Dynamic Probabilistic Modeling of Environmental Emissions of Engineered Nanomaterials

TLDR

Engineered nanomaterials lack reliable environmental concentration data, and existing static models fail to account for rapid production growth and delayed releases from in-use stocks. The study aims to predict the environmental flows of four engineered nanomaterials and quantify their amounts in temporary and final sinks. Dynamic probabilistic material flow modeling is employed to achieve this. The model predicts rising concentrations of all four ENM, with nano‑TiO2 reaching up to ~40,000 µg/kg in sediment and mg/kg levels in incineration residues, providing the most accurate mass inputs for future fate models.

Abstract

The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental concentrations. Despite significant advances in analytical methods, it is still not possible to measure the concentrations of ENM in natural systems. Material flow and environmental fate models have been used to provide predicted environmental concentrations. However, almost all current models are static and consider neither the rapid development of ENM production nor the fact that many ENM are entering an in-use stock and are released with a lag phase. Here we use dynamic probabilistic material flow modeling to predict the flows of four ENM (nano-TiO2, nano-ZnO, nano-Ag and CNT) to the environment and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. Caused by the increase in production, the concentrations of all ENM in all compartments are increasing. Nano-TiO2 had far higher concentrations than the other three ENM. Sediment showed in our worst-case scenario concentrations ranging from 6.7 μg/kg (CNT) to about 40 000 μg/kg (nano-TiO2). In most cases the concentrations in waste incineration residues are at the "mg/kg" level. The flows to the environment that we provide will constitute the most accurate and reliable input of masses for environmental fate models which are using process-based descriptions of the fate and behavior of ENM in natural systems and rely on accurate mass input parameters.