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Priority setting for existing chemicals: Automated data selection routine
Author(s) -
van Haelst Anniek G.,
Hansen Bjorn G.
Publication year - 2000
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620190930
Subject(s) - ranking (information retrieval) , european union , selection (genetic algorithm) , computer science , production (economics) , control (management) , risk assessment , risk analysis (engineering) , operations research , data mining , engineering , information retrieval , business , machine learning , computer security , macroeconomics , artificial intelligence , economics , economic policy
One of the four steps within Council Regulation 793/93/EEC on the evaluation and control of existing chemicals is the priority setting step. The priority setting step is concerned with selecting high‐priority substances from a large number of substances, initially starting with 2,474 high‐production‐volume chemicals. In order to be able to efficiently carry out the priority setting step, an automated priority setting method was developed, the so‐called European Union risk ranking method (EURAM). The EURAM produces rankings among the high‐production‐volume chemicals appearing in the International Uniform ChemicaL Information Database (IUCLID). As part of a publication series, this paper deals with the data selection criteria used in EURAM and furthermore with data validation of the selected data. To validate the selected data in EURAM, comparisons are made with validated data of priority chemicals for which European Union risk assessment reports have been completed or with data of priority chemicals from the Screening Information Data Set initial assessment reports of the Organization for Economic Cooperation and Development. Regression analysis between the selected data in EURAM and the data selected by expert judgment in the assessment procedure resulted in good correlation for physicochemical properties and aquatic toxicity data. In addition, the type of biodegradation (i.e., readily biodegradable, inherently biodegradable, or nonbiodegradable) selected by EURAM also was in agreement with the type of biodegradation selected by expert judgment in the risk assessment procedure. Hence, the good correlation between the automated data selection procedure of EURAM and the data selection by expert judgment indicates that the data selection criteria of EURAM, which are developed in order to select data for priority purposes among high‐production‐volume chemicals in IUCLID, seem to perform well.