Supplementary file 1_Chemical exposure and risk in river basins: Bayesian integration of e

A probabilistic modeling framework developed for assessing chemical risk to aquatic ecosystems at the watershed level across Europe. The framework synthesizes multiple information sources, including process-based simulation data and the pan-European Waterbase Water Quality monitoring dataset. A pilot study using a subset of pesticides in Belgium serves as a proof-of-concept.

Use Cases

• Update prior exposure probabilities derived from process-based simulations based on chemical monitoring data.
• Identify chemicals with high contribution to risk in the aquatic environment based on spatial exposure patterns in watersheds.
• Assess chemical risk to aquatic ecosystems at the watershed level across large European regions.
• Test a Bayesian network model implementation for integrating environmental model simulations and monitoring data.

Strengths

• Integrates multiple sources of information, including the pan-European Waterbase Water Quality dataset.
• Framework accounts for uncertainty in both exposure and hazard information.
• Pilot study provides a concrete proof-of-concept for the modeling approach.

Limitations

• Dataset size is 13.3 KB, indicating a very limited scope or sample.
• Row count and column-level documentation are unknown, limiting suitability assessment.
• Data may reflect geographic bias inherent to the pilot study focused on Belgium.

Provenance

Source

Sophie Mentzel via figshare, associated with the ENCORE project.

Collection Method

Bayesian network model integrating environmental model simulations and monitoring data.

Freshness

Last updated 2026-05-04 05:32:16

Geography

Pilot study focuses on Belgium; framework intended for larger European regions.