Predicting Extreme Environmental Values with Hybrid Models: A Perspective Across Air Quality, Wind Energy, and Sensor Networks

This Perspective synthesizes recent (2023-2025) progress in predicting extreme environmental values by combining empirical formulations, physics-based simulation outputs, and sensor-network data. We argue that hybrid approaches-spanning physics-informed machine learning, digital/operational twins, and edge/embedded AI-can deliver faster and more robust maxima estimates than standalone CFD or purely data-driven models, particularly for urban air quality and wind-energy applications. We distill lessons from cross-domain case studies and highlight five open challenges (uncertainty quantification, reproducibility and benchmarks, sensor layout optimization, real-time inference at the edge, and trustworthy model governance). Building on these, we propose a 2025-2030 research agenda: (i) standardized, open benchmarks with sensor-CFD pairs; (ii) physics-informed learners for extremes; (iii) adaptive source-term estimation pipelines; (iv) lightweight inference for embedded sensing; (v) interoperable digital-twin workflows; and (vi) reporting standards for uncertainty and ethics. The goal is a pragmatic path that couples scientific validity with deployability in operational environments. This Perspective is intended for researchers and practitioners in environmental sensing, urban dispersion, and renewable energy who seek actionable, cross-disciplinary directions for the next wave of extreme-value prediction. For instance, in validation studies using CFD-RANS and sensor data, the proposed hybrid models achieved prediction accuracies for peak pollutant concentrations and wind speeds within ~90-95% of high-fidelity simulations, with a computational cost reduction of over 80%. These results underscore the practical viability of the approach for operational use cases such as urban air quality alerts and wind farm micro-siting.