A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland

Javier Martínez Torres, Jorge Pastor Pérez, Joaquín Sancho Val, Aonghus McNabola, Miguel Martínez Comesaña and John Gallagher
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Javier Martínez Torres: Department of Applied Mathematics I. Telecommunications Engineering School, University of Vigo, 36310 Vigo (Pontevedra), Spain
Jorge Pastor Pérez: Centro de Evaluación, Formación y Calidad de Aragón, 50018 Zaragoza, Spain
Joaquín Sancho Val: Centro Universitario de la Defensa. Academia General Militar, 50090 Zaragoza, Spain
Aonghus McNabola: Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, University of Dublin, Dublin D02 PN40, Ireland
Miguel Martínez Comesaña: Escuela de Ingeniería Industrial, University of Vigo, 36310 Vigo (Pontevedra), Spain
John Gallagher: Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, University of Dublin, Dublin D02 PN40, Ireland

Mathematics, 2020, vol. 8, issue 2, 1-19

Abstract: Ground level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO 2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO 2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures.

Keywords: air pollution; functional data analysis; non-normal data; statistical process control; outlier (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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