Modeling nanomaterial fate and uptake in the environment: Current knowledge and future trends

Research output: Contribution to journalArticle

Authors

  • M. Baalousha
  • G. Cornelis
  • T. A J Kuhlbusch
  • C. Nickel
  • W. Peijnenburg
  • N. W. Van Den Brink

Colleges, School and Institutes

External organisations

  • Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, South Carolina, USA.
  • University of Gothenburg
  • University Duisburg-Essen
  • Leiden University
  • Wageningen University and Research Centre

Abstract

Modeling the environmental fate of nanomaterials (NMs) and their uptake by cells and organisms in the environment is essential to underpin experimental research, develop overarching theories, improve our fundamental understanding of NM exposure and hazard, and thus enable risk assessment of NMs. Here, we critically review the state-of-the-art of the available models that can be applied/adapted to quantify/predict NM fate and uptake in aquatic and terrestrial systems and make recommendations regarding future directions for model development. Fate models have evolved from substance flow analysis models that lack nano-specific processes to more advanced mechanistic models that (at least partially) take nano-specific (typically non-equilibrium, dynamic) processes into account, with a focus on key fate processes such as agglomeration, sedimentation and dissolution. Similarly, NM uptake by organisms is driven by dynamic processes rather than by equilibrium partitioning. Hence, biokinetic models are more suited to model NM uptake, compared with the simple bioaccumulation factors used for organic compounds. Additionally, biokinetic models take speciation processes (e.g. particulate versus ionic uptake) into account, although identifying essential environment-specific processes to include in models remains a challenge. The models developed so far require parameterization, calibration and validation with available data, e.g. field data (if available), or experimental data (e.g. aquatic and terrestrial mesocosms), rather than extension to more complex and sophisticated models that include all possible transformation processes. Collaborative efforts between experimentalists and modelers to generate appropriate ground-truth data would advance the field most rapidly.

Details

Original languageEnglish
Pages (from-to)323-345
Number of pages23
JournalEnvironmental Science: Nano
Volume3
Issue number2
Early online date29 Feb 2016
Publication statusPublished - 1 Apr 2016