• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.25 no.1
• /
• pp.95-103
• /
• 2015

Objectives: This study was conducted in order to better understand the conceptual model and Stoffenmanager nano module and apply it to the synthesis and packing processes of nanomaterials. Methods: Site visits were conducted to five nanomaterial production processes. Product and exposure variables were investigated in these workplaces. Hazard banding and exposure classification of the synthesis and packing processes of nanomaterials were conducted using documents and the website of Stoffenmanager Nano. Results: The five sites featured different products, packing tasks, ventilation and local exhaust, and others. The hazards for nano-nickel and copper were classified as E. The hazards for both fumed silica and indium tin oxide were classified as D. The hazard for spherical silica was classified as C. The exposure classes in the synthesis process of nanomaterials ranged from 2 through 4. The exposure classes in the packing process of nanomaterials ranged from 1 through 4. Conclusions: Application of Stoffenmanager nano to the synthesis and packing processes of nanomaterials helped to better understand the control level of the work environment and to suggest appropriate actions. The comparison of each process showed the effect of the production process and handling of solids and ventilation on exposure class.

• Journal of Environmental Health Sciences
• /
• v.44 no.5
• /
• pp.460-467
• /
• 2018

Objectives: The objectives of this study are to estimate the inhalation exposure level of benzene for workers using Tier 1 exposure models ECETOC TRA (European Center for Ecotoxicology and Toxicology of Chemicals Target Risk Assessment) and Stoffenmanager, and to investigate their reliability for exposure assessment in K-REACH. Methods: Two exposure scenarios, 'manufacture of benzene' and 'use as solvents,' were developed for assessment of workers' exposure to benzene. The Process Category (PROC) for ECETOC TRA was collected from the European Chemical Agency (ECHA) registration dossier, and the Activity for Stoffenmanager was converted from PROC using translation of exposure models (TREXMO). The information related to exposure, such as working duration, Respiratory Protective Equipment (RPE), Local Exhaust Ventilation (LEV), and Risk Management Measure (RMM) were classified into high, medium, and low exposure conditions. The risk was determined by the ratio of the estimated exposure and occupational exposure limits of benzene. Results: Under high exposure conditions, the worker exposure level calculated from all PROCs and Activities exceeded the risk level, with the exception of PROC 1 and Activity 1. In the medium exposure condition, PROC 8a, 8b, and 9 and Activity 3, 7, and 8 all exceeded the risk, whereas in the low condition, all PROCs and Activities were determined to be safe. As a result, action corresponding with the low exposure condition is required to reduce the risk of exposure among workers in workplaces where benzene is manufactured or used as a solvent. In addition, the predicted exposure levels derived from the exposure models were lower than measured levels. The exposure levels estimated from Stoffenmanager were more conservative than those from ECETOC TRA. Conclusions: This study demonstrates the feasibility of exposure models for exposure assessment through the example of occupational inhalation exposure assessment for benzene. For more active utilization of exposure models in K-REACH, the exact application of collected information and accurate interpretation of obtained results are necessary.

• Safety and Health at Work
• /
• v.7 no.3
• /
• pp.185-193
• /
• 2016

Background: Management and workers in small and medium-sized enterprises (SMEs) often find it hard to comprehend the requirements related to controlling risks due to exposure to substances. An intervention study was set up in order to support 45 SMEs in improving the management of the risks of occupational exposure to chemicals, and in using the control banding tool and exposure model Stoffenmanager in this process. Methods: A 2-year intervention study was carried out, in which a mix of individual and collective training and support was offered, and baseline and effect measurements were carried out by means of structured interviews, in order to measure progress made. A seven-phase implementation evolutionary ladder was used for this purpose. Success and failure factors were identified by means of company visits and structured interviews. Results: Most companies clearly moved upwards on the implementation evolutionary ladder; 76% of the companies by at least one phase, and 62% by at least two phases. Success and failure factors were described. Conclusion: Active training and coaching helped the participating companies to improve their chemical risk management, and to avoid making mistakes when using and applying Stoffenmanager. The use of validated tools embedded in a community platform appears to support companies to organize and structure their chemical risk management in a business-wise manner, but much depends upon motivated occupational health and safety (OHS) professionals, management support, and willingness to invest time and means.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.24 no.2
• /
• pp.219-228
• /
• 2014

Objectives: The major objective of this study was to develop a tier 2 exposure model combining tier 1 exposure model estimates and worker monitoring data and suggesting narrower exposure ranges than tier 1 results. Methods: Bayesian statistics were used to develop a tier 2 exposure model as was done for the European Union (EU) tier 2 exposure models, for example Advanced REACH Tools (ART) and Stoffenmanager. Bayesian statistics required a prior and data to calculate the posterior results. In this model, tier 1 estimated serving as a prior and worker exposure monitoring data at the worksite of interest were entered as data. The calculation of Bayesian statistics requires integration over a range, which were performed using a Riemann sum algorithm. From the calculated exposure estimates, 95% range was extracted. These algorithm have been realized on Excel spreadsheet for convenience and easy access. Some fail-proof features such as locking the spreadsheet were added in order to prevent errors or miscalculations derived from careless usage of the file. Results: The tier 2 exposure model was successfully built on a separate Excel spreadsheet in the same file containing tier 1 exposure model. To utilize the model, exposure range needs to be estimated from tier 1 model and worker monitoring data, at least one input are required. Conclusions: The developed tier 2 exposure model can help industrial hygienists obtain a narrow range of worker exposure level to a chemical by reflecting a certain set of job characteristics.