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Author's personal copy
Journal of Geochemical Exploration 104 (2010) 69–86
Contents lists available at ScienceDirect
Journal of Geochemical Exploration
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j g e o ex p
Discrimination of lithogenic and anthropogenic sources of metals and sulphur in soils
of 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
b
Czech Geological Survey, Klárov 3, CZ-11821 Prague 1, Czech Republic
University of Zambia, School of Mines, Geology Department, P. O. Box 32 379, Lusaka, Zambia
a r t i c l e
i n f o
Article history:
Received 20 February 2009
Accepted 17 December 2009
Available online 6 January 2010
Keywords:
Heavy metals
Smelters
Soil contamination
Copperbelt
Zambia
a b s t r a c t
Samples of topsoil together with reference samples of subsurface soil from a depth of 80–90 cm were
collected in the central-northern part of the Zambian Copperbelt to distinguish lithogenic sources of metals
from anthropogenic contamination of soils caused by fallout of dust from mining operations, flotation ore
treatment plants, tailings dams, smelters and slag dumping grounds. The total sulphur, Cu and Co contents
were found to be significantly higher in topsoil relative to subsurface soil over a large part of the surveyed
area, and Zn, Pb, As and Hg contents showed a definite increase in the close neighbourhood of smelters and
in the direction of prevailing winds. This indicates that the increase of these elements in the topsoil is due to
anthropogenic activities. The areal extent and degree of anthropogenic contamination of topsoil can be
expressed by an enrichment index (EI) based on the average ratio of the actual and median concentrations of
the given contaminants. Although the contamination of soil by dust fallout decreases progressively with
depth in the soil profile, in areas strongly affected by mining and mineral processing the anthropogenic
contamination by sulphur and copper can be traced to a depth of 80–90 cm. In contrast, the concentration of
elements such as Cr, Ni, and V, that show a direct correlation with the content of iron in the soils, increases in
the subsurface soil relative to the topsoil. This is particularly evident in areas underlain by rocks of the
Katanga Supergroup.
Lithogenic and anthropogenic sources of metals and sulphur can also be distinguished by using factor
analysis. This analysis of data acquired from the topsoil revealed five factors governing the source and nature
of 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 five
factors account for 69.4% of the total variance in the data structure of the system. The interpretation of factors
is based on the geographical inspection of factor score distribution, the knowledge of the chemical character
of the sources of contamination, the local geology and the agrochemical properties of the soils. The factor
analysis of data obtained from subsurface soils showed that only the bedrock specific factors had an
influence.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
The issue of anthropogenic contamination of soils in mining
districts has been discussed in numerous reports and publications
(e.g., Dudka and Adriano, 1997; Barcan and Kovnatsky, 1998; Farago
et 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
⁎ 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
et al., 2005; Voegelin et al., 2008; Ruan et al., 2008). There have been
also many investigations of the mineralogy and chemical composition
of dust fallout (Khan et al., 2004; Castellano et al., 2004; Beavington
et al., 2004), the chemistry of flotation tailings and waste rock dumps
(Levy et al., 1997; Johnson et al., 2000; Kim et al., 2002; CourtinNormade et al., 2002; Carlsson et al., 2003; Zhang et al., 2004; Šráček
et 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 to
the origin of individual metals, and the factors governing their
migration, e.g., the bonding of metals to individual components in the
soil substrate, and to factors governing the mobility of elements in soil
solutions (Maiz et al., 1997; Li and Thornton, 2001; Lee et al., 2001;
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
Kabala and Singh, 2001; Liu et al., 2003; Chatain et al., 2005; Luo et al.,
2006). However, these studies were mostly undertaken on rather
small areas with simple geological structure. In large mining districts
it is generally difficult to determine the relative contributions to the
geochemistry of soil samples based solely on the concentration of
major or trace elements in the soils themselves, especially in areas
with varied lithology and where both lithogenic and anthropogenic
sources of contamination are present (Reimann et al., 2005; Reimann
and Garrett, 2005). In the case when the geochemical composition of
uncontaminated soil or bedrock is known and relatively simple, the
anthropogenic contamination can be expressed in the form of an
enrichment factor, i.e. by the normalization of element concentrations
in contaminated soils by their contents in uncontaminated soils
(Reimann and Garrett, 2005). Alternatively, the content of metals in
contaminated soil can be normalized relative to local or worldwide
geochemical soil standards (Kabata-Pendias and Pendias, 1984;
Reimann and de Caritat, 1998). A variety of statistical methods has
been developed that significantly improve the chances of successfully
differentiating 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 the
Zambian part of the Copperbelt is poorly quantified in spatial terms
because the availability of high precision and up-to-date regional
geochemical data for both unpolluted and polluted areas is limited.
Moreover, a high natural background of heavy metals in soils and the
varied lithology of the surveyed terrain make the evaluation of the
degree of industrial pollution difficult. Therefore, in order to
distinguish natural concentrations of metals from those ascribed to
contamination by dust fallout, the soil sampling was undertaken at
two depth horizons: firstly the topsoil, which is most affected by dust
fallout, 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 all
contaminated by dust fallout, so that the values established are
considered to represent the natural geochemical background. The
authors of this study are of course aware of the danger arising from
different physical and chemical properties of separate soil horizons
and their effect on the distribution of relevant metals. As a
consequence, the study of the distribution of metals was complemented by the determination of soil pH, the contents of organic and
carbonate carbon, and total sulphur as well as by the analysis of total
iron, both in topsoil and subsurface soils. The present study was carried
out as a part of the larger research programme dealing with the
pollution of soils in the Copperbelt area. To establish the extent of
industrial contamination an environmental–geochemical survey of
soils was carried out within the framework of the Development
Cooperation Programme of the Czech Republic during the years 2002
to 2006 (Kříbek and Nyambe, 2002, 2004, 2005, 2006) and Kříbek et al.
(2003, 2004, 2007). The main 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 and
subsurface soils in order to determine the pattern of dispersion in
relation to the distance from the sources of contamination, (2) to
discriminate between lithogenic (geological) and anthropogenic
sources of metals in this mineralized and polluted area, and (3) to
provide some initial data on the identification of lithogenic and
anthropogenic sources using statistical correlation and factor analysis.
2. Study area
The research covered 4700 km2 of soils in the urban and rural
areas of the central-northern part of the Copperbelt Province of
Zambia. The surveyed area covers 63% of the total area of the Zambian
Copperbelt amounting to 7500 km2. The majority of the mining and
mineral 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 of
866,646, then Chingola (177,445), and Mufulira (152,664). Other
important towns are Chililabombwe (84,866), Kalulushi, and Chambishi. Mining activities (open pits and underground mines) are
located in the vicinity of the individual towns. Smelters are located at
Mufulira, Kitwe (the Nkana smelter) and at Chambishi (Fig. 1).
2.1. Soils
The typical section of freely drained soils sampled within this
project consists of A1, A2, B1, B2 and C horizons (Kříbek and Nyambe,
2005):
A1 horizon (0–5 cm), topsoil. Dark greyish brown silt; moderate
medium granular structure; slightly hard, friable, slightly plastic;
roots abundant; smooth and gradual boundary.
A2 horizon (5–30 cm). Dark reddish brown silt or clay; weak medium
granular 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 medium
subangular breaking down into weak fine granular structure; friable,
slightly plastic, slightly sticky; a few roots; smooth and gradual
boundary; abundant ferromanganese nodules.
B2 horizon (50–120 cm). Dark red clay; massive porous breaking
down into weak granular structure; soft, very friable, slightly plastic,
slightly sticky; no roots; clear, undulating boundary; abundant
ferromanganese nodules.
C horizon (120–140±). Clay loam; horizon comprising weathered
rock 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 the
ferrasoil group (acric, orthic or rhodic ferrasoils).
Ferrasoils in the surveyed area are usually acidic, poor in organic
carbon and nitrogen, and display low values of cation exchange
capacity (Table 1). Compared with the subsurface soil horizon (B2
horizon, depth 80–90 cm), the topsoil (A1 horizon, depth 0–5 cm) has
higher contents of organic carbon (Corg), higher values of pH, higher
amounts of exchangeable cations (except for K+), and a lower amount
of clay and silt fractions (Table 1). The soils of the dambo-type (poorly
drained soils, cambisoils) according to the FAO-UNESCO (1997)
classification that occur locally along the riverbanks were not sampled.
2.2. Climate
Three climatic seasons are defined: (i) a rainy season, (ii) a cool
dry season, and (iii) a hot season. The rainy season lasts roughly from
the beginning of November until the end of April and is characterized
by tropical thunderstorms. The cool dry season lasts from the first half
of May until the end of August and is characterized by light winds.
Precipitation during this season is negligible. Daily temperatures in
June and July range from 6° to 24° and fall to 5 °C at night. The hot
season lasts from September until the end of January. The average
temperatures are over 30 °C during the day and range between 21 °C
and 26 °C at night. The annual rainfall averages are 1320 mm in Kitwe
and 1270 mm in Mufulira. The wind flow is dominated by strong
winds from the south-easterly quadrant from March until October.
During January, February, November and December, wind flow is
dominated by light north-easterly winds.
2.3. Geology and mineralization
In the Zambian Copperbelt, the oldest Pre-Katanga Basement
Complex consists of a Paleoproterozoic magmatic arc sequence,
comprising schists and intrusive granitoids dated at about 1980±8 Ma
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
71
Fig. 1. Geological sketch map of the surveyed part of the Copperbelt Province in Zambia. Compiled, simplified and taken from Garrard (1994), Marjonen (2002) and Mukwila (2002)
with markings of major sources of contamination and location of the studied soil profiles.
(Rainauld et al., 2005; Fig. 1). This Basement Complex is overlain
uconformably by quartzites and metapelites of the Muva Supergroup.
The Basement Complex as well as the Muva Supergroup are penetrated
by the pink microcline granite and adamellite that is assumed to be of
Pre-Katangan age.
Overlying metasediments of the Katanga Supergroup are traditionally divided into the ore-bearing Mine Series and the Kundelungu
Groups. The deposition of the Katanga Supergroup sediments started
at some time after 880 Ma (Armstrong et al., 1999). Structurally, the
Copperbelt region belongs to the Lufilian Arc Terrane. The Neoproterozoic sedimentation in this terrane began in a continental rift
environment (Binda, 1994; Porada and Berhorst, 2000). New
geochronological data indicate that metasedimentary rocks of the
Katanga Supergroup were deformed and partly metamorphosed
during 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 uplift
was accompanied by the formation of ENE-directed thrusting and
later by strike-slip faulting. A number of N-trending basic dykes cut
the Pre-Katanga basement as well as the Katanga Supergroup.
Spatially associated with the dykes are a number of irregularly shaped
dolerite stocks (Key et al., 2001).
The Zambian Copperbelt is one of the world's largest copper and
cobalt ore districts. The Cu–Co mineralizion in the Copperbelt is confined
to the lower section of the Katanga Supergroup (Mine Series) close to its
contact with the underlying Pre-Katanga units. The Copperbelt ores
form sediment-hosted deposits of strata-bound and/or stratiform type
characterized by finely disseminated copper–(cobalt)–iron sulfides
consisting mostly of chalcopyrite, cobalt-rich pyrite and bornite ±
carrolite. The host rocks include quartzite (arkose), shale and dolomite
that 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
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Table 1
Agrochemical 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)
pHKCl
Exchangeable H+ (mmol/100 g)
Exchangeable Ca2+ (mmol/100 g)
Exchangeable Mg2+ (mmol/100 g)
Exchangeable K+ (mmol/100 g)
Exchangeable Al3+ (mmol/100 g)
CEC (mmol/100 g)
Ntot (wt.%)
Corg (wt.%)
Size fraction
Subsurface soil (70–90 cm depth)
Min.
Median
Max
Min.
Median
Max
4.18
2.0
0.1
0.01
0.04
0.0
0.9
b0.05
0.20
4.75
4.42
1.48
0.49
0.21
0.22
6.00
0.11
0.81
5.37
8.5
4.26
1.01
0.41
0.6
12.6
0.33
1.49
4.03
1.5
0.07
0.03
0.05
0.0
2.4
b0.05
0.12
4.25
3.75
0.13
0.17
0.30
0.0
2.92
0.05
0.13
4.47
5.5
0.19
0.24
1.18
0.0
4.0
0.06
0.24
Grain size distribution (in %)
Topsoil
b 0.001 mm
b 0.002 mm
b 0.01 mm
b 0.05 mm
0.01–0.05 mm
0.05–0.25 mm
0.25–2.0 mm
Subsurface soil
Min.
Average
Max.
Min.
Average
Max.
6.4
7.4
7.4
10.1
2.7
42.1
7.4
11.8
14.2
15.2
23.4
11.6
50.3
17.2
18.6
23.5
25.3
47.8
22.5
68.6
23.5
24.1
26.0
26.2
36.1
5.7
33.2
8.5
28.9
32.2
33.4
40.4
7.9
40.6
11.2
39.3
41.6
46.9
52.8
10.9
50.0
18.7
CEC = Cation exchange capacity, Ntot = total nitrogen, Corg = total organic carbon.
both metals are extracted. Trace amounts of Au, Pt and Ag were
recovered from the copper slimes during the smelting process. Ca
30 million metric tons of copper metal were produced since the largescale mining operations began in 1930 (Kamona and Nyambe, 2002).
2.4. Copper and cobalt industry
Significant mineral exploration in the Copperbelt started during
the 1920s and resulted in the discovery and the development of
several mines. During the 1960s, the annual Zambian copper
production peaked at over 755,000 metric tonnes. In 1969, the
Zambian government nationalized the industry and reorganized all
mining utilities into the Nchanga Consolidated Copper Mines Limited
and Roan Copper Mines Limited. In 1982, both companies were
merged into the Zambia Consolidated Copper Mines Limited. When
the world price of copper fell, the Zambian government was unable to
adjust social services provided by the mines to accommodate this
drop in income and thus the capital available to maintain and update
mining technology declined and production deteriorated. In response,
Zambia began to consider the options for privatization in the early
1990s. The Nkana and Mufulira mines were purchased by Mopani
Copper Mines, Chambishi mine was taken over by the China NonFerrous Metal Corporation, the Nchanga and Konkola Mines were
transferred to Vedanta Resources (India) and the Chibuluma-West
and Chibuluma-South underground mines to the Chibuluma Mines
PLC. In 2008, the annual production of copper in the whole of the
Copperbelt Mining District amounted to ca 569,891 metric tons and
that of cobalt was ca 5275 metric tons. Significant volumes of
selenium (17 t) and silver (8 t) together with minor gold and platinum 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 ore
treatment plants, and smelted and refined at the Mufulira and Kitwe
(Nkana) smelters. The Chambishi smelter re-processes old slags from
the Kitwe (Nkana) smelter, which are rich in copper and cobalt.
3. Materials and methods of investigation
Regional environmental–geochemical surveying of soils was
carried 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 grid
of square cells 4 × 4 km in size. Each cell was characterized by at least
one sampling point, from which, after removal of plant remains and
the thin humus layer, a composite sample of topsoil was collected at a
depth of 0–5 cm. A composite sample was prepared by blending soil
samples taken on the edges and in the central point of a square 25 by
25 m in size. Weight of composite topsoil samples was 0.6 to 1.7 kg. In
heavily contaminated areas, additional samples were taken in the
neighbourhood of contamination “hot spots”. At selected sampling
points, composite samples of the subsurface soil were taken from a
depth of 80 to 90 cm using a soil probe. Weight of composite subsurface soil samples was 0.3 to 0.4 kg. Points for the subsurface soil
sampling were selected on the basis of the different underlying
bedrock lithologies so that soils formed on the different rock types of
bedrock could be characterized. Site descriptions were made at the
time of sampling to record the location of the sample in relation to
land use and major environmental features. During the project, 719
composite samples of the topsoil, and 129 samples of the subsurface
soil were collected and analysed to create a representative geochemical database for the central-northern part of the Copperbelt. To
characterize dust fallout from overburden dumps and waste rocks
dumps, from ore crushers, tailings ponds, ore concentrate depots,
from smelters and slag dumps were collected using low volume personal total suspended particles (TPS) samplers (LVPs, type PV-1.7,
Kubik, Czech Republic). The samplers are multi-functional equipment
for sampling either the dust fallout in workplaces or outdoors. Other
dust fallout samples were collected from the leaves of trees and from
wind-blown tailings material. Soil samples were air-dried, and, after
homogenization, half of each sample was passed through 0.2 mm
mesh screen using a U.S. Geological Survey Standard Sieving Set and
pulverized in an agate ball mill to less than 0.063 mm mesh. To
determine the content of metals, soil and dust samples were digested
with aqua regia in accordance with the ISO 11466 procedure
(International Organization for Standardization, 1995). The choice of
this method was dictated by international regulations, which set
norms for soils according to this procedure. All reagents were declared
pro analysi, and all solutions were prepared with double distilled
water. Standard working solutions were prepared from original
certified stock solutions (MERCK) concentration 1000 mg L− 1 in 1%
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
super pure HNO3. Fe, Co, Cr, Cu, Ni, Pb, V and Zn were determined
using Flame Atomic Absorption Spectroscopy (FAAS, Perkin Elmer
4000 Spectrometer). Arsenic was determined by a Hydride-Generation Atomic Absorption Spectrometry (HGAAS, Perkin Elmer 503
equipment), and Hg was determined mercurometrically, using an
AMA 254 Mercury Analyser.
All samples were analysed at the accredited Central Geochemical
Laboratories of the Czech Geological Survey. The quality control
procedure involved analysis of reagent blanks, duplicate samples and
several referenced soils. Analytical precision was determined by the
10% analysis (in duplicate) of randomly chosen samples and reference
samples as well, with a variation coefficient for all investigated
elements b8%, with the exception of Pb and Ni (b22%). Reliability of
analyses 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 Pb
due to the large number of samples in which the concentrations of Ni
and Pb were near the limits of analytical detection.
The amount of total carbon (Ctot) was determined using an ELTRA
CS 500 instrument. Samples were combusted at 1400 °C and the Ctot
was measured as CO2 using an IR detector. The amount of carbonate
carbon (C carb ) was determined using another ELTRA CS 500
instrument. Samples were digested in a saturated solution of H3PO4
and the amount of CO2 liberated was recalculated to that of carbonate
carbon (Ccarb). The amount of organic carbon (Corg) was determined
by subtraction of carbonate carbon from total carbon content
Corg = Ctot − Ccarb. Total sulphur (Stot) was determined using the
ELTRA CS 500 equipment. Samples were combusted at a temperature
of 1400 °C and the Stot, measured as released SO2, was determined by
an infrared detector. The variation coefficient for Ctot and Ccarb is
b0.5%, for Stot it is b1%. Relative errors of Ctot, Ccarb and Stot determined
using reference materials were ±2.5% for Ctot and Stot, and ±2% for
Ccarb.
To determine the pH value of soils, 2.5 g of material, sieved
through sieve mesh 0.2 mm was leached in periodically shaken
solution of 1 M KCl. The pH measurements were made with a
precision of 0.01 pH unit using a pHC 2085 pH electrode connected to
a PHM 201 pH-meter after 24-hour leaching. Differences in water
temperature were automatically compensated using a T 201 temperature compensator. Calibrations were carried out using two standard
IUPAC (Radiometer A/S Copenhagen, Denmark) buffers with pH
values of 4.01 and 7.00, respectively. The measured pH value was
recorded automatically, with a precision of 0.01 pH unit.
Determination of the mineralogical identity and location of metals
in the soils and dusts was attempted using a CamScan 3200 electron
microprobe in SEM mode equipped with an energy-dispersive
analyzer LINK-ISIS. Prior to analysis, the selected soil samples were
separated according to density using polyvinylpyrrolidone and
diiodomethane. Analyses were undertaken using an accelerating
voltage of 15 keV, and a beam current of 3 × 10−9 A. The XRD
characteristics of minerals and their relative proportions in dust
from ore concentrates, crushers, dust fallout from smelters and
tailings ponds were established using a Philips PW 7310 diffractometer with CuKα radiation and a Ni filter in standard configuration.
3.1. Processing of analytical data
3.1.1. Univariate statistics and data transformation
Summary statistics of the data set were first calculated to evaluate
the distributions. The frequency distribution for each of the elements
analysed was examined using histograms, background normality tests
were made and kurtosis and skewness calculated. Kurtosis and
skewness were calculated using the S-Plus programme version 4.5
(MathSoft Inc., Seattle, Washington, U.S.A. 1997). Because the
statistical distribution of most variables determined by chemical
analyses was not normal, a non-parametric method was used to
evaluate the main statistical characteristics of the individual data
73
populations using again the S-Plus programme version 4.5. For the
purpose of statistical treatment, data from chemical analyses lower
than the detection limit were replaced by values equal to 2/5 of the
limit of detection. In order to construct contour maps, the data were
transformed to a regular grid using the kriging method. The distance
between grid nodes was 500 m. To calculate the grid node, generally
up to 10 adjacent data points in the “search area” were taken into
account. In addition, the search area within the 10 km radius was
divided into 4 sectors, in each of these up to 4 adjacent data points
were taken into account. A minimum condition for the retrieval of the
grid node value was the presence of at least one sampling point in any
sector of the search area. All data sets displayed statistical distributions close to lognormal; therefore, logarithmic values were used for
construction of maps and were recalculated from logarithmic back to
normal (geometric) values that appear on the maps. Categories of
concentration for the contour maps of surface soils were selected at
the 10%, 25%, 50% (median) 75% and 90% percentiles for the individual
data sets. In cases in which a large number of values fall below the
detection limit, the value of the limit was used as the lowest
boundary. The final category represents extreme data (outliers). The
same categories were used for maps of subsurface soils, irrespective of
the range and distribution of the concentrations in subsurface soils.
The grid was calculated and results were mapped using the program
Surfer version 8 (Golden Software Inc., Golden, Colorado, USA 2002).
3.1.2. Factor analysis
Logarithmic data were processed by means of R-mode factor
analysis (FA), applying the varimax-raw rotational technique. As a
multivariate method, it facilitates the reduction, transformation and
organization of the original data by the use of intricate mathematical
techniques, which eventually results in a simple form of factor model.
Factor analysis using the same amount of information creates a new
set of uncorrelated variables which are the linear combinations of the
original ones. If the original variables have significant linear
intercorrelations, when the FA is carried out the first few factors
will include the largest part of the total variance. The interpretation of
dominant factors was made by taking into account the highest factor
loadings for each of the chemical elements. The theoretical details of
FA are given by Johnson (1998). The statistical analysis was carried
out using the program S-Plus version 4.5 (MathSoft Inc., Seattle,
Washington, U.S.A. 1997).
4. Results
4.1. Sources of contamination
The main sources of soil contamination in the surveyed area are dust
fallout from open-pit operations and ore transport, waste rock and
overburden dumps, from crushers, ore concentrate stockpiles and ore
concentrate transport, smelters, slag dumps and the dry areas of tailings
ponds. Dust collected in the vicinity of the Chingola (Nchanga) open pit
revealed only a slightly increased copper content (Table 2, analysis 1).
The concentration of individual elements in dust fallout sampled in the
crusher areas is very variable and reflects the primary geochemical
variability of ore from individual deposits being mined. For example, the
cobalt content of dust collected at Kitwe (the Nkana processing plant) is
high, corresponding to the high cobalt content of ores mined in this area
(Table 2, analyses 2 and 3). In contrast, the dust collected in the crusher
area at Mufulira has a low content of cobalt correlated with the low
cobalt content of the local ores (Table 2, analysis 4). In dust fallout from
crushers, XRD analyses revealed small amount of chalcopyrite in
addition to the prevailing quartz and muscovite and small amount of
K feldspar, plagioclase, calcite, dolomite, talc, chlorite and amphibole.
The chemical variability of ore concentrates reflects both the geochemical variability of ores and variable flotation techniques used to separate
copper- or cobalt-rich products (Table 2, analyses 5–7). The copper
Author's personal copy
74
B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
Table 2
Concentrations 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
2
3
4
5
6
7
8
9
10
11
12
13
14
Nchanga Open Pit, Chingola
Crushers' area, Kitwe 2005
Crushers' area, Kitwe 2005
Crushers' area, Mufulira 2002
Co concentrate, Kitwe
Cu concentrate, Kitwe
Cu concentrate, Chibuluma
Kitwe smelter, dust fallout
Kitwe slag1
Mufulira slag
Mufulira tailings
Chambishi tailings
Kitwe tailings
Chingola tailings2
1.9
15.9
28.4
0.77
236.9
2.41
9.76
28.24
12.56
24.91
0.35
160
2.86
2.3
b0.8
0.3
0.9
b0.8
0.3
3.7
0.3
b0.8
b0.8
b0.8
b0.8
b0.8
0.28
b0.8
13
917
1437
12
7790
3930
4160
1542
3170
3200
b5
2820
200
750
63
22
37
20
32
20
25
182
1107
629
10
10
23
72
169
15,340
21,830
39,100
58,130
248,000
203,000
20,170
6972
8810
1650
7600
1250
40,200
0.005
0.099
0.064
0.027
1.13
0.45
1.13
0.009
0.007
0.007
0.005
0.005
0.041
0.005
6
13
11
b5
25
10
25
20
41
127
b5
60
6
10
8
7
22
5
12
10
8
25
62
98
b5
38
15
25
4
21
12
20
35
39
35
102
15
79
b10
65
10
18
0.2
6.5
8.5
1.24
38
33
20
21.1
0.63
0.96
b 0.2
6.00
0.64
b 0.2
b15
b15
47
b15
55
42
b15
b15
70
75
b15
20
25
34
15
137
92
41
67
1440
67
288
123
1165
5
52
10
144
1
Old slag from the Kitwe (Nkana) smelter re-processed at the Chambishi Smelter, 2Old flotation tailings reprocessed at the Chemical Treatment Plant at Chingola.
concentrate from the Kitwe and Chibuluma flotation plant is composed
essentially of chalcopyrite and pyrite with minor amount of muscovite,
quartz and talc. Cobalt concentrate from the Kitwe ore dressing plant is
composed predominantly of minerals of the linnaeite group (linnaeite,
Co3S4, siegenite, (Co,Ni)3S4 or carrolite, Co2S3) and minor amount of
chalcopyrite, pyrite and arsenopyrite. Non-ore minerals are represented
by orthoclase, muscovite and talc.
The chemical composition of dust fallout from the smelter in Kitwe is
enriched in “volatile elements”, as for example Pb, Zn, As and Se (Table 2,
analysis 8). The investigation of the morphology and chemical
composition of the dust particles from the Kitwe smelter captured by
filters in the TDS samplers revealed sharp-edged or drop-like particles
with significant concentrations of Ca, Mg, Fe, Si and Al and minor
concentrations of Ti, Zn N Cu and S. These are most likely very fine
particles of silicate slag (Fig. 2). Small amount of pyrite, chalcopyrite,
unidentified Fe–Cu–S sulphate, quartz, barite and anhydrite were also
identified. Metallic particles composed of Fe, Cr, and Ni with trace
amount of Cu and Zn captured by filters probably formed by abrasion of
the Kitwe smelter equipment. Using XRD, magnetite, fayalite and quartz
were identified in dust fallout from the Kitwe smelter. In contrast to
other materials studied, particles of slag dust are very rich in “refractory”
chromium (Table 2, analyses 9 and 10) which is related to the
occurrence of numerous chromite inclusions in the slag particles as
revealed by micro-chemical analyses (Fig. 3). In addition to chromite,
Fig. 2. Chemical composition of dust particles captured on the surface of the filter
during the monitoring of dust fallout in the Kitwe (Nkana) smelter area. Length of the
white bar (bottom part of the image) corresponds to 20 µm.
tiny particles of sulphide melt with a chemical composition resembling
that of bornite were also identified (Fig. 4).
The wide variation in the chemical composition of dust from the
dry areas of tailings dams reflects both the variation in the chemical
composition of the ores and the progress in flotation technology over
time. Old slimes enriched in copper and cobalt (Table 2, analyses 12
and 14) are currently re-processed by chemical leaching. The
prevailing minerals in all flotation tailings are quartz, feldspars
(plagioclase N K feldspar), dolomite, calcite, muscovite, chlorite and
amphibole. Evidence of the weathering of the accessory sulphides in
tailings is provided by the efflorescence of sulphates on the surface.
XRD analyses revealed that gypsum (CaSO4·6 H2O) is the prevailing
secondary 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, total
sulphur, pH values and correlations among variables
The principal statistical data, i.e. minimum, maximum and median
analytical 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 and
subsurface soil data sets are given in Tables 3 and 4. The medians of
pH, 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 characteristic
of areas strongly affected by dust fallout from smelters and tailings
ponds. In contrast, the medians of Fe, V, Cr and Ni are higher in
Fig. 3. Inclusions of chromite (Chr) in dust particles of slag collected in the Mufulira
smelter.
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
Fig. 4. Particles of sulphide melt (BN) with chemical composition close to bornite in slag
particles. The Mufulira smelter.
subsurface soils. The dispersion of values in subsurface soil is
generally much lower as compared to that in the topsoil.
The characteristic distribution of values for some elements in topsoil
and in the subsurface soil in the area underlain by the Pre-Katanga units
(Basement Complex and Muva Supergroup) and in the area formed by
the Katanga Supergroup is shown in Fig. 5. Contents of Cu (Fig. 5a) were
found to be mostly higher in topsoil than in the subsurface soil horizon
overlying both geological formations. Extremely high contents of cobalt
in topsoil covering all geological formations (Fig. 5b) are characteristic of
strongly contaminated areas. In contrast, higher concentrations of the
same 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 from
the Katanga Supergroup. Zinc (Fig. 5c), arsenic (Fig. 5d), lead and mercury
(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 the
picture) are mostly higher in subsurface soil regardless of the bedrock
75
geology. The only exceptions are the enhanced contents of nickel
detected in the close vicinity of smelters.
Because the distributions of metals and sulphur were skewed (the
skewness coefficient exceeds 1.0), tightness of the mutual relationships between variables, i.e. between the concentrations of metals,
Corg, Ccarb, and Stot in soils in the surveyed area were investigated
using non-parametric statistics. Tables 5 and 6 are the correlation
matrix, listing Spearman's rank correlation coefficients. Significant
correlations at the probability level p b 0.001 (99.9%) are printed in
bold, at the probability level p b 0.01 (99%) in bold italics and at the
level p b 0.05 (95%) in small italics. The significance was estimated
using t-test. Insignificant correlations are printed in normal letters.
In topsoils, all variables show significant correlations at the
probability level p b 0.001. The high value of Spearman's coefficient
of correlation (r N 0.5) with pH is shown only by Zn. For Stot, high
values of coefficients (r N 0.5) are shown by Co, Cu, Zn, Pb and Hg, for
Ccarb by Co, Cu, and Zn, and for Corg only by Hg.
In contrast to the set of data for topsoil, the values of Spearman's
coefficients of correlation between metals and Stot are generally not
significant in the subsurface soil data set. The significant correlation
between 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 the
profile not affected by contamination (Profile I), in the area of tailings
dams (Profiles II and III), and in a heavily contaminated area located
near the Kitwe (Nkana) smelter (Profile IV, Fig. 6). All the given
profiles and the samples collected were from soils developed on
bedrocks of the Katanga Supergroup. The position of the profiles is
shown on Fig. 1. In all profiles, the moisture and the content of silt and
clay fractions were found to increase gradually with depth. This
matches the grain size distribution in topsoils and subsurface soils
given in Table 1. The distribution of selected metals in all profiles
Table 3
Statistical summary of parameters of topsoil (n = 719).
Parameter
pH
Stot
(wt.%)
Ccarb
(wt.%)
Corg
(wt.%)
As
(ppm)
Co
(ppm)
Min. value
3.56
b0.010
b0.003
0.05
b 0.10
b5
Percentiles
10%
25%
50% (Median)
75%
90%
Max. value
Kurtosis
Skewness
4.23
4.46
4.88
5.70
6.70
9.17
0.72
1.09
b0.010
0.011
0.018
0.029
0.051
1.423
286.26
14.56
0.008
0.013
0.019
0.033
0.055
2.828
96.53
9.09
0.77
1.26
1.91
3.11
4.37
12.84
4.01
1.55
b 0.10
0.16
0.46
1.04
2.70
254.90
631.60
24.45
b5
5.0
10.0
19.0
60.0
606.0
32.77
5.12
Cr
(ppm)
1.6
Cu
(ppm)
15.0
7.0
10.0
16.0
25.0
36.0
595.0
371.81
16.72
72.0
134.0
289.0
627.5
1885.6
41900.0
93.95
8.14
Cr
(ppm)
Cu
(ppm)
Fe
(wt.%)
Hg
(ppm)
0.10
0.002
0.33
0.56
0.97
1.59
2.43
7.13
4.90
1.85
0.007
0.009
0.014
0.021
0.035
0.441
94.22
8.25
Ni
(ppm)
Pb
(ppm)
V
(ppm)
Zn
(pm)
b5
b10
b10
b5
b5
b5
b5
7.0
12.0
42.0
9.94
2.71
b10
b10
b10
11.0
21.0
503.0
185.72
12.40
b10
13.0
21.0
33.0
52.0
227.0
13.66
2.82
6.0
9.0
13.0
21.0
41.2
450.0
55.86
6.40
Ni
(ppm)
Pb
(ppm)
V
(ppm)
Zn
(ppm)
b10
b10
b10
b10
b10
11.0
18.2
67.0
15.99
3.63
22.4
33.0
56.0
102.0
159.2
330.0
3.40
1.66
Table 4
Statistical summary of parameters of subsurface soils (n = 129).
Parameter
Min. value
Percentiles
10%
25%
50% (Median)
75%
90%
Max. value
Curtosity
Skewness
Stot
(wt.%)
Ccarb
(wt.%)
Corg
(wt.%)
As
(ppm)
Co
(ppm)
3.91
b 0.010
b 0.003
0.14
b0.10
b5
4.04
4.12
4.27
4.50
4.84
7.12
11.30
2.87
b 0.010
b 0.010
0.011
0.016
0.021
0.038
59.68
6.69
0.005
0.007
0.010
0.016
0.023
0.095
27.20
4.52
0.17
0.22
0.29
0.42
0.60
3.26
59.36
6.66
b0.10
0.15
0.47
1.64
5.13
33.20
21.75
4.37
b5
b5
5.0
11.0
22.0
65.0
5.80
2.40
pH
20.0
29.8
37.0
54.0
85.0
120.6
256.0
3.42
1.63
6.0
17.0
24.0
34.0
54.0
100.2
1560.0
83.16
8.63
Fe
(wt.%)
Hg
(ppm)
0.47
b0.005
1.2
1.69
2.58
4.50
6.7
13.10
2.48
1.47
0.006
0.007
0.009
0.012
0.015
0.072
56.19
6.33
b5
7.0
11.0
15.0
23.0
36.0
132.0
15.62
3.39
b5
7.0
9.0
13.0
27.0
54.0
185.0
14.18
3.26
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
shows that contents of Fe, Cr, V, and Ni generally increase with depth
and the pH value and contents of Corg decrease. Contents of Stot, Cu, Co
and Pb were found to be higher in the topsoil of only slightly
contaminated profiles (Profiles II and III) but their concentrations in
deeper parts of the soil horizon are similar to those in the
uncontaminated profile. In the profile of highly contaminated soils
sampled in the vicinity of the Kitwe smelter (Profile IV), very high
amounts of sulphur and other elements, including vanadium, nickel
and chromium, were detected in topsoil. At a depth of 10 to 20 cm in
Profile IV, the concentrations of these elements decrease sharply.
However, contents of sulphur and copper are still much higher in the
lower part of soil Profile IV as compared to contents of the same
elements in uncontaminated or slightly contaminated soils.
4.4. Distribution of sulphur and metals in soils on a regional scale
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 and
Basement Complex) and from the Katanga Supergroup in surveyed area of the
Zambia 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).
The distribution of sampling sites and the results of the
environmental–geochemical surveying are presented in individual
maps (Fig. 7). Several types of data presentation are used, including
contour maps of topsoil, contour maps of subsurface soil, contour
maps of the ratio of concentrations of the chemical elements in the
topsoils and subsurface soils, and combined maps, in which the
surface data are expressed in the form of contour maps and data from
the subsurface soil horizon are shown in classed post maps. This type
of graphic presentation enables the concentration of variables in both
topsoil and subsurface soil layers at the same sampling site to be
compared.
Concentrations of Stot are higher in topsoil relative to subsurface
soil (Fig. 7b,c). The high content of sulphur in topsoil is evidently a
result of sulphur emissions from smelters and dust fallout from the
dry beaches of tailings ponds, crushers and mining operations.
Contamination by sulphur around the Chambishi smelter is relatively
low because this smelter re-processes sulphur-poor slag from the
Kitwe (Nkana) smelter. In the Chingola mining area and surroundings,
high total sulphur contents in topsoil are explained as being due to
dust fallout from mining operations and the ore processing plant
(dust fallout from crushers, concentrate piles and tailings at the
Chingola processing plant).
Contents of copper in topsoil (Fig. 7d) at individual sampling sites
are also higher than those in the subsurface soil (Fig. 7e). Therefore,
the regional distribution of copper in topsoils is indicative of
anthropogenic contamination. Taking into account the prevailing
direction of winds from the south-east, the highest copper concentrations (N1800 ppm) are recorded around the smelters at Kitwe and
Mufulira and downwind from them in the northwest direction. Other,
less contaminated areas are located around tailings ponds and in the
vicinity of active or abandoned open pits (the Chingola and
Chililabombwe areas). The pattern of distribution of copper in topsoil
and subsurface soils is not related to the lithology of the bedrock (see
Fig. 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 cobalt
values in subsurface soils (shown in green). A narrow corridor of
contamination between Chingola, Chambishi and Kitwe probably
indicates topsoil contamination due to cobalt-rich concentrate
transport 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 at
least on two factors. In industrial districts, the content of arsenic in the
topsoil layer is higher as compared to that in the subsurface soil and is
indicative of anthropogenic contamination, especially around the
Kitwe and Mufulira smelters (shown in red in the map). Mining areas
without smelters (the Chingola and Chililabombwe areas) are less
affected by arsenic contamination. In other parts of the surveyed area,
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
77
Table 5
Matrix of Spearman's correlation coefficients for topsoil, upper triangle (n = 719).
pH
Stot
Ccarb
Corg
V
Cr
Co
Ni
Cu
Zn
Pb
Fe
As
Stot
Ccarb
Corg
V
Cr
Co
Ni
Cu
Zn
Pb
Fe
As
Hg
0.403
0.423
0.705
0.127
0.665
0.548
0.255
0.364
0.394
0.236
0.300
0.414
0.372
0.228
0.803
0.376
0.672
0.543
0.340
0.464
0.493
0.300
0.343
0.354
0.172
0.553
0.647
0.510
0.283
0.736
0.568
0.445
0.276
0.316
0.815
0.313
0.535
0.694
0.584
0.492
0.517
0.590
0.634
0.474
0.578
0.222
0.561
0.426
0.396
0.249
0.298
0.426
0.242
0.486
0.586
0.243
0.494
0.428
0.306
0.819
0.859
0.530
0.515
0.400
0.594
0.373
0.209
0.488
0.457
0.321
0.395
0.361
0.449
0.165
0.527
0.583
0.468
0.455
0.133
0.751
0.586
0.690
0.367
0.348
0.596
0.284
0.688
0.613
0.556
0.472
0.479
however, the ratio is close to zero or negative, thus indicating a
lithogenic source of arsenic. These areas are shown in green on the
map. The map of the ratio of mercury in topsoil and subsurface soils
(Fig. 7h) indicates that the contamination by mercury is restricted to
the vicinity of the Kitwe and Mufulira smelters.
Concentrations of lead (Fig. 7i) are low in both topsoil and
subsurface soils with the exception of a high lead concentration
(N60 ppm) around the Kitwe and Mufulira smelters and in the area of
the Chingola processing plant.
Concentrations of zinc in topsoil (Fig. 7j) are also generally low, in
some places higher, and in others lower than in the subsurface soils.
Nevertheless, in the industrial regions of Kitwe, Mufulira and
Chingola, the concentration of zinc in topsoil is higher than in
subsurface soils and therefore can be related to industrial contamination. In other areas, for example, in the area east of Mufulira or
north of Chililabombwe, higher concentrations of zinc in topsoil and
subsurface soils probably reflect higher concentrations of this element
in soils derived from the Katanga Supergroup. In contrast to total
sulphur, cobalt, copper and many other elements that usually display
higher contents in topsoil, contents of nickel (Fig. 7k), vanadium and
chromium (not shown), are usually higher in subsurface soils. This
suggests that vanadium, chromium, and to a great degree also nickel
values do not indicate the extent of anthropogenic contamination. The
higher contents of nickel, chromium and vanadium in topsoil and
subsurface soils generally correspond to areas of rocks of the Katanga
Supergroup (compare with Fig. 1).
The EI is based on the average ratio of the actual and median
concentrations of the given contaminants:
EI =
As
mAs
+
Co
mCo
+
Cu
mCu
+ mHg + mPb +
Hg
Pb
Zn
mZn
6
where mMe is the median value of concentration for a given metal in
topsoil.
The enrichment index actually reflects a higher-than-median or
lower-than-median average content for the six elements. Nevertheless, the EI values correlate well with the ratio of topsoil to subsurface
soil metal contents (compare, for example, Fig. 7f and i with l). This
indicates that the EI values to a large degree reflect the enrichment
from anthropogenic sources.
Boundaries between individual intervals of EI values were
established based on the statistical distribution of data, and are
expressed in percentiles. It is evident from the contour map of EI
values, that medium to very strong contamination is restricted to the
industrial areas of Kitwe, Mufulira and Chingola and downwind, in a
north-westerly direction. The area east of Mufulira, close to the border
with Democratic Republic of Congo, with N2 is interpreted as being
affected by the former smelting operations at the abandoned
Luanshya smelter, located south-east of the surveyed area.
4.6. Factor analysis
Using factor analysis (FA), complex linear correlations between
pH, and logarithmic values of the Ccarb, Corg and metal concentrations
in topsoil and subsurface soils were estimated, which enabled the
interpretation of correlations between elements in the surveyed area.
Using the logarithmic data the influence of potential outliers is
4.5. Enrichment index for topsoil
Generally, the extent of anthropogenic contamination can be
expressed using the enrichment index (EI, Fig. 7l; Kříbek et al., 2004)
Table 6
Matrix of Spearman's correlation coefficients for subsurface soils, upper triangle (correlation matrix for subsurface soils, upper triangle) (n = 129).
pH
Stot
Ccarb
Corg
V
Cr
Co
Ni
Cu
Zn
Pb
Fe
As
Stot
Ccarb
Corg
V
Cr
Co
Ni
Cu
Zn
Pb
Fe
As
Hg
0.134
0.518
0.274
0.179
0.212
0.307
0.429
0.063
0.540
0.319
0.399
−0.054
0.273
0.170
0.629
0.543
0.260
0.408
0.207
0.548
0.396
0.283
0.072
0.182
0.240
0.371
0.705
0.480
0.409
0.208
0.389
0.308
0.355
0.323
0.594
0.344
0.161
−0.180
0.143
0.121
0.134
0.170
0.043
−0.011
0.085
0.136
−0.102
0.342
0.083
0.259
0.282
0.118
0.113
0.109
0.335
0.526
0.104
0.531
0.261
0.885
0.755
0.494
0.391
0.348
0.255
0.294
0.198
−0.294
0.229
0.145
0.424
0.421
−0.044
0.121
−0.036
0.299
0.481
0.444
0.366
0.232
0.506
0.372
0.275
0.162
0.201
0.029
0.377
0.192
0.331
0.324
0.163
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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: Heavily
contaminated 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) Contour
map 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 to
subsurface 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 in
subsurface soil. (l) Contour map of the enrichment index (EI) in topsoil in the surveyed area of the Zambian Copperbelt.
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
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Fig. 7 (continued).
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B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
eliminated or reduced substantially. Individual components of the
system belonging to a given factor were defined by a factor matrix
after varimax rotation, with those having strong correlation grouped
into factors. The identification of factors is based on the dominant
influence. 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 in
topsoils 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 grouped
into the second component (F2; accounting for 16.5% of variance), Stot,
Co, Cu and Hg are grouped into the third component (F3; accounting
for 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 factor
analysis enables a calculation of factor scores, which then replace the
values of original variables. The factor scores determined by varimax
rotation factor analysis can be plotted on maps. As an example,
positive anomalies reaching factor scores N1 in the F1 to F4 score map
for topsoil, are plotted in Fig. 8.
By comparison with the results of the factor analysis of data from
topsoil, the factor matrix of the components from subsurface soils
reveals quite different groupings (Table 8). The first component (F1)
groups V, Cr, Co, Fe and As (accounting for 16.6% of variance), F2
groups pH, Stot, Ccarb, Corg, Co and Hg (accounting for 14.9% of
variance), F3 groups pH, Ccarb, Co and Cu (accounting for 14.2% of
variance), 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 copper
smelters are not only a source of copper, but also of other elements
such 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 copper
smelters. It is also notable that even coal can be a source of
contaminants such as As, Se and Hg, in particular during metallurgical
processes in which it is used as a fuel or reducing agent (Dudka and
Adriano, 1997; Mukherjee and Zevenhoven, 2006).
As noted above, the medians of Cu, Co and Hg in topsoil of the
surveyed part of the Copperbelt are considerably higher than the same
81
values in subsurface soils, and the highest values of correlation
coefficients for these metals with total sulphur were identified in
topsoil (Table 3). The dust fallout and gaseous emissions from smelters
in particular, and dust fallout from the dried out parts of tailings dams,
are believed to be the principal source of sulphur and the abovementioned metals. In 1999, Guerreiro (1999) reported SO2 concentrations between 0.005 and 0.984 µg m− 3 in air from the vicinity of the
Kitwe smelter, and concentrations between 272.5 and 511.75 µg SO2.
m− 3 in the environs of the Mufulira smelter. Now, due to the
installation of efficient dust collectors and sulphur dioxide separators,
the contents of SO2 in the environs of the smelter at Kitwe have
decreased 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 from
the 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 of
convertor No. 3 ranged betweeen 0.15 and 0.46 t h− 1, and during the
smelting of Cu concentrate in the same convertor the range was 0.15 to
0.65 t h− 1 during the period from August 2003 to January 2004. The
monitoring of solid particles on chimneys of the smelter at Mufulira
revealed contents of arsenic in the range between 17.5 and 775 ppm,
copper between 19,210 and 63,600 ppm, cobalt between 29 and
490 ppm, lead between 50 and 3619 ppm and mercury between 0.001
and 0.1 ppm (Knight, 2004). In addtion to these metals, Kříbek and
Nyambe (2006) also found traces of Be, Cr, Mn, Ni, Zn, Mo and Cd in the
dust fallout.
The dust from dry parts of tailings dams also contains high
concentrations of sulphur and metals. Sulphur in flotation tailings is in
the form of sulphides as well as sulphates and the content of sulphates
increases with the age of tailings in the tailings dam (Šráček et al.,
2010). As the surface of tailings dams is often covered by efflorescent
sulphates, mostly gypsum and hexahydrite, the dust particles can be
expected to contain sulphur in the form of sulphates.
Significant correlation between Cu, Co and other metals and Ccarb
values can be explained by the presence of carbonates (calcite and
dolomite) in the dust from extracted ores, and also by their use as
fluxing agents in metallurgical processes and particularly because of
their addition to flotation tailings. The dust collected in the
neighbourhood of tailing dams at Mufulira contains, for example,
0.07% Ccarb, and that sampled in the environs of tailings dams at
Chingola contains as much as 1.2% Ccarb (Kříbek and Nyambe, 2005).
The values of correlation coefficients between metals and the pH of
soil extract are much lower as compared to those between total
sulphur and carbonate carbon. A relatively high correlation coefficient
Table 7
Main factors (sum of squares of loadings N 1) 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
Sum of squares of loadings
2.48
2.30
1.92
1.64
1.38
Rotated factor loadings
Component
pH
Stot
Ccarb
Corg
V
Cr
Co
Ni
Cu
Zn
Pb
Fe
As
Hg
Explained variance
Proportion of total variance
0.066
0.056
0.004
0.070
0.046
0.870
0.238
0.275
0.266
0.481
0.603
0.114
0.939
0.121
17.7%
17.7%
0.120
0.139
0.058
0.219
0.973
0.454
0.150
0.550
0.114
0.111
0.006
0.839
0.042
0.102
16.5%
34.2%
0.102
0.716
0.086
0.098
−0.021
0.047
0.422
0.249
0.880
0.231
0.286
0.179
0.109
0.429
13.7%
47.9%
0.426
0.175
0.789
−0.067
0.036
0.088
0.779
0.310
0.185
0.150
0.032
0.072
0.086
0.121
11.7%
59.6%
0.070
0.245
−0.049
0.400
0.111
0.071
0.206
0.117
0.232
0.642
0.359
0.210
0.103
0.655
9.9%
69.4%
Communalities
0.215
0.625
0.636
0.227
0.963
0.979
0.905
0.550
0.946
0.733
0.576
0.799
0.914
0.653
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Fig. 8. Contour maps of the distribution of factor scores N 1 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”.
between pH and zinc (r = 0.54) is an exception that is ascribed to the
easy extractability of zinc and its ability to migrate in soil solutions.
Similarly, with the exception of zinc, Hu et al. (2007) did not find any
significant correlation between contents of metals in soils near the
copper smelter in China and the pH of soil solutions.
Relatively low values of correlation coefficients between the
majority of metals and the content of organic carbon in topsoil are
ascribed to generally low concentrations of organic matter and to the
low degree of humification of the soil. Due to the rapid process of
mineralization, the organic matter in topsoil consists mostly of the
remains of dead plants so that the absorption capacity is evidently
low.
The forms and chemical bonding of copper and other metals in
soils have been studied by numerous authors. For instance, Burt et al.
(2003) report that copper and other metals in soils in the neighbourhood of a Cu smelter in Montana (USA) occur as both sulphate and
sulphide forms. These authors also state that the amount of copper in
the H2O-soluble fraction, the exchangeable fraction, the carbonate-
bound fraction, the iron- and manganese oxides fraction, the organic
matter and the sulphide fraction is greater than the contents of copper
and other metals in the residual fraction. In strongly contaminated
soils around the copper smelter in an industrial complex at Port
Kembla in Australia the major part of the copper was preferentially
bound to hydrous Fe–Mn oxides, crystalline oxides, sulphides, and
organic matter (Martley et al., 2004). Similarly, Kabala and Singh
(2001) provide evidence that copper in strongly contaminated soils is
mostly present in the exchangeable fraction and a specifically
adsorbed form, while in less contaminated soils the copper and
other metals are distributed in the following order of abundance:
residual fraction ≫ iron- and manganese oxides fraction N organic
fraction N exchangeable fraction and specifically adsorbed form.
Adamo et al. (2001) report that copper in the environs of a copper
and nickel smelter in Sudbury (Canada) is almost uniformly
distributed throughout all the fractions studied.
Enhanced contents of copper (and sulphur) in subsurface soils of
the surveyed area of the Copperbelt correspond directly with areas
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Table 8
Main factors (sum of squares of loadings N 1) factor loadings, communalities, explained
variance and proportion of total variance for subsurface soils of the surveyed area of the
Copperbelt.
Sum of squares
of loadings
Factor
1
Factor
2
Factor
3
Factor
4
Factor
5
2.32
2.09
1.99
1.64
1.50
0.393
0.559
0.537
0.689
0.143
−0.077
0.310
0.077
0.157
0.122
0.111
0.071
−0.084
0.815
16.5%
0.515
0.098
0.710
0.293
0.034
0.007
0.389
0.016
0.961
0.137
0.013
0.025
0.003
0.189
13.7%
0.024
0.100
− 0.006
0.007
0.085
0.653
0.234
1.011
0.032
− 0.036
0.285
0.178
− 0.031
− 0.025
11.7%
− 0.021
−0.169
0.172
0.049
0.235
0.355
− 0.149
0.028
− 0.039
0.119
0.584
0.307
0.866
0.188
9.9%
34.2%
47.9%
59.6%
69.4%
Rotated factor loadings
Component
pH
0.269
0.008
Stot
0.159
Ccarb
−0.045
Corg
V
0.921
Cr
0.478
Co
0.499
Ni
0.087
Cu
− 0.033
Zn
0.124
Pb
0.111
Fe
0.866
As
0.317
Hg
− 0.078
Explained
17.7%
variance
Proportion of
17.7%
total variance
Communalities
0.492
0.360
0.847
0.564
0.933
0.788
0.574
1.037
0.951
0.064
0.447
0.882
0.859
0.742
where there are also high concentrations in topsoil (Fig. 7b–d). This
indicates that a certain proportion of copper and sulphur is washed
out of topsoil during the rainy season and transported down the soil
profile. This explains the much higher contents of sulphur and copper
in 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 in
strongly contaminated regions has been studied by a number of
authors. For instance, Martley et al. (2004) report that strong
contamination of soils by copper and other elements occurs in an
industrial complex with a copper smelter at Port Kembla (NSW,
Australia). This contamination can be traced down to a depth of
50 cm. Ruan et al. (2008) state that the average migration rate of Cu
down the profile in forest soils near a source of industrial emissions is
approximately 0.33 cm per year.
As distinct from sulphur, copper, cobalt and mercury, the median
contents of chromium, vanadium and nickel are higher in subsurface
soil relative to topsoil (Tables 3 and 4, Fig. 5). Moreover, vanadium
and 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 soil
profiles studied (Fig. 5). The nature of Cr, V and Ni in lateritic soils has
been studied by a number of authors. It is well known that most of the
Cr, V, Ni (and Co) in laterites is associated with goethite (Schwertmann and Pfab, 1994, 1996; de Oliveira et al., 2001). However, it is not
yet properly understood how V, Ni, Cr and Co are incorporated in
goethite — whether as ions adsorbed on crystal surfaces, as ions
replacing Fe in the crystal lattice, or as hydroxides intimately
intergrown with FeOOH. The structural incorporation of foreign ions
into the goethite lattice has been demonstrated by a linear change in
unit-cell parameters in synthetic and natural samples (Cornell, 1991;
Schwertmann and Pfab, 1994, 1996). On the other hand, the
adsorption of ions (Ni, Zn, Cd, Co, Cu) on the crystal surfaces of
goethite is also a well-known phenomenon (Forbes et al., 1976;
Cornel and Schwertmann, 1996). Moreover, at least a part of those
metals can be bound to clay fractions (Singh and Cornelius, 2006). The
highest contents of V, Cr and Ni were found in areas underlain by the
Katanga Supergroup. This could be explained either (1) by the
abundance of numerous intrusions of dolerite and related rocks
83
(Fig. 1) within this unit or (2) by the high initial contents of these
elements in Katanga rocks or (3) by the higher rate of weathering
(more intense lateritization) of unmetamorphosed or only slightly
metamorphosed rocks of the Katanga Supergroup as compared to the
more highly metamorphosed rocks of the Pre-Katanga Muva
Supergroup and the Basement Complex. Because the contents of V,
Cr and Ni in soils covering areas underlain by the Katanga Supergroup
do not appear to be explained either by the distribution of dolerites or
by the very varied lithology of this geological unit, we are of the
opinion that a higher degree of weathering (i.e. the higher degree of
lateritization) of the sediments of this formation is the main reason for
the enhanced median concentrations of V, Cr, Ni (and Fe) in topsoil
and 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 to
establish 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 the
concentrations of the measured element to the hazard criteria or to
the soil quality guidelines for that element. It is notable that neither in
Zambia nor in any country in sub-Saharan Africa have soil quality
guidelines been established. The determination of permissible levels
of element concentrations in soil and the application of such
standards as developed and used by other countries is not easy in
Zambia because of the high contents of copper and other metals in
tropical soils of the Copperbelt. For example, the median value of
copper in essentially uncontaminated subsurface soils in the Copperbelt is much higher (median = 34 ppm, Table 4) than the copper
concentrations 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 the
European Union, in Canada or Australia fluctuate within a wide range
from 32 to 91 ppm depending on the method of determination and
also on land use (EPT, 1999). For instance, if the Canadian soil quality
guidelines for metals are applied (Table 9), it is evident that the
majority of samples of topsoil collected in the Copperbelt exceed
ecological norms for copper (Table 9). Therefore, the enrichment
index in this study was modified so that it is expressed as a ratio of the
concentrations of the measured element to the hazard criteria but as a
shared average of actual and median concentrations of potential
contaminants (As, Co, Cu, Hg, Pb, and Zn) in topsoil. All areas with EI
values N1 are suspected to have been affected by industrial activities.
However, it should be pointed out that in cases where the EI value
falls in the range 1–2, the contours may be significantly influenced by
variations in the geochemical character of the soils in the surveyed
area. For this reason, only areas with EI N2 are considered to have
been seriously affected by contamination.
Table 9
Canadian soil quality guidelines for protection of environmental and human health (mg
kg− 1; CCME, 2007). Percent of samples exceeding permissible values for different land
uses in the surveyed Copperbelt area is given in brackets.
Chemical
element
Land use
Agricultural
Residential/parkland
Commercial
Industrial
Arsenic
Chromium
Cobalt
Copper
Lead
Mercury
Nickel
Vanadium
Zinc
Sulphur
12 (0.83%)
64 (1.11%)
40 (13.35%)
63 (91.23%)
70 (0.97%)
6.6 (0.00%)
50 (0.00%)
130 (0.56%)
200 (0.70%)
500 (8.34%)
12
64
50
63
140
6.6
50
130
200
12 (0.83%)
87 (0.42%)
300 (1.25%)
91 (85.26%)
260 (0.14%)
6.6 (0.00%)
50 (0.00%)
130 (0.56%)
360 (0.14%)
12
87
300
91
600
6.6
50
130
360
(0.83%)
(1.11%)
(11.3%)
(91.23%)
(0.56%)
(0.00%)
(0.00%)
(0.56%)
(0.70%)
(0.83%)
(0.42%)
(1.25%)
(85.26%)
(0.00%)
(0.00%)
(0.00%)
(0.56%)
(0.14%)
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5.3. Interpretation of factor analysis in topsoil
The interpretation of multielement factor loadings and factor
scores is often difficult and also very subjective (Reimann and Garrett,
2005; Glavin and Hooda, 2005; Lima, 2008). Therefore, when
interpreting the results of factor analysis for topsoil in the Copperbelt
region, the areal distribution of sources of contamination, the
chemical composition of emissions and the type of local geology
were all taken into account because they, to a large extent, govern the
geochemical properties of the soil profile. Multielement factors for
topsoil can be divided into two groups: (1) factors indicating strong
anthropogenic influence (Factors 1, 3 and 4) and, (2) factors
indicative of predominantly natural processes (lithogenic and
pedogenic, Factors 2 and 5). It should be noted that the first group
of factors does not completely exclude the influence of natural
processes.
5.3.1. Factors indicating strong anthropogenic influence in topsoils
Score 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 complex
comprising a flotation treatment plant for the ore and a smelter, old
slags from the Kitwe smelter are also stockpiled for crushing and
remelting in a smelter at Chambishi. These slags are highly enriched in
chromium relative to the ore concentrate (Table 1, Analysis 8)
because this metal is concentrated in the silicate melt during the
smelting process. As a consequence, contents of chromium in the dust
from the crushers are very high. Therefore, Factor 1 is designated “slag
specific” and high values of the factor scores are interpreted as being
due to fallout of dust from the slag treatment plant. Because of the low
content of sulphur in the slag, the metals of the Factor 1 group are not
linked with sulphur.
Factor 3 which is the grouping Stot, Co, Cu and Hg also reflects
anthropogenic influence. The highest values of this factor score (Fig. 8c)
demarcate the environs of the smelters at Mufulira and Kitwe. On the
other hand, the values of the scores for this factor in the neighbourhood
of the smelter at Chambishi are low because old slags low in sulphur are
remelted at this smelter. Consequently, Factor 3 is marked as “smelter
specific” and high values of this factor score are ascribed to emissions
from 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 Mufulira
where waste from the extraction of copper ores hosted in sandstones
was stockpiled. These ores were very poor in carbonates. Therefore,
Factor 4 is interpreted as a result of dust fallout from tailings dams
containing flotation wastes with a higher content of carbonate or from
tailings 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 topsoils
Areas 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 be
interpreted as resulting from the accumulation of these metals in soils
formed by fast weathering of the slightly metamorphosed or
nonmetamorphosed Katanga Supergroup rocks.
Similarly, Factor 5 (the grouping Corg, Zn, Pb and Hg) is interpreted as
being the result of natural processes that lead to the bonding of these
metals to organic matter. The score values of N 1 for this factor that is
designated as “organic carbon specific” are not presented as a map
because they are scattered randomly over the whole of the mapped area.
5.3.3. Interpretation of factor analysis in subsurface soils
The 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 the
accumulation of metals in the subsurface soil environment, including
their bonding with individual soil components, i.e. organic carbon,
carbonates, Fe-hydroxides or the residual fraction. Moreover, because
of very slight influence of anthropogenic pollution, the scores may
possibly more reflect the geochemical character of the parent rocks.
Although the factor analysis was unable to discriminate the nature of
bonding with individual components, it is evident that the results of
factor analysis for subsurface soils differ considerably from those
obtained for topsoil.
6. Conclusions
The results of this study show that sampling of the soils at two
different levels, combined with statistical treatment of data and factor
analysis, enables lithogenic and anthropogenic sources of metals and
sulphur to be distinguished, especially in heavily contaminated areas.
However, over a great part of the surveyed area, interpretation of the
distribution of the other metals is more problematic. Difficulties arise
because the contents of metals in the topsoil of slightly contaminated
areas may be the product of both anthropogenic contamination and
also naturally enhanced concentrations derived from the bedrock and
transferred by weathering into the overlying soils. This can be true for
the elements arsenic, zinc, lead, chromium and nickel, the contents of
which are usually higher in topsoil and subsurface soils over areas
underlain by the Katanga Supergroup. However, the same metals form
a marked aureole of contamination around smelters. In addition, in
strongly contaminated areas, some metals, in particular copper, are
transported into deeper parts of the soil horizon. Manifestations of
primary copper and cobalt mineralization in these heavily polluted
areas may be overprinted to a depth of several centimetres by
anthropogenic contamination. Despite of the difficulties discussed
above, regional presentation of geochemical data combined with
geostatistical methods, can prove effective in assessing the levels of
anthropogenic contamination in soils, even in areas where the
composition of the bedrocks shows significant differences. However,
unambiguous interpretation of the results of environmental–geochemical surveys of large areas depends on a good knowledge of the
local geology and particularly of the constituents of geological
formations because, to a large extent, the lithology of the bedrock
governs the rate and depth of lateritic weathering, and consequently
the chemical and physical properties of the soil profile. The location
and distribution of single sources of contamination and the chemical
and mineralogical compositions of pollutants is another essential
prerequisite for correct interpretation of the geochemical patterns
that 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 (Geological
Department, Ministry of Mines and Mineral Development of the
Republic of Zambia) and S. Simasiku (University of Zambia, School of
Mines, Geology Department) and J. Adamovič (Geological Institute,
Academy of Science, Czech Republic), for their participation and
initiative in field operations and in the interpretation of the interim
results produced during the years 2002–2006 by the Development
Co-operation Programme of the Czech Republic in Zambia. The
authors are also obliged to K. Liyungu, the Director of the Geological
Department of the Zambian Ministry of Mines and Mineral Development for wide-ranging help in the organization of all aspects of the
field programme and to T. Henderson, the Chief Executive of the
Moppani Copper Mines Plc., for his efficient co-operation and
sponsorship of the project. The synthesis, statistical treatment and
interpretation of the geochemical data were undertaken within a
Author's personal copy
B. Kříbek et al. / Journal of Geochemical Exploration 104 (2010) 69–86
grant 774 No. 205/08/0321 of the Czech Academy of Sciences. Our
thanks are also directed to R. Koole, the journal manager and to S. Pirc
and W. De Vos, the reviewers, for their effort to go carefully through
the text and for their reasonable and inspiring recommendations.
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