Search results

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO 2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95 th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m 3 room, no local ventilation, and NF ventilation of 1.6 m 3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the ...

Abstract
STOFFENMANAGER® and the Advanced REACH Tool (ART) are recommended tools by the European Chemical Agency for regulatory chemical safety assessment. The models are widely used and accepted within the scientific community. STOFFENMANAGER® alone has more than 37 000 users globally and more than 310 000 risk assessment have been carried out by 2020. Regardless of their widespread use, this is the first study evaluating the theoretical backgrounds of each model. STOFFENMANAGER® and ART are based on a modified multiplicative model where an exposure base level (mg m−3) is replaced with a dimensionless intrinsic emission score and the exposure modifying factors are replaced with multipliers that are mainly based on subjective categories that are selected by using exposure taxonomy. The intrinsic emission is a unit of concentration to the substance emission potential that represents the concentration generated in a standardized task without local ventilation. Further information or scientific justification for this selection is not provided. The multipliers have mainly discrete values given in natural logarithm steps (…, 0.3, 1, 3, …) that are allocated by expert judgements. The multipliers scientific reasoning or link to physical quantities is not reported. The models calculate a subjective exposure score, which is then translated to an exposure level (mg m−3) by using a calibration factor. The calibration factor is assigned by comparing the measured personal exposure levels with the exposure score that is calculated for the respective exposure scenarios. A mixed effect regression model was used to calculate correlation factors for four exposure group [e.g. dusts, vapors, mists (low-volatiles), and solid object/abrasion] by using ~1000 measurements for STOFFENMANAGER® and 3000 measurements for ART. The measurement data for calibration are collected from different exposure groups. For example, for dusts the calibration data were pooled from exposure measurements sampled from pharmacies, bakeries, construction industry, and so on, which violates the empirical model basic principles. The calibration databases are not publicly available and thus their quality or subjective selections cannot be evaluated. STOFFENMANAGER® and ART can be classified as subjective categorization tools providing qualitative values as their outputs. By definition, STOFFENMANAGER® and ART cannot be classified as mechanistic models or empirical models. This modeling algorithm does not reflect the physical concept originally presented for the STOFFENMANAGER® and ART. A literature review showed that the models have been validated only at the 'operational analysis' level that describes the model usability. This review revealed that the accuracy of STOFFENMANAGER® is in the range of 100 000 and for ART 100. Calibration and validation studies have shown that typical log-transformed predicted exposure concentration and measured exposure levels often exhibit weak Pearson's correlations (r is <0.6) for both STOFFENMANAGER® and ART. Based on these limitations and performance departure from regulatory criteria for risk assessment models, it is recommended that STOFFENMANAGER® and ART regulatory acceptance for chemical safety decision making should be explicitly qualified as to their current deficiencies.

Advanced material development, including at the nanoscale, comprises costly and complex challenges coupled to ensuring human and environmental safety. Governmental agencies regulating safety have announced interest toward acceptance of safety data generated under the collective term New Approach Methodologies (NAMs), as such technologies/approaches offer marked potential to progress the integration of safety testing measures during innovation from idea to product launch of nanomaterials. Divided in overall eight main categories, searchable databases for grouping and read across purposes, exposure assessment and modeling, in silico modeling of physicochemical structure and hazard data, in vitro high-throughput and high-content screening assays, dose-response assessments and modeling, analyses of biological processes and toxicity pathways, kinetics and dose extrapolation, consideration of relevant exposure levels and biomarker endpoints typify such useful NAMs. Their application generally agrees with articulated stakeholder needs for improvement of safety testing procedures. They further fit for inclusion and add value in nanomaterials risk assessment tools. Overall 37 of 50 evaluated NAMs and tiered workflows applying NAMs are recommended for considering safer-by-design innovation, including guidance to the selection of specific NAMs in the eight categories. An innovation funnel enriched with safety methods is ultimately proposed under the central aim of promoting rigorous nanomaterials innovation. ; acceptedVersion ; Peer reviewed