Fingerprinting sources of salinity in a coastal chalk aquifer in Denmark using trace elements

Abstract

Salinity levels above the drinking water standard (>250 mg/l Cl–) are observed at shallow depth in a Maastrichtian chalk aquifer on the island of Falster, south-eastern Denmark. To understand the source of the salt, 63 samples from 12 individual, 1 m, screened intervals between 14 and 26 m b.s. were collected from 1 May to 4 June 2018. The samples were collected during a tracer test to estimate the dual porosity properties of the chalk and were analysed for a wide range of elements. Furthermore, samples from the Baltic Sea and from deeper saline aquifers in the area (40 and 85 m b.s.) were analysed for comparison. The geochemical data were analysed using an unsupervised machine-learning algorithm, self-organising maps, to fingerprint water sources. The water composition in the screened intervals at various stratigraphic levels has specific geochemical fingerprints that are maintained for the first days of pumping and are distinct amongst the different levels. This suggests an evolution in water composition because of reaction with the chalk. Water composition is distinct from both seawater from the nearby Baltic Sea and salty water from deeper levels of the reservoir. Thus, neither up-coning of salty water nor intrusion of seawater caused the elevated salinity levels in the area. The slightly saline composition of groundwater in the shallow aquifer (14–26 m b.s.) is more likely because of incomplete refreshing of the salty connate water in the chalk during the Pleistocene and Holocene. Furthermore, the geochemical fingerprint of salty water from the deeper aquifer at 40 m was similar to water from the Baltic Sea, suggesting a Baltic Sea source for salt in the aquifer at 40 m b.s., c. 100 m from the coast. Statistical analysis based on self-organising maps is an effective tool for interpreting a large number of variables to understand the compositional variation in an aquifer and a useful alternative to linear dimensionality-reduction methods such as principal component analysis. The approach using the multi-element analysis combined with the analysis of self-organising maps may be useful in future studies of groundwater quality.