Airborne particulate matter (PM) poses growing threats to human health and the environment, especially in the context of emerging infectious diseases. Conventional petroleum-based filters are constrained by an inherent trade-off between filtration efficiency and air resistance, and additionally suffer from a lack of mechanical durability, thermal protection, and environmental degradability. Here, a multifunctional triboelectric-assisted aerogel filter is fabricated through a synergistic approach combining cell wall nano-reconstruction with diatom-inspired mineralization. This strategy constructs a hierarchical porous framework that regulates charge storage sites, leading to a 71% improvement in charge retention compared with native wood and a significant enhancement in triboelectric output. As a self-powered filtration platform, the system achieves outstanding removal efficiencies for PM0.3 (98.75% ± 0.08%), PM0.5 (99.51% ± 0.21%), and PM1 (99.98% ± 0.02%) with a low pressure drop of 53 Pa. Meanwhile, the aerogel demonstrates remarkable compressive resilience (18.1 MPa at 60% radial strain) and complete elastic recovery, together with robust thermal insulation and flame retardancy. Notably, the integrated triboelectric signal is analyzed using deep learning algorithms to identify respiratory patterns, thereby enabling real-time health monitoring and intelligent respiratory management. This work establishes a sustainable, high-performance material platform for advanced air purification and wearable healthcare applications in extreme environments.