This study presents a UAV-based fuzzy inference framework for real-time air quality monitoring that integrates symmetric and asymmetric fuzzy rules. Symmetric rules capture baseline pollutant dynamics, ensuring computational stability, while asymmetric rules account for local anomalies, turbulence, and environmental disturbances, effectively regularizing the inherently ill-posed backward problem of reconstructing pollutant concentrations from noisy UAV measurements. Simulation and field experiments demonstrate that this hybrid fuzzy approach provides both mathematical robustness and practical reliability, outperforming purely symmetric models in dynamic, asymmetric environments. The proposed framework offers a generalizable methodology for environmental monitoring, emphasizing the critical role of symmetry and its breaking in modeling real-world ecological processes. Future developments will focus on atmospheric dispersion integration, fuzzy rule optimization, and large-scale UAV deployment. The results indicate that the hybrid fuzzy inference system can enhance the accuracy and reliability of UAV-based air quality monitoring under real-world disturbances, providing a robust framework applicable for urban planning, environmental policy, and large-scale deployment scenarios.