000109098 001__ 109098
000109098 005__ 20211216151749.0
000109098 0247_ $$2doi$$a10.3390/math8020225
000109098 0248_ $$2sideral$$a125226
000109098 037__ $$aART-2020-125226
000109098 041__ $$aeng
000109098 100__ $$0(orcid)0000-0001-6359-895X$$aMartínez Torres, Javier
000109098 245__ $$aA Functional Data Analysis Approach for the Detection of Air Pollution Episodes and Outliers: A Case Study in Dublin, Ireland
000109098 260__ $$c2020
000109098 5060_ $$aAccess copy available to the general public$$fUnrestricted
000109098 5203_ $$aGround level concentrations of nitrogen oxide (NOx) can act as an indicator of air quality in the urban environment. In cities with relatively good air quality, and where NOx concentrations rarely exceed legal limits, adverse health effects on the population may still occur. Therefore, detecting small deviations in air quality and deriving methods of controlling air pollution are challenging. This study presents different data analytical methods which can be used to monitor and effectively evaluate policies or measures to reduce nitrogen oxide (NOx) emissions through the detection of pollution episodes and the removal of outliers. This method helps to identify the sources of pollution more effectively, and enhances the value of monitoring data and exceedances of limit values. It will detect outliers, changes and trend deviations in NO2 concentrations at ground level, and consists of four main steps: classical statistical description techniques, statistical process control techniques, functional analysis and a functional control process. To demonstrate the effectiveness of the outlier detection methodology proposed, it was applied to a complete one-year NO2 dataset for a sub-urban site in Dublin, Ireland in 2013. The findings demonstrate how the functional data approach improves the classical techniques for detecting outliers, and in addition, how this new methodology can facilitate a more thorough approach to defining effect air pollution control measures.
000109098 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/RTI2018-096296-B-C21
000109098 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000109098 590__ $$a2.258$$b2020
000109098 591__ $$aMATHEMATICS$$b24 / 330 = 0.073$$c2020$$dQ1$$eT1
000109098 592__ $$a0.495$$b2020
000109098 593__ $$aMathematics (miscellaneous)$$c2020$$dQ2
000109098 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000109098 700__ $$0(orcid)0000-0002-4251-7695$$aPastor Pérez, Jorge
000109098 700__ $$0(orcid)0000-0001-6762-9960$$aSancho Val, Joaquín
000109098 700__ $$aMcNabola, Aonghus
000109098 700__ $$aMartínez Comesaña, Miguel
000109098 700__ $$aGallagher, John
000109098 773__ $$g8, 2 (2020), 225 [19 pp.]$$pMathematics (Basel)$$tMATHEMATICS$$x2227-7390
000109098 8564_ $$s1584151$$uhttps://zaguan.unizar.es/record/109098/files/texto_completo.pdf$$yVersión publicada
000109098 8564_ $$s2585451$$uhttps://zaguan.unizar.es/record/109098/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000109098 909CO $$ooai:zaguan.unizar.es:109098$$particulos$$pdriver
000109098 951__ $$a2021-12-16-13:12:22
000109098 980__ $$aARTICLE