Machine Learning-Driven Prediction of Residential VOC Emissions from Building Materials and Associated Population Health Risks
Keywords:
volatile organic compounds, building materials, machine learning, indoor air quality, population health risk, digital twin, environmental justice, smart buildingsAbstract
Residential exposure to volatile organic compounds emitted by building materials constitutes a persistent yet under-characterized burden on public health, contributing to respiratory disease, neurological effects, and cancer. Traditional approaches for estimating emissions and associated risks rely on chamber tests and deterministic mass transfer models, which struggle to capture real-world variability driven by temperature, humidity, ventilation, and occupant behavior. This paper presents an interdisciplinary systems analysis of machine learning-driven prediction frameworks designed to bridge that gap. We examine the structural architecture required to integrate heterogeneous data streams, comprising material composition databases, environmental sensor networks, building metadata, and population activity patterns, into robust emission forecasting pipelines. The discussion emphasizes trade-offs among model complexity, interpretability, regulatory compliance, and computational sustainability. We further interrogate governance dimensions, including fairness across socioeconomic groups, privacy-preserving deployment strategies, and alignment with evolving building standards. The integration of digital twins and federated learning emerges as a promising paradigm for scalable and equitable indoor air quality management. Throughout, the paper maintains a system-level perspective, foregrounding the socio-technical interdependencies that will determine whether these predictive technologies reduce health disparities or inadvertently reinforce them. The analysis culminates in a set of policy and design recommendations for embedding machine learning tools into public health surveillance, material certification, and long-term urban housing strategies.
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