Probabilistic T-spherical hesitant fuzzy decision support for selecting advanced tertiary wastewater treatment...

Pharmaceuticals, microplastics, per- and polyfluoroalkyl substances (PFAS) and endocrine-disrupting compounds are increasingly discharged in treated wastewater, are not effectively removed by conventional secondary treatment, and have important implications for the environment and human health. Advanced tertiary treatment is needed for safe reuse of urban non-potable water, but the choice among competing technologies is a complex decision involving technical, economic, environmental and regulatory considerations, as well as uncertainty and reluctance in expert evaluations. To address this issue, this research uses a decision support system based on Probabilistic T-Spherical Hesitant Fuzzy Numbers (Pt-SHFNs) that represents probabilistic uncertainty, ambiguity and expert hesitance consistently. A hybrid method is used to find criteria weights. The hybrid method combines the Level-Based Weight Assessment (LBWA) technique and changes of empirical data. This allows the subjective preferences of experts and objective evidence to be simultaneously considered. Then the alternatives are ranked using Comprehensive Distance Based Ranking (COBRA) technique. The results show that in the practical case of seven tertiary treatment systems, the effectiveness of removal of emerging pollutants (0.2319) and capital expenditures (0.1713) are the most influential factors. The most efficient systems are hybrid membrane-advanced oxidation process systems (0.0338) followed by ozonation-biofiltration (0.0203) and nanofiltration membranes (-0.0022). The stability analysis and comparison studies with other MCDM techniques increase the dependability and consistency of the obtained rankings. The proposed technique provides an explicit and uncertainty-aware basis for ranking tertiary alternatives to treatment and could assist wastewater utilities and regulators in integrating technical decisions with sustainability and water reuse objectives.