Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles

Rianne Jacobs, Johannes A.J. Meesters, Cajo J.F. ter Braak, Dik van de Meent, Hilko van der Voet*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)


There is a growing need for good environmental risk assessment of engineered nanoparticles (ENPs). Environmental risk assessment of ENPs has been hampered by lack of data and knowledge about ENPs, their environmental fate, and their toxicity. This leads to uncertainty in the risk assessment. To deal with uncertainty in the risk assessment effectively, probabilistic methods are advantageous. In the present study, the authors developed a method to model both the variability and the uncertainty in environmental risk assessment of ENPs. This method is based on the concentration ratio and the ratio of the exposure concentration to the critical effect concentration, both considered to be random. In this method, variability and uncertainty are modeled separately so as to allow the user to see which part of the total variation in the concentration ratio is attributable to uncertainty and which part is attributable to variability. The authors illustrate the use of the method with a simplified aquatic risk assessment of nano-titanium dioxide. The authors' method allows a more transparent risk assessment and can also direct further environmental and toxicological research to the areas in which it is most needed.

Original languageEnglish
Pages (from-to)2958-2967
JournalEnvironmental Toxicology and Chemistry
Issue number12
Publication statusPublished - 2016


  • 2-dimensional Monte Carlo
  • Biostatistics
  • Hazard/risk assessment
  • Nanoparticle
  • Species sensitivity distribution
  • Uncertainty/variability


Dive into the research topics of 'Combining exposure and effect modeling into an integrated probabilistic environmental risk assessment for nanoparticles'. Together they form a unique fingerprint.

Cite this