Bayesian Networks as a screening tool for exposure assessment

A tiered approach to contamination exposure assessment is currently adopted in many countries. Increasing the site-specific information in exposure assessments is generally recommended when guideline values for contaminants in soil are exceeded. This work details a Bayesian Network (BN) approach to developing a site-specific environmental exposure assessment that focuses on the simple mapping and assessment of assumptions and the effect of new data on assessment outcomes. The BN approach was applied to a floodplain system in New South Wales, Australia, where site-specific information about elevated antimony (Sb) concentrations and distribution in soils was available. Guidelines for exposure assessment in Australia are used as a template for this site, although the approach is generic. The BN-based assessment used an iterative approach starting with limited soil Sb data (41 samples ranging from 0 to 18 mg kg−1 Sb) and extending the model with more detailed Sb data (145 samples ranging from 0 to 40 mg kg−1 Sb). The analyses identified dominant exposure pathways and assessed the sensitivity of these pathways to changes in assumptions and the level of site-specific information available. In particular, there was a 10.8% probability of exceeding the tolerable daily intake of Sb in the case study when the limited soil Sb data was used, which increased to 26.2% with the more detailed sampling regime. There was also a 47% decrease in the probability of overexposure to Sb when the dermal bioavailability of arsenic (a similar metalloid) was used as a surrogate measure instead of a default bioavailability of 100%. We conclude that the BN approach to soil exposure assessment has merit both in the context of Australian and international soil exposure assessments. •We develop Bayesian Networks as screening tools for exposure assessment.•BNs with limited data can be used to assess importance of exposure assumptions.•The proposed framework helps target additional data collection.