How do hydrogeological and socio-economic parameters influence the likelihood of NO3− pollution and Cl− salinization? An application within the campania region (Italy)

Mojgan Bordbar, Gianluigi Busico (), Stefania Stevenazzi and Micòl Mastrocicco
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Mojgan Bordbar: Campania University “Luigi Vanvitelli”
Gianluigi Busico: Campania University “Luigi Vanvitelli”
Stefania Stevenazzi: University of Naples Federico II
Micòl Mastrocicco: Campania University “Luigi Vanvitelli”

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 11, No 21, 12887-12907

Abstract: Abstract Groundwater pollution is increasing because of long-term human activities. This study aims at assessing the probability of nitrate (NO3−) and chloride (Cl−) pollution. The approach firstly involved applying a Gaussian simulation to reconstruct the spatial distribution of the pollutants in three areas in the Campania Region (Italy). Then, probability maps were used to determine how different hydrogeological and socio-economic parameters affect groundwater quality in the three regions. To prioritize the factors affecting the target pollutions, two distinct groups of parameters were considered: hydraulic head, recharge, distance from inland water, distance from the coastline, ground elevation, hydraulic conductivity, and fine sediment content to assess Cl− salinization; while hydraulic conductivity, recharge, fine sediment content, crops fertilizer request, depth to water table, and distance from wells to assess NO3− pollution. Three different algorithms, Decision Tree (DT), Random Forest (RF), and Information Gain Ratio (IGR), were employed. The results of the prioritization of parameters affecting NO3− pollution indicate that recharge, hydraulic conductivity, water depth, and crops fertilizer request are the most influential factors, while the results for Cl− salinization show that hydraulic head, recharge, hydraulic conductivity, distance from inland water, and fine sediment content have the strongest impact. This study highlights that, as different processes govern NO3− pollution and Cl− salinization, an informed management is essential to effectively tackle protection measures to safeguard groundwater resources. The protocol here employed can be extended to other regions, assisting policymakers and managers in identifying areas exposed to potential human and naturally driven pollution processes.

Keywords: Gaussian imulation; Probability mapping; Machine learning; Nitrate pollution; Chloride salinization; Groundwater resources (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07300-5

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