Testing the near field/far field model performance for prediction of particulate matter emissions in a paint factory

Abstract

A Near Field/Far Field (NF/FF) model is a well-accepted tool for precautionary exposure assessment but its capability to estimate particulate matter (PM) concentrations is not well studied. The main concern is related to emission source characterization which is not as well defined for PM emitters compared to e.g. for solvents. One way to characterize PM emission source strength is by using the material dustiness index which is scaled to correspond to industrial use by using modifying factors, such as handling energy factors. In this study we investigate how well the NF/FF model predicts PM concentration levels in a paint factory. PM concentration levels were measured during big bag and small bag powder pouring.Rotating drum dustiness indices were determined for the specific powders used and applied in the NF/FF model to predict mass concentrations. Modeled process specific concentration levels were adjusted to be similar to the measured concentration levels by adjusting the handling energy factor. The handling energy factors were found to vary considerably depending on the material and process even-though they have the same values as modifying factors in the exposure models. This suggests that the PM source characteristics and process-specific handling energies should be studied in more detail to improve the model-based exposure assessment. Environmental impactThe REACH requires that manufacturers or importers within the European Union must estimate human exposure by all routes for each potential exposure scenario which will then be used in risk assessment. To fulll this regulation several exposure assessment models/tools were developed but their performance in predicting particulate matter exposure has not been tested in work environments. In this study we tested how well a NF/FF dispersion model predicts particulate matter concentrations when source emission potency was estimated using the material dustiness index. It was found that the dustiness index did not describe source emission potency well. To overcome this problem, particle emissions from different sources should be studied with well-controlled work simulations where emission rates are assessed using indoor aerosol models.

Indoor dispersion of airborne nano and fine particles: Main factors affecting spatial and temporal distribution in the frame of exposure modeling

“…Some aspects of these results can be put in perspective with several real-scale studies in the literature. In the vast majority of situations, which can be either field studies 11,14,15 or laboratory studies, 10,12,13,[20][21][22][23][24][25][26][27][28][29][30] the existence of a NF around the source presenting a noticeably higher contaminant concentration than the rest of the indoor environment is attested in steady state. This was actually the main motivation for the development of the two-zone modeling concept.…”

“…[8][9][10][11][12][13][14] Overall, evaluations of the NF-FF model have found predicted values to be within 0.49-2.5 times measured concentrations in 99% of situations reported in the literature. 9,13,14 The use of the two-box models in scenarios involving nano-sized aerosols was tested by Koivisto et al 15 to predict exposure levels in a paint factory during pouring process. In that case, the NF-FF model predicted values to be within 0.2-2.4 times measured concentrations.…”

Size-Resolved Characterization of Particles and Fibers Released during Abrasion of Fiber-Reinforced Composite in a Workplace Influenced by Ambient Background Sources

“…Currently, there is a consensus that the best approach to assess the exposure to engineered nanomaterials in workplaces is represented by the nanoparticle emission assessment technique (NEAT, Methner et al, 2009, 2010 applying instruments measuring the number concentrations of the nanomaterials together with filters (or other sampling techniques) that allow offline analysis of samples for particle (mass,) morphology, size, count, and elemental composition (adopted from Savolainen in Vogel et al, 2013, changes in parenthesis). One of the greatest challenges is discrimination between different factory (primary) emissions and the outdoor background, which enter through mechanical ventilation and other more or less controlled ventilation paths in the workplace (e.g., Ono-Ogasawara et al, 2009;Ramachandran et al, 2011;Koivisto et al, 2015). The characteristics and composition of this outdoor contribution may vary considerably from place to place and over time depending on the location and outdoor activities.…”

“…A common approach is to use online measurements with two or more high time-resolution measurement devices simultaneously by which the major near and far-field sources can be distinguished (e.g., Jensen et al, 2015;Kaminski et al, 2015;Koivisto et al, 2015). However, such measurements are still challenged by the fact that the different airborne particles and their sources are not unequivocally identified.…”

Can We Trust Real Time Measurements of Lung Deposited Surface Area Concentrations in Dust from Powder Nanomaterials?

“…Therefore, the comparability in these measurements and the reliability in conversions from number and volume concentrations metrics is still not well documented for non-spherical particles such as aggregates and agglomerates commonly observed in dustiness tests, covering a wide range of sizes from nano-to µm-size (Ibaseta and Biscans, 2007;Schneider and Jensen, 2008;Jensen et al, 2009;Tsai et al, 2009;Levin et al, 2014;Koivisto et al, 2015;Koponen et al, 2015). Even in case of direct release from an airborne synthesis processes, primary particles can already start to agglomerate or aggregate in the reactor and may further coagulate at or very near the point of release with similar result (Makela et al, 2009;Hämeri et al, 2009;Schneider et al, 2011;Koivisto et al, 2012;Koivisto et al, 2015). Measurements are therefore challenging when the aerosol structure is not known, because this in principle prevents the possibility to convert number-size-distribution measurements into reliable surface area values (Fissan et al, 2013).…”