A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

• Published in: Journal of contaminant hydrology Vol. 188; pp. 12 - 28
• Main Authors: Thomsen, Nanna I., Binning, Philip J., McKnight, Ursula S., Tuxen, Nina, Bjerg, Poul L., Troldborg, Mads
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-05-2016
• Subjects: Bayes Theorem; Bayesian belief networks; Conceptual models; Conceptual uncertainty; Contaminated sites; Environmental Pollution - analysis; Groundwater - analysis; Hazardous Waste Sites; Humans; Models, Statistical; Models, Theoretical; Risk Assessment; Screening level; Soil - chemistry; Uncertainty; Contaminated sites; Conceptual uncertainty; CPT; DCE; FN; LIF; Screening level; PID; VC; Conceptual models; BBN; UD; DNAPL; PCE; MIP; BMA; TCE; UN; CSM; FD; FLUTE; Bayesian belief networks
• ISSN: 0169-7722; 1873-6009
• DOI: 10.1016/j.jconhyd.2016.02.003

A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information becomes available.
•We present a Bayesian belief network method for constructing conceptual site models.•The method facilitates systematic construction of multiple conceptual site models.•The method works regardless of the knowledge level at the contaminated site.•The method is flexible with regards to datatype.•A belief is calculated for each of a number of conceptual site models.