Author's personal copy - · PDF file(Levy et al., 1997; Johnson et al., 2000; Kim et al.,...

This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution

and sharing with colleagues.

Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party

websites are prohibited.

In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information

regarding Elsevier’s archiving and manuscript policies areencouraged to visit:

http://www.elsevier.com/copyright

http://www.elsevier.com/copyright

Author's personal copy

Discrimination of lithogenic and anthropogenic sources of metals and sulphur in soilsof the central-northern part of the Zambian Copperbelt Mining District: A topsoil vs.subsurface soil concept

Bohdan Kříbek a,⁎, Vladimír Majer a, František Veselovský a, Imasiku Nyambe b

a Czech Geological Survey, Klárov 3, CZ-11821 Prague 1, Czech Republicb University of Zambia, School of Mines, Geology Department, P. O. Box 32 379, Lusaka, Zambia

a b s t r a c ta r t i c l e i n f o

Article history:Received 20 February 2009Accepted 17 December 2009Available online 6 January 2010

Keywords:Heavy metalsSmeltersSoil contaminationCopperbeltZambia

Samples of topsoil together with reference samples of subsurface soil from a depth of 80–90 cm werecollected in the central-northern part of the Zambian Copperbelt to distinguish lithogenic sources of metalsfrom anthropogenic contamination of soils caused by fallout of dust from mining operations, flotation oretreatment plants, tailings dams, smelters and slag dumping grounds. The total sulphur, Cu and Co contentswere found to be significantly higher in topsoil relative to subsurface soil over a large part of the surveyedarea, and Zn, Pb, As and Hg contents showed a definite increase in the close neighbourhood of smelters andin the direction of prevailing winds. This indicates that the increase of these elements in the topsoil is due toanthropogenic activities. The areal extent and degree of anthropogenic contamination of topsoil can beexpressed by an enrichment index (EI) based on the average ratio of the actual and median concentrations ofthe given contaminants. Although the contamination of soil by dust fallout decreases progressively withdepth in the soil profile, in areas strongly affected by mining and mineral processing the anthropogeniccontamination by sulphur and copper can be traced to a depth of 80–90 cm. In contrast, the concentration ofelements such as Cr, Ni, and V, that show a direct correlation with the content of iron in the soils, increases inthe subsurface soil relative to the topsoil. This is particularly evident in areas underlain by rocks of theKatanga Supergroup.Lithogenic and anthropogenic sources of metals and sulphur can also be distinguished by using factoranalysis. This analysis of data acquired from the topsoil revealed five factors governing the source and natureof individual elements or their groups: the “slag specific” grouping of Cr, Zn, Pb and As, the “bedrock specific”grouping of V, Cr, Ni and Fe, the “smelter specific” grouping of Stot, Co, Cu and Hg, the “tailings specific”grouping of pH, Ccarb, Co and Ni, and finally the “organic carbon specific” grouping of Corg and Hg. These fivefactors account for 69.4% of the total variance in the data structure of the system. The interpretation of factorsis based on the geographical inspection of factor score distribution, the knowledge of the chemical characterof the sources of contamination, the local geology and the agrochemical properties of the soils. The factoranalysis of data obtained from subsurface soils showed that only the bedrock specific factors had aninfluence.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

The issue of anthropogenic contamination of soils in miningdistricts has been discussed in numerous reports and publications(e.g., Dudka and Adriano, 1997; Barcan and Kovnatsky, 1998; Faragoet al., 1999; Lee et al., 2001; Goodarzi et al., 2002; Liu et al., 2003;Krzaklewski et al., 2004; Beavington et al., 2004; Ashey et al., 2004; Lin

et al., 2005; Voegelin et al., 2008; Ruan et al., 2008). There have beenalso many investigations of the mineralogy and chemical compositionof dust fallout (Khan et al., 2004; Castellano et al., 2004; Beavingtonet al., 2004), the chemistry of flotation tailings and waste rock dumps(Levy et al., 1997; Johnson et al., 2000; Kim et al., 2002; Courtin-Normade et al., 2002; Carlsson et al., 2003; Zhang et al., 2004; Šráčeket al., 2004; Walker et al., 2005; Liu et al., 2005; Romero et al., 2006)and slags (Orescanin et al., 2006; Ganne et al., 2006; Ettler et al.,2009). Much attention in contaminated soil studies has been paid tothe origin of individual metals, and the factors governing theirmigration, e.g., the bonding of metals to individual components in thesoil substrate, and to factors governing themobility of elements in soilsolutions (Maiz et al., 1997; Li and Thornton, 2001; Lee et al., 2001;

Journal of Geochemical Exploration 104 (2010) 69–86

⁎ Corresponding author. Czech Geological Survey, Klárov 3, CZ-11821 Prague 1,Czech Republic. Tel.: +420 25108518; fax: +420 543 212 370.

E-mail addresses: [email protected] (B. Kříbek),[email protected] (V. Majer), [email protected](F. Veselovský), [email protected] (I. Nyambe).

0375-6742/$ – see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.gexplo.2009.12.005

Contents lists available at ScienceDirect

Journal of Geochemical Exploration

j ourna l homepage: www.e lsev ie r.com/ locate / jgeoexp


Kabala and Singh, 2001; Liu et al., 2003; Chatain et al., 2005; Luo et al.,2006). However, these studies were mostly undertaken on rathersmall areas with simple geological structure. In large mining districtsit is generally difficult to determine the relative contributions to thegeochemistry of soil samples based solely on the concentration ofmajor or trace elements in the soils themselves, especially in areaswith varied lithology and where both lithogenic and anthropogenicsources of contamination are present (Reimann et al., 2005; Reimannand Garrett, 2005). In the case when the geochemical composition ofuncontaminated soil or bedrock is known and relatively simple, theanthropogenic contamination can be expressed in the form of anenrichment factor, i.e. by the normalization of element concentrationsin contaminated soils by their contents in uncontaminated soils(Reimann and Garrett, 2005). Alternatively, the content of metals incontaminated soil can be normalized relative to local or worldwidegeochemical soil standards (Kabata-Pendias and Pendias, 1984;Reimann and de Caritat, 1998). A variety of statistical methods hasbeen developed that significantly improve the chances of successfullydifferentiating the relative contributions from multiple sources(Facchinelli et al., 2001; Romic and Romic, 2003; Perez and Valiente,2005; Simenov et al., 2005; Glavin and Hooda, 2005; Chen et al., 2008;Lima, 2008; Lopéz et al., 2008). The environmental degradation of theZambian part of the Copperbelt is poorly quantified in spatial termsbecause the availability of high precision and up-to-date regionalgeochemical data for both unpolluted and polluted areas is limited.Moreover, a high natural background of heavy metals in soils and thevaried lithology of the surveyed terrain make the evaluation of thedegree of industrial pollution difficult. Therefore, in order todistinguish natural concentrations of metals from those ascribed tocontamination by dust fallout, the soil sampling was undertaken attwo depth horizons: firstly the topsoil, which is most affected by dustfallout, and secondly a reference soil horizon at a depth of 80–90 cm.Here, the subsurface soil is believed to be little or not at allcontaminated by dust fallout, so that the values established areconsidered to represent the natural geochemical background. Theauthors of this study are of course aware of the danger arising fromdifferent physical and chemical properties of separate soil horizonsand their effect on the distribution of relevant metals. As aconsequence, the study of the distribution of metals was complemen-ted by the determination of soil pH, the contents of organic andcarbonate carbon, and total sulphur as well as by the analysis of totaliron, both in topsoil and subsurface soils. Thepresent studywas carriedout as a part of the larger research programme dealing with thepollution of soils in the Copperbelt area. To establish the extent ofindustrial contamination an environmental–geochemical survey ofsoils was carried out within the framework of the DevelopmentCooperation Programme of the Czech Republic during the years 2002to 2006 (Kříbek andNyambe, 2002, 2004, 2005, 2006) and Kříbek et al.(2003, 2004, 2007). Themain objectives of the investigation were: (1)to evaluate the concentrations of metals (As, Hg, Co, Cr, Cu, Ni, V, Pb,and Zn) and total sulphur, and to map their distribution in topsoil andsubsurface soils in order to determine the pattern of dispersion inrelation to the distance from the sources of contamination, (2) todiscriminate between lithogenic (geological) and anthropogenicsources of metals in this mineralized and polluted area, and (3) toprovide some initial data on the identification of lithogenic andanthropogenic sources using statistical correlation and factor analysis.

2. Study area

The research covered 4700 km2 of soils in the urban and ruralareas of the central-northern part of the Copperbelt Province ofZambia. The surveyed area covers 63% of the total area of the ZambianCopperbelt amounting to 7500 km2. The majority of the mining andmineral processing facilities and grounds of the Copperbelt (ca 92%)are located in the mapped area. Within the studied area, the largest

center of population is the city of Kitwe with a total population of866,646, then Chingola (177,445), and Mufulira (152,664). Otherimportant towns are Chililabombwe (84,866), Kalulushi, and Cham-bishi. Mining activities (open pits and underground mines) arelocated in the vicinity of the individual towns. Smelters are located atMufulira, Kitwe (the Nkana smelter) and at Chambishi (Fig. 1).

2.1. Soils

The typical section of freely drained soils sampled within thisproject consists of A1, A2, B1, B2 and C horizons (Kříbek and Nyambe,2005):

A1 horizon (0–5 cm), topsoil. Dark greyish brown silt; moderatemedium granular structure; slightly hard, friable, slightly plastic;roots abundant; smooth and gradual boundary.A2horizon (5–30 cm). Dark reddish brown silt or clay;weakmediumgranular structure; soft, friable, slightly plastic, sticky; few roots;smooth and diffuse boundary; a few ferromanganese nodules.B1 horizon (30–50 cm). Dark reddish brown silty clay; weak mediumsubangular breaking down into weak fine granular structure; friable,slightly plastic, slightly sticky; a few roots; smooth and gradualboundary; abundant ferromanganese nodules.B2 horizon (50–120 cm). Dark red clay; massive porous breakingdown intoweak granular structure; soft, very friable, slightly plastic,slightly sticky; no roots; clear, undulating boundary; abundantferromanganese nodules.C horizon (120–140±). Clay loam; horizon comprising weatheredrock fragments and material of B2 horizon.

According to the FAO classification of soils (FAO-UNESCO, 1997)freely drained soils of the Copperbelt region can be assigned to theferrasoil group (acric, orthic or rhodic ferrasoils).

Ferrasoils in the surveyed area are usually acidic, poor in organiccarbon and nitrogen, and display low values of cation exchangecapacity (Table 1). Compared with the subsurface soil horizon (B2horizon, depth 80–90 cm), the topsoil (A1 horizon, depth 0–5 cm) hashigher contents of organic carbon (Corg), higher values of pH, higheramounts of exchangeable cations (except for K+), and a lower amountof clay and silt fractions (Table 1). The soils of the dambo-type (poorlydrained soils, cambisoils) according to the FAO-UNESCO (1997)classification that occur locally along the riverbankswere not sampled.

2.2. Climate

Three climatic seasons are defined: (i) a rainy season, (ii) a cooldry season, and (iii) a hot season. The rainy season lasts roughly fromthe beginning of November until the end of April and is characterizedby tropical thunderstorms. The cool dry season lasts from the first halfof May until the end of August and is characterized by light winds.Precipitation during this season is negligible. Daily temperatures inJune and July range from 6° to 24° and fall to 5 °C at night. The hotseason lasts from September until the end of January. The averagetemperatures are over 30 °C during the day and range between 21 °Cand 26 °C at night. The annual rainfall averages are 1320 mm in Kitweand 1270 mm in Mufulira. The wind flow is dominated by strongwinds from the south-easterly quadrant from March until October.During January, February, November and December, wind flow isdominated by light north-easterly winds.

2.3. Geology and mineralization

In the Zambian Copperbelt, the oldest Pre-Katanga BasementComplex consists of a Paleoproterozoic magmatic arc sequence,comprising schists and intrusive granitoids dated at about 1980±8Ma

70 B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86


(Rainauld et al., 2005; Fig. 1). This Basement Complex is overlainuconformably by quartzites and metapelites of the Muva Supergroup.The Basement Complex as well as the Muva Supergroup are penetratedby the pink microcline granite and adamellite that is assumed to be ofPre-Katangan age.

Overlying metasediments of the Katanga Supergroup are tradi-tionally divided into the ore-bearing Mine Series and the KundelunguGroups. The deposition of the Katanga Supergroup sediments startedat some time after 880 Ma (Armstrong et al., 1999). Structurally, theCopperbelt region belongs to the Lufilian Arc Terrane. The Neoproter-ozoic sedimentation in this terrane began in a continental riftenvironment (Binda, 1994; Porada and Berhorst, 2000). Newgeochronological data indicate that metasedimentary rocks of theKatanga Supergroup were deformed and partly metamorphosedduring the Pan-African Lufilian Orogeny between ca 600–480 Ma.Regional uplift and cooling that affected the whole Katangan Basin is

dated at between 495–480 Ma (Rainauld et al., 2002, 2005). The upliftwas accompanied by the formation of ENE-directed thrusting andlater by strike-slip faulting. A number of N-trending basic dykes cutthe Pre-Katanga basement as well as the Katanga Supergroup.Spatially associated with the dykes are a number of irregularly shapeddolerite stocks (Key et al., 2001).

The Zambian Copperbelt is one of the world's largest copper andcobalt ore districts. TheCu–Comineralizion in theCopperbelt is confinedto the lower section of the Katanga Supergroup (Mine Series) close to itscontact with the underlying Pre-Katanga units. The Copperbelt oresform sediment-hosted deposits of strata-bound and/or stratiform typecharacterized by finely disseminated copper–(cobalt)–iron sulfidesconsisting mostly of chalcopyrite, cobalt-rich pyrite and bornite±carrolite. The host rocks include quartzite (arkose), shale and dolomitethat are believed to have deposited in a continental rift environment.The ore grades average 3 wt.% Cu and 0.18% Co in deposits from which

Fig. 1. Geological sketch map of the surveyed part of the Copperbelt Province in Zambia. Compiled, simplified and taken from Garrard (1994), Marjonen (2002) andMukwila (2002)with markings of major sources of contamination and location of the studied soil profiles.

71B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86


both metals are extracted. Trace amounts of Au, Pt and Ag wererecovered from the copper slimes during the smelting process. Ca30 million metric tons of copper metal were produced since the large-scale mining operations began in 1930 (Kamona and Nyambe, 2002).

2.4. Copper and cobalt industry

Significant mineral exploration in the Copperbelt started duringthe 1920s and resulted in the discovery and the development ofseveral mines. During the 1960s, the annual Zambian copperproduction peaked at over 755,000 metric tonnes. In 1969, theZambian government nationalized the industry and reorganized allmining utilities into the Nchanga Consolidated Copper Mines Limitedand Roan Copper Mines Limited. In 1982, both companies weremerged into the Zambia Consolidated Copper Mines Limited. Whenthe world price of copper fell, the Zambian government was unable toadjust social services provided by the mines to accommodate thisdrop in income and thus the capital available to maintain and updatemining technology declined and production deteriorated. In response,Zambia began to consider the options for privatization in the early1990s. The Nkana and Mufulira mines were purchased by MopaniCopper Mines, Chambishi mine was taken over by the China Non-Ferrous Metal Corporation, the Nchanga and Konkola Mines weretransferred to Vedanta Resources (India) and the Chibuluma-Westand Chibuluma-South underground mines to the Chibuluma MinesPLC. In 2008, the annual production of copper in the whole of theCopperbelt Mining District amounted to ca 569,891 metric tons andthat of cobalt was ca 5275 metric tons. Significant volumes ofselenium (17 t) and silver (8 t) together with minor gold and plat-inum group elements were produced in the above year (BMI, 2009).Ores are processed by flotation at Kitwe (Nkana processing plant),Chingola, Chililabombwe, Chambishi, Chibuluma and Mufulira oretreatment plants, and smelted and refined at the Mufulira and Kitwe(Nkana) smelters. The Chambishi smelter re-processes old slags fromthe Kitwe (Nkana) smelter, which are rich in copper and cobalt.

3. Materials and methods of investigation

Regional environmental–geochemical surveying of soils wascarried out using the methodology recommended for regional

geochemical mapping by the FOREGS Geochemistry Working Group(Salminen et al., 1998). The surveyed area was divided into a base gridof square cells 4×4 km in size. Each cell was characterized by at leastone sampling point, from which, after removal of plant remains andthe thin humus layer, a composite sample of topsoil was collected at adepth of 0–5 cm. A composite sample was prepared by blending soilsamples taken on the edges and in the central point of a square 25 by25 m in size. Weight of composite topsoil samples was 0.6 to 1.7 kg. Inheavily contaminated areas, additional samples were taken in theneighbourhood of contamination “hot spots”. At selected samplingpoints, composite samples of the subsurface soil were taken from adepth of 80 to 90 cm using a soil probe. Weight of composite sub-surface soil samples was 0.3 to 0.4 kg. Points for the subsurface soilsampling were selected on the basis of the different underlyingbedrock lithologies so that soils formed on the different rock types ofbedrock could be characterized. Site descriptions were made at thetime of sampling to record the location of the sample in relation toland use and major environmental features. During the project, 719composite samples of the topsoil, and 129 samples of the subsurfacesoil were collected and analysed to create a representative geochem-ical database for the central-northern part of the Copperbelt. Tocharacterize dust fallout from overburden dumps and waste rocksdumps, from ore crushers, tailings ponds, ore concentrate depots,from smelters and slag dumps were collected using low volume per-sonal total suspended particles (TPS) samplers (LVPs, type PV-1.7,Kubik, Czech Republic). The samplers are multi-functional equipmentfor sampling either the dust fallout in workplaces or outdoors. Otherdust fallout samples were collected from the leaves of trees and fromwind-blown tailings material. Soil samples were air-dried, and, afterhomogenization, half of each sample was passed through 0.2 mmmesh screen using a U.S. Geological Survey Standard Sieving Set andpulverized in an agate ball mill to less than 0.063 mm mesh. Todetermine the content of metals, soil and dust samples were digestedwith aqua regia in accordance with the ISO 11466 procedure(International Organization for Standardization, 1995). The choice ofthis method was dictated by international regulations, which setnorms for soils according to this procedure. All reagentswere declaredpro analysi, and all solutions were prepared with double distilledwater. Standard working solutions were prepared from originalcertified stock solutions (MERCK) concentration 1000 mg L−1 in 1%

Table 1Agrochemical properties and grain size distribution of topsoil and subsurface soils in the surveyed area of the Zambian Copperbelt. Number of samples: 11 (After Kříbek and Nyambe,2005).

Topsoil (0–5 cm depth) Subsurface soil (70–90 cm depth)

Min. Median Max Min. Median Max

pHKCl 4.18 4.75 5.37 4.03 4.25 4.47Exchangeable H+ (mmol/100 g) 2.0 4.42 8.5 1.5 3.75 5.5Exchangeable Ca2+ (mmol/100 g) 0.1 1.48 4.26 0.07 0.13 0.19Exchangeable Mg2+ (mmol/100 g) 0.01 0.49 1.01 0.03 0.17 0.24Exchangeable K+ (mmol/100 g) 0.04 0.21 0.41 0.05 0.30 1.18Exchangeable Al3+ (mmol/100 g) 0.0 0.22 0.6 0.0 0.0 0.0CEC (mmol/100 g) 0.9 6.00 12.6 2.4 2.92 4.0Ntot (wt.%) b0.05 0.11 0.33 b0.05 0.05 0.06Corg (wt.%) 0.20 0.81 1.49 0.12 0.13 0.24

Size fraction Grain size distribution (in %)

Topsoil Subsurface soil

Min. Average Max. Min. Average Max.

b0.001 mm 6.4 11.8 18.6 24.1 28.9 39.3b0.002 mm 7.4 14.2 23.5 26.0 32.2 41.6b0.01 mm 7.4 15.2 25.3 26.2 33.4 46.9b0.05 mm 10.1 23.4 47.8 36.1 40.4 52.80.01–0.05 mm 2.7 11.6 22.5 5.7 7.9 10.90.05–0.25 mm 42.1 50.3 68.6 33.2 40.6 50.00.25–2.0 mm 7.4 17.2 23.5 8.5 11.2 18.7

CEC = Cation exchange capacity, Ntot = total nitrogen, Corg = total organic carbon.



super pure HNO3. Fe, Co, Cr, Cu, Ni, Pb, V and Zn were determinedusing Flame Atomic Absorption Spectroscopy (FAAS, Perkin Elmer4000 Spectrometer). Arsenic was determined by a Hydride-Genera-tion Atomic Absorption Spectrometry (HGAAS, Perkin Elmer 503equipment), and Hg was determined mercurometrically, using anAMA 254 Mercury Analyser.

All samples were analysed at the accredited Central GeochemicalLaboratories of the Czech Geological Survey. The quality controlprocedure involved analysis of reagent blanks, duplicate samples andseveral referenced soils. Analytical precision was determined by the10% analysis (in duplicate) of randomly chosen samples and referencesamples as well, with a variation coefficient for all investigatedelements b8%, with the exception of Pb and Ni (b22%). Reliability ofanalyses determined by reference materials (RMs) was ±5% for Cu,Co and Zn, ±12% for Fe, ±10% for As and Hg, and ±22% for Ni and Pbdue to the large number of samples in which the concentrations of Niand Pb were near the limits of analytical detection.

The amount of total carbon (Ctot) was determined using an ELTRACS 500 instrument. Samples were combusted at 1400 °C and the Ctotwas measured as CO2 using an IR detector. The amount of carbonatecarbon (Ccarb) was determined using another ELTRA CS 500instrument. Samples were digested in a saturated solution of H3PO4

and the amount of CO2 liberated was recalculated to that of carbonatecarbon (Ccarb). The amount of organic carbon (Corg) was determinedby subtraction of carbonate carbon from total carbon contentCorg=Ctot−Ccarb. Total sulphur (Stot) was determined using theELTRA CS 500 equipment. Samples were combusted at a temperatureof 1400 °C and the Stot, measured as released SO2, was determined byan infrared detector. The variation coefficient for Ctot and Ccarb isb0.5%, for Stot it is b1%. Relative errors of Ctot, Ccarb and Stot determinedusing reference materials were ±2.5% for Ctot and Stot, and ±2% forCcarb.

To determine the pH value of soils, 2.5 g of material, sievedthrough sieve mesh 0.2 mm was leached in periodically shakensolution of 1 M KCl. The pH measurements were made with aprecision of 0.01 pH unit using a pHC 2085 pH electrode connected toa PHM 201 pH-meter after 24-hour leaching. Differences in watertemperature were automatically compensated using a T 201 temper-ature compensator. Calibrations were carried out using two standardIUPAC (Radiometer A/S Copenhagen, Denmark) buffers with pHvalues of 4.01 and 7.00, respectively. The measured pH value wasrecorded automatically, with a precision of 0.01 pH unit.

Determination of the mineralogical identity and location of metalsin the soils and dusts was attempted using a CamScan 3200 electronmicroprobe in SEM mode equipped with an energy-dispersiveanalyzer LINK-ISIS. Prior to analysis, the selected soil samples wereseparated according to density using polyvinylpyrrolidone anddiiodomethane. Analyses were undertaken using an acceleratingvoltage of 15keV, and a beam current of 3×10−9 A. The XRDcharacteristics of minerals and their relative proportions in dustfrom ore concentrates, crushers, dust fallout from smelters andtailings ponds were established using a Philips PW 7310 diffractom-eter with CuKα radiation and a Ni filter in standard configuration.

3.1. Processing of analytical data

3.1.1. Univariate statistics and data transformationSummary statistics of the data set were first calculated to evaluate

the distributions. The frequency distribution for each of the elementsanalysedwas examined using histograms, background normality testswere made and kurtosis and skewness calculated. Kurtosis andskewness were calculated using the S-Plus programme version 4.5(MathSoft Inc., Seattle, Washington, U.S.A. 1997). Because thestatistical distribution of most variables determined by chemicalanalyses was not normal, a non-parametric method was used toevaluate the main statistical characteristics of the individual data

populations using again the S-Plus programme version 4.5. For thepurpose of statistical treatment, data from chemical analyses lowerthan the detection limit were replaced by values equal to 2/5 of thelimit of detection. In order to construct contour maps, the data weretransformed to a regular grid using the kriging method. The distancebetween grid nodes was 500 m. To calculate the grid node, generallyup to 10 adjacent data points in the “search area” were taken intoaccount. In addition, the search area within the 10 km radius wasdivided into 4 sectors, in each of these up to 4 adjacent data pointswere taken into account. A minimum condition for the retrieval of thegrid node value was the presence of at least one sampling point in anysector of the search area. All data sets displayed statistical distribu-tions close to lognormal; therefore, logarithmic values were used forconstruction of maps and were recalculated from logarithmic back tonormal (geometric) values that appear on the maps. Categories ofconcentration for the contour maps of surface soils were selected atthe 10%, 25%, 50% (median) 75% and 90% percentiles for the individualdata sets. In cases in which a large number of values fall below thedetection limit, the value of the limit was used as the lowestboundary. The final category represents extreme data (outliers). Thesame categories were used for maps of subsurface soils, irrespective ofthe range and distribution of the concentrations in subsurface soils.The grid was calculated and results were mapped using the programSurfer version 8 (Golden Software Inc., Golden, Colorado, USA 2002).

3.1.2. Factor analysisLogarithmic data were processed by means of R-mode factor

analysis (FA), applying the varimax-raw rotational technique. As amultivariate method, it facilitates the reduction, transformation andorganization of the original data by the use of intricate mathematicaltechniques, which eventually results in a simple form of factor model.Factor analysis using the same amount of information creates a newset of uncorrelated variables which are the linear combinations of theoriginal ones. If the original variables have significant linearintercorrelations, when the FA is carried out the first few factorswill include the largest part of the total variance. The interpretation ofdominant factors was made by taking into account the highest factorloadings for each of the chemical elements. The theoretical details ofFA are given by Johnson (1998). The statistical analysis was carriedout using the program S-Plus version 4.5 (MathSoft Inc., Seattle,Washington, U.S.A. 1997).

4. Results

4.1. Sources of contamination

Themain sources of soil contamination in the surveyed area are dustfallout from open-pit operations and ore transport, waste rock andoverburden dumps, from crushers, ore concentrate stockpiles and oreconcentrate transport, smelters, slag dumps and the dry areas of tailingsponds. Dust collected in the vicinity of the Chingola (Nchanga) open pitrevealed only a slightly increased copper content (Table 2, analysis 1).The concentration of individual elements in dust fallout sampled in thecrusher areas is very variable and reflects the primary geochemicalvariability of ore from individual deposits beingmined. For example, thecobalt content of dust collected at Kitwe (theNkana processing plant) ishigh, corresponding to the high cobalt content of oresmined in this area(Table 2, analyses 2 and 3). In contrast, the dust collected in the crusherarea at Mufulira has a low content of cobalt correlated with the lowcobalt content of the local ores (Table 2, analysis 4). In dust fallout fromcrushers, XRD analyses revealed small amount of chalcopyrite inaddition to the prevailing quartz and muscovite and small amount ofK feldspar, plagioclase, calcite, dolomite, talc, chlorite and amphibole.The chemical variability of ore concentrates reflects both the geochem-ical variability of ores and variable flotation techniques used to separatecopper- or cobalt-rich products (Table 2, analyses 5–7). The copper



concentrate from the Kitwe and Chibuluma flotation plant is composedessentially of chalcopyrite and pyrite with minor amount of muscovite,quartz and talc. Cobalt concentrate from the Kitwe ore dressing plant iscomposed predominantly of minerals of the linnaeite group (linnaeite,Co3S4, siegenite, (Co,Ni)3S4 or carrolite, Co2S3) and minor amount ofchalcopyrite, pyrite and arsenopyrite. Non-oreminerals are representedby orthoclase, muscovite and talc.

The chemical composition of dust fallout from the smelter inKitwe isenriched in “volatile elements”, as for examplePb, Zn,As and Se (Table 2,analysis 8). The investigation of the morphology and chemicalcomposition of the dust particles from the Kitwe smelter captured byfilters in the TDS samplers revealed sharp-edged or drop-like particleswith significant concentrations of Ca, Mg, Fe, Si and Al and minorconcentrations of Ti, ZnNCu and S. These are most likely very fineparticles of silicate slag (Fig. 2). Small amount of pyrite, chalcopyrite,unidentified Fe–Cu–S sulphate, quartz, barite and anhydrite were alsoidentified. Metallic particles composed of Fe, Cr, and Ni with traceamount of Cu and Zn captured by filters probably formed by abrasion ofthe Kitwe smelter equipment. UsingXRD,magnetite, fayalite and quartzwere identified in dust fallout from the Kitwe smelter. In contrast toothermaterials studied, particles of slag dust are very rich in “refractory”chromium (Table 2, analyses 9 and 10) which is related to theoccurrence of numerous chromite inclusions in the slag particles asrevealed by micro-chemical analyses (Fig. 3). In addition to chromite,

tiny particles of sulphide melt with a chemical composition resemblingthat of bornite were also identified (Fig. 4).

The wide variation in the chemical composition of dust from thedry areas of tailings dams reflects both the variation in the chemicalcomposition of the ores and the progress in flotation technology overtime. Old slimes enriched in copper and cobalt (Table 2, analyses 12and 14) are currently re-processed by chemical leaching. Theprevailing minerals in all flotation tailings are quartz, feldspars(plagioclaseNK feldspar), dolomite, calcite, muscovite, chlorite andamphibole. Evidence of the weathering of the accessory sulphides intailings is provided by the efflorescence of sulphates on the surface.XRD analyses revealed that gypsum (CaSO4·6 H2O) is the prevailingsecondary mineral, together with hexahydrite (MgSO4·6 H2O),epsomite (MgSO4·7 H2O), syngenite (K2Ca(SO4)2·H2O), picromerite(K2Mg(SO4)2·6 H2O), blödite (Na2Mg(SO4)2·4 H2O), and mooreite(Mg9Zn4Mn2(SO4)2(OH)26·8 H2O).

4.2. Concentrations of metals, carbonate carbon, organic carbon, totalsulphur, pH values and correlations among variables

The principal statistical data, i.e. minimum, maximum and mediananalytical values, values of the 10%, 25%, 75% and 90% percentiles,kurtosis and skewness for the pH of soil leachate, total sulphur,carbonate carbon, organic carbon and metals in topsoils andsubsurface soil data sets are given in Tables 3 and 4. The medians ofpH, total sulphur (Stot), carbonate carbon (Ccarb), organic carbon(Corg), Co, Cu and Hg are higher in topsoil compared to subsurface soil.Maximum values of these metals and Stot in topsoil are characteristicof areas strongly affected by dust fallout from smelters and tailingsponds. In contrast, the medians of Fe, V, Cr and Ni are higher in

Table 2Concentrations of chemical elements in dust samples collected at the surroundings of mining operations, crushers, smelters, slag and tailings deposits.

Sample no. Dust samples from As Cd Co Cr Cu Hg Mo Ni Pb Se V Zn

1 Nchanga Open Pit, Chingola 1.9 b0.8 13 63 169 0.005 6 8 4 0.2 b15 152 Crushers' area, Kitwe 2005 15.9 0.3 917 22 15,340 0.099 13 7 21 6.5 b15 1373 Crushers' area, Kitwe 2005 28.4 0.9 1437 37 21,830 0.064 11 22 12 8.5 47 924 Crushers' area, Mufulira 2002 0.77 b0.8 12 20 39,100 0.027 b5 5 20 1.24 b15 415 Co concentrate, Kitwe 236.9 0.3 7790 32 58,130 1.13 25 12 35 38 55 676 Cu concentrate, Kitwe 2.41 3.7 3930 20 248,000 0.45 10 10 39 33 42 14407 Cu concentrate, Chibuluma 9.76 0.3 4160 25 203,000 1.13 25 8 35 20 b15 678 Kitwe smelter, dust fallout 28.24 b0.8 1542 182 20,170 0.009 20 25 102 21.1 b15 2889 Kitwe slag1 12.56 b0.8 3170 1107 6972 0.007 41 62 15 0.63 70 12310 Mufulira slag 24.91 b0.8 3200 629 8810 0.007 127 98 79 0.96 75 116511 Mufulira tailings 0.35 b0.8 b5 10 1650 0.005 b5 b5 b10 b0.2 b15 512 Chambishi tailings 160 b0.8 2820 10 7600 0.005 60 38 65 6.00 20 5213 Kitwe tailings 2.86 0.28 200 23 1250 0.041 6 15 10 0.64 25 1014 Chingola tailings2 2.3 b0.8 750 72 40,200 0.005 10 25 18 b0.2 34 144

1Old slag from the Kitwe (Nkana) smelter re-processed at the Chambishi Smelter, 2Old flotation tailings reprocessed at the Chemical Treatment Plant at Chingola.

Fig. 2. Chemical composition of dust particles captured on the surface of the filterduring the monitoring of dust fallout in the Kitwe (Nkana) smelter area. Length of thewhite bar (bottom part of the image) corresponds to 20 µm.

Fig. 3. Inclusions of chromite (Chr) in dust particles of slag collected in the Mufulirasmelter.



subsurface soils. The dispersion of values in subsurface soil isgenerally much lower as compared to that in the topsoil.

The characteristic distribution of values for some elements in topsoiland in the subsurface soil in the area underlain by the Pre-Katanga units(Basement Complex and Muva Supergroup) and in the area formed bythe Katanga Supergroup is shown in Fig. 5. Contents of Cu (Fig. 5a) werefound to be mostly higher in topsoil than in the subsurface soil horizonoverlying both geological formations. Extremely high contents of cobaltin topsoil covering all geological formations (Fig. 5b) are characteristic ofstrongly contaminated areas. In contrast, higher concentrations of thesame element in the subsurface soil horizon are indicative of lithogenic(bedrock-related) source. Compared with Pre-Katanga geological units,contents of cobalt are generally higher in subsurface soils derived fromtheKatangaSupergroup. Zinc (Fig. 5c), arsenic (Fig. 5d), leadandmercury(not shown) show a pattern of distribution similar to that of cobalt.

The contents of Cr (Fig. 5e), Ni (Fig. 5f) and V (not shown in thepicture) are mostly higher in subsurface soil regardless of the bedrock

geology. The only exceptions are the enhanced contents of nickeldetected in the close vicinity of smelters.

Because the distributions of metals and sulphur were skewed (theskewness coefficient exceeds 1.0), tightness of the mutual relation-ships between variables, i.e. between the concentrations of metals,Corg, Ccarb, and Stot in soils in the surveyed area were investigatedusing non-parametric statistics. Tables 5 and 6 are the correlationmatrix, listing Spearman's rank correlation coefficients. Significantcorrelations at the probability level pb0.001 (99.9%) are printed inbold, at the probability level pb0.01 (99%) in bold italics and at thelevel pb0.05 (95%) in small italics. The significance was estimatedusing t-test. Insignificant correlations are printed in normal letters.

In topsoils, all variables show significant correlations at theprobability level pb0.001. The high value of Spearman's coefficientof correlation (rN0.5) with pH is shown only by Zn. For Stot, highvalues of coefficients (rN0.5) are shown by Co, Cu, Zn, Pb and Hg, forCcarb by Co, Cu, and Zn, and for Corg only by Hg.

In contrast to the set of data for topsoil, the values of Spearman'scoefficients of correlation between metals and Stot are generally notsignificant in the subsurface soil data set. The significant correlationbetween metals and pH was found only for V, Cr, Co, Cu and Fe.

4.3. Distribution of pH, Corg, Stot and metals in soil profiles

Distribution of pH, Corg, Stot and relevant metals was studied in theprofile not affected by contamination (Profile I), in the area of tailingsdams (Profiles II and III), and in a heavily contaminated area locatednear the Kitwe (Nkana) smelter (Profile IV, Fig. 6). All the givenprofiles and the samples collected were from soils developed onbedrocks of the Katanga Supergroup. The position of the profiles isshown on Fig. 1. In all profiles, the moisture and the content of silt andclay fractions were found to increase gradually with depth. Thismatches the grain size distribution in topsoils and subsurface soilsgiven in Table 1. The distribution of selected metals in all profiles

Fig. 4. Particles of sulphidemelt (BN) with chemical composition close to bornite in slagparticles. The Mufulira smelter.

Table 3Statistical summary of parameters of topsoil (n=719).

Parameter pH Stot Ccarb Corg As Co Cr Cu Fe Hg Ni Pb V Zn(wt.%) (wt.%) (wt.%) (ppm) (ppm) (ppm) (ppm) (wt.%) (ppm) (ppm) (ppm) (ppm) (pm)

Min. value 3.56 b0.010 b0.003 0.05 b0.10 b5 1.6 15.0 0.10 0.002 b5 b10 b10 b5

Percentiles10% 4.23 b0.010 0.008 0.77 b0.10 b5 7.0 72.0 0.33 0.007 b5 b10 b10 6.025% 4.46 0.011 0.013 1.26 0.16 5.0 10.0 134.0 0.56 0.009 b5 b10 13.0 9.050% (Median) 4.88 0.018 0.019 1.91 0.46 10.0 16.0 289.0 0.97 0.014 b5 b10 21.0 13.075% 5.70 0.029 0.033 3.11 1.04 19.0 25.0 627.5 1.59 0.021 7.0 11.0 33.0 21.090% 6.70 0.051 0.055 4.37 2.70 60.0 36.0 1885.6 2.43 0.035 12.0 21.0 52.0 41.2Max. value 9.17 1.423 2.828 12.84 254.90 606.0 595.0 41900.0 7.13 0.441 42.0 503.0 227.0 450.0Kurtosis 0.72 286.26 96.53 4.01 631.60 32.77 371.81 93.95 4.90 94.22 9.94 185.72 13.66 55.86Skewness 1.09 14.56 9.09 1.55 24.45 5.12 16.72 8.14 1.85 8.25 2.71 12.40 2.82 6.40

Table 4Statistical summary of parameters of subsurface soils (n=129).

Parameter pH Stot Ccarb Corg As Co Cr Cu Fe Hg Ni Pb V Zn(wt.%) (wt.%) (wt.%) (ppm) (ppm) (ppm) (ppm) (wt.%) (ppm) (ppm) (ppm) (ppm) (ppm)

Min. value 3.91 b0.010 b0.003 0.14 b0.10 b5 20.0 6.0 0.47 b0.005 b5 b10 b10 b5

Percentiles10% 4.04 b0.010 0.005 0.17 b0.10 b5 29.8 17.0 1.2 0.006 7.0 b10 22.4 7.025% 4.12 b0.010 0.007 0.22 0.15 b5 37.0 24.0 1.69 0.007 11.0 b10 33.0 9.050% (Median) 4.27 0.011 0.010 0.29 0.47 5.0 54.0 34.0 2.58 0.009 15.0 b10 56.0 13.075% 4.50 0.016 0.016 0.42 1.64 11.0 85.0 54.0 4.50 0.012 23.0 11.0 102.0 27.090% 4.84 0.021 0.023 0.60 5.13 22.0 120.6 100.2 6.7 0.015 36.0 18.2 159.2 54.0Max. value 7.12 0.038 0.095 3.26 33.20 65.0 256.0 1560.0 13.10 0.072 132.0 67.0 330.0 185.0Curtosity 11.30 59.68 27.20 59.36 21.75 5.80 3.42 83.16 2.48 56.19 15.62 15.99 3.40 14.18Skewness 2.87 6.69 4.52 6.66 4.37 2.40 1.63 8.63 1.47 6.33 3.39 3.63 1.66 3.26



shows that contents of Fe, Cr, V, and Ni generally increase with depthand the pH value and contents of Corg decrease. Contents of Stot, Cu, Coand Pb were found to be higher in the topsoil of only slightlycontaminated profiles (Profiles II and III) but their concentrations indeeper parts of the soil horizon are similar to those in theuncontaminated profile. In the profile of highly contaminated soilssampled in the vicinity of the Kitwe smelter (Profile IV), very highamounts of sulphur and other elements, including vanadium, nickeland chromium, were detected in topsoil. At a depth of 10 to 20 cm inProfile IV, the concentrations of these elements decrease sharply.However, contents of sulphur and copper are still much higher in thelower part of soil Profile IV as compared to contents of the sameelements in uncontaminated or slightly contaminated soils.

4.4. Distribution of sulphur and metals in soils on a regional scale

The distribution of sampling sites and the results of theenvironmental–geochemical surveying are presented in individualmaps (Fig. 7). Several types of data presentation are used, includingcontour maps of topsoil, contour maps of subsurface soil, contourmaps of the ratio of concentrations of the chemical elements in thetopsoils and subsurface soils, and combined maps, in which thesurface data are expressed in the form of contour maps and data fromthe subsurface soil horizon are shown in classed post maps. This typeof graphic presentation enables the concentration of variables in bothtopsoil and subsurface soil layers at the same sampling site to becompared.

Concentrations of Stot are higher in topsoil relative to subsurfacesoil (Fig. 7b,c). The high content of sulphur in topsoil is evidently aresult of sulphur emissions from smelters and dust fallout from thedry beaches of tailings ponds, crushers and mining operations.Contamination by sulphur around the Chambishi smelter is relativelylow because this smelter re-processes sulphur-poor slag from theKitwe (Nkana) smelter. In the Chingolamining area and surroundings,high total sulphur contents in topsoil are explained as being due todust fallout from mining operations and the ore processing plant(dust fallout from crushers, concentrate piles and tailings at theChingola processing plant).

Contents of copper in topsoil (Fig. 7d) at individual sampling sitesare also higher than those in the subsurface soil (Fig. 7e). Therefore,the regional distribution of copper in topsoils is indicative ofanthropogenic contamination. Taking into account the prevailingdirection of winds from the south-east, the highest copper concentra-tions (N1800 ppm) are recorded around the smelters at Kitwe andMufulira and downwind from them in the northwest direction. Other,less contaminated areas are located around tailings ponds and in thevicinity of active or abandoned open pits (the Chingola andChililabombwe areas). The pattern of distribution of copper in topsoiland subsurface soils is not related to the lithology of the bedrock (seeFig. 1 for comparison).

The map of the ratio of topsoil to subsurface soil cobalt contents(Fig. 7f) clearly demarcates areas with heavy contamination of topsoil(shown in red in the map) from those with naturally increased cobaltvalues in subsurface soils (shown in green). A narrow corridor ofcontamination between Chingola, Chambishi and Kitwe probablyindicates topsoil contamination due to cobalt-rich concentratetransport from the Chingola flotation processing plant to the Kitwe(Nkana) smelter.

The map of the ratio of topsoil to subsurface soil arsenic contents(Fig. 7g) indicates that the concentration of this element depends atleast on two factors. In industrial districts, the content of arsenic in thetopsoil layer is higher as compared to that in the subsurface soil and isindicative of anthropogenic contamination, especially around theKitwe and Mufulira smelters (shown in red in the map). Mining areaswithout smelters (the Chingola and Chililabombwe areas) are lessaffected by arsenic contamination. In other parts of the surveyed area,

Fig. 5. Correlation of (a) Cu, (b) Co, (c) Zn, (d) As, (e) Cr and (f) Ni in topsoil vs.subsurface soil derived from Pre-Katanga lithologies (the Muva Supergroup andBasement Complex) and from the Katanga Supergroup in surveyed area of theZambia Copperbelt. Straight line corresponds to the Element(concentration in topsoil)/Element(concentration in subsurface soil) ratio=1. Circles=Katanga Supergroup, rhombuses=Pre-Katanga formations (the Muva Supergroup and Basement Complex).



however, the ratio is close to zero or negative, thus indicating alithogenic source of arsenic. These areas are shown in green on themap. The map of the ratio of mercury in topsoil and subsurface soils(Fig. 7h) indicates that the contamination by mercury is restricted tothe vicinity of the Kitwe and Mufulira smelters.

Concentrations of lead (Fig. 7i) are low in both topsoil andsubsurface soils with the exception of a high lead concentration(N60 ppm) around the Kitwe and Mufulira smelters and in the area ofthe Chingola processing plant.

Concentrations of zinc in topsoil (Fig. 7j) are also generally low, insome places higher, and in others lower than in the subsurface soils.Nevertheless, in the industrial regions of Kitwe, Mufulira andChingola, the concentration of zinc in topsoil is higher than insubsurface soils and therefore can be related to industrial contami-nation. In other areas, for example, in the area east of Mufulira ornorth of Chililabombwe, higher concentrations of zinc in topsoil andsubsurface soils probably reflect higher concentrations of this elementin soils derived from the Katanga Supergroup. In contrast to totalsulphur, cobalt, copper and many other elements that usually displayhigher contents in topsoil, contents of nickel (Fig. 7k), vanadium andchromium (not shown), are usually higher in subsurface soils. Thissuggests that vanadium, chromium, and to a great degree also nickelvalues do not indicate the extent of anthropogenic contamination. Thehigher contents of nickel, chromium and vanadium in topsoil andsubsurface soils generally correspond to areas of rocks of the KatangaSupergroup (compare with Fig. 1).

4.5. Enrichment index for topsoil

Generally, the extent of anthropogenic contamination can beexpressed using the enrichment index (EI, Fig. 7l; Kříbek et al., 2004)

The EI is based on the average ratio of the actual and medianconcentrations of the given contaminants:

EI =AsmAs

+ ComCo

+ CumCu

+ HgmHg

+ PbmPb

+ ZnmZn

� �

6

where mMe is the median value of concentration for a given metal intopsoil.

The enrichment index actually reflects a higher-than-median orlower-than-median average content for the six elements. Neverthe-less, the EI values correlate well with the ratio of topsoil to subsurfacesoil metal contents (compare, for example, Fig. 7f and i with l). Thisindicates that the EI values to a large degree reflect the enrichmentfrom anthropogenic sources.

Boundaries between individual intervals of EI values wereestablished based on the statistical distribution of data, and areexpressed in percentiles. It is evident from the contour map of EIvalues, that medium to very strong contamination is restricted to theindustrial areas of Kitwe, Mufulira and Chingola and downwind, in anorth-westerly direction. The area east of Mufulira, close to the borderwith Democratic Republic of Congo, with N2 is interpreted as beingaffected by the former smelting operations at the abandonedLuanshya smelter, located south-east of the surveyed area.

4.6. Factor analysis

Using factor analysis (FA), complex linear correlations betweenpH, and logarithmic values of the Ccarb, Corg and metal concentrationsin topsoil and subsurface soils were estimated, which enabled theinterpretation of correlations between elements in the surveyed area.Using the logarithmic data the influence of potential outliers is

Table 6Matrix of Spearman's correlation coefficients for subsurface soils, upper triangle (correlation matrix for subsurface soils, upper triangle) (n=129).

Stot Ccarb Corg V Cr Co Ni Cu Zn Pb Fe As Hg

pH 0.134 0.518 0.179 0.429 0.399 0.543 0.283 0.409 0.161 0.136 0.526 0.198 0.366Stot 0.274 0.212 0.063 −0.054 0.260 0.072 0.208 −0.180 −0.102 0.104 −0.294 0.232Ccarb 0.307 0.540 0.273 0.408 0.182 0.389 0.143 0.342 0.531 0.229 0.506Corg 0.319 0.170 0.207 0.240 0.308 0.121 0.083 0.261 0.145 0.372V 0.629 0.548 0.371 0.355 0.134 0.259 0.885 0.424 0.275Cr 0.396 0.705 0.323 0.170 0.282 0.755 0.421 0.162Co 0.480 0.594 0.043 0.118 0.494 −0.044 0.201Ni 0.344 −0.011 0.113 0.391 0.121 0.029Cu 0.085 0.109 0.348 −0.036 0.377Zn 0.335 0.255 0.299 0.192Pb 0.294 0.481 0.331Fe 0.444 0.324As 0.163

Table 5Matrix of Spearman's correlation coefficients for topsoil, upper triangle (n=719).

Stot Ccarb Corg V Cr Co Ni Cu Zn Pb Fe As Hg

pH 0.403 0.423 0.127 0.255 0.300 0.376 0.300 0.283 0.535 0.222 0.243 0.209 0.133Stot 0.705 0.665 0.364 0.414 0.672 0.343 0.736 0.694 0.561 0.494 0.488 0.751Ccarb 0.548 0.394 0.372 0.543 0.354 0.568 0.584 0.426 0.428 0.457 0.586Corg 0.236 0.228 0.340 0.172 0.445 0.492 0.396 0.306 0.321 0.690V 0.803 0.464 0.553 0.276 0.517 0.249 0.819 0.395 0.367Cr 0.493 0.647 0.316 0.590 0.298 0.859 0.361 0.348Co 0.510 0.815 0.634 0.426 0.530 0.449 0.596Ni 0.313 0.474 0.242 0.515 0.165 0.284Cu 0.578 0.486 0.400 0.527 0.688Zn 0.586 0.594 0.583 0.613Pb 0.373 0.468 0.556Fe 0.455 0.472As 0.479



Fig. 6. Distribution of pH, Corg, Stot and metals in soil profiles in the surveyed Copperbelt area. Profile I: Uncontaminated profile, II and III: Slightly contaminated profiles, IV: Heavilycontaminated profile. Location of profiles is shown in Fig. 1.

Fig. 7. Environmental–geochemical maps of the central-northern part of the Zambian Copperbelt. (a) Position of soil sampling points in the surveyed area of the Zambian Copperbelt.(b) Contour map of the total sulphur values in topsoil. (c) Contour map of the total sulphur values in the subsurface soil. (d) Contour map of the copper values in topsoil. (e) Contourmap of the copper values in subsurface soil. (f) Contour map of the ratio of cobalt values in topsoil to subsurface soil. (g) Contour map of the ratio of arsenic values in topsoil tosubsurface soil. (h) Contour map of the mercury values in topsoil to subsurface soil. (i) Contour map of lead values in topsoil and classed point map of lead values in subsurface soil.(j) Contour map of zinc values in topsoil and classed point map of zinc values in subsurface soil. (k) Contour map of nickel values in topsoil and classed point map of nickel values insubsurface soil. (l) Contour map of the enrichment index (EI) in topsoil in the surveyed area of the Zambian Copperbelt.





Fig. 7 (continued).



eliminated or reduced substantially. Individual components of thesystem belonging to a given factor were defined by a factor matrixafter varimax rotation, with those having strong correlation groupedinto factors. The identification of factors is based on the dominantinfluence. Five main factors (with a sum of squares of loading N1)explain 69.4% of the total variance of the data set for topsoil (Table 7).

The factor matrix after varimax rotation of the components intopsoils shows a grouping of Cr, Zn, Pb and As into the first component(F1; accounting for 17.7% of variance), V, Cr, Ni and Fe are groupedinto the second component (F2; accounting for 16.5% of variance), Stot,Co, Cu and Hg are grouped into the third component (F3; accountingfor 13.7% of variance), pH, Ccarb, Co and Ni in F4 (11.7% variance) and,Corg, Zn, Pb, and Hg in F5 (9.9% variance). The procedure of factoranalysis enables a calculation of factor scores, which then replace thevalues of original variables. The factor scores determined by varimaxrotation factor analysis can be plotted on maps. As an example,positive anomalies reaching factor scores N1 in the F1 to F4 score mapfor topsoil, are plotted in Fig. 8.

By comparison with the results of the factor analysis of data fromtopsoil, the factor matrix of the components from subsurface soilsreveals quite different groupings (Table 8). The first component (F1)groups V, Cr, Co, Fe and As (accounting for 16.6% of variance), F2groups pH, Stot, Ccarb, Corg, Co and Hg (accounting for 14.9% ofvariance), F3 groups pH, Ccarb, Co and Cu (accounting for 14.2% ofvariance), F4 groups Cr and Ni (11.7% variance) and F5 group Cr, Pb,Fe, As (10.7% variance).

5. Discussion

5.1. Distribution of metals and sulphur in topsoil and subsurface soils

Numerous investigators have shown that emissions from coppersmelters are not only a source of copper, but also of other elementssuch as Pb, Zn, Cd, Cr, Ni, Se, Ag and Sb (Kabala and Singh, 2001;Adamo et al., 2001; Martley and Gulson, 2003; Beavington et al., 2004;Martley et al., 2004; Hu et al., 2007). The majority of these elements,although they occur only in small amounts in ore concentrates,accumulate during prolonged deposition in topsoil near coppersmelters. It is also notable that even coal can be a source ofcontaminants such as As, Se and Hg, in particular during metallurgicalprocesses in which it is used as a fuel or reducing agent (Dudka andAdriano, 1997; Mukherjee and Zevenhoven, 2006).

As noted above, the medians of Cu, Co and Hg in topsoil of thesurveyed part of the Copperbelt are considerably higher than the same

values in subsurface soils, and the highest values of correlationcoefficients for these metals with total sulphur were identified intopsoil (Table 3). The dust fallout and gaseous emissions from smeltersin particular, and dust fallout from the dried out parts of tailings dams,are believed to be the principal source of sulphur and the above-mentionedmetals. In 1999, Guerreiro (1999) reported SO2 concentra-tions between 0.005 and 0.984 µg m−3 in air from the vicinity of theKitwe smelter, and concentrations between 272.5 and 511.75 µg SO2.m−3 in the environs of the Mufulira smelter. Now, due to theinstallation of efficient dust collectors and sulphur dioxide separators,the contents of SO2 in the environs of the smelter at Kitwe havedecreased to 18.6–66.2 µg m−3 (background value: b1.4 µg m−3;Kříbek and Nyambe, 2005). Results summarized by Knight (2004)reveal that at the Mufulira smelter, the amount of dust emitted fromthe stack of electric furnace No. 2 ranged between 1.19 and 3.86 t h−1,during the smelting of old slag the emissions from the stack ofconvertor No. 3 ranged betweeen 0.15 and 0.46 t h−1, and during thesmelting of Cu concentrate in the same convertor the rangewas 0.15 to0.65 t h−1 during the period from August 2003 to January 2004. Themonitoring of solid particles on chimneys of the smelter at Mufulirarevealed contents of arsenic in the range between 17.5 and 775 ppm,copper between 19,210 and 63,600 ppm, cobalt between 29 and490 ppm, lead between 50 and 3619 ppm andmercury between 0.001and 0.1 ppm (Knight, 2004). In addtion to these metals, Kříbek andNyambe (2006) also found traces of Be, Cr,Mn, Ni, Zn,Mo and Cd in thedust fallout.

The dust from dry parts of tailings dams also contains highconcentrations of sulphur andmetals. Sulphur in flotation tailings is inthe form of sulphides as well as sulphates and the content of sulphatesincreases with the age of tailings in the tailings dam (Šráček et al.,2010). As the surface of tailings dams is often covered by efflorescentsulphates, mostly gypsum and hexahydrite, the dust particles can beexpected to contain sulphur in the form of sulphates.

Significant correlation between Cu, Co and other metals and Ccarbvalues can be explained by the presence of carbonates (calcite anddolomite) in the dust from extracted ores, and also by their use asfluxing agents in metallurgical processes and particularly because oftheir addition to flotation tailings. The dust collected in theneighbourhood of tailing dams at Mufulira contains, for example,0.07% Ccarb, and that sampled in the environs of tailings dams atChingola contains as much as 1.2% Ccarb (Kříbek and Nyambe, 2005).The values of correlation coefficients between metals and the pH ofsoil extract are much lower as compared to those between totalsulphur and carbonate carbon. A relatively high correlation coefficient

Table 7Main factors (sum of squares of loadings N1) factor loadings, communalities, explained variance and proportion of total variance for topsoil of the surveyed area of the Copperbelt.

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Communalities

Sum of squares of loadings 2.48 2.30 1.92 1.64 1.38

Rotated factor loadingsComponentpH 0.066 0.120 0.102 0.426 0.070 0.215Stot 0.056 0.139 0.716 0.175 0.245 0.625Ccarb 0.004 0.058 0.086 0.789 −0.049 0.636Corg 0.070 0.219 0.098 −0.067 0.400 0.227V 0.046 0.973 −0.021 0.036 0.111 0.963Cr 0.870 0.454 0.047 0.088 0.071 0.979Co 0.238 0.150 0.422 0.779 0.206 0.905Ni 0.275 0.550 0.249 0.310 0.117 0.550Cu 0.266 0.114 0.880 0.185 0.232 0.946Zn 0.481 0.111 0.231 0.150 0.642 0.733Pb 0.603 0.006 0.286 0.032 0.359 0.576Fe 0.114 0.839 0.179 0.072 0.210 0.799As 0.939 0.042 0.109 0.086 0.103 0.914Hg 0.121 0.102 0.429 0.121 0.655 0.653Explained variance 17.7% 16.5% 13.7% 11.7% 9.9%Proportion of total variance 17.7% 34.2% 47.9% 59.6% 69.4%



between pH and zinc (r=0.54) is an exception that is ascribed to theeasy extractability of zinc and its ability to migrate in soil solutions.Similarly, with the exception of zinc, Hu et al. (2007) did not find anysignificant correlation between contents of metals in soils near thecopper smelter in China and the pH of soil solutions.

Relatively low values of correlation coefficients between themajority of metals and the content of organic carbon in topsoil areascribed to generally low concentrations of organic matter and to thelow degree of humification of the soil. Due to the rapid process ofmineralization, the organic matter in topsoil consists mostly of theremains of dead plants so that the absorption capacity is evidentlylow.

The forms and chemical bonding of copper and other metals insoils have been studied by numerous authors. For instance, Burt et al.(2003) report that copper and other metals in soils in the neighbour-hood of a Cu smelter in Montana (USA) occur as both sulphate andsulphide forms. These authors also state that the amount of copper inthe H2O-soluble fraction, the exchangeable fraction, the carbonate-

bound fraction, the iron- and manganese oxides fraction, the organicmatter and the sulphide fraction is greater than the contents of copperand other metals in the residual fraction. In strongly contaminatedsoils around the copper smelter in an industrial complex at PortKembla in Australia the major part of the copper was preferentiallybound to hydrous Fe–Mn oxides, crystalline oxides, sulphides, andorganic matter (Martley et al., 2004). Similarly, Kabala and Singh(2001) provide evidence that copper in strongly contaminated soils ismostly present in the exchangeable fraction and a specificallyadsorbed form, while in less contaminated soils the copper andother metals are distributed in the following order of abundance:residual fraction≫ iron- and manganese oxides fractionNorganicfractionNexchangeable fraction and specifically adsorbed form.Adamo et al. (2001) report that copper in the environs of a copperand nickel smelter in Sudbury (Canada) is almost uniformlydistributed throughout all the fractions studied.

Enhanced contents of copper (and sulphur) in subsurface soils ofthe surveyed area of the Copperbelt correspond directly with areas

Fig. 8. Contour maps of the distribution of factor scores N1 for the four factors for topsoil of the surveyed Copperbelt area. (a) Factor 1,“ slag specific ”. (b) Factor 2, “bedrock (soil)specific”. (c) Factor 3, “smelter specific”. (d) Factor 4 “tailings specific”.



where there are also high concentrations in topsoil (Fig. 7b–d). Thisindicates that a certain proportion of copper and sulphur is washedout of topsoil during the rainy season and transported down the soilprofile. This explains the much higher contents of sulphur and copperin the deeper parts of the extremely contaminated soil Profile IV(Fig. 6) relative to less contaminated or uncontaminated soil profiles.The gradual migration of contaminants down the soil profile instrongly contaminated regions has been studied by a number ofauthors. For instance, Martley et al. (2004) report that strongcontamination of soils by copper and other elements occurs in anindustrial complex with a copper smelter at Port Kembla (NSW,Australia). This contamination can be traced down to a depth of50 cm. Ruan et al. (2008) state that the average migration rate of Cudown the profile in forest soils near a source of industrial emissions isapproximately 0.33 cm per year.

As distinct from sulphur, copper, cobalt and mercury, the mediancontents of chromium, vanadium and nickel are higher in subsurfacesoil relative to topsoil (Tables 3 and 4, Fig. 5). Moreover, vanadiumand chromium show high coefficients of correlation with iron(Table 6). Gradual increase in the contents of iron, chromium,vanadium and nickel with depth can be seen in individual soilprofiles studied (Fig. 5). The nature of Cr, V and Ni in lateritic soils hasbeen studied by a number of authors. It is well known that most of theCr, V, Ni (and Co) in laterites is associated with goethite (Schwert-mann and Pfab, 1994, 1996; de Oliveira et al., 2001). However, it is notyet properly understood how V, Ni, Cr and Co are incorporated ingoethite — whether as ions adsorbed on crystal surfaces, as ionsreplacing Fe in the crystal lattice, or as hydroxides intimatelyintergrown with FeOOH. The structural incorporation of foreign ionsinto the goethite lattice has been demonstrated by a linear change inunit-cell parameters in synthetic and natural samples (Cornell, 1991;Schwertmann and Pfab, 1994, 1996). On the other hand, theadsorption of ions (Ni, Zn, Cd, Co, Cu) on the crystal surfaces ofgoethite is also a well-known phenomenon (Forbes et al., 1976;Cornel and Schwertmann, 1996). Moreover, at least a part of thosemetals can be bound to clay fractions (Singh and Cornelius, 2006). Thehighest contents of V, Cr and Ni were found in areas underlain by theKatanga Supergroup. This could be explained either (1) by theabundance of numerous intrusions of dolerite and related rocks

(Fig. 1) within this unit or (2) by the high initial contents of theseelements in Katanga rocks or (3) by the higher rate of weathering(more intense lateritization) of unmetamorphosed or only slightlymetamorphosed rocks of the Katanga Supergroup as compared to themore highly metamorphosed rocks of the Pre-Katanga MuvaSupergroup and the Basement Complex. Because the contents of V,Cr and Ni in soils covering areas underlain by the Katanga Supergroupdo not appear to be explained either by the distribution of dolerites orby the very varied lithology of this geological unit, we are of theopinion that a higher degree of weathering (i.e. the higher degree oflateritization) of the sediments of this formation is themain reason forthe enhanced median concentrations of V, Cr, Ni (and Fe) in topsoiland subsurface soils derived from the Katanga Supergroup.

5.2. Evaluation of the enrichment index

The enrichment index (EI) is used by numerous authors in order toestablish the degree of contamination by metals (Nishida et al., 1982;Chon et al., 1995; Kim et al., 1998; Lee et al., 1998, 2001; da Silva et al.,2005). This index is usually computed by averaging the ratios of theconcentrations of the measured element to the hazard criteria or tothe soil quality guidelines for that element. It is notable that neither inZambia nor in any country in sub-Saharan Africa have soil qualityguidelines been established. The determination of permissible levelsof element concentrations in soil and the application of suchstandards as developed and used by other countries is not easy inZambia because of the high contents of copper and other metals intropical soils of the Copperbelt. For example, the median value ofcopper in essentially uncontaminated subsurface soils in the Copper-belt is much higher (median=34 ppm, Table 4) than the copperconcentrations in the upper continental crust (14 ppm; Wedepohl,1995) or in the average soil (25 ppm,median; Reimann and de Caritat,1998). Moreover, permissible levels for copper in countries of theEuropean Union, in Canada or Australia fluctuate within a wide rangefrom 32 to 91 ppm depending on the method of determination andalso on land use (EPT, 1999). For instance, if the Canadian soil qualityguidelines for metals are applied (Table 9), it is evident that themajority of samples of topsoil collected in the Copperbelt exceedecological norms for copper (Table 9). Therefore, the enrichmentindex in this study wasmodified so that it is expressed as a ratio of theconcentrations of the measured element to the hazard criteria but as ashared average of actual and median concentrations of potentialcontaminants (As, Co, Cu, Hg, Pb, and Zn) in topsoil. All areas with EIvalues N1 are suspected to have been affected by industrial activities.However, it should be pointed out that in cases where the EI valuefalls in the range 1–2, the contours may be significantly influenced byvariations in the geochemical character of the soils in the surveyedarea. For this reason, only areas with EI N2 are considered to havebeen seriously affected by contamination.

Table 8Main factors (sum of squares of loadings N1) factor loadings, communalities, explainedvariance and proportion of total variance for subsurface soils of the surveyed area of theCopperbelt.

Factor1

Factor2

Factor3

Factor4

Factor5

Communalities

Sum of squaresof loadings

2.32 2.09 1.99 1.64 1.50

Rotated factor loadingsComponentpH 0.269 0.393 0.515 0.024 −0.021 0.492Stot 0.008 0.559 0.098 0.100 −0.169 0.360Ccarb 0.159 0.537 0.710 −0.006 0.172 0.847Corg −0.045 0.689 0.293 0.007 0.049 0.564V 0.921 0.143 0.034 0.085 0.235 0.933Cr 0.478 −0.077 0.007 0.653 0.355 0.788Co 0.499 0.310 0.389 0.234 −0.149 0.574Ni 0.087 0.077 0.016 1.011 0.028 1.037Cu −0.033 0.157 0.961 0.032 −0.039 0.951Zn 0.124 0.122 0.137 −0.036 0.119 0.064Pb 0.111 0.111 0.013 0.285 0.584 0.447Fe 0.866 0.071 0.025 0.178 0.307 0.882As 0.317 −0.084 0.003 −0.031 0.866 0.859Hg −0.078 0.815 0.189 −0.025 0.188 0.742Explainedvariance

17.7% 16.5% 13.7% 11.7% 9.9%

Proportion oftotal variance

17.7% 34.2% 47.9% 59.6% 69.4%

Table 9Canadian soil quality guidelines for protection of environmental and human health (mgkg−1; CCME, 2007). Percent of samples exceeding permissible values for different landuses in the surveyed Copperbelt area is given in brackets.

Chemicalelement

Land use

Agricultural Residential/parkland Commercial Industrial

Arsenic 12 (0.83%) 12 (0.83%) 12 (0.83%) 12 (0.83%)Chromium 64 (1.11%) 64 (1.11%) 87 (0.42%) 87 (0.42%)Cobalt 40 (13.35%) 50 (11.3%) 300 (1.25%) 300 (1.25%)Copper 63 (91.23%) 63 (91.23%) 91 (85.26%) 91 (85.26%)Lead 70 (0.97%) 140 (0.56%) 260 (0.14%) 600 (0.00%)Mercury 6.6 (0.00%) 6.6 (0.00%) 6.6 (0.00%) 6.6 (0.00%)Nickel 50 (0.00%) 50 (0.00%) 50 (0.00%) 50 (0.00%)Vanadium 130 (0.56%) 130 (0.56%) 130 (0.56%) 130 (0.56%)Zinc 200 (0.70%) 200 (0.70%) 360 (0.14%) 360 (0.14%)Sulphur 500 (8.34%)



5.3. Interpretation of factor analysis in topsoil

The interpretation of multielement factor loadings and factorscores is often difficult and also very subjective (Reimann and Garrett,2005; Glavin and Hooda, 2005; Lima, 2008). Therefore, wheninterpreting the results of factor analysis for topsoil in the Copperbeltregion, the areal distribution of sources of contamination, thechemical composition of emissions and the type of local geologywere all taken into account because they, to a large extent, govern thegeochemical properties of the soil profile. Multielement factors fortopsoil can be divided into two groups: (1) factors indicating stronganthropogenic influence (Factors 1, 3 and 4) and, (2) factorsindicative of predominantly natural processes (lithogenic andpedogenic, Factors 2 and 5). It should be noted that the first groupof factors does not completely exclude the influence of naturalprocesses.

5.3.1. Factors indicating strong anthropogenic influence in topsoilsScore values of N1 for the Factor 1 grouping (Cr, Zn, Pb and As)

demarcate a small area NW of the Nkana industrial complex in Kitwe(in the direction of the prevailing winds; Fig. 8a). In this complexcomprising a flotation treatment plant for the ore and a smelter, oldslags from the Kitwe smelter are also stockpiled for crushing andremelting in a smelter at Chambishi. These slags are highly enriched inchromium relative to the ore concentrate (Table 1, Analysis 8)because this metal is concentrated in the silicate melt during thesmelting process. As a consequence, contents of chromium in the dustfrom the crushers are very high. Therefore, Factor 1 is designated “slagspecific” and high values of the factor scores are interpreted as beingdue to fallout of dust from the slag treatment plant. Because of the lowcontent of sulphur in the slag, the metals of the Factor 1 group are notlinked with sulphur.

Factor 3 which is the grouping Stot, Co, Cu and Hg also reflectsanthropogenic influence. The highest values of this factor score (Fig. 8c)demarcate the environs of the smelters at Mufulira and Kitwe. On theother hand, the values of the scores for this factor in the neighbourhoodof the smelter at Chambishi are low because old slags low in sulphur areremelted at this smelter. Consequently, Factor 3 is marked as “smelterspecific” and high values of this factor score are ascribed to emissionsfrom smelters processing sulphide concentrates.

Areas with high factor scores for Factor 4 (the grouping pH, Ccarb,Co and Ni), occur in the close vicinity of large flotation tailings dams(Fig. 8d). The only exception is the tailings dam north of Mufulirawhere waste from the extraction of copper ores hosted in sandstoneswas stockpiled. These ores were very poor in carbonates. Therefore,Factor 4 is interpreted as a result of dust fallout from tailings damscontaining flotationwastes with a higher content of carbonate or fromtailings dams where flotation waste is stabilized by added carbonates.This factor is then designated as “tailings specific”.

5.3.2. Factors indicating predominantly natural processes in topsoilsAreas with high scores for Factor 2, (the grouping V, Cr, Ni and Fe),

are mostly confined to lithologies formed by the Katanga Supergroup(Fig. 8b). This factor is designated as “bedrock specific” and can beinterpreted as resulting from the accumulation of these metals in soilsformed by fast weathering of the slightly metamorphosed ornonmetamorphosed Katanga Supergroup rocks.

Similarly, Factor 5 (thegrouping Corg, Zn, Pb andHg) is interpreted asbeing the result of natural processes that lead to the bonding of thesemetals to organic matter. The score values of N 1 for this factor that isdesignated as “organic carbon specific” are not presented as a mapbecause they are scattered randomlyover thewholeof themappedarea.

5.3.3. Interpretation of factor analysis in subsurface soilsThe interpretation of individual factors in subsurface soils

(Table 8) turned out to be problematic. We assume that all these

factors reflect natural (lithogenic) processes that lead to theaccumulation of metals in the subsurface soil environment, includingtheir bonding with individual soil components, i.e. organic carbon,carbonates, Fe-hydroxides or the residual fraction. Moreover, becauseof very slight influence of anthropogenic pollution, the scores maypossibly more reflect the geochemical character of the parent rocks.Although the factor analysis was unable to discriminate the nature ofbonding with individual components, it is evident that the results offactor analysis for subsurface soils differ considerably from thoseobtained for topsoil.

6. Conclusions

The results of this study show that sampling of the soils at twodifferent levels, combined with statistical treatment of data and factoranalysis, enables lithogenic and anthropogenic sources of metals andsulphur to be distinguished, especially in heavily contaminated areas.However, over a great part of the surveyed area, interpretation of thedistribution of the other metals is more problematic. Difficulties arisebecause the contents of metals in the topsoil of slightly contaminatedareas may be the product of both anthropogenic contamination andalso naturally enhanced concentrations derived from the bedrock andtransferred by weathering into the overlying soils. This can be true forthe elements arsenic, zinc, lead, chromium and nickel, the contents ofwhich are usually higher in topsoil and subsurface soils over areasunderlain by the Katanga Supergroup. However, the samemetals forma marked aureole of contamination around smelters. In addition, instrongly contaminated areas, some metals, in particular copper, aretransported into deeper parts of the soil horizon. Manifestations ofprimary copper and cobalt mineralization in these heavily pollutedareas may be overprinted to a depth of several centimetres byanthropogenic contamination. Despite of the difficulties discussedabove, regional presentation of geochemical data combined withgeostatistical methods, can prove effective in assessing the levels ofanthropogenic contamination in soils, even in areas where thecomposition of the bedrocks shows significant differences. However,unambiguous interpretation of the results of environmental–geo-chemical surveys of large areas depends on a good knowledge of thelocal geology and particularly of the constituents of geologicalformations because, to a large extent, the lithology of the bedrockgoverns the rate and depth of lateritic weathering, and consequentlythe chemical and physical properties of the soil profile. The locationand distribution of single sources of contamination and the chemicaland mineralogical compositions of pollutants is another essentialprerequisite for correct interpretation of the geochemical patternsthat emerge as a result of mapping.

Acknowledgements

The authors are grateful to P. Bezusko, J. Godanyi, I. Knésl, J. Pašava,V. Pecina, P. Rambousek, E. Zítová (Czech Geological Survey) F.Chibesakunda, M. Mwale, K. Mwamba, A. Dokowe (GeologicalDepartment, Ministry of Mines and Mineral Development of theRepublic of Zambia) and S. Simasiku (University of Zambia, School ofMines, Geology Department) and J. Adamovič (Geological Institute,Academy of Science, Czech Republic), for their participation andinitiative in field operations and in the interpretation of the interimresults produced during the years 2002–2006 by the DevelopmentCo-operation Programme of the Czech Republic in Zambia. Theauthors are also obliged to K. Liyungu, the Director of the GeologicalDepartment of the Zambian Ministry of Mines and Mineral Develop-ment for wide-ranging help in the organization of all aspects of thefield programme and to T. Henderson, the Chief Executive of theMoppani Copper Mines Plc., for his efficient co-operation andsponsorship of the project. The synthesis, statistical treatment andinterpretation of the geochemical data were undertaken within a



grant 774 No. 205/08/0321 of the Czech Academy of Sciences. Ourthanks are also directed to R. Koole, the journal manager and to S. Pircand W. De Vos, the reviewers, for their effort to go carefully throughthe text and for their reasonable and inspiring recommendations.

References

Adamo, P., Dudka, S., Wilson, M.J., Mchardy,W.J., 2001. Distribution of trace elements insoils from the Sudbury smelting area (Ontario, Canada). Water, Air & Soil Pollution137, 95–116.

Armstrong, R.A., Robb, L.J., Masters, S., Kruger, F.J., Mumba, P.A.C.C., 1999. NewU–Pb ageconstraints on the Katangan Sequence, Central Africa Copperbelt. Journal of AfricanEarth Sciences 28, 6–7.

Ashey, P.M., Lottermoser, B.G., Collins, A.J., Grant, C.D., 2004. Environmentalgeochemistry of Webbs Consols Mine, New South Wales, Australia. EnvironmentalGeology 46, 591–604.

Barcan, V., Kovnatsky, E., 1998. Soil surface geochemical anomaly around the copper–nickel metallurgical smelter. Water Air & Soil Pollution 103, 197–218.

Beavington, F., Cawse, P.A., Wakenshaw, A., 2004. Comparative studies of atmospherictrace elements: improvement in air quality near a copper smelter. Science of theTotal Environment 332, 39–49.

Binda, P.L., 1994. Stratigraphy of Zambian Copperbelt orebodies. Journal of AfricanEarth Sciences 19, 251–264.

BMI, 2009. Zambia Mining Report Q3 2009.–Agriculture, Farming & Raw MaterialsMarket. Business Monitor International. . http://www.companiesandmarkets.com/Summary-Market-Report/zambia-mining-report, visited 06-08-2009.

Burt, R., Wilson, M.A., Keck, T.J., Dougherty, B.D., Strom, D.E., Lindahl, J.A., 2003. Traceelement speciation in selected smelter-contaminated soils in Anaconda and DeerLodge Valley, Montana, USA. Advances in Environmental Research 8, 51–67.

Carlsson, E., Ohlander, B., Holmstrom, H., 2003. Geochemistry of the infiltrating water inthe vadose zone of a remediated tailings impoundment, Kristineberg Mine, NorthernSweden. Applied Geochemistry 18, 659–674.

Castellano, A.V., Cancio, J.A.L., Martin, S.S., Sanchez, A.D., 2004. Metallic water-solublespecies present in atmospheric particles. Afinidad 61, 286–293.

CCME (Canadian Council of Ministres of the Environment) 2007. Canadian soil qualityguidelines for the protection of environmental and human health. Summary tables,Table 1. Canadian soil quality guidelines. Update 7.0. CCME Ottawa, Canada. http://www.ccme.ca/assets/pdf/rev_soil_summary_tbl_7.0_e.pdf. Visited 14-01-2009.

Chatain, V., Sanchez, F., Bayard, W., Moskowicz, P., Gourdon, R., 2005. Effect of exper-imentally induced reducing conditions on the mobility of arsenic from a mining soil.Journal of Hazardous Materials 122, 119–128.

Chen, T.B., Liu, X., Zhu, M., Zhao, K., Wu, J., Xu, J., Huang, P., 2008. Identification of traceelements sources and associated risk assessment in vegetable soils of the urban–rural 811 transitional area of Hangzhou, China. Environmental Pollution 151, 67–78.

Chon, H.T., Cho, C.H., Kim, K.W., Moon, H.S., 1995. The occurrence and dispersion ofpotentially toxic elements in areas covered with black shales and slates in Korea.Applied Geochemistry 11, 69–76.

Cornel, R.M., Schwertmann, U., 1996. The Iron Oxides. VCH Publishers, New York.Cornell, R.M., 1991. Simultaneous incorporation ofMn,Ni andCo in thegoethite (α-FeOOH)

structure. Clay Minerals 26, 427–430.Courtin-Normade, A., Nell, C., Bril, H., Davranche, M., 2002. Trapping andmobilisation of

arsenic and lead in former mine tailings — environmental conditions effects.Bulletin de la Société Géologique de France 173, 479–485.

da Silva, E.F., Fonseca, E.C., Matos, J.X., Patinha, C., Reis, P., Oliveria, J.M.S., 2005. Theeffect of unconfined mine tailings on the geochemistry of soils, sediments andsurface waters of the Lousal area (Iberian Pyrite Belt, Southern Portugal). LandDegradation & Development 16, 213–228.

Dudka, S., Adriano, D.C., 1997. Environmental impacts of metal ore mining andprocessing: a review. Journal of Environmental Quality 26, 590–602.

EPT (Ecological Planning and Toxicology, Inc.), 1999. A Critical Review of Methods forDeveloping Ecological Soil Quality Guidelines and Criteria. MS, Ecological Planningand Toxicology Inc, S. W. Hout Street, Corvallis, USA.

Ettler, V., Johan, Z., Kříbek, B., Šebek, O., Mihaljevič, M., 2009. Mineralogy and environmentalstability of slags from the Tsumeb smelter, Namibia. Applied Geochemistry 24, 1–15.

Facchinelli, A., Sacchi, E., Mallen, L., 2001. Multivariate statistical and GIS-based approachto identify heavy metal sources in soils. Environmental Pollution 114, 313–324.

FAO-UNESCO, (1997): Soil map of the world 1:500 000. Volume VI. Africa. UNESCO,Paris.

Farago, M.E., Thornton, I., White, N.D., Tell, I., Martensson, M.B., 1999. Environmentalimpacts of a secondary lead smelter in Landskrona, southern Sweden. Environ-mental Geochemistry & Health 21, 67–82.

Forbes, E.A., Posner, A.M., Quirk, J.P., 1976. The specific adsorption of divalent Cd, Co, Cu,Pb and Zn on goethite. Journal of Soil Science 28, 297–305.

Ganne, P., Cappuyns, V., Vervoort, A., Buve, L., Swennen, R., 2006. Leachability of heavymetals and arsenic from slags of metal extraction industry at Angleur (easternBelgium). Science of the Total Environment 356, 69–85.

Garrard, P., 1994. Geological map of the Chingola area to the scale 1: 100, 000. Degreesheet 1227, SE quarter. Geological Survey Department, Lusaka.

Glavin, R.J., Hooda, P.S., 2005. A practical examination of the use of geostatistics in theremediation of a site with a complexmetal contamination history. Soil & SedimentsContamination 14, 155–169.

Goodarzi, F., Sanchi, H., Garrett, R., Duncan, W., 2002. Accumulation of trace metals onthe surface soils around the Trail Smelter, British Columbia, Canada. EnvironmentalGeology 43, 29–38.

Guerreiro, C., 1999. Passive Sampling of SO2 and NO2 Ambient Air Concentrations inZambia. MS., Norwegian Institute for Air Research, Norway.

Hu, N.J., Luo, Y.M., Wu, L.H., Song, J., 2007. A field lysimeter study of heavy metalmovement down the profile of soils with multiple metal pollution during chelate-enhanced phytoremediation. International Journal of Phytoremediation 9, 257–268.

International Organization for Standardization, 1995. ISO 11466. Soil quality: extractionof trace elements soluble in aqua regia. ISO, Geneva.

Johnson, D.E., 1998. Applied Multivariate Methods for Data Analysis. Duxbury Press,Pacific Grove, California.

Johnson, R.H., Blowes, D.W., Robertson,W.D., Jambor, J.L., 2000. The hydrogeochemistry oftheNickel Rimmine tailings impoundment, Sudbury, Ontario. Journal of ContaminantHydrology 41, 49–80.

Kabala, C., Singh, B.R., 2001. Fractionation and mobility of copper lead, zinc in soilprofiles in the vicinity of a copper smelter. Journal of Environmental Quality 30,485–492.

Kabata-Pendias, A., Pendias, H., 1984. Trace Elements in Soils and Plants. CRC Press,Boca Raton, Florida.

Kamona, A.F., Nyambe, I.A., 2002. Geological characteristics and genesis of stratiformsediment-hosted Cu–(Co) deposits, Zambian Copperbelt. In: Miller, R.E. (Ed.), Proc.11th IAGOD Quadrennial Symposium and Geocongress, Extended Abstracts.Geological Survey of Namibia, Windhoek Namibia (CD version).

Key, R.M., Liyungu, A.K., Njamu, F.M., 2001. The western arm of the Lufilian Arc in NWZambia and its potential for copper mineralization. Journal of African EarthSciences 33, 503–528.

Khan, F.U., Rahman, A.U., Jan, A., Riaz, M., 2004. Toxic and trace metals (Pb, Cd, Zn, Cu,Mn, Ni, Co and Cr) in dust, dustfall/soil. Journal of the Chemical Society of Pakistan26, 453–456.

Kim, K.W., Lee, H.K., Yoo, B.C., 1998. The environmental impact of gold mines in theYugu-Kwangcheon Au–Ag metallogenic province, Republic of Corea. Environmen-tal Technology 19, 291–298.

Kim, K.K., Kim, K.W., Kim, J.Y., Kim, I.S., Cheong, Y.N., Min, J.S., 2002. Characteristics oftailings from the closed metal mines as potential contamination source in SouthKorea. Environmental Geology 41, 358–364.

Knight, A., 2004. Emissionmonitoring from theMufulira Smelter. Mopani CopperMinesPlc. MS. Internal Audit of the Mopani Copper Mines, Plc. Alfred Knight Plc. Kitwe,Zambia.

Kříbek, B., Nyambe, I. (Eds.), 2002. Evaluation of the environmental impacts of copperand cobalt mining in the Zambian part of the Copperbelt and a heavy metals andsulphur contamination monitoring system proposal. — Kitwe Area — Project of theTechnical Aid of the Czech Republic to the Republic of Zambia in the year 2002. MS,Czech Geological Survey, Praha. 140 pp.

Kříbek, B., Nyambe, I. (Eds.), 2004. Evaluation of the environmental impacts of copperand cobalt mining in the Zambian part of the Copperbelt and a heavy metals andsulphur contamination monitoring system proposal — Nsato Area. — Project of theTechnical Aid of the Czech Republic to the Republic of Zambia in the year 2004. MS,Czech Geological Survey, Praha. 160 pp.

Kříbek, B., Nyambe, I. (Eds.), 2005. Impact assessment of mining and processing ofcopper and cobalt ores on the environment in the Copperbelt, Zambia.— The Kitweand Mufulira areas. Project of the development assistance programme of the CzechRepublic for the years 2004–2006. Final report for year 2004. MS, Czech GeologicalSurvey, Praha. 196 pp.

Kříbek, B., Nyambe, I. (Eds.), 2006. Evaluation of the environmental impacts of copperand cobalt mining in the Zambian part of the Copperbelt and a heavy metals andsulphur contamination monitoring system proposal. —The Kitwe-east andMufulira-east area.— Project of the Technical Aid of the Czech Republic to theRepublic of Zambia in the year 2005. MS, Czech Geological Survey, Praha. 135 pp.

Kříbek, B., Pašava, J., Majer, V.L., Knésl, I., Rambousek, P., Nyambe, I., Mwale, M., 2003.Use of geochemical mapping for the evaluation of environmental impacts of miningin the Zambian Copperbelt. In: Eliopoulos, D.G., (Ed.), 1996. Mineral explorationand sustainable development. Millpres Rottgerdam, Netherlands, pp. 33–36.

Kříbek, B., Pašava, J., Majer, V.L., Nyambe, I., Mwale, M., 2004. Impact of copper andcobalt mining on the environment in the Zambian Copperbelt. 20th Colloquium ofAfrican Geology. BRGM, Orleans, p. 238.

Kříbek, B., Majer, V., Nyambe, I., 2007. Environmental–Geochemical Atlas of the Central-Northern Part of the Copperbelt Province of Zambia. Czech Geological Survey, Prague.

Krzaklewski, W., Barszcz, J., Malek, S., Koziol, K., Pietrowski, M., 2004. Contamination offorest soils in the vicinity of the sedimentation pond after zinc and lead oreflotation (in the region of Olkusz, southern Poland). Water Air & Soil Pollution 24,151–164.

Lee, J.S., Chon, H.T., Kim, J.S., Kim, K.W., Moon, H.S., 1998. Enrichment of potentiallytoxic elements in areas underlain by black shales and slates in Korea.Environmental Geochemistry & Health 20, 135–147.

Lee, Ch.G., Chon, H.T., Jung, M.Ch., 2001. Heavy metal contamination in the vicinity ofthe Daduk Au–Ag–Pb–Zn mine in Korea. Applied Geochemistry 16, 1377–1386.

Levy, D.B., Curtis, K.H., Casey, W.H., 1997. A comparison of metal attenuation in mineresidue and overburden material from an abandoned copper mine. AppliedGeochemistry 12, 203–211.

Li, X., Thornton, I., 2001. Chemical partitioning of trace and major elements in soilscontaminatedbyminingandsmeltingactivities.AppliedGeochemistry16, 1693–1706.

Lima, A., 2008. Evaluation of geochemical background at regional and local scales byfractal filtering technique: case studies in selected Italian areas. In: de Vivo, B.,Belkin, H.E., Lima, A. (Eds.), Environmental Geochemistry. Site Characterization,Data Analysis and Case Stories. Elsevier, Amsterdam, pp. 135–153.

Lin, C., Tong, X., Lu, W., Yan, L., Wu, Y., Nie, C., Chu, C., Long, J., 2005. Environmentalimpacts of surface mining on mined lands, affected streams and agricultural landsin the DabaoshanMine region, southern Korea. Applied Geochemistry 20, 256–271.



Liu, W.X., Coveney, R.M., Chen, J.L., 2003. Environmental quality assessment on a riversystem polluted by mining activities. Applied Geochemistry 18, 749–764.

Liu, Y.G., Zhang, H.Z., Zeng, G.M., Huang, B.R., Li, X., Xu, W.H., 2005. Characteristics oftailings from metal mines in Hunan Province, China. Journal of Central SouthUniversity of Technology 12, 225–228.

Lopéz, J.M., Borrajo, J.L., de Miguel Garcia, E., Arrans, M., Hidalgo Estéves, C., SáezCastillo, A.J., 2008. Multivariante analysis of contamination in the district of Linaréz(Jaén, Spain). Applied Geochemistry 23, 2324–2336.

Luo, X.S., Zhou, D.M., Liu, X.H., Wang, Y.J., 2006. Solid/solution partitioning and speciationofheavymetals in thecontaminatedagricultural soils. Journal ofHazardeousMaterials131, 19–27.

Maiz, I., Esnaola, M.V., Millán, E., 1997. Evaluation of heavy metal availability incontaminated soils by a short sequential extraction procedure. Science of the TotalEnvironment 206, 107–115.

Marjonen, R., 2002. Geological map of the Kitwe–Mufulira area to the scale 1: 100 000.Quarter degree sheets 122 SWand part of 1228 NW. Geological Survey Department,Lusaka.

Martley, E., Gulson, B.L., 2003. Heavy metal partitioning in soil profiles in the vicinity ofan industrial complex and potential health implications. Journal de Physique 107,831–834.

Martley, E.,Gulson, B., Louie,H.,Wu,M.,Di, P., 2004.Metal partitioning in soil profiles in thevicinity of an industrial complex, New South Wales. Geochemistry, Exploration &Environment Analysis, vol. 4. Geological Soc Publ. House, Avon, England, pp. 171–179.

Mukherjee, A.B., Zevenhoven, R., 2006. Mercury in coal ash and its fate in the IndianSubcontinent. A synoptic review. Science of the Total Environment 368, 364–392.

Mukwila, H., 2002. Geological map of the Copperbelt area to the scale 1: 150 000.— MSConsulting Geologists Department, Konkola Copper Mines Plc., Chingola.

Nishida, H., Miyai, M., Tada, F., Suzuki, S., 1982. Computation of the index of pollutioncaused by heavy metals in river environment. Environmental Pollution Series B4,241–248.

Oliveira, S.M.B., de Moya Partiti, C.S., Enzweiler, J., 2001. Ochreous laterite: a nickel orefrom Punta Gorda, Cuba. Journal of South American Earth Science 14, 307–317.

Orescanin, V., Mikelic, L., Lovrencic, I., Barisic, D., Mikulic, N., Lulic, S., 2006. Environ-mental contamination assessment of the surroundings of the ex-ferrochromiumsmelter Dugi Rat, Croatia. Journal of Environmental Science and Health Part A—Toxic/Hazardeous Substances & Environmental engineering 41, 2547–2555.

Perez, G., Valiente, M., 2005. Determination of pollution trends in an abandonedminingsite by application of a multivariate statistical analysis to heavy metalsfractionation using SM&T-SES. Journal of Environmental Monitoring 7, 29–36.

Porada, H., Berhorst, V., 2000. Toward a new understanding of the Neoproterozoic–Early Palaeozoic Lufilian and northern Zambezi Belts in Zambia and the DemocraticRepublic of Congo. Journal of African Earth Sciences 30, 727–771.

Rainauld, C.L., Masters, S., Armstrong, R.A., Phillips, D., Robb, L.J., 2002. Contributions to thegeology and mineralization of the Central African Copperbelt: IV Monazite U–Pbdating and

40K–

39Ar thermochronology of metamorphic events during the Lufulian

Orogeny. In: Miller, R.E. (Ed.), Proc. 11th IAGOD Quadrennial Symposium andGeocongress, Extended Abstracts. Geological Survey of Namibia, Windhoek Namibia(CD version).

Rainauld, C.L., Masters, S., Armstrong, R.A., Robb, L.J., 2005. Geochronology and natureof the Palaeoproterozoic basement in the Central African Copperbelt (Zambia and

the Democratic republic of Kongo), with regional implications. Journal of AfricanEarth Sciences 42, 1–32.

Reimann, C., de Caritat, P., 1998. Chemical Elements in the Environment-Factsheets forthe Geochemist and Environmental Scientist. Springer-Verlag, Berlin, Germany.

Reimann, C., Garrett, G., 2005. Geochemical background — concept and reality. Scienceof the Total Environment 350, 12–27.

Reimann, C., Filzmoser, P., Garrett, R.G., 2005. Background and threshold: criticalcomparison of methods of determination. Science of the Total Environment 346,1–16.

Romero, F.M., Armienta, M.A., Villasenor, G., Gonzales, J., 2006. Mineralogicalconstraints on the mobility of arsenic in tailings from Zimapan, Hidalgo, Mexico.International Journal of Environment and Pollution 26, 23–40.

Romic, M., Romic, D., 2003. Heavy metals distribution in agricultural topsoils in urbanarea. Environmental Geochemistry 43, 795–805.

Ruan, X.L., Zhang, G.L., Ni, L.J., He, Y., 2008. Distribution and migration of heavy metals inundisturbed forest soils: a high resolution samplingmethod. Pedosphere18, 386–393.

Salminen, R., Tarvainen, T., Demetriades, A., Duriš, M., Fordyce, F.M., Gregorauskiene, V.,Kahelin, H., Kivisilla, J., Klaver, G., Klein, H., Larson, J.O., Lis, J., Locutura, J., Marsina,K., Mjartanova, H., Mouvet, C., O'Connor, P., Odor, L., Ottonello, G., Paukola, T., Plant,J.A., Reimann, C., Schermann, O., Siewers, U., Steenfelt, A., Van der Sluys, J., DeVivo,B., Williams, L., 1998. FOREGS geochemical mapping field manual. GeologicalSurvey of Finland, Guide, vol. 47. Geological Survey of Finland, Espoo.

Schwertmann, U., Pfab, G., 1994. Structural vanadium in synthetic goethite. Geochimicaet Cosmochimica Acta 58, 4349–4352.

Schwertmann, U., Pfab, G., 1996. Structural vanadium and chromium in lateritic ironoxides. Geochimica et Cosmochimica Acta 60, 4279–4283.

Simenov, V., Einax, J., Tsakovski, S., Kraft, J., 2005. Multivariate statistical assessment ofpolluted soils. Central European Journal of Chemistry 3, 1–9.

Singh, B., Cornelius, M., 2006. Geochemistry and mineralogy of the regolith profile overthe Aries kimberlite pipe, Western Australia. Geochemistry, Exploration &Environment Analysis 6, 311–323.

Šráček, O., Choquette, M., Gélinas, P., Lefebvre, R., Nicholson, R.V., 2004. Geochemicalcharacterization of acid mine drainage from awaste rock pile, Mine Doyon, Québec,Canada. Journal of Contaminant Hydrology 69, 45–71.

Šráček, O., Mihaljevič, M., Kříbek, B., Majer, V., Veselovský, F., 2010. Geochemistry andmineralogy of Cu and Co in mine tailings at the Copperbelt, Zambia. Journal ofAfrican Earth Sciences 57, 14–30.

Voegelin, A., Tokpa, G., Jacquat, O., Barmettler, K., Kretzschmar, R., 2008. Zinc fractionationin contaminated soils by sequential and single extractions: influence of soil propertiesand zinc content. Journal of Environmental Quality 37, 190–1200.

Walker, S.R., Jamieson,H.E., Lanzirotti, A., Andrade, C.F., Hall, G.E.M., 2005. The speciationofarsenic in iron oxides in mine wastes from the Giant gold mine, NWT: application ofsynchrotron micro-XRD and micro-XANES at the grain scale. Canadian Mineralogist43, 1205–1224.

Wedepohl, K.H., 1995. The composition of the continental crust. Geochimica etCosmochimica Acta 59, 1217–1232.

Zhang, G., Liu, C.Q., Wu, P.Y., 2004. The geochemical characteristics of mine-wastecalcines and runoff from the Wanshan mercury mine, Guizhon, China. AppliedGeochemistry 19, 1735–1744.


Date post:	23-Feb-2018
Category:	Documents
Upload:	lynga
View:	214 times
Download:	2 times

Author's personal copy - · PDF file(Levy et al., 1997; Johnson et al., 2000; Kim et al.,...

Documents