Medición de CO₂ para la Mejora de la Calidad del Aire en Espacios Interiores: Una Revisión de Literatura

John Barco Jiménez; Sixto Campaña

doi:10.22490/ECBTI.9981

Vol. 5 Núm. 1 (2024)

Publicado 2025-10-08

Licencia

Derechos de autor 2025 Documentos de Trabajo ECBTI

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.

Artículos

Medición de CO₂ para la Mejora de la Calidad del Aire en Espacios Interiores: Una Revisión de Literatura

DOI: https://doi.org/10.22490/ECBTI.9981

John Barco Jiménez Universidad Nacional Abierta y a Distancia

Sixto Campaña Universidad Nacional Abierta y a Distancia

PDF

Resumen
Referencias

Mantener una buena calidad del aire interior es vital para la salud y la seguridad en los laboratorios clínicos, ya que implica proteger al personal y garantizar la exactitud de la investigación. La pandemia de COVID-19 demostró la importancia de prevenir la propagación de enfermedades transmitidas por el aire. El CO₂ constituye un indicador clave en este contexto: sus niveles muestran si la ventilación funciona correctamente y si los espacios están sobreocupados. Las concentraciones elevadas suelen reflejar una ventilación deficiente, lo cual aumenta el riesgo de transmisión de enfermedades respiratorias.

A pesar de los avances tecnológicos, existe una carencia de estudios centrados en el uso de la monitorización de CO₂ y de ventilación inteligente en laboratorios clínicos; la mayoría de las investigaciones se enfocan en hogares o edificios comerciales, cuyos requisitos son menos estrictos. Herramientas como los sensores de infrarrojo no dispersivo (NDIR) y modelos de IA, por ejemplo, redes de memoria a largo plazo (LSTM) o algoritmos Random Forest, pueden predecir la calidad del aire y optimizar la ventilación. Sin embargo, su uso en entornos clínicos sigue siendo limitado.

Para profundizar en este aspecto, esta revisión examina tecnologías de sensores, modelos predictivos y la interacción humano-ambiente. Se recopilaron artículos de las bases de datos Scopus e IEEE publicados entre 2020 y 2025, seleccionando 41 para un análisis detallado bajo criterios específicos. Los hallazgos resaltan la necesidad de sistemas adaptativos que consideren tanto los datos ambientales como el comportamiento humano. Dichos sistemas pueden conducir a una gestión de calidad del aire más segura, eficiente y adecuada para entornos clínicos, donde la fiabilidad y la resiliencia son esenciales.

Palabras clave: Monitorización de CO₂, Calidad del aire, IoT, salud ambiental, ocupación de edificios, laboratorios clínicos

[1] V. Motuzienė, J. Bielskus, R. Džiugaitė-Tumėnienė, and V. Raudonis, “Occupancy-Based Predictive AI-Driven Ventilation Control for Energy Savings in Office Buildings,” Sustainability, vol. 17, no. 9, p. 4140, May 2025, doi: 10.3390/su17094140.

[2] X. Liang, J. Shim, O. Anderton, and D. Song, “Low-cost data-driven estimation of indoor occupancy based on carbon dioxide (CO2) concentration: A multi-scenario case study,” Journal of Building Engineering, vol. 82, p. 108180, Dec. 2023, doi: 10.1016/j.jobe.2023.108180.

[3] A. Sánchez-Fernández, E. C. Aliaga, V. Lerma-Arce, and E. Lorenzo‐Sáez, “Evaluation of Different Natural Ventilation Strategies by Monitoring the Indoor Air Quality Using CO2 Sensors,” International Journal of Environmental Research and Public Health, vol. 20, no. 18, p. 6757, Sep. 2023, doi: 10.3390/ijerph20186757.

[4] H.-C. Zhu, C. Ren, and S.-J. Cao, “Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system,” Building Simulation, vol. 14, no. 3, p. 649, Sep. 2020, doi: 10.1007/s12273-020-0709-z.

[5] H. Q. Flayyih, J. Waleed, and A. M. Ibrahim, “Indoor Air Quality Prediction in Sick Building Using Machine and Deep Learning: Comparative Analysis,” Diyala Journal of Engineering Sciences, p. 203, Mar. 2025, doi: 10.24237/djes.2025.18112.

[6] C. Kanthila, A. Boodi, K. Beddiar, Y. Amirat, and M. Benbouzid, “Building Occupancy Behavior and Prediction Methods: A Critical Review and Challenging Locks,” IEEE Access, vol. 9. Institute of Electrical and Electronics Engineers, p. 79353, Jan. 01, 2021. doi: 10.1109/access.2021.3083534.

[7] A. Carrera-Rivera, W. Ochoa, F. Larrinaga, and G. Lasa, “How-to conduct a systematic literature review: A quick guide for computer science research,” MethodsX, vol. 9, p. 101895, Jan. 2022, doi: 10.1016/j.mex.2022.101895.

[8] V. King, A. Stevens, B. Nußbaumer-Streit, C. Kamel, and C. Garritty, “Paper 2: Performing rapid reviews,” Systematic Reviews, vol. 11, no. 1. BioMed Central, Jul. 30, 2022. doi: 10.1186/s13643-022-02011-5.

[9] Q. Sun, T. Liu, J. Xu, H. Li, and M. Huang, “Rapid Recognition and Concentration Prediction of Gas Mixtures Based on SMLP,” IEEE Transactions on Instrumentation and Measurement, vol. 73, p. 1, Jan. 2024, doi: 10.1109/tim.2024.3386203.

[10] S. Soleimani-Alyar, M. Soleimani-Alyar, R. Yarahmadi, P. Beyk-Mohammadloo, and P. L. Fazeli, “The study of indoor particulate matter in office buildings based on artificial intelligence,” International Journal of Environmental Science and Technology, Dec. 2024, doi: 10.1007/s13762-024-06277-1.

[11] S. Summa, G. Remia, C. Di Perna, and F. Stazi, “Mechanically ventilated classrooms in central Italy’s heritage school buildings: Proposal of archetypes and CO2 prediction models.” 2025.

[12] “MH-Z19C Datasheet.” Aug. 2023. Accessed: Jun. 26, 2025. [Online]. Available: https://www.alldatasheet.com/datasheet-pdf/pdf/1303687/WINSEN/MH-Z19C.html

[13] M. Ródenas et al., “Review of low-cost sensors for indoor air quality: Features and applications,” Applied Spectroscopy Reviews, vol. 57, p. 747, Jun. 2022, doi: 10.1080/05704928.2022.2085734.

[14] H. Chojer, P. T. B. S. Branco, F. G. Martins, M. C. M. Alvim-Ferraz, and S. Sousa, “Development of low-cost indoor air quality monitoring devices: Recent advancements,” The Science of The Total Environment, vol. 727. Elsevier BV, p. 138385, Apr. 12, 2020. doi: 10.1016/j.scitotenv.2020.138385.

[15] M. Carminati, G. R. Sinha, S. Mohdiwale, and S. L. Ullo, “Miniaturized Pervasive Sensors for Indoor Health Monitoring in Smart Cities,” Smart Cities, vol. 4, no. 1, p. 146, Jan. 2021, doi: 10.3390/smartcities4010008.

[16] X. Liu, Z. Wang, J. Li, A. Cangelosi, and C. Yang, “Demonstration learning and generalization of robotic motor skills based on wearable motion tracking sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 72, p. 1, 2023, doi: 10.1109/TIM.2023.3288240.

[17] A. Tsanousa, C. Moschou, E. Bektsis, S. Vrochidis, and I. Kompatsiaris, “Fusion of Environmental Sensors for Occupancy Detection in a Real Construction Site,” Sensors, vol. 23, no. 23, p. 9596, Dec. 2023, doi: 10.3390/s23239596.

[18] S. H. Godasiaei, O. Ejohwomu, H. Zhong, and D. Booker, “Integrating Experimental Analysis and Machine Learning for Enhancing Energy Efficiency and Indoor Air Quality in Educational Buildings,” Building and Environment, p. 112874, Mar. 2025, doi: 10.1016/j.buildenv.2025.112874.

[19] G. Piras, S. Agostinelli, and F. Muzi, “Smart Buildings and Digital Twin to Monitoring the Efficiency and Wellness of Working Environments: A Case Study on IoT Integration and Data-Driven Management,” Applied Sciences, vol. 15, no. 9, p. 4939, Apr. 2025, doi: 10.3390/app15094939.

[20] S. Oh and S. Song, “Detailed Analysis of Thermal Comfort and Indoor Air Quality Using Real-Time Multiple Environmental Monitoring Data for a Childcare Center,” Energies, vol. 14, no. 3, p. 643, Jan. 2021, doi: 10.3390/en14030643.

[21] B. Ravelo, M. Guerin, and W. Rahajandraibe, “Negative Group Delay Predictor Application for CO2 Gas Concentration Real-Time Forecasting.” Feb. 01, 2024.

[22] H. S. Alavi, S. Zhong, and D. Lalanne, “Indoor Air Quality Forecast in Shared Spaces– Predictive Models and Adaptive Design Proposals,” DOAJ (DOAJ: Directory of Open Access Journals), May 2022, Accessed: Mar. 2025. [Online]. Available: https://doaj.org/article/850024a37d6e4e2991494d317b173d25

[23] G. Cretu, I. Stamatescu, and G. Stâmâtescu, “Modeling and Prediction of Occupancy in Buildings Based on Sensor Data Using Deep Learning Methods,” IEEE Access, vol. 12, p. 102994, Jan. 2024, doi: 10.1109/access.2024.3432584.

[24] J.-S. Shieh, “Development of a Human-Centric Autonomous Heating, Ventilation, and Air Conditioning Control System Enhanced for Industry 5.0 Chemical Fiber Manufacturing.” May 2025. Accessed: Jul. 08, 2025. [Online]. Available: https://www.mdpi.com/2075-1702/13/5/421

[25] S. Seseña, A. Rodrı́guez, and M. L. Palop, “Indoor air quality analysis in naturally ventilated university training laboratories: a health risk assessment,” Air Quality Atmosphere & Health, vol. 15, no. 10, p. 1817, Jul. 2022, doi: 10.1007/s11869-022-01220-0.

[26] S. Stamp, E. Burman, C. Shrubsole, L. Chatzidiakou, D. Mumovic, and M. Davies, “Long-term, continuous air quality monitoring in a cross-sectional study of three UK non-domestic buildings,” Building and Environment, vol. 180, p. 107071, Jul. 2020, doi: 10.1016/j.buildenv.2020.107071.

[27] J. K. Basak et al., “Application of Machine Learning Models for Predicting Methane Concentrations in Pig Barns Using Biophysical Data,” Water Air & Soil Pollution, vol. 236, no. 5, Apr. 2025, doi: 10.1007/s11270-025-07954-8.

[28] M. B. Trueblood et al., “Calibration of gas flow meters using choked flow and an evacuated vessel,” Measurement Science and Technology, vol. 32, no. 10, p. 105105, May 2021, doi: 10.1088/1361-6501/ac055d.

[29] Y. Sun, Y. Hu, H. Zhang, F. Wang, and H. Chen, “A Parallel Supervision System for Vehicle CO2 Emissions Based on OBD-Independent Information.” Mar. 2023.

[30] V. J. L. Gan, H. Luo, Y. Tan, M. Deng, and H. L. Kwok, “BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment,” Sensors, vol. 21, no. 13, p. 4401, Jun. 2021, doi: 10.3390/s21134401.

[31] M. K. Diarra, A. Maniar, J.-B. Masson, B. Marhic, and L. Delahoche, “Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context,” Sensors, vol. 23, no. 23, p. 9603, Dec. 2023, doi: 10.3390/s23239603.

[32] W. Zhang, J. K. Calautit, P. W. Tien, Y. Wu, and S. Wei, “Deep Learning Models for Vision-Based Occupancy Detection in High Occupancy Buildings,” Journal of Building Engineering, p. 111355, Nov. 2024, doi: 10.1016/j.jobe.2024.111355.

[33] Y. Zhao, C. Xiong, R. Li, Z. Luo, T. Hussein, and T. Zhao, “Research on an Adaptive Prediction Method for Restaurant Air Quality Based on Occupancy Detection,” Jan. 2024, doi: 10.2139/ssrn.4893706.

[34] D. Sood et al., “Machine learning based multi-objective optimisation of energy consumption, thermal comfort and CO2 concentration in energy-efficient naturally ventilated residential dwellings,” Building and Environment, vol. 267, p. 112255, Nov. 2024, doi: 10.1016/j.buildenv.2024.112255.

[35] F. Almeida, M. Castelli, N. Côrte-Real, and L. Manzoni, “Optimizing Space Heating in Buildings: A Deep Learning Approach for Energy Efficiency,” Energies, vol. 18, no. 10, p. 2471, May 2025, doi: 10.3390/en18102471.

[36] M.-H. Benzaama, K. Touati, Y. E. Mendili, M. L. Guern, F. Streiff, and S. Goodhew, “Machine Learning-Based Indoor Relative Humidity and CO2 Identification Using a Piecewise Autoregressive Exogenous Model: A Cob Prototype Study,” Energies, vol. 17, no. 1, p. 243, Jan. 2024, doi: 10.3390/en17010243.

[37] Y. C. Hu and K. Lv, “Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine,” IEEE Access, vol. 8, p. 20616, Jan. 2020, doi: 10.1109/access.2020.2968585.

[38] E. X. Chen, X. Han, A. Malkawi, R. Zhang, and N. Li, “Adaptive model predictive control with ensembled multi-time scale deep-learning models for smart control of natural ventilation,” Building and Environment, vol. 242, p. 110519, Jun. 2023, doi: 10.1016/j.buildenv.2023.110519.

[39] Y. Wei, J. Jang‐Jaccard, W. Xu, F. Sabrina, S. Camtepe, and M. Boulic, “LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series Data,” arXiv (Cornell University), Jan. 2022, doi: 10.48550/arXiv.2204.06701.

[40] S. H. Kim and C. S. Park, “Quantification of occupant response to influencing factors of window adjustment behavior using explainable AI,” Energy and Buildings, vol. 296, p. 113349, Jul. 2023, doi: 10.1016/j.enbuild.2023.113349.

[41] W. Su, Z. Ai, J. Liu, B. Yang, and F. Wang, “Maintaining an acceptable indoor air quality of spaces by intentional natural ventilation or intermittent mechanical ventilation with minimum energy use,” Applied Energy, vol. 348, p. 121504, Jul. 2023, doi: 10.1016/j.apenergy.2023.121504.

[42] S. Altomonte et al., “Ten questions concerning well-being in the built environment,” Building and Environment, vol. 180, p. 106949, May 2020, doi: 10.1016/j.buildenv.2020.106949.

[43] H. Ziwei et al., “The applications of internet of things in smart healthcare sectors: a bibliometric and deep study,” Heliyon, vol. 10, no. 3, Feb. 2024, doi: 10.1016/j.heliyon.2024.e25392.

Licencia

Cómo citar

Barco Jiménez, J., & Campaña, S. (2025). Medición de CO₂ para la Mejora de la Calidad del Aire en Espacios Interiores: Una Revisión de Literatura. Documentos De Trabajo ECBTI, 5(1). https://doi.org/10.22490/ECBTI.9981

Descargar cita

Almétricas