Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges

Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integra...

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Autores:
Tarazona Alvarado, Miguel
Salamanca-Coy, J. L.
Forero-Gutièrrez, K.
Núñez, L. A.
Pisco-Guabave, J.
Escobar-Diaz, Fr.
Sierra Porta, David
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/12703
Acceso en línea:
https://hdl.handle.net/20.500.12585/12703
Palabra clave:
Air quality
Low-cost sensor
Citizen science
Calibration models
LEMB
Rights
openAccess
License
http://creativecommons.org/publicdomain/zero/1.0/
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dc.title.spa.fl_str_mv Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
title Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
spellingShingle Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
Air quality
Low-cost sensor
Citizen science
Calibration models
LEMB
title_short Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
title_full Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
title_fullStr Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
title_full_unstemmed Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
title_sort Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges
dc.creator.fl_str_mv Tarazona Alvarado, Miguel
Salamanca-Coy, J. L.
Forero-Gutièrrez, K.
Núñez, L. A.
Pisco-Guabave, J.
Escobar-Diaz, Fr.
Sierra Porta, David
dc.contributor.author.none.fl_str_mv Tarazona Alvarado, Miguel
Salamanca-Coy, J. L.
Forero-Gutièrrez, K.
Núñez, L. A.
Pisco-Guabave, J.
Escobar-Diaz, Fr.
Sierra Porta, David
dc.subject.keywords.spa.fl_str_mv Air quality
Low-cost sensor
Citizen science
Calibration models
topic Air quality
Low-cost sensor
Citizen science
Calibration models
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-08-05T16:41:14Z
dc.date.available.none.fl_str_mv 2024-08-05T16:41:14Z
dc.date.issued.none.fl_str_mv 2024-06-09
dc.date.submitted.none.fl_str_mv 2024-08-05
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dc.identifier.citation.spa.fl_str_mv Tarazona Alvarado, M., Salamanca-Coy, J. L., Forero-Gutièrrez, K., Núñez, L. A., Pisco-Guabave, J., Escobar-Diaz, Fr., & Sierra-Porta, D. (2024). Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges. International Journal of Remote Sensing, 45(17), 5713–5736. https://doi.org/10.1080/01431161.2024.2373338
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/12703
dc.identifier.doi.none.fl_str_mv 10.1080/01431161.2024.2373338
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Tarazona Alvarado, M., Salamanca-Coy, J. L., Forero-Gutièrrez, K., Núñez, L. A., Pisco-Guabave, J., Escobar-Diaz, Fr., & Sierra-Porta, D. (2024). Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges. International Journal of Remote Sensing, 45(17), 5713–5736. https://doi.org/10.1080/01431161.2024.2373338
10.1080/01431161.2024.2373338
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/12703
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.format.extent.none.fl_str_mv 25 páginas
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dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv International Journal of Remote Sensing Vol. 45, N° 17 (2024)
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spelling Tarazona Alvarado, Miguelef024c8f-0c62-47e6-90e1-9de2e2566b23Salamanca-Coy, J. L.cccf10ce-a9e8-4032-9a30-850b430f86d9Forero-Gutièrrez, K.b172d6d5-8c49-4a0e-8c9f-f8a88cd29d92Núñez, L. A.74772062-25f8-471b-ab30-70d79df9021fPisco-Guabave, J.122bd472-26e2-44fa-a32b-e4d1c6123cd4Escobar-Diaz, Fr.2908ceb6-e09c-45e7-ad6d-e4d00bbd7e01Sierra Porta, David62fe46fe-2160-4eac-8b0c-89e7fd6ce2932024-08-05T16:41:14Z2024-08-05T16:41:14Z2024-06-092024-08-05Tarazona Alvarado, M., Salamanca-Coy, J. L., Forero-Gutièrrez, K., Núñez, L. A., Pisco-Guabave, J., Escobar-Diaz, Fr., & Sierra-Porta, D. (2024). Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges. International Journal of Remote Sensing, 45(17), 5713–5736. https://doi.org/10.1080/01431161.2024.2373338https://hdl.handle.net/20.500.12585/1270310.1080/01431161.2024.2373338Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarAir pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.25 páginasapplication/pdfenghttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccessCC0 1.0 Universalhttp://purl.org/coar/access_right/c_abf2International Journal of Remote Sensing Vol. 45, N° 17 (2024)Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challengesinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Air qualityLow-cost sensorCitizen scienceCalibration modelsLEMBCartagena de IndiasPúblico generalAbdi, H., and L. J. Williams. 2010. “Principal Component Analysis.” Wiley Interdisciplinary Reviews: Computational Statistics 2 (4): 433–459. https://doi.org/10.1002/wics.101 .Abu El-Magd, S., G. Soliman, M. Morsy, and S. Kharbish. 2023. “Environmental Hazard Assessment and Monitoring for Air Pollution Using Machine Learning and Remote Sensing.” International Journal of Environmental Science and Technology 20 (6): 6103–6116. https://doi.org/10.1007/ s13762-022-04367-6 .Adong, P., E. Bainomugisha, D. Okure, R. Sserunjogi, and P. Nachev. 2022. “Generative Model-Enhanced Human Motion Prediction.” Applied AI Letters 3 (2): e76. https://doi.org/10. 1002/ail2.63 .Afshar-Mohajer, N., and C.-Y. Wu. 2023. “Use of a Drone-Based Sensor As a Field-Ready Technique for Short-Term Concentration Mapping of Air Pollutants: A Modeling Study.” Atmospheric Environment 294:119476. https://doi.org/10.1016/j.atmosenv.2022.119476 .Aleixandre, M., and M. 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