A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studies

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

Álvaro Gómez-Losada (Autor) Outra; Francisca M. Santos (Autor) Outra; Karina Gibert (Autor) Outra; José C. M. Pires (Autor) FEUP

Revista

Título: Computers, Environment and Urban Systems

Vol. 75

Páginas: 1-11

ISSN: 0198-9715

Editora: Elsevier

Indexação

ISI Web of Knowledge - 12 Citações

ISI Web of Science

Scopus - 16 Citações

INSPEC

Outras Informações

ID Authenticus: P-00Q-0MT

DOI: 10.1016/j.compenvurbsys.2018.12.005

Abstract (EN): Model developments to assess different air pollution exposures within cities are still a key challenge in environmental epidemiology. Background air pollution is a long-term resident and low-level concentration pollution difficult to quantify, and to which population is chronically exposed. In this study, hourly time series of four key air pollutants were analysed using Hidden Markov Models to estimate the exposure to background pollution in Madrid, from 2001 to 2017. Using these estimates, its spatial distribution was later analysed after combining the interpolation results of ordinary kriging and inverse distance weighting. The ratio of ambient to background pollution differs according to the pollutant studied but is estimated to be on average about six to one. This methodology is proposed not only to describe the temporal and spatial variability of this complex exposure, but also to be used as input in new modelling approaches of air pollution in urban areas. © 2018 The Authors

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11