A Pythagorean Fuzzy decision making framework for PM2.5 monitoring station placement in urban areas: A case study of Trabzon, Türkiye

The selection of suitable PM2.5 monitoring locations in urban areas is defined as a complex decision-making problem due to the heterogeneity of the urban structure and the uncertainties inherent in expert evaluations. In this study, an integrated Multi-Criteria Decision-Making (MCDM) approach based on Pythagorean Fuzzy Sets (PFSs) is proposed to analytically support the selection of PM2.5 monitoring sites. Eight evaluation criteria were identified based on a comprehensive literature review and expert opinions. The Pythagorean fuzzy Step-wise Weight Assessment Ratio Analysis (PF-SWARA) method was employed to calculate the weights of the criteria, while the Pythagorean Fuzzy Combinative Distance-based Assessment (PF-CODAS) method was used to evaluate and rank the alternative locations. The proposed methodology accounts for uncertainty in expert judgments by incorporating membership and non-membership degrees, from which the hesitation degree is implicitly derived according to the PFS definition. A real-world case study involving five alternative locations was conducted in the city of Trabzon, Türkiye. The results indicate that PM2.5 concentration level is the most influential criterion, followed by population density and distance to existing monitoring stations. The Shopping Mall area was identified as the most suitable monitoring location, followed by Pelitli and Değirmendere. Taken together, the findings demonstrate that the proposed framework provides a practical decision-support tool for urban air quality monitoring.İ̇mplication statement: This study provides policy-relevant evidence for improving urban PM2.5 monitoring by showing how monitoring locations can be prioritized based on population exposure and network coverage rather than convenience or ad hoc placement. By integrating expert consensus (Modified Delphi) with an uncertainty-aware multi-criteria framework (PF-SWARA and PF-CODAS), the proposed approach helps municipalities and environmental agencies design more representative monitoring networks under budget and infrastructure constraints. The results support practical policy actions such as targeting underserved areas, reducing spatial overlap among stations, and strengthening the detection of local pollution hotspots that affect vulnerable populations. Overall, the framework can guide transparent, data-informed decisions for monitoring network expansion and enable more effective evaluation of air quality regulations and mitigation programs.