2019
DOI: 10.1016/j.scitotenv.2018.09.379
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Testing the performance of one and two box models as tools for risk assessment of particle exposure during packing of inorganic fertilizer

Abstract: 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.

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Cited by 14 publications
(22 citation statements)
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“…This is not the case for coarse and fine particles, for which the Exposure Control Efficacy Library (ECEL) provides information on the efficacy of control methods for inhalation exposure (Fransman et al, 2008), mainly focusing on particle mass concentration as main metric (as opposed to particle number concentration, used for NPs). It should be added that the quantitative data on exposure reduction for specific technological measures are also a key input for exposure prediction models applied to indoor settings in the framework of risk assessment (e.g., one-and two-box models; Hewett and Ganser, 2017;Hussein and Kulmala, 2008;Nazaroff, 2004;Ribalta et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…This is not the case for coarse and fine particles, for which the Exposure Control Efficacy Library (ECEL) provides information on the efficacy of control methods for inhalation exposure (Fransman et al, 2008), mainly focusing on particle mass concentration as main metric (as opposed to particle number concentration, used for NPs). It should be added that the quantitative data on exposure reduction for specific technological measures are also a key input for exposure prediction models applied to indoor settings in the framework of risk assessment (e.g., one-and two-box models; Hewett and Ganser, 2017;Hussein and Kulmala, 2008;Nazaroff, 2004;Ribalta et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Key challenges for model application are source characterization, local controls and air mixing rates [30,32,33,34,35,36]. In addition, there is a need to test model performance under real-world conditions.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, there is a need to test model performance under real-world conditions. Because of the large variety of indoor micro-environments and emission sources, libraries compiling model parameters would be highly useful for modeling studies [33,37]. Source characterization requires dedicated attention, as it is the main determinant of exposure.…”
Section: Introductionmentioning
confidence: 99%
“…However, to work well, these models require specific details of the scenario being modelled, including the mass emission rate from the source or sources, information that is not generally available for REACH exposure scenarios or even for specific individual workplaces. Certainly, knowing the emission rates and contextual parameters can make it possible to predict exposure or contaminant concentrations in specific situations relatively well by compartment modelling, e.g., papers by Ribalta et al [17] and Jensen et al [18]. However, even when all key information is known to a high degree, there can still be challenges in predicting the exposure levels accurately.…”
Section: Exposure Model Structure and Exposure Modifiersmentioning
confidence: 99%
“…Until now, we have not seen any publications that transparently describe mass-balanced modelling for chemical regulatory risk assessment, including the parameter uncertainties and assumptions that have to be made, although some progress is being made in developing models for the more restricted case of nanomaterials, for example work by Ribalta et al [17] and Belut et al [31]. Also, we have not been able to identify any validation studies demonstrating that mass-balance modelling gives results that perform better for exposure assessment at scenario level than the currently available model tools for REACH.…”
Section: Calibration and Validation Of The Toolsmentioning
confidence: 99%