Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation

Franco Sotelo Gómez; Paulo Nazareno Maia Sampaio; Laura Margarita Rodríguez Peralta; Fabián Leonardo Cuesta Astudillo; Éldman de Oliveira Nunes

doi:10.1007/978-3-031-93106-2_24

Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation

Franco Sotelo Gómez
, Paulo Nazareno Maia Sampaio
, Laura Margarita Rodríguez Peralta
, Fabián Leonardo Cuesta Astudillo
, Éldman de Oliveira Nunes

Research Group on Finance, Auditing, Accounting and Taxation (GIFACT)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Air pollution is a global challenge that affects both outdoor and indoor environments. This issue is exacerbated in enclosed spaces where everyday activities like cooking or using cleaning products contribute to the accumulation of pollutants, leading to both acute and chronic health effects. Sick Building Syndrome (SBS) is one of the phenomena associated with poor indoor air quality. It is characterized by medical symptoms experienced by building occupants, which disappear once they leave the premises. In this context, the need for advanced predictive models to manage indoor air quality arises. Quantum Machine Learning (QML) offers an innovative solution for accurately predicting indoor air contaminants. By using historical and real-time data, QML can identify complex patterns and predict dangerous pollution levels, enabling proactive interventions. This model focuses on forecasting specific pollutants like CO2, TVOC, PM2.5, and PM10, and assesses its efficiency in determining risk levels. Integrating this model with IoT technologies allows for continuous and accurate monitoring, contributing to the creation of safer and healthier indoor environments.

Original language	English
Title of host publication	Information Technology and Systems, ICITS 2025
Editors	Alvaro Rocha, Carlos Ferrás, Hiram Calvo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	276-286
Number of pages	11
ISBN (Print)	9783031931055
DOIs	https://doi.org/10.1007/978-3-031-93106-2_24
State	Published - 2025
Event	International Conference on Information Technology and Systems, ICITS 2025 - Mexico City, Mexico Duration: 22 Jan 2025 → 25 Jan 2025

Publication series

Name	Lecture Notes in Networks and Systems
Volume	1448 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Information Technology and Systems, ICITS 2025
Country/Territory	Mexico
City	Mexico City
Period	22/01/25 → 25/01/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Big Data
IoT
Machine Learning Quantum Neural Networks
Monitoring
Sick Building Syndrome

Access to Document

10.1007/978-3-031-93106-2_24

Cite this

Gómez, F. S., Sampaio, P. N. M., Peralta, L. M. R., Cuesta Astudillo, F. L., & de Oliveira Nunes, É. (2025). Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation. In A. Rocha, C. Ferrás, & H. Calvo (Eds.), Information Technology and Systems, ICITS 2025 (pp. 276-286). (Lecture Notes in Networks and Systems; Vol. 1448 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-93106-2_24

Gómez, Franco Sotelo ; Sampaio, Paulo Nazareno Maia ; Peralta, Laura Margarita Rodríguez et al. / Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation. Information Technology and Systems, ICITS 2025. editor / Alvaro Rocha ; Carlos Ferrás ; Hiram Calvo. Springer Science and Business Media Deutschland GmbH, 2025. pp. 276-286 (Lecture Notes in Networks and Systems).

@inproceedings{697b9f84bfca4658a9418b24b755480e,

title = "Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation",

abstract = "Air pollution is a global challenge that affects both outdoor and indoor environments. This issue is exacerbated in enclosed spaces where everyday activities like cooking or using cleaning products contribute to the accumulation of pollutants, leading to both acute and chronic health effects. Sick Building Syndrome (SBS) is one of the phenomena associated with poor indoor air quality. It is characterized by medical symptoms experienced by building occupants, which disappear once they leave the premises. In this context, the need for advanced predictive models to manage indoor air quality arises. Quantum Machine Learning (QML) offers an innovative solution for accurately predicting indoor air contaminants. By using historical and real-time data, QML can identify complex patterns and predict dangerous pollution levels, enabling proactive interventions. This model focuses on forecasting specific pollutants like CO2, TVOC, PM2.5, and PM10, and assesses its efficiency in determining risk levels. Integrating this model with IoT technologies allows for continuous and accurate monitoring, contributing to the creation of safer and healthier indoor environments.",

keywords = "Big Data, IoT, Machine Learning Quantum Neural Networks, Monitoring, Sick Building Syndrome",

author = "G{\'o}mez, \{Franco Sotelo\} and Sampaio, \{Paulo Nazareno Maia\} and Peralta, \{Laura Margarita Rodr{\'i}guez\} and \{Cuesta Astudillo\}, \{Fabi{\'a}n Leonardo\} and \{de Oliveira Nunes\}, {\'E}ldman",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; International Conference on Information Technology and Systems, ICITS 2025 ; Conference date: 22-01-2025 Through 25-01-2025",

year = "2025",

doi = "10.1007/978-3-031-93106-2\_24",

language = "English",

isbn = "9783031931055",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "276--286",

editor = "Alvaro Rocha and Carlos Ferr{\'a}s and Hiram Calvo",

booktitle = "Information Technology and Systems, ICITS 2025",

address = "Germany",

}

Gómez, FS, Sampaio, PNM, Peralta, LMR, Cuesta Astudillo, FL & de Oliveira Nunes, É 2025, Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation. in A Rocha, C Ferrás & H Calvo (eds), Information Technology and Systems, ICITS 2025. Lecture Notes in Networks and Systems, vol. 1448 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 276-286, International Conference on Information Technology and Systems, ICITS 2025, Mexico City, Mexico, 22/01/25. https://doi.org/10.1007/978-3-031-93106-2_24

Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation. / Gómez, Franco Sotelo; Sampaio, Paulo Nazareno Maia; Peralta, Laura Margarita Rodríguez et al.
Information Technology and Systems, ICITS 2025. ed. / Alvaro Rocha; Carlos Ferrás; Hiram Calvo. Springer Science and Business Media Deutschland GmbH, 2025. p. 276-286 (Lecture Notes in Networks and Systems; Vol. 1448 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation

AU - Gómez, Franco Sotelo

AU - Sampaio, Paulo Nazareno Maia

AU - Peralta, Laura Margarita Rodríguez

AU - Cuesta Astudillo, Fabián Leonardo

AU - de Oliveira Nunes, Éldman

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

Y1 - 2025

N2 - Air pollution is a global challenge that affects both outdoor and indoor environments. This issue is exacerbated in enclosed spaces where everyday activities like cooking or using cleaning products contribute to the accumulation of pollutants, leading to both acute and chronic health effects. Sick Building Syndrome (SBS) is one of the phenomena associated with poor indoor air quality. It is characterized by medical symptoms experienced by building occupants, which disappear once they leave the premises. In this context, the need for advanced predictive models to manage indoor air quality arises. Quantum Machine Learning (QML) offers an innovative solution for accurately predicting indoor air contaminants. By using historical and real-time data, QML can identify complex patterns and predict dangerous pollution levels, enabling proactive interventions. This model focuses on forecasting specific pollutants like CO2, TVOC, PM2.5, and PM10, and assesses its efficiency in determining risk levels. Integrating this model with IoT technologies allows for continuous and accurate monitoring, contributing to the creation of safer and healthier indoor environments.

AB - Air pollution is a global challenge that affects both outdoor and indoor environments. This issue is exacerbated in enclosed spaces where everyday activities like cooking or using cleaning products contribute to the accumulation of pollutants, leading to both acute and chronic health effects. Sick Building Syndrome (SBS) is one of the phenomena associated with poor indoor air quality. It is characterized by medical symptoms experienced by building occupants, which disappear once they leave the premises. In this context, the need for advanced predictive models to manage indoor air quality arises. Quantum Machine Learning (QML) offers an innovative solution for accurately predicting indoor air contaminants. By using historical and real-time data, QML can identify complex patterns and predict dangerous pollution levels, enabling proactive interventions. This model focuses on forecasting specific pollutants like CO2, TVOC, PM2.5, and PM10, and assesses its efficiency in determining risk levels. Integrating this model with IoT technologies allows for continuous and accurate monitoring, contributing to the creation of safer and healthier indoor environments.

KW - Big Data

KW - IoT

KW - Machine Learning Quantum Neural Networks

KW - Monitoring

KW - Sick Building Syndrome

UR - https://www.scopus.com/pages/publications/105012823987

U2 - 10.1007/978-3-031-93106-2_24

DO - 10.1007/978-3-031-93106-2_24

M3 - Conference contribution

AN - SCOPUS:105012823987

SN - 9783031931055

T3 - Lecture Notes in Networks and Systems

SP - 276

EP - 286

BT - Information Technology and Systems, ICITS 2025

A2 - Rocha, Alvaro

A2 - Ferrás, Carlos

A2 - Calvo, Hiram

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Information Technology and Systems, ICITS 2025

Y2 - 22 January 2025 through 25 January 2025

ER -

Gómez FS, Sampaio PNM, Peralta LMR, Cuesta Astudillo FL, de Oliveira Nunes É. Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation. In Rocha A, Ferrás C, Calvo H, editors, Information Technology and Systems, ICITS 2025. Springer Science and Business Media Deutschland GmbH. 2025. p. 276-286. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-93106-2_24

Leveraging Quantum Machine Learning for Accurate Indoor Air Quality Forecasting and Risk Mitigation

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this