Semi-automated harmonization and selection of chemical data for risk and impact assessment

Chemical data for thousands of substances are available for safety, risk, life cycle and substitution assessments, as submitted for example under the European Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) Regulation. However, to widely disseminate reported physicochemical properties as well as human and ecological exposure and toxicological data for use in various science and policy fields, systematic methods for data harmonization and selection are necessary. In the present study, we developed a semi-automated method for deriving appropriate substance property values as input for various risk and impact assessment frameworks with different requirements for resolution and data quality. Starting with data reported for a given substance and property submitted under the REACH regulation as example data source, we propose a set of aligned data selection and harmonization criteria to obtain a representative mean value with related confidence intervals per chemical-property combination. The proposed method was tested on a set of octanol-water partition coefficients (Kow) reported for a set of 20 example substances. Our method is applicable to any set of substances, and can assess specific distribution in quality and variability across reported data. Further research can likely extend the method for mining information from text fields and adapt it to available data reported or collected from other sources and other substance properties to improve the reliability of input data for risk and impact assessment.