Journal
COMPUTERS ENVIRONMENT AND URBAN SYSTEMS
Volume 75, Issue -, Pages 1-11Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2018.12.005
Keywords
Air pollution exposure; Background pollution levels; Hidden Markov Models; Inverse distance weighting; Ordinary kriging
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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.
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