Including Spatial Variability in Monte Carlo Simulations of Pesticide Leaching

A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (DT50) and topsoil o...

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Published inEnvironmental science & technology Vol. 41; no. 21; pp. 7444 - 7450
Main Authors Leterme, Bertrand, Vanclooster, Marnik, van der Linden, Ton, Tiktak, Aaldrik, Rounsevell, Mark D. A
Format Journal Article
LanguageEnglish
Published Washington, DC American Chemical Society 01.11.2007
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Abstract A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (DT50) and topsoil organic matter (OM) content as uncertain input parameters. Atrazine DT50 is taken as a non-georeferenced parameter, so that DT50 values sampled from the input distribution are randomly allocated in the study area for every simulation. Organic matter content is a georeferenced parameter, so that a fixed uncertainty distribution is given at each location. Spatially variable DT50 values are found to have a significant influence on the amount of simulated leaching. In the stochastic simulation, concentra tions exist above the regulatory level of 0.1 μg L-1, but virtually no leaching occurs in the deterministic simulation. It is axiomatic that substance parameters (DT50, sorption coefficient, etc.) are spatially variable, but pesticide registration procedures currently ignore this fact. Including this spatial variability in future registration policies would have significant consequences on the amount and pattern of leaching simulated, especially if risk assessments are implemented in a spatially distributed way.
AbstractList A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (...) and topsoil organic matter (OM) content as uncertain input parameters. Atrazine ... is taken as a non-georeferenced parameter, so that ... values sampled from the input distribution are randomly allocated in the study area for every simulation. Organic matter content is a georeferenced parameter, so that a fixed uncertainty distribution is given at each location. Spatially variable ... values are found to have a significant influence on the amount of simulated leaching. In the stochastic simulation, concentrations exist above the regulatory level of 0.1 ...g L..., but virtually no leaching occurs in the deterministic simulation. It is axiomatic that substance parameters (..., sorption coefficient, etc.) are spatially variable, but pesticide registration procedures currently ignore this fact. Including this spatial variability in future registration policies would have significant consequences on the amount and pattern of leaching simulated, especially if risk assessments are implemented in a spatially distributed way. (ProQuest: ... denotes formulae/symbols omitted.)
A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (DT sub(50)) and topsoil organic matter (OM) content as uncertain input parameters. Atrazine DT sub(50) is taken as a non-georeferenced parameter, so that DT sub(50) values sampled from the input distribution are randomly allocated in the study area for every simulation. Organic matter content is a georeferenced parameter, so that a fixed uncertainty distribution is given at each location. Spatially variable DT sub(50) values are found to have a significant influence on the amount of simulated leaching. In the stochastic simulation, concentra tions exist above the regulatory level of 0.1 mg L super(-) super(1), but virtually no leaching occurs in the deterministic simulation. It is axiomatic that substance parameters (DT sub(50), sorption coefficient, etc.) are spatially variable, but pesticide registration procedures currently ignore this fact. Including this spatial variability in future registration policies would have significant consequences on the amount and pattern of leaching simulated, especially if risk assessments are implemented in a spatially distributed way.
A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (DT50) and topsoil organic matter (OM) content as uncertain input parameters. Atrazine DT50 is taken as a non-georeferenced parameter, so that DT50 values sampled from the input distribution are randomly allocated in the study area for every simulation. Organic matter content is a georeferenced parameter, so that a fixed uncertainty distribution is given at each location. Spatially variable DT50 values are found to have a significant influence on the amount of simulated leaching. In the stochastic simulation, concentra tions exist above the regulatory level of 0.1 μg L-1, but virtually no leaching occurs in the deterministic simulation. It is axiomatic that substance parameters (DT50, sorption coefficient, etc.) are spatially variable, but pesticide registration procedures currently ignore this fact. Including this spatial variability in future registration policies would have significant consequences on the amount and pattern of leaching simulated, especially if risk assessments are implemented in a spatially distributed way.
A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters. A Monte Carlo analysis of atrazine leaching is performed in the Dyle river catchment (Belgium) with pesticide half-life (DT50) and topsoil organic matter (OM) content as uncertain input parameters. Atrazine DT50 is taken as a non-georeferenced parameter, so that DT50 values sampled from the input distribution are randomly allocated in the study area for every simulation. Organic matter content is a georeferenced parameter, so that a fixed uncertainty distribution is given at each location. Spatially variable DT50 values are found to have a significant influence on the amount of simulated leaching. In the stochastic simulation, concentrations exist above the regulatory level of 0.1 microg L(-1), but virtually no leaching occurs in the deterministic simulation. It is axiomatic that substance parameters (DT50, sorption coefficient, etc.) are spatially variable, but pesticide registration procedures currently ignore this fact. Including this spatial variability in future registration policies would have significant consequences on the amount and pattern of leaching simulated, especially if risk assessments are implemented in a spatially distributed way.
Author Leterme, Bertrand
Rounsevell, Mark D. A
Tiktak, Aaldrik
Vanclooster, Marnik
van der Linden, Ton
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Issue 21
Keywords Stochastic model
Monte Carlo method
Uncertainty
Organic matter
Spatial variability
Pollutant behavior
Pesticides
Property of soil
Environmental factor
Soil pollution
Herbicide
Case study
Watershed
Triazine derivatives
Lixiviation
Numerical simulation
Organic compounds
Atrazine
Language English
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Snippet A methodology is developed to quantify the uncertainty in a pesticide leaching assessment arising from the spatial variability of non-georeferenced parameters....
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StartPage 7444
SubjectTerms Adsorption
Agronomy. Soil science and plant productions
Applied sciences
Atrazine - chemistry
Belgium
Biological and medical sciences
Biological and physicochemical properties of pollutants. Interaction in the soil
Computer Simulation
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Environmental science
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Herbicides - chemistry
Leaching
Models, Theoretical
Monte Carlo Method
Monte Carlo simulation
Pesticides
Pollution
Pollution, environment geology
Reproducibility of Results
Risk assessment
Rivers
Simulation
Soil and sediments pollution
Soil and water pollution
Soil Pollutants - chemistry
Soil science
Uncertainty
Water Movements
Title Including Spatial Variability in Monte Carlo Simulations of Pesticide Leaching
URI http://dx.doi.org/10.1021/es0714639
https://api.istex.fr/ark:/67375/TPS-9FNSD152-7/fulltext.pdf
https://www.ncbi.nlm.nih.gov/pubmed/18044524
https://www.proquest.com/docview/230169699/abstract/
https://search.proquest.com/docview/14825270
https://search.proquest.com/docview/19963879
Volume 41
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