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 in | Environmental science & technology Vol. 41; no. 21; pp. 7444 - 7450 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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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. |
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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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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 |
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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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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 |
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