  Air pollution monitoring platforms play a very important role in preventing
and mitigating the effects of pollution. Recent advances in the field of graph
signal processing have made it possible to describe and analyze air pollution
monitoring networks using graphs. One of the main applications is the
reconstruction of the measured signal in a graph using a subset of sensors.
Reconstructing the signal using information from sensor neighbors can help
improve the quality of network data, examples are filling in missing data with
correlated neighboring nodes, or correcting a drifting sensor with neighboring
sensors that are more accurate. This paper compares the use of various types of
graph signal reconstruction methods applied to real data sets of Spanish air
pollution reference stations. The methods considered are Laplacian
interpolation, graph signal processing low-pass based graph signal
reconstruction, and kernel-based graph signal reconstruction, and are compared
on actual air pollution data sets measuring O3, NO2, and PM10. The ability of
the methods to reconstruct the signal of a pollutant is shown, as well as the
computational cost of this reconstruction. The results indicate the superiority
of methods based on kernel-based graph signal reconstruction, as well as the
difficulties of the methods to scale in an air pollution monitoring network
with a large number of low-cost sensors. However, we show that scalability can
be overcome with simple methods, such as partitioning the network using a
clustering algorithm.
