Suchergebnisse

Abstract

Introduction
EN 14325:2018 and ISO 16602:2012 provide guidelines for testing chemical protective clothing against chemicals with different criteria. Taiwan's CNS primarily favours ISO 16602. This research tested Type 4 chemical protective clothing according to EN and ISO standards to evaluate data consistency and unity.


Methodology
Type 4 protective clothing from two manufacturers were selected for this study. Both fabric and seam components were encompassed for examinations. The tested chemicals were sulfuric acid, sodium hydroxide, and toluene. The permeation experiment procedures followed ISO and EN standards by applying horizontal and vertical permeation test apparatus in triplication. In total, 36 samples were tested. The breakthrough times (BT) were recorded according to the definitions of EN at a permeation rate of 1.0 μg/cm2-min and ISO at 0.1 μg/cm2-min.


Results
There were no significant differences in the BTs between the ISO and EN criteria with the p-Value > 0.05 by the Mann–Whitney U test. However, comparing types of test apparatus (vertical vs. horizontal), types of seams (binding vs. taping), and components (fabric vs. seam), all the BTs are significantly different (p-Value < 0.05). Toluene immediately broke through the tested Type 4 clothing, i.e., less than 15 seconds.


Conclusions
The BTs of the vertical test apparatus are shorter than the BTs of horizontal results. The effect of gravity might be counted for the results. Seams are usually considered to be the weak parts of protective clothing. The binding seams were more vulnerable than the fabric. The chemical resistance tests of these components should be included as ISO requirements.

Abstract
Addressing occupational health and safety concerns early in the design stage anticipates hazards and enables health professionals to recommend control measures that can best protect workers' health. This method is a well-established tool in public health. Importantly, its success depends on a comprehensive exposure assessment that incorporates previous exposure data and outcomes. Traditional methods for characterizing similar occupational exposure scenarios rely on expert judgment or qualitative descriptions of relevant exposure data, which often include undisclosed underlying assumptions about specific exposure conditions. Thus, improved methods for predicting exposure modeling estimates based on available data are needed. This study proposes that cluster analysis can be used to quantify the relevance of existing exposure scenarios that are similar to a new scenario. We demonstrate how this method improves exposure predictions. Exposure data and contextual information of the scenarios were collected from past exposure assessment reports. Prior distributions for the exposure distribution parameters were specified using Stoffenmanager® 8 predictions. Gower distance and k-Medoids clustering algorithm analyses grouped existing scenarios into clusters based on similarity. The information was used in a Bayesian model to specify the degree of correlation between similar scenarios and the scenarios to be assessed. Using the distance metric to characterize the degree of similarity, the performance of the Bayesian model was improved in terms of the average bias of model estimates and measured data, reducing from 0.77 (SD: 2.0) to 0.49 (SD: 1.8). Nevertheless, underestimation of exposures still occurred for some rare scenarios, which tended to be those with highly variable exposure data. In conclusion, the cluster analysis approach may enable transparent selection of similar exposure scenarios for factoring into design-phase assessments and thereby improve exposure modeling estimates.