Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia
ABSTRACT: Air pollution is an environmental issue that concerns human health all around the world. The air quality is affected by human emissions, meteorological conditions, and topography. The measurement of pollutants is an important task to make better decisions for controlling high pollution con...
- Autores:
-
Botello Velasquez, Maria Camila
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2021
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/20032
- Acceso en línea:
- http://hdl.handle.net/10495/20032
- Palabra clave:
- Environmental quality
Calidad ambiental
Air pollution
Contaminación atmosférica
Meteorology
Meteorología
Measurement
Medición
Data analysis
Análisis de datos
Air quality
Missing data
Data reconstruction
Graph signal processing
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- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 2.5 Colombia
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Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| title |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| spellingShingle |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia Environmental quality Calidad ambiental Air pollution Contaminación atmosférica Meteorology Meteorología Measurement Medición Data analysis Análisis de datos Air quality Missing data Data reconstruction Graph signal processing http://vocabularies.unesco.org/thesaurus/concept4533 http://vocabularies.unesco.org/thesaurus/concept1946 http://vocabularies.unesco.org/thesaurus/concept185 http://vocabularies.unesco.org/thesaurus/concept5899 http://vocabularies.unesco.org/thesaurus/concept2214 |
| title_short |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| title_full |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| title_fullStr |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| title_full_unstemmed |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| title_sort |
Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, Colombia |
| dc.creator.fl_str_mv |
Botello Velasquez, Maria Camila |
| dc.contributor.advisor.none.fl_str_mv |
Rendón Perez, Ángela María |
| dc.contributor.author.none.fl_str_mv |
Botello Velasquez, Maria Camila |
| dc.subject.unesco.none.fl_str_mv |
Environmental quality Calidad ambiental Air pollution Contaminación atmosférica Meteorology Meteorología Measurement Medición Data analysis Análisis de datos |
| topic |
Environmental quality Calidad ambiental Air pollution Contaminación atmosférica Meteorology Meteorología Measurement Medición Data analysis Análisis de datos Air quality Missing data Data reconstruction Graph signal processing http://vocabularies.unesco.org/thesaurus/concept4533 http://vocabularies.unesco.org/thesaurus/concept1946 http://vocabularies.unesco.org/thesaurus/concept185 http://vocabularies.unesco.org/thesaurus/concept5899 http://vocabularies.unesco.org/thesaurus/concept2214 |
| dc.subject.proposal.spa.fl_str_mv |
Air quality Missing data Data reconstruction Graph signal processing |
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http://vocabularies.unesco.org/thesaurus/concept4533 http://vocabularies.unesco.org/thesaurus/concept1946 http://vocabularies.unesco.org/thesaurus/concept185 http://vocabularies.unesco.org/thesaurus/concept5899 http://vocabularies.unesco.org/thesaurus/concept2214 |
| description |
ABSTRACT: Air pollution is an environmental issue that concerns human health all around the world. The air quality is affected by human emissions, meteorological conditions, and topography. The measurement of pollutants is an important task to make better decisions for controlling high pollution concentrations. However, air quality sensing usually has problems due to machine failures, routine maintenance, among others. As a result, air quality datasets could have missing information that sometimes could represent more than 10% of the data. The correct reconstruction of these missing values plays an essential role in further environmental studies. In this work, we model the reconstruction of missing data as a problem of recovery of graph signals. Therefore, we evaluate the robustness of a graph signal reconstruction method in a dataset of Particular Matter PM2.5 in the Aburrá Valley, Colombia. We observe that 1) the model has better performance during dry months than in wet or transition seasons, and 2) the model could not follow pollution peaks because the algorithm assumes smooth changes in time. This model could be suitable to reconstruct data in the Aburrá Valley in dry seasons for other environmental studies. |
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2021 |
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2021-06-09T19:43:40Z |
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2021-06-09T19:43:40Z |
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2021 |
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http://hdl.handle.net/10495/20032 |
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Medellín, Colombia |
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Rendón Perez, Ángela MaríaBotello Velasquez, Maria Camila2021-06-09T19:43:40Z2021-06-09T19:43:40Z2021http://hdl.handle.net/10495/20032ABSTRACT: Air pollution is an environmental issue that concerns human health all around the world. The air quality is affected by human emissions, meteorological conditions, and topography. The measurement of pollutants is an important task to make better decisions for controlling high pollution concentrations. However, air quality sensing usually has problems due to machine failures, routine maintenance, among others. As a result, air quality datasets could have missing information that sometimes could represent more than 10% of the data. The correct reconstruction of these missing values plays an essential role in further environmental studies. In this work, we model the reconstruction of missing data as a problem of recovery of graph signals. Therefore, we evaluate the robustness of a graph signal reconstruction method in a dataset of Particular Matter PM2.5 in the Aburrá Valley, Colombia. We observe that 1) the model has better performance during dry months than in wet or transition seasons, and 2) the model could not follow pollution peaks because the algorithm assumes smooth changes in time. This model could be suitable to reconstruct data in the Aburrá Valley in dry seasons for other environmental studies.RESUMEN: La contaminación atmosférica es un problema ambiental que afecta a la salud humana mundialmente. La calidad del aire se ve afectada por emisiones antropogénicas, por condiciones meteorológicas y por la topografía. La medición de contaminantes atmosféricos es una tarea importante para la toma de decisiones, por ejemplo, para controlar altas concentraciones de contaminación en una ciudad. Sin embargo, en la medición de la calidad del aire generalmente hay problemas debidos a fallas de los equipos, mantenimiento de rutina, entre otros. Como resultado, los conjuntos de datos de calidad del aire pueden tener información faltante, que a veces puede representar más del 10% de los datos. La reconstrucción de estos valores faltantes juega un papel importante en los estudios ambientales. En este trabajo, modelamos la imputación de datos faltantes como un problema de reconstrucción de señales gráficas. Evaluamos la robustez de un método de procesamiento de señales gráficas en un conjunto de datos de Material Particulado PM2.5 en el Valle de Aburrá, Colombia. Observamos que 1) el modelo tiene un mejor desempeño durante los meses secos que durante temporadas húmedas o de transición y 2) el modelo puede no predecir picos de contaminación dado que el algoritmo asume cambios suaves en el tiempo. Este modelo podría ser útil para reconstruir datos en el Valle de Aburrá en temporadas secas para ser utilizados en futuros estudios de calidad del aire.36application/pdfspainfo:eu-repo/semantics/draftinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fhttps://purl.org/redcol/resource_type/TPTesis/Trabajo de grado - Monografía - Pregradohttp://purl.org/coar/version/c_b1a7d7d4d402bcceAtribución-NoComercial-CompartirIgual 2.5 Colombiainfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by-nc-sa/4.0/Evaluation of a Graph Reconstruction Method of Missing Data in Air Quality: Application to the Aburrá Valley, ColombiaMedellín, ColombiaEnvironmental qualityCalidad ambientalAir pollutionContaminación atmosféricaMeteorologyMeteorologíaMeasurementMediciónData analysisAnálisis de datosAir qualityMissing dataData reconstructionGraph signal processinghttp://vocabularies.unesco.org/thesaurus/concept4533http://vocabularies.unesco.org/thesaurus/concept1946http://vocabularies.unesco.org/thesaurus/concept185http://vocabularies.unesco.org/thesaurus/concept5899http://vocabularies.unesco.org/thesaurus/concept2214Ingeniera AmbientalPregradoFacultad de Ingeniería. Carrera de Ingeniería AmbientalUniversidad de AntioquiaORIGINALBotelloMaria_2021_ReconstructionAirQuality.pdfBotelloMaria_2021_ReconstructionAirQuality.pdfTrabajo de grado de pregradoapplication/pdf24661616http://bibliotecadigital.udea.edu.co/bitstream/10495/20032/1/BotelloMaria_2021_ReconstructionAirQuality.pdf86efdee717be4c0a34723cd13528f759MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-81051http://bibliotecadigital.udea.edu.co/bitstream/10495/20032/3/license_rdfe2060682c9c70d4d30c83c51448f4eedMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/20032/4/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5410495/20032oai:bibliotecadigital.udea.edu.co:10495/200322021-06-19 18:52:16.213Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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 |