Probabilistic risk assessment of emerging materials: case study of titanium dioxide nanoparticles. Issue 4 (21st April 2017)

• Record Type: Journal Article
• Authors: Tsang, Michael P.; Hristozov, Danail; Zabeo, Alex; Koivisto, Antti Joonas; Jensen, Alexander Christian Østerskov; Jensen, Keld Alstrup; Pang, Chengfang; Marcomini, Antonio; Sonnemann, Guido
• Is Part Of: Nanotoxicology. Volume 11:Issue 4(2017)
• Page Start: 558
• Page End: 568
• Publication Date: 2017-04-21
• Subjects: Benchmark dose -- occupational exposure -- Monte Carlo analysis -- engineered nanomaterials -- TiO2; Toxicology -- Periodicals; 615.9
• DOI: 10.1080/17435390.2017.1329952
• Languages: English
• ISSNs: 1743-5390

Abstract

Abstract: The development and use of emerging technologies such as nanomaterials can provide both benefits and risks to society. Emerging materials may promise to bring many technological advantages but may not be well characterized in terms of their production volumes, magnitude of emissions, behaviour in the environment and effects on living organisms. This uncertainty can present challenges to scientists developing these materials and persons responsible for defining and measuring their adverse impacts. Human health risk assessment is a method of identifying the intrinsic hazard of and quantifying the dose–response relationship and exposure to a chemical, to finally determine the estimation of risk. Commonly applied deterministic approaches may not sufficiently estimate and communicate the likelihood of risks from emerging technologies whose uncertainty is large. Probabilistic approaches allow for parameters in the risk assessment process to be defined by distributions instead of single deterministic values whose uncertainty could undermine the value of the assessment. A probabilistic approach was applied to the dose–response and exposure assessment of a case study involving the production of nanoparticles of titanium dioxide in seven different exposure scenarios. Only one exposure scenario showed a statistically significant level of risk. In the latter case, this involved dumping high volumes of nano-TiO2 powders into an open vessel with no personal protection equipment. The probabilistic approach not only provided the likelihood of but also the major contributing factors to the estimated risk (e.g. emission potential).