Environmental Pollution xxx (2011) 1e6
Contents lists available at ScienceDirect
Environmental Pollution
journal homepage: www.elsevier.com/locate/envpol
Indoor metallic pollution related to mining activity in the Bolivian Altiplano
Francisco E Fontúrbel a, *, Enio Barbieri b, Cristian Herbas c, Flavia L Barbieri b, d, Jacques Gardon b, d
a
Departamento de Ciencias Ecológicas, Facultad de Ciencias, Universidad de Chile, Santiago, Chile
IRD-HSM (Institut de Recherche pour le Développement), La Paz, Bolivia
c
Universidad Mayor de San Andrés, IGEMA Institute (Instituto de Investigaciones Geológicas y del Medio Ambiente), La Paz, Bolivia
d
Universidad Mayor de San Andrés, SELADIS Institute (Instituto de Servicios de Laboratorio para el Diagnóstico e Investigación en Salud), La Paz, Bolivia
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 31 December 2010
Received in revised form
26 April 2011
Accepted 29 April 2011
The environmental pollution associated with mining and metallurgical activities reaches its greatest
extent in several Andean cities and villages. Many locations in this area have accumulated through
centuries a large amount of mining wastes, often disregarding the magnitude of this situation. However,
in these naturally mineralized regions, there is little information available stating the exact role of mining
and metallurgical industries in urban pollution. In this study, we demonstrated that the various metallic
elements present in indoor dust (As, Cd, Cu, Pb, Sb, Sn, Zn) had a common origin and this contamination
was increased by the proximity to the mines. Lead dust concentration was found at concerning levels for
public health. In addition, wrong behaviors such as carrying mining workwear home contributed to this
indoor dust pollution. Consequently, the constant exposure of the population could represent a potential
health hazard for vulnerable groups, especially children.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
Geostatistical analysis
Household dust
Human exposure
Metallic trace element
Mining pollution
1. Introduction
Altiplanic regions in South America are naturally rich in polymetallic deposits (Clark et al., 1990), which is why mining and
metallurgical activities play a fundamental role in the economy of
these populations. In the Bolivian Altiplano mining has been an
essential activity since pre-Colonial times (Abbott and Wolfe, 2003)
and many villages, towns and even cities were developed in close
proximity to the mines and smelters. In the last years, in spite of the
strong variations in the trading value of base metals, the global
demand still stands. As a consequence, extractive activities are
currently intense even at several formerly abandoned mines. For
economic, political, and social reasons, there is little or no control over
the consequent contamination. The mining wastes create a permanent source of pollution (Miller et al., 2004; Reif et al., 1989; Smolders
et al., 2003), especially in these arid and windy regions where metallic
elements deposited in soils can be easily dispersed through air.
In the Oruro mining district, where numerous mines and large
smelters are currently in activity, the environmental contamination
by a complex cocktail of Pb, Sn, Sb, As, Cd, and other elements is
known (Díaz-Barriga et al., 1997a, 1997b; PPO, 1997). The consequences of chronic exposure to these metallic elements are
potentially dangerous for children and women of childbearing age
(Calderón et al., 2001; Díaz-Barriga et al., 1997a; Koller et al., 2004;
* Corresponding author.
E-mail address: [email protected] (F.E. Fontúrbel).
Landrigan et al., 2004; Lidsky and Schneider, 2003). Handto-mouth dust ingestion constitutes an important pathway of
exposure especially for toddlers and young children. As a consequence, metallic element concentration in indoor dust is crucial in
describing the risk of exposure.
Our study aimed to verify the existence of a significantly higher
household contamination in a Bolivian mining town, comparing
indoor dust trace element concentrations obtained from the mining
district and from a peripheral district, relatively far from mining or
metallurgical activities.
2. Material and methods
2.1. Study area
The city of Oruro (17.97 Se67.10 W; Fig. 1) is located in the central Bolivian
Altiplanic region, at 3700 m of altitude above sea level. Oruro city has 220,000
inhabitants (w2.4% of the Bolivian population, according to the National 2001
Census: INE, 2001), mainly dedicated to mining and metallurgical activities and
trading (Langer, 2009; Montes de Oca, 1997). Because of its location at the topmost of
the Altiplano, Oruro is a windy city. According to the Bolivian Meteorological Service,
the average of daily maximum wind speeds was w15 km/h in 2006 (SENAMHI,
2011), reaching annual maximum wind speeds around w37 km/h from 2001 to
2010. The total annual precipitation for the same decade was w400 mm, with
a remarkable difference between the rainy season (w250 mm from December to
February) and the dry season (w11 mm from May to July). In the arid environment of
the Altiplano, the vegetation coverage of the soil is rather scarce. Additionally, its
location near several mining centers (which usually do not properly cover residual
materials, storage facilities, and trucks) and sand dunes makes Oruro a dusty city.
Those are risk factors for the metallic elements widespread.
0269-7491/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envpol.2011.04.039
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039
2
F.E. Fontúrbel et al. / Environmental Pollution xxx (2011) 1e6
Fig. 1. Sampling sites location in the urban area of Oruro.
2.2. Samples and data collection
We sampled two locations in Oruro; the first was San José (17.95 Se67.12 W;
hereafter mining district) chosen due to its proximity to two important mines of
Oruro: San José and La Colorada. The second location sampled was Villa Challacollo
(18.01 Se67.14 W; hereafter peripheral district), located at the southern periphery
of the city, approximately at 3.5 km from the nearest mine.
We sampled a total of 98 households, 57 at the mining district and 41 at the
peripheral district. We collected samples of around 1 g of dust from the top surfaces
of furniture or windowsills using a 1.5” brush previously treated with deionized and
ultrafiltered water. We kept the samples in sealed polyethylene bags to avoid
contamination and delivered them directly to the laboratory. We georeferenced each
house, using a GPS (Global Positioning System).
We also conducted a questionnaire on several socioeconomic factors, focusing
on the working habits of each family, especially regarding mining activities. We
specifically inquired whether someone in the family worked at the mine or not; if so,
we inquired if they carried home their work clothes or tools.
2.3. Sample preparation and analysis
We analyzed dust samples at the Geological and Environmental Research
Institute (IGEMA) of the Universidad Mayor de San Andrés in La Paz, Bolivia. We
determined metallic elements concentrations using ICPeOES (Inductively Coupled
PlasmaeOptical Emission Spectrometer). Considering the health impact of the
potential human exposure, we chose to measure Pb, As, Cd, Sb and Sn as nonessential (or potentially toxic) elements, as well as Cu and Zn as essential elements.
Each sample was previously homogenized; a sub-sample of 0.5 0.0001 g was
transferred to a 50 mL digestion tube with 6 mL of HCl and 2 mL of HNO3, and
digested in a microwave oven for 45 min. Samples were degasified, cooled, and
transferred to a 100 mL flask in order to extract some remaining insoluble silicate
particles. Standard solutions were prepared and the samples were analyzed at the
ICPeOES spectrometer. We used two types of reference materials, the first set was
provided by Canadian Association for Environmental Analytical Laboratories
(CAEAL), and the second set was provided by the National Research Council of
Canada; reference standards of the first were: C17 (metals in soils), C17e3 (low
range), C17e4 (high range); and for the second set: HISS-1 (low range, marine
sediment reference materials for trace elements and other constituents), MESS-3
(mid range), and PACS-2 (high range).
2.4. Data analysis
We checked data for normality with the KolmogoroveSmirnov test and used
logarithmic or BoxeCox transformations to normalize data distributions, when
necessary. To assess differences in metal concentrations between the mining and
peripheral districts, we performed a one-way Multivariate Analysis of Variance
(MANOVA), operating the seven metallic concentration variables as a single
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039
F.E. Fontúrbel et al. / Environmental Pollution xxx (2011) 1e6
Table 1
Theoretical semivariogram models of prediction and data transformations.
Metal
Method
Semivariogram
model
Nugget/sill
Transformation
As
Cd
Cu
Pb
Sb
Sn
Zn
Ordinary Kriging
Ordinary Kriging
Ordinary Kriging
Ordinary Kriging
Simple Kriging
Ordinary Kriging
Ordinary Kriging
Spherical
Circular
Rational quadratic
Rational quadratic
Rational quadratic
Exponential
Exponential
0.776
0.761
1.506
0.939
1.699
0.997
0.956
Log
BoxeCox
Log
Log
Log
Log
Log
3
At the mining district, having at least one family member working
in a mine significantly increased the polymetallic indoor dust
concentrations (Wilks’ lambda ¼ 4.85, hypothesis df ¼ 7, error
df ¼ 49, p < 0.001). Regarding people’s habits, carrying work tools
home had no significant effect on indoor polymetallic concentrations (Wilks’ lambda ¼ 0.76, hypothesis df ¼ 7, error df ¼ 20, p ¼ 0.
62), but these significantly increased when work clothes were
carried home (Wilks’ lambda ¼ 3.06, hypothesis df ¼ 7, error
df ¼ 20, p ¼ 0.02).
3.2. Geostatistical analysis and spatial distribution
response vector. Then, we performed additional MANOVA tests only for the mining
district data (because there were no miner workers at the peripheral district), in
order to test effects of: (1) having at least one person working at the mine, (2) if work
clothes were carried home, and (3) if work tools were carried home. We did not test
interactions because the statistic power was <0.85 for those tests. We chose
Spearman correlation to construct correlation matrix separately for each district
(based on the untransformed data), because the normalizations of trace element
distributions were not obtained with the same transformation. For each test,
equality of variances was checked through a Levene test (Sokal and Rohlf, 1995). All
analyses were conducted using PASW Statistics 18 (SPSS Inc., 2009).
2.5. Geostatistical analysis based on GIS
We estimated the distances from each sampling point to the mines using ArcGIS
v.9.1 (ESRI Co., Redlands CA). We employed spatial interpolation and GIS mapping
techniques to generate prediction maps with the spatial distributions of the seven
assessed metallic elements. All procedures were conducted using the Geostatistical
Analyst module of ArcGIS, following Shi et al. (2008). We used a Kriging interpolation, with data transformations when necessary to ensure the normality of
residuals (Table 1). Theoretical semivariogram models were used to build the
prediction maps, a nugget/sill ratio criterion was used to optimize data sets reducing
the spatial autocorrelation (Shi et al., 2008).
Using only the mining district points (n ¼ 57), we have estimated critical
distances for each metallic element. As there are no maximum tolerable limit values
published for domestic dust, for all the elements assessed here, we defined the
critical concentration area as the maximum radius of the highest concentration
contour on the prediction maps, which is calculated from the largest distance vector,
derived from the semivariogram model. Consequently, ranges with the highest
metal concentration were: 364e636 ppm for As, 48e101 for Cd, 382e617 for Cu,
4036e8413 for Pb, 2712e6199 for Sb, 638e1172 for Sn, and 1310e2295 for Zn. We
repeated this analysis using only the Pb Kriging, since it is the most health-risky
element and the only one with a maximum tolerable limit in dust published by
the EPA (2001). Then, we estimated the risky area of Oruro, as the area comprised in
all contours, which Pb content in dust was above 400 ppm.
3. Results
3.1. Metallic trace element concentrations
For all metallic elements assessed, concentrations were higher
and more variable at the mining than at the peripheral district
(Table 2). Metal concentrations were strong and significantly
correlated at the mining district (Table 3), and varied significantly
between the mining and the peripheral districts (Wilks’
lambda ¼ 13.11, hypothesis df ¼ 7, error df ¼ 90, p < 0.001; Fig. 2).
Prediction maps showed a differential spatial distribution
among the seven assessed metals in Oruro (Fig. 3). Concentrations
at the mining district were critical for As, Cu, Pb, and Zn, whereas Sb
and Sn showed a more homogeneous spatial distribution, despite
also being higher at the mining district. From the mining district
data, we estimated a high-metallic concentration distance of
1113 184 m (mean 1SE), based on the higher concentration
contour of each element. When considering only Pb content in dust,
and the 400-ppm limit defined by EPA for Pb contents in soil, we
estimated a high exposure area of 1305 ha, above the permitted
limits. With such approach, this area is not limited to the mining
district, but also comprises downtown and the some peripheral
area southward.
4. Discussion
The present results allowed us to assess the general situation of
indoor metallic trace elements contamination in the city of Oruro. It
showed us effectively which elements were present in the household dust and, consequently, in direct contact with the people from
the two selected districts.
As seen on the prediction maps, the spatial distribution of the
metallic elements in dust is not homogeneous. We found
a considerable difference in the metallic elements concentrations
between the two districts, being consistently higher in the Mining
district. The most remarkable case is in the concentrations of Pb
and Sb, which were four times higher in the Mining district than in
the Peripheral district.
There was a strong correlation among most metallic trace
elements (Table 3), suggesting a common origin for the polymetallic contents in household dust. This correlation was consistent
in both districts. Likewise, our prediction maps show that, in spite
of slight differences in the different metallic elements’ spatial
distribution, there is an identifiable pattern common to all of them.
The areas with the highest concentrations were very similar for all
the elements, significantly higher near around the Mining district.
Cases reporting metallic elements exposure emitted from workplaces can be found in the literature (Abdul-Wahab and Yaghi, 2004;
Maharachpong et al., 2006). Previous studies have demonstrated
Table 2
Metal concentrations (ppm) determined in domestic dust in Oruro.
Metallic element
As
Cd
Cu
Pb
Sb
Sn
Zn
Mining district
Peripheral district
Arithmetic mean
(Standard error)
Geometric mean
MineMax value
Arithmetic mean
(Standard error)
Geometric mean
MineMax value
143.38
15.96
114.30
1289.78
494.44
114.13
583.75
90.28
11.48
88.97
619.11
152.91
76.39
442.91
12.90e635.55
4.60e101.34
30.30e471.25
66.60e8413.45
21.50e6199.35
19.60e1172.35
119.30e2295.22
42.50
6.05
77.02
221.43
46.43
36.21
252.06
41.68
5.94
56.56
133.66
39.23
33.90
228.34
30.19e68.75
4.19e10.31
23.90e617.20
48.51e2415.25
18.11e203.65
19.49e76.87
132.00e843.38
(19.33)
(2.49)
(12.43)
(235.36)
(138.50)
(21.73)
(64.69)
(1.40)
(0.20)
(15.27)
(60.59)
(5.93)
(2.16)
(21.00)
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039
4
F.E. Fontúrbel et al. / Environmental Pollution xxx (2011) 1e6
Table 3
Spearman correlation coefficients of metal concentrations, values above the diagonal correspond to the mining district (n ¼ 57), and values below the diagonal correspond to
the peripheral district (n ¼ 41).
As
Cd
Cu
Pb
Sb
Sn
Zn
As
Cd
Cu
Pb
Sb
Sn
Zn
1.000
0.689**
0.674**
0.304
0.407*
0.666**
0.456*
0.882**
1.000
0.610**
0.332*
0.418*
0.690**
0.478*
0.806**
0.791**
1.000
0.324*
0.373*
0.673**
0.607**
0.880**
0.894**
0.766**
1.000
0.807**
0.403*
0.523**
0.785**
0.820**
0.795**
0.834**
1.000
0.505**
0.423*
0.839**
0.761**
0.783**
0.766**
0.761**
1.000
0.551**
0.734**
0.710**
0.532**
0.683**
0.523**
0.694**
1.000
* Significant at p < 0.05.
** Significant at p < 0.001.
Italic values stand for rank correlation above 0.6 and bold values above 0.8.
that when a stationary metal source exists nearby the city, there are
usually high metal concentrations in soils (Gallagher et al., 2008;
Madrid et al., 2008; Shi et al., 2008), which are usually correlated
with the dust concentrations (Louekari et al., 2004; Murgueytio et al.,
1998; Thornton et al., 1990; von Lindern et al., 2003). As a matter of
fact, the most significant factor in our study was the distance
between the household and the mines. Our geostatistical analyses
showed a high-exposure radius (ranging from 543 m to 1.8 km)
around the mines and mineral processing facilities, in the Mining
district. The San José district and its surroundings were not intended
to be a residential area (in fact, it was designed to be a mining camp),
but it gradually went populated by miners and their families, due to
its proximity to the work place. Later on, non-miners families also
contributed to the population of these mining areas, bringing also
commercial activities, a hospital, a school, etc.
However, metallic elements concentrations not only depended
on the distance to the mines. In the particular case of the miners,
the habits of carrying their workwear home contributed to increase
dust indoor metal concentrations, by means of carrying every day
metal particles into the household. This kind of relationship was
Fig. 2. Metallic elements profile by district. Z-score represents standardized data after
a log10 transformation (to smooth outliers). All mining-peripheral district pairwise
comparisons were significantly different (p < 0.05, after a ManneWhitney test), except
for Zn.
previously observed in other studies (Maharachpong et al., 2006;
Sutton et al., 1995).
Metal particles present in dust will no self-degrade or naturally
diminish in the environment, residing for a long time (Stigliani
et al., 1991). Consequently, they will accumulate as long as dust is
not removed, which is why the indoor dust metal concentrations
will increase proportionally according to the amount of dust. This
contaminated dust can be easily dispersed through wind (Fields,
2003; Louekari et al., 2004), and deposited as dust in houses,
schools, and streets.
For many physiological and behavioral reasons, children are
a vulnerable group for this particular scenario (Landrigan et al.,
2004). Furthermore, they are more likely to spend time doing
sports and other outdoors activities. This can increase their exposure, not only by airborne particles inhalation, but most importantly dust ingestion (Díaz-Barriga et al., 1997a; Landrigan and
Todd, 1994; Mielke et al., 1997).
Taking Pb as a major concern, the concentration in dust was
significantly higher at the mining district than in peripheral district
(see Pb bars on Fig. 2), being the mining district concentrations
considerably above the EPA (2001) reference levels for soil. Based
on EPA (2001), ATSDR (2005), and Díaz-Barriga et al. (1997a)
criteria, Pb ingestion in the mining district children might be up
to six times higher than those from the peripheral district, and also
up to five times higher than the Tolerable Daily Intake (TDI) values
recommended by FAO and WHO (Table 4).
Certain particular characteristics in the city of Oruro might
increase dust accumulation and the subsequent human exposure to
the metallic elements, such as the dry weather with strong winds,
lack of vegetation and dust roads. Besides, in the studied districts
the housing conditions are representative of a difficult socioeconomic situation (Table 5). Many houses lack ceiling or wall coating,
and around half of them have precarious floors, being considerably
unprotected against the wind and dust.
A recently published study assessed children exposure using
a biomarker in the city of Oruro (Barbieri et al., 2011). The
authors sampled hair from school-age children from different
districts of Oruro, including the mining district and the peripheral
district, aiming to compare the general situation of metallic trace
element exposure. The houses sampled in our study corresponded to the same children who participated in the study by
Barbieri et al. (2011). Consistently, in their study the children
from the mining and the metallurgical district showed significantly higher metallic concentrations in hair, compared to the
children from the other districts, being the peripheral district the
one with the lowest hair metallic levels in Oruro. Those results
corroborated our assumption that the children from the mining
district were more exposed to metallic elements, considering that
the sampled dust from their houses also showed the highest
metallic concentrations.
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039
F.E. Fontúrbel et al. / Environmental Pollution xxx (2011) 1e6
5
Fig. 3. Prediction maps of metal distribution in domestic dust in Oruro. Dotted polygons indicate the study locations and the dots indicate the sampling points.
Table 4
Pb daily intake (mg/day) for children according to location and for different dust
ingestion scenarios using geometric mean (GM) and 75th percentile (P75) of lead
concentrations in household dust.
Location
EPAa
GM e P75
ATSDRb
GM e P75
Díaz-Barriga
et al. (1997a),c
GM e P75
TDId
Mining district
Peripheral district
53e99
11e17
62e117
13e20
217e408
47e69
87.5
Dust ingestion criteria:
a
EPA: 85 mg of dust a day for a 6 years old child (2001).
b
ATSDR: 100 mg of dust a day (2005).
c
Díaz-Barriga et al.: 350 mg of dust a day (1997a).
d
Tolerable Daily Intake Values are based on FAO/WHO guidelines (UNEP, 2006),
and calculated with a mean weight of 25 kg.
5. Conclusions
There is a clear situation of metallic trace elements contamination in the city of Oruro, much higher in the surroundings of the
mines and mineral processing facilities. This constant exposure of
the population, especially children, could represent a significant
risk. We can only theorize about the potentially harmful impact on
human health and children development, but so far no health
impact studies have been published in this particular region. Either
way, considering the toxic potential of these elements, measures
could be undertaken to minimize children contact with contaminated dust, including some important changes in the population’s
habits. It would be suitable, in this mining city, to reconsider the
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039
6
F.E. Fontúrbel et al. / Environmental Pollution xxx (2011) 1e6
Table 5
General characteristics of the households in the city of Oruro, by district.
Household characteristics
Roof without ceiling
Wall without coating*
Precarious floor
House without essential services
Miner in the household
They bring home their clothes**
They bring home their tools
They bring home both their
clothes and their tools
Mining district
(n ¼ 57)
12
39
30
33
28
21
24
18
(21.05%)
(69.64%)
(52.63%)
(57.89%)
(49.12%)
(75.00%)
(85.71%)
(64.29%)
Peripheral district
(n ¼ 41)
12 (29.27%)
20 (48.78%)
19 (46.34%)
30 (73.17%)
None
*Significant difference at p ¼ 0.04.
**Significant difference at p ¼ 0.02.
general organization of their land use planning, in order to keep
a healthy distance between their mining/metallurgical areas and
their urban areas.
Acknowledgments
We thank all the house owners who allowed us to take samples
in their homes. S. Ignacio helped with the questionnaire surveys.
E. Silva helped with the statistical analyses. R. Llano collaborated
with a map. FEF was supported by a doctoral fellowship of the
Chilean Foundation for Science (CONICYT). This study was conducted in the frame of the ToxBol project, supported by the Institut
de Recherche pour le Développement (IRD) and the Agence
Nationale de la Recherche (ANR).
References
Abbott, M.B., Wolfe, A., 2003. Intensive pre-Incan metallurgy recorded by lake
sediments from the Bolivian Andes. Science 301, 1893e1895.
Abdul-Wahab, S., Yaghi, B., 2004. Total suspended dust and heavy metals levels
emitted from a workplace compared with nearby residential houses. Atmospheric Environment 38, 745e750.
ATSDR, 2005. Toxicological Profile for Lead. Agency for Toxic Substances and
Disease Registry, U.S. Department of Health and Human Services.
Barbieri, F., Cournil, A., Souza Sarkis, J., Bénéfice, E., Gardon, J., 2011. Hair trace
elements concentration to describe polymetallic mining waste exposure in
Bolivian Altiplano. Biological Trace Element Research 139, 10e23.
Calderón, J., Navarro, M.E., Jiménez-Capdeville, M.E., Santos-Díaz, M.A., Golden, A.,
Rodríguez-Leyva, I., Borja-Aburto, V., Díaz-Barriga, F., 2001. Exposure to arsenic
and lead and neuropsychological development in Mexican children. Environmental Research 85, 69e76.
Clark, A.H., Farrar, E., Kontak, D.J., Langridge, R.J., Arenas, M.J., France, L.J.,
McBride, S.L., Woodman, P.L., Wasteneys, H.A., Sandeman, H.A., Archibald, D.A.,
1990. Geologic and geochronological contrains on the metallogenic evolution of
the Andes of southeastern Peru. Economic Geology and the Bulletin of the
Society of Economic Geologists 85, 1520e1583.
Díaz-Barriga, F., Batres, L., Calderón, J., Lugo, A., Galvao, L., Lara, I., Rizo, P.,
Arroyave, M.E., McConnell, R., 1997a. The El Paso smelter 20 years later: residual
impact on Mexican children. Environmental Research 74, 11e16.
Díaz-Barriga, F., Hamel, J., Paz, E., Carrizales, L., Batres, L., Calderón, J., Galvao, L.,
Caldas, L.Q., McConnell, R., 1997b. Evaluación de riesgos para la salud en la
población expuesta a metales en Bolivia. Centro Panamericano de Ecología
Humana y Salud, México DF.
EPA, 2001. Residential Lead Hazard StandardseTSCA Section 403. U.S. Enviromental
Protection Agency.
Fields, S., 2003. The Earth’s open wounds. Abandoned and orphaned mines. Environmental Health Perspectives 111, 154e161.
Gallagher, F.J., Pechman, I., Bodgen, J.D., Grabosky, J., Weis, P., 2008. Soil metal
concentrations and productivity of Betula populifolia (gray birch) as measured
by field spectrometry and incremental annual growth in an abandoned urban
Brownfield in New Jersey. Environmental Pollution 156, 699e706.
INE, 2001. Censo de Población y Vivienda. Instituto Nacional de Estadística, La Paz.
http://www.ine.gob.bo (accesses 28.10.10).
Koller, K., Brown, T., Spurgeon, A., Levy, L., 2004. Recent developments in low-level
lead exposure and intellectual impairment in children. Environmental Health
Perspectives 112, 987e994.
Landrigan, P.J., Kimmel, C.A., Correa, A., Eskenazi, B., 2004. Children’s health and the
environment: public health issues and challenges for risk assessment. Environmental Health Perspectives 112, 257e265.
Landrigan, P.J., Todd, A.C., 1994. Lead poisoning. Western Journal of Medicine 161,
153e159.
Langer, E.D., 2009. Bringing the economic back in: Andean Indians and the
construction of the nation-state in nineteenth-century Bolivia. Journal of Latin
American Studies 41, 527e551.
Lidsky, T.I., Schneider, J.S., 2003. Lead neurotoxicity in children: basic mechanisms
and clinical correlates. Brain 126, 5e19.
Louekari, K., Mroueh, U.M., Maidell-Münster, L., Valkonen, S., Tuomi, T.,
Savolainen, K., 2004. Reducing the risks of children living near the site of
a former lead smeltery. Science of the Total Environment 319, 65e75.
Madrid, F., Díaz-Barrientos, E., Madrid, L., 2008. Availability and bio-accessibility of
metals in the clay fraction of urban soils of Sevilla. Environmental Pollution 156,
605e610.
Maharachpong, N., Geater, A., Chongsuvivatwong, V., 2006. Environmental and
childhood lead contamination in the proximity of boat-repair yards in southern
ThailanddI: pattern and factors related to soil and household dust lead levels.
Environmental Research 101, 294e303.
Mielke, H.W., Dugas, D., Mielke, P.W., Smith, K.S., Smith, S.L., Gonzales, C.R., 1997.
Associations between soil lead and childhood blood lead in urban new Orleans
and rural Lafourche Parish of Louisiana. Environmental Health Perspectives 105,
950e954.
Miller, J.R., Hudson-Edwards, K.A., Lechler, P.J., Preston, D., Macklin, M.G., 2004.
Heavy metal contamination of water, soil and produce within riverine
communities of the Rio Pilcomayo basin, Bolivia. Science of the Total Environment 320, 189e209.
Montes de Oca, I., 1997. Geografía y recursos naturales de Bolivia. EDOBOL, La Paz.
Murgueytio, A., Evans, R., Roberts, D., 1998. Relationship between soil and dust lead
in a lead mining area and blood lead levels. Journal of Exposure Analysis and
Environmental Epidemiology 8, 173e185.
PPO, 1997. The Oruro Pilot Project Ministerio de Desarrollo Sostenible y Medio
Ambiente, Swedish Geological AB, La Paz.
Reif, J.S., Ameghino, E., Aaronson, M.J., 1989. Chronic exposure of sheep to a zinc
smelter in Peru. Environmental Research 49, 40e49.
SENAMHI, 2011. SisMeteBase de datos oficial del Servicio Nacional de Meteorología
e Hidrología. accessed 21.04.11. Servicio Nacional de Meteorología e Hidrología,
La Paz. http://www.senamhi.gob.bo/sismet/index.php.
Shi, G., Chen, Z., Xu, S., Zhang, J., Wang, L., Bi, C., Teng, J., 2008. Potentially toxic
metal contamination of urban soils and roadside dust in Shanghai, China.
Environmental Pollution 156, 251e260.
Smolders, A.J., Lock, R.A., Van der Velde, G., Medina-Hoyos, R.I., Roelofs, J.G., 2003.
Effects of mining activities on heavy metal concentrations in water, sediment,
and macroinvertebrates in different reaches of the Pilcomayo River, South
America. Archives of Environmental Contamination and Toxicology 44, 314e323.
Sokal, R.R., Rohlf, F.J., 1995. Biometry: The Principles and Practice of Statistics in
Biological Research, third ed. Freeman and Co., New York.
SPSS Inc., 2009. PASW Statistics Version 18 Chicago, IL.
Stigliani, W.M., Doelman, P., Salomons, W., Schulin, R., Schmidt, G.R.B., Van Der
Zee, S.E.A., 1991. Chemical time bombs-predicting the unpredictable. Environment 33, 4e30.
Sutton, P.M., Athanasoulis, M., Flessel, P., Guirguis, G., Haan, M., Schlag, R.,
Goldman, L.R., 1995. Lead levels in the household environment of children in
three high-risk communities in California. Environmental Research 68, 45e57.
Thornton, I., Davies, D., Watt, J., Quinn, M., 1990. Lead exposure of young children
from dust and soil in the United Kingdom. Environmental Health Perspectives
89, 55e60.
UNEP, 2006. Interim Review of Scientific Information on Lead. United Nations
Environment Programme.
von Lindern, I.H., Spalinger, S.M., Bero, B.N., Petrosyan, V., von Braund, M.C., 2003.
The influence of soil remediation on lead in house dust. Science of the Total
Environment 303, 59e78.
Please cite this article in press as: Fontúrbel, F.E., et al., Indoor metallic pollution related to mining activity in the Bolivian Altiplano, Environmental Pollution (2011), doi:10.1016/j.envpol.2011.04.039