Air Quality Data Analysis with Symbolic Principal Components

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2025

Autores

Loureiro, P (Autor) Outra; Oliveira, M (Autor) Outra; brito, p (Autor) FEP; Oliveira, L (Autor) Outra

Ata de Conferência Internacional

Título: Springer Proceedings in Mathematics and Statistics

Páginas: 335-348

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-017-T5Q

DOI: 10.1007/978-3-031-68949-9_25

Abstract (EN): Air pollution is a global challenge with deep implications in public health and environment. We examine air quality data from a monitoring station in Entrecampos, Lisbon, Portugal, using Symbolic Data Analysis. The dataset consists of hourly concentrations of nine pollutants during three years, which are logarithmically transformed and aggregated in intervals, taking the daily minimum and maximum values. The symbolic mean and variance are estimated for each variable through the method of moments, and the pairwise dependencies are captured using a bivariate copula. Symbolic principal component scores are obtained from the estimated covariance matrix and used to fit generalized extreme value distributions. Outlier maps, based on these distributions¿ quantiles, are used to identify outlying observations. A comparative analysis with daily average-based outlier detection methods is conducted. The results show the relevance of Symbolic Data Analysis in revealing new insights into air quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13