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UID:pretalx-flowpath-2025-PFZLGW@talks.staging.osgeo.org
DTSTART;TZID=CET:20250612T165000
DTEND;TZID=CET:20250612T170000
DESCRIPTION:Geogenic arsenic (As) contamination is a known issue affecting 
 groundwater quality worldwide. In heterogeneous aquifers\, As mobility res
 ults from complex physical and geochemical interactions. Extensive monitor
 ing data are required to reliably assess these underlying processes and th
 e natural As heterogeneity. However\, effective groundwater characterizati
 on is often hindered by limited data availability\, high monitoring costs\
 , and resource constraints. This study exploits an alternative source of g
 eochemical information\, aggregating data from monitoring wells of sites u
 nder remediation\, a pervasive network widespread in urbanized areas. We p
 reviously demonstrated that\, when properly processed to remove anthropoge
 nic influences\, these data can provide meaningful insights into groundwat
 er’s pristine composition. We developed a random forest model to predict
  the probability of As concentrations exceeding the 10 µg/L regulatory th
 reshold. The method was applied to the shallow aquifer of Ferrara province
  in the Po Valley (northern Italy)\, a highly anthropized region with know
 n geogenic As issues. Here\, local assessments of As natural background le
 vels are often required to distinguish geogenic from anthropogenic source 
 of contamination in remediation procedures\, since provided regional-scale
  assessments lack sufficient resolution. Our model identified areas with h
 igh probability of As exceeding 10 µg/L\, mostly close to the Po River de
 lta. As mobilization was linked to natural processes driven by the stratig
 raphic architecture of the area: widespread peat deposits promote redox re
 actions associated with organic matter degradation\, leading to the reduct
 ion of Fe/Mn oxides originating from Apennine sediment sources. This study
  provides a useful tool for groundwater management\, improving chemical co
 mposition knowledge through an integrated approach\, relevant for both loc
 al-scale decision-making and large-scale groundwater quality assessments.
DTSTAMP:20260428T025549Z
LOCATION:Room R3
SUMMARY:A machine learning model trained on data from sites under remediati
 on to predict geogenic arsenic distribution in shallow groundwater - Laura
  Landi
URL:https://talks.staging.osgeo.org/flowpath-2025/talk/PFZLGW/
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