Highlights: Occupational exposure to particles during industrial packing was assessed. No significant increases were found during packing of a granulate fertilizer. One and two box models predicted adequately actual worker exposure. Including outdoor concentrations in models was seen to improve their performance. Models parametrization was seen to be a key issue to adequately predict exposure.

Packing of raw materials in work environments is a known source of potential health impacts (respiratory, cardiovascular) due to exposure to airborne particles. This activity was selected to test different exposure and risk assessment tools, aiming to understand the effectiveness of source enclosure as a strategy to mitigate particle release. Worker exposure to particle mass and number concentrations was monitored during packing of 7 ceramic materials in 3 packing lines in different settings, with low (L), medium (M) and high (H) degrees of source enclosure. Results showed that packing lines L and M significantly increased exposure concentrations (119-609 µg m -3 respirable, 1150-4705 µg m -3 inhalable, 24755-51645 cm -3 particle number), while nonsignificant increases were detected in line H. These results evidence the effectiveness of source enclosure as a mitigation strategy, in the case of packing of ceramic materials. Total deposited particle surface area during packing ranged between 5.4-11.8x10 5 µm 2 min -1 , with particles depositing mainly in the alveoli (51-64%) followed by head airways (27-41%) and trachea bronchi (7-10%). The comparison between the results from different risk assessment tools (Stoffenmanager, ART, NanoSafer) and the actual measured exposure concentrations evidenced that all of the tools overestimated exposure concentrations, by factors of 1.5-8. Further research is necessary to bridge the current gap between measured and modelled health risk assessments.

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 (S1), 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. 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 S1, Q1, Q2, Q3 and K1 respectively. Impacts were not significant with regard to particle number concentration: background particle number concentrations ranged between 10620 -46421 /cm 3 while during handling under high energy settings they were 20880 -40498 /cm 3 . Mean lung deposited surface area during background ranged between 27 -101 μm 2 /cm 3 whereas it ranged between 22 -42 μm 2 /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 (R 2 = 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.