Evaluation of Risk Assessment and Control Banding Models for Engineered Nanomaterials: Application to Exposure Scenarios in Semiconductor Fabrication
Shepard, Dr. Michele
(Colden Corporation, Albany, NY)
With the continued growth of engineered nanomaterial (ENM) applications and nanoproducts in the workplace, methods are needed to evaluate potential worker exposures and provide a basis for risk management decisions. Risk assessment may be challenging due to current limitations in available data on nanomaterial properties and hazards, as well as exposure assessment methods. A number of risk screening and control banding models for ENMs have been developed to assist in occupational risk assessment and management. Further evaluation and application of these models is necessary to help refine and validate their utility and better understand limitations. ENMs are used in the nanoelectronics and related industries for chemical mechanical planarization (CMP) processes as well as other applications being investigated. This presentation provides an overview of four risk banding tools for ENMs, and presents the findings from applying these to six exposure scenarios at a research and development site using ENMs in CMP processes. The risk banding approaches-ANSES, CB Nanotool, ISO 12901-2, and the Precautionary Matrix-were reviewed to identify model characteristics and determine areas of agreement or contrast in design and application. Output from control banding was compared to existing site engineering controls to determine if additional controls or actions were recommended, and to conclusions from air sampling previously conducted at the site. The models have differences in their scope and complexity, input parameters and other design features. The control levels recommended were not consistent across the control banding models evaluated, although two approaches generally agreed. No significant differences were found between conclusions from control banding and air sampling based on the limited evaluation. While broader application of these results may be limited based on the small sample size, this study helped highlight areas of agreement and dissimilarity in the approaches and provides additional data to consider in selecting or refining risk banding models for ENMs.