title: Interception of wet deposited atmospheric pollutants by herbaceous vegetation: Data review and modelling.
abstract: 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 substances 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 variability of the interception fraction is satisfactorily explained and quite accurately modelled by a process-based alternative 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.
authors.affiliation: Institute of Radiation Protection and Nuclear Safety, CE Cadarache-Bat 153, BP3-13115 St-Paul-lez-Durance Cedex, France. Electronic address: marc-andre.gonze@irsn.fr.
authors.forename: M-A
authors.initials: MA
authors.lastname: Gonze
authors.affiliation: Institute of Radiation Protection and Nuclear Safety, CE Cadarache-Bat 153, BP3-13115 St-Paul-lez-Durance Cedex, France.
authors.forename: M M
authors.initials: MM
authors.lastname: Sy
mesh_terms: D001499:Bayes Theorem; D004784:Environmental Monitoring / Q000379:methods*; D004787:Environmental Pollution / Q000032:analysis* / Q000706:statistics & numerical data*; D008962:Models, Theoretical; D010944:Plants / Q000737:chemistry* / Q000378:metabolism*; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000378:metabolism*
chemical_list: D014874:Water Pollutants, Chemical
keywords: Bayesian inference; Interception by plant; Process-based modelling; Wet deposition