Evaluating nanotechnology opportunities and risks through integration of life-cycle and risk assessment.

It has been some 15 years since the topics of sustainability and nanotechnologies first appeared together in the scientific literature and became a focus of organizations' research and policy developments. On the one hand, this focus is directed towards approaches and tools for risk assessment and management and on the other hand towards life-cycle thinking and assessment. Comparable to their application for regular chemicals, each tool is seen to serve separate objectives as it relates to evaluating nanotechnologies' safety or resource efficiency, respectively. While nanomaterials may provide resource efficient production and consumption, this must balance any potential hazards they pose across their life-cycles. This Perspective advocates for integrating these two tools at the methodological level for achieving this objective, and it explains what advantages and challenges this offers decision-makers while highlighting what research is needed to further enhance integration.

Framework for the uncertainty assessment in the impact pathway analysis with an application on a local scale in Spain.

The estimation of damage estimates due to air emissions gives important basic knowledge for decision-making on the level of environmental politics and business strategies. Nowadays, a frequently applied method to estimate environmental damages is the Impact Pathway Analysis (IPA), which can be easily carried out using models such as EcoSense or PathWays. These models produce results in a relatively short term. However, there is a lack of reliability in the results. As in many other environmental software tools, the uncertainty is the key problem that makes it difficult to convince decision-makers by the outcomes of a study. Therefore, a framework that allows assessing the uncertainties within studies in which the IPA is applied on a local scale has been developed. In this assessment framework, the uncertainties of the used parameters, including their spatial and temporal variability, are taken into account. As the model is processing a huge quantity of data, one step of the assessment consists of a screening procedure to determine the parameters that are supposed to be fixed. For the other data, probability distributions have to be selected and classified into two groups: extensively available data for which average and standard deviation can be calculated and data based on little information. A quantification of the uncertainty can be completed by a stochastic model in the form of Monte Carlo (MC) simulation on the basis of the framework. As an illustration of the framework, we have applied it to a study on the installation of an advanced gas treatment in the municipal waste incinerator of Tarragona. It can be shown that the presented stochastic approach gives a lower geometric deviation than the analytical one and that the new gas treatment reduces the environmental damages without any doubt.