Air pollution in our cities is a very significant cause of death and worsening of
the quality of life in current times. Knowing in depth this phenomenon and its
details and building tools that help us to mitigate its effects can be key to a better
habitability of present and future urban environments. This is precisely the purpose
of this work. Using specific data from the city of Barcelona, the problem of spatial
prediction of air quality at the urban level has been addressed. The objective ...
Air pollution in our cities is a very significant cause of death and worsening of
the quality of life in current times. Knowing in depth this phenomenon and its
details and building tools that help us to mitigate its effects can be key to a better
habitability of present and future urban environments. This is precisely the purpose
of this work. Using specific data from the city of Barcelona, the problem of spatial
prediction of air quality at the urban level has been addressed. The objective is
to obtain a resolution at street level starting from the known values of a series of
detectors, dispersed throughout the city. Certain particularities of this case study
have forced us to carry out novel research in this aspect. The contributions of
this work are the following. Firstly, a formalization of the problem based on Markov
Random Fields has been carried out, following the recommendations of recent works
in this sense, in order to favor the unification of the theoretical frameworks of the
Graph Signal Processing field. After this, a novel algorithm has been developed for
the resolution of the problem. This algorithm is based on a message passing scheme
between nodes. In the proposed method, this algorithm is combined with a Graph
Neural Network that refines the obtained result for a better approximation.
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