Freshwater ecotoxicity characterisation factor for metal oxide nanoparticles: A case study on titanium dioxide nanoparticle

Salieri, Beatrice; Righi, Serena; Pasteris, Andrea; Olsen, Stig Irving

doi:10.1016/j.scitotenv.2014.09.107

Cited by 64 publications

(70 citation statements)

References 48 publications

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“…21,46 In accordance with the work of Barberio et al, 33 to date, only two publications pioneer in quantifying the freshwater nanotoxicity. 29,48 Additionally, model's satisfactory level is low, which means improvement actions are needed for this model and apply with caution. 46 Therefore, further research is needed to develop the characterization model addressing nano-toxicity.…”

Section: Impact Of Different Packaging Materialsmentioning

confidence: 99%

Selection of Nanomaterial-Based Active Agents for Packaging Application: Using Life Cycle Assessment (LCA) as a Tool

Zhang

Hortal

Dobon

et al. 2016

Packag. Technol. Sci.

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The interest for using metallic nanoparticles for active packaging derives from its superior antimicrobial efficacy, no negative impacts on the food sensory properties and compatibility with harsh polymer processing conditions. There are a few candidates for this purpose, and the selection of which can be assisted by considering the packaging's environmental impacts. As a case study, three packaging films incorporating different active agents (silver nanoparticles, titanium dioxide nanoparticles and a mixture of both) were evaluated using life cycle assessment and the associated impacts were calculated and compared. The functional unit was set in such a way that it directly focuses on the function that the antimicrobial packaging is supposed to deliver: to ensure food safety and quality. To this end, the antimicrobial efficacy of each agent was integrated in the environmental impact calculation. The results show that the film incorporating the mixture of nanoparticles had the lowest environmental impacts.

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Section: Impact Of Different Packaging Materialsmentioning

confidence: 99%

Selection of Nanomaterial-Based Active Agents for Packaging Application: Using Life Cycle Assessment (LCA) as a Tool

Zhang

Hortal

Dobon

et al. 2016

Packag. Technol. Sci.

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show abstract

“…A key and open issue addressed in these reviews is the human toxicity and ecotoxicity characterization factors (CFs) for nanomaterials [12]. Thus far, CFs for a toxicity assessment have been published for two nanoparticles only, namely carbon nanotubes (CNT) [20] with graphene oxide [21] and TiO 2 nanoparticles (nanoTiO 2 ) [22,23].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Environmental Performance of TiO2 Nanoparticles Coated Self-Cleaning Float Glass

et al. 2017

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Abstract:In recent years, superhydrophilic and photocatalytic self-cleaning nanocoatings have been widely used in the easy-to-clean surfaces field. In the building sector, self-cleaning glass was one of the first nanocoating applications. These products are based on the photocatalytic property of a thin layer of titanium dioxide (TiO 2 ) nanoparticles deposited on the surface of any kind of common glass. When exposed to UV radiation, TiO 2 nanoparticles react with the oxygen and water molecules adsorbed on their surface to produce radicals leading to oxidative species. These species are able to reduce or even eliminate airborne pollutants and organic substances deposited on the material's surface. To date, TiO 2 nanoparticles' benefits have been substantiated; however, their ecological and human health risks are still under analysis. The present work studies the ecodesign of the industrial scale-up of TiO 2 nanoparticles self-cleaning coated float glass production performed by the life cycle assessment (LCA) methodology and applies new human toxicity indicators to the impact assessment stage. Production, particularly the TiO 2 nanoparticle application, is the life cycle phase most contributing to the total damage. According to the ecodesign approach, the production choices carried out have exacerbated environmental burdens.

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“…Widespread concern regarding 4 potential toxicity-related impacts associated with emissions of ENMs galvanized an active research community and produced volumes of published data that demonstrates high variability between published parameter estimates (NSTCCT 2014). The suitability of human and ecotoxicity LCIA models for ENMs is a known issue (Klopffer 2007) and relatively well covered in recent literature (Gilbertson et al 2015;Miseljic and Olsen 2014b;Salieri et al 2015). Less emphasized are critical data-related challenges include:…”

Section: Introductionmentioning

confidence: 99%

“…Nanomaterial LCA review articles identified the lack of ENM-specific CFs as preventing quantification of toxicity impacts associated with ENM emissions (Gavankar et al 2012;Hischier and Walser 2012;Miseljic and Olsen 2014a). In the literature fewer than five studies have developed aquatic ecotoxicity CFs for ENMs, predominantly through innovative modifications of USEtox including: development of realistic and worst-case scenarios for the ENM's CF (Eckelman et al 2012), precautionary assumptions (Miseljic and Olsen 2014a), qualitative discussion of uncertainty (Rodriguez-Garcia et al 2014), and development of simplified colloidal transport models within USEtox (Salieri et al 2015). Only Eckelman et al (2012) conducts a thorough Monte Carlo sensitivity analysis on substance properties, but the emphasis was on comparing the magnitude of cumulative upstream ecotoxicity impacts with those directly from ENM releases, and therefor did not include the relative influence of variable substance data on characterization results.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity-based research prioritization through stochastic characterization modeling

Wender

Prado

Fantke

et al. 2017

Int J Life Cycle Assess

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Purpose Product developers using life cycle toxicity impact assessment models to understand potential impacts of material substitutions face serious challenges related to large data demands and high uncertainty. This motivates greater focus on model sensitivity toward input parameter variability, particularly in the context of emerging contaminants like engineered nanomaterials (ENMs), to guide future efforts in data refinement and design of experiments. This study presents a Monte Carlo tool designed for use with USEtox 1.0 that allows researchers to rapidly prioritize data needs according to influence on characterization factors (CFs).Methods Using Monte Carlo analysis we demonstrate a sensitivity-based approach to prioritize research through a case study comparing aquatic ecotoxicity CFs for the ENM C60 and the vitamin B derivative niacinamide, two antioxidants used in personal care products. We calculate CFs via 10,000 iterations assuming plus-or-minus one order of magnitude variance for fate and exposure-relevant inputs. Spearman Rank Correlation Indices are used for all variable inputs to identify parameters with the largest influence on CFs, which we prioritize for data refinement and future experimental investigation. Based on the importance of aggregate multi-species toxicity (average log EC50) and studies suggesting solvent residues may yield erroneous toxicity estimates, we recalculate C60 CFs omitting all studies using solvents in sample preparation.Results and discussion For emissions to freshwater, the C60 CF is log-normally distributed with a geometric mean of 280 and geometric standard deviation (GSD) of 2.1 PAF m 3 day/kg compared to 2.6 with a GSD=1.8 PAF m 3 day/kg for niacinamide. C60 CFs are most sensitive to varied suspended solids partitioning coefficients (Kpss) and average log EC50, whereas variation of other substance parameters has comparatively little effect on model results. Insufficient experimental evidence hampers to revise assumptions for Kpss, and we suggest prioritizing future experiments that elucidate C60 interactions with suspended solids. Recalculating C60 CFs without toxicity studies that use solvents reduces the geometric mean by more than a factor of ten. This reinforces the importance of thorough characterization of released ENMs, in this case regarding the presence of solvent residues. ConclusionCalculating stochastic CFs allows sensitivity-based prioritization of data needs and future experiments, which is particularly helpful in the context of emerging contaminants like C60. Researchers can conserve resources and address parameter uncertainty by applying our approach when developing new or refining existing CFs for the inventory items that contribute most to toxicity impacts. The Monte Carlo tool can be applied to current toxicity characterization models like USEtox and is freely available at

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Freshwater ecotoxicity characterisation factor for metal oxide nanoparticles: A case study on titanium dioxide nanoparticle

Cited by 64 publications

References 48 publications

Selection of Nanomaterial-Based Active Agents for Packaging Application: Using Life Cycle Assessment (LCA) as a Tool

Selection of Nanomaterial-Based Active Agents for Packaging Application: Using Life Cycle Assessment (LCA) as a Tool

Assessment of Environmental Performance of TiO2 Nanoparticles Coated Self-Cleaning Float Glass

Sensitivity-based research prioritization through stochastic characterization modeling

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