A hybrid approach for smart city air quality monitoring using q-rung orthopair fuzzy fairly aggregation with Z-numbers

This study presents a novel approach for enhancing air quality monitoring (AQM) in smart cities by employing q-rung orthopair fuzzy Z-numbers (q-ROFZNs) within a robust multi-criteria decision-making framework. The proposed methodology addresses the challenges of uncertainty and imprecision in environmental data through the integration of q-ROFFZN aggregation operators. In particular, we develop and apply the aggregated operation weighted averaging and ordered weighted averaging operators under the proposed set, enabling more flexible and accurate data fusion. By leveraging Grey Relational Analysis (GRA), the model demonstrates superior assessment accuracy and resilience compared to existing techniques. The hybrid computational strategy effectively supports dynamic decision-making by incorporating real-time traffic and industrial data, leading to optimized environmental control and public health protection. The findings highlight the potential of advanced fuzzy set theories in developing sustainable and intelligent solutions for urban air pollution management.