title: Assessment of chemicals applying partial order ranking techniques.
abstract: This review summarizes the use of partial order ranking (POR) techniques for the assessment of chemicals. Simple partial order ranking may advantageously be applied to give the single chemicals investigated an identity in relation to other substances. Thus, it constitutes an effective tool for the prioritization of chemicals, e.g., based on their PBT (Persistence, Bioaccumulating, Toxicity) characteristics. In more elaborate cases where a larger number of descriptors are taken into account, e.g., comprising physico-chemical characteristics, atmospheric parameters, geospecific factors, and possibly socio-economic factors, hierarchical partial order ranking (HPOR) may be applied. Thus, in a first ordering step, a series of meta-descriptors are generated that later subsequently may be used as descriptors in subsequent ordering. HPOR allows a sensible ranking model even if a relatively high number of descriptors are included. Finally, accumulation partial order ranking (APOR) is illustrated. Accumulating partial APOR is a technique where data from a series of individual tests of various characteristics are aggregated while maintaining the basics of the partial order ranking methodology. APOR offers prioritization based on mutual probabilities derived from the aggregated data. Alternatively, prioritization may be achieved based on averaged ranks derived from the APOR. The application APOR is demonstrated by an assessment of a series of potential PBT substances. In all cases, an absolute ranking can be achieved based on the average ranks of the single substances. Alternatively, ranking probabilities can be derived.
authors.affiliation: Awareness Center, Hyldeholm 4, Vendee, Roskilde, Denmark. LC@AwarenessCenter.dk
authors.forename: Lars
authors.initials: L
authors.lastname: Carlsen
mesh_terms: D002621:Chemistry / Q000379:methods*; D020650:Combinatorial Chemistry Techniques; D004784:Environmental Monitoring; D011336:Probability; D021281:Quantitative Structure-Activity Relationship