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

Modelling of particle exposure is a useful tool for preliminary exposure assessment in workplaces with low and high exposure concentrations. However, actual exposure measurements are needed to assess models reliability. Worker exposure was monitored during packing of an 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 mass concentrations. The concentrations in the worker area during pre-activity ranged 63,797-81,073 cm-3, 4.6 × 106 to 7.5 × 106 µm2 cm-3, and 354 to 634 µg m-3 (respirable mass fraction) and during packing 50,300 to 85,949 cm-3, 4.3 × 106 to 7.6 × 106 µm2 cm-3, and 279 to 668 µg m-3 (respirable mass fraction). Thus, the packing process did not significantly increase the exposure levels. Chemical exposure was also under control based on REACH standards. The particle surface area deposition rate in respiratory tract was up to 7.6 × 106 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 ±â€¯0.19 and 0.84 ±â€¯0.12 for small and big bags, respectively, when using the one box model, and 0.88 ±â€¯0.25 and 0.82 ±â€¯0.12, 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 in a low emission industrial scenario, e.g. during packing of a fertilizer, can be predicted with a reasonable accuracy by using the concept of dustiness and mass balance models.

On the Relationship between Exposure to Particles and Dustiness during Handling of Powders in Industrial Settings.

Exposure to ceramic powders, which is frequent during handling operations, is known to cause adverse health effects. Finding proxy parameters to quantify exposure is useful for efficient and timely exposure assessments. Worker exposure during handling of five materials [a silica sand (SI1), three quartzes (Q1, Q2, and Q3), and a kaolin (K1)] with different particle shape (prismatic and platy) and sizes (3.4-120 µm) was assessed. Materials handling was simulated using a dry pendular mill under two different energy settings (low and high). Three repetitions of two kilos of material were carried out per material and energy conditions with a flow rate of 8-11 kg h-1. The performance of the dustiness index as a predictor of worker exposure was evaluated correlating material's dustiness indexes (with rotating drum and continuous drop) with exposure concentrations. Significant impacts on worker exposure in terms of inhalable and respirable mass fractions were detected for all materials. Mean inhalable mass concentrations during background were always lower than 40 µg m-3 whereas during material handling under high energy settings mean concentrations were 187, 373, 243, 156, and 430 µg m-3 for SI1, Q1, Q2, Q3, and K1, respectively. Impacts were not significant with regard to particle number concentration: background particle number concentrations ranged between 10 620 and 46 421 cm-3 while during handling under high energy settings they were 20 880 - 40 498 cm-3. Mean lung deposited surface area during background ranged between 27 and 101 µm2 cm-3 whereas it ranged between 22 and 42 µm2 cm-3 during materials handling. TEM images evidenced the presence of nanoparticles (≤100 nm) in the form of aggregates (300 nm-1 µm) in the worker area, and a slight reduction on mean particle size during handling was detected. Dustiness and exposure concentrations showed a high degree of correlation (R2 = 0.77-0.97) for the materials and operating conditions assessed, suggesting that dustiness could be considered a relevant predictor for workplace exposure. Nevertheless, the relationship between dustiness and exposure is complex and should be assessed for each process, taking into account not only material behaviour but also energy settings and workplace characteristics.