Functional Data Analysis for the Detection of Outliers and Study of the Effects of the COVID-19 Pandemic on Air Quality: A Case Study in Gijón, Spain

Xurxo Rigueira, María Araújo, Javier Martínez, Paulino José García-Nieto and Iago Ocarranza
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Xurxo Rigueira: CINTECX, GESSMin Group, Department of Natural Resources and Environmental Engineering, University of Vigo, 36310 Vigo, Spain
María Araújo: CINTECX, GESSMin Group, Department of Natural Resources and Environmental Engineering, University of Vigo, 36310 Vigo, Spain
Javier Martínez: CINTECX, GESSMin Group, Department of Applied Mathematics I, University of Vigo, 36310 Vigo, Spain
Paulino José García-Nieto: Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
Iago Ocarranza: Possible Incorporated SL, 36211 Vigo, Spain

Mathematics, 2022, vol. 10, issue 14, 1-27

Abstract: Air pollution, especially at the ground level, poses a high risk for human health as it can have serious negative effects on the population of certain areas. The high variability of this type of data, which are affected by weather conditions and human activities, makes it difficult for conventional methods to precisely detect anomalous values or outliers. In this paper, classical analysis, statistical process control, and functional data analysis are compared for this purpose. The results obtained motivate the development of a new outlier detector based on the concept of functional directional outlyingness. The validation of this algorithm is perfomed on real air quality data from the city of Gijón, Spain, aiming to detect the proven reduction in NO 2 levels during the COVID-19 lockdown in that city. Three more variables ( SO 2 , PM 10 , and O 3 ) are studied with this technique. The results demonstrate that functional data analysis outperforms the two other methods, and the proposed outlier detector is well suited for the accurate detection of outliers in data with high variability.

Keywords: functional data analysis; air pollution; magnitude outlyingness; shape outlyingness; COVID-19 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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