Tampere University of Technology

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Teollisten nanohiukkasten ympäristöriskien arviointi

Research output: Book/ReportMaster's ThesisScientific

Details

Original languageFinnish
PublisherTampere University of Technology
Number of pages105
Publication statusPublished - 2016
Publication typeG2 Master's thesis, polytechnic Master's thesis

Abstract

In the 21st century, increasing number of nanoscale materials such as engineered nanoparticles (ENP) are produced for various industrial applications and everyday products. However, recent studies have shown that some ENPs may cause adverse effects on human health and the environment, thus a need to assess and govern risks has arisen, to ensure development and production of low-risk products containing ENPs. In order to reliably assess the risks, analytical measurement methods are needed, along with modeling methods that take into account the processes ENPs undergo in the nature. Present risk assessment models do not sufficiently consider the processes affecting ENPs after they are released into the atmosphere.

In this Master’s thesis, how the effect of possible atmospheric processes can be considered in assessments of the environmental and health risks of ENPs is studied. A Gaussian atmospheric dispersion model is proposed to model the behavior and fate of ENPs after their release into the atmosphere. The model is developed to compute exposure estimates to be used in risk assessment. Additionally, a SimpleBox4Nano- model, found during the literature review, is recommended as an exemplary model that takes into account some of the atmospheric processes. The thesis focuses also on how the uncertainty that is present in the current models can be reduced through sensitivity analysis. A sensitivity testing was performed for the dispersion model considered in this thesis and it was found that exposure estimates change the most when model input parameters such as particle mass, particle size, atmospheric stability class, wind speed and size of the particle plume are varied. This kind of sensitivity testing could be a way to increase accuracy and reliability of the current risk assessment models.

Field of science, Statistics Finland