[show abstract][hide abstract] ABSTRACT: In the scope of a Dutch programme to reinforce the working conditions policy on hazardous substances, an internet-based tool was developed to help small- and medium-sized companies to handle hazardous substances with more care. The heart of this tool, called the Stoffenmanager, is a risk banding scheme. It combines a hazard banding scheme similar to that of COSHH Essentials and an exposure banding scheme based on an exposure model originally presented by Cherrie et al. (1996) and further developed by Cherrie and Schneider (1999). The exposure model has been modified to allow non-expert users to understand and use the model. Exposure scores are calculated based on categorization of determinants of emission, transmission and immission. These exposure scores are assigned to exposure bands. The comparison of exposure bands and hazard bands leads to a risk band or priority band. Following the evaluation of the priority of tasks done with products, generic exposure control measures can be evaluated for their possibility to lower the risks. Relevant control measures can be put into an action plan and into workplace instruction cards. The tool has several other functionalities regarding registration and storage of products. The exposure model in the Stoffenmanager leads to exposure scores. These have been compared with measured exposure levels. The exposure scores correlated well with measured exposure levels. The development of the Stoffenmanager has facilitated a whole range of further developments of useful tools for small- and medium-sized enterprises.
Annals of Occupational Hygiene 07/2008; 52(6):429-41. · 2.16 Impact Factor
[show abstract][hide abstract] ABSTRACT: In The Netherlands, the web-based tool called 'Stoffenmanager' was initially developed to assist small- and medium-sized enterprises to prioritize and control risks of handling chemical products in their workplaces. The aim of the present study was to explore the accuracy of the Stoffenmanager exposure algorithm. This was done by comparing its semi-quantitative exposure rankings for specific substances with exposure measurements collected from several occupational settings to derive a quantitative exposure algorithm. Exposure data were collected using two strategies. First, we conducted seven surveys specifically for validation of the Stoffenmanager. Second, existing occupational exposure data sets were collected from various sources. This resulted in 378 and 320 measurements for solid and liquid scenarios, respectively. The Spearman correlation coefficients between Stoffenmanager scores and exposure measurements appeared to be good for handling solids (r(s) = 0.80, N = 378, P < 0.0001) and liquid scenarios (r(s) = 0.83, N = 320, P < 0.0001). However, the correlation for liquid scenarios appeared to be lower when calculated separately for sets of volatile substances with a vapour pressure >10 Pa (r(s) = 0.56, N = 104, P < 0.0001) and non-volatile substances with a vapour pressure < or =10 Pa (r(s) = 0.53, N = 216, P < 0.0001). The mixed-effect regression models with natural log-transformed Stoffenmanager scores as independent parameter explained a substantial part of the total exposure variability (52% for solid scenarios and 76% for liquid scenarios). Notwithstanding the good correlation, the data show substantial variability in exposure measurements given a certain Stoffenmanager score. The overall performance increases our confidence in the use of the Stoffenmanager as a generic tool for risk assessment. The mixed-effect regression models presented in this paper may be used for assessment of so-called reasonable worst case exposures. This evaluation is considered as an ongoing process and when more good quality data become available, the analyses described in this paper will be expanded. Based on these analyses, the algorithm will be refined in the near future.
Annals of Occupational Hygiene 07/2008; 52(6):443-54. · 2.16 Impact Factor