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FeinPhone: Low-cost Smartphone Camera-based 2D Particulate Matter Sensor

Budde, Matthias 1; Leiner, Simone; Köpke, Marcel 1; Riesterer, Johannes 1; Riedel, Till ORCID iD icon 1; Beigl, Michael 1
1 Karlsruher Institut für Technologie (KIT)


Precise, location-specific fine dust measurement is central for the assessment of urban air quality. Classic measurement approaches require dedicated hardware, of which professional equipment is still prohibitively expensive (>10k$) for dense measurements, and inexpensive sensors do not meet accuracy demands. As a step towards filling this gap, we propose FeinPhone, a phone-based fine dust measurement system that uses camera and flashlight functions that are readily available on today’s off-the-shelf smart phones. We introduce a cost-effective passive hardware add-on together with a novel counting approach based on light-scattering particle sensors. Since our approach features a 2D sensor (the camera) instead of a single photodiode, we can employ it to capture the scatter traces from individual particles rather than just retaining a light intensity sum signal as in simple photometers. This is a more direct way of assessing the particle count, it is robust against side effects, e.g., from camera image compression, and enables gaining information on the size spectrum of the particles. Our proof-of-concept evaluation comparing several FeinPhone sensors with data from a high-quality APS/SMPS (Aerodynamic Particle Sizer/Scanning Mobility Particle Sizer) reference device at the World Calibration Center for Aerosol Physics shows that the collected data shows excellent correlation with the inhalable coarse fraction of fine dust particles (r > 0.9) and can successfully capture its levels under realistic conditions.

Verlagsausgabe §
DOI: 10.5445/IR/1000090936
Veröffentlicht am 13.02.2019
DOI: 10.3390/s19030749
Zitationen: 9
Web of Science
Zitationen: 5
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Experimentelle Teilchenphysik (ETP)
Institut für Telematik (TM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2019
Sprache Englisch
Identifikator ISSN: 1424-8220
KITopen-ID: 1000090936
Erschienen in Sensors
Verlag MDPI
Band 19
Heft 3
Seiten Article: 749
Bemerkung zur Veröffentlichung Gefördert durch den KIT-Publikationsfonds
Schlagwörter mobile sensing; PM; ubiquitous computing; atmospheric dust; Particulate Matter; low-cost sensor
Nachgewiesen in Dimensions
Web of Science
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