Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning

The scientific community has recently shown a rising interest in figuring out how atmospheric pollution affects the behavior of all-encompassing quality-of-life measures. The purpose is to show that bad air quality can have an impact on economic and educational factors in addition to people's h...

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Autores:: Paul Sanmartin Mendoza
Margarita Castillo Ramirez
Ste`phanie Galvis Castro
Vera Santiago Martinez
Parody Muñoz, Alexander Elias

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Tecnológica de Bolívar

Repositorio:: Repositorio Institucional UTB

Idioma:: eng

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dc.title.spa.fl_str_mv	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
dc.title.translated.spa.fl_str_mv	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
spellingShingle	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title_short	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title_full	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title_fullStr	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title_full_unstemmed	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
title_sort	Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning
dc.creator.fl_str_mv	Paul Sanmartin Mendoza Margarita Castillo Ramirez Ste`phanie Galvis Castro Vera Santiago Martinez Parody Muñoz, Alexander Elias
dc.contributor.author.eng.fl_str_mv	Paul Sanmartin Mendoza Margarita Castillo Ramirez Ste`phanie Galvis Castro Vera Santiago Martinez Parody Muñoz, Alexander Elias
description	The scientific community has recently shown a rising interest in figuring out how atmospheric pollution affects the behavior of all-encompassing quality-of-life measures. The purpose is to show that bad air quality can have an impact on economic and educational factors in addition to people's health. This research seeks to establish the statistical association between the variables: population, demographic density, percentage of population at risk of poverty and the Human Development Index (HDI) of the United Nations, with the behavior of the average annual concentration of the pollutant PM2.5 in the USA. To achieve this multivariate regression models such as the generalized linear model and the logistic regression model were generated, in addition to generating a Bayesian classifier of neural networks to measure the predictive ability of the variables under study. As a result, the study was able to show that there is a connection between the Human Development Index, population size, and the proportion of the population at risk of poverty, as well as be-tween the average concentration of the pollutant PM2.5 and the likelihood that it will exceed the World Health Organization's upper limit. The major finding of the study is that poorer quality of life is related to higher levels of PM2.5 pollution concentration in the atmosphere. This is demonstrated by the pollutant's inverse link to both the percentage of the population at risk of poverty and the Human Development Index.
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-02-06 00:00:00 2025-08-16T14:15:13Z
dc.date.available.none.fl_str_mv	2025-02-06 00:00:00
dc.date.issued.none.fl_str_mv	2025-02-06
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.uri.none.fl_str_mv	https://hdl.handle.net/20.500.12585/14165
dc.identifier.url.none.fl_str_mv	https://doi.org/10.32397/tesea.vol6.n1.627
dc.identifier.doi.none.fl_str_mv	10.32397/tesea.vol6.n1.627
dc.identifier.eissn.none.fl_str_mv	2745-0120
url	https://hdl.handle.net/20.500.12585/14165 https://doi.org/10.32397/tesea.vol6.n1.627
identifier_str_mv	10.32397/tesea.vol6.n1.627 2745-0120
dc.language.iso.eng.fl_str_mv	eng
language	eng
dc.relation.references.eng.fl_str_mv	H. Briceño Scremin and G. Díaz Clavo. Relationship between the increase in Carbon Dioxide (CO2) emissions and the growth of GDP per capita in the US and Brazil (1960-2014) based on the Environmental Kuznets Curve. Universidad Católica Andrés Bello, 2019. [2] D. Alandete. Pollution in the united states reaches its maximum in eight years. ABC, 2019. [last accessed: 10 diciembre 2021]. [3] U.S. Environmental Protection Agency. Integrated Science Assessment (ISA) for Particulate Matter (Final Report). Technical report, U.S. EPA, Washington, DC, 2019. [4] S. LaMotte. Air quality in the US is dramatically worse than in previous years, says new "State of the Air" report. CNN Español, 2020. [last accessed: 10 diciembre 2021]. [5] Health Effects Institute. State of Global Air 2020: A Special Report On Global Exposure To Air Pollution And Its Health Impacts. Technical report, Health Effects Institute, Boston, 2020. [6] American Lung Association. Health impact of air pollution. American Lung Association, 2021. [last accessed: 03 octubre 2021]. [7] American Lung Association. State of the Air 2021. Technical report, American Lung Association, 2021. [8] IQAir. Air quality index (AQI) and PM2.5 air pollution in the United States. Technical report, IQAir, Goldach, 2021. [9] R. Ramirez. Millions of deaths could be prevented by meeting new air quality guidelines, says WHO, 2021. [last accessed: 10 diciembre 2021]. [10] D. Méndez. 10 reasons why China is so polluted, 2014. [last accessed: 11 diciembre 2021]. [11] Xiaolan Li, Yanjun Ma, Yangfeng Wang, Ningwei Liu, and Ye Hong. Temporal and spatial analyses of particulate matter (PM 10 and PM 2.5 ) and its relationship with meteorological parameters over an urban city in northeast China. Atmospheric Research, 198:185–193, December 2017. [12] Xiaoyu Qi, Gang Mei, Salvatore Cuomo, Chun Liu, and Nengxiong Xu. Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing. Internet of Things, 14:100127, June 2021. [13] Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, and Meryem Ay¸se Yücel. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Frontiers in Human Neuroscience, 14, February 2020. [14] Ping Wang, Junji Cao, Xuexi Tie, Gehui Wang, Guohui Li, Tafeng Hu, Yaoting Wu, Yunsheng Xu, Gongdi Xu, Youzhi Zhao, Wenci Ding, Huikun Liu, Rujin Huang, and Changlin Zhan. Impact of Meteorological Parameters and Gaseous Pollutants on PM2.5 and PM10 Mass Concentrations during 2010 in Xi’an, China. Aerosol and Air Quality Research, 15(5):1844–1854, 2015. [15] Abhik Das. Logistic Regression, page 1–2. Springer International Publishing, 2021. [16] E. J. D. L. H. Granadillo, J. M. Gómez, and L. L. Polo. Evaluación de la competitividad comercial del sector industrial colombiano mediante el coeficiente de apertura exportadora. Aglala, 10(1):180–195, 2019. [17] P. Chiarvetto, L. Lourdes, F. Saravia, and N. Brignole. Aplicación de redes neuronales artificiales para la predicción de calidad de aire. 2008. [18] Jessie María Ramírez Suárez. Modelo de predicción de la calidad del aire a partir de datos meteorológicos e información del tráfico automovilístico, 2018. [19] Jackson G Lu. Air pollution: A systematic review of its psychological, economic, and social effects. Current Opinion in Psychology, 32:52–65, April 2020. [20] A. Pandey, M. Brauer, M. L. Cropper, K. Balakrishnan, P. Mathur, S. Dey, and L. Dandona. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. The Lancet Planetary Health, 5(1):e25–e38, 2021. [21] Andrew Adewale Alola, Edmund Ntom Udemba, Chikaodinaka Iwuagwu, and Ibrahim Abdallah. Assessing the human development aspects of CO, PM2.5, PM10, NOX, and SO2 in the United States. Heliyon, 9(7):e18072, July 2023. [22] Halil Altınta¸s and Yacouba Kassouri. Is the environmental Kuznets Curve in Europe related to the per-capita ecological footprint or CO2 emissions? Ecological Indicators, 113:106187, June 2020. [23] Muhammad Awais Baloch, Danish, Salah Ud-Din Khan, Zübeyde ¸Sentürk Ulucak, and Ashfaq Ahmad. Analyzing the relationship between poverty, income inequality, and CO2 emission in Sub-Saharan African countries. Science of The Total Environment, 740:139867, October 2020. [24] Kizito Uyi Ehigiamusoe, Muhammad Tariq Majeed, and Eyup Dogan. The nexus between poverty, inequality and environmental pollution: Evidence across different income groups of countries. Journal of Cleaner Production, 341:130863, March 2022. [25] Salim Khan and Wang Yahong. Income inequality, ecological footprint, and carbon dioxide emissions in Asian developing economies: what effects what and how? Environmental Science and Pollution Research, 29(17):24660–24671, November 2021. [26] Atif Awad and M. Hersi Warsame. The poverty-environment nexus in developing countries: Evidence from heterogeneous panel causality methods, robust to cross-sectional dependence. Journal of Cleaner Production, 331:129839, January 2022.
dc.relation.ispartofjournal.eng.fl_str_mv	Transactions on Energy Systems and Engineering Applications
dc.relation.citationvolume.eng.fl_str_mv	6
dc.relation.citationstartpage.none.fl_str_mv	1
dc.relation.citationendpage.none.fl_str_mv	13
dc.relation.bitstream.none.fl_str_mv	https://revistas.utb.edu.co/tesea/article/download/627/450
dc.relation.citationedition.eng.fl_str_mv	Núm. 1 , Año 2025 : Transactions on Energy Systems and Engineering Applications
dc.relation.citationissue.eng.fl_str_mv	1
dc.rights.eng.fl_str_mv	Paul Sanmartin, Margarita Castillo, Stephanie Galvis, Vera Santiago, Alexander Parody - 2025
dc.rights.uri.eng.fl_str_mv	https://creativecommons.org/licenses/by/4.0
dc.rights.accessrights.eng.fl_str_mv	info:eu-repo/semantics/openAccess
dc.rights.creativecommons.eng.fl_str_mv	This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.rights.coar.eng.fl_str_mv	http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv	Paul Sanmartin, Margarita Castillo, Stephanie Galvis, Vera Santiago, Alexander Parody - 2025 https://creativecommons.org/licenses/by/4.0 This work is licensed under a Creative Commons Attribution 4.0 International License. http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.mimetype.eng.fl_str_mv	application/pdf
dc.publisher.eng.fl_str_mv	Universidad Tecnológica de Bolívar
dc.source.eng.fl_str_mv	https://revistas.utb.edu.co/tesea/article/view/627
institution	Universidad Tecnológica de Bolívar
repository.name.fl_str_mv	Repositorio Digital Universidad Tecnológica de Bolívar
repository.mail.fl_str_mv	bdigital@metabiblioteca.com
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spelling	Paul Sanmartin MendozaMargarita Castillo RamirezSte`phanie Galvis CastroVera Santiago MartinezParody Muñoz, Alexander Elias2025-02-06 00:00:002025-08-16T14:15:13Z2025-02-06 00:00:002025-02-06https://hdl.handle.net/20.500.12585/14165https://doi.org/10.32397/tesea.vol6.n1.62710.32397/tesea.vol6.n1.6272745-0120The scientific community has recently shown a rising interest in figuring out how atmospheric pollution affects the behavior of all-encompassing quality-of-life measures. The purpose is to show that bad air quality can have an impact on economic and educational factors in addition to people's health. This research seeks to establish the statistical association between the variables: population, demographic density, percentage of population at risk of poverty and the Human Development Index (HDI) of the United Nations, with the behavior of the average annual concentration of the pollutant PM2.5 in the USA. To achieve this multivariate regression models such as the generalized linear model and the logistic regression model were generated, in addition to generating a Bayesian classifier of neural networks to measure the predictive ability of the variables under study. As a result, the study was able to show that there is a connection between the Human Development Index, population size, and the proportion of the population at risk of poverty, as well as be-tween the average concentration of the pollutant PM2.5 and the likelihood that it will exceed the World Health Organization's upper limit. The major finding of the study is that poorer quality of life is related to higher levels of PM2.5 pollution concentration in the atmosphere. This is demonstrated by the pollutant's inverse link to both the percentage of the population at risk of poverty and the Human Development Index.application/pdfengUniversidad Tecnológica de BolívarPaul Sanmartin, Margarita Castillo, Stephanie Galvis, Vera Santiago, Alexander Parody - 2025https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/627Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learningRelationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learningArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85H. Briceño Scremin and G. Díaz Clavo. Relationship between the increase in Carbon Dioxide (CO2) emissions and the growth of GDP per capita in the US and Brazil (1960-2014) based on the Environmental Kuznets Curve. Universidad Católica Andrés Bello, 2019. [2] D. Alandete. Pollution in the united states reaches its maximum in eight years. ABC, 2019. [last accessed: 10 diciembre 2021]. [3] U.S. Environmental Protection Agency. Integrated Science Assessment (ISA) for Particulate Matter (Final Report). Technical report, U.S. EPA, Washington, DC, 2019. [4] S. LaMotte. Air quality in the US is dramatically worse than in previous years, says new "State of the Air" report. CNN Español, 2020. [last accessed: 10 diciembre 2021]. [5] Health Effects Institute. State of Global Air 2020: A Special Report On Global Exposure To Air Pollution And Its Health Impacts. Technical report, Health Effects Institute, Boston, 2020. [6] American Lung Association. Health impact of air pollution. American Lung Association, 2021. [last accessed: 03 octubre 2021]. [7] American Lung Association. State of the Air 2021. Technical report, American Lung Association, 2021. [8] IQAir. Air quality index (AQI) and PM2.5 air pollution in the United States. Technical report, IQAir, Goldach, 2021. [9] R. Ramirez. Millions of deaths could be prevented by meeting new air quality guidelines, says WHO, 2021. [last accessed: 10 diciembre 2021]. [10] D. Méndez. 10 reasons why China is so polluted, 2014. [last accessed: 11 diciembre 2021]. [11] Xiaolan Li, Yanjun Ma, Yangfeng Wang, Ningwei Liu, and Ye Hong. Temporal and spatial analyses of particulate matter (PM 10 and PM 2.5 ) and its relationship with meteorological parameters over an urban city in northeast China. Atmospheric Research, 198:185–193, December 2017. [12] Xiaoyu Qi, Gang Mei, Salvatore Cuomo, Chun Liu, and Nengxiong Xu. Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing. Internet of Things, 14:100127, June 2021. [13] Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, and Meryem Ay¸se Yücel. Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective. Frontiers in Human Neuroscience, 14, February 2020. [14] Ping Wang, Junji Cao, Xuexi Tie, Gehui Wang, Guohui Li, Tafeng Hu, Yaoting Wu, Yunsheng Xu, Gongdi Xu, Youzhi Zhao, Wenci Ding, Huikun Liu, Rujin Huang, and Changlin Zhan. Impact of Meteorological Parameters and Gaseous Pollutants on PM2.5 and PM10 Mass Concentrations during 2010 in Xi’an, China. Aerosol and Air Quality Research, 15(5):1844–1854, 2015. [15] Abhik Das. Logistic Regression, page 1–2. Springer International Publishing, 2021. [16] E. J. D. L. H. Granadillo, J. M. Gómez, and L. L. Polo. Evaluación de la competitividad comercial del sector industrial colombiano mediante el coeficiente de apertura exportadora. Aglala, 10(1):180–195, 2019. [17] P. Chiarvetto, L. Lourdes, F. Saravia, and N. Brignole. Aplicación de redes neuronales artificiales para la predicción de calidad de aire. 2008. [18] Jessie María Ramírez Suárez. Modelo de predicción de la calidad del aire a partir de datos meteorológicos e información del tráfico automovilístico, 2018. [19] Jackson G Lu. Air pollution: A systematic review of its psychological, economic, and social effects. Current Opinion in Psychology, 32:52–65, April 2020. [20] A. Pandey, M. Brauer, M. L. Cropper, K. Balakrishnan, P. Mathur, S. Dey, and L. Dandona. Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. The Lancet Planetary Health, 5(1):e25–e38, 2021. [21] Andrew Adewale Alola, Edmund Ntom Udemba, Chikaodinaka Iwuagwu, and Ibrahim Abdallah. Assessing the human development aspects of CO, PM2.5, PM10, NOX, and SO2 in the United States. Heliyon, 9(7):e18072, July 2023. [22] Halil Altınta¸s and Yacouba Kassouri. Is the environmental Kuznets Curve in Europe related to the per-capita ecological footprint or CO2 emissions? Ecological Indicators, 113:106187, June 2020. [23] Muhammad Awais Baloch, Danish, Salah Ud-Din Khan, Zübeyde ¸Sentürk Ulucak, and Ashfaq Ahmad. Analyzing the relationship between poverty, income inequality, and CO2 emission in Sub-Saharan African countries. Science of The Total Environment, 740:139867, October 2020. [24] Kizito Uyi Ehigiamusoe, Muhammad Tariq Majeed, and Eyup Dogan. The nexus between poverty, inequality and environmental pollution: Evidence across different income groups of countries. Journal of Cleaner Production, 341:130863, March 2022. [25] Salim Khan and Wang Yahong. Income inequality, ecological footprint, and carbon dioxide emissions in Asian developing economies: what effects what and how? Environmental Science and Pollution Research, 29(17):24660–24671, November 2021. [26] Atif Awad and M. Hersi Warsame. The poverty-environment nexus in developing countries: Evidence from heterogeneous panel causality methods, robust to cross-sectional dependence. Journal of Cleaner Production, 331:129839, January 2022.Transactions on Energy Systems and Engineering Applications6113https://revistas.utb.edu.co/tesea/article/download/627/450Núm. 1 , Año 2025 : Transactions on Energy Systems and Engineering Applications120.500.12585/14165oai:repositorio.utb.edu.co:20.500.12585/141652025-08-16 09:15:13.932https://creativecommons.org/licenses/by/4.0Paul Sanmartin, Margarita Castillo, Stephanie Galvis, Vera Santiago, Alexander Parody - 2025metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com

Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning

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