Exposure assessments of nanoparticles in aquatic environments – considerations, review and recommendations

Synthetic nanoparticles are new forms of chemical substances. They can be found in severaldifferent forms, such as free particles, surface bound and dissolved in liquid. Nanoparticlescan also exist as free, individual particles or agglomerate consisting of multiple particles. Thisreport discusses the assessment of possible risks of nanoparticles. Chemical risk is usuallyconsidered to consist of two elements: (1) Exposure to the substance, and (2) the substance\u27stoxicity. So far, the risk-related research on nanoparticles has had a strong focus on theparticles \u27 toxic effects. In this report, we would instead focus on how exposure tonanoparticles can be calculated and assessed, with focus on nanoparticles in water. In thereport, we provide an initial background and definitions of nanomaterials and nanoparticles,and describe briefly a standard method of risk assessment of chemicals in the environment.Then we go through important considerations that should be made in the exposure assessmentof nanoparticles. First we discuss three considerations related to the emissions ofnanoparticles, namely the lack of data for annual production of nanoparticles, the importanceof applying a substance flow perspective, and lack of data for so-called emission factors fornanoparticles of various products and materials. Furthermore, we discuss considerations formodeling of nanoparticles behavior in water, mainly by listing a number of key processeswith large influence. These are agglomeration, sedimentation, and dissolution. Related to that,we discuss how natural organic materials, coatings and aging of particles can affect theseprocesses. We note here three particle properties that are important in order to describenanoparticles dispersion in water, in a similar way that the octanol-water partition coefficientand half-life is important to describe the fate of organic chemicals in the environment. Fornanoparticles these are the particle size (a) and the density (ρ). We also identify a number ofmore complex parameters affecting particle behavior in the environment, but not only becauseof the different particle characteristics, but also depending on characteristics related to theenvironment. These are the collision efficiency (α), point of zero charge (pHpzc), Hamakerconstant (A) and a so-called form factor (β) that affect the sedimentation. In addition to thegeneral difficulty to measure or calculate these parameters they also co-vary. Furthermore, wemake a review of 11 currently available exposure models for nanoparticles in aquaticenvironment. We note that the studies differ regarding modeling method, which sources ofemissions that are included, the nanoparticles taken into account, estimated concentrations inthe environment, and whether the results are presented as mass or particle concentration. Onlytwo studies trying to model the nanoparticle exposure based on particle properties in a mannersimilar to standard methods for chemical risk assessment. The other modeling studies areinstead based on data on flows of specific nanomaterials, and not on generic algorithms. Next,we describe a number of challenges that occur when measuring nanoparticles in theenvironment. Finally, we provide the following recommendations to ensure good exposureassessment of nanoparticles in the future:1. Information of flows and stocks of nanoparticles in society need to be collected.2. Emission factors would need to be developed for each product that makes use ofnanoparticles.3. Emissions should be reported both as mass and particle number until it becomesclearer which one is most relevant.4. More research is needed in order to determine which particle properties need to beknown in order to calculate the concentration of nanoparticles in the environment.5. At least the particle size and particle size distribution, as well as the specific particledensity should be reported.6. More research is required to improve the experimental measurements of nanoparticlesto be able to validate exposure models