Missing Data Estimation in a Low-Cost Sensor Network for Measuring Air Quality: a Case Study in Aburrá Valley
According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sen...
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Published in | Water, air, and soil pollution Vol. 232; no. 10 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
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01.10.2021
Springer Springer Nature B.V |
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Abstract | According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40
%
missing data with less than 12
%
errors, obtaining better results than those presented by other methods found in the literature. |
---|---|
AbstractList | According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40
%
missing data with less than 12
%
errors, obtaining better results than those presented by other methods found in the literature. According to the World Health Organization (WHO), air pollution is currently one leading cause of death around the world. As a result, some projects have emerged to monitor air quality through the implementation of low-cost Wireless Sensor Networks (WSNs). However, the type of technology and the sensors’ location have an impact on data quality, resulting in a considerable amount of missing data. This hinders the proper implementation of methodologies for sensor calibration and data leverage for dispersion analysis of pollutants and prediction of pollution episodes. This paper presents a methodology based on matrix factorization (MF) to recover missing data from a low-cost WSN for particulate matter PM2.5 measurement. Using the proposed methodology with the study case in Aburrá Valley, Colombia, it is shown that is possible to recover 40% missing data with less than 12% errors, obtaining better results than those presented by other methods found in the literature. |
ArticleNumber | 436 |
Audience | Academic |
Author | Rivera-Muñoz, León M. Gallego-Villada, Juan D. Giraldo-Forero, Andrés F. Martinez-Vargas, Juan D. |
Author_xml | – sequence: 1 givenname: León M. orcidid: 0000-0002-2559-236X surname: Rivera-Muñoz fullname: Rivera-Muñoz, León M. email: leonrivera281329@correo.itm.edu.co organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 2 givenname: Juan D. surname: Gallego-Villada fullname: Gallego-Villada, Juan D. organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 3 givenname: Andrés F. surname: Giraldo-Forero fullname: Giraldo-Forero, Andrés F. organization: Instituto Tecnológico Metropolitano (ITM) – sequence: 4 givenname: Juan D. surname: Martinez-Vargas fullname: Martinez-Vargas, Juan D. organization: Instituto Tecnológico Metropolitano (ITM) |
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CitedBy_id | crossref_primary_10_1016_j_scitotenv_2024_171522 crossref_primary_10_1016_j_ecoinf_2022_101775 crossref_primary_10_1007_s11270_023_06127_9 crossref_primary_10_1002_met_2161 crossref_primary_10_1007_s11270_022_05679_6 crossref_primary_10_5194_amt_16_5415_2023 |
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Title | Missing Data Estimation in a Low-Cost Sensor Network for Measuring Air Quality: a Case Study in Aburrá Valley |
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