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L. Guadagnini

Researcher at Polytechnic University of Milan

Publications -  25
Citations -  458

L. Guadagnini is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Aquifer & Groundwater. The author has an hindex of 11, co-authored 24 publications receiving 389 citations.

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Natural background levels and threshold values of chemical species in three large-scale groundwater bodies in Northern Italy

TL;DR: The results highlight the benefit of a dynamic monitoring process and analysis of well demarcated groundwater bodies to update the associated NBLs as a function of the temporal dependence of natural processes occurring in the subsurface.
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Probabilistic study of well capture zones distribution at the Lauswiesen field site.

TL;DR: The findings suggest that the uncertainty associated with the prediction of the location of the outer boundary of well catchment at the "Lauswiesen" site is significantly affected by the conceptual model adopted to incorporate the heterogeneous nature of the aquifer domain in a predictive framework.
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Arsenic release from deep natural solid matrices under experimentally controlled redox conditions

TL;DR: The results suggest that the environmentally critical As concentrations detected in some aquifers in the Emilia-Romagna Region are consistent with the occurrence of high natural As content in the component of the host porous medium associated with vegetal matter.
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Probabilistic reconstruction of geologic facies

TL;DR: In this paper, a probabilistic reconstruction of boundaries between geologic facies using random domain decomposition (RDD) has been proposed to quantify uncertainty in flow simulations when both the geologic makeup of a porous medium and its hydraulic parameters are under-specified by data.
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Delineation of source protection zones using statistical methods

TL;DR: In this article, the authors investigate the requirements for the establishment of probabilistic source protection zones, the practical use of stochastic methods in their delineation, and the use of data-assimilation for uncertainty reduction.