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.

Investigating Potential Water Quality Impacts of Fungicides Used to Combat Soybean Rust in Indiana

“…Beigel et al (1999) demonstrated that the persistence of the fungicide triticonazole increased with application rate. Similar studies with pesticides have revealed that application rate is a dominant factor in determining pesticide concentration in surface waters (Sorensen et al 2003).…”

Analysis of breeding bird communities along an urban-rural gradient in Berlin, Germany, by Hasse Diagram Technique

“…Therefore, in our study we tested the appropriateness of a concept of partial order theory, namely the Hasse Diagram Technique (HDT) (Brüggemann et al 2001a), to analyze patterns in the changes of species communities, in this case along an urban-rural gradient. HDT has already been successfully used in environmental science and chemistry (Halfon and Reggiani 1986;Brüggemann et al 1999;Klein 2000;Felinks 2001;Brüggemann et al 2001b;Carlsen et al 2001;Lerche et al 2002;Brüggemann et al 2003a;Brüggemann et al 2003b;Sørensen et al 2003;Thinh et al 2004;Simon et al 2004a;Simon et al 2004b). However, as far as we know, no such formalized data analysis, including the use of explanatory background information, has been applied to date in the context of gradient analysis.…”

Partial‐Order Ranking and Linear Modeling: Their Use in Predictive QSAR/QSPR Studies

“…If the model ranking is able to reproduce the experimental ranking, then other compounds that do not poses an experimental value can be assigned a position in the ranking and then their experimental properties can be predicted. A type of similarity index necessary to define for QSAR based on POR is the modified Tanimoto index T (0,0), reflecting the percentage of rankings in the model that can be found in the experimental data [64]. It is defined as…”