Interception of wet deposited atmospheric pollutants by herbaceous vegetation: Data review and modelling M.-A. Gonze ⁎, M.M. Sy Institute of Radiation Protection and Nuclear Safety, CE Cadarache-Bat 153, BP3-13115 St-Paul-lez-Durance Cedex, France HIGHLIGHTS • Literature data on the interception of atmospheric pollutants by herbs were reviewed • Predictive models were developed and evaluated in the Bayesian modelling framework • Sensitivity of interception to environ- mental conditions was satisfactorily explained • 81% of the observations were satisfactorily predicted by a semi- mechanistic model • This model challenges empirical rela- tionships currently used in risk assess- ment tools GRAPHICAL ABSTRACT abstract article info Article history: Received 22 January 2016 Received in revised form 5 April 2016 Accepted 5 April 2016 Available online xxxx Editor: P Elena PAOLETTI Better understanding and predicting interception of wet deposited pollutants by vegetation remains a key issue in risk assessment studies of atmospheric pollution. We develop different alternative models, following either empirical or semi-mechanistic descriptions, on the basis of an exhaustive dataset consisting of 440 observations obtained in controlled experiments, from 1970 to 2014, for a wide variety of herbaceous plants, radioactive sub- stances and rainfall conditions. The predictive performances of the models and the uncertainty/variability of the parameters are evaluated under Hierarchical Bayesian modelling framework. It is demonstrated that the variabil- ity of the interception fraction is satisfactorily explained and quite accurately modelled by a process-based alter- native in which absorption of ionic substances onto the foliage surfaces is determined by their electrical valence. Under this assumption, the 95% credible interval of the predicted interception fraction encompasses 81% of the observations, including situations where either plant biomass or rainfall intensity are unknown. This novel approach is a serious candidate to challenge existing empirical relationships in radiological or chemical risk assessment tools. © 2016 Elsevier B.V. All rights reserved. Keywords: Wet deposition Interception by plant Process-based modelling Bayesian inference Science of the Total Environment 565 (2016) 49–67 ⁎ Corresponding author. E-mail address: marc-andre.gonze@irsn.fr (M.-A. Gonze). http://dx.doi.org/10.1016/j.scitotenv.2016.04.024 0048-9697/© 2016 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv