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Open AccessArticle

A New Black Carbon Sensor for Dense Air Quality Monitoring Networks

1
Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
2
Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
3
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(3), 738; https://doi.org/10.3390/s18030738
Received: 26 January 2018 / Revised: 23 February 2018 / Accepted: 26 February 2018 / Published: 1 March 2018
(This article belongs to the Special Issue Air Pollution Sensors: A New Class of Tools to Measure Air Quality)
Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)—a major component of particulate matter pollution associated with adverse human health risks—is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD’s sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval). View Full-Text
Keywords: air quality monitoring; black carbon; wireless sensor network air quality monitoring; black carbon; wireless sensor network
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MDPI and ACS Style

Caubel, J.J.; Cados, T.E.; Kirchstetter, T.W. A New Black Carbon Sensor for Dense Air Quality Monitoring Networks. Sensors 2018, 18, 738.

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