg, Hg 0.13 mg/kg, Mn 441 mg/ kg, Mo 1.38 mg/kg, Pb 51 mg/kg, and Zn 113 mg/kg. As a whole, the median concentration of trace elements in the agricultural topsoil was similar to that in the non-agricultural topsoil. Background concentrations of elements in topsoil of China have been investigated from 1986 to 1990 (Wei et al., 1991). Mean and median concentrations of 62 elements have been computed for topsoil samples from 4095 locations through mainland China (Chen et al., 1991). Some detailed procedures of determination background concentrations of elements including sample collection, chemical analysis, and data presentation were provided by Wei et al. (1991) and Chen et al. (1991). Wei et al. (1991) computed median concentrations of trace elements in topsoil: As 9.6 mg/kg; Cd 0.079 mg/kg; Cr 57.3 mg/kg; Cu 20.7 mg/kg; Hg 0.038 mg/kg; Mn 540 mg/kg; Mo 1.1 mg/kg, Pb 23.5 mg/kg; and Zn 68 mg/kg. In additionally, He et al. (2001) calculated Jiangxi topsoil background concentration of trace elements which followed: Cu 20.8 mg/kg, Pb 32.1 mg/kg, Zn 69.0 mg/kg, Cd 0.10 mg/kg, Cr 48.0 mg/ kg, Hg 0.08 mg/kg, As 10.4 mg/kg, and Mn 259 mg/kg. The “regional background value” of Mo and Mn in non-agricultural and agricultural topsoil in the study area was lower than the median background concentrations of those in topsoil of China.

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Fig. 2. Geochemical patterns of non-agricultural topsoil pollution.

Fig. 3. Geochemical patterns of agricultural topsoil pollution.

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Fig. 4. Spatial distribution of cultivation pollution index.

4.2. Topsoil pollution assessment 4.2.1. Industrial pollution sources In the study area, potential industrial pollution sources mainly came from mining and smelting activities, and the description of these pollution sources was illustrated in Table 4. In coal mining area, the pollution sources included dumping gangues and coal dusts. The average content of trace elements in the coal in study area was: As 10 mg/kg, Cd 1 mg/kg, Cr 11 mg/kg, Cu 26 mg/kg, Hg 0.25 mg/kg, Mn 96 mg/kg, Mo 5 mg/kg, Pb 17 mg/kg, and Zn 32 mg/kg (Zhao et al., 2007a), therefore anthropogenic source of trace elements input into topsoil through coal dust deposition. The average content of trace elements in the gangue: Cu 108 mg/kg, Pb 27–102 mg/kg, Zn 12.8–66.3 mg/kg, As 0.45–3.21 mg/kg, Cr 2.31– 11.6 mg/kg, Hg 0.65–1.23 mg/kg, and Cd 0.48–1.06 mg/kg (Xu et al., 2002), and the trace elements would be released into soil by leaching of gangue piles. In Au, Cu, Mo, Pb, and Zn mining area, acid mine drainage (AMD) which had high concentration of trace elements was the most important pollution source, In some areas which located in and around Cu, Pb, and Zn extraction plant and smelting mill, alkaline effluent which had high concentration of trace elements was another important pollution source. In general, the AMD and alkaline effluent would meet into Lean River and its branches. Especially in Dawu River which was a tributary of Lean River, the concentrations of trace elements in river water were: Cu 22–16419 μg/L, Pb <1.0–4 μg/L, Zn 3–840 μg/L, Cd 0.6–4.1 μg/L, Mo <1.0–226 μg/L, As <0.1–9.2 μg/L, Hg <0.001–0.06 μg/L, and Cr 3–250 μg/L (Zhao et al., 2007b), while in Jishui river which was another tributary of Lean River, the concentrations of trace elements in river water were: Cu 118 μg/L, Pb 1.42 μg/L, Zn 362 μg/L, Cd 0.86 μg/L, Mo 0.52 μg/L, As 1.58 μg/L, and Hg 0.05 μg/L (Zhao et al., 2007b). In addition, the effluent with high content heavy metal came from electrochemical plant. Therefore, the topsoil could be influenced by irrigating polluted river water.

Fig. 5. Tree-clustering diagram of trace elements in non-agricultural and agricultural topsoil.

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4.2.2. Topsoil pollution assessment Soil pollution (enrichment) indices permit a simplified estimation of the intensity of soil contamination by using the multiple element data sets (Bityukova et al., 2000). A pollution index map of the soils also permits to classify the soils on the basis of their geochemistry (Chon et al., 1996). According to the calculation method by Bityukova et al. (2000), we selected “regional background value” substitute for the average concentration of the worldwide soils. So the pollution index (PI) was calculated by averaging the ratios of the concentrations of the soils divided by “regional background values”: Pollution index =

1 n element concentration in soili ∑ n i = 1 Regional background valuei

where n is a number of the measured elements, and As, Cd, Cr, Cu, Hg, Mn, Mo, Pb, and Zn were concerned in the study area. In non-agricultural topsoil, there are about 52% sites with its PI below 1.0; and about 34% from 1.0 to 2.0; about 14% above 2.0 (Table 5). However, in agricultural topsoil, there are about 37% sites with its PI below 1.0; and about 51% from 1.0 to 2.0; about 12% above 2.0 (Table 5). So the pollution degree of agricultural topsoil was slightly higher than that of non-agricultural topsoil. The maps of the PI for As, Cd, Cr, Cu, Hg, Mn, Mo, Pb and Zn of nonagricultural topsoil was illustrated in Fig. 2 and agricultural topsoil was illustrated in Fig. 3. Either in non-agricultural topsoil or in agricultural topsoil, the higher PI was distributed in and around specific some pollution sources such as Ec1 (electrochemical plant); Ex1 (Cu and Mo extracting plant); Cm8, Cm9 and Cm11 (Coal mining site); Mm1 (Mn mining site); Mz1 and Mz2 (Pb and Zn mining site); Mc3, Mc4, Mc5, Mc7, Mc8, and Mc11 (Cu and Mo mining site); Sm1

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(Pb and Zn smelter). And the heaviest pollution of topsoil was located in the neighboring and surrounding the Dexing and Leping mining area. 4.2.3. Cultivation impact assessment In order to assess the impact of cultivation on agricultural topsoil pollution, we compiled the PI to cultivation pollution index (CPI). The CPI was calculated by the following formulation:

Cultivation pollution index =

1 n element concentration in agricultural topsoili ∑ n i = 1 element concentration in nonagricultural topsoili

where n is a number of the measured elements. There were a total of 303 samples with its CPI was higher than 1, which indicated that the pollution was more serious in agricultural topsoil than in non-agricultural topsoil. The CPI patterns revealed that the topsoil pollution distribution was located in the neighboring mining area (Fig. 4). The quality of agricultural topsoil was pejorative due to irrigating with polluted water. 4.3. Geochemical assemblage In geochemical exploration, the element association or assemblage in surficial environment can be used for indicating mineralized target. While in environmental geochemistry, the element association of heavy metals in the sediment is mainly restricted with the local environmental features, geological processes, and the characteristics of heavy metals. Fig. 5 showed the result of the cluster analysis and Table 6 supplied the correlation matrix for the 17 elements in agricultural topsoil and non-agricultural topsoil.

Table 6 Correlation matrix of trace elements. Mo

Cd

Cr

Zn

Cu

Mn

Ti

Pb

Fe

Ca

K

Si

Al

Mg

Na

Non-agricultural topsoil As 1.00 Hg 0.13 1.00 Mo 0.20 0.23 Cd 0.38 0.14 Cr 0.13 0.01 Zn 0.27 0.01 Cu 0.10 0.15 Mn 0.22 0.04 Ti 0.04 0.05 Pb 0.37 0.14 Fe 0.35 −0.01 Ca 0.05 0.01 K 0.01 −0.08 Si − 0.21 0.05 Al 0.11 −0.03 Mg 0.25 −0.08 Na − 0.02 −0.05

As

Hg

1.00 0.03 0.06 0.01 0.29 0.09 0.06 0.03 0.29 0.03 −0.03 − 0.15 0.04 0.15 0.08

1.00 0.01 0.78 0.22 0.18 0.01 0.86 0.16 0.09 −0.02 − 0.12 0.01 0.12 − 0.01

1.00 0.01 0.07 0.08 0.21 0.01 0.46 0.02 − 0.17 −0.26 0.23 0.52 −0.22

1.00 0.01 0.02 0.01 0.83 0.11 0.02 − 0.02 − 0.12 0.03 0.03 0.01

1.00 0.03 0.15 0.04 0.25 0.01 − 0.04 − 0.13 0.13 0.09 −0.04

1.00 0.01 0.12 0.46 0.06 0.16 − 0.34 0.21 0.36 0.17

1.00 0.02 0.39 − 0.17 −0.49 0.03 0.07 − 0.06 − 0.45

1.00 0.12 0.04 − 0.01 − 0.12 0.03 0.01 0.01

1.00 − 0.02 0.02 − 0.60 0.52 0.55 − 0.11

1.00 −0.12 −0.39 −0.16 0.14 − 0.03

1.00 − 0.49 0.60 0.30 0.62

1.00 − 0.69 − 0.58 − 0.31

1.00 0.30 0.13

1.00 0.26

1.00

Agricultural topsoil As 1.00 Hg 0.33 1.00 Mo 0.06 0.12 Cd 0.18 0.16 Cr 0.07 0.02 Zn 0.25 0.23 Cu 0.07 0.18 Mn 0.08 0.07 Ti 0.06 0.05 Pb 0.21 0.13 Fe 0.17 0.08 Ca 0.01 0.02 K 0.05 0.01 Si − 0.15 −0.08 Al 0.09 0.04 Mg 0.09 0.02 Na 0.01 −0.01

1.00 0.06 0.03 0.08 0.49 0.01 0.04 0.13 0.25 0.21 − 0.05 −0.20 0.05 0.08 −0.02

1.00 0.02 0.71 0.33 0.10 0.02 0.66 0.10 0.10 − 0.02 − 0.07 −0.02 0.17 −0.03

1.00 0.09 0.05 0.11 0.14 0.04 0.40 0.04 − 0.18 − 0.20 0.17 0.43 − 0.24

1.00 0.20 0.09 0.08 0.80 0.23 0.03 0.03 − 0.23 0.15 0.20 −0.02

1.00 0.06 0.13 0.19 0.31 0.04 0.01 − 0.25 0.19 0.13 − 0.06

1.00 0.10 0.04 0.34 0.05 0.03 − 0.27 0.15 0.17 0.02

1.00 0.06 0.41 −0.15 − 0.41 −0.07 0.10 0.02 − 0.41

1.00 0.11 0.02 −0.02 − 0.09 0.07 0.03 − 0.05

1.00 − 0.02 0.08 −0.71 0.60 0.43 − 0.22

1.00 − 0.15 −0.20 − 0.14 0.08 0.01

1.00 − 0.54 0.60 0.30 0.59

1.00 − 0.81 −0.49 − 0.19

1.00 0.26 0.05

1.00 0.26

1.00

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In the agricultural topsoil, As, Hg, Mo, Cu, Cd, Zn, Pb, Cr, Fe, Al, K, Na, Mg, Ti, and Mn were classified by three groups. The group one included As and Hg; the group two included Mo, Cu, Cd, Zn, and Pb; and the group three included Cr, Fe, Al, K, Na, Mg, Ti, and Mn. While in the non-agricultural topsoil, the classification is gently different from that in the agricultural topsoil. The first group included As, Cd, Pb, and Zn; the second group included Cr, Fe, Mg, K, Na, Al, Mn, and Ti; the third group included Cu and Mo. According to Goldschmidt's geochemical classification (Goldschmidt, 1954), Cr, Mg and Fe were siderophile group; As, Mo, Cu, Hg, Cd, Pb, and Zn were chalcophile group; and, K, Na, and Al were lithophile group. In the investigation of Jiangxi Province soil background (He et al., 2001), 12 elements were divided into chalcophile group (Cu, Pb, Cd, Hg, As and F) and siderophile group (Ni, Cr, V, Co, Zn and Mn). This study area was important mineralized region of base metals (Cu, Mo, Pb and Zn), so the geochemical association of trace elements was firstly controlled by geogenic process. In addition, trace element assemblage was influenced by mining and processing activities. Here, the principal component factor analytical method was applied, and the correlation matrix of tracer elements was obtained. As shown in Table 6, the analysis results revealed that: factor 1 included As, Cd, Pb, and Zn; factor 2 included Cu, Hg, and Mo; and factor 3 included As, Cr, and Mn, so these trace elements could be from the same sources (Xuan, 2007). The possible reason inducing the phenomenon was inferred that was, in the processing of metal mining and extracting, some sulfides (i.e. sphalerite, galenite, sphalerite, pyrite, chalcopyrite, molybdenite, bornite, chalcocite, tennatite) would be oxidized to release As, Cd, Cu, Pb, Zn, Hg, Mo into the environment. 5. Conclusion The geochemical background and threshold of trace elements was determined with the use of MAD in the study area. The topsoil geochemical background of Cu, Pb, Zn, Cd, Cr, Hg, As and Mn in the study area was higher than that in Jiangxi Province. In the nonagricultural soil, trace elements were classified by three groups: group one included As and Hg; the group two included Mo, Cu, Cd, Zn, and Pb; and the group three included Cr, Fe, Al, K, Na, Mg, Ti, and Mn; which was gently different from that in the non-agricultural topsoil. The maps of the pollution indices for As, Cd, Cr, Cu, Hg, Mn, Mo, Pb, Ti and Zn of non-agricultural topsoil and agricultural topsoil showed that the highest level of pollution is distributed nearby and along the Lean River, especially in the neighboring and surrounding the Dexing and Leping mining activities area. Therefore, this area needs to be monitored regularly for heavy metal contamination in the soil, especially in mining and its neighboring area. Acknowledgement This study is granted by China Natural Science Foundation (No.40603017) and program for NCET. The authors would like to thank the members of this consortium from Beijing Normal University, China Academy of Geological Sciences, Chengdu University of Technology. References Abrahams, P.W., 2002. Soils: their implications to human health. Science of the Total Environment 22, 1–32. Aelion, C.M., Davis, H.T., McDermott, S., Lawson, A.B., 2008. Metal concentrations in rural topsoil in South Carolina: potential for human health impact. Science of the Total Environment 402, 149–156. Bilos, C., Colombo, J.C., Skorupka, C.C., Rodriguez Presa, M.J., 2001. Sources, distribution and vaiability of airborne trace metals in La Plata City area, Argentina. Environmental Pollution 111, 49–158. Bityukova, L., Shogenova, A., Birke, M., 2000. Urban geochemistry: a study of element distributions in the soils of Tallinn (Estonia). Environmental Geochemistry and Health 22, 173–193. Boyd, F.R., Mertzman, S.A., 1987. Composition and structure of the Kaapvaal lithosphere, southern Africa. In: Mysen, B.O. (Ed.), Magmatic Processes: Physico-

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