Bayesian approach for simultaneous recognition of contaminant sources in groundwater and surface-water resources
YeoJin Ju, Jürgen Mahlknecht, Kang‐Kun Lee, Dugin Kaown
Abstract
Accurate source apportionment is required to develop a water management plan for controlling harmful pollutants in water. Environmental tracers can be used for the source apportionment of pollutants. They inevitably exhibit diverse uncertainties stemming from measurement errors, spatiotemporal variability of sources, biochemical transformation, and dynamic mixing. To reflect the uncertainties involved in source apportionment, a statistical approach, the Bayesian mixing model (BMM) has been actively adopted. Current BMM studies are mostly limited to understanding the spatiotemporal diversity of water contamination, which is similar to previous deterministic calculations. Considering the nature of BMM, which is capable of printing estimation uncertainty, the course of future research should focus on improving the precision of the current designs of source apportionment analysis.