Probabilistic environmental risk assessment of five nanomaterials (nano-TiO₂, nano-Ag, nano-ZnO, CNT, and fullerenes)

Abstract

The environmental risks of five engineered nanomaterials (nano-TiO2, nano-Ag, nano-ZnO, CNT, and fullerenes) were quantified in water, soils, and sediments using probabilistic Species Sensitivity Distributions (pSSDs) and probabilistic predicted environmental concentrations (PECs). For water and soil, enough ecotoxicological endpoints were found for a full risk characterization (between 17 and 73 data points per nanomaterial for water and between 4 and 20 for soil) whereas for sediments, the data availability was not sufficient. Predicted No Effect Concentrations (PNECs) were obtained from the pSSD and used to calculate risk characterization ratios (PEC/PNEC). For most materials and environmental compartments, exposure and effect concentrations were separated by several orders of magnitude. Nano-ZnO in freshwaters and nano-TiO2 in soils were the combinations where the risk characterization ratio was closest to one, meaning that these are compartment/ENM combinations to be studied in more depth with the highest priority. The probabilistic risk quantification allows us to consider the large variability of observed effects in different ecotoxicological studies and the uncertainty in modeled exposure concentrations. The risk characterization results presented in this work allows for a more focused investigation of environmental risks of nanomaterials by consideration of material/compartment combinations where the highest probability for effects with predicted environmental concentrations is likely.

Assessing the Risk of Engineered Nanomaterials in the Environment: Development and Application of the nanoFate Model

“…MFAs apply transfer coefficients to move mass from one compartment to another at a given rate, generally without considering the mechanics of the transfer process. [10][11][12]46,52,53,63,64 Since nanoFate falls within the multimedia models, we discuss the differences with similar models (i.e., MendNano and SimpleBox4Nano) in more detail. 47,48 MendNano includes air, water, soil, sediment, and terrestrial and aquatic biota.…”

Assessing nanomaterial exposures in aquatic ecotoxicological testing: Framework and case studies based on dispersion and dissolution

“…For the simplicity of demonstrating the testing framework, the standard toxicological organism C. dubia was used for the case studies. C. dubia is often among the most sensitive organisms to various ENMs (Coll et al, 2016;Garner et al, 2015). The acute C. dubia USEPA test method (2002a) was used to assess survival after 48 h exposure.…”

“…ENM settling may also result in a predominance of smaller suspended particles and released ions or other constituent substances in the water column (Hull et al, 2009, Kennedy et al, 2015b, suggesting a need to test three different fractions of the exposure; the entire ENM exposure, the suspended fraction and the settled fraction (Potthoff et al, 2015). The focus of this study was on basic water-column assays, which use test organisms that are highly sensitive to ENMs (Coll et al, 2016;Garner et al, 2015). Elutriate methods (USEPA/USACE, 1998) were used to understand the hazard contribution from the soluble or dispersible fractions of the low or non-dispersible ENMs.…”

Statistical modelling of variability and uncertainty in risk assessment of nanoparticles

“…Bayesian networks (Chio et al, 2012;Coll et al, 2015;Gottschalk et al, 2013a;Money et al, 2012). Although these methods quantify the variation in the various components of the risk assessment, this variation is referred to as uncertainty only in the mentioned publications.…”

“…Here again, uncertainty and variability are treated simultaneously, making separation between them in the risk quantification impossible. Recently, this risk quantification method was extended (Coll et al, 2015) to include more toxicity data and extra uncertainty on the assessment factors used in the probabilistic SSD method. Money et al (2012) developed the Forecasting the Impacts of Nanomaterials in the Environment model for nanoparticle risk forecasting.…”