Evidence map›Paper›PMID 40025328›Full record

ArticleEnvironmental science and pollution research international2025

Tracking the spatiotemporal evolution of groundwater chemistry in the Quaternary aquifer system of Debrecen area, Hungary: integration of classical and unsupervised learning methods.

Musaab A A Mohammed, Norbert P Szabó, Viktória Mikita, Péter Szűcs

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Article in Environmental science and pollution research international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Musaab A A MohammedFaculty of Earth and Environmental Sciences and Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary. mohammed.musaab@uni-miskolc.hu.ORCID http://orcid.org/0000-0002-0097-0037
Norbert P SzabóFaculty of Earth and Environmental Sciences and Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary.
Viktória MikitaFaculty of Earth and Environmental Sciences and Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary.
Péter SzűcsFaculty of Earth and Environmental Sciences and Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Monitoring changes in groundwater quality over time helps identify time-dependent factors influencing water safety and supports the development of effective management strategies. This study investigates the spatiotemporal evolution of groundwater chemistry in the Debrecen area, Hungary, from 2019 to 2024, using indexing, machine learning, and multivariate statistical techniques. These techniques include self-organizing maps (SOM), hierarchical cluster analysis (HCA), principal component analysis (PCA), and groundwater quality indexing (GWQI). The hydrochemical analysis revealed that Ca-Mg-HCO₃ is the dominant water type, with a temporal shift toward Na-HCO₃, reflecting increased salinity driven by ongoing rock-water interactions. SOM analysis showed a transition from heterogeneous to more uniform groundwater chemistry over time, suggesting greater stability in the aquifer system. Elevated salinity zones shifted spatially due to changes in groundwater recharge and flow patterns, while hardness intensified and expanded, indicating continued carbonate dissolution. HCA highlighted temporal shifts in groundwater composition, with six clusters identified in 2019 and five clusters in 2024, reflecting a gradual homogenization of water quality. PCA further confirmed this trend, linking it to underlying hydrochemical processes, such as water-rock interactions, with limited contributions from anthropogenic influences. The GWQI analysis indicated a general improvement in groundwater quality over time, with most regions meeting drinking water standards. However, specific areas exhibited signs of localized contamination, requiring targeted management. These findings underscore the importance of continuous groundwater quality monitoring to detect emerging trends and guide resource management. The study highlights the need for sustainable practices to safeguard water resources and ensure long-term water security in the Debrecen area.

Indexed as

Environmental MonitoringGroundwaterUnsupervised Machine LearningCluster AnalysisHungaryPrincipal Component AnalysisWater Pollutants, ChemicalWater QualityWater Pollutants, ChemicalGroundwater quality indexMultivariate statisticsPannonian BasinSaturation indexSelf-organizing maps

Identifiers

PMID40025328
PMCPMC11928377

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.