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Despite the widespread use of pesticides in the Pampa region of Argentina, mathematical models are rarely employed to predict pesticide fate due to the lack of regionally tested models and the absence of readily available databases to run such models. The objective of the current study was to perform a sensitivity analysis of the Pesticide in Water Calculator (PWC) model for the Pampa Region of Argentina. The sensitivity analysis was performed while simulating applications of 2,4-D (mobile, low Kd) and glyphosate (soil-binding, high Kd) in five localities of the Pampa region: Anguil, Paraná, Marcos Juárez, Pergamino and Tres Arroyos. The sensitivity of the various parameters involved in PWC modelling was evaluated though a two-steps sensitivity analysis which included a first screening of less sensitive parameters with Morris method, followed by a fully global sensitivity analysis of the remaining parameters using Sobol method. When ran under soil and climate conditions typical of the Pampa region of Argentina, PWC was most sensitive to 25% of the parameters evaluated. The sensitive parameters identified depended mainly on the nature of the pesticide molecule being modelled; the location and endpoint considered having much less influence on the sensitivity results. Sensitive parameters belonged to two main grand categories: (i) degradation rates of the pesticide in soil and water, and (ii) parameters descriptive of soil binding, runoff and erosion. The sensitivity analysis of the model PWC performed in the current study represents a crucial first step towards the development and expansion of probabilistic pesticide risk assessment in Argentina, and provides important parameterization criteria that will help obtaining more certain modelling results from PWC in Argentina and elsewhere.
This article was published in the following journal.
Name: The Science of the total environment
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Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.
A principle of estimation in which the estimates of a set of parameters in a statistical model are those quantities minimizing the sum of squared differences between the observed values of a dependent variable and the values predicted by the model.
Analysis based on the mathematical function first formulated by Jean-Baptiste-Joseph Fourier in 1807. The function, known as the Fourier transform, describes the sinusoidal pattern of any fluctuating pattern in the physical world in terms of its amplitude and its phase. It has broad applications in biomedicine, e.g., analysis of the x-ray crystallography data pivotal in identifying the double helical nature of DNA and in analysis of other molecules, including viruses, and the modified back-projection algorithm universally used in computerized tomography imaging, etc. (From Segen, The Dictionary of Modern Medicine, 1992)
Techniques of nucleotide sequence analysis that increase the range, complexity, sensitivity, and accuracy of results by greatly increasing the scale of operations and thus the number of nucleotides, and the number of copies of each nucleotide sequenced. The sequencing may be done by analysis of the synthesis or ligation products, hybridization to preexisting sequences, etc.
A polychlorinated pesticide that is resistant to destruction by light and oxidation. Its unusual stability has resulted in difficulties in residue removal from water, soil, and foodstuffs. This substance may reasonably be anticipated to be a carcinogen: Fourth Annual Report on Carcinogens (NTP-85-002, 1985). (From Merck Index, 11th ed)