Monitoring urban air pollution using low-cost sensor devices
dc.rights.license | Atribución-NoComercial 4.0 Internacional |
dc.contributor.advisor | Yris, Olaya Morales |
dc.contributor.author | Rios Martinez, Jenny Rocio |
dc.date.accessioned | 2024-02-13T18:22:10Z |
dc.date.available | 2024-02-13T18:22:10Z |
dc.date.issued | 2023-02-13 |
dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/85696 |
dc.description | Ilustraciones, gráficas, tablas |
dc.description.abstract | The 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. |
dc.description.abstract | 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) |
dc.description.sponsorship | Colciencias Convocatoria 727 doctorados nacionales |
dc.format.extent | 196 páginas |
dc.format.mimetype | application/pdf |
dc.language.iso | eng |
dc.publisher | Universidad Nacional de Colombia |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ |
dc.subject.ddc | 000 - Ciencias de la computación, información y obras generales::003 - Sistemas |
dc.title | Monitoring urban air pollution using low-cost sensor devices |
dc.type | Trabajo de grado - Doctorado |
dc.type.driver | info:eu-repo/semantics/doctoralThesis |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
dc.publisher.program | Medellín - Minas - Doctorado en Ingeniería - Sistemas |
dc.contributor.researchgroup | Ciencias de la Decision |
dc.description.degreelevel | Doctorado |
dc.description.degreename | Doctor en Ingeniería |
dc.description.researcharea | Investigación de operaciones |
dc.identifier.instname | Universidad Nacional de Colombia |
dc.identifier.reponame | Repositorio Institucional Universidad Nacional de Colombia |
dc.identifier.repourl | https://repositorio.unal.edu.co/ |
dc.publisher.faculty | Facultad de Minas |
dc.publisher.place | Medellín, Colombia |
dc.publisher.branch | Universidad Nacional de Colombia - Sede Medellín |
dc.relation.indexed | LaReferencia |
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dc.rights.accessrights | info:eu-repo/semantics/openAccess |
dc.subject.lemb | Contaminación del aire |
dc.subject.lemb | Minería de datos |
dc.subject.proposal | Contaminación de aire |
dc.subject.proposal | Aprendizaje de máquinas |
dc.subject.proposal | Sensores de bajo costo |
dc.subject.proposal | Calibración de sensores |
dc.subject.proposal | Air pollution |
dc.subject.proposal | Particulate matter |
dc.subject.proposal | Low-cost sensors |
dc.subject.proposal | Sensor calibration |
dc.subject.proposal | Machine learning |
dc.subject.proposal | Data mining |
dc.title.translated | Monitoreo de la contaminación del aire urbano utilizando dispositivos sensores de bajo costo |
dc.type.coar | http://purl.org/coar/resource_type/c_db06 |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa |
dc.type.content | Text |
dc.type.redcol | http://purl.org/redcol/resource_type/TD |
oaire.accessrights | http://purl.org/coar/access_right/c_abf2 |
oaire.fundername | Colciencias |
dcterms.audience.professionaldevelopment | Estudiantes |
dc.description.curriculararea | Área Curricular de Ingeniería de Sistemas e Informática |
dc.subject.wikidata | Sensores |
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