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The assessment of the implementation of green chemistry principles in the syntheses of nanomaterials is a complex decision-making problem that necessitates the integration of several evaluation criteria. Multiple Criteria Decision Aiding (MCDA) provides support for such a challenge. One of its methods – Dominance-based Rough Set Approach (DRSA) – was used in this research to develop a model for the green chemistry-based classification of silver nanoparticle synthesis protocols into preference-ordered performance classes. DRSA allowed integration of knowledge from both peer-reviewed literature and experts (decision makers, DMs) in the field, resulting in a model composed of decision rules that are logical statements in the form: “if conditions, then decision”. The approach provides the basis for the design of rules for the greener synthesis of silver nanoparticles. Decision rules are supported by synthesis protocols that enforce the principles of green chemistry to various extents, resulting in robust recommendations for the development and assessment of silver nanoparticle synthesis that perform at one of five pre-determined levels. The DRSA-based approach is transparent and structured and can be easily updated. New perspectives and criteria could be added into the model if relevant data were available and domain-specific experts could collaborate through the MCDA procedure. [ABSTRACT FROM AUTHOR]

Substantial limitations and uncertainties hinder the exposure assessment of engineered nanomaterials (ENMs). The present deficit of reliable measurements and models will inevitably lead in the near term to qualitative and uncertain exposure estimations, which may fail to support adequate risk assessment and management. Therefore it is necessary to complement the current toolset with user-friendly methods for near-term nanosafety evaluation. This paper proposes an approach for relative exposure screening of ENMs. For the first time, an exposure model explicitly implements quantitative weight of evidence (WoE) methods and utilises expert judgement for filling data gaps in the available evidence-base. Application of the framework is illustrated for screening of exposure scenarios for nanoscale titanium dioxide, carbon nanotubes and fullerenes, but it is applicable to other nanomaterials as well. The results show that the WoE-based model overestimates exposure for scenarios where expert judgement was substantially used to fill data gaps, which suggests its conservative nature. In order to test how variations in input data influence the obtained results, probabilistic Monte Carlo sensitivity analysis was applied to demonstrate that the model performs in stable manner. [ABSTRACT FROM AUTHOR]

We consider a problem of multi-decision sorting subject to multiple criteria. In the newly formulated decision problem, besides performances on multiple criteria, alternatives get evaluations on multiple interrelated decision attributes involving preference-ordered classes. We propose a dedicated method for dealing with such a problem, incorporating a threshold-based value-driven sorting procedure. The Decision Maker (DM) is expected to holistically evaluate a subset of reference alternatives by indicating the quality or risk level on a pre-defined scale of each decision attribute. Based on these evaluations, we construct a set of interrelated preference models, one for each decision attribute, compatible with intra- and inter-decision constraints imposed by such indirect preference information. We also formulate a new way of dealing with potentially non-monotonic criteria by discovering local monotonicity changes in different performance scale regions. The marginal value functions for criteria with unknown monotonicity are represented as a sum of two value functions assuming opposing preference directions, one non-decreasing and the other non-increasing. This permits to obtain an aggregated marginal value function with an arbitrary non-monotonic shape. The practical usefulness of the approach is demonstrated on a case study concerning risk management related to handling (i.e., production, use, manipulation, and processing) nanomaterials in different conditions. We analyze the expert judgments and discuss the inferred preference models, which can be applied to support health and safety managers in reducing the possible risk associated with the respective exposure scenario. • We introduce a problem of multi-decision sorting in Multi-Criteria Decision Analysis. • We propose a dedicated value-driven threshold-based preference disaggregation method. • We translate the assignment examples into intra- and inter-decision constraints. • We consider a study on the nanomaterials' exposure management in various conditions. • We propose a new way of dealing with potentially non-monotonic criteria. [ABSTRACT FROM AUTHOR]