title: Analysis of monitoring data of pesticide residues in surface waters using partial order ranking theory.
abstract: In this investigation, a new and simple way to analyze, interpret, and generalize monitoring data of occurrence of pesticide active ingredients in surface waters was developed. The occurrence is quantified using the variables frequency of detection and the concentration level. These two parameters are associated with basically different ecotoxicological effects; for example, a high frequency of detection may be related to bioaccumulation problems, while the level of concentration also controls the acute toxicological effects. The active ingredients were ranked on the basis of the monitoring data in relation to both the frequency of finding and concentration level using the concept of partial ordered sets. The resulting rankings was correlated with other rankings based on descriptors such as sprayed area, applied dose, adsorption to soil organic carbon, vapor pressure, and soil dissipation half-life. A similarity index was applied in order to compare the ranking of the monitoring data with the ranking of the descriptors. It is shown how partial order theory can be used to evaluate the relevance of every single descriptor. The dosage is found to be the most important descriptor, followed by the sprayed area and the adsorption to organic carbon ending up a very close similarity between, respectively, the rankings using monitoring data and rankings using these three descriptors.
authors.affiliation: National Environmental Research Institute, Department of Environmental Chemistry, Frederiksborgvej 399, Post Box 358, DK-4000 Roskilde, Denmark. pbs@dmu.dk
authors.forename: Peter B
authors.initials: PB
authors.lastname: Sørensen
authors.forename: Rainer
authors.initials: R
authors.lastname: Brüggemann
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsen
authors.forename: Betty B
authors.initials: BB
authors.lastname: Mogensen
authors.forename: Jenny
authors.initials: J
authors.lastname: Kreuger
authors.forename: Stefan
authors.initials: S
authors.lastname: Pudenz
mesh_terms: D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D010573:Pesticide Residues / Q000032:analysis*; D018570:Risk Assessment; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D010573:Pesticide Residues; D014874:Water Pollutants, Chemical