Predicting environmental concentrations of nanomaterials for exposure assessment - a review

There have been major advances in the science to predict the likely environmental concentrations of nanomaterials, which is a key component of exposure and subsequent risk assessment. Considerable progress has been since the first Material Flow Analyses (MFAs) in 2008, which were based on very limited information, to more refined current tools that take into account engineered nanoparticle (ENP) size distribution, form, dynamic release, and better-informed release factors. These MFAs provide input for all environmental fate models (EFMs), that generate estimates of particle flows and concentrations in various environmental compartments. While MFA models provide valuable information on the magnitude of ENP release, they do not account for fate processes, such as homo- and heteroaggregation, transformations, dissolution, or corona formation. EFMs account for these processes in differing degrees. EFMs can be divided into multimedia compartment models (e.g., atmosphere, waterbodies and their sediments, soils in various landuses), of which there are currently a handful with varying degrees of complexity and process representation, and spatially-resolved watershed models which focus on the water and sediment compartments. Multimedia models have particular applications for considering predicted environmental concentrations (PECs) in particular regions, or for developing generic “fate factors” (i.e., overall persistence in a given compartment) for life-cycle assessment. Watershed models can track transport and eventual fate of emissions into a flowing river, from multiple sources along the waterway course, providing spatially and temporally resolved PECs. Both types of EFMs can be run with either continuous sources of emissions and environmental conditions, or with dynamic emissions (e.g., temporally varying for example as a new nanomaterial is introduced to the market, or with seasonal applications), to better understand the situations that may lead to peak PECs that are more likely to result in exceedance of a toxicological threshold. In addition, bioaccumulation models have been developed to predict the internal concentrations that may accumulate in exposed organisms, based on the PECs from EFMs. The main challenge for MFA and EFMs is a full validation against observed data. To date there have been no field studies that can provide the kind of dataset(s) needed for a true validation of the PECs. While EFMs have been evaluated against a few observations in a small number of locations, with results that indicate they are in the right order of magnitude, there is a great need for field data. Another major challenge is the input data for the MFAs, which depend on market data to estimate the production of ENPs. The current information has major gaps and large uncertainties. There is also a lack of robust analytical techniques for quantifying ENP properties in complex matrices; machine learning may be able to fill this gap. Nevertheless, there has been major progress in the tools for generating PECs. With the emergence of nano- and microplastics as a leading environmental concern, some EFMs have been adapted to these materials. However, caution is needed, since most nano- and microplastics are not engineered, therefore their characteristics are difficult to generalize, and there are new fate and transport processes to consider.