Advanced REACH Tool (ART): Calibration of the mechanistic model

Schinkel, Jody; Warren, Nicholas; Fransman, Wouter; Tongeren, Martie van; McDonnell, Patricia E.; Voogd, Eef; Cherrie, John W.; Tischer, Martin; Kromhout, Hans; Tielemans, Erik

doi:10.1039/c1em00007a

Cited by 55 publications

(60 citation statements)

References 37 publications

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“…The difference between estimated GM and measured GM exposure levels resulted in a maximum factor difference of 8. For 75% of the scenarios the uncertainty in exposure estimates was within the UF of 4.4, which is less than the 90% of scenarios found in the original calibration study 5 and indicates more uncertainty in the ART estimates in this industry. 90% of the scenarios had a factor difference of less than 5.5.…”

Section: Validation Of the Art Mechanistic Modelmentioning

confidence: 81%

“…75% of the scenarios had an estimated GM within the UF of 4.4 of the measured GM exposure levels, a smaller percentage than the 90% expected from the calibration study. 5 An overall relative bias of À32% for estimated versus measured GM exposure levels indicated on average a one-third underestimation of GM exposure levels for scenarios from the pharmaceutical industry.…”

Section: Glove Boxmentioning

confidence: 99%

“…5 Linear mixed effects models were used to evaluate the association between relative ART model scores and measurements. A random scenario component of variance was introduced to reflect the model uncertainty.…”

Section: à3mentioning

confidence: 99%

“…The ART mechanistic model was developed and calibrated for the purposes of REACH exposure scenarios and estimates a geometric mean (GM) exposure level for any given exposure scenario from across many companies and countries. 5 Where traditional exposure assessment methods are not available, exposure models such as the ART may be the most viable option for derivation of REACH exposure scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Validation of the inhalable dust algorithm of the Advanced REACH Tool using a dataset from the pharmaceutical industry

et al. 2011

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As it is often difficult to obtain sufficient numbers of measurements to adequately characterise exposure levels, occupational exposure models may be useful tools in the exposure assessment process. This study aims to refine and validate the inhalable dust algorithm of the Advanced REACH Tool (ART) to predict airborne exposure of workers in the pharmaceutical industry. The ART was refined to reflect pharmaceutical situations. Largely task based workplace exposure data (n ¼ 192) were collated from a multinational pharmaceutical company with exposure levels ranging from 5 Â 10 À5 to 12 mg m À3 . Bias, relative bias and uncertainty around geometric mean exposure estimates were calculated for 16 exposure scenarios. For 12 of the 16 scenarios the ART geometric mean exposure estimates were lower than measured exposure levels with on average, a one-third underestimation of exposure (relative bias À32%). For 75% of the scenarios the exposure estimates were, within the 90% uncertainty factor of 4.4, as reported for the original calibration study, which may indicate more uncertainty in the ART estimates in this industry. While the uncertainty was higher than expected this is likely due to the limited number of measurements per scenario, which were largely derived from single premises.

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Section: Validation Of the Art Mechanistic Modelmentioning

confidence: 81%

Section: Glove Boxmentioning

confidence: 99%

Section: à3mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of the inhalable dust algorithm of the Advanced REACH Tool using a dataset from the pharmaceutical industry

et al. 2011

Self Cite

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“…In the SB pouring there was no localized control and hence we used a multiplier of 1 whereas low specication containment by the discharge cone was present in the BB pouring, leading us to use a localized control multiplier of 0.1. The ART concentrations, which were calculated according to Fransman et al, 8 were corrected by using a calibration factor of ln(a) ¼ 3.01 as dened by Schinkel et al 9 and dustiness multipliers given in Table 1. Table 1 shows measured dustiness indices in units of mass and particle number.…”

Section: Art Model Parameterizationmentioning

confidence: 99%

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

Koivisto

Jensen

Levin

et al. 2015

Environ. Sci.: Processes Impacts

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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.

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Control Banding: Background, Evolution, and Application

Zalk

West

Nelson³

2021

Patty's Industrial Hygiene

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Control banding (CB) strategies offer a solution for controlling worker exposures to constituents that are found in the workplace in the absence of firm toxicological and exposure data. The CB approach is now recognized in developed countries and economically developing countries and is considered as the most practical approach for the prevention of work‐related risks. As CB strategies are more prevalent in practice, the need for newer professionals to understand their strengths and weaknesses becomes an essential component of their ongoing education.

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Advanced REACH Tool (ART): Calibration of the mechanistic model

Cited by 55 publications

References 37 publications

Validation of the inhalable dust algorithm of the Advanced REACH Tool using a dataset from the pharmaceutical industry

Validation of the inhalable dust algorithm of the Advanced REACH Tool using a dataset from the pharmaceutical industry

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

Control Banding: Background, Evolution, and Application

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