Chemosphere 76 (2009) 774–783
Contents lists available at ScienceDirect
Chemosphere
journal homepage: www.elsevier.com/locate/chemosphere
Dioxin-like chemicals in soil and sediment from residential and industrial
areas in central South Africa
Claudine Nieuwoudt a,*, Laura P. Quinn a, Rialet Pieters a, Ilse Jordaan a, Maret Visser a,
Henrik Kylin b,c, Anders R. Borgen d, John P. Giesy e,f,g,h, Henk Bouwman a
a
School of Environmental Sciences and Development (Zoology), North-West University, Potchefstroom Campus, Private Bag X6001, Potchefstroom 2520, South Africa
Norwegian Institute for Air Research, The Polar Environmental Centre, NO-9296 Tromsø, Norway
c
Department of Aquatic Science and Assessment, Swedish University of Agricultural Sciences, P.O. Box 7050, SE-75007 Uppsala, Sweden
d
Norwegian Institute for Air Research, P.O. Box 100, NO-2027 Kjeller, Norway
e
Zoology Department and Center for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA
f
Department of Biomedical Veterinary Sciences and Toxicology Centre, University of Saskatchewan, Saskatoon, SK, Canada S7N 5B3
g
Department of Chemistry and Biology, City University of Hong Kong, Kowloon, SAR, China
h
School of Environment, Nanjing University, Nanjing, China
b
a r t i c l e
i n f o
Article history:
Received 12 February 2009
Received in revised form 28 April 2009
Accepted 28 April 2009
Available online 29 May 2009
Keywords:
PCDD/Fs
PCBs
H4IIE
Persistent organic pollutants
Gauteng
a b s t r a c t
Persistent organic pollutants (POPs) are a global concern due to their ubiquitous presence and toxicity.
Currently, there is a lack of information regarding POPs from South Africa. Here we report and interpret
concentrations of polychlorinated dibenzo-p-dioxins (PCDDs), -dibenzofurans (PCDFs) and co-planarbiphenyls (PCBs) in soils and sediments collected from central South Africa. High resolution gas chromatography–high resolution mass spectrometry (HRGC/HRMS) and the H4IIE-luc bio-assay were used to
identify and quantify individual PCDD/F congeners and to report the total concentration of 2,3,7,8-tetrachloro dibenzo-p-dioxin equivalents (TCDD-EQ), respectively. TCDD-EQs determined by use of the bioassay, and concentrations of WHO2005-TEQ (toxic equivalents) determined by chemical analysis, were
similar. The limit of detection (LOD) for the bio-assay was 0.82 and 2.8 ng TCDD-EQ kg1, dw for sediment
and soil, respectively. EQ20 concentrations determined by use of the bio-assay ranged from <LOD to
70 ng TCDD-EQ kg1, dw for soil, and from <LOD to 45 ng TCDD-EQ kg1, dw for sediment. Concentrations
of WHO2005-TEQ in soils were generally greater than those in sediments, and soils from the industrial area
of Vanderbijlpark and the residential area of Klerksdorp contained the greatest concentrations. Based on
the congener-specific HRGC/HRMS analyzes, concentrations of WHO2005-TEQ ranged from 0.12 to 32 ng
WHO2005-TEQ kg1, dw in sediments, and between 0.34 and 20 ng WHO2005-TEQ kg1, dw in soils. The
sources, processes and threats that govern and are associated with the lesser concentrations in sediment
and greater concentrations in soils need further investigation.
Ó 2009 Elsevier Ltd. All rights reserved.
1. Introduction
Persistent organic pollutants commonly referred to as POPs, are
a global concern. These substances are capable of long-range transport and have been dispersed world-wide, affecting areas where
they have never been used or produced. Their physico-chemical
characteristics, which include hydrophobicity and resistance to
degradation, make these pollutants a challenge to control (Lohmann et al., 2007). Some of these chemicals are endocrine-disruptive and toxic, and have the ability to bio-accumulate in food webs,
posing significant health threats to humans, animals and the environment (Klánová et al., 2007).
* Corresponding author. Tel.: +27 18 299 2512; Mobile: +27 83 308 2764; fax:
+27 18 299 2503.
E-mail address: claudine.nieuwoudt@nwu.ac.za (C. Nieuwoudt).
0045-6535/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved.
doi:10.1016/j.chemosphere.2009.04.064
In May 1995, the United Nations Environment Programme
(UNEP) initiated work on the Stockholm Convention (SC) on POPs
with the intention of reducing, and ultimately eliminating these
pollutants. South Africa signed and ratified the treaty on 4 September 2002; on 17 May 2004, the SC on POPs entered into force for
parties to the Convention (Bouwman, 2004). As a party to the Convention, South Africa is legally obligated to abide by the objectives
of the treaty, and is encouraged to support research on POPs.
In South Africa, some studies have been conducted into the distributions of selected POPs, such as the intentionally released
insecticide DDT, but little is known about the unintentionally released POPs. The latter include polychlorinated dibenzo-p-dioxins
(PCDDs), polychlorinated dibenzofurans (PCDFs) and co-planar
(mono- and non-ortho-substituted congeners) polychlorinated
biphenyls (PCBs), collectively known as dioxin-like chemicals
(DLCs). PCDD/Fs (PCDDs and PCDFs) are produced as by-products
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
of industrial or thermal processes, and are not produced deliberately except for scientific research (Schecter et al., 2006). PCBs
were manufactured for industrial purposes since the early 1930s,
but production and use were banned in the 1980s. Presently, coplanar PCBs are still being formed unintentionally, in the same
way as PCDD/Fs (Koppe and Keys, 2001). The unintentional formation makes it more difficult to control the release of these pollutants. Potential sources of DLCs are chemical- and petrochemical
plants, ferrous and non-ferrous metal smelting operations, paper
and pulp industries, cement production, and fuel combustion.
Smaller non-point sources include domestic burning of wood,
landfill fires and open burning, as well as by natural processes such
as vegetation fires (UNEP, 2005). All of these sources occur in South
Africa, either concentrated in industrial parks or distributed in residential and rural landscapes. The few previous investigations done
in South Africa established the presence of PCDD/Fs and PCBs in air
(Lohmann et al., 2001), sediment (Vosloo and Bouwman, 2005) and
mining-related waste (Jordaan et al., 2007).
The main reservoirs where PCDD/Fs ultimately accumulate are
soils and sediments (Eljarrat et al., 2001). Therefore, this study
was focused on these matrices. Specifically, this study was focused
on measuring concentrations of lesser-researched DLCs in soils and
sediments of central South Africa, comparing industrial to nonindustrial areas. For this purpose, an extensive area was selected
to represent a rural–residential–industrial transect, to evaluate
the possible sources of dioxin-like pollution of soil and sediment.
Chromatographic techniques (high resolution gas chromatography–high resolution mass spectrometry or HRGC/HRMS) are traditionally used for these types of analysis. The major advantage of
HRGC/HRMS analysis is the technique’s ability to determine the
identity and concentrations of numerous individual PCDD/F and
PCB congeners with precision. However, the technique is laborious
and quite expensive for a developing country such as South Africa.
For this reason, samples were screened with H4IIE-luc reporter
gene bio-assay, which is more time- and cost-effective (Vanderperren et al., 2004), at the North-West University in Potchefstroom,
South Africa. Thereafter, selected samples were analyzed by
HRGC/HRMS at the Norwegian Institute for Air Research in Kjeller,
Norway, to verify the results of the bio-assay and to determine the
concentrations of individual PCDD/F and dioxin-like PCB congeners.
2. Materials and methods
2.1. Study areas
Sampling sites were classified as one of three categories; nonindustrial (Hertzogville, Hoopstad, Wesselsbron, Bothaville and
Klerksdorp), industrial (Vanderbijlpark, Sasolburg, SkbrRiv3,
KlipRiv, VaalRiv, TbosSpr and RietSpr), and rural reference (SkbrRiv1 and 2). These sites are spread across three adjoining provinces; Gauteng, North-West and the Free State (Fig. 1). Both soil
and sediment samples were collected from the industrial region.
From the non-industrial sites, which were located primarily in
the more arid western region, only soil was collected, and from
the rural reference sites only sediments were collected assuming
that run-off from all sources would accumulate in sediments.
2.1.1. Non-industrial areas
Hertzogville, Hoopstad, Wesselsbron, and Bothaville are nonindustrial, rural towns with small populations ranging from 500
in Hertzogville to 6100 in Bothaville (StatsSA, 2001). These four
towns are on a West–East 140 km transect (Fig. 1), with an ‘‘urbanization gradient” from Hertzogville (least urbanized, in the west) to
Bothaville (most urbanized, in the east). The main agricultural
activities include livestock farming (cattle and sheep) and dry-land
775
maize cultivation. In addition to the four towns mentioned above,
in a fifth town Klerksdorp, farming is also important, but there is
also large-scale gold- and uranium mining, manufacturing and servicing of mining equipment, food and beverage industries, and
medical waste incineration. The town has a population of approximately 55,000 people (StatsSA, 2001) of which many live in highdensity low-income residential areas. Most of the dwellings in
these low-income areas are supplied with potable water and electricity, but many are not, and residents are dependent on domestic
fires for cooking and heating; a potential source of PCDD/F and PCB
releases.
2.1.2. Industrial areas
Sasolburg, Vanderbijlpark, and Vereeniging are collectively
known as the ‘‘Vaal Triangle” area (Fig. 1), and this area forms
the largest industrial complex in South Africa. Many of the industries located in the Vaal Triangle have the potential to release DLCs.
These industries include ferrous and non-ferrous metal production,
petrochemical plants, paper- and pulp treatment plants, plasticand building material manufacturers, cokes production, electricity
from coal, and coal- and dolomite mining (Vaal Triangle Info,
2008). Numerous low-income residential settlements are located
in this area, which may have domestic sources of DLCs as described
earlier. There are an estimated 31,000, 115,000, and 60,000 people
living in Sasolburg, Vanderbijlpark and Vereeniging, respectively,
but these cities are interspersed with many large informal settlements (StatsSA, 2001).
2.2. Sampling
In each of the towns of Hertzogville, Hoopstad, Wesselsbron,
Bothaville, and Klerksdorp, a composite soil sample was collected
from the more affluent residential (R) area and another from the
low-income residential (LIR) area (Table 1). Composite soil samples
were also collected in Sasolburg and Vanderbijlpark, specifically
targeting the areas surrounding the ferrous- and non-ferrous metal
producer (Vanderbijlpark) and the petrochemical plant (Sasolburg). Both of these industries are probable sources of DLCs (UNEP,
2005). Samples were collected from the LIR areas close to both
industrial plants (VandrblLIR and SasolLIR). Since dry-land maize
farming occurs in close proximity to these industries, samples
were taken from farmlands as well (VandrblAGR and SasolAGR). A
third set of samples was collected next to the public access roads
of both plants (VandrblIND and SasolIND), where ‘‘IND” denotes
industry, to distinguish these samples from the others also associated with the plants (Table 1).
The Vaal River, the second largest river in South Africa, drains
the industrialized Vaal Triangle, and sediment samples were collected from the catchment area: the Klip River and lower Suikerbosrand River near Vereeniging; Riet Spruit and the Vaal River in
the vicinity of Vanderbijlpark; and Taaibos Spruit close to Sasolburg (Fig. 1, Table 1). The hydrology of the rivers and sedimentation rates were not taken into account. Sampling sites were
selected based on their accessibility and their proximity to industry. Samples from the upper Suikerbosrand River catchment (between Nigel and Balfour; Fig. 1, Table 1) were used as reference
since these sites were located outside the industrial area with little
or no residential areas or industries.
Samples were collected from the surface layers of soil and sediment (top 1–5 cm), during June 2006. Sampling was done with
pre-cleaned steel or glass equipment, according to US EPA method
1613 (US EPA, 1994). Composite soil samples comprised of equal
volumes of soil collected randomly from three separate sites within a one to two kilometre radius, depending on the size of the town
or city. This is referred to as ‘‘pooled” collection types in Table 1.
Sediment samples were assembled by combining sediment from
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C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
Fig. 1. Map of South Africa (A), indicating the study area in the dotted square (B) and the sediment sampling sites of the industrial region and reference sites are indicated in
(C). N1, N3, R26, and R568 indicate major roads.
five random points within each of the aquatic sites, to obtain representative composite samples. These collection types are classified as ‘‘point” samples (Table 1). The samples were protected
against sunlight, and transported and stored at 4 °C until
extracted (Mai et al., 2007).
2.3. Sample extraction, clean-up and analysis with the H4IIE-luc bioassay
Soil and sediment samples were treated similarly. Samples
were air-dried, ground and sieved (0.5 mm mesh size) (Mai et al.,
2007). A 40 g sub-sample was mixed with an equal volume of
anhydrous sodium sulphate to remove residual moisture (Hilscherová et al., 2003). An Accelerated Solvent Extractor (ASE ) (Dionex,
ASE 100) was used to extract samples using two cycles with the
following parameters: 10 342 kPa, 100 °C, 5 min static and heat
time, 60% flush volume and 100 sec nitrogen purge (McCant
et al., 1999). The solvent used was a 3:1 (v/v) mixture of dichloromethane (DCM) and hexane (HPLC grade; Burdick and Jackson ).
The extracts were evaporated to 1 ml with a TurboVapÒ II (Caliper),
and the volumes were adjusted to 10 ml with hexane (US EPA,
1999). Elemental sulphur, which may be toxic to the H4IIE-luc
cells, was removed with activated copper (US EPA, 1986). To prevent non-target compounds, such as polycyclic aromatic hydrocarbons (PAHs) from contributing to the response elicited from the
H4IIE-luc cells, extracts were treated with 98% sulphuric acid (US
EPA, 1996). Subsequently, the extracts were concentrated to
1 ml, from which a three-time dilution series were prepared using
hexane for use in the bio-assay (Khim et al., 1999).
The H4IIE-luc bio-assay uses a rat hepatoma cell line stably
transfected with a firefly luciferase reporter gene. The binding of
TM
TM
DLCs to the aryl hydrocarbon receptor (AhR) of the cell results in a
light-producing reaction once the substrate (luciferin) is added to
the cells. The amount of light emitted from cells is quantified, equivalent to the toxicant exposure of the cells (Hilscherová et al., 2000).
The cells were dosed with the sample extract or reference compound [2,3,7,8-tetrachloro dibenzo-p-dioxin (TCDD)]. Cells were
exposed in triplicate to six different sample concentrations of which
the first in the series was the pure extract. TCDD was dosed at the
following concentrations: 120.0, 30.0, 7.50, 1.88, 0.47 and 0.12 pg
TCDD/well (Giesy et al., 1997). The plates were inspected microscopically for confluency and viability. An additional viability test
was performed, using 3-[4,5-dimethyltiazol-2yl]-2,5-diphenyl tetrazolium bromide (MTT) on duplicate plates (Vistica et al., 1991).
LucliteÒ reagent (PerkinElmer) was added and the amount of light
emitted by the cells (expressed as relative light units; RLU) measured with a luminometer (Microplate Fluorescence Reader
Flx800, Bio-Tek Instruments) (Hilscherová et al., 2000).
Sample responses were expressed as percentage maximal
induction to 2,3,7,8-TCDD (%TCDDmax) and plotted as a function
of log lL sample. Relative effect potencies (REP) were calculated
(where REP20–80 are based on the effective concentrations (EC) eliciting 20%, 50% and 80% response), to address the phenomenon of
non-parallelism between the reference dose–response curve and
the sample dose–response curve (Finney, 1971; Besselink et al.,
2004). It cannot be assumed that the complete mixtures from the
environment will exhibit equal efficacy to TCDD. For this reason,
the %TCDDmax of the samples must be greater than 20 (Villeneuve
et al., 2000). Since the concentrations of DLCs in the South African
soil and sediment samples did not elicit responses of 50% or greater, only REP20 concentrations were converted to TCDD-equivalent
(TCDD-EQ) concentrations by back-calculation (Koh et al., 2006).
Table 1
Site- and sample specific information for sediment and soil samples collected from central South Africa and the percentage oxidable (OXC) and total organic carbon (TOC), and bio-assay and GC–HRMS results for soil and sediment
samples reported in ng kg1, dry weight (dw).
Latitudea
(S)
Longitudea
(E)
Collection
type
Matrix
Non-industrial region
Hertzogville low-income residential
Hertzogville residential
Hoopstad low-income residential
Hoopstad residential
Wesselsbron low-income residential
Wesselsbron residential
Bothaville low-income residential
Bothaville residential
Klerksdorp low-income residential
Klerksdorp residential
HertzLIR
HertzR
HoopLIR
HoopR
WeslbrLIR
WeslbrR
BothaLIR
BothaR
KlerkLIR
KlerkR
28°070
28°070
27°490
27°490
27°510
27°510
27°230
27°230
26°510
26°510
25°300
25°300
25°540
25°540
26°210
26°210
26°360
26°360
26°400
26°400
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Industrial region
Vanderbijlpark industrial
Vanderbijlpark low-income residential
Vanderbijlpark agricultural
Sasolburg industrial
Sasolburg low-income residential
Sasolburg agricultural
Suikerbosrand River3 (Vereeniging)
Klip River (Vereeniging)
Vaal River (Vanderbijlpark)
Taaibos Spruit (Sasolburg)
Riet Spruit (Vanderbijlpark)
VandrblIND
VandrblLIR
VandrblAGR
SasolIND
SasolLIR
SasolAGR
SkbrRiv3
KlipRiv
VaalRiv
TbosSpr
RietSpr
26°400
26°400
26°400
26°490
26°500
26°490
26°40.2590
26°36.5480
26°43.5350
26°45.1900
26°41.9250
27°490
27°460
27°490
27°510
27°510
27°540
28°00.9980
27°59.8560
27°53.7330
27°52.4950
27°44.3940
Reference sites
Suikerbosrand River1 (Nigel/Balfour)
Suikerbosrand River2 (Nigel/Balfour)
SkbrRiv1
SkbrRiv2
26°32.0420
26°31.3020
28°34.6260
28°39.9600
a
b
c
d
e
OXC
(%)
TOCb
(%)
Bio-assay results
TCDD-EQ20
(ng kg1, dw)
Normalized TCDD-EQ20
(ng kg1, dw)
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
Soil
1.9
3.9
0.77
0.94
1.1
1.3
2.4
1.5
1.4
2.6
2.7
5.1
1.3
1.5
1.7
1.9
3.3
2.2
2.1
3.5
20
5.4
2.8e
2.8e
20
9.9
35
2.8e
12
70
7.8
1.1
2.2
1.9
12
5.2
11
1.3
5.8
20
Pooled
Pooled
Pooled
Pooled
Pooled
Pooled
Point
Point
Point
Point
Point
Soil
Soil
Soil
Soil
Soil
Soil
Sediment
Sediment
Sediment
Sediment
Sediment
2.6
2.1
1.7
3.7
1.7
2.2
0.53
3.4
1.3
0.55
0.88
3.6
2.9
2.4
4.9
2.4
3.1
1
4.5
2
1
1.4
70
18
2.8e
25
16
4.9
0.82e
0.82e
0.82e
0.82e
45
20
7.8
0.99
5.1
6.7
1.6
0.82
0.18
0.41
0.8
32
Point
Point
Sediment
Sediment
0.72
0.63
1.2
1.1
0.82e
0.82e
0.66
0.73
GC–HRMS results
c
Totald WHO2005-TEQ
(ng kg1, dw)
Normalizedc WHO2005-TEQ
(ng kg1, dw)
16
0.82
4.3
0.34
6.3
1.5
1.3
0.61
0.42
1.4
0.3
0.2
0.66
0.42
0.3
0.15
0.19
0.46
0.12
0.13
0.09
0.12
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
Site
abbreviation
Site name
GPS co-ordinates for ‘‘pooled” samples are representative of the approximate positioning of the entire town or city, whereas co-ordinates for ‘‘point” samples are specific to an exact location.
The TOC was converted from OXC as described in ‘‘Determination of oxidable and total organic carbon”.
Data was normalized to 1% TOC.
Total WHO2005-TEQ = RWHO2005-TEQ(PCDDs, PCDFs and PCBs).
Where TCDD-EQ20’s could not be calculated, the LOD was reported.
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The limit of detection (LOD) for the bio-assay was calculated by
determining the average EC0 (TCDD concentration at which no response was elicited from cells) for the entire study’s TCDD dose–
response curves. The 95% confidence interval was subsequently
determined, added to the average (Thomsen et al., 2003), and converted to ng TCDD-EQ kg1, dw.
2.4. Sample extraction, clean-up and analysis using HRGC/HRMS
All sediment and soil samples were subjected to the bio-assay,
and all sediment samples and selected soil samples were chemically analyzed by the Norwegian Institute for Air Research (NILU).
The selected soil samples were from the Vaal Triangle, because of
the industrialized nature and subsequent high population density
of this area. Of the six possible soil samples from the Vaal Triangle,
the four that elicited the greatest response in the bio-assay, VanderblIND, VanderblLIR, SasolIND, and SasolLIR were analyzed by HRGC/
HRMS (Table 1).
Samples were spiked with 13C-labelled 2,3,7,8-chloro substituted PCDD and PCDF congeners and extracted with toluene.
Clean-up was done with multi-column chromatography on a
tri-functional column with neutral and basic silica, followed by
concentration on graphitized carbon, and a final clean-up on acidic
silica and alumina. Suitable isotope labelled recovery control standards were added, after which analysis were done with HRGC/
HRMS using an Agilent 6890 N gas chromatograph coupled to an
Autospec (Micromass Waters, Manchester UK) mass spectrometer.
The mass spectrometer was operated at a resolution of >10,000
using electron ionization. Further analytical details are given
by Knutzen et al. (2003) and Bengtson Nash et al. (2008). Two
masses were monitored for each isomer group, and the added
13
C-labelled isomers were used as internal standard. In addition,
the recoveries of the added internal standard compounds were
established.
The concentration of the toxic 2,3,7,8-chloro substituted congeners were determined, and the toxic equivalent (WHO2005-TEQ,
Van den Berg et al., 2006) of the congeners were calculated for each
sample, assuming that chemicals in the mixture have additive
responses.
This method complies with the directives given by the European Commission for determination of dioxins for the official control of foodstuffs (2002/69/EC) and feedstuffs (2002/70/EC). The
following conditions had to be met for quality assurance for an
unequivocal identification and quantification of the DLCs:
(a) The retention time had to be in a window of +3 to 0 s compared to the corresponding 13C-labelled isomer.
(b) The signal-to-noise ratio had to be more than 3:1 for
identification.
(c) The recovery of the added 13C-labelled internal standards
had to be within 40–120%.
2.5. Determination of oxidable and total organic carbon
Since DLCs preferentially associate with organic carbon particles,
the oxidable organic carbon content of samples was determined
using the Walkley-Black wet oxidation method (Schumacher,
2002). A correction factor of 1.4 was applied because this method
leads to the incomplete oxidation of organic carbon. The total organic carbon (TOC) was calculated from the oxidable organic carbon
(OXC), using the following equation: TOC = 1.23(OXC) + 0.35
(Sánchez-Monedero et al., 1996). TCDD-EQ and WHO2005-TEQ concentrations were normalized to 1% TOC to allow for comparison
with environmental quality guidelines proposed by the USA,
Germany and Canada (Table 1).
3. Results and discussion
In this section, the H4IIE-luc-assay results will be compared to
the instrumentally derived results, before the congener specific
profiles are addressed. The normalized data will not be used to
compare sites, since the %TOC correlated weakly with the TCDDEQ and WHO2005-TEQ (R2 = 0.32), compared to strong correlations
that have been found in other studies (Sánchez-Monedero et al.,
1996; Wisconsin Department of Natural Resources, 2003; Wevers
et al., 2004). However, the normalized data is useful for comparison of our results to environmental sediment quality guidelines.
3.1. Bio-assay results
The MTT tests showed that all the cells were more than 80% viable, and it was therefore unlikely that cell viability affected the
credibility of the luminescence assay’s results. The LOD for sediment and soils was 0.82 and 2.8 ng TCDD-EQ kg1, dw, respectively. TCDD-EQ20 concentrations ranged from <LOD to 70 ng
TCDD-EQ kg1, dw (Table 1). Where TCDD-EQ’s could not be calculated, the LOD was reported.
Arranging the sites in declining order of TCDD-EQ20 concentrations and comparing the TCDD-EQ20 concentrations to those normalized to 1% TOC (Fig. 2) resulted in similar patterns. Except for
the RietSpr site, soil contained significantly greater concentrations
of DLCs than sediment (p < 0.05; Mann-Whitney U test). The RietSpr site, approximately 10 km down stream of a ferrous metal producer, receives effluent from the metal plant and might explain its
greater concentration of DLCs, compared to the other sediment
sites in the industrial region not directly associated to a particular
point source. Concentrations of TCDD-EQ at these six sites were
less than the assay’s LOD. The three sites with the greatest concentrations were the same for the non-normalized (Fig. 2) and normalized concentrations. The industrial and non-industrial sites are
randomly distributed in the continuum of greater to lesser concentrations of dioxin-like pollution and not grouped together, i.e. not
all industrial sites had concentrations greater than the non-industrial sites. This may indicate different sources of DLCs throughout
the study area, such as domestic fires and alternative fuel usage
for cooking and heating in the LIR areas of towns. Comparing the
mean concentration of TCDD-EQ in soil from the non-industrial
area to the industrial area, the industrial area had a slightly greater
concentration (23 vs. 18 ng TCDD-EQ kg1, dw), but the difference
was not statistically significant (p = 0.63; Mann-Whitney U test).
3.1.1. Non-industrial sites
Concentrations of TCDD-EQ at both the Hoopstad sites (HoopLIR
and HoopR) were less than the LOD (Table 1). Of the four remaining
towns, the more affluent residential area of Klerksdorp (KlerkR) had
a greater concentration of TCDD-EQ than its corresponding LIR.
This concentration was similar to that of VandrblIND from the
industrial group of sites (Table 1). This may be ascribed to the more
industrial character of Klerksdorp, with potential DLC-producing
industries situated close to the residential area. Comparing the
LIR areas of the other towns to their corresponding R areas, the
LIR concentrations were greater, but this was not statistically significant (p > 0.5; Wilcoxon matched pairs test) (Table 1).
The greater concentrations of TCDD-EQ measured in soils from
the LIR areas might be due to the greater incidence of open fires for
cooking and heating usually found in these kinds of residential
areas, especially during the winter months, which corresponded
with the sampling period. Many studies have found that open
burning may contribute significantly to the local deposition of
PCDD/Fs and PCBs (Launhardt et al., 1998; Wevers et al., 2004;
Andersson and Ottesen, 2008). Especially mixtures of waste,
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
Fig. 2. The South African soil and sediment sites arranged from greatest to least TCDD-EQ20.
sites.
containing paper, cartons, plastics and painted wood, may produce
relatively large releases of DLCs. PCDD/F concentrations ranging
between 380 and 2200 ng WHO2005-TEQ kg1, dw were found in
chimney soot after incineration of such waste mixtures (Launhardt
et al., 1998).
3.1.2. Industrial sites
At Vanderbijlpark and Sasolburg, the greatest TCDD-EQ20 concentrations were measured at VanderblIND and SasolIND, followed
by the respective LIR sites (SasolLIR and VanderblLIR), with the least
concentrations found in agricultural soils (SasolAGR and VanderblAGR) (Table 1). Of the sediment samples collected in the industrial area, only Riet Spruit sediments had detectable TCDD-EQ
(Table 1). Lesser concentrations of DLCs in South African sediments
are confirmed by other unpublished data. The total concentrations
and relative patterns of DLCs measured at industrial sites will be
discussed in greater detail in the next section.
3.2. HRGC/HRMS results
It was expected that the industrial area would be more impacted than the non-industrial areas, and therefore only the samples collected from the Vaal Triangle were analyzed chemically.
HRGC/HRMS results provided the concentrations of the individual
PCDD/F and dioxin-like PCB congeners that contributed to the
TCDD-EQ determined by the bio-assay (Table 1). The concentrations of WHO2005-TEQ, (determined by summing the product of
the concentration of each individual congener multiplied by it’s
corresponding WHO2005-TEF value) had a trend corresponding to
that of the bio-assay data, although the bio-assay TCDD-EQ results
consistently were almost an order of magnitude greater (Table 1).
This was also found in other studies (Carbonnelle et al., 2004;
Vanderperren et al., 2004; Van Wouwe et al., 2004). However, SasolLIR had greater concentrations of WHO2005-TEQ than VanderblLIR
(Table 1), which was contrary to the TCDD-EQ results.
The normalized TCDD-EQ20 of all samples correlated weakly
with the normalized WHO2005-TEQ (R2 = 0.22; p > 0.05). However,
the RietSpr site could be considered an outlier; when it was removed the correlation improved to R2 = 0.85, which was statistically significant (p < 0.05). It should be noted, though, that eleven
sites are too few to derive a correlation, and that six of these sites
had responses less than the detection limit of the bio-assay. The
779
= industrial regions; = sediment sites; soil sites unmarked; and = reference
reason for inconsistencies in biological and chemical results may
be because the bio-assay accounts for all of the compounds in
the extracts that have the ability to bind to the AhR. Mixtures of
DLCs may be inhibitory or synergistic, leading to a reduced or enhanced response in cells, which is not quantifiable with chemical
analysis (Safe, 1995). The WHO2005-TEQ, on the other hand, estimates the total equivalency based on only those congeners that
were selected for GC/MS analysis, assuming that congeners have
only additive effects (Carbonnelle et al., 2004). It should, however,
be taken into consideration that TEF values used for the calculation
of WHO2005-TEQs are based on a wide range of in vitro and in vivo
studies in various species, and not based on data from the H4IIE-luc
bio-assay only. It was, therefore, not expected that H4IIE derived
assay data would correlate exactly with the WHO2005-TEQs, but
is rather meant as a screening tool to give an estimated indication
of the extent of DLC pollution in a sample.
Concentrations of WHO2005-TEQ in soil were significantly greater than those in sediment (p < 0.05; Mann-Whitney U test). The
Klip River had the greatest sediment TEQ (1.4 ng WHO2005TEQ kg1, dw; Table 1) when compared to the other sediments,
while Riet Spruit had the greatest TCDD-EQ20. However, when
comparing the normalized data, Riet Spruit had both the greatest
TCDD-EQ20 as well as WHO2005-TEQ (Table 1). When the total
TEQs of PCDDs, PCDFs and PCBs were considered individually,
the greatest concentration of RPCDDs was determined at SasolIND
(3.6 ng WHO2005-TEQ kg1, dw), while the greatest concentrations
of RPCDF and RPCB were measured at VanderblIND (9.6 and 4.4 ng
WHO2005-TEQ kg1, dw, respectively) (Table 2). According to literature (Schuhmacher et al., 2004; Nadal et al., 2006) petrochemical
processes produce more PCBs than PCDD/Fs, making the results
found at SasolIND unexpected (if assuming that the surrounding soil
reflects the same pattern released by the industry). Other sites that
also showed relatively greater concentrations of RPCDD, RPCDFs
or RPCBs are SasolLIR and KlipRiv (Table 2), although the concentrations at these two were less than at the two industrial sites.
Concentrations of DLCs were the least at the two reference sites
(SkbrRiv1 and 2) and TbosSpr (Table 2).
3.3. PCDD/F and PCB profiles
One of the major advantages of HRGC/HRMS data is that it
allows for the comparison of congener-and/or homologue- specific
780
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
Table 2
Individual and total concentrations (ng kg1, dw) and total TEQWHO (ng WHO2005-TEQ kg1, dw) for PCDD/Fs and PCBs in soils and sediments from central South Africa.
Chemicals
Site name
SasolLIR
VandrblLIR
SkbrRiv1
VaalRiv
KlipRiv
TbosSpr
RietSpr
SkbrRiv2
SkbrRiv3
SasolIND
VandrblIND
2378-TCDD
12378-PeCDD
123478-HxCDD
123678-HxCDD
123789-HxCDD
1234678-HpCDD
OCDD
R[PCDD]
RPCDD WHO2005-TEQ
0.05
0.18
0.19
0.67
0.45
15
150
170
0.54
0.02
0.07
0.06
0.12
0.15
0.76
4
5.2
0.14
0.03
0.03
0.02
0.03
0.03
0.13
0.72
0.99
0.06
0.01
0.04
0.03
0.06
0.06
0.41
5.9
6.5
0.07
0.03
0.14
0.16
0.58
0.33
15
150
170
0.44
0.01
0.04
0.04
0.05
0.05
0.45
2.2
2.8
0.06
0.02
0.08
0.12
0.37
0.22
9.1
75
85
0.27
0.03
0.03
0.03
0.04
0.03
0.25
2.6
3
0.06
0.02
0.04
0.04
0.09
0.11
0.96
7.3
8.6
0.09
0.2
1.2
1.9
4.9
3.6
120
740
870
3.6
0.14
0.8
0.82
1.7
1.7
16
75
96
1.5
2378-TCDF
12378/12348-PeCDF
23478-PeCDF
123478/123479-HxCDF
123678-HxCDF
123789-HxCDF
234678-HxCDF
1234678-HpCDF
1234789-HpCDF
OCDF
R[PCDF]
RPCDF WHO2005-TEQ
0.37
0.94
0.55
1.3
0.92
0.21
0.66
5.9
0.66
11
22
0.75
0.2
0.34
0.17
0.23
0.19
0.1
0.15
0.59
0.09
0.41
2.5
0.2
0.03
0.04
0.02
0.02
0.02
0.05
0.02
0.08
0.02
0.11
0.41
0.02
0.04
0.07
0.02
0.03
0.03
0.09
0.03
0.16
0.02
0.37
0.86
0.02
0.43
0.36
0.29
0.43
0.29
0.09
0.34
3.4
0.26
7.5
13
0.35
0.07
0.09
0.06
0.09
0.05
0.03
0.04
0.29
0.03
0.41
1.2
0.06
0.27
0.18
0.12
0.19
0.13
0.08
0.16
1.4
0.08
1.3
3.9
0.17
0.03
0.06
0.03
0.03
0.02
0.09
0.03
0.12
0.05
0.24
0.7
0.03
0.07
0.14
0.06
0.12
0.07
0.22
0.08
0.71
0.07
0.76
2.3
0.11
1.5
2.2
1.3
3.6
2.3
0.39
1.4
30
2.5
120
160
2
24
25
5.2
17
5.8
2.1
4.5
30
6.1
43
160
9.6
PCB105
PCB114
PCB118
PCB123
PCB156
PCB157
PCB167
PCB189
PCB77
PCB81
PCB126
PCB169
R[PCBs]
RPCB WHO2005-TEQ
R[PCDD/F&PCB]
RPCDD/F&PCB WHO2005-TEQ
80
10
320
10
90
20
50
10
7
0.48
0.95
0.22
600
0.2
790
1.5
360
40
1600
20
340
70
200
50
5.9
0.39
0.63
0.13
2700
0.48
2700
0.82
30
10
110
10
30
10
10
10
0.77
0.04
0.04
0.02
220
0.04
220
0.12
180
20
770
10
150
30
100
20
2.1
0.11
0.13
0.05
1300
0.21
1300
0.3
280
30
1000
10
210
40
120
30
76
3
3.1
0.43
1800
0.58
2000
1.4
10
10
60
10
10
10
10
10
5.1
0.21
0.39
0.03
130
0.08
130
0.2
110
10
320
10
60
10
30
10
50
1.5
1.3
0.18
610
0.22
700
0.66
10
10
40
10
20
10
10
10
0.77
0.06
0.05
0.03
120
0.04
120
0.13
150
20
560
10
170
30
90
20
2.2
0.11
0.44
0.1
1000
0.22
1000
0.42
30
10
100
10
30
10
10
10
78
1.1
6.1
0.96
300
0.67
1300
6.3
670
50
2200
40
720
120
390
160
350
12
35
7
4700
4.4
4900
16
patterns. When concentrations of the PCDD/F homologues in the
eleven soil and sediment sites were combined, the homologue
contributions for central South Africa consisted of OCDD >
OCDF > HpCDD > HpCDF (Fig. 3). The contributions of tetra-, pentaand hexa-CDD/Fs were less significant. The relatively greater OCDD
concentrations measured in South African soil and sediment shows
good agreement to what had been found in other studies (Masunga
et al., 2003; Chi et al., 2007). PCB-118, followed by PCB-105 > PCB156 > PCB-167 > PCB-77 were the dominant dioxin-like PCBs at the
South African sites (Fig. 4).
Similar homologue/congener profiles were observed for individual sites. The congener profile of SasolIND was an exception, with
this site also having notable concentrations of PCB-77. The congener profile of this site was PCB-118 > PCB-77 > PCB-156 > PCB-105.
Although PCB-77 was not present at such large concentrations at
VandrblIND, this congener was also more prevalent than at the
other sites. In an investigation by Alcock et al. (1998), Aroclor formulations (1221, 1232, and 1242) were dominated by PCB-105,
PCB-118, and PCB-77, with PCB-126 and PCB-156 present in smaller quantities. The Japanese commercial PCB preparations Kanechlor 300, 400, and 500 were responsible for PCB-118, PCB-105,
and PCB-77 (Kannan et al., 1987). Emissions from combustion
sources contributed towards additional PCB126, a non-ortho
substituted PCB. According to Chi et al. (2007), non-ortho PCBs
are characteristic of coal combustion and industrial waste incineration and do not originate solely from commercial PCB mixtures.
Municipal solid waste and medical waste incinerators typically release PCB-118 and PCB-123 into the atmosphere (Dyke et al.,
2003). Another study ascribed the presence of PCB-189, PCB-126,
and PCB-169 in air samples to thermal processes, and PCB-118,
PCB-77, PCB-167, and PCB-123 to volatilisation from commercial
PCB products (Masunga et al., 2003). It seems likely that the main
source of dioxin-like PCBs in the South African sites were from
commercial PCB preparations (according to Fig. 4). Although combustion processes also contributed, it was to a smaller extent. The
concentrations of PCB-123, PCB-167, and PCB-81 were less than
the HRGC/HRMS detection limit (Table 2).
Although it was evident that PCB-118 was the most abundant
DLC at the majority of the sites (Table 2), SasolIND was once again
an exception with OCDD being the dominant congener. A large
concentration of OCDD was also measured at SasolLIR, with PCB118 the dominant congener at this site as well. When only
PCDD/Fs were considered, the homologue profile for the majority
of the individual sites were identical to that observed for all the
sites together (OCDD > OCDF > HpCDD > HpCDF) (Fig. 3). However,
the pattern was not as apparent at VandrblIND with almost equal
concentrations of the various PCDD/F congeners. The concentrations of OCDD, HpCDF, and OCDF were only a fraction greater than
the other homologues.
Of the PCDD congeners, OCDD generally had the greatest concentrations, followed by HpCDD at concentrations five to six times
less than OCDD (Table 2). For PCDF homologues, it was clear that
all sediment samples had similar profiles, with all five homologue
groups (tetra through octa) present at almost equal concentrations.
The homologue patterns of the four soil sites were more distinguishable, with OCDF being the most dominant PCDF congener at
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
781
Fig. 3. Relative contributions of the 17 PCDD/F congener homologues for the eleven sites from central South Africa.
Fig. 4. Relative contributions of the different dioxin-like PCB congeners for the eleven sites from central South Africa.
SasolIND. Although OCDF also dominated at VandrblIND, its concentration was in the same order of magnitude as the other four PCDF
homologue groups, which was present at equal ratios (Table 2).
The profiles of Sasolburg and Vanderbijlpark’s LIR areas were similar to their respective industrial areas that may indicate that the
industrial and LIR areas might have been polluted by a similar
source.
Relatively more PCDFs than PCDDs are formed during thermal
processes, especially in the iron and steel industry (Buekens
et al., 2000). This tendency was also reflected at VanderblIND soil.
The iron- and steel plant, which is the major industry in this area,
makes use of electric arc, blast, and basic oxygen furnaces, and
have associated coke ovens, which are known potential sources
of PCDD/Fs and PCBs (UNEP, 2005). Apart from the sintering process, other stages in the steel production process have the potential
to form DLCs (Buekens et al., 2000). Octa- and hepta- PCDD/F-isomers are generally associated with emissions from uncontrolled
low temperature sources, such as inefficient bio-mass combustion
and/or open-burning of wood and domestic waste (Kouimtzis
et al., 2002). Other OCDD/Fs and HpCDD/Fs sources include cement
kilns and sinter plants. The penta- and tetra-isomers, which generally had lesser concentrations, but were present at more significant
concentrations at VandrblIND are mostly indicative of ferrous- and
non-ferrous metal operations and sintering plants (Masunga et al.,
2003). It seems that various combustion and high-temperature
processes might have been responsible for PCDD/F pollution at
the majority of the sites.
3.4. South Africa’s position in the global POPs issue
Compared with North America, Europe, and Japan, knowledge
in South Africa of especially the concentrations of DLCs are limited.
The results of this study were normalized to 1% TOC to allow comparison to environmental soil and sediment quality guidelines of
Canada, the USA, and Germany (ATSDR, 1998; BMU, 1999; CCME,
2002; Wisconsin Department of Natural Resources, 2003). When
concentrations of TCDD-EQ and WHO2005-TEQ were considered,
the normalized TCDD-EQ20 concentrations in sediment ranged between 0.12 and 32 ng kg1, dw (Table 1). All of the sediment samples, except one (RietSpr), had PCDD/F and PCB concentrations that
were less than 0.85 ng WHO2005-TEQ kg1, dw, which is the strictest guideline proposed by the Canadian Council of Ministers of the
Environment (CCME, 2002). Concentrations of DLCs measured at
RietSpr require closer scrutiny, since the concentrations exceeded
782
C. Nieuwoudt et al. / Chemosphere 76 (2009) 774–783
Table 3
Levels of PCDD/Fs and PCBs measured in soil and sediment from EU member states.
Soil (ng TEQ kg1, dw)
Rural
Austria
Belgium
Finland
Germany
Greece
Italy
Luxembourg
The Netherlands
Spain
Sweden
United Kingdom
a
Sediments (ng TEQ kg1, dw)
Background
Contaminateda
<1–100
1.2–19
80,000
1500
<1–10
570
98,000
1–10
4000
11,000
1600
<1–200
1700
7000
Contaminated
a
330
2.1–2.3
>90,000
30,000
1100
1.4
2.2–16
<1–8.4
<1
<1–20
Maximum measured concentration at contaminated sites.
Canadian and German guidelines (5–10 ng WHO2005-TEQ kg1),
and were greater than the lesser range (10 ng WHO2005-TEQ kg1)
proposed by the USA.
Normalized concentrations in soil samples ranged from 0.34 to
20 ng WHO2005-TEQ kg1, dw (Table 1). These concentrations were
less than the German and USA action levels (1000 and 50–1000 ng
WHO2005-TEQ kg1, respectively), although some soil samples exceeded the Canadian action levels of 4 ng WHO2005-TEQ kg1. It
was especially true for the industrial and LIR sites (Table 1), where
concentrations of DLCs greater than 4.0 ng WHO2005-TEQ kg1
were measured. Further investigation is specifically advised for
KlerkR and VanderblIND soils, as these sites had normalized concentrations of 20 ng TCDD-EQ kg1 (Table 1), and because of the close
proximity of the residents to these environments. Concentrations
of DLCs measured in our study were less than the majority of concentrations measured in soils and sediments of some European Union (EU) Member States (Fiedler et al., 1999; Yoon et al., 2004)
(Table 3).
4. Conclusions and recommendations
The H4IIE-luc bio-assay was an efficient method for measuring
concentrations of DLCs in soils and sediments. While concentrations of TCDD-EQ tended to be greater than those of WHO2005TEQ, there was a good correlation between biological and chemical
results, when one of the sites was removed from the equation.
Overall, sediments had lesser concentrations of DLCs than soils.
The reason for this is uncertain. It may be that run-off does not
transport soil residues to the water environment, that the residues
break down quickly once it reaches the water environment, that
the residues in the water environment does not associate quickly
with the sediment and gets transported downstream, or combinations hereof. Concentrations of PCDD/Fs and PCBs were generally
less in both soil and sediments. It seems that combustion sources
(such as inefficient bio-mass- and/or open-burning of wood and
domestic waste) were the most likely sources of PCDD/F pollution,
with smaller contributions from industrial and chemical sources.
Potential sources responsible for greater concentrations of dioxin-like PCB pollution were mainly commercial PCB formulations
and, to a lesser extent, combustion.
The majority of the samples did not exceed the proposed environmental quality guidelines, and the main areas of concern identified in this survey were the Klerksdorp residential area, the
industrial area of Vanderbijlpark, and Riet Spruit. It is recommended
that more intensive assessments of these sites be conducted, due to
human health and environmental concerns (Buekens et al., 2000;
Bouwman, 2004). Because DLCs are endocrine disruptive, extracts
could also be assayed with the MDA-kb2 and MVLN cell lines. These
assays determine the potency of environmental extracts to mimic
testosterone and estrogen at the relevant steroid hormone receptors. It is further suggested that an in-depth survey be done to cover
a larger part of South African soils, major rivers, and areas where humans may be exposed.
The processes that govern the residue concentrations in sediments, as well as the appreciably greater concentrations in soils
in residential and industrial areas need further investigation
regarding sources and the threats that they might pose. Bio-accumulation into biota and humans from both terrestrial and aquatic
food chains under developing-country conditions such as we have
investigated are little known, and needs further investigation before conclusive statements about threats and the need for mitigating actions can be made.
Acknowledgements
This work was funded by the Water Research Commission (WRC)
of South Africa (K5/1561) and the South African/Norwegian Bilateral
Scientific Agreement administrated by the National Research Foundation of South Africa and the Research Council of Norway (UID
64489). The authors also acknowledge the contributions of the
National Research Foundation of South Africa and the North-West
University, Potchefstroom Campus. Prof. Giesy was supported by
the Canada Research Chair program and an at large Chair Professorship in the Department of Biology and Chemistry and Research Centre for Coastal Pollution and Conservation, City University of Hong
Kong. The research was supported by a Discovery Grant from the National Science and Engineering Research Council of Canada (Project
# 6807) and a Grant from the Western Economic Diversification
Canada (Project # 6971 and 6807). The authors wish to acknowledge
the support of an instrumentation Grant from the Canada Foundation for Infrastructure to the University of Saskatchewan.
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