Needs and challenges for assessing the environmental impacts of engineered nanomaterials (ENMs)

• Michelle Romero-Franco,
• Hilary A. Godwin,
• Muhammad Bilal and
• Yoram Cohen

Beilstein J. Nanotechnol. 2017, 8, 989–1014, doi:10.3762/bjnano.8.101

• potential environmental impacts of ENMs. Assessment frameworks and tools that are suitable for the different decision analysis scenarios are then identified. In addition, we identify the gaps that currently exist between the needs of decision makers, for a range of decision scenarios, and the abilities of

• the Swiss Precautionary Matrix (SPM) [31], Risk Classification System based on Multi Criteria Decision Analysis (MCDA risk classification) [32][33][34], NanoRiskCat [35], the Decision-making framework for the grouping and testing of nanomaterials (DF4Nano grouping) [36], and the modified GreenScreen

• Precautionary Matrix, (b) “Risk” Classification Systems Based on Multi Criteria Decision Analysis, (c) NanoRiskCat, and (d) Decision-making framework for the grouping and testing of nanomaterials. Although several of the above approaches include “risk” in their titles, in practice they have been used either to

Nanoinformatics for environmental health and biomedicine

• Rong Liu and
• Yoram Cohen

Beilstein J. Nanotechnol. 2015, 6, 2449–2451, doi:10.3762/bjnano.6.253

• nanomaterials, which provides critical information for the environmental impact assessment of nanomaterials [18]. Another contribution addresses the issue of nanomaterial risk assessment and proposes a decision analysis scheme for furthering nanoinformatics work [19]. This work considers an array of decision

• analysis techniques (e.g., multicriteria decision analysis, value of information, weight of evidence, and portfolio decision analysis) that are potentially capable of assessing and classifying the multitude of available nanomaterial data. Such an approach can serve as the basis for both establich a

How decision analysis can further nanoinformatics

• Matthew E. Bates,
• Sabrina Larkin,
• Jeffrey M. Keisler and
• Igor Linkov

Beilstein J. Nanotechnol. 2015, 6, 1594–1600, doi:10.3762/bjnano.6.162

• techniques of multicriteria decision analysis (MCDA), value of information (VOI), weight of evidence (WOE), and portfolio decision analysis (PDA) can bridge the gap from current data collection and visualization efforts to present information relevant to specific decision needs. Decision analytic and

• Bayesian models could be a natural extension of mechanistic and statistical models for nanoinformatics practitioners to master in solving complex nanotechnology challenges. Keywords: decision analysis; nanoinformatics; policy; portfolio analysis; risk assessment; value of information; weight of evidence

• the data needed and the data available to make informed decisions about a new technology. Decision analysis typically formulates models for important decisions in order to identify which alternatives are most desirable given the available information and the preferences of the decision makers, thus

Simulation tool for assessing the release and environmental distribution of nanomaterials

• Haoyang Haven Liu,
• Muhammad Bilal,
• Anastasiya Lazareva,
• Arturo Keller and
• Yoram Cohen

Beilstein J. Nanotechnol. 2015, 6, 938–951, doi:10.3762/bjnano.6.97

• an acceptable level for compartmental models [22][23][24]. Compartmental models can be used to provide a first-tier analysis for estimating the magnitudes of potential ENM exposure concentrations. However, in order to support timely decision analysis regarding the potential environmental impact of