PCE point source apportionment using a GIS-based statistical technique combined with stochastic modelling

Licia C Pollicino; Loris Colombo; Luca Alberti; Marco Masetti

doi:10.1016/j.scitotenv.2020.142366

PCE point source apportionment using a GIS-based statistical technique combined with stochastic modelling

Sci Total Environ. 2021 Jan 1:750:142366. doi: 10.1016/j.scitotenv.2020.142366. Epub 2020 Sep 15.

Authors

Licia C Pollicino¹, Loris Colombo², Luca Alberti¹, Marco Masetti³

Affiliations

¹ Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milan, Italy.
² Dipartimento di Ingegneria Civile e Ambientale (DICA), Politecnico di Milano, 20133 Milan, Italy. Electronic address: loris.colombo@polimi.it.
³ Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, 20133 Milan, Italy.

PMID: 33182200
DOI: 10.1016/j.scitotenv.2020.142366

Abstract

To meet the continuous growth of urbanised areas with the ever-increasing demand for safe water supplies, the implementation of new scientifically based methodologies can represent a key support for preventing groundwater quality deterioration. In this study, a new combined approach based on the application of the Weights of Evidence and the Null-Space Monte Carlo particle back-tracking methods was set up to assess tetrachloroethylene (PCE) contamination due to Point Sources in the densely urbanised north-eastern sector of the Milano FUA (Functional Urban Area). This combined approach offers the advantage of further enhancing the power of each individual technique by integrating both the advective transport mechanism, neglected by the Weights of Evidence, and the influence of specific factors, such as the land use variation, not considered by the Null-Space Monte Carlo particle tracking. To accurately test and explore the performance of this new approach, the analysis was carried out based on the simulation of synthetic PCE plumes using a groundwater numerical model already implemented in a previous study. The Weights of Evidence method revealed that the areas characterised by a groundwater depth lower than 17 m, a groundwater velocity higher than 2.6 × 10^-6 m/s, a recharge higher than 0.26 m/y and a significant variation of the industrial activities extent are the most susceptible to groundwater pollution. The Null-Space Monte Carlo particle back-tracking has proved to be effective in delineating the potential source zones and contaminant travel path. The proposed approach can offer additional insights for the protection of groundwater resource. The end-product provides crucial information on the zones that require to be prioritised for investigations and can be easily understood by non-expert decision-makers constituting an advanced tool for enhancing groundwater protection strategies.

Keywords: Milan; Point source contamination; Spatial statistical method; Synthetic model; Uncertainty prediction; Urban groundwater.