Science of the Total Environment 487 (2014) 13–19
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Indoor metallic pollution and children exposure in a mining city Enio Barbieri a,⁎, Francisco E. Fontúrbel b, Cristian Herbas c, Flavia L. Barbieri a,d,e, Jacques Gardon a,d,f a
IRD (Institut de Recherche pour le Développement), La Paz, Bolivia Departamento de Ciencias Ecológicas, Facultad de Ciencias, Universidad de Chile, Santiago de Chile, Chile c Instituto IGEMA, Universidad Mayor de San Andrés, La Paz, Bolivia d Universidad Mayor de San Andrés, SELADIS (Instituto de Servicios de Laboratorio para el Diagnóstico e Investigación en Salud), La Paz, Bolivia e Berlin School of Public Health, Charité Universitätsmedizin, Berlin, Germany f IRD, HSM, Montpellier, France b
H I G H L I G H T S • • • • •
Mining activities are an important source of environmental pollution. Mining pollution contaminated also indoor homes, creating a risk to population. Indoor dust and hair concentrations in As, Cd, Pb, Sb and Sn were correlated. No correlation was found for essential elements such as Cu or Zn. Children behavior modifies the exposure to certain elements.
a r t i c l e
i n f o
Article history: Received 6 January 2014 Received in revised form 24 March 2014 Accepted 31 March 2014 Available online xxxx Editor: P. Kassomenos Keywords: Children exposure Hair samples Household dust Metallic trace elements Mining pollution Metallic pollution
a b s t r a c t Mining industries are known for causing strong environmental contamination. In most developing countries, the management of mining wastes is not adequate, usually contaminating soil, water and air. This situation is a source of concern for human settlements located near mining centers, especially for vulnerable populations such as children. The aim of this study was to assess the correlations of the metallic concentrations between household dust and children hair, comparing these associations in two different contamination contexts: a mining district and a suburban non-mining area. We collected 113 hair samples from children between 7 and 12 years of age in elementary schools in the mining city of Oruro, Bolivia. We collected 97 indoor dust samples from their households, as well as information about the children's behavior. Analyses of hair and dust samples were conducted to measure As, Cd, Pb, Sb, Sn, Cu and Zn contents. In the mining district, there were significant correlations between non-essential metallic elements (As, Cd, Pb, Sb and Sn) in dust and hair, but not for essential elements (Cu and Zn), which remained after adjusting for children habits. Children who played with dirt had higher dust-hair correlations for Pb, Sb, and Cu (P = 0.006; 0.022 and 0.001 respectively) and children who put hands or toys in their mouths had higher dust-hair correlations of Cd (P = 0.011). On the contrary, in the suburban area, no significant correlations were found between metallic elements in dust and children hair and neither children behavior nor gender modified this lack of associations. Our results suggest that, in a context of high metallic contamination, indoor dust becomes an important exposure pathway for children, modulated by their playing behavior. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Several metallic trace elements are potentially toxic for human health, such as lead, arsenic, mercury, cadmium, manganese or antimony (Hu et al., 2007; Jarup, 2003; Walker et al., 2007). As most of these elements are known to be neurotoxic, most of the epidemiological ⁎ Corresponding author at: IRD Bolivia, Av. Hernando Siles # 5290, La Paz, Bolivia. Tel.: + 591 2 2782969; fax: +591 2 2782944. E-mail address: [email protected] (E. Barbieri).
http://dx.doi.org/10.1016/j.scitotenv.2014.03.136 0048-9697/© 2014 Elsevier B.V. All rights reserved.
studies about metallic elements have focused on their deleterious effects for the neurological development (Grandjean and Landrigan, 2006). Children are especially vulnerable to metallic exposure because of their physiological traits and also because of their particular behavioral characteristics such as crawling, playing with dirt and sucking or chewing toys (Hubal et al., 2000; Landrigan et al., 2004). Mining and metallurgical industries are known to have a strong impact on the environment. The exploited metals usually constitute just a portion of the mineral compounds present at most mining sites, while the remaining materials are discharged as waste. No other
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Fig. 1. Overview of the study area, showing the districts sampled, the main mining and mineral processing facilities, and the area covered by the Kriging interpolation depicted on Fig. 4.
industry generates such an amount of solid waste (Ayres and Ayres, 2002). Due to geochemical processes, the compounds present in the tailings and mining waste are potentially the origin of persistent sources of metallic pollution for water resources, air, and soil (Ayres and Ayres, 2002; Schwarzenbach et al., 2010). Soil pollution by metallic trace elements has been studied in many different contexts (Bjerre et al., 1993; Han et al., 2002; Markus and McBratney, 2001; Mielke et al., 1999; Senesi et al., 1999). It is common to observe that the intensity of the contamination is proportional to the distance to the pollution sources (Meyer et al., 1999b; Qu et al., 2012; Zota et al., 2011). Moreover, it has been demonstrated that metallic pollution can also reach indoor environments (e.g., households), increasing the risk of non-occupational exposure (Fontúrbel et al., 2011; Glorennec et al., 2012; Meyer et al., 1999a; Thornton et al., 1990; Zota et al., 2011). In developed countries, mining waste is often adequately managed. However, the situation is different in some mining sites located in developing countries. When resources are limited and the implementation of the laws and regulations is not very effective, tailings and waste piles remain exposed and subject to wind erosion and leaching
processes, becoming a certain source of pollution and may even be used as bulk-fill in improvised construction projects (Yáñez et al., 2002). Furthermore, it is not rare to find settlements and even towns in direct contact with mining pits, waste piles, ponds and artisanal smelters (e.g., Schaider et al., 2007; van Geen et al., 2012). A clear example of this situation is the city of Oruro (17.97°S– 67.10°W), a major Andean mining city in the Bolivian plateau at ~3700 m above sea level. With a current population of approximately 250,000 inhabitants (INE, 2001), this city has been historically dedicated to mining, metallurgical and metal trading activities for centuries. Like many other Andean cities, Oruro is cold and arid, with long dry seasons – usually from April to November – and strong winds, which contribute to making it a dusty city. Besides, even though it is in an urban context, many roads are still unpaved and most mining tailings are uncovered. Previous studies have shown the presence of metallic trace elements in Oruro (Goix et al., 2011), particularly inside the households (Fontúrbel et al., 2011) of the mining and metallurgical districts, where the contamination largely exceeds the United States Environmental Protection Agency (US EPA) recommendations for residential
Table 1 Median concentrations of trace elements measured at mining (N = 56) and suburban (N = 41) districts. Percentiles 5% and 95% are shown in parentheses. Element
As Cd Pb Sb Sn Cu Zu
Dust samples (ppm)
Hair samples (μg/g of hair)
Mining
Suburban
Mining
Suburban
88.60 (18.54–469.51) 10.40 (5.10–38.15) 560.60 (87.86–5198.00) 108.70 (27.08–2770.28) 63.40 (25.96–309.12) 80.15 (31.70–325.50) 414.54 (167.80–1705.50)
40.61 (32.50–58.21) 5.78 (4.80–8.99) 103.65 (57.95–742.78) 33.96 (23.47–84.31) 32.17 (19.85–61.57) 49.18 (25.86–175.18) 199.75 (142.00–442.64)
0.89 (0.10–3.19) 0.14 (0.00–1.65) 13.26 (3.36–65.36) 0.37 (0.00–4.11) 0.19 (0.06–0.66) 10.73 (3.61–39.96) 118.97 (37.44–350.70)
0.38 (0.12–1.52) 0.08 (0.03–0.26) 1.88 (0.56–8.01) 0.09 (0.05–0.19) 0.09 (0.04–0.17) 8.02 (4.59–10.83) 122.52 (40.80–187.35)
E. Barbieri et al. / Science of the Total Environment 487 (2014) 13–19 Table 2 Household characteristics for the mining and suburban districts. Distance to mining centers (San José, Itos, and La Colorada mines) was estimated using GPS data; figures correspond to mean ± 1SE. The remaining data were obtained using questionaires and interviews. Feature
District Mining
Suburban
Distance to mining centers (m) Roof without ceiling Uncoated walls Precarious floors Essential services coverage
759 ± 57 21.05% 69.64% 52.63% 42.11%
4600 ± 65 29.27% 48.78% 46.34% 26.83%
dust (US EPA, 2001). School-aged children seemed exposed to multiple metallic trace elements in this population, particularly in the mining and metallurgical districts (Barbieri et al., 2011). The aim of this study was to assess the correlations of the metallic concentrations between household dust and children hair, comparing these associations in two different contamination contexts: a mining district and a suburban non-mining area. Additionally, we assessed the influence of children behavior and other determinants on the correlations between metallic elements in household dust and children hair.
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2. Materials and methods The present cross-sectional study was conducted in the frame of the ToxBol project, a multidisciplinary research project that assessed the origin of poly-metallic contamination and its impact on the environment, health and society in a Bolivian mining city. 2.1. Data collection On the first phase of the ToxBol project, we selected five elementary schools in five different districts of Oruro. After parental consent was signed, we collected hair samples from children between 7 and 12 years old for metallic trace elements (MTE) measurements using ICP-MS. Detailed procedures of the sample collection and laboratory analysis can be found in a previous study (Barbieri et al., 2011) and on Appendix A, available online. A few personal data were also collected, including home address and a brief physical examination. For the second phase, we chose two of the five districts from the first phase to collect indoor dust samples from the households of the sampled children for MTE measurements using ICP-OES. Detailed procedures of the sample collection and laboratory analysis can be found in a previous study (Fontúrbel et al., 2011) and on Appendix A. We chose to contrast a mining urban district, located directly around mining tailings, and a suburban non-mining district, distant from mining and metallurgical centers (Fig. 1). A detailed questionnaire was filled by interview, gathering information about household characteristics, socioeconomic data and children behavior (handto-mouth behavior, sucking toys, eating dirt, playing with dirt, etc.). The geographic coordinates of each household were taken using a Garmin Vista GPS device (Garmin Co., Olathe, KS). 2.2. Statistical analyses
Fig. 2. Spearman correlation coefficients and 95% bias-corrected confidence intervals for (a) the Mine district, and (b) the Suburban district.
As our data were non-normally distributed, we calculated the Spearman correlation coefficients between hair and dust metallic element concentrations, analyzing mining and suburban districts separately. We calculated the bias-corrected and accelerated (BCa) 95% confidence intervals using a bootstrapping procedure with 1000 permutations. In order to evaluate the metallic mixture as a whole, we used a Canonical Correlation Analysis (CCA) procedure (González et al., 2008) aiming to compare the hair–dust correlations between the mining and the suburban districts. We ran the CCA analysis with the whole dataset (i.e., both mining and suburban districts) of metallic elements (As, Cd, Pb, Sb, Sn, Cu, Zn). We conducted a classical CCA in order to obtain the correlation-temperature plot and a base CCA model that allow a visual comparison of the correlation between trace element concentrations of hair and indoor dust samples. Then, we conducted a regularized CCA procedure using the lambda values estimated from the classical CCA, in order to maximize the variance explained by the first two canonical dimensions (González et al., 2008), which were plotted to visually depict the correlations by element and source (hair or indoor dust), as well as by case ID (i.e., each sampled house). We depicted spatial representations for the purpose of comparing two contrasting elements (lead as non-essential and zinc as essential), based on the associations observed between their concentrations in household dust and children hair. Using the geographic coordinates and the metallic concentrations in household dust, we constructed Kriging maps for both elements, where the darker shades of blue represent higher concentrations. Next, we overlaid hair concentrations of lead and zinc as dots in a color scale ranging from yellow (lowest) to red (highest). These maps were elaborated using ArcGIS v.10.1 (ESRI Co., Redlands, CA), following the methods detailed in Fontúrbel et al. (2011). To assess the possible associations between the children behavioral determinants and their exposure to metallic trace elements, we
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Fig. 3. (a) Temperature correlation plot for essential and non-essential elements measured. (b) Canonical distribution of the variables (left panel, “D” denotes dust samples and “H” hair samples) and the study cases (right panel). Individual houses were represented by numbers, from which 1 to 56 corresponded to the mine district and 57 to 97 to the suburban district.
conducted a generalized linear model (GLM) with each element concentration in hair samples as dependent variable and indoor dust metallic trace element concentrations as independent variable. The covariates included in the model were gender and behavioral determinants: playing with dirt, sucking toys, sucking hands/fingers, etc. Because our variables were non-normally distributed, we used a bootstrapping procedure to test the significance level of each coefficient of GLM models with 5000 permutations per model, using the package ‘boot’ in R 2.15 (Canty and Ripley, 2012; R Core Development Team, 2012). 3. Results From the 61 sampled children in the mining district and the 52 sampled children in the suburban district, we obtained 56 and 41 household dust samples respectively (Table 1). Eight hair samples showed values of Sb and Cd below detection limit and were not considered for the analyses. The main household characteristics can be seen on Table 2.
As seen in Fig. 2a, metallic concentrations in household dust were significantly correlated with their concentrations in children hair for lead (Spearman rs = 0.535, P = 0.001), arsenic (rs = 0.408, P = 0.018) and antimony (rs = 0.492, P = 0.001) in the mining area. We observed similar associations in cadmium and tin, even though were statistically non-significant (rs = 0.266, P = 0.125 and rs = 0.261, P = 0.235, respectively). In the suburban area no significant correlations were observed (Fig. 2b). The canonical correlation analysis showed that correlations of trace element concentrations between hair and indoor dust samples were stronger at the mining district than at the suburban district, and also were stronger for non-essential elements (arsenic, cadmium, lead, antimony and tin) than for essential ones (copper and zinc), especially for lead (Fig. 3a) at the cross-correlation analysis (comparing both districts), which was consistent with the results of univariate Spearman correlations. Correlations between hair and indoor dust metallic concentrations showed a relatively even distribution along the first
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Fig. 4. Spatial distribution of the hair samples (points) and dust samples (expressed through a Kriging layer) for (a) lead and (b) zinc.
canonical dimension, but more variable at the second one (Fig. 3b) separating two clouds of points: one for the trace element concentrations in indoor dust samples (marked with ‘_D’) and another for concentration on hair samples, drawing attention the proximity of lead concentration in both hair (Pb_H) and dust (Pb_D) concentrations. On the contrary, the case ID plot showed a fairly variable distribution in both dimensions, being the correlations from the mining districts (case IDs 1 to 56) more ‘disperse’ than those from the suburban district (case IDs 57 to 97).
Table 3 GLM estimates of the determinants of the hair concentration in (a) mining and (b) suburban districts. Variable
As
(a) Mine district Intercept 2.01⁎ Dust 0.23⁎ Male 0.37⁎ Playa Mouthb
0.21 0.03
(b) Suburban district Intercept 2.46⁎ Dust −0.10 Male −0.20 0.23 Playa b Mouth 0.15
Cd
Pb
Sb
Sn
Cu 4.17⁎ −0.15 −0.09 0.56⁎ −0.27
4.89⁎ 0.73 0.06 0.25 0.50
4.06⁎ −0.09 0.02 0.07 −0.09
5.01⁎ −0.26 0.24 0.10 0.01
1.42⁎ 0.59⁎
2.74⁎ 0.36⁎
1.37⁎ 0.41⁎
−0.13 0.23 0.23⁎
0.09 0.42⁎ −0.01
−0.15 0.50⁎ 0.23
1.73⁎ 0.19 0.12 0.20 −0.02
1.28⁎ −0.04 0.06 0.22 0.56
2.80⁎ 0.17 −0.30 0.15 0.20
1.78⁎ 0.29 −0.11 −0.10 0.04
1.33⁎ 0.14 −0.15 0.03 0.36
a “Play with dirt” (0/1); b“hand-to-mouth behavior or sucking toys” (0/1). ⁎Significant coefficients at P b 0.01 after bootstrapping procedures.
Zn
The spatial representation of Pb (Fig. 4a) and Zn (Fig. 4b) shows higher concentrations of both metals in the mining district, both for household dust and children hair, observed by the shade of blue on the Kriging representation and the red and orange dots. In fact, even within the mining district, the highest hair concentrations correspond also to the highest dust concentrations. In the suburban non-mining district, both metals show lower concentrations in household dust and children hair; both metallic concentrations look very heterogeneous and no pattern or tendency can be observed. After including gender and behavioral determinants in the GLM as covariates, the associations between indoor dust concentrations and hair concentrations remained significant for arsenic, cadmium, lead and antimony in the mining district (Table 3a). In particular, these associations were significantly stronger in children who played with dirt for Pb, Sb and Cu, in children who sucked their hands or toys for Cd and in boys, compared to girls, for As. On the contrary, in the suburban area neither dust concentrations nor gender and behavioral variables were associated with hair concentration of metallic trace elements (Table 3b). 4. Discussion The obtained results showed that indoor dust metallic contamination seems to be strongly associated with children exposure to these metallic elements, particularly in populations living in close proximity of mining centers. Even considering that the combination or “cocktail” of metallic trace elements found in household dust and children
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hair corresponds to the chemical background of the Oruro area (Tapia et al., 2012), there was a clear increase in metallic elements concentrations in the mining district. Not only the overall indoor dust and hair concentrations of nonessential metallic elements were significantly higher in the mining district (Barbieri et al., 2011; Fontúrbel et al., 2011), but also the associations between these elements in household dust and children hair were stronger, particularly in the case of lead, as confirmed by a strong correlation coefficient (rs = 0.535, P = 0.001). Socioeconomic and environmental determinants could be interacting with mining pollution in this case. Unpaved roads and household characteristics (e.g., walls without coating, precarious floors) might also increase the exposure to potentially toxic trace elements, because they may allow more residues of metallic trace elements to accumulate in household dust (Fontúrbel et al., 2011). Children behavior seemed to be a modulating factor in the exposure to metallic elements available in indoor dust (Lanphear et al., 1995). Even though gender appeared to have a direct association with hair metallic concentrations in the univariate analysis, the statistical significance was lost when behavioral determinants were included (namely playing with dirt), suggesting a difference between both genders based on behavioral and cultural factors, such as boys playing outside more than girls, for example. Indeed, the correlation between lead concentrations in indoor dust and children hair was stronger in children with the habit of playing with dirt in the mining neighborhood. Either for indoor dust or for child exposure we used unconventional indicators. Wipes are commonly used to evaluate trace element concentrations by surface (mg/cm2), existing references for some elements (US EPA, 1995). In the vast majority of the households that we visited during this study, the accumulation of dust was abundant for several reasons, which is why we considered that the concentration by weight of collected dust would be optimal in this population, instead of the calculation by surface. The concentration of lead in household dust has shown to be a good predictor of blood lead concentrations in children (Lanphear et al., 1995). However, as discussed in the previous publication by Barbieri et al. (2011), hair was chosen as a non-invasive biomarker because without a clear clinical impact of this situation, in this context it was almost impossible to obtain adhesion of the parents for blood screening campaigns (Fraser, 2006). It is always difficult to interpret whether the correlations observed between indoor dust and hair concentrations are the result of a passive adsorption of dust facilitated by the scale structure of the surface of hair, or the product of biological integration of elements absorbed and linked with the sulfur-containing amino acids, abundantly present in keratin (Robbins, 2012). The total lack of correlation with zinc could reinforce the second hypothesis: even though the Kriging representation shows higher Zn concentrations in hair which seem to correspond to higher Zn concentrations in dust, no significant correlation was observed and the GLM found no association. In humans, most ingested zinc is eliminated in the feces, comprising unabsorbed zinc and endogenous zinc from bile, pancreatic fluid and intestinal mucosa cells (World Health Organization, 2011). The complex mechanisms involved in the regulation of Zn elimination could explain the absence of correlation between contamination and exposure. This type of context, with concomitant Pb–Zn pollution provided an interesting situation and it would be interesting to test our results elsewhere. 5. Conclusion Even though the populations surrounding mining areas are exposed to metallic trace elements from a number of different sources and pathways, our results suggest that the household dust contamination with non-essential (and toxic) metallic elements plays an important role in human exposure, particularly for children. In a context of high metallic contamination, this association between the indoor environment and the exposure seems to be modulated by the children's playing behavior.
In this context, children grow in close contact with several pollution sources through various pathways and it can be particularly difficult for the authorities to balance risk prevention within industrial development policies. However, indoor household mining pollution appears to be an important issue and should be taken into consideration for prevention and mitigation actions to be taken. Besides improving household conditions per se, it is important to also address social and cultural determinants with educational campaigns aimed at both adults and children. The assessment of the actual health impact of this exposure on such mining populations is open to further studies. Conflict of interest We declare no interest conflicts among us or with other colleagues. Acknowledgments We are grateful to the children and their families, who allowed taking hair and dust samples, and kindly answered our questionnaire. S. Ignacio assisted with the interviews. This study was conducted in the frame of the ToxBol project, funded by the Institut de Recherche pour le Développement (IRD) and the Agence Nationale de la Recherche (ANR) (39SEST06). Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2014.03.136. These data include the Google map of the most important areas described in this article. References Ayres RU, Ayres LW. A handbook of industrial ecology. Cheltenham, UK; Northampton, MA: Edward Elgar Pub.; 2002. Barbieri FL, Cournil A, Souza Sarkis JE, Benefice E, Gardon J. Hair trace elements concentration to describe polymetallic mining waste exposure in Bolivian Altiplano. Biol Trace Elem Res 2011;139:10–23. http://dx.doi.org/10.1007/s12011-010-8641-1. Bjerre B, Berglund M, Harsbo K, Hellman B. Blood lead concentrations of Swedish preschool children in a community with high lead levels from mine waste in soil and dust. Scand J Work Environ Health 1993;19:154–61. Canty A, Ripley B. Bootstrap R (S-plus) functions. R package version 1.3-5; 2012. Fontúrbel FE, Barbieri E, Herbas C, Barbieri FL, Gardon J. Indoor metallic pollution related to mining activity in the Bolivian Altiplano. Environ Pollut 2011;159:2870–5. http://dx.doi.org/10.1016/j.envpol.2011.04.039. Fraser B. Peruvian mining town must balance health and economics. Lancet 2006;367: 889–90. http://dx.doi.org/10.1016/S0140-6736(06)68363-3. Glorennec P, Lucas JP, Mandin C, Le Bot B. French children's exposure to metals via ingestion of indoor dust, outdoor playground dust and soil: contamination data. Environ Int 2012;45:129–34. http://dx.doi.org/10.1016/j.envint.2012.04.010. Goix S, Point D, Oliva P, Polve M, Duprey JL, Mazurek H, et al. Influence of source distribution and geochemical composition of aerosols on children exposure in the large polymetallic mining region of the Bolivian Altiplano. Sci Total Environ 2011;412:170–84. http://dx.doi.org/10.1016/J.Scitotenv.2011.09.065. González I, Dejean S, Martin PGP, Baccini A. CCA: an R package to extend canonical correlation analysis. J Stat Softw 2008;23:1–14. Grandjean P, Landrigan PJ. Developmental neurotoxicity of industrial chemicals. Lancet 2006;368:2167–78. http://dx.doi.org/10.1016/S0140-6736(06)69665-7. Han FX, Banin A, Su Y, Monts DL, Plodinec MJ, Kingery WL, et al. Industrial age anthropogenic inputs of heavy metals into the pedosphere. Naturwissenschaften 2002;89: 497–504. http://dx.doi.org/10.1007/s00114-002-0373-4. Hu H, Shine J, Wright RO. The challenge posed to children's health by mixtures of toxic waste: the Tar Creek superfund site as a case-study. Pediatr Clin North Am 2007; 54:155–75. http://dx.doi.org/10.1016/j.pcl.2006.11.009. Hubal EAC, Sheldon LS, Burke JM, McCurdy TR, Barry MR, Rigas ML, et al. Children's exposure assessment: a review of factors influencing children's exposure, and the data available to characterize and assess that exposure. Environ Health Perspect 2000;108:475–86. http://dx.doi.org/10.2307/3454607. INE. Censo de Población y Vivienda. La Paz: Instituto Nacional de Estadística; 2001 [Accessed 28/10/2010. [http://www.ine.gob.bo]]. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003;68:167–82. http://dx.doi.org/10.1093/Bmb/Ldg032. Landrigan PJ, Kimmel CA, Correa A, Eskenazi B. Children's health and the environment: public health issues and challenges for risk assessment. Environ Health Perspect 2004;112:257–65. http://dx.doi.org/10.1289/ehp.6115.
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Indoor metallic pollution and children exposure in a mining city Enio Barbieri a,⁎, Francisco E. Fontúrbel b, Cristian Herbas c, Flavia L. Barbieri a,d,e, Jacques Gardon a,d,f a
IRD (Institut de Recherche pour le Développement), La Paz, Bolivia Departamento de Ciencias Ecológicas, Facultad de Ciencias, Universidad de Chile, Santiago de Chile, Chile c Instituto IGEMA, Universidad Mayor de San Andrés, La Paz, Bolivia d Universidad Mayor de San Andrés, SELADIS (Instituto de Servicios de Laboratorio para el Diagnóstico e Investigación en Salud), La Paz, Bolivia e Berlin School of Public Health, Charité Universitätsmedizin, Berlin, Germany f IRD, HSM, Montpellier, France b
H I G H L I G H T S • • • • •
Mining activities are an important source of environmental pollution. Mining pollution contaminated also indoor homes, creating a risk to population. Indoor dust and hair concentrations in As, Cd, Pb, Sb and Sn were correlated. No correlation was found for essential elements such as Cu or Zn. Children behavior modifies the exposure to certain elements.
a r t i c l e
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Article history: Received 6 January 2014 Received in revised form 24 March 2014 Accepted 31 March 2014 Available online xxxx Editor: P. Kassomenos Keywords: Children exposure Hair samples Household dust Metallic trace elements Mining pollution Metallic pollution
a b s t r a c t Mining industries are known for causing strong environmental contamination. In most developing countries, the management of mining wastes is not adequate, usually contaminating soil, water and air. This situation is a source of concern for human settlements located near mining centers, especially for vulnerable populations such as children. The aim of this study was to assess the correlations of the metallic concentrations between household dust and children hair, comparing these associations in two different contamination contexts: a mining district and a suburban non-mining area. We collected 113 hair samples from children between 7 and 12 years of age in elementary schools in the mining city of Oruro, Bolivia. We collected 97 indoor dust samples from their households, as well as information about the children's behavior. Analyses of hair and dust samples were conducted to measure As, Cd, Pb, Sb, Sn, Cu and Zn contents. In the mining district, there were significant correlations between non-essential metallic elements (As, Cd, Pb, Sb and Sn) in dust and hair, but not for essential elements (Cu and Zn), which remained after adjusting for children habits. Children who played with dirt had higher dust-hair correlations for Pb, Sb, and Cu (P = 0.006; 0.022 and 0.001 respectively) and children who put hands or toys in their mouths had higher dust-hair correlations of Cd (P = 0.011). On the contrary, in the suburban area, no significant correlations were found between metallic elements in dust and children hair and neither children behavior nor gender modified this lack of associations. Our results suggest that, in a context of high metallic contamination, indoor dust becomes an important exposure pathway for children, modulated by their playing behavior. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Several metallic trace elements are potentially toxic for human health, such as lead, arsenic, mercury, cadmium, manganese or antimony (Hu et al., 2007; Jarup, 2003; Walker et al., 2007). As most of these elements are known to be neurotoxic, most of the epidemiological ⁎ Corresponding author at: IRD Bolivia, Av. Hernando Siles # 5290, La Paz, Bolivia. Tel.: + 591 2 2782969; fax: +591 2 2782944. E-mail address: [email protected] (E. Barbieri).
http://dx.doi.org/10.1016/j.scitotenv.2014.03.136 0048-9697/© 2014 Elsevier B.V. All rights reserved.
studies about metallic elements have focused on their deleterious effects for the neurological development (Grandjean and Landrigan, 2006). Children are especially vulnerable to metallic exposure because of their physiological traits and also because of their particular behavioral characteristics such as crawling, playing with dirt and sucking or chewing toys (Hubal et al., 2000; Landrigan et al., 2004). Mining and metallurgical industries are known to have a strong impact on the environment. The exploited metals usually constitute just a portion of the mineral compounds present at most mining sites, while the remaining materials are discharged as waste. No other
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Fig. 1. Overview of the study area, showing the districts sampled, the main mining and mineral processing facilities, and the area covered by the Kriging interpolation depicted on Fig. 4.
industry generates such an amount of solid waste (Ayres and Ayres, 2002). Due to geochemical processes, the compounds present in the tailings and mining waste are potentially the origin of persistent sources of metallic pollution for water resources, air, and soil (Ayres and Ayres, 2002; Schwarzenbach et al., 2010). Soil pollution by metallic trace elements has been studied in many different contexts (Bjerre et al., 1993; Han et al., 2002; Markus and McBratney, 2001; Mielke et al., 1999; Senesi et al., 1999). It is common to observe that the intensity of the contamination is proportional to the distance to the pollution sources (Meyer et al., 1999b; Qu et al., 2012; Zota et al., 2011). Moreover, it has been demonstrated that metallic pollution can also reach indoor environments (e.g., households), increasing the risk of non-occupational exposure (Fontúrbel et al., 2011; Glorennec et al., 2012; Meyer et al., 1999a; Thornton et al., 1990; Zota et al., 2011). In developed countries, mining waste is often adequately managed. However, the situation is different in some mining sites located in developing countries. When resources are limited and the implementation of the laws and regulations is not very effective, tailings and waste piles remain exposed and subject to wind erosion and leaching
processes, becoming a certain source of pollution and may even be used as bulk-fill in improvised construction projects (Yáñez et al., 2002). Furthermore, it is not rare to find settlements and even towns in direct contact with mining pits, waste piles, ponds and artisanal smelters (e.g., Schaider et al., 2007; van Geen et al., 2012). A clear example of this situation is the city of Oruro (17.97°S– 67.10°W), a major Andean mining city in the Bolivian plateau at ~3700 m above sea level. With a current population of approximately 250,000 inhabitants (INE, 2001), this city has been historically dedicated to mining, metallurgical and metal trading activities for centuries. Like many other Andean cities, Oruro is cold and arid, with long dry seasons – usually from April to November – and strong winds, which contribute to making it a dusty city. Besides, even though it is in an urban context, many roads are still unpaved and most mining tailings are uncovered. Previous studies have shown the presence of metallic trace elements in Oruro (Goix et al., 2011), particularly inside the households (Fontúrbel et al., 2011) of the mining and metallurgical districts, where the contamination largely exceeds the United States Environmental Protection Agency (US EPA) recommendations for residential
Table 1 Median concentrations of trace elements measured at mining (N = 56) and suburban (N = 41) districts. Percentiles 5% and 95% are shown in parentheses. Element
As Cd Pb Sb Sn Cu Zu
Dust samples (ppm)
Hair samples (μg/g of hair)
Mining
Suburban
Mining
Suburban
88.60 (18.54–469.51) 10.40 (5.10–38.15) 560.60 (87.86–5198.00) 108.70 (27.08–2770.28) 63.40 (25.96–309.12) 80.15 (31.70–325.50) 414.54 (167.80–1705.50)
40.61 (32.50–58.21) 5.78 (4.80–8.99) 103.65 (57.95–742.78) 33.96 (23.47–84.31) 32.17 (19.85–61.57) 49.18 (25.86–175.18) 199.75 (142.00–442.64)
0.89 (0.10–3.19) 0.14 (0.00–1.65) 13.26 (3.36–65.36) 0.37 (0.00–4.11) 0.19 (0.06–0.66) 10.73 (3.61–39.96) 118.97 (37.44–350.70)
0.38 (0.12–1.52) 0.08 (0.03–0.26) 1.88 (0.56–8.01) 0.09 (0.05–0.19) 0.09 (0.04–0.17) 8.02 (4.59–10.83) 122.52 (40.80–187.35)
E. Barbieri et al. / Science of the Total Environment 487 (2014) 13–19 Table 2 Household characteristics for the mining and suburban districts. Distance to mining centers (San José, Itos, and La Colorada mines) was estimated using GPS data; figures correspond to mean ± 1SE. The remaining data were obtained using questionaires and interviews. Feature
District Mining
Suburban
Distance to mining centers (m) Roof without ceiling Uncoated walls Precarious floors Essential services coverage
759 ± 57 21.05% 69.64% 52.63% 42.11%
4600 ± 65 29.27% 48.78% 46.34% 26.83%
dust (US EPA, 2001). School-aged children seemed exposed to multiple metallic trace elements in this population, particularly in the mining and metallurgical districts (Barbieri et al., 2011). The aim of this study was to assess the correlations of the metallic concentrations between household dust and children hair, comparing these associations in two different contamination contexts: a mining district and a suburban non-mining area. Additionally, we assessed the influence of children behavior and other determinants on the correlations between metallic elements in household dust and children hair.
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2. Materials and methods The present cross-sectional study was conducted in the frame of the ToxBol project, a multidisciplinary research project that assessed the origin of poly-metallic contamination and its impact on the environment, health and society in a Bolivian mining city. 2.1. Data collection On the first phase of the ToxBol project, we selected five elementary schools in five different districts of Oruro. After parental consent was signed, we collected hair samples from children between 7 and 12 years old for metallic trace elements (MTE) measurements using ICP-MS. Detailed procedures of the sample collection and laboratory analysis can be found in a previous study (Barbieri et al., 2011) and on Appendix A, available online. A few personal data were also collected, including home address and a brief physical examination. For the second phase, we chose two of the five districts from the first phase to collect indoor dust samples from the households of the sampled children for MTE measurements using ICP-OES. Detailed procedures of the sample collection and laboratory analysis can be found in a previous study (Fontúrbel et al., 2011) and on Appendix A. We chose to contrast a mining urban district, located directly around mining tailings, and a suburban non-mining district, distant from mining and metallurgical centers (Fig. 1). A detailed questionnaire was filled by interview, gathering information about household characteristics, socioeconomic data and children behavior (handto-mouth behavior, sucking toys, eating dirt, playing with dirt, etc.). The geographic coordinates of each household were taken using a Garmin Vista GPS device (Garmin Co., Olathe, KS). 2.2. Statistical analyses
Fig. 2. Spearman correlation coefficients and 95% bias-corrected confidence intervals for (a) the Mine district, and (b) the Suburban district.
As our data were non-normally distributed, we calculated the Spearman correlation coefficients between hair and dust metallic element concentrations, analyzing mining and suburban districts separately. We calculated the bias-corrected and accelerated (BCa) 95% confidence intervals using a bootstrapping procedure with 1000 permutations. In order to evaluate the metallic mixture as a whole, we used a Canonical Correlation Analysis (CCA) procedure (González et al., 2008) aiming to compare the hair–dust correlations between the mining and the suburban districts. We ran the CCA analysis with the whole dataset (i.e., both mining and suburban districts) of metallic elements (As, Cd, Pb, Sb, Sn, Cu, Zn). We conducted a classical CCA in order to obtain the correlation-temperature plot and a base CCA model that allow a visual comparison of the correlation between trace element concentrations of hair and indoor dust samples. Then, we conducted a regularized CCA procedure using the lambda values estimated from the classical CCA, in order to maximize the variance explained by the first two canonical dimensions (González et al., 2008), which were plotted to visually depict the correlations by element and source (hair or indoor dust), as well as by case ID (i.e., each sampled house). We depicted spatial representations for the purpose of comparing two contrasting elements (lead as non-essential and zinc as essential), based on the associations observed between their concentrations in household dust and children hair. Using the geographic coordinates and the metallic concentrations in household dust, we constructed Kriging maps for both elements, where the darker shades of blue represent higher concentrations. Next, we overlaid hair concentrations of lead and zinc as dots in a color scale ranging from yellow (lowest) to red (highest). These maps were elaborated using ArcGIS v.10.1 (ESRI Co., Redlands, CA), following the methods detailed in Fontúrbel et al. (2011). To assess the possible associations between the children behavioral determinants and their exposure to metallic trace elements, we
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Fig. 3. (a) Temperature correlation plot for essential and non-essential elements measured. (b) Canonical distribution of the variables (left panel, “D” denotes dust samples and “H” hair samples) and the study cases (right panel). Individual houses were represented by numbers, from which 1 to 56 corresponded to the mine district and 57 to 97 to the suburban district.
conducted a generalized linear model (GLM) with each element concentration in hair samples as dependent variable and indoor dust metallic trace element concentrations as independent variable. The covariates included in the model were gender and behavioral determinants: playing with dirt, sucking toys, sucking hands/fingers, etc. Because our variables were non-normally distributed, we used a bootstrapping procedure to test the significance level of each coefficient of GLM models with 5000 permutations per model, using the package ‘boot’ in R 2.15 (Canty and Ripley, 2012; R Core Development Team, 2012). 3. Results From the 61 sampled children in the mining district and the 52 sampled children in the suburban district, we obtained 56 and 41 household dust samples respectively (Table 1). Eight hair samples showed values of Sb and Cd below detection limit and were not considered for the analyses. The main household characteristics can be seen on Table 2.
As seen in Fig. 2a, metallic concentrations in household dust were significantly correlated with their concentrations in children hair for lead (Spearman rs = 0.535, P = 0.001), arsenic (rs = 0.408, P = 0.018) and antimony (rs = 0.492, P = 0.001) in the mining area. We observed similar associations in cadmium and tin, even though were statistically non-significant (rs = 0.266, P = 0.125 and rs = 0.261, P = 0.235, respectively). In the suburban area no significant correlations were observed (Fig. 2b). The canonical correlation analysis showed that correlations of trace element concentrations between hair and indoor dust samples were stronger at the mining district than at the suburban district, and also were stronger for non-essential elements (arsenic, cadmium, lead, antimony and tin) than for essential ones (copper and zinc), especially for lead (Fig. 3a) at the cross-correlation analysis (comparing both districts), which was consistent with the results of univariate Spearman correlations. Correlations between hair and indoor dust metallic concentrations showed a relatively even distribution along the first
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Fig. 4. Spatial distribution of the hair samples (points) and dust samples (expressed through a Kriging layer) for (a) lead and (b) zinc.
canonical dimension, but more variable at the second one (Fig. 3b) separating two clouds of points: one for the trace element concentrations in indoor dust samples (marked with ‘_D’) and another for concentration on hair samples, drawing attention the proximity of lead concentration in both hair (Pb_H) and dust (Pb_D) concentrations. On the contrary, the case ID plot showed a fairly variable distribution in both dimensions, being the correlations from the mining districts (case IDs 1 to 56) more ‘disperse’ than those from the suburban district (case IDs 57 to 97).
Table 3 GLM estimates of the determinants of the hair concentration in (a) mining and (b) suburban districts. Variable
As
(a) Mine district Intercept 2.01⁎ Dust 0.23⁎ Male 0.37⁎ Playa Mouthb
0.21 0.03
(b) Suburban district Intercept 2.46⁎ Dust −0.10 Male −0.20 0.23 Playa b Mouth 0.15
Cd
Pb
Sb
Sn
Cu 4.17⁎ −0.15 −0.09 0.56⁎ −0.27
4.89⁎ 0.73 0.06 0.25 0.50
4.06⁎ −0.09 0.02 0.07 −0.09
5.01⁎ −0.26 0.24 0.10 0.01
1.42⁎ 0.59⁎
2.74⁎ 0.36⁎
1.37⁎ 0.41⁎
−0.13 0.23 0.23⁎
0.09 0.42⁎ −0.01
−0.15 0.50⁎ 0.23
1.73⁎ 0.19 0.12 0.20 −0.02
1.28⁎ −0.04 0.06 0.22 0.56
2.80⁎ 0.17 −0.30 0.15 0.20
1.78⁎ 0.29 −0.11 −0.10 0.04
1.33⁎ 0.14 −0.15 0.03 0.36
a “Play with dirt” (0/1); b“hand-to-mouth behavior or sucking toys” (0/1). ⁎Significant coefficients at P b 0.01 after bootstrapping procedures.
Zn
The spatial representation of Pb (Fig. 4a) and Zn (Fig. 4b) shows higher concentrations of both metals in the mining district, both for household dust and children hair, observed by the shade of blue on the Kriging representation and the red and orange dots. In fact, even within the mining district, the highest hair concentrations correspond also to the highest dust concentrations. In the suburban non-mining district, both metals show lower concentrations in household dust and children hair; both metallic concentrations look very heterogeneous and no pattern or tendency can be observed. After including gender and behavioral determinants in the GLM as covariates, the associations between indoor dust concentrations and hair concentrations remained significant for arsenic, cadmium, lead and antimony in the mining district (Table 3a). In particular, these associations were significantly stronger in children who played with dirt for Pb, Sb and Cu, in children who sucked their hands or toys for Cd and in boys, compared to girls, for As. On the contrary, in the suburban area neither dust concentrations nor gender and behavioral variables were associated with hair concentration of metallic trace elements (Table 3b). 4. Discussion The obtained results showed that indoor dust metallic contamination seems to be strongly associated with children exposure to these metallic elements, particularly in populations living in close proximity of mining centers. Even considering that the combination or “cocktail” of metallic trace elements found in household dust and children
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hair corresponds to the chemical background of the Oruro area (Tapia et al., 2012), there was a clear increase in metallic elements concentrations in the mining district. Not only the overall indoor dust and hair concentrations of nonessential metallic elements were significantly higher in the mining district (Barbieri et al., 2011; Fontúrbel et al., 2011), but also the associations between these elements in household dust and children hair were stronger, particularly in the case of lead, as confirmed by a strong correlation coefficient (rs = 0.535, P = 0.001). Socioeconomic and environmental determinants could be interacting with mining pollution in this case. Unpaved roads and household characteristics (e.g., walls without coating, precarious floors) might also increase the exposure to potentially toxic trace elements, because they may allow more residues of metallic trace elements to accumulate in household dust (Fontúrbel et al., 2011). Children behavior seemed to be a modulating factor in the exposure to metallic elements available in indoor dust (Lanphear et al., 1995). Even though gender appeared to have a direct association with hair metallic concentrations in the univariate analysis, the statistical significance was lost when behavioral determinants were included (namely playing with dirt), suggesting a difference between both genders based on behavioral and cultural factors, such as boys playing outside more than girls, for example. Indeed, the correlation between lead concentrations in indoor dust and children hair was stronger in children with the habit of playing with dirt in the mining neighborhood. Either for indoor dust or for child exposure we used unconventional indicators. Wipes are commonly used to evaluate trace element concentrations by surface (mg/cm2), existing references for some elements (US EPA, 1995). In the vast majority of the households that we visited during this study, the accumulation of dust was abundant for several reasons, which is why we considered that the concentration by weight of collected dust would be optimal in this population, instead of the calculation by surface. The concentration of lead in household dust has shown to be a good predictor of blood lead concentrations in children (Lanphear et al., 1995). However, as discussed in the previous publication by Barbieri et al. (2011), hair was chosen as a non-invasive biomarker because without a clear clinical impact of this situation, in this context it was almost impossible to obtain adhesion of the parents for blood screening campaigns (Fraser, 2006). It is always difficult to interpret whether the correlations observed between indoor dust and hair concentrations are the result of a passive adsorption of dust facilitated by the scale structure of the surface of hair, or the product of biological integration of elements absorbed and linked with the sulfur-containing amino acids, abundantly present in keratin (Robbins, 2012). The total lack of correlation with zinc could reinforce the second hypothesis: even though the Kriging representation shows higher Zn concentrations in hair which seem to correspond to higher Zn concentrations in dust, no significant correlation was observed and the GLM found no association. In humans, most ingested zinc is eliminated in the feces, comprising unabsorbed zinc and endogenous zinc from bile, pancreatic fluid and intestinal mucosa cells (World Health Organization, 2011). The complex mechanisms involved in the regulation of Zn elimination could explain the absence of correlation between contamination and exposure. This type of context, with concomitant Pb–Zn pollution provided an interesting situation and it would be interesting to test our results elsewhere. 5. Conclusion Even though the populations surrounding mining areas are exposed to metallic trace elements from a number of different sources and pathways, our results suggest that the household dust contamination with non-essential (and toxic) metallic elements plays an important role in human exposure, particularly for children. In a context of high metallic contamination, this association between the indoor environment and the exposure seems to be modulated by the children's playing behavior.
In this context, children grow in close contact with several pollution sources through various pathways and it can be particularly difficult for the authorities to balance risk prevention within industrial development policies. However, indoor household mining pollution appears to be an important issue and should be taken into consideration for prevention and mitigation actions to be taken. Besides improving household conditions per se, it is important to also address social and cultural determinants with educational campaigns aimed at both adults and children. The assessment of the actual health impact of this exposure on such mining populations is open to further studies. Conflict of interest We declare no interest conflicts among us or with other colleagues. Acknowledgments We are grateful to the children and their families, who allowed taking hair and dust samples, and kindly answered our questionnaire. S. Ignacio assisted with the interviews. This study was conducted in the frame of the ToxBol project, funded by the Institut de Recherche pour le Développement (IRD) and the Agence Nationale de la Recherche (ANR) (39SEST06). Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.scitotenv.2014.03.136. These data include the Google map of the most important areas described in this article. References Ayres RU, Ayres LW. A handbook of industrial ecology. Cheltenham, UK; Northampton, MA: Edward Elgar Pub.; 2002. Barbieri FL, Cournil A, Souza Sarkis JE, Benefice E, Gardon J. Hair trace elements concentration to describe polymetallic mining waste exposure in Bolivian Altiplano. Biol Trace Elem Res 2011;139:10–23. http://dx.doi.org/10.1007/s12011-010-8641-1. Bjerre B, Berglund M, Harsbo K, Hellman B. Blood lead concentrations of Swedish preschool children in a community with high lead levels from mine waste in soil and dust. Scand J Work Environ Health 1993;19:154–61. Canty A, Ripley B. Bootstrap R (S-plus) functions. R package version 1.3-5; 2012. Fontúrbel FE, Barbieri E, Herbas C, Barbieri FL, Gardon J. Indoor metallic pollution related to mining activity in the Bolivian Altiplano. Environ Pollut 2011;159:2870–5. http://dx.doi.org/10.1016/j.envpol.2011.04.039. Fraser B. Peruvian mining town must balance health and economics. Lancet 2006;367: 889–90. http://dx.doi.org/10.1016/S0140-6736(06)68363-3. Glorennec P, Lucas JP, Mandin C, Le Bot B. French children's exposure to metals via ingestion of indoor dust, outdoor playground dust and soil: contamination data. Environ Int 2012;45:129–34. http://dx.doi.org/10.1016/j.envint.2012.04.010. Goix S, Point D, Oliva P, Polve M, Duprey JL, Mazurek H, et al. Influence of source distribution and geochemical composition of aerosols on children exposure in the large polymetallic mining region of the Bolivian Altiplano. Sci Total Environ 2011;412:170–84. http://dx.doi.org/10.1016/J.Scitotenv.2011.09.065. González I, Dejean S, Martin PGP, Baccini A. CCA: an R package to extend canonical correlation analysis. J Stat Softw 2008;23:1–14. Grandjean P, Landrigan PJ. Developmental neurotoxicity of industrial chemicals. Lancet 2006;368:2167–78. http://dx.doi.org/10.1016/S0140-6736(06)69665-7. Han FX, Banin A, Su Y, Monts DL, Plodinec MJ, Kingery WL, et al. Industrial age anthropogenic inputs of heavy metals into the pedosphere. Naturwissenschaften 2002;89: 497–504. http://dx.doi.org/10.1007/s00114-002-0373-4. Hu H, Shine J, Wright RO. The challenge posed to children's health by mixtures of toxic waste: the Tar Creek superfund site as a case-study. Pediatr Clin North Am 2007; 54:155–75. http://dx.doi.org/10.1016/j.pcl.2006.11.009. Hubal EAC, Sheldon LS, Burke JM, McCurdy TR, Barry MR, Rigas ML, et al. Children's exposure assessment: a review of factors influencing children's exposure, and the data available to characterize and assess that exposure. Environ Health Perspect 2000;108:475–86. http://dx.doi.org/10.2307/3454607. INE. Censo de Población y Vivienda. La Paz: Instituto Nacional de Estadística; 2001 [Accessed 28/10/2010. [http://www.ine.gob.bo]]. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003;68:167–82. http://dx.doi.org/10.1093/Bmb/Ldg032. Landrigan PJ, Kimmel CA, Correa A, Eskenazi B. Children's health and the environment: public health issues and challenges for risk assessment. Environ Health Perspect 2004;112:257–65. http://dx.doi.org/10.1289/ehp.6115.
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