Biomonitoring of several toxic metal(loid)s in different biological matrices from environmentally and occupationally exposed populations from Panasqueira mine area, Portugal

Abstract

In the Panasqueira mine area of central Portugal, some environmental media show higher metal(loid) concentrations when compared with the local geochemical background and the values proposed in the literature for these environmental media. In order to evaluate the effect of the external contamination on selected indexes of internal dose, As, Cd, Cu, Cr, Fe, Hg, Mg, Mn, Mo, Ni, Pb, S, Se, Si, and Zn were quantified by inductively coupled plasma mass spectrometry and inductively coupled plasma optical emission spectrometry in blood, urine, hair and nail samples from individuals environmentally (N = 41) and occupationally exposed (N = 41). A matched control group (N = 40) was also studied, and data from the three groups were compared. Results obtained agreed with those reported by environmental studies performed in this area, pointing to populations living nearby and working in the mine being exposed to metal(loid)s originated from mining activities. Arsenic was the element with the highest increase in exposed populations. The concentration of other elements such as Cr, Mg, Mn, Mo, Ni, Pb, S, Se, and Zn was also increased, although at a lesser extent, specifically in the individuals environmentally exposed and in females. These findings confirm the need for competent authorities to act as soon as possible in this area and implement strategies aimed to protect exposed populations and the entire ecosystem.

Full text

1 23
Environmental Geochemistry and
Health
Official Journal of the Society for
Environmental Geochemistry and
Health
ISSN 0269-4042
Environ Geochem Health
DOI 10.1007/s10653-013-9562-7
Biomonitoring of several toxic metal(loid)s
in different biological matrices from
environmentally and occupationally
exposed populations from Panasqueira
mine area, Portugal
P.Coelho, S.Costa, C.Costa, S.Silva,
A.Walter, J.Ranville, M.R.Pastorinho,
C.Harrington, A.Taylor, V.Dall’Armi,
R.Zoffoli, et al.

1 23
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ORIGINAL PAPER
Biomonitoring of several toxic metal(loid)s in different
biological matrices from environmentally
and occupationally exposed populations from Panasqueira
mine area, Portugal
P. Coelho
•
S. Costa
•
C. Costa
•
S. Silva
•
A. Walter
•
J. Ranville
•
M. R. Pastorinho
•
C. Harrington
•
A. Taylor
•
V. Dall’Armi
•
R. Zoffoli
•
C. Candeias
•
E. Ferreira da Silva
•
S. Bonassi
•
B. Laffon
•
J. P. Teixeira
Received: 26 October 2012 / Accepted: 17 July 2013
Ó Springer Science+Business Media Dordrecht 2013
Abstract In the Panasqueira mine area of central
Portugal, some environmental media show higher
metal(loid) concentrations when compared with the
local geochemical background and the values pro-
posed in the literature for these environmental media.
In order to evaluate the effect of the external
contamination on selected indexes of internal dose,
As, Cd, Cu, Cr, Fe, Hg, Mg, Mn, Mo, Ni, Pb, S, Se, Si,
and Zn were quantified by inductively coupled plasma
mass spectrometry and inductively coupled plasma
optical emission spectrometry in blood, urine, hair
and nail samples from individuals environmentally
(N = 41) and occupationally exposed (N = 41). A
matched control group (N = 40) was also studied, and
data from the three groups were compared. Results
obtained agreed with those reported by environmental
studies performed in this area, pointing to populations
living nearby and working in the mine being exposed
to metal(loid)s originated from mining activities.
Arsenic was the element with the highest increase in
exposed populations. The concentration of other
elements such as Cr, Mg, Mn, Mo, Ni, Pb, S, Se, and
Zn was also increased, although at a lesser extent,
specifically in the individuals environmentally
exposed and in females. These findings confirm the
need for competent authorities to act as soon as
possible in this area and implement strategies aimed to
protect exposed populations and the entire ecosystem.
Keywords Environmental contamination 
Human exposure  Internal dose  Metal(loid)s 
Mining activities
P. Coelho (&)  S. Costa  C. Costa  S. Silva 
J. P. Teixeira
Environmental Health Department, National Institute of
Health, Porto, Portugal
e-mail: pcscoelho@gmail.com
C. Costa  J. P. Teixeira
Institute of Public Health, University of Porto, Porto,
Portugal
A. Walter  C. Harrington  A. Taylor
SAS Trace Element Laboratory, University of Surrey,
Guildford, UK
J. Ranville  M. R. Pastorinho
Department of Chemistry and Geochemistry, Colorado
School of Mines, Coolbaugh Hall, Golden, CO, USA
V. Dall’Armi  R. Zoffoli  S. Bonassi
Unit of Clinical and Molecular Epidemiology, Istituto di
Ricovero e Cura a Carattere Scientifico San Raffaele
Pisana, Rome, Italy
C. Candeias  E. F. da Silva
Department of Geosciences, GeoBioTec-Geobiosciences,
Geotechnologies and Geoengineering Research Center,
University of Aveiro, Aveiro, Portugal
B. Laffon
Toxicology Unit, Department of Psychobiology,
University of A Corun
˜
a, A Corun
˜
a, Spain
123
Environ Geochem Health
DOI 10.1007/s10653-013-9562-7
Author's personal copy

Introduction
Mining is one of the oldest activities in human
civilization and is a vital economic sector for many
countries. It is also one of the most hazardous activities
both in occupational and environmental contexts.
The remarkable increase in exposure to metal(loid)s
in several environmental and occupational settings
represents a worldwide pattern, affecting a significant
number of individuals. Most metal(loid)s have toxic
properties and can represent an important threat for
human health. Major health effects include develop-
ment retardation, endocrine disruption, kidney damage,
immunological and neurologic effects, and several
types of cancer (Mudgal et al. 2010).
Human health status can be monitored by measur-
ing biological indicators (biomarkers), in different
matrices, such as blood, urine, nails, hair, milk, and
saliva. This activity of biological monitoring has the
main purpose of relating the biomarker concentration
to the internal dose and then investigates the possibil-
ity that the extent of exposure is associated with
disease occurrence (Needham et al. 2007).
For decades, scientists have analysed biological
samples such as liver, kidney, brain, blood, and urine
to assess concentrations of trace elements in the human
body (Mehra and Juneja 2005). Information on the
elemental content of these samples is well established,
although most of them need an invasive method for
collection that may limit their use in biomonitoring
studies. Hair and nail samples provide a much less
invasive option and offer a number of advantages,
including the easy sample collection, storage, and
transportation; possible time course of exposure; the
long-term sample stability; and the simple analysis using
inductively coupled plasma mass spectrometry (ICP-
MS) and inductively coupled plasma optical spectrom-
etry (ICP-OES) (He 2011). These techniques are the
most commonly performed, and they are routinely used
for multielemental analysis of biological matrices
(Delves 1988;Carolietal.1992; Taylor et al. 2011).
Hair and nails incorporate elements in proportion to
the dietary intakes and exogenous exposure depending
upon various mechanisms, such as protein synthesis
and chemical binding with sulfhydryl groups. These
biological matrixes are useful to investigate exposure
to trace elements and are currently employed in
clinical studies with increasing frequency. As
suggested by Rodushkin and Axelsson (2000), human
hair and nails in trace elements analysis may provide
suitable data that point towards diseases such as
diabetes mellitus, coronary artery disease, and other
cardiovascular diseases.
Panasqueira (40°9
0
57
00
N, 7°45
0
37
00
W) was studied
between 2002 and 2007 in the scope of an e-EcoRisk
project (e-Ecorisk 2001, 2007). This area was chosen
by the consortium to be an e-EcoRisk test site due to
several factors: (a) it is an active mine; (b) there are
huge tailing piles and mud impoundments; (c) small
villages, S. Jorge da Beira, Panasqueira, Barroca
Grande, and S. Francisco de Assis are located near the
mine site; (d) the Ze
ˆ
zere River crosses the area and
feeds the Castelo do Bode dam (located 90 km
downstream), the main water supply source for
Lisbon; and (e) the local population strongly depends
on the use of land and water for their subsistence
(water supply, agriculture, cattle breeding, fishing, and
forestry). From the geochemical results obtained in
this project, it was concluded that anomalous distri-
bution of several metals (Ag, Bi, Cu, Cd, Sb, Sn, W,
and Zn) and metalloids (As) in stream sediments and
surface waters from local water streams, and soils
from nearby villages occurs (A
´
vila et al. 2008;
Salgueiro et al. 2008; Grangeia et al. 2011). Starting
from 2010, the Department of Geosciences, University
of Aveiro (GeoBioTec) together with the National
Laboratory of Energy and Geology (LNEG) have been
collecting and analysing different types of samples,
such as road dusts, soils, plants for human consump-
tion, superficial and groundwater’s, and stream sedi-
ments. Results published by Ferreira da Silva et al.
(2013) agree with those from the previous study
reporting extremely high concentrations of metals and
metalloids in all these matrices. According to the
results, the wind and hydrological factors are respon-
sible for the chemical elements transport mechanisms,
the water being the main transporter medium and soils
as one of the possible retention media (Ferreira da
Silva et al. 2013).
The aim of the present study was to evaluate
whether the contamination (environmental and occu-
pational) related to Panasqueira mine activities is
associated with the internal dose of several elements
by quantifying them in blood, urine, hair, and nails by
ICP-MS (As, Cd, Cr, Hg, Mn, No, Ni, Pb, and Se) and
by ICP-OES (Cu, Fe, Mg, S, Si, and Zn).
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Study Area
The Panasqueira mine (Sn-W) is located in Castelo
Branco district, central Portugal—Fig. 1.
The topography of this area is heterogeneous,
characterized by deep valleys and hills ranging from
350 to 1,080 m in altitude (Reis 1971). Streams are
generally dry in summer and flooded in winter.
Although historical data show that Romans worked
the area for tin (Sn), the first prospecting licence was
granted in 1886 (Cavey and Gunning 2006). The
Panasqueira mining concession covers an area of more
than 2,000 ha and since 1928 has been running by
Beralt Tin and Wolfram Lda (Corre
ˆ
adeSa
´
et al. 1999;
D’Orey 1967).
The ore complex paragenesis of Panasqueira contains
several minerals, which the most important are wol-
framite, cassiterite, pyrite, arsenopyrite, pyrrhotite,
sphalerite, chalcopyrite, marcasite, siderite, galena,
Pb–Bi–Ag sulphosalts, dolomite, calcite, and siderite.
Until 1996—for approximately 90 years—the rejected
materials were deposited in the Rio tailing and/or in the
open-air impoundment located in the vicinity of Rio
tailing (Fig. 2). The open-air impoundment contains
731,034 m
3
of rejected ore concentrates with high metal
levels. This rejected material is exposed to the atmo-
sphere and rain water, and the oxidized material
generates acidic mine drainage (AMD). Also there are
several slippage zones evident in the Rio tailings,
indicating a risk of collapse. In this scenario, the stocked
material will enter directly into the Ze
ˆ
zere River (e-
EcoRisk 2007). As previously mentioned Ze
ˆ
zere River
feeds the Castelo do Bode dam (located 90 km down-
stream from the mine—Fig. 1), the principal water
supply for the Lisbon metropolitan area (where about
one-third of the Portuguese population lives).
Today the mining and beneficiation processes are
carried out exclusively in Barroca Grande. The
rejected materials are deposited in the Barroca Grande
tailing and/or in the open-air impoundment.
Fig. 1 Location of Panasqueira mine (arrow on the left map), the Castelo do Bode dam, the Ze
ˆ
zere river flow (line across the map on
the right), and the city of Lisbon (Lisboa, circle)
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The Barroca Grande site includes a huge tail-
ing ([7Mm
3
) and two open-air impoundments
(*1,2 Mm
3
—one of these dams still active while the
older one is deactivated, both placed over the tailing).
These tailings and impoundments are also exposed to
the atmospheric conditions, and surface run-off and
water percolation leaching are observed. The tailing
piles at Barroca Grande are adjacent to the small, but
perennially flowing, Casinhas stream, which drains
also to the Ze
ˆ
zere River. The Salgueira water treatment
plant receives surface water from the old tailing pond
area, water from the new tailings pond, mine drainage
water, and seepage from the base of the tailings. These
waters are mainly treated with lime. The precipitated
sludge is pumped to the tailings pond while the treated
water is pumped into holding tanks for later use in the
mill or released to the creek channel adjacent to the
plant and discharged into the Ze
ˆ
zere River.
Materials and methods
Study population
The study population consisted of a total of 122
subjects living in villages near the Panasqueira mine,
in central Portugal (Fig. 2).
The study population consisted of a total of 122
subjects living in the area of the Panasqueira mine.
Forty-one individuals living in villages located in the
vicinity of the mine were classified as environmentally
exposed (16 males and 25 females), 41 male workers
from the Panasqueira mine represented the group of
occupationally exposed, and 40 additional subjects
without environmental and/or occupational exposure
to mining activities, or other known toxic exposure,
were the controls. This latter group included individ-
uals living in non-contaminated areas, working mainly
in administrative offices and matched with the envi-
ronmentally exposed group by age, gender, lifestyle,
and smoking habits (17 males and 23 females). Only
individuals aged over 18 years and living in the same
village for at least 5 years before the study were
selected.
Health conditions, medical history, medication,
diagnostic tests (X-rays, etc.), and lifestyle factors
were assessed by means of questionnaires. Subjects
also provided information about the presence of
specific symptoms related to metal(loid)s exposure
and chronic respiratory diseases, such as bronchitis
and others; drinking and agricultural water source;
agricultural practices, including pesticides usage; diet.
All subjects were fully informed about the procedures
Fig. 2 Map showing the location of the 4 study villages: 2
exposed in rectangles (S. Francisco de Assis and Barroca do
Ze
ˆ
zere) and 2 control in circles (Casegas and Unhais-o-Velho).
In squares are the main sources of contamination: the active
tailing—Barroca Grande and the old one—Rio tailing. The
current location of the mine is also shown inside the Barroca
Grande square.Ze
ˆ
zere river flow is highlighted in a thick white
line and Casinhas stream flow in a thinner white line
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and objectives of this study, and signed an informed
consent form. Approval for this study was obtained
from the Institutional Ethical Board of the Portuguese
National Institute of Health.
The general characteristics of the study groups are
summarized in Table 1. Smoking habits groups were
established as never/ever smokers, with the former
group composed of individuals that never smoked and
the second one of current and ex-smokers. This was
due to the fact that the number of ex-smokers was
extremely high, and the majority of these individuals
had been heavy smokers.
Sample collection
Blood (B) samples were collected by venipuncture in
tubes containing ethylenediamine tetraacetic acid
(EDTA). First morning, urine (U) samples (minimum
volume: 20 ml) were collected in polyethylene sterile
bottles. Hair (H) and nail (finger, FN, and toe, TN)
samples were collected with stainless steel scissors
and nail clippers and stored in polyethylene bags.
Samples were transported under refrigeration and kept
at approximately ?4 °C (blood) and –20 °C (urine,
hair, and nails) until analysis. All samples were coded
and analysed under blind conditions.
Sample preparation and analysis
Blood samples (1–2 g) were added to 2 mL of HNO
3
in Teflon vials and digested for 24 h at 100 °C. After
cooling, the digested solutions were diluted with
deionized water up to 10 mL in polypropylene tubes
for elemental analysis by ICP-MS and ICP-OES. For
analysis of blood samples, In and Sc were the chemical
elements used as internal standards, while Ge and Ir
were used for urine, hair, and nail samples.
Hair and nail samples were washed and digested
following the procedures described by Coelho et al.
(2012). The solutions were transferred to plastic tubes
and made up to 5 mL with ultra-pure water for direct
determination via ICP-MS and ICP-OES. The ICP-MS
instruments used in this study were a PerkinElmer
Elan DRC II and a Thermo Elemental X Series.
Table 1 Characteristics of
the study population:
baseline comparison
between controls and
exposed groups by
demographics and lifestyle
factors
Statistically significant
results are highlighted in
bold
a
Mean ± standard
deviation;
b
Chi-square test;
c
ANOVA test
Total of subjects Controls Environmentally
exposed
Occupationally
exposed
P value
40 41 41
Gender
Males 17 (43 %) 16 (39 %) 41 (100 %) <0.001
b
Females 23 (59 %) 25 (61 %) 0 (0 %)
Age (years)
a
56.60 ± 12.58 61.71 ± 13.50 62.05 ± 7.57 0.063
c
Smoking habits
Never smokers 25 (62 %) 32 (78 %) 16 (39 %) 0.001
b
Ever smokers 15 (38 %) 9 (22 %) 25 (61 %)
Water consumption
Bottled water 2 (5 %) 3 (7 %) 4 (10 %) 0.714
b
Tap water 20 (51 %) 23 (56 %) 17 (41 %)
Spring water 17 (44 %) 15 (37 %) 20 (49 %)
Fish consumption
0–2 portions/week 19 (47 %) 25 (61 %) 23 (56 %) 0.541
b
[2 portions/week 20 (53 %) 16 (39 %) 18 (44 %)
Agriculture
No 6 (15 %) 14 (34 %) 1 (2 %) 0.001
b
Yes 34 (85 %) 27 (66 %) 40 (98 %)
Pesticide usage (last
year)
No 10 (26 %) 16 (39 %) 11 (28 %) 0.408
b
Yes 28 (74 %) 25 (61 %) 29 (72 %)
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A PerkinElmer Optima 5300 and a Thermo Scientific
iCAP 6300 Duo were used for ICP-OES analysis. For
microwave digestion, an Anton Paar Multiwave 3000
oven, equipped with 16 vessels, was used.
Urine samples were defrosted 24 h before the
analysis, centrifuged for 3 min at 2,500 rpm, and
diluted up to 25-fold with 1 % v/v HNO
3
for
elemental analysis by ICP-MS and ICP-OES. Results
of urine samples were adjusted, and they are reported
as lg/g creatinine. This procedure (creatinine adjust-
ment) is routinely used to reduce the influence of
factors that are not related to metal(loid) exposure,
such as urine concentration and urine volume (Hin-
wood et al. 2002). Creatinine was measured spectro-
photometrically using the Jaffe reaction (Roche
Diagnostics).
All reagents used were trace analysis grade or better
quality. All aqueous solutions were prepared using
ultra-pure water.
Quality control and quality assurance
Blood analyses were validated using European Union,
Institute for Reference Materials and Measurements—
Certified Reference Materials (CRMs): BCR
Ò
634,
BCR
Ò
635, and BCR
Ò
636. Freeze-dried human urine
CRM from National Institute of Environmental Stud-
ies (NIES), Japan, was used in addition to validating
the metal(loid) quantification in urine samples. This
material was also analysed during each analytical run
as a quality control (QC) sample. As no nail CRM is
currently available, the methods for digestion and
analysis of hair, fingernail, and toenail samples were
validated using a human hair CRM—NCS DC 73347a
and NCS ZC 81002b human hair (NCS Beijing,
China). The CRMs were also used as QC items by
digesting a portion with each batch of samples and
determining the metal(loid) concentration along with
the other digests. Other QC measures used in the
different matrices included the periodic analysis of
suitable standards to check on instrument drift and
short-term stability. Results from all the CRMs were
within their certificated ranges.
Statistical data analysis
A general description of the study population was
performed through univariate analysis. The distribu-
tion within the three study groups of gender, age, and
lifestyle factors potentially influencing the concentra-
tion of metal(loid)s in the biological samples (i.e.
smoking habits, source of drinking water, fish con-
sumption, agricultural practices, and pesticide use)
was evaluated with the chi-square test for categorical
variables and with the analysis of variance (ANOVA)
for continuous variables.
The effect of exposure on the concentration of
metal(loid)s was preliminarily tested with the ANOVA
of log-transformed data. A multiple regression analysis
was performed to estimate the effect of the exposure,
adjusted for actual confounders. All models included
age and smoking habits. Three multiple regression
models were applied for the multivariate analysis,
depending on the characteristics and statistical distri-
bution of variables. Details are given in Table 2.
Associations between two variables were tested by
Spearman correlation. The critical limit for signifi-
cance was set at P \ 0.05. The statistical software
used for the analyses were StataCorp. 2011, Stata
Statistical Software: Release 12, College Station, TX:
StataCorp LP. and SPSS Inc. Released 2004, SPSS for
Windows, Version 13, Chicago, SPSS Inc.
Results
The general characteristics of the study groups are
summarized in Table 1. No significant differences in
age were observed between the three groups.
Gender and smoking habit difference between
groups were mostly due to the fact that the occupa-
tionally exposed group was composed only of males,
Table 2 Multiple regression models applied according to the characteristics and statistical distribution of variables
Log-linear Poisson Negative-binominal
Fe-B, Mg-B,
S-FN, S-TN,
S-H, Se-B, Si-B
As-H, Cd-B, Cd-U, Cd-FN, Cd-TN, Cd-H, Cr-U, Cr-H,
Hg-U, Hg-FN, Hg-TN, Hg-H, Mn-H, Ni-H, Pb-FN,
Pb-TN, Se-FN, Se-TN, Se-H
As-B, As-U, As-FN, As-TN, Cr-FN, Cr-TN, Cu-B, Cu-
H, Fe-H, Mg-FN, Mg-TN, Mg-H, Mn-B, Mn-U, Mn-
FN, Mn-TN, Mo-B, Ni-U, Ni-FN, Ni-TN, Pb-B, Pb-
U, Pb-H, Se-U, Zn-B, Zn-FN, Zn-TN, Zn-H
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the vast majority of whom were smokers. Significant
differences were also found for the frequency of
individuals reporting their involvement in agricultural
practice, while no significant differences were found
for the source of drinking water, quantity of fish
consumed, and pesticide use in the last year.
Results of metal(loid) quantification showed sig-
nificant differences among groups, for several ele-
ments, in different matrices (Table 3).
Some of the elements—As, Cd, Cr, Hg, Mg, Mn,
Mo, Ni, and Pb—presented higher values in exposed
groups (environmental and/or occupational) in all the
matrices when compared to controls. Among all the
elements analysed, As, Cr, Mn, and Pb showed
significantly different values in three different matri-
ces; Se in two; and Cd, Cu, Mg, Mo, S and Zn in one.
No significant differences were observed for Fe, Hg,
Ni, and Si in any matrix, while Cu and S presented
significantly lower values in both exposed groups in
blood and hair, respectively.
When comparing these results with reference
ranges of exposure (Table 3), the level of exposure
in our study groups was generally higher, for some
elements in some of the matrices, particularly in blood
and urine samples. Concerning nails and hair samples,
the vast majority of concentrations were within or
relatively close to the reference ranges (except for
Hg).
Results of the multivariable analysis for the effect
of exposure adjusted by age, smoking, and metal-
specific actual confounders, and stratified by gender in
those cases for which a significant effect modification
(excluding the occupationally exposed group) was
found are presented in Table 4.
After taking into account all possible confounding
factors, elements which still presented significantly
higher values in the exposed groups in one or more
biological matrices were As, Cr, Mg, Mn, Mo, Ni, Pb,
S, Se, and Zn. Copper was the only element showing
significantly lower values in the exposed groups, both
in blood and hair. All significant differences were
obtained in the groups with environmental or both,
environmental and occupational exposure, except for
Cu, Mn and Pb in hair, and Se in blood, which
significantly differed from controls only in the occu-
pationally exposed group. No significant differences
were obtained for Cd, Fe, Hg, and Si. For some of
these elements, the significant differences were only
obtained for one of the genders, namely Cr in toenails
(only in females), Mg in blood (only in females), Mn
in toenails (only in females), Ni in toenails (only in
females), and Zn in blood (only in males).
Table 5 shows the correlations between the con-
centration of the same metal(loid) in different matrices
(only significant results are shown).
Only As, Hg, Mg, Mn, and Pb presented more than
one significant and positive correlation between two or
more matrices, and among these elements, only As and
Hg showed correlations between blood or urine and
fingernails, toenails, or hair. Fingernail and toenail
samples were significantly correlated in all elements
shown but Cd. Significant positive correlations were
also obtained between fingernails and hair for As, Hg,
Mn, and Pb, and between toenails and hair for As, Hg,
and Mg.
The effects of gender, age, and smoking habits on
the levels of metal(loid)s in the different matrices were
also evaluated, and the results are presented in
Table 6.
Concerning gender, the majority of the elements
showed significantly higher values in males in blood
and hair samples (except for Mg in hair, higher in
females), and significantly higher values in females in
urine, fingernails, and toenail. Regarding age, the
effect depended mainly on the matrix. All elements
showed a significant increase in older groups in blood
and urine samples, except Cu in blood, and a
significant decrease with age in fingernails, toenails,
and hair samples, except for Mn in toenails and Fe in
hair. As regards smoking, significantly higher levels
were generally observed in smokers in the different
matrices, except for Hg in hair which was significantly
lower. These results are also shown schematically in
Fig. 3.
Discussion
A high degree of contamination by metal(loid)s was
reported by geochemical campaigns involving various
sample types around the Panasqueira mine (A
´
vila et al.
2008; Salgueiro et al. 2008; Grangeia et al. 2011).
Moreover, local health statistics report an elevated
number of health complaints, with a significant
number of individuals with cardiac, respiratory, and
urinary diseases, and a high rate of deaths by cancer.
To evaluate the role of environmental contamination
in populations living and working nearby, we carried
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Table 3 Concentrations of metal(loid)s in the different biological matrices in the exposure groups (mean ± SD)
Controls Environmentally
exposed
Occupationally
exposed
Statistical
significance
Published/
Reference
ranges
a
As-B (lg/L) 11.67 ± 6.58 17.23 ± 11.13 14.71 ± 12.48 2.6–17.8
As-U (lg/g creat) 60.17 ± 72.28 93.34 ± 105.38 71.38 ± 118.07 2.3–161
As-FN (lg/g) 0.14 ± 0.10 0.61 ± 1.04 1.31 ± 2.88 ** 0.07–1.09
As-TN (lg/g) 0.22 ± 0.30 0.65 ± 0.56 1.01 ± 2.36 ** 0.07–1.09
As-H (lg/g) 0.12 ± 0.14 0.14 ± 0.15 0.32 ± 0.52 ** 0.03–0.32
Cd-B (lg/L) 2.44 ± 0.68 2.31 ± 0.32 2.62 ± 0.94 0.15–2.04
b
Cd-U (lg/g creat) 0.47 ± 0.34 0.57 ± 0.45 0.47 ± 0.31 0.06–0.79
Cd-FN (lg/g) 0.12 ± 0.13 0.15 ± 0.22 0.10 ± 0.12 0.01–0.44
Cd-TN (lg/g) 0.04 ± 0.04 0.05 ± 0.04 0.07 ± 0.13 0.01–0.44
Cd-H (lg/g) 0.04 ± 0.05 0.06 ± 0.08 0.15 ± 0.24 ** 0.01–0.36
Cr-U (lg/g creat) 1.23 ± 0.98 1.58 ± 0.83 1.12 ± 0.68 ** 0.24–1.80
Cr-FN (lg/g) 1.27 ± 1.42 1.66 ± 2.82 0.95 ± 0.62 0.22–3.20
Cr-TN (lg/g) 1.19 ± 0.99 2.17 ± 2.41 0.91 ± 0.92 ** 0.22–3.20
Cr-H (lg/g) 0.07 ± 0.06 0.07 ± 0.08 0.18 ± 0.33 * 0.05–0.53
Cu-B (lg/L) 1,386.84 ± 590.29 1,017.44 ± 235.46 948.19 ± 106.98 ** 780–1,760
Cu-H (lg/g) 19.93 ± 21.28 16.96 ± 10.60 14.73 ± 6.47 8.5–96
Fe-B (lg/L) 500,542.58 ± 90,827.76 449,104.09 ± 118,606.55 497,669.02 ± 47,017.44 390,000–550,000
Fe-H (l
g/g) 12.22 ± 12.57 11.15 ± 7.65 13.94 ± 9.20 4.9–23
Hg-U (lg/g creat) 1.09 ± 0.70 1.13 ± 0.93 1.09 ± 0.89 0.14–2.21
Hg-FN (lg/g) 0.62 ± 0.34 0.61 ± 0.42 0.72 ± 0.37 0.03–0.31
Hg-TN (lg/g) 0.48 ± 0.26 0.46 ± 0.48 0.51 ± 0.36 0.03–0.31
Hg-H (lg/g) 1.58 ± 0.90 1.50 ± 1.27 1.77 ± 0.89 0.05–0.93
Mg-B (lg/L) 27,623.66 ± 4,741.42 30,179.31 ± 4,162.05 28,268.58 ± 3,441.56 * 31,200–34,200
Mg-FN (lg/g) 120.83 ± 40.50 161.67 ± 141.34 123.17 ± 26.59 55–191
Mg-TN (lg/g) 164.54 ± 97.02 175.55 ± 120.18 198.46 ± 129.93 55–191
Mg-H (lg/g) 51.52 ± 65.34 56.47 ± 47.84 34.17 ± 25.71 8.5–141
Mn-B (lg/L) 21.12 ± 23.14 22.35 ± 7.53 25.39 ± 31.75 5–12.8
Mn-U (lg/g creat) 1.51 ± 2.32 3.07 ± 2.52 1.45 ± 1.91 ** 0.11–1.32
Mn-FN (lg/g) 1.63 ±
2.88 2.09 ± 2.28 1.88 ± 1.58 0.19–3.30
Mn-TN (lg/g) 1.25 ± 1.29 2.84 ± 3.17 1.98 ± 3.19 * 0.19–3.30
Mn-H (lg/g) 0.70 ± 0.84 0.77 ± 0.69 1.50 ± 1.65 * 0.08–2.41
Mo-B (lg/L) 3.75 ± 2.64 9.25 ± 8.48 6.78 ± 7.20 * 0.77–7.86
Ni-U (lg/g creat) 6.14 ± 3.33 7.94 ± 6.20 7.53 ± 6.89 0.59–4.06
Ni-FN (lg/g) 2.43 ± 2.36 3.48 ± 6.10 2.10 ± 2.28 0.14–6.95
Ni-TN (lg/g) 1.24 ± 1.03 4.12 ± 9.20 0.98 ± 0.90 0.14–6.95
Ni-H (lg/g) 0.37 ± 0.25 0.30 ± 0.24 0.47 ± 0.85 0.11–1.60
Pb-B (lg/L) 36.01 ± 25.81 34.08 ± 39.39 63.72 ± 58.56 ** 11.4–62.8
Pb-U (lg/g creat) 2.43 ± 2.26 2.81 ± 5.3 4.59 ± 6.82 * 0.01–2.14
Pb-FN (lg/g) 1.33 ± 0.97 1.61 ± 1.65 1.72 ± 1.28 0.27–4.75
Pb-TN (lg/g) 0.99 ± 1.08 1.25 ±
1.33 1.16 ± 1.38 0.27–4.75
Pb-H (lg/g) 1.55 ± 2.92 1.45 ± 2.00 3.02 ± 3.00 ** 0.22–7.26
S-FN (lg/g) 30,090.67 ± 8,361.79 37,401.52 ± 21,791.54 31,868.07 ± 4,487.16 23,400–43,500
S-TN (lg/g) 25,737.22 ± 7,822.65 28,490.47 ± 19,176.70 25,158.77 ± 5,925.07 23,400–43,500
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Table 4 Effect of exposure on metal(loid)s concentration. Adjustment for age, smoking, and metal(loid)-specific confounders
Controls Environmentally exposed Occupationally exposed
MR [95 % CI] MR [95 % CI] MR [95 % CI]
As-B (lg/L) O 1.00 1.38* [1.03; 1.86] 1.15 [0.84; 1.58]
As-Ul (lg/g creat) M 1.00 1.13 [0.62; 2.06] 1.38 [0.84; 2.26]
F 2.02* [1.13; 3.62] 2.56** [1.45; 4.49]
As-FN (lg/g) O 1.00 5.65** [1.8; 17.76] 9.20** [3.09; 27.36]
As-TN (lg/g) O 1.00 3.42** [1.37; 8.52] 4.39** [1.78; 10.80]
As-H (lg/g) O 1.00 1.36 [0.38; 4.86] 2.66 [0.85; 8.33]
Cd-B (lg/L) O 1.00 1.02 [0.65; 1.58] 1.08 [0.67; 1.74]
Cd-U (lg/g creat) O 1.00 1.29 [0.66; 2.49] 0.82 [0.42; 1.60]
Cd-FN (lg/g) O 1.00 1.54 [0.40; 5.95] 0.67 [0.16; 2.78]
Cd-TN (lg/g) O 1.00 1.19 [0.14; 10.16] 1.46 [0.18; 11.91]
Cd-H (lg/g) O 1.00 1.81 [0.21; 15.62] 4.20 [0.60; 29.27]
Cr-U (lg/g creat) O 1.00 1.16 [0.77; 1.74] 0.79 [0.51; 1.23]
Cr-FN (lg/g) M 1.00 1.52 [0.65; 3.52] 1.21 [0.58; 2.52]
F 2.28* [1.05; 4.93] 3.17** [1.45; 6.89]
Cr-TN (lg/g) M 1.00 1.34 [0.62; 2.92] 1.09 [0.55; 2.19]
F 1.64 [0.77; 3.47] 2.85**,
§
[1.39; 5.85]
Cr-H (lg/g) O 1.00 0.88 [0.15; 4.98] 2.44 [0.53; 11.25]
Cu-B (lg/L) O 1.00 0.76** [0.65; 0.89] 0.71** [0.60; 0.85]
Cu-H (lg/g) O 1.00 0.91 [0.68; 1.22] 0.71* [0.53; 0.95]
Fe-B (lg/L) O 1.00 0.75 [0.46; 1.23] 1.13 [0.64; 1.99]
Fe-H (lg/g) M 1.00 0.98 [0.65; 1.49] 0.87 [0.62; 1.23]
F 0.61* [0.41; 0.90] 0.62* [0.41; 0.93]
Hg-U (lg/g creat) O 1.00 0.99 [0.64; 1.53] 1.07 [0.68; 1.69]
Hg-FN (lg/g) O 1.00 0.94 [0.52; 1.71] 1.28 [0.70; 2.35]
Hg-TN (lg/g) O 1.00 0.81 [0.41; 1.61] 1.06 [0.53; 2.14]
Table 3 continued
Controls Environmentally
exposed
Occupationally
exposed
Statistical
significance
Published/
Reference
ranges
a
S-H (lg/g) 63,848.11 ± 16,470.62 52,112.68 ± 14,635.14 57,239.98 ± 17,749.52 ** 40,700–55,000
Se-B (lg/L) 198.44 ± 41.98 200.38 ± 39.3 226.21 ± 52.16 * 89–154
Se-U (lg/g creat) 29.17 ± 12 31.89 ± 11.82 33.14 ± 25.65 10.5–45.5
Se-FN (lg/g) 0.75 ± 0.37 1.13 ± 1.16 0.64 ± 0.28 ** 0.62–1.53
Se-TN (lg/g) 0.58 ± 0.46 0.63 ± 0.35 0.51 ± 0.19 0.62–1.53
Se-H (lg/g) 0.50 ± 0.34 0.43 ± 0.16 0.49 ± 0.27 0.48–1.84
Si-B (lg/L) 1,361.34 ± 460.50 1,264.9 ± 401.09 1,285.47 ± 154.60 n.a.
Zn-B (lg/L) 12,805.39 ± 7,443.16 19,739.24 ± 15,515.37 22,055.71 ± 17,454.97 4,076–7,594
Zn-FN (lg/g) 198.30 ± 111.98 199.63 ± 107.79 176.94 ± 50.05 80–191
Zn-TN (lg/g) 142.60 ± 106.74 136.49 ± 80.72 126.14 ± 43.21 80–191
Zn-H (lg/g) 158.49 ± 80.40 176.47 ± 89.28 196.30 ± 88.48 * 68–198
a
Values in blood were obtained from Alimonti et al. (2005) and Goulle
´
et al. (2005); values in urine were obtained from Goulle
´
et al.
(2005); values in fingernails, toe nails, and hair were obtained from Rodushkin and Axelsson (2000);
b
Higher values can be found in
smokers; creat creatinine, n.a. not available; * P \ 0.05; ** P \ 0.01 (ANOVA test)
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Table 4 continued
Controls Environmentally exposed Occupationally exposed
MR [95 % CI] MR [95 % CI] MR [95 % CI]
Hg-H (lg/g) M 1.00 1.17 [0.68; 2.01] 1.08 [0.67; 1.74]
F 0.88 [0.52; 1.50] 0.62 [0.35; 1.09]
Mg-B (lg/L)
INT
M 1.00 0.97 [0.87; 1.07] 0.93 [0.86; 1.02]
F 0.86** [0.78; 0.95] 1.02 [0.92; 1.12]
§§
Mg-FN (lg/g) O 1.00 1.36** [1.10; 1.67] 1.03 [0.84; 1.26]
Mg-TN (lg/g)
INT
M 1.00 0.78 [0.56; 1.10] 0.99 [0.74; 1.32]
F 0.83 [0.59; 1.16] 1.09 [0.78; 1.52]
Mg-H (lg/g) M 1.00 0.90 [0.53; 1.55] 1.15 [0.75; 1.78]
F 1.87* [1.12; 3.09] 2.34** [1.40; 3.90]
Mn-B (lg/L) O 1.00 1.10 [0.80; 1.51] 1.06 [0.74; 1.50]
Mn-U (lg/g creat) O 1.00 1.86** [1.20; 2.90] 0.92 [0.56; 1.52]
Mn-FN (lg/g) O 1.00 1.31 [0.82; 2.10] 1.10 [0.69; 1.75]
Mn-TN (lg/g) M 1.00 1.25 [0.56; 2.78] 1.41 [0.73; 2.70]
F 1.49 [0.67; 3.28] 4.07**,
§§
[1.93; 8.59]
Mn-H (lg/g) O 1.00 1.12 [0.65; 1.92] 1.88** [1.17; 3.03]
Mo-B (lg/L) O 1.00 2.83** [1.85; 4.32] 1.42 [0.90; 2.23]
Ni-U (lg/g creat) O 1.00 1.29 [0.95; 1.75] 1.17 [0.85; 1.60]
Ni-FN (lg/g) O 1.00 1.49 [0.91; 2.44] 1.38 [0.78; 2.43]
Ni-TN (lg/g)
INT
M 1.00 1.08 [0.42; 2.73] 1.20 [0.53; 2.72]
F 1.06 [0.41; 2.70] 4.76**,
§§
[2.05; 11.05]
Ni-H (lg/g) O 1.00 0.81 [0.37; 1.76] 1.47 [0.68; 3.16]
Pb-B (lg/L) M 1.00 1.09 [0.70; 1.71] 1.19 [0.83; 1.71]
F 0.65* [0.43; 0.98] 0.68 [0.44; 1.05]
Pb-U (lg/g creat) O 1.00 1.16 [0.71; 1.90] 1.46 [0.91; 2.33]
Pb-FN (lg/g) O 1.00 1.42 [0.95; 2.11] 1.28 [0.86; 1.89]
Pb-TN (lg/g) O 1.00 1.13 [0.72; 1.77] 1.07 [0.67; 1.73]
Pb-H (lg/g) O 1.00 1.27 [0.73; 2.21] 2.17** [1.27; 3.71]
S-FN (lg/g) O 1.00 1.26* [1.05; 1.50] 1.13 [0.94; 1.35]
S-TN (lg/g) O 1.00 1.01 [0.83; 1.22] 1.03 [0.84; 1.26]
S-H (lg/g) M 1.00 0.83 [0.65; 1.06] 0.85 [0.70; 1.05]
F 0.86 [0.68; 1.08] 0.72** [0.57; 0.90]
Se-B (lg/L) O 1.00 1.01 [0.88; 1.56] 1.17* [1.02; 1.34]
Se-U (lg/g creat) M 1.00 0.96 [0.69; 1.33] 1.17 [0.89; 1.52]
F 1.15 [0.85; 1.57] 1.30 [0.95; 1.77]
Se-FN (lg/g) O 1.00 1.54 [0.93; 2.55] 0.90 [0.50; 1.60]
Se-TN (lg/g) O 1.00 1.10 [0.60; 2.00] 0.89 [0.46; 1.73]
Se-H (lg/g) O 1.00 0.91 [0.45; 1.84] 0.91 [0.46; 1.78]
Si-B (lg/L) O 1.00 0.91 [0.73; 1.13] 0.96 [0.74; 1.23]
Zn-B (lg/L) M 1.00 2.15** [1.37; 3.40] 1.53* [1.05; 2.23]
F 0.92 [0.60; 1.41] 1.13 [0.74; 1.73]
Zn-FN (lg/g) O 1.00 1.07 [0.89; 1.27] 1.00 [0.82; 1.21]
Zn-TN (lg/g) O 1.00 0.96 [0.78; 1.17] 0.90 [0.73; 1.12]
Zn-H (lg/g) O 1.00 1.23* [1.02; 1.48] 1.34** [1.11; 1.61]
Comparison versus reference level (control group or males control group): * P \ 0.05; ** P \ 0.01; comparison versus females control group:
§
P \ 0.05;
§§
P \ 0.01; creat creatinine; MR mean ratio; O overall, F females, M males, O effect indicates no effect of gender. INT indicates gender-
by-exposure interaction
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Table 5 Correlation coefficients between the same metal(loid) in different matrices
B - U B - FN B -TN U - FN U - TN U - H FN -TN FN - H TN - H
As
0.375** 0.314** 0.282* 0.298** 0.220* 0.183* 0.487** 0.246** 0.216*
Cd
0.294*
Hg
0.219* 0.311** 0.519** 0.489** 0.441**
Mg
0.224* 0.224* 0.234*
Mn
0.227* 0.188*
Ni
0.320**
Pb
0.487** 0.241* 0.306**
S
0.201*
Se
0.327**
* P \ 0.05; ** P \ 0.01
Grey cells one of the matrices was not analysed
Table 6 Effect of gender, age, and smoking on the levels of metal(loid)s in the different biological matrices
Gender (effect in males) Age (effect with regard to 25–50 years) Smoking (effect in smokers)
51–60 years 61–70 years C71 years
As-U (lg/g creat) ;:::
As-TN (lg/g) ;
Cr-U (lg/g creat) :
Cr-FN (lg/g) ;;
Cr-TN (lg/g) ;
Cu-B (lg/L) ;;
Cu-H (lg/g) ::
Fe-H (lg/g) :: : ::
Hg-H (lg/g) : ;
Mg-B (lg/L) ::
Mg-TN (lg/g) ;; :
Mg-H (lg/g) ;; ;
Mn-TN (lg/g) ; : :: :: :
Pb-B (lg/L) :: : :
S-H (lg/g) :
Se-U (lg/g creat) ;; :
Zn-B (lg/L) ::
Zn-FN (lg/g) ;; ;
Zn-TN (lg/g) ;
Zn-H (lg/g) ;; ;
Only significant effect on at least one parameter is shown
Arrows up indicate increase, and arrows down indicate decrease; one arrow P \ 0.05; two arrows P \ 0.01; creat creatinine
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out the present study, with the purpose of quantifying
the level of several elements—As, Cd, Cu, Cr, Fe, Hg,
Mg, Mn, Mo, Ni, Pb, S, Se, Si, and Zn—in different
biological matrices—blood, urine, finger and toe nails,
and hair.
Blood and urine are still the specimens of choice to
be analysed in biomonitoring studies. The levels of
most elements have been extensively studied in these
matrices, and numerous validated biomarkers of
exposure are available. As regards nails and hair
samples, in recent years, a number of studies using
these matrices have been carried out bringing new and
improved information that can help to quantify
elements in these matrices and validate this approach
to measure exposure in the near future.
In biomonitoring of environmental and occupa-
tional exposure to toxic elements, nails are generally
preferred to hair, as they do not become so easily
contaminated (Button et al. 2009). Further, toenails
may be a more reliable sample than fingernails, since
the latter come into contact with the atmosphere,
metallic objects, and other substances containing trace
elements such as dyes. In contrast, toenails most of the
time are hidden in shoes, and therefore, they have
lower contact with trace elements.
Up to now, few studies have been conducted
comparing the levels of diverse elements in all five
different matrices (He 2011). The information pro-
vided by each of them is rather different as blood and
urine generally reflect recent exposures (days/few
weeks), and hair and nails, particularly toenails, reflect
exposures occurring in the last weeks/months. This
distinction may be not straightforward for some
elements, such as Cd and Pb which accumulate in
the human body for years (half life of 10–12 years)
(Dorne et al. 2011). The combination of results from
all the matrices will allow better characterization of
the exposure.
Taking into account all this information, our results
point to different types of exposure (past/recent) for
different elements in the exposed groups (Table 4). The
group exposed to environmental toxins only apparently
experienced a pronounced and continuous (past and
recent) exposure to As, a moderate but continuous
exposure to Mg, Mn, and Zn, a recent exposure to Mo,
and past exposure to Cr, Ni, and S. Since Mo was only
analysed in blood samples, little can be said about the
timeframe of exposure to this compound. The second
group (occupationally exposed) experienced a contin-
uous exposure to Zn, recent exposure to Se, and long
standing exposure to As, Mn, and Pb.
We also investigated correlations between different
measures of the same element in the four matrices
analysed (Table 5). If exposure occurs continuously,
significant and positive correlations between the
different sample types are expected. As and Hg were
the only metal(loid)s that showed correlations
between matrices compatible with the presence of
recent and past exposure. As expected, significant and
positive correlations between fingernails and toenails
were obtained for most of these elements; significant
correlations between finger/toenails and hair, and
between blood and urine, were achieved for some of
them. It is important to highlight the fact that for some
of the elements, not all the matrices were analysed.
For some of the elements studied, the concentra-
tions reported here for blood and urine were above the
published reference ranges (Table 3). These reference
ranges were obtained from samples collected from
Italian and Swedish healthy volunteers without a
detailed description of the environmental or occupa-
tional exposure of these subjects, and therefore are
only indicative and may change significantly from
population to population. Numerous factors such as
site of residence, gender, age, diet, lifestyle, or
geochemical environment need to be taken into
All matrices
SMOKING HABITS
All matrices
NEVER
SMOKERS EVER SMOKERS
Blood
Hair
Urine
Fingernails
Toenails
Urine
Fingernails
Toenails
Blood
Hair
MALES FEMALES
GENDER
Fingernails
Toenails
Hair
Blood
Urine
AGE
INCREASING AGE
Fig. 3 Schematic representation of the effect of gender, age, and smoking on the concentration of metal(loid)s in the different matrices.
Variations in the relative concentration of metal(loid)s are represented horizontally
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account when establishing the reference ranges for a
population (Rodushkin and Axelsson 2000), and any
comparison must be interpreted carefully. As regards
the Portuguese population, there are few studies that
report metal(loid) levels in exposed populations, and
they show wide changes in levels within the same
population in different geographic areas (reviewed in
Coelho et al. 2012). Therefore, in this study, we
compared the element concentration obtained for the
exposed populations with those of the controls, as they
were matched for age, gender (only the environmen-
tally exposed group), diet, lifestyle, geochemical
environment, and residence.
Our previous studies at the Panasqueira mine area
(Coelho et al. 2011, 2012) indicated that the exposed
populations were experiencing severe health effects
derived from exposure to the environmental and
occupational contamination resulting from mining
activities; however, a more restricted number of
samples and a lower number of internal dose bio-
markers limited major conclusions and lead us to
design and perform a more complete and robust study.
From the results in Table 3, it can be seen that the
concentrations of As, Cr, Mn, and Pb vary signifi-
cantly among the three groups, with significant
differences in three different media. These results
were influenced by confounding variables such as
gender, age, smoking habits, and factors directly
associated with exposure, such as agriculture practice,
fish consumption, and source of water for consump-
tion. When adjusting for all these variables, some of
the elements were no longer significantly different
between groups (Tables 3 vs. 4).
Modifying factors such as gender, age, socioeconomic
status, and lifestyle factors in exposure and susceptibility
to metal(loid)s have been reported in the literature to play
a role in modulating exposure to metal(loid)s. However,
most of these parameters are often overlooked, and
additional studies needed to be performed in order to fill
gaps. Identification of these gaps provides information on
susceptibility factors which is critical to design guide-
lines for preventive measures (Berglund et al. 2011).
Regarding the effect of gender on the metal(loid) levels in
the different matrices, our results show that females have
higher concentrations of As, Cr, Mg, Mn, and Se in urine,
fingernails, and toenails, while males have higher levels
of Fe, Hg, Mg, Pb, S, and Zn in blood and hair samples.
The only exception was Mg in hair which presented
with significantly higher concentrations in females.
Interestingly, this difference was reported before in
different studies aiming to evaluate the concentration of
several trace elements in hair samples (Quereshi 1982;
Takeuchi et al. 1982; Nowak and Kozlowski 1998;
Chojnacka et al. 2006). The higher levels of Pb in blood
in males are also known and reported in several studies
with non-exposed, environmentally and/or occupation-
ally exposed individuals. Milman et al. (1994) suggested
that the difference in Pb blood levels between females
and males could be explained by the higher content of
haemoglobin in men.
Gender differences in the exposure to toxic metals
are well documented. In a review paper by Vahter
et al. (2007), several references to studies describing
significant differences in internal levels of elements
between males and females were described and
commented. Berglund et al. (2011) reported that
females seemed to be more at risk for toxic metal
exposure than males, and this finding was confirmed in
our study, where females had significantly higher
levels of several genotoxic elements, i.e. As, Cr, and
Mn. Differences between genders can be due to
different patterns of exposure, with one of the genders
being more exposed to certain metal(loid)s, although
the presence of different toxicokinetic mechanisms
between both genders should be taking into account.
Concerning the effect of age, we found older
individuals having higher concentrations in blood and
urine (As, Cu, Mg, and Zn), and younger individuals
having higher concentrations in nails and hair (As, Cr,
Fe, Mg, Mn, and Se). These differences, besides
different patterns of exposure, may be due to different
toxicokinetic rates, possibly because of the lower
proportion of water in the organism, lower absorption,
and excretion rates, and possible nutritional deficien-
cies, and comorbidities in older individuals.
Finally, the analysis of the effect of smoking showed
that smokers have significantly higher concentration of
several elements, although the levels of Hg in hair were
higher in non-smokers. This finding was not expected
since tobacco smoke contains Hg, but a similar result
was previously reported by Chojnacka et al. (2006).
Conclusions
Overall our results agree with those obtained in the
environmental studies performed in Panasqueira mine
area, showing as populations living nearby and
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working in the mine are exposed to several metal(loid)s
originated by mining activities. The most important
exposure seemed to be to As, particularly in individuals
exposed through the environment. Exposure to other
elements such as Cr, Mn, Ni, Pb, Se, and Zn also occurs
but not at the same extent (amount of element/time of
exposure). Our results indicate that the environmen-
tally exposed group is more affected, specifically
females, as they presented significantly higher values
of the most toxic elements, i.e. As, Cr, Mn, and Ni.
These findings highlight once more the need for the
competent authorities to intervene in this area in order
to prevent that severe health consequences occur in
exposed populations. Not only human populations are
at risk but the entire ecosystem.
Our results show also major effects of host factors
and smoking habits on the levels of metal(loid)s in the
different biological samples analysed, strongly influ-
encing the results obtained. Studies with a higher
number of individuals, analysing all the elements in all
the matrices, need to be performed in order to better
characterize the factors influencing the exposure, the
toxicokinetic processes in the population groups, and
the feasibility of the different biological samples for
exposure assessment.
Acknowledgments This workwas supported by the Portuguese
Foundation for Science and Technology through Grants SFRH/
BD/47781/2008, SFRH/BD/63343/2009, and SFRH/BPD/26689/
2006, and project PTDC/SAU-ESA/102367/2008. The work of
Stefano Bonassi, Valentina Dall’Armi, and Roberto Zoffoli was
supported by a grant funded by the Associazione Italiana per la
Ricerca sul Cancro (AIRC).
Conflict of interest The authors declare that there are no
conflicts of interest.
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