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Updated: Mar 27, 2026

Extraction and Detection of Geosmin and 2-Methylisoborneol in Water and Fish using High-Capacity Sorptive Extraction Probes and GC-MS
Published on: July 3, 2025
Comparative assessment of 2-methylisoborneol and geosmin removal techniques using multicriteria decision analysis
Betul Aykut-Senel1, Nuray Ates2, Sehnaz Sule Kaplan-Bekaroglu1
1Environmental Engineering Department, Faculty of Engineering and Natural Sciences, Suleyman Demirel University, Isparta, Türkiye.
Abstract:
The removal of 2-methylisoborneol and geosmin from drinking water is a persistent challenge due to their resistance to conventional treatment methods. This study employs a multicriteria decision-making approach, integrating the analytic hierarchy process, technique for order preference by similarity to ideal solution, VlseKriterijumska Optimizacija I Kompromisno Resenje (multicriteria optimization and compromise solution), and stochastic multicriteria acceptability analysis, to evaluate five treatment alternatives: (A1) activated carbon adsorption, (A2) modified activated carbon adsorption, (A3) peroxone oxidation, (A4) integrated original activated carbon and peroxone process, and (A5) integrated modified activated carbon and peroxone process. The assessment was conducted across seven criteria: technical performance, environmental sustainability, economic feasibility, operational feasibility, usability and monitoring, safety and health risks, and adaptability and suitability. Results indicate that A2 exhibits the highest removal efficiency, while A3 offers the fastest degradation but has high chemical demands and safety risks. The analytic hierarchy process, technique for order preference by similarity to ideal solution, and stochastic multicriteria acceptability analysis ranked A2 as the most favorable, whereas VlseKriterijumska Optimizacija I Kompromisno Resenje favored A5, suggesting that it provides a balanced performance across multiple criteria. A sensitivity analysis confirmed the stability of rankings, highlighting the impact of criterion weight variations on final decisions. These findings underscore the importance of a hybrid evaluation framework in selecting effective water treatment strategies. Future studies should explore the integration of machine learning techniques to enhance decision-making reliability.
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