Comparing ecotoxicity risks for nanomaterial production and release under uncertainty

Abstract

Innovations in clean energy technology are expected to reduce fossil fuel dependence and mitigate greenhouse gas emissions, but these benefits are contingent on concurrent innovations in energy materials that can improve performance and reduce cost. Engineered nanomaterials have been promoted as a transformative advance in renewable energy generation and storage, but their adoption has also raised concerns that potential environmental impacts of nanomaterial production and use may outweigh their potential benefits. Life cycle assessment (LCA) has the potential to quantify nanomaterial environmental impacts and compare trade-offs between potential benefits and adverse impacts. However, LCA for nanomaterials is sparse due to lack of data and models that link physicochemical parameters with the overall toxicity and chemical fate of nanomaterial emissions. This study develops preliminary life cycle impact characterization factors for representative case study nanomaterials used in clean energy applications and then compares the environmental impact of direct nanomaterial release to the indirect impacts across the nanomaterial supply chain. Scenario analysis is used to model the uncertainty and variability of nanomaterial parameters that contribute to estimated freshwater aquatic ecotoxicity for carbon nanotubes, C60 fullerenes, nano-TiO₂, and nano-Ag. Results suggest that supply chain energy consumption, largely due to complex nanomaterial synthesis processes, may result in greater ecotoxicity than the direct nanomaterial release in many realistic cases. The exception to this trend was nano-Ag, which was intrinsically more toxic, primarily due to upstream processes leading to metal releases (silver mining), but also due to the potential for downstream silver releases in the worst-case scenario. This systematic approach can aid decision-makers in mitigating unintended consequences from nanomaterial use in clean energy technologies through informed life cycle and uncertainty modeling.

Graphical abstract

from Energy Ecology Environment Ambio via Terpsi Hori on Inoreader https://ift.tt/2LiO1uH