AI-Driven Indoor Air Quality Prediction and Health Risk Assessment of Volatile Organic Compounds
Keywords:
indoor air quality; volatile organic compounds; artificial intelligence; health risk assessment; smart buildings; data governanceAbstract
Indoor air quality is a persistent public health challenge because volatile organic compounds emitted from building materials, furnishings, cleaning products, and occupant activities can produce chronic and acute health effects that are not well captured by episodic monitoring. The increasing availability of low-cost sensors, building management data, and machine learning methods has created new opportunities for continuous indoor air quality prediction. However, the translation of predicted volatile organic compound concentrations into meaningful health risk information requires more than model accuracy. It demands careful attention to system architecture, exposure science, data governance, uncertainty communication, robustness, fairness, and deployment context. This paper presents a system-level analysis of AI-driven indoor air quality prediction and health risk assessment for volatile organic compounds. It examines the structural design of sensing and modeling pipelines, the integration of toxicological and exposure considerations, the governance of occupant-related data, and the policy trade-offs that arise when predictive systems are deployed in heterogeneous buildings. The discussion emphasizes that AI-based prediction should be understood as part of a larger socio-technical infrastructure rather than as an isolated computational task. A well-designed system must balance predictive performance with interpretability, privacy protection, equity, and operational sustainability. The paper concludes by identifying directions for interdisciplinary research and policy development to ensure that AI-enhanced indoor air quality management improves health outcomes without exacerbating existing environmental disparities.
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