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

• Type: Article in International Scientific Journal
• Date: 2019

Authors

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

Journal

Title: Computers, Environment and Urban Systems

Vol. 75

Pages: 1-11

ISSN: 0198-9715

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 12 Citations

ISI Web of Science

Scopus - 16 Citations

INSPEC

Other information

Authenticus ID: 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

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11