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

TLDR

Probabilistic risk quantification accounts for variability in ecotoxicological effects and exposure uncertainty. The study quantified risks of five engineered nanomaterials in water, soil, and sediment by deriving probabilistic species sensitivity distributions and predicted environmental concentrations, then calculating risk ratios from predicted no‑effect concentrations. Risk characterization revealed sufficient data for water and soil but not sediments, with most materials showing exposure–effect gaps of several orders of magnitude; Nano‑ZnO in freshwater and Nano‑TiO₂ in soil had risk ratios near one, highlighting them as priority combinations for further study.

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.