An integrated health risk assessment with control banding for nanomaterials exposure. (8th December 2021)

• Record Type: Journal Article
• Authors: Halil, Norsuzieanah; Rusli, Risza; Zainal Abidin, Mardhati; Jamen, Shahrulnizam; Khan, Faisal
• Other Names: Abidin Mardhati Zainal guestEditor.; Rusli Risza Bt guestEditor.; Willey Ronald J. guestEditor.
• Is Part Of: Process safety progress. Volume 41(2022)Supplement 1
• Page Start: S84
• Page End: S97
• Publication Date: 2021-12-08
• Subjects: Bayesian Network -- control banding -- health risk -- nanomaterials risk -- paint industry; Chemical plants -- Management -- Periodicals; 660
• DOI: 10.1002/prs.12327
• Languages: English
• ISSNs: 1066-8527

Abstract

Abstract: Nanomaterials are known to cause biological effects to humans through various routes of exposure such as injection, intravenous, oral, and inhalation. The risk analyses through conventional qualitative or semi‐quantitative approaches, such as control banding tools with limited safety data, and information on the risks posed by nanomaterials, have created uncertainties in decision‐making by various stakeholders. Therefore, an integrated Nanomaterial Risk (NanoRisk) framework that incorporates the Bayesian Network (BN) model, control banding, and process parameters focusing on humidity, the mass of nanomaterials, and operating temperatures was developed to assess the hazards of nanomaterials and their potential biological effects to human health as a result of exposure. The proposed risk assessment was applied to nanomaterials used in the paint and coating industry (nano‐silica, nano‐titanium, and nano‐silver), and the nodes of the BN model were constructed from physiochemical properties, biological effects, routes of exposure, and types of studies extracted from published data. The flexible analytic approach of the BN model allows for a valuable prediction of hazard exposure towards nanomaterials, thus facilitating decision‐making. Furthermore, the integrated framework proposes suitable control measures to reduce the hazard exposure according to the hazard level at different modes of operation. The distinctive feature of NanoRisk demonstrates comprehensive analysis and results that are comparable with previously developed methods.