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

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

A Functional Data Analysis for Assessing the Impact of a Retrofitting in the Energy Performance of a Building

There is an increasing interest in reducing the energy consumption in buildings and in improving their energy efficiency. Building retrofitting is the employed solution for enhancing the energy efficiency in existing buildings. However, the actual performance after retrofitting should be analysed to check the effectiveness of the energy conservation measures. The aim of this work was to detect and to quantify the impact that a retrofitting had in the electrical consumption, heating demands, lighting and temperatures of a building located in the north of Spain. The methodology employed is the application of Functional Data Analyses (FDA) in comparison with classic mathematical techniques such as the Analysis of Variance (ANOVA). The methods that are commonly used for assessing building refurbishment are based on vectorial approaches. The novelty of this work is the application of FDA for assessing the energy performance of renovated buildings. The study proves that more accurate and realistic results are obtained working with correlated datasets than with independently distributed observations of classical methods. Moreover, the electrical savings reached values of more than 70% and the heating demands were reduced more than 15% for all floors in the building.This paper was funded by the Spanish Government (Science, Innovation and Universities Ministry) under the project RTI2018-096296-B-C21

Desempenho operacional de smartphones em levantamentos planimétricos GNSS sobre coberturas vegetais de pastagem e Pinus elliottii Engelm / Operational performance of smartphones in GNSS planimetric surveys on pasture coverages and Pinus Elliottii Engelm

Este trabalho teve como objetivo avaliar o desempenho de receptores de baixo custo, equipados com tecnologia GNSS convencional e assistida por telefonia móvel (A-GNSS), em ambientes que oferecem diferentes níveis de obstrução vegetal aos sinais de satélite para a obtenção de levantamentos planimétricos na superfície terrestre. Para isso, foram conduzidas estimativas de índices de acurácia horizontal, através de métodos de posicionamento GNSS estático, onde os tratamentos foram compostos pela combinação dos níveis de dois fatores, sendo estes: cobertura vegetal (Pastagem e Pinus) e cinco receptores GNSS (um de navegação e quatro smartphones), constituindo um total de 10 tratamentos, avaliados durante um período de 10 horas de coleta simultânea, com quatro repetições. A presença de cobertura vegetal de Pinus, independente do receptor utilizado, proporcionou os piores índices de acurácia horizontal, reduzindo o número de satélites visíveis no horizonte, e ocasionando perda na qualidade da distribuição geométrica dos satélites, sendo o melhor índice de acurácia nesta condição obtido por um smartphone, indicando uma possível melhoria de desempenho, oferecida pela operadora de telefonia móvel através do sistema A-GNSS. Para o ambiente com cobertura vegetal de Pastagem, o melhor índice de acurácia horizontal foi obtido pelo receptor de navegação, demonstrando maior estabilidade e precisão experimental dos dados ao longo do período de coleta frente aos smartphones, que apresentaram alta variabilidade amostral. O uso de um sistema de rastreamento de sinais mono-constelação (apenas GPS) influenciou negativamente os índices de acurácia horizontal, em comparação com os receptores que utilizaram sistemas multi-constelação (GPS+Glonass)