Decision
makers in the Columbia River Basin (CRB) are
currently
challenged with identifying and characterizing the extent of per-
and polyfluoroalkyl substances (PFAS) contamination and human exposure
to PFAS. This work aims to develop and pilot a methodology to help
decision makers target and prioritize sampling investigations and
identify contaminated natural resources. Here we use random forest
models to predict ∑PFAS in fish tissue; understanding PFAS
levels in fish is particularly important in the CRB because fish can
be a major component of tribal and indigenous people diet. Geospatial
data, including land cover and distances to known or potential PFAS
sources and industries, were leveraged as predictors for modeling.
Models were developed and evaluated for Washington state and Oregon
using limited available empirical data. Mapped predictions show several
areas where detectable concentrations of PFAS in fish tissue are predicted
to occur, but prior sampling has not yet confirmed. Variable importance
is analyzed to identify potentially important sources of PFAS in fish
in this region. The cost-effective methodologies demonstrated here
can help address sparsity of existing PFAS occurrence data in environmental
media in this and other regions while also giving insights into potentially
important drivers and sources of PFAS in fish.