Monitoring urban air pollution using low-cost sensor devices

Ilustraciones, gráficas, tablas

Autores:: Rios Martinez, Jenny Rocio

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Monitoring urban air pollution using low-cost sensor devices
dc.title.translated.spa.fl_str_mv	Monitoreo de la contaminación del aire urbano utilizando dispositivos sensores de bajo costo
title	Monitoring urban air pollution using low-cost sensor devices
spellingShingle	Monitoring urban air pollution using low-cost sensor devices 000 - Ciencias de la computación, información y obras generales::003 - Sistemas Contaminación del aire Minería de datos Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining Sensores
title_short	Monitoring urban air pollution using low-cost sensor devices
title_full	Monitoring urban air pollution using low-cost sensor devices
title_fullStr	Monitoring urban air pollution using low-cost sensor devices
title_full_unstemmed	Monitoring urban air pollution using low-cost sensor devices
title_sort	Monitoring urban air pollution using low-cost sensor devices
dc.creator.fl_str_mv	Rios Martinez, Jenny Rocio
dc.contributor.advisor.none.fl_str_mv	Yris, Olaya Morales
dc.contributor.author.none.fl_str_mv	Rios Martinez, Jenny Rocio
dc.contributor.researchgroup.spa.fl_str_mv	Ciencias de la Decision
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::003 - Sistemas
topic	000 - Ciencias de la computación, información y obras generales::003 - Sistemas Contaminación del aire Minería de datos Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining Sensores
dc.subject.lemb.none.fl_str_mv	Contaminación del aire Minería de datos
dc.subject.proposal.none.fl_str_mv	Contaminación de aire Aprendizaje de máquinas Sensores de bajo costo Calibración de sensores
dc.subject.proposal.eng.fl_str_mv	Air pollution Particulate matter Low-cost sensors Sensor calibration Machine learning Data mining
dc.subject.wikidata.none.fl_str_mv	Sensores
description	Ilustraciones, gráficas, tablas
publishDate	2023
dc.date.issued.none.fl_str_mv	2023-02-13
dc.date.accessioned.none.fl_str_mv	2024-02-13T18:22:10Z
dc.date.available.none.fl_str_mv	2024-02-13T18:22:10Z
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/85696
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/85696 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	LaReferencia
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Doctorado en Ingeniería - Sistemas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Yris, Olaya Morales110f54339555420e316d32df97ad67afRios Martinez, Jenny Rocio6d0f23048a80bbfd3898a7a2f9cfc437Ciencias de la Decision2024-02-13T18:22:10Z2024-02-13T18:22:10Z2023-02-13https://repositorio.unal.edu.co/handle/unal/85696Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Ilustraciones, gráficas, tablasThe study of urban air pollution holds paramount significance within the realms of environmental science and public health. Urban areas are epicenters of diverse human activities, industrial operations, and vehicular traffic, collectively contributing to elevated concentrations of air pollutants. As the use of LCS devices becomes more prevalent in citizen science initiatives, educational purposes, rise of information and awareness, it is crucial to establish their performance characteristics and evaluation metrics for air pollution monitoring. This thesis focuses on evaluating the performance of Low-cost sensors (LCS) in the monitoring of PM2.5 concentrations in outdoor urban environments in Colombia using data mining and machine learning models. The results show that the polynomial regression and Artificial Neural Networks models present a better enhancing in the accuracy and precision of the measurements of the different models of LCS used in this study compared with simple linear regression and other machine learning models. Lastly, the project endeavors to demonstrate the applicability of LCS devices for monitoring PM2.5 concentration in various transportation modes within the city of Medellin. This research contributes to the broader understanding of LCS devices' potential in enhancing air quality monitoring and their suitability for citizen-driven initiatives in regions lacking regulatory-grade instruments.El estudio de la contaminación del aire urbano tiene una importancia fundamental en los ámbitos de la ciencia ambiental y la salud pública. Las áreas urbanas son epicentros de diversas actividades humanas, operaciones industriales y tráfico vehicular, contribuyendo colectivamente a concentraciones elevadas de contaminantes atmosféricos. A medida que el uso de dispositivos LCS se vuelve más frecuente en iniciativas de ciencia ciudadana, con fines educativos, aumento de información y conciencia, es crucial establecer sus características de rendimiento y métricas de evaluación para el monitoreo de la contaminación del aire. Esta tesis se centra en evaluar el rendimiento de los sensores de bajo costo (LCS) en el monitoreo de las concentraciones de PM2.5 en entornos urbanos al aire libre en Colombia mediante la minería de datos y modelos de aprendizaje automático. Los resultados muestran que los modelos de regresión polinómica y redes neuronales artificiales mejoran la precisión y la exactitud de las mediciones de los diferentes modelos de LCS utilizados en este estudio en comparación con la regresión lineal simple y otros modelos de aprendizaje de máquinas. Por último, el proyecto pretende demostrar la aplicabilidad de los dispositivos LCS para monitorear la concentración de PM2.5 en diversos modos de transporte dentro de la ciudad de Medellín. Esta investigación contribuye a una comprensión más amplia del potencial de los dispositivos LCS para mejorar el monitoreo de la calidad del aire y su idoneidad para iniciativas impulsadas por ciudadanos en regiones que carecen de instrumentos de calidad regulatoria. (text tomado de la fuente)Colciencias Convocatoria 727 doctorados nacionalesDoctoradoDoctor en IngenieríaInvestigación de operacionesÁrea Curricular de Ingeniería de Sistemas e Informática196 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Minas - Doctorado en Ingeniería - SistemasFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::003 - SistemasContaminación del aireMinería de datosContaminación de aireAprendizaje de máquinasSensores de bajo costoCalibración de sensoresAir pollutionParticulate matterLow-cost sensorsSensor calibrationMachine learningData miningSensoresMonitoring urban air pollution using low-cost sensor devicesMonitoreo de la contaminación del aire urbano utilizando dispositivos sensores de bajo costoTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDLaReferenciaAgarwal, A., Kaushik, A., Kumar, S., & Mishra, R. 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Sensors and Actuators B: Chemical, 215, 249–257. https://doi.org/10.1016/j.snb.2015.03.031ColcienciasEstudiantesLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85696/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL63560781.2023.pdf.pdf63560781.2023.pdf.pdfTesis de Doctorado en Ingenieria de Sistemasapplication/pdf5319819https://repositorio.unal.edu.co/bitstream/unal/85696/2/63560781.2023.pdf.pdf21cece1afd7f7d1ac48d7ceb59d68b21MD52THUMBNAIL63560781.2023.pdf.pdf.jpg63560781.2023.pdf.pdf.jpgGenerated Thumbnailimage/jpeg4296https://repositorio.unal.edu.co/bitstream/unal/85696/3/63560781.2023.pdf.pdf.jpge129e58763a5b9af81d9a584cf31692aMD53unal/85696oai:repositorio.unal.edu.co:unal/856962024-02-13 23:03:35.72Repositorio Institucional Universidad Nacional de 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Monitoring urban air pollution using low-cost sensor devices

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