Air pollution is a growing global health threat, exacerbated by climate change and linked to cardiovascular and respiratory diseases. While personal sensing devices enable real-time physiological monitoring, their integration with environmental data for individualised health prediction remains underdeveloped. Here, we present a modular, cloud-based framework that predicts personalised physiological responses to pollution by combining wearable-derived data with real-time environmental exposures. At its core is an Adversarial Autoencoder (AAE), initially trained on high-resolution pollution-health data from the INHALE study and fine-tuned using smartwatch data via transfer learning to capture individual-specific patterns. Consistent with changes in pollution levels commonly observed in the real-world, simulated pollution spikes (+100%) revealed modest but measurable increases in vital signs (e.g., +2.5% heart rate, +3.5% breathing rate). To assess clinical relevance, we analysed U-BIOPRED data and found that individuals with such subclinical vital sign elevations had higher asthma burden scores or elevated Fractional Exhaled Nitric Oxide (FeNO), supporting the physiological validity of these AI-predicted responses. This integrative approach demonstrates the feasibility of anticipatory, personalised health modelling in response to environmental challenges, offering a scalable and secure infrastructure for AI-driven environmental health monitoring.

翻译：空气污染是一个日益严重的全球健康威胁，因气候变化而加剧，并与心血管和呼吸系统疾病相关联。虽然个人传感设备能够实现实时生理监测，但它们与环境数据结合用于个体化健康预测的能力仍不成熟。本文提出了一种模块化、基于云的框架，通过将可穿戴设备数据与实时环境暴露相结合，预测个体对污染的生理反应。其核心是一个对抗自编码器（AAE），该模型首先在INHALE研究提供的高分辨率污染-健康数据上进行训练，然后通过迁移学习利用智能手表数据进行微调，以捕捉个体特异性模式。与真实世界中常见的污染水平变化一致，模拟的污染峰值（+100%）揭示了生命体征的轻微但可测量的增加（例如，心率+2.5%，呼吸频率+3.5%）。为评估临床相关性，我们分析了U-BIOPRED数据，发现具有此类亚临床生命体征升高的个体具有更高的哮喘负担评分或升高的呼出气一氧化氮分数（FeNO），这支持了这些AI预测反应的生理有效性。这种集成方法展示了针对环境挑战进行预测性、个性化健康建模的可行性，为AI驱动的环境健康监测提供了一个可扩展且安全的基础设施。