4.0 Review

Retrieval, Selection, and Evaluation of Chemical Property Data for Assessments of Chemical Emissions, Fate, Hazard, Exposure, and Risks

Journal

ACS ENVIRONMENTAL AU
Volume 2, Issue 5, Pages 376-395

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acsenvironau.2c00010

Keywords

chemical property; partition ratio; half-life; quantitative structure-activity relationship; chemical assessment; modeling; risk; hazard

Funding

  1. European Chemistry Industry Council Long-range Research Initiative (CEFIC-LRI) [ECO54]

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This comprehensive review offers practical guidance for using chemical property data in chemical assessments, including obtaining experimentally derived and in silico predicted property data sources, as well as strategies for evaluating and curating the data. It emphasizes the uncertainty and variability present in both experimentally derived and in silico predicted property data, advocating for the harmonization of multiple experimental data or consolidating predictions from multiple in silico tools.
Reliable chemical property data are the key to defensible and unbiased assessments of chemical emissions, fate, hazard, exposure, and risks. However, the retrieval, evaluation, and use of reliable chemical property data can often be a formidable challenge for chemical assessors and model users. This comprehensive review provides practical guidance for use of chemical property data in chemical assessments. We assemble available sources for obtaining experimentally derived and in silico predicted property data; we also elaborate strategies for evaluating and curating the obtained property data. We demonstrate that both experimentally derived and in silico predicted property data can be subject to considerable uncertainty and variability. Chemical assessors are encouraged to use property data derived through the harmonization of multiple carefully selected experimental data if a sufficient number of reliable laboratory measurements is available or through the consensus consolidation of predictions from multiple in silico tools if the data pool from laboratory measurements is not adequate.

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