Next Article in Journal
Conjugacy of Dynamical Systems on Self-Similar Groups
Previous Article in Journal
New Framework for Quality Function Deployment Using Linguistic Z-Numbers
Previous Article in Special Issue
Constructing a Control Chart Using Functional Data
Open AccessArticle

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

1
Department of Applied Mathematics I. Telecommunications Engineering School, University of Vigo, 36310 Vigo (Pontevedra), Spain
2
Centro de Evaluación, Formación y Calidad de Aragón, 50018 Zaragoza, Spain
3
Centro Universitario de la Defensa. Academia General Militar, 50090 Zaragoza, Spain
4
Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, University of Dublin, Dublin D02 PN40, Ireland
5
Escuela de Ingeniería Industrial. University of Vigo, 36310 Vigo (Pontevedra), Spain
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(2), 225; https://doi.org/10.3390/math8020225
Received: 14 January 2020 / Revised: 30 January 2020 / Accepted: 4 February 2020 / Published: 10 February 2020
(This article belongs to the Special Issue Functional Statistics: Outliers Detection and Quality Control)
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 NO2 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 NO2 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 air pollution; functional data analysis; non-normal data; statistical process control; outlier
MDPI and ACS Style

Torres, J.M.; Pastor Pérez, J.; Sancho Val, J.; McNabola, A.; Martínez Comesaña, M.; Gallagher, J. A Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland. Mathematics 2020, 8, 225.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop