Distributed, Low-cost Particulate Matter Sensing: Scenarios, Challenges, Approaches

In this paper, we present different scenarios enabled by distributed PM measurement, discuss the major challenges in such systems and show different approaches that can be used to address them.
Budde et al (2014)

The observation and control of particulate matter (PM) pollution in ambient air is increasingly being
recognized as an important topic in societies across the globe. Classic measurement approaches
provide accurate daily means but are static, expensive and suffer from low spatial resolution and high
latency. Distributed measurement grids using real-time capable instrumentation not only can provide
spatio-temporally fine-grained readings, but also have the potential to enable novel applications.
However, in order for them to be feasible, sufficiently accurate low-cost measurement devices are
needed. The noise as well as typically low sensitivity and stability of commercially available low-cost
dust sensors result in additional challenges that need to be solved when basing distributed
measurement grids on them. This includes signal processing, sufficiently frequent recalibration,
suitable data processing and – where applicable – incentivization of users. In this paper, we present
different scenarios enabled by distributed PM measurement, discuss the major challenges in such
systems and show different approaches that can be used to address them.

PM10, PM2.5
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