  We present a management and data correction framework for low-cost
electrochemical sensors for nitrogen dioxide (NO2) deployed within a
hierarchical network of low-cost and regulatory-grade instruments. The
framework is founded on the idea that it is possible in a suitably configured
network to identify a source of reliable proxy data for each sensor site that
has a similar probability distribution of measurement values over a suitable
time period. Previous work successfully applied these ideas to a sensor system
with a simple linear 2-parameter (slope and offset) response. Applying these
ideas to electrochemical sensors for NO2 presents significant additional
difficulties for which we demonstrate solutions. The three NO2 sensor response
parameters (offset, ozone (O3) response slope, and NO2 response slope) are
known to vary significantly as a consequence of ambient humidity and
temperature variations. Here we demonstrate that these response parameters can
be estimated by minimising the Kullback-Leibler divergence between
sensor-estimated and proxy NO2 distributions over a 3-day window. We then
estimate an additional offset term by using co-location data. This offset term
is dependent on climate and spatially correlated and can thus be projected
across the network. Co-location data also estimates the time-, space- and
concentration-dependent error distribution between sensors and regulatory-grade
instruments. We show how the parameter variations can be used to indicate both
sensor failure and failure of the proxy assumption. We apply the procedures to
a network of 56 sensors distributed across the Inland Empire and Los Angeles
County regions, demonstrating the need for reliable data from dense networks of
monitors to supplement the existing regulatory networks.
