This page has only limited features, please log in for full access.

Ms. Licia Pollicino
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan 20133, Italy

Basic Info


Research Keywords & Expertise

0 GIS
0 PEST
0 Transport Modelling
0 Diffuse pollution
0 stochastic modelling

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 14 May 2021 in Water Research
Reads 0
Downloads 0

In heavily urbanised areas, groundwater diffuse pollution is recognised as one of the most insidious threats to groundwater quality. Diffuse pollution originates from multiple small sources releasing a low contaminant mass over a relatively large area; the lack of a defined plume in groundwater, the limited leaked mass, and the fact that leakage may have occurred in the past and be now ceased, make these sources difficult to locate and characterise. In addressing this environmental issue, an inverse approach based on the Null space Monte Carlo stochastic method has been applied in the framework of an innovative methodology with the aim to locate potential source areas distributed in a large (120 km2) urban area. To simplify the problem and better understand the limitations and effectiveness of the proposed methodology, the analysis has been performed using a groundwater model with fixed (i.e., determined by a previous calibration) hydraulic conductivity and flow boundary conditions. The only source of uncertainty considered in the study is the PCE mass discharge from all model cells of the topmost layer. After implementing and calibrating a deterministic solute transport model, multiple random realisations of mass discharge fields were generated, all of which are history-match constrained and hydrogeologically plausible. The obtained stochastic parameter sets were used to investigate the statistical distribution of the solute mass discharge and map the areas that are more likely to host unknown sources of PCE. Although the application of the NSMC stochastic method on the synthetic case study has provided promising results, it has also highlighted that multiple sources of uncertainty (e.g., continuity and duration of each source, attenuation processes) could adversely affect the reliability of the results in a real-world context, in which the effect of other uncertain parameters (hydraulic conductivity amongst all) would need to be considered in addition. This study offers new insights to the problem of aquifer diffuse pollution by providing key information on the potential source zones and on the areas that urgently need to be prioritised for further investigations.

ACS Style

Licia C. Pollicino; Loris Colombo; Giovanni Formentin; Luca Alberti. Stochastic modelling of solute mass discharge to identify potential source zones of groundwater diffuse pollution. Water Research 2021, 200, 117240 .

AMA Style

Licia C. Pollicino, Loris Colombo, Giovanni Formentin, Luca Alberti. Stochastic modelling of solute mass discharge to identify potential source zones of groundwater diffuse pollution. Water Research. 2021; 200 ():117240.

Chicago/Turabian Style

Licia C. Pollicino; Loris Colombo; Giovanni Formentin; Luca Alberti. 2021. "Stochastic modelling of solute mass discharge to identify potential source zones of groundwater diffuse pollution." Water Research 200, no. : 117240.

Journal article
Published: 28 July 2020 in Water
Reads 0
Downloads 0

Karst aquifers are indispensable, yet vulnerable, resources; therefore, they require a comprehensive protection strategy. Since springs are the terminal points of the karst flow systems, knowledge of their distribution is a key element for the better understanding of groundwater flow, availability and vulnerability. The present study aims to introduce a data-driven analysis by the application of a spatial statistical technique (Weights of Evidence (WofE)) for the evaluation of factors influencing spring distribution in karst areas. A workflow was developed for investigating two questions: where will the springs locate, and where will the permanent springs evolve? This workflow has the potential for application to unconfined karst areas. This enhanced approach was applied to an unconfined transboundary aquifer, the Gömör–Torna Karst (HU and SK). The roles of five factors was statistically investigated: terrain elevation, distance to faults, distance of the carbonate–non-carbonate rock contact, distance to sinkholes, and precipitation distribution. The validation procedures confirmed the effectiveness of the approach. The resulting predictive maps are useful for decision-makers to delineate areas holding potential karst springs and to address water availability problems and protection measures. In addition, the WofE technique improved the comprehension of the geological conditions favourable for the formation of the springs.

ACS Style

Veronika Iván; Stefania Stevenazzi; Licia C. Pollicino; Marco Masetti; Judit Mádl-Szőnyi. An Enhanced Approach to the Spatial and Statistical Analysis of Factors Influencing Spring Distribution on a Transboundary Karst Aquifer. Water 2020, 12, 2133 .

AMA Style

Veronika Iván, Stefania Stevenazzi, Licia C. Pollicino, Marco Masetti, Judit Mádl-Szőnyi. An Enhanced Approach to the Spatial and Statistical Analysis of Factors Influencing Spring Distribution on a Transboundary Karst Aquifer. Water. 2020; 12 (8):2133.

Chicago/Turabian Style

Veronika Iván; Stefania Stevenazzi; Licia C. Pollicino; Marco Masetti; Judit Mádl-Szőnyi. 2020. "An Enhanced Approach to the Spatial and Statistical Analysis of Factors Influencing Spring Distribution on a Transboundary Karst Aquifer." Water 12, no. 8: 2133.

Journal article
Published: 10 June 2019 in Water
Reads 0
Downloads 0

Contamination by chlorinated solvents is typically associated with point sources, which are able to release high concentrations and to generate well defined plumes. Nevertheless, in urban settings (especially in functional urban areas—FUAs), multiple-point sources are frequently present, consisting of a series of unidentifiable small sources clustered within large areas, generating a diffuse, anthropogenic contamination. This situation results in the coexistence of single plumes with higher contaminant concentrations, and larger areas where the concentration is lower but still higher than the maximum admissible concentration limits. This paper proposes a methodology devised to cope with the diffuse contamination by chlorinated solvents within shallow aquifers due to multiple-point sources in FUAs. The approach is based on a Bayesian model that helps to spatially evaluate the likelihood of having active multiple-point sources, and to relate their impact on the shallow aquifer to the hydrogeological features of the area. Moreover, the approach allows testing of the efficiency of the monitoring network to properly characterize the contamination in the aquifer. The consistency of the results of the analysis was also checked for the Milan FUA (Italy) by a comparison to a previous study, performed through an inverse numerical modelling approach within a Monte Carlo statistical framework to identify the areas with the highest likelihood to host potential multiple-point sources.

ACS Style

Licia C. Pollicino; Marco Masetti; Stefania Stevenazzi; Loris Colombo; Luca Alberti. Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas. Water 2019, 11, 1211 .

AMA Style

Licia C. Pollicino, Marco Masetti, Stefania Stevenazzi, Loris Colombo, Luca Alberti. Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas. Water. 2019; 11 (6):1211.

Chicago/Turabian Style

Licia C. Pollicino; Marco Masetti; Stefania Stevenazzi; Loris Colombo; Luca Alberti. 2019. "Spatial Statistical Assessment of Groundwater PCE (Tetrachloroethylene) Diffuse Contamination in Urban Areas." Water 11, no. 6: 1211.