Meta-analysis of the quantitative effectiveness of risk management measures (RMM) in the workplace.

BACKGROUND AND OBJECTIVES: This paper describes an evaluation and analysis of an updated version of ECEL v3.0-an integrated risk management measure (RMM) library developed as part of a CEFIC LRI initiative. The occupational module contains extensive data on the quantitative effectiveness of RMMs to control inhalation and dermal exposure in the workplace. The objective was to investigate the effectiveness and variability in effectiveness of RMM and to explore the difference between optimal and non-optimal RMM applications in the workplace. METHODS: A new database structure and interface were developed and the content of the database was updated with a systematic literature review and integration with other databases (totalling 3373 records from 548 studies). To analyse the data, Bayesian linear mixed models were constructed with the study as a random effect and various study characteristics and RMM categories as fixed effects individually in separate models. A multivariate mixed model was used on a stratified dataset to test (amongst others) the conditions of RMM use. RESULTS: Analyses of the data indicated effectiveness values for each RMM category (for example ~87% for technical emission controls compared with ~60% for technical dispersion controls). Substantial variability in effectiveness was observed within and between different types of RMM. Seven study characteristics (covariables) were included in the analyses, which indicated a pronounced difference in as-built (optimal/experimental) and as-used (workplace) conditions of RMM use (93.3% and 74.6%, respectively). CONCLUSIONS: This library provides a reliable evidence base to derive base estimates of RMM effectiveness-beneficial for both registrant and downstream users. It stresses the importance of optimal use of RMMs in the workplace (technical design/functioning, use, and maintenance). Various challenges are foreseen to further update ECEL to improve guidance, for deriving improved estimates and ensure user-friendliness of the library.

Development of handling energy factors for use of dustiness data in exposure assessment modelling.

Several exposure assessment models use dustiness as an input parameter for scaling or estimating exposure during powder handling. Use of different dustiness methods will result in considerable differences in the dustiness values as they are based on different emission generation principles. EN17199:2019 offers 4 different dustiness test methods considering different dust release scenarios (e.g. powder pouring, mixing and gentle agitation, and vibration). Conceptually, the dustiness value by a given method can be multiplied with a scenario-specific modifier, called a handling energy factor (Hi), that allows conversion of a dustiness value to a release constant. Therefore, a Hi, scaling the effective mechanical energy in the process to the energy supplied in the specific dustiness test, needs to be applied. To improve the accuracy in predictive exposure modelling, we derived experimental Hi to be used in exposure algorithms considering both the mass- and number-based dust release fraction determined by the EN17199-3 continuous drop (CD) and the EN17199-4 small rotating drum (SRD) test methods. Three materials were used to evaluate the relationship between dustiness and dust levels during pouring powder from different heights in a controlled environment. The results showed increasing scatter and difference between the Hi derived for the 2 test methods with increasing pouring height. Nearly all the Hi values obtained for both SRD and CD were <1 indicating that the dustiness tests involved more energy input than the simulated pouring activity and consequently de-agglomeration and dust generation were higher. This effect was most pronounced in CD method showing that SRD mechanistically resembles more closely the powder pouring.