Improving Public Health Policies with Indoor Air Quality Predictive Models

Ariel Isaac Posada Barrera; Laura Margarita Rodriguez Peralta; Eldman De Oliveira Nunes; Paulo Nazareno Maia Sampaio; Fabian Leonardo Cuesta Astudillo

doi:10.1109/CSCI62032.2023.00040

Improving Public Health Policies with Indoor Air Quality Predictive Models

Ariel Isaac Posada Barrera
, Laura Margarita Rodriguez Peralta
, Eldman De Oliveira Nunes
, Paulo Nazareno Maia Sampaio
, Fabian Leonardo Cuesta Astudillo

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

4 Scopus citations

Abstract

Indoor air quality is important for public health. This study was designed to develop predictive models focusing on indoor air quality, specifically targeting levels of CO2, TVOC, PM2.5, and PM10. We implemented and trained Machine Learning Models-Regression Forest Model and Gradient-Boosted Tree Model-using a dataset from the states of Puebla and Morelos in Mexico. The dataset incorporated various environmental variables, including pollutant levels, temperature, relative humidity, population density, and ventilation characteristics, all of which were found to significantly influence the presence of indoor air contaminants. These findings are instrumental in formulating policies to mitigate poor indoor air quality. Moreover, the study suggests that it is feasible to predict when contaminants will reach harmful levels by monitoring changes in these variables.

Original language	English
Title of host publication	Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	221-226
Number of pages	6
ISBN (Electronic)	9798350361513
DOIs	https://doi.org/10.1109/CSCI62032.2023.00040
State	Published - 2023
Externally published	Yes
Event	2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 - Las Vegas, United States Duration: 13 Dec 2023 → 15 Dec 2023

Publication series

Name	Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

Conference

Conference	2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023
Country/Territory	United States
City	Las Vegas
Period	13/12/23 → 15/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Big Data
IoT
Machine learning
Monitoring
Sick buildings syndrome

Access to Document

10.1109/CSCI62032.2023.00040

Cite this

Posada Barrera, A. I., Rodriguez Peralta, L. M., De Oliveira Nunes, E., Maia Sampaio, P. N., & Cuesta Astudillo, F. L. (2023). Improving Public Health Policies with Indoor Air Quality Predictive Models. In Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 (pp. 221-226). (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSCI62032.2023.00040

Posada Barrera, Ariel Isaac ; Rodriguez Peralta, Laura Margarita ; De Oliveira Nunes, Eldman et al. / Improving Public Health Policies with Indoor Air Quality Predictive Models. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 221-226 (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023).

@inproceedings{d66fb51957ba47d48377a5cb4231d04c,

title = "Improving Public Health Policies with Indoor Air Quality Predictive Models",

abstract = "Indoor air quality is important for public health. This study was designed to develop predictive models focusing on indoor air quality, specifically targeting levels of CO2, TVOC, PM2.5, and PM10. We implemented and trained Machine Learning Models-Regression Forest Model and Gradient-Boosted Tree Model-using a dataset from the states of Puebla and Morelos in Mexico. The dataset incorporated various environmental variables, including pollutant levels, temperature, relative humidity, population density, and ventilation characteristics, all of which were found to significantly influence the presence of indoor air contaminants. These findings are instrumental in formulating policies to mitigate poor indoor air quality. Moreover, the study suggests that it is feasible to predict when contaminants will reach harmful levels by monitoring changes in these variables.",

keywords = "Big Data, IoT, Machine learning, Monitoring, Sick buildings syndrome",

author = "\{Posada Barrera\}, \{Ariel Isaac\} and \{Rodriguez Peralta\}, \{Laura Margarita\} and \{De Oliveira Nunes\}, Eldman and \{Maia Sampaio\}, \{Paulo Nazareno\} and \{Cuesta Astudillo\}, \{Fabian Leonardo\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023 ; Conference date: 13-12-2023 Through 15-12-2023",

year = "2023",

doi = "10.1109/CSCI62032.2023.00040",

language = "Ingl{\'e}s",

series = "Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "221--226",

booktitle = "Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023",

}

Posada Barrera, AI, Rodriguez Peralta, LM, De Oliveira Nunes, E, Maia Sampaio, PN & Cuesta Astudillo, FL 2023, Improving Public Health Policies with Indoor Air Quality Predictive Models. in Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 221-226, 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023, Las Vegas, United States, 13/12/23. https://doi.org/10.1109/CSCI62032.2023.00040

Improving Public Health Policies with Indoor Air Quality Predictive Models. / Posada Barrera, Ariel Isaac; Rodriguez Peralta, Laura Margarita; De Oliveira Nunes, Eldman et al.
Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 221-226 (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023).

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

TY - GEN

T1 - Improving Public Health Policies with Indoor Air Quality Predictive Models

AU - Posada Barrera, Ariel Isaac

AU - Rodriguez Peralta, Laura Margarita

AU - De Oliveira Nunes, Eldman

AU - Maia Sampaio, Paulo Nazareno

AU - Cuesta Astudillo, Fabian Leonardo

PY - 2023

Y1 - 2023

N2 - Indoor air quality is important for public health. This study was designed to develop predictive models focusing on indoor air quality, specifically targeting levels of CO2, TVOC, PM2.5, and PM10. We implemented and trained Machine Learning Models-Regression Forest Model and Gradient-Boosted Tree Model-using a dataset from the states of Puebla and Morelos in Mexico. The dataset incorporated various environmental variables, including pollutant levels, temperature, relative humidity, population density, and ventilation characteristics, all of which were found to significantly influence the presence of indoor air contaminants. These findings are instrumental in formulating policies to mitigate poor indoor air quality. Moreover, the study suggests that it is feasible to predict when contaminants will reach harmful levels by monitoring changes in these variables.

AB - Indoor air quality is important for public health. This study was designed to develop predictive models focusing on indoor air quality, specifically targeting levels of CO2, TVOC, PM2.5, and PM10. We implemented and trained Machine Learning Models-Regression Forest Model and Gradient-Boosted Tree Model-using a dataset from the states of Puebla and Morelos in Mexico. The dataset incorporated various environmental variables, including pollutant levels, temperature, relative humidity, population density, and ventilation characteristics, all of which were found to significantly influence the presence of indoor air contaminants. These findings are instrumental in formulating policies to mitigate poor indoor air quality. Moreover, the study suggests that it is feasible to predict when contaminants will reach harmful levels by monitoring changes in these variables.

KW - Big Data

KW - IoT

KW - Machine learning

KW - Monitoring

KW - Sick buildings syndrome

UR - https://www.scopus.com/pages/publications/85199966089

U2 - 10.1109/CSCI62032.2023.00040

DO - 10.1109/CSCI62032.2023.00040

M3 - Articulo (Contribución a conferencia)

AN - SCOPUS:85199966089

T3 - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

SP - 221

EP - 226

BT - Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

Y2 - 13 December 2023 through 15 December 2023

ER -

Posada Barrera AI, Rodriguez Peralta LM, De Oliveira Nunes E, Maia Sampaio PN, Cuesta Astudillo FL. Improving Public Health Policies with Indoor Air Quality Predictive Models. In Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 221-226. (Proceedings - 2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023). doi: 10.1109/CSCI62032.2023.00040

Improving Public Health Policies with Indoor Air Quality Predictive Models

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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