Literature DB >> 29228505

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

Emily Walker1,2, Melen Leclerc3, Jean-François Rey1, Rémy Beaudouin4, Samuel Soubeyrand1, Antoine Messéan2.   

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.
© 2017 Society for Risk Analysis.

Entities:  

Keywords:  Environmental risk assessment; GMO; landscape management; particle dispersal; stochastic geometry

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Year:  2017        PMID: 29228505     DOI: 10.1111/risa.12941

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  1 in total

1.  EFSA is working to advance the environmental risk assessment of genetically modified crops to better protect butterflies and moths.

Authors:  Yann Devos; Giacomo De Sanctis; Franco Maria Neri; Antoine Messéan
Journal:  EFSA J       Date:  2021-04-12
  1 in total

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