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Issue 1, 2015
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Testing the near field/far field model performance for prediction of particulate matter emissions in a paint factory

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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.

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

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Supplementary files

Article information


Submitted
07 Oct 2014
Accepted
06 Nov 2014
First published
06 Nov 2014

Environ. Sci.: Processes Impacts, 2015,17, 62-73
Article type
Paper
Author version available

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

A. J. Koivisto, A. C. Ø. Jensen, M. Levin, K. I. Kling, M. D. Maso, S. H. Nielsen, K. A. Jensen and I. K. Koponen, Environ. Sci.: Processes Impacts, 2015, 17, 62
DOI: 10.1039/C4EM00532E

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