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Spatial perspectives enhance modeling of nanomaterial risks
Author(s) -
Moore Elizabeth A.,
Babbitt Callie W.,
Tomaszewski Brian,
Tyler Anna Christina
Publication year - 2020
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12976
Subject(s) - context (archaeology) , life cycle assessment , environmental science , industrial ecology , ecotoxicity , ecosystem , renewable energy , production (economics) , environmental resource management , risk analysis (engineering) , business , ecology , sustainability , geography , chemistry , archaeology , organic chemistry , toxicity , biology , economics , macroeconomics
Novel engineered nanomaterials (ENMs) are increasingly being manufactured and integrated into renewable energy generation and storage technologies. Past research estimated the potential impact of this increased demand on environmental systems, due to both the life cycle impact of ENM production and the potential for their direct release into ecosystems. However, many models treat ENM production and use as spatially implicit, without considering the specific geographic location of potential emissions. By not considering geographical context, ENM accumulation or impact may be underestimated. Here, we introduce an integrated predictive model that forecasts likely ENM manufacturing locations and potential emissions to the environment, with a focus on critical environmental areas and freshwater ecosystems. Spatially explicit ENM concentrations are estimated for four case study ENMs that have promising application in lithium‐ion battery production. Results demonstrate that potential ENM exposure from manufacturing locations within buffer zones of sensitive ecosystems would accumulate to levels associated with measured ecotoxicity risk under high release scenarios, underscoring the importance of adding a spatial and temporal perspective to life cycle toxicity impact assessment. This predictive integrated modeling approach is novel to the nanomaterial literature and can be adapted to other regions and material case studies to proactively inform life cycle tradeoffs and decision‐making.