Volume 39, Issue 1
Original Research Article

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

Emily Walker

Corresponding Author

E-mail address: emily.walker@inra.fr

BioSP, INRA, Avignon, France

EcoInnov, INRA, Thiverval‐Grignon, France

Address correspondence to Emily Walker, BioSP, INRA, 228 Route de l'Aérodrome, 84914 Avignon, France; tel: +33 432 72 2155; E-mail address: emily.walker@inra.fr.Search for more papers by this author
Rémy Beaudouin

INERIS, Unité Modèles pour l'Ecotoxicologie et la Toxicologie (METO), Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil en Halatte, France

Search for more papers by this author
Antoine Messéan

EcoInnov, INRA, Thiverval‐Grignon, France

Search for more papers by this author
First published: 11 December 2017
Citations: 5

Abstract

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (biological risk assessment with R) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.

Number of times cited according to CrossRef: 5

  • When the average hides the risk of Bt-corn pollen on non-target Lepidoptera: Application to Aglais io in Catalonia, Ecotoxicology and Environmental Safety, 10.1016/j.ecoenv.2020.111215, 207, (111215), (2021).
  • Software tools for toxicology and risk assessment, Information Resources in Toxicology, 10.1016/B978-0-12-813724-6.00072-4, (791-812), (2020).
  • Advances in Spatial Risk Analysis, Risk Analysis, 10.1111/risa.13260, 39, 1, (1-8), (2019).
  • Spatial exposure-hazard and landscape models for assessing the impact of GM crops on non-target organisms, Science of The Total Environment, 10.1016/j.scitotenv.2017.11.329, 624, (470-479), (2018).
  • Toxicokinetic models and related tools in environmental risk assessment of chemicals, Science of The Total Environment, 10.1016/j.scitotenv.2016.10.146, 578, (1-15), (2017).

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.