Combination of multivariate data analysis and mixing modelling to assess tracer potential of contaminants of emerging concern in aquifers

Collected evidence has shown that contaminants of emerging concern (CECs) in conjunction with more conventional tracers (major ions, nutrients, isotopes etc.) can be used to trace pollution origin in aquatic systems. However, in highly mixed aquifer systems signals obtained from conventional tracers overlap diminishing their potential to be used as tracers. In this study, we present an approach that incorporates multivariate statistical analysis (principal component analysis (PCA) and Kohonen's Self-Organizing Map method (SOM)) and mixing modelling to identify the most suitable CECs to be employed as anthropogenic tracers. The study area is located in the Besòs River Delta (Barcelona, NE Spain) and represents the highly mixed aquifer system. A one-year monthly based monitoring campaign was performed to collect the information about the concentrations of 105 CECs as well as major and minor ions in the river and along the groundwater flow. The dimensionality of the obtained dataset was reduced to 25 CECs, based on their estimated health risk effects, for multivariate data analysis. The obtained results showed the overlap of conventional tracers' signals obtained from PCA. In case of CECs, PCA revealed differences in their distributions allowing the differentiation of the roles of natural attenuation processes, local and regional flows on their occurrence in different parts of the aquifer. This was not possible to do using solely CECs' distribution profiles. SOMs provided the lacking information about the modality of the distribution of each CECs, revealing their ability to represent factors controlling the groundwater hydrochemistry, which assist in defining their tracer potential. Based on the obtained results four identified persistent CECs, two with unimodal (lamotrigine and 5-Desamino-5-oxo-lamotrigine) and two with bimodal (carbamazepine and diazepam (higher modality was not revealed)) distributions, were selected to run a mixing model to compare their tracer performance.