Can commercial low-cost sensor platforms contribute to air qualitymonitoring and exposure estimates?

Can commercial low-cost sensor platforms contribute to air qualitymonitoring and exposure estimates?
Castell et al (2017)
The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high
spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitor-
ing systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of ca-
pabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms
are often of questionable quality.
We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform
against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capa-
bility over time and a range of environmental conditions. Our results show that their performance varies spatially
and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results
show that the performance varies from unit to unit, which makes it necessary to examine the data quality of
each node before its use.
In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to mar-
keting these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be
employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air qual-
ity legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale,
for purposes such as awareness raising).
Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and
interpreting the data.In general, whilelow-costplatforms present low accuracy for regulatory orhealth purposes
they can provide relative and aggregated information about the observed air quality
NO2, PM2.5, O3, PM10
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