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.

The presence of harmonics in power systems and their problems and impact on quality of electricity supply is increasing due to the increase in society's use of the nonlinear loads that generate them, especially the electronic kind. Classic techniques such as Statistical Process Control (SPC) and Process Capability Analysis (PCA) that form the Statistical Quality Control, can be used to identify outliers or special causes of variability, and proceed with the implementation of actions allowing corrective disposal, provided that the harmonics are normally distributed. In the event that the distribution is unknown novel tools such as Functional Data Analysis (FDA), have been used successfully in this communication to study these phenomena in situations where the classic quality control cannot