Biomonitoring of manganese in blood, urine and axillary hair following low-dose exposure during the manufacture of dry cell batteries
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Objectives: A cross-sectional study was carried out on 100 workers from three different workplace areas in a dry cell battery manufacturing plant and on 17 currently nonexposed referents, to examine the relationship between the external exposure to manganese dioxide (MnO2) and the body burden of manganese in blood, urine and hair. Methods: Inhalable dust was measured gravimetrically after stationary active sampling. Manganese was analyzed in dust samples, blood, urine and axillary hair by atomic absorption spectro- metry. Results: The average air concentrations of manganese in the three workplace areas were 4 μg/m3 (range: 1–12 μg/m3), 40 μg/m3 (12–64 μg/m3) and 400 μg/m3 (137–794 μg/m3). Manganese in blood and axillary hair correlated with airborne manganese in group-based calculations but not on an individual level. The manganese concentrations varied between 3.2 μg/l and 25.8 μg/l in the blood (mean: 12.2 ± 4.8 μg/l) and between 0.4 μg/g and 49.6 μg/g in hair (mean: 6.2 ± 6.2 μg/g in the proximal sequence), respectively. The results for the nonexposed referents were 7.5 ± 2.7 μg/l (mean) in the blood (range: 2.6–15.1 μg/l) and 2.2 ± 1.8 μg/g (mean) in axillary hair (range: 0.4–6.2 μg/g). In these matrices, manganese differed significantly between the highly exposed workers and both the reference and the low-exposure group. Manganese in blood revealed the lowest background variance. No differences for manganese in urine were observed between workers (mean: 0.36 ± 0.42 μg/l, range: 0.1–2.2 μg/l) and referents (mean: 0.46 ± 0.47 μg/l, range: 0.1–1.7 μg/l). Conclusions: Manganese in blood is a specific and suitable parameter for the biomonitoring of MnO2 exposure, although its validity is limited to group-based calculations. Urinary manganese failed to allow a differentiation between exposed workers and referents. The suitability of manganese analysis in hair for biomonitoring purposes suffers from a relatively great background variation as well as from analytical problems.
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