Can contamination indices performance be improved by accounting for the dilution effect when dealing with stream sediments? A case study from southern Italy.

Geochemical prospecting is a valuable tool for investigating the influence of the geological context on the composition of stream sediment and determining the existence of any natural or anthropogenic geochemical anomaly within a river catchment basin. Several indices have been proposed in scientific literature to assess sediment's environmental quality and the ecological condition of catchment basins. In environmental studies, these indices are usually based on applying ratios among the raw geochemical composition of the sediment at the sampling location and a value assumed as a reference for the undisturbed conditions (Background/baseline). However, the reference values are often determined while overlooking the potential influence of river dynamics on the variability of sediment composition, and this can compromise the robustness of the contamination assessment.

As a matter of fact, the chemical composition of a stream sediment sample is representative of the relative upstream catchment basin. The Sample Catchment Basin (SCB) method, which accounts for the dynamic nature of rivers, has been largely used in literature to correct the dilution effect impacting the composition of stream sediment, aiming at determining reliable geochemical background values to be used for mineral prospecting.

The main purpose of the study was to check if the use of the correction of the dilution effect to determine background values could also improve the performance of some contamination indices, favouring a more effective and accurate assessment of the environmental degradation affecting a river basin.

The Sarno River, known for its susceptibility to contamination from urban and industrial sources, served as a pertinent case study. A total of 96 samples were used to define the zone of influence of each sample through the elaboration of geomorphological and hydrological features; then, the background concentrations of each element were estimated by calculating the weighted average element content based on the areal proportions of lithologic units in each sample catchment basin. The values deriving from this step were then used as a reference to calculate the degree of contamination for each SCB.

The contamination indices were calculated and mapped by using both non-diluted and dilution-corrected background data.

A comparative analysis was performed among the results obtained to assess if sensitive changes occurred to the spatial and statistical distribution of used indices and to determine if an improvement in performance could be obtained.

 This study represents a methodological benchmark for future research focusing on environmental risk assessment of stream sediment.