By the same authors

From the same journal

Impact of correlation between pesticide parameters on estimates of environmental exposure

Research output: Contribution to journalArticle

Published copy (DOI)

Author(s)

Department/unit(s)

Publication details

JournalPest management science
DatePublished - Jul 2006
Issue number7
Volume62
Number of pages7
Pages (from-to)603-609
Original languageEnglish

Abstract

Monte Carlo techniques are increasingly used in pesticide exposure modelling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to modelling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled from statistical distributions. A key decision in setting up a probabilistic analysis is whether there is correlation between any of the inputs to the analysis. Pesticide proper-ties are often the most sensitive in exposure assessment. Analysis of the literature demonstrated that there are examples of both positive and negative correlation between the sorption and degradation behaviour of a pesticide, but that general trends are not apparent at present. The inclusion of even weak correlation between sorption and degradation was found to greatly influence a probabilistic analysis of leaching through soil. Correlation will reduce the predicted extent of leaching for pesticides, and it is recommended to set the correlation to zero unless the experimental data support an alternative assumption (i.e. where the correlation is statistically significant (P <= 0.05) and experimental artefacts can be excluded). (c) Crown copyright 2006.

    Research areas

  • Monte Carlo, correlation, pesticide exposure modelling, degradation, sorption, review, SPATIAL VARIABILITY, SOIL-PH, DEGRADATION, ADSORPTION, UNCERTAINTY, SORPTION, MODEL, IMAZAQUIN, RATES, BIODEGRADATION

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations