(338a) A Framework for Assessing the Adequacy of Information for Environmental Impact Assessment of Engineered Nanomaterials

Nanotechnology is developing rapidly with engineered nanomaterials (ENMs) now being used in myriad of applications. At the same time there are concerns regarding the potential environmental and health impacts of ENMs and thus the need for environmental impact assessments (EIAs) that are reliant on the available body of information. Conducting EIA for the target ENM requires, at the initial stage of analysis, evaluation of the adequacy of available information regarding: (i) the rate of ENM releases, (ii) environmental exposure concentrations, and (iii) toxicity of the ENM of concern. Tools are then needed to integrate the body of quantitative and qualitative information to arrive at the needed EIA. However, prior to investing significant resources in conducting quantitative EIA, it is critical to first assess the adequacy (i.e., availability and quantitative value) of the existing knowledge base for the specific EIA scenario of interest (e.g., exposure and ENM release scenarios). Accordingly, the present study presents a rigorous decision support tool (DST) that provides: (i) organization of information relevant for EIA into a decision tree (of information flow) suitable for EIA, (ii) representation of the adequacy of information as categories that are quantified in terms of scores signifying the extent of available information, as well as quantification of the uncertainty associated with the unavailability of information, and (iii) aggregation of the information scores that quantify the adequacy of available information for conducting EIA for ENMs. The constructed DST uses evidential reasoning algorithm that handles nonlinearity in the assessments of the range of available information (data extracted and curated from scientific literature) and integrates them into combined scores that reflect whether the available information is adequate (or sufficient) for carrying out the EIA scenario of interest. In order to illustrate the approach, a case study was conducted for three ENMs (i.e., nano Cu, nano ZnO and nano TiO₂) in which baseline conditions were first set based on literature exposure and toxicity data that were curated following a QA/QC screening of 256 publications. Four different scenarios were then designed to explore the impact of deviations due to the attribute weights associated with the different attributes under the different information categories and EIA scenarios. The base case analysis indicated that under baseline conditions, the results suggest that the body of available information for the three selected was adequate for assessing exposure potentials. When considering the various EIA scenarios, it was apparent that there is a significant body of hazard information for Nano ZnO and Nano CuO particularly in terms of in vivo toxicity studies directed at assessing ecological outcomes. The analysis also suggests that the current body of knowledge is sufficient for conducting occupational risk assessment for Nano TiO₂, followed by ecological risk assessment for Nano Cu-CuO. In all cases, the analysis demonstrated that additional information will be required regarding the fate and transport, environmental release and exposure data. The scenarios considered in the present study demonstrate an approach to Nano-EIA that can serve to support decision analysis in a broader context of ensuring sustainable nanotechnology. In summary, the DST approach enables the analyst to assign specific exposure scenarios and adjust weights, depending on the purpose of the analysis, and visualize the information domain via suitable information maps.