AI-Driven Digital Twin Framework for Indoor Chemical Exposure Risk Prediction: Integrating Phthalate Emission Characterization, Occupant Behavior Modeling, and Smart Building Management
Keywords:
digital twin, indoor air quality, phthalates, exposure assessment, occupant behavior, smart building, artificial intelligence, cyber-physical systemAbstract
Indoor environments concentrate a multitude of semi-volatile organic compounds, with phthalates representing a ubiquitous yet poorly managed chemical hazard. Traditional exposure assessment relies on sparse monitoring, stationary sampling, and simplistic fate models that fail to capture the dynamic interplay between emission sources, building materials, ventilation, occupant activities, and individual time-activity patterns. This paper presents a comprehensive AI-driven digital twin framework designed to predict indoor chemical exposure risk in near real-time by fusing physics-based phthalate emission characterization, data-driven occupant behavior modeling, and smart building management integration. The proposed architecture conceptualizes the building as a cyber-physical system wherein digital replicas continuously ingest sensor streams, update indoor air quality states through coupled mass balance and machine learning surrogates, and project personal exposure trajectories for diverse occupant profiles. We detail the structural trade-offs inherent in harmonizing heterogeneous data sources, balancing model fidelity with computational latency, and ensuring privacy-preserving occupant sensing. System-level challenges related to sensor density, data assimilation, edge-cloud orchestration, and uncertainty quantification are critically examined. Governance dimensions are analyzed through the lenses of algorithmic fairness—ensuring that exposure predictions do not systematically disadvantage vulnerable populations—and infrastructural robustness. The framework establishes a pathway toward proactive building management that moves beyond energy-centric optimization to incorporate chemical health burdens as a first-class control objective. By connecting material science, behavioral science, and intelligent building systems, this work contributes a holistic research agenda for resilient, health-aware indoor environments.
References
1. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.
2. Khajavi, S. H., Motlagh, N. H., Jaribion, A., Werner, L. C., & Holmström, J. (2019). Digital twin: Vision, benefits, boundaries, and creation for buildings. IEEE Access, 7, 169241–169256.
3. Xu, Y., & Little, J. C. (2006). Predicting emissions of SVOCs from polymeric materials and their interaction with airborne particles. Environmental Science & Technology, 40(2), 456–461.
4. Weschler, C. J., & Nazaroff, W. W. (2008). Semivolatile organic compounds in indoor environments. Atmospheric Environment, 42(40), 9018–9040.
5. Little, J. C., Hodgson, A. T., & Gadgil, A. J. (1994). Modeling emissions of volatile organic compounds from new carpets. Atmospheric Environment, 28(2), 227–234.
6. Klepeis, N. E., Nelson, W. C., Ott, W. R., Robinson, J. P., Tsang, A. M., Switzer, P., Behar, J. V., Hern, S. C., & Engelmann, W. H. (2001). The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants. Journal of Exposure Analysis and Environmental Epidemiology, 11(3), 231–252.
7. Yan, D., O’Brien, W., Hong, T., Feng, X., Burak Gunay, H., Tahmasebi, F., & Mahdavi, A. (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, 264–278.
8. Chen, J., Chen, X., Zhang, S., & Zhang, J. (2021). Deep learning for occupant behavior prediction in buildings: A state-of-the-art review. Building and Environment, 202, 108036.
9. Minoli, D., Sohraby, K., & Occhiogrosso, B. (2017). IoT considerations, requirements, and architectures for smart buildings—Energy optimization and next-generation building management systems. IEEE Internet of Things Journal, 4(1), 269–279.
10. Allen, J. G., MacNaughton, P., Satish, U., Santanam, S., Vallarino, J., & Spengler, J. D. (2016). Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic compound exposures in office workers: A controlled exposure study of green and conventional office environments. Environmental Health Perspectives, 124(6), 805–812.
11. Bellinger, C., Mohomed Jabbar, M. S., Zaïane, O., & Osornio-Vargas, A. (2017). A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17, 907.
12. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
13. Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.
14. Liang, Y., Xu, Y., & Pei, X. (2019). Bayesian inverse modeling of semivolatile organic compound emissions from indoor sources. Indoor Air, 29(4), 638–651.
15. Hsu, P.-H., & Ma, Y. (2020). Privacy by design for smart building systems. Journal of Systems Architecture, 106, 101714.
16. Yang, T., Wang, H., Zhang, X., Xiong, J., Huang, S., & Koutrakis, P. (2020). Characterization of phthalates in sink and source materials: measurement methods and the impact on exposure assessment. Journal of Hazardous Materials, 396, 122689.
17. Sha, K., Chen, Z., Wei, W., Yang, T. A., & Wang, Z. (2020). A survey of edge computing-based designs for IoT security. Digital Communications and Networks, 6(2), 195–202.
18. Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova, A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G. S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri, M., Ning, Z., Motta, N., … Williams, R. (2018). Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone? Environment International, 116, 286–299.
19. MacNaughton, P., Satish, U., Laurent, J. G. C., Flanigan, S., Vallarino, J., Coull, B., Spengler, J. D., & Allen, J. G. (2017). The impact of working in a green certified building on cognitive function and health. Building and Environment, 114, 178–186.
20. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
21. Braun, T., Fung, B. C. M., Iqbal, F., & Shah, B. (2019). Security and privacy challenges in smart cities. Sustainable Cities and Society, 47, 101472.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
