Application of Gaussian mixture models to quantify the upper background threshold for perfluorooctane sulfonate (PFOS) in U.S. surface soil.

Studies on the occurrence and environmental distribution of per- and polyfluoroalkyl substances (PFAS) have clearly demonstrated their ubiquity in surface soil as a result of historic and ongoing emissions from various manufacturing and industrial activities worldwide. Given global efforts to characterize and mitigate risk from point source-impacted sites, there is, thus, an urgent need to quantify nonpoint source threshold concentrations (i.e., background) to support site management decisions particularly for perfluorooctane sulfonate (PFOS) as a top priority. Accordingly, this study evaluated the application of Gaussian mixture models (GMMs) fitted to log-transformed PFOS concentrations using nation-wide metadata consisting of thousands of surface soil samples representative of both background and aqueous film-forming foam (AFFF) impacts with unknown proportion. Multiple GMMs were fitted for a given number of components using different methods to account for bias associated with a marginal non-detect fraction (n = 8%) including exclusion, substitution, and imputation. Careful evaluation of the rate of change among multiple goodness-of-fit measures universally justified fitting a 2-component GMM; thus, discriminating between background and AFFF-impacted samples among the metadata. Background threshold PFOS concentrations were defined as the intersection of the probability density functions and ranged between 1.9 and 13.8 µg/kg within a broader concentration range extending up to ~ 50,000 µg/kg reflecting AFFF impacts. By demonstrating an innovative statistical approach that intelligently incorporates different criteria for model selection, this research makes significant contributions to risk mitigation efforts at point source-impacted sites and lays the groundwork for future targeted regulatory actions.

Evaluation of geochemical associations as a screening tool for identifying anthropogenic trace metal contamination.

Geochemical association plots are used as a screening tool for environmental site assessments and use empirical log-log relationships between total trace metal concentrations and concentrations of a major (i.e., reference) soil metal constituent, such as iron (Fe), to discern sites with naturally elevated trace metal levels from sites with anthropogenic contamination. Log-log relationships have been consistently observed between trace metal and reference metal concentrations and are often considered constant. Consequently, we used a regional geochemistry data set to evaluate background trace metal/Fe log-log associations across soils with highly diverse composition. Our results indicate that, although geochemical associations may be proportional, they significantly differ across predominant United States Department of Agriculture (USDA) soil orders. This suggests that highly complex interactions between soil-forming factors and variable secondary clay mineral composition affect the ratio of trace metals to Fe concentrations in soils. Also, intra-order variability in trace metal/Fe ratios generally ranged multiple orders of magnitude which suggest that the order level of the USDA soil taxonomic system is insufficient to reasonably classify background trace metal concentrations. Consequently, geochemical association plots are a useful screening tool for environmental site assessments, but ubiquitous application of generic background metal data sets could result in erroneous conclusions. Because significantly different ratios were observed across predominant USDA soil orders, an agglomerative clustering technique was used to elucidate hierarchical patterns of association. We present these results as a mechanism to aid environmental assessors in screening candidate background metal data sets for their applicability to site-specific soil composition; although site-specific background metal data should be utilized if ample pristine reference sites with similar (i.e., sub-order) soil composition can be identified and sampled.