Exposure Models for REACH and Occupational Safety and Health Regulations

Cherrie, John W.; Fransman, Wouter; Heussen, G.A.H.; Koppisch, Dorothea; Jensen, Keld Alstrup

doi:10.3390/ijerph17020383

Cited by 23 publications

(30 citation statements)

References 31 publications

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“…While external exposure is an essential precursor to this process, the mathematical models to predict external dose for different products, pollutants and scenarios are beyond the scope of the current article (the reader is referred to previous reviews of exposure modelling software). 4,5…”

Section: Introductionmentioning

confidence: 99%

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

et al. 2020

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Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal care products, food additives and their associated regulatory agencies, there is a need to develop robust and reliable methods to reduce or replace animal testing. It is generally recognised that no single alternative method will be able to provide a one-to-one replacement for assays based on more complex toxicological endpoints. Hence, information from a combination of techniques is required. A greater understanding of the time and concentration-dependent mechanisms, underlying the interactions between chemicals and biological systems, and the sequence of events that can lead to apical effects, will help to move forward the science of reducing and replacing animal experiments. In silico modelling, in vitro assays, high-throughput screening, organ-on-a-chip technology, omics and mathematical biology, can provide complementary information to develop a complete picture of the potential response of an organism to a chemical stressor. Adverse outcome pathways (AOPs) and systems biology frameworks enable relevant information from diverse sources to be logically integrated. While individual researchers do not need to be experts across all disciplines, it is useful to have a fundamental understanding of what other areas of science have to offer, and how knowledge can be integrated with other disciplines. The purpose of this review is to provide those who are unfamiliar with predictive in silico tools, with a fundamental understanding of the underlying theory. Current applications, software, barriers to acceptance, new developments and the use of integrated approaches are all discussed, with additional resources being signposted for each of the topics.

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Section: Introductionmentioning

confidence: 99%

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

et al. 2020

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“…McDonnell and collaborators [32] described scenarios by the means of main modifying factors: (i) activity emission potential, (ii) substance emission potential (categories grouped to dust or granules) and (iii) localized controls. Koivisto and collaborators [38] provided an extensive work on the general ventilation multipliers, which as then further discussed by Cherrie et al [39], while Park et al [40] evaluate the ventilation rate, as well as the room size and the amount of aerosol sprayed. Further, two studies performed a sensitivity analysis for investigating modifying factors' impact on estimates results [12,37].…”

Section: Results Discussionmentioning

confidence: 99%

Comparison between Communicated and Calculated Exposure Estimates Obtained through Three Modeling Tools

Spinazzè

Borghi

Magni

et al. 2020

IJERPH

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This study aims to evaluate the risk assessment approach of the REACH legislation in industrial chemical departments with a focus on the use of three models to calculate exposures, and discuss those factors that can determine a bias between the estimated exposure (and therefore the expected risk) in the extended safety data sheets (e-SDS) and the expected exposure for the actual scenario. To purse this goal, the exposure estimates and risk characterization ratios (RCRs) of registered exposure scenarios (ES; “communicated exposure” and “communicated RCR”) were compared with the exposure estimates and the corresponding RCRs calculated for the actual, observed ES, using recommended tools for the evaluation of exposure assessment and in particular the following tools: (i) the European Centre for Ecotoxicology and Toxicology of Chemicals Targeted Risk Assessment v.3.1 (ECETOC TRA), (ii) STOFFENMANAGER® v.8.0 and (iii) the Advanced REACH Tool (ART). We evaluated 49 scenarios in three companies handling chemicals. Risk characterization ratios (RCRs) were calculated by dividing estimated exposures by derived no-effect levels (DNELs). Although the calculated exposure and RCRs generally were lower than communicated, the correlation between communicated and calculated exposures and RCRs was generally poor, indicating that the generic registered scenarios do not reflect actual working, exposure and risk conditions. Further, some observed scenarios resulted in calculated exposure values and RCR higher than those communicated through chemicals’ e-SDSs; thus ‘false safe’ scenarios (calculated RCRs > 1) were also observed. Overall, the obtained evidences contribute to doubt about whether the risk assessment should be performed using generic (communicated by suppliers) ES with insufficient detail of the specific scenario at all companies. Contrariwise, evidences suggested that it would be safer for downstream users to perform scenario-specific evaluations, by means of proper scaling approach, to achieve more representative estimates of chemical risk.

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“…The NF and FF volumes interact with each other through the exchange of aerosol droplets at a rate defined by their concentration differences and ventilation rate (Cherrie et al, 2020(Cherrie et al, , 2011Jayjock et al, 2011;Jensen et al, 2018;Koivisto et al, 2015;Ribalta et al, 2019;Zhang et al, 2009). The bi-compartmental approach in the present study considers only two discrete concentrations at NF and FF, and hence, it may not be able to capture the continuous spatial variation of particulate concentration in the entire volume.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Under natural convection, the air entering the NF will be equal to air leaving the NF, else NF will be pressurized or evacuated of air. Hence, flow rates , and , can be written as (Cherrie et al, 2020(Cherrie et al, , 2011Hussein and Kulmala, 2010;Jayjock et al, 2011;Jensen et al, 2018;Ribalta et al, 2019;Zhang et al, 2009):…”

Section: Air-exchangementioning

confidence: 99%

Survival of Expiratory Aerosols in a Room: Study Using a Bi-compartment and Bi-component Indoor Air Model

Bathula

Anand

Thajudeen

et al. 2021

Aerosol Air Qual. Res.

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Recent studies argue that inhalation of respiratory droplets in indoor environments is one of the significant routes of COVID-19 infection. In many cases, patients are isolated in hospitals and quarantine centers to minimize the spread. However, the rooms allocated to these patients are accessed by health care and sanitization workers a couple of times in a day. Since the expiratory activities release airborne droplets with certain viral load, there is a greater need to study the survival of these droplets in the room of a patient to control the exposure to the accessing people. A bi-compartment numerical model is developed to study the survival of these droplets in a room, taking into consideration the deposition rates of the droplets and the ventilation rates in the room. The vital aspects related to the survival of the droplets, such as the effect of the severity of the infection, types of releases, size-dependent deposition and role of ventilation are discussed.

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Exposure Models for REACH and Occupational Safety and Health Regulations

Cited by 23 publications

References 31 publications

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Comparison between Communicated and Calculated Exposure Estimates Obtained through Three Modeling Tools

Survival of Expiratory Aerosols in a Room: Study Using a Bi-compartment and Bi-component Indoor Air Model

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