Abstract. Ambient air pollution poses a major global public health
risk. Lower-cost air quality sensors (LCSs) are increasingly being explored
as a tool to understand local air pollution problems and develop effective
solutions. A barrier to LCS adoption is potentially larger measurement
uncertainty compared to reference measurement technology. The technical
performance of various LCSs has been tested in laboratory and field
environments, and a growing body of literature on uses of LCSs primarily focuses on
proof-of-concept deployments. However, few studies have demonstrated the
implications of LCS measurement uncertainties on a sensor network's ability
to assess spatiotemporal patterns of local air pollution. Here, we present
results from a 2-year deployment of 100 stationary electrochemical nitrogen
dioxide (NO2) LCSs across Greater London as part of the Breathe London pilot project (BL). We evaluated sensor performance using collocations with
reference instruments, estimating ∼ 35 % average uncertainty
(root mean square error) in the calibrated LCSs, and identified infrequent,
multi-week periods of poorer performance and high bias during summer months.
We analyzed BL data to generate insights about London's air pollution,
including long-term concentration trends, diurnal and day-of-week patterns,
and profiles of elevated concentrations during regional pollution episodes.
These findings were validated against measurements from an extensive
reference network, demonstrating the BL network's ability to generate robust
information about London's air pollution. In cases where the BL network did
not effectively capture features that the reference network measured,
ongoing collocations of representative sensors often provided evidence of
irregularities in sensor performance, demonstrating how, in the absence of
an extensive reference network, project-long collocations could enable
characterization and mitigation of network-wide sensor uncertainties. The
conclusions are restricted to the specific sensors used for this study, but
the results give direction to LCS users by demonstrating the kinds of air
pollution insights possible from LCS networks and provide a blueprint for
future LCS projects to manage and evaluate uncertainties when collecting,
analyzing, and interpreting data.
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