Published May 7, 2024 | Version v1
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Exploring Organic-Carbon–Water Partition Ratio (KOC) Data for Mobility Hazard and Exposure Assessments Using Big Data Approaches

  • 1. ROR icon Norwegian Geotechnical Institute
  • 2. ROR icon Norwegian University of Science and Technology

Description

Chemical hazard and risk assessments often use physical-chemical properties to categorize and identify chemicals of concern. In Europe, recent legislation regarding the classification, labelling and packaging of consumer products includes new chemical hazard categories, including persistent, mobile, and toxic (PMT) and very persistent and very mobile (vPvM). While persistency and toxicity are currently used in chemical regulation, mobility is a new criterion for chemical hazard classification. As a result, the mobility of thousands of chemicals on the global market must be assessed. Mobility is determined based on the organic carbon–water partition ratio (KOC) or octanol–water partition ratio (KOW) for neutral compounds and the octanol–water distribution ratio (DOW) for ionizable compounds. However, measured KOC values can vary by orders of magnitude depending on the environmental conditions particularly for ionizable compounds.

Ideally such property data is available in machine-readable formats in standardized units with meta data regarding the environmental conditions of the measurement. However, current regulatory databases and experimental datasets have thousands of KOC data values of diverging quality available, and the environmental conditions associated with this data are not easily accessible. Data must be cross-referenced to ensure the name and CAS number associated with a KOC value matches the SMILES used in prediction models. The data must be tidied up to remove duplicated entries and incorrectly reported units.

In this work, we use all available KOC data for chemicals on the global market in a consensus-based approach, which considers the number of KOC values and their individual variability and uncertainty, and source of KOC data. Data sources include the OECD's eChemPortal and QSAR Toolbox, experimental log10 KOC datasets, and multiple KOC prediction models of varying quality and domains of applicability.

With Bayesian statistical inference approaches we aggregate values and their errors from these different sources to derive a probability distribution for the "true" log10 KOC value. The use of probability distributions is advantageous in these instances because we are less concerned about the environmental conditions for which the KOC is measured and can apply mobility classifications based on relative risk tolerance of the uncertainty for a given KOC value.

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Additional details

Funding

ZeroPM – ZeroPM: Zero pollution of Persistent, Mobile substances 101036756
European Commission