A Bayesian Approach to Incorporating Spatiotemporal Variation and Uncertainty Limits into Modeling of Predicted Environmental Concentrations from Chemical Monitoring Campaigns

Environmental monitoring
studies provide key information to assess
ecosystem health. Results of chemical monitoring campaigns can be
used to identify the exposure scenarios of regulatory concern. In
environmental risk assessment (ERA), measured concentrations of chemicals
can be used to model predicted environmental concentrations (PECs).
As the PEC is, by definition, a predicted variable, it is highly dependent
on the underlying modeling approach from which it is derived. We demonstrate
the use of Bayesian distributional regression models to derive PECs
by incorporating spatiotemporal conditional variances, and limits
of quantification (LOQ) and detection (LOD) as de facto data censoring. Model accuracies increase when incorporating spatiotemporal
conditional variances, and the inclusion of LOQ and LOD results in
potentially more robust PEC distributions. The methodology is flexible,
credibly quantifies uncertainty, and can be adjusted to different
scientific and regulatory needs. Posterior sampling allows to express
PECs as distributions, which makes this modeling procedure directly
compatible with other Bayesian ERA approaches. We recommend the use
of Bayesian modeling approaches with chemical monitoring data to make
realistic and robust PEC estimations and encourage the scientific
debate about the benefits and challenges of Bayesian methodologies
in the context of ERA.