Testing the performance of one and two box models as tools for risk assessment of particle exposure during packing of inorganic fertilizer

Abstract

Modelling of particle exposure is a useful tool for preliminary exposure assessment in workplaces. However, actual exposure measurements are needed to assess models reliability. Worker exposure was monitored during packing of a complex inorganic granulate fertilizer at industrial scale using small and big bags. Particle concentrations were modelled with one and two box models, where the emission source was estimated with the fertilizer’s dustiness index. The exposure levels were used to calculate inhaled dose rates and test accuracy of the exposure modellings. The particle number concentrations were measured from worker area by using a mobility and optical particle sizer which were used to calculate surface area and mass30 concentrations. The concentrations in the worker area during pre-activity ranged from 63797 - 81073 cm-3, 4.6x106 to 7.5x106 um2 cm-3, and 354 to 634 μg m-3 31 (respirable mass fraction) and during packing from 50300 to 85949 cm-3, 4.3x106 to 7.6x106 um2 32 cm-3, and 279 to 668 μg m-3 33 (respirable mass fraction). Thus, the packing process did not significantly increase the exposure levels. High particle number concentration was partly due to the use of diesel-powered forklifts. The particle surface area deposition rate in respiratory tract was up to 7.6x106 μm2 min-1 during packing, with 52% - 61% of deposition occurring in the alveolar region. Ratios of the modelled and measured concentrations were 0.98 ± 0.19 and 0.84 ± 0.12 for small and big bags, respectively, when using the one box model, and 0.88 ± 0.25 and 0.82 ± 0.12, for small and big bags, respectively, when using the one box model, and 0.88 ± 0.25 and 0.82 ± 0.12, respectively, when using the two box model. The modelling precision improved for both models when outdoor particle concentrations were included. This study shows that exposure concentrations during packing of fertilizers can be predicted with a reasonable accuracy by using a concept of dustiness and mass balance models.