The Experts below are selected from a list of 39 Experts worldwide ranked by ideXlab platform
Carole Bedos - One of the best experts on this subject based on the ideXlab platform.
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Fungicide Volatilization Measurements: Inverse Modeling, Role of Vapor Pressure, and State of Foliar Residue
Environmental science & technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique BarriusoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnal volatilization patterns were observed: fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha−1 and 5 g ha−1, respectively (0.8% and 0.6% of the theoretical applicati...
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Fungicide volatilization measurements: inverse Modeling, Role of vapor pressure, and state of foliar residue
Environmental Science and Technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique Barriuso BenitoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnalvolatilizationpatternswereobserved:fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.
Enrique Barriuso Benito - One of the best experts on this subject based on the ideXlab platform.
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Fungicide volatilization measurements: inverse Modeling, Role of vapor pressure, and state of foliar residue
Environmental Science and Technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique Barriuso BenitoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnalvolatilizationpatternswereobserved:fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.
Dominique Flura - One of the best experts on this subject based on the ideXlab platform.
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Fungicide Volatilization Measurements: Inverse Modeling, Role of Vapor Pressure, and State of Foliar Residue
Environmental science & technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique BarriusoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnal volatilization patterns were observed: fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha−1 and 5 g ha−1, respectively (0.8% and 0.6% of the theoretical applicati...
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Fungicide volatilization measurements: inverse Modeling, Role of vapor pressure, and state of foliar residue
Environmental Science and Technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique Barriuso BenitoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnalvolatilizationpatternswereobserved:fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.
Olivier Briand - One of the best experts on this subject based on the ideXlab platform.
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Fungicide Volatilization Measurements: Inverse Modeling, Role of Vapor Pressure, and State of Foliar Residue
Environmental science & technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique BarriusoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnal volatilization patterns were observed: fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha−1 and 5 g ha−1, respectively (0.8% and 0.6% of the theoretical applicati...
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Fungicide volatilization measurements: inverse Modeling, Role of vapor pressure, and state of foliar residue
Environmental Science and Technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique Barriuso BenitoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnalvolatilizationpatternswereobserved:fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.
M-france Rousseau Djabri - One of the best experts on this subject based on the ideXlab platform.
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Fungicide Volatilization Measurements: Inverse Modeling, Role of Vapor Pressure, and State of Foliar Residue
Environmental science & technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique BarriusoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnal volatilization patterns were observed: fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha−1 and 5 g ha−1, respectively (0.8% and 0.6% of the theoretical applicati...
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Fungicide volatilization measurements: inverse Modeling, Role of vapor pressure, and state of foliar residue
Environmental Science and Technology, 2010Co-Authors: Carole Bedos, M-france Rousseau Djabri, Benjamin Loubet, Brigitte Durand, Dominique Flura, Olivier Briand, Enrique Barriuso BenitoAbstract:Few data sets of pesticide volatilization from plants at the field scale are available. In this work, we report measurements of fenpropidin and chlorothalonil volatilization on a wheat field using the aerodynamic gradient (AG) method and an inverse dispersion Modeling approach (using the FIDES model). Other data necessary to run volatilization models are also reported: measured application dose, crop interception, plant foliage residue, upwind concentrations, and meteorological conditions. The comparison of the AG and inverse Modeling methods proved the latter to be reliable and hence suitable for estimating volatilization rates with minimized costs. Different diurnal/nocturnalvolatilizationpatternswereobserved:fenpropidin volatilization peaked on the application day and then decreased dramatically, while chlorothalonil volatilization remained fairly stable over a week-long period. Cumulated emissions after 31 h reached 3.5 g ha-1 and 5 g ha-1, respectively (0.8% and 0.6% of the theoretical application dose). A larger difference in volatilization rates was expected given differences in vapor pressure, and for fenpropidin, volatilization should have continued given that 80% of the initial amount remained on plant foliage for 6 days. We thus ask if vapor pressure alone can accurately estimate volatilization just after application and then question the state of foliar residue. We identified adsorption, formulation, and extraction techniques as relevant explanations.