Exploring KOC Data Using Big Data Approaches for Mobility Hazard &amp; Exposure Assessments
Creators: name: Baskaran, Sivani
name: Wolf, Raoul
name: Arp, Hans Peter H.
Publisher: name: Zenodo
Year: 2023
Subjects: PMT/vPvM
Bayesian Analysis
Hazard and Risk Assessment

description: Presentation given at the American Chemical Society's Fall 2023 Meeting. Presentation submitted under the title, "Exploring organic carbon–water partition ratio (<em>K</em><sub>OC</sub>) data for mobility assessments using data science and Bayesian statistical approaches" to the Chemical Information session called "Chemical Data Interoperability, Validation &amp; Evaluation". Abstract: 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 (<em>K</em><sub>OC</sub>) or octanol–water partition ratio (<em>K</em><sub>OW</sub>) for neutral compounds and the octanol–water distribution ratio (<em>D</em><sub>OW</sub>) for ionizable compounds. However, measured <em>K</em><sub>OC</sub> 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 <em>K</em><sub>OC</sub> data values of diverging quality available, and the environmental conditions associated with this data is not easily accessible. Data must be cross-referenced to ensure the name and CAS number associated with a <em>K</em><sub>OC</sub> value matches the SMILES used in prediction models. The data must be tidied to remove duplicated entries and incorrectly reported units. In this work, we use all available <em>K</em><sub>OC</sub> data for chemicals on the global market in a consensus-based approach, which considers the number of <em>K</em><sub>OC</sub> values and their individual variability and uncertainty, and source of <em>K</em><sub>OC</sub> data. Data sources include the OECD's eChemPortal, experimental log<sub>10</sub> <em>K</em><sub>OC</sub> datasets, and multiple <em>K</em><sub>OC</sub> 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" log<sub>10</sub> <em>K</em><sub>OC</sub> value. The use of probability distributions is advantageous in these instances because we are less concerned about the environmental conditions for which the <em>K</em><sub>OC</sub> is measured and can apply mobility classifications based on relative risk tolerance of the uncertainty for a given <em>K</em><sub>OC</sub> value. <br>
description: SB acknowledges support from Natural Sciences and Engineering Research Council of Canada via a Postdoctoral Fellowship and travel support to the ACS Fall Meeting from the CAS 2023 Future Leaders Program.