Air Quality Data Analysis with Symbolic Principal Components

• Type: Article in International Conference Proceedings Book
• Date: 2025

Authors

Loureiro, P (Author) Other; Oliveira, M (Author) Other; brito, p (Author) FEP; Oliveira, L (Author) Other

Conference proceedings International

Title: Springer Proceedings in Mathematics and Statistics

Pages: 335-348

26th Congress of the Portuguese Statistical Society, SPE 2023

Evora, 13 October 2021 through 16 October 2021

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13