Previous methods of estimating characterization factors (CFs) of metals in life cycle impact assessment (LCIA) models were based on multimedia fate, exposure, and effect models originally developed to address the potential impacts of organic chemicals. When applied to metals, the models neglect the influence of ambient chemistry on metal speciation, bioavailability and toxicity. Gandhi et al. (2010) presented a new method of calculating CFs for freshwater ecotoxicity that addresses these metal-specific issues. In this paper, we compared and assessed the consequences of using the new method versus currently available LCIA models for calculating freshwater ecotoxicity, as applied to two case studies previously examined by Gloria et al. (2006): (1) the production of copper (Cu) pipe and (2) a zinc (Zn) gutter system. Using the same inventory data as presented by Gloria et al. (2006), we calculated and compared the LCIA outcomes for freshwater ecotoxicity of each case study using four models: USES-LCA 1.0, USES-LCA 2.0, USEtox™ using the previous approach, and USEtox™ using the new method. Since the new method requires specification of water chemistry for the freshwater compartment, we explored the effect of using seven freshwater archetypes. We analyzed the freshwater ecotoxicity outcomes of the two case studies with respect to the different models, infinite versus 100 years time scales for calculating impacts after metal emissions, and water chemistries representing environmental variability. Significant differences in CFs, overall freshwater ecotoxicity score (Σ CF × emissions) and the contributions of individual metals to the overall score were traced back to differences in modeling methods (e.g., variations in compartments included in the fate model), the choice of metal partition coefficients versus those explicitly calculated based on water chemistry (USEtox™ (new)), and the calculation of effect factors. Metal CFs calculated using USES-LCA 1.0 ranked Co > Ni > Cd ≈ Cu > Zn > Pb, but changed using USEtox™ (new) to Cd > Co > Ni > Zn > Cu > Pb for the archetype of hard alkaline water and Cd > Ni > Co > Cu ≈ Zn > Pb for the archetype of soft, acidic water. For the Cu pipe, total freshwater ecotoxicity scores for metal emissions into air and water ranged from 0.01 to 0.02 for USES-LCA1.0, ~1 for USEtox™ (previous) to 0.0002–0.01 1, 4-dichlorobenzene (DCB) eq. for USEtox™ (new) depending on the archetype. Whereas Cu followed by Ni emissions contributed most to total freshwater ecotoxicity estimated by USES-LCA1.0, Cu, Cd, Ni, and Zn, emissions were all important contributors towards freshwater ecotoxicity with USEtox™ (new), with differences in contributions dependent on the freshwater archetype. For the Zn gutter case study, the total scores varied from 10 for USEtox™ (previous) to 0.008 for USES-LCA 2.0 and 0.02–0.11 equal to 1, 4-DCB for USEtox™ (new). Zn contributed ~98% towards the freshwater ecotoxicity scores of metals in all models. For both case studies, differences in ecotoxicity scores were not significant for the infinite vs. 100 years time scale. Accounting for metal bioavailability and speciation by using USEtox™ (new) when calculating CFs decreased by 1–4 orders of magnitude the total metal freshwater ecotoxicity scores (Σ CF × emissions) attributable to metal emissions tallied for Cu pipe and Zn gutter system case studies (Gloria et al. 2006). This broad range came from the model used in comparison to USEtox™ (new) and the choice of freshwater archetype. Additionally, contributions of each metal to the total score of the Cu pipe case study changed significantly from the use of previous CFs (Huijbregts et al. 2000) versus the revised CFs (Gandhi et al. 2010). Metal CFs calculated using the method proposed by Gandhi et al. (2010) significantly lowers the total freshwater ecotoxicity impact of metal emissions. It is suggested that this lower estimate of potential impact from metal emissions is consistent with our understanding of metal chemistry. The magnitude of the potential freshwater ecotoxicity of metals depends on the chemistry of the modeled freshwater compartment, similarly to the dependence of acidification potential on regionally variant freshwater chemistry.
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