The assessment of
environmental hazard indicators such as persistence,
mobility, toxicity, or bioaccumulation of chemicals often results
in highly variable experimental outcomes. Persistence is particularly
affected due to a multitude of influencing environmental factors,
with biodegradation experiments resulting in half-lives spanning several
orders of magnitude. Also, half-lives may lie beyond the limits of
reliable half-life quantification, and the number of available data
points per substance may vary considerably, requiring a statistically
robust approach for the characterization of data. Here, we apply Bayesian
inference to address these challenges and characterize the distributions
of reported soil half-lives. Our model estimates the mean, standard
deviation, and corresponding uncertainties from a set of reported
half-lives experimentally obtained for a single substance. We apply
our inference model to 893 pesticides and pesticide transformation
products with experimental soil half-lives of varying data quantity
and quality, and we infer the half-life distribution for each compound.
By estimating average half-lives, their experimental variability,
and the uncertainty of the estimations, we provide a reliable data
source for building predictive models, which are urgently needed by
regulatory authorities to manage existing chemicals and by industry
to design benign, nonpersistent chemicals. Our approach can be readily
adapted for other environmental hazard indicators.