University of Connecticut Health Center, Department of Public Health Sciences, UConn School of Medicine, 263 Farmington Avenue, Farmington, CT 06032-1941, USA
Bloomberg School of Public Health, Environmental Health and Engineering, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205-2103, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Colby Buehler
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Chemical and Environmental Engineering, Yale University, P.O. Box 208286, New Haven, CT 06520, USA
Abhirup Datta
Bloomberg School of Public Health, Department of Biostatistics, Johns Hopkins University, 615 N Wolfe Street, Baltimore, MD 21205-2103, USA
Drew R. Gentner
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
Chemical and Environmental Engineering, Yale University, P.O. Box 208286, New Haven, CT 06520, USA
Kirsten Koehler
Bloomberg School of Public Health, Environmental Health and Engineering, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205-2103, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale University, New Haven, CT 06520, USA
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1,560
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15
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35
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Total article views: 2,084 (including HTML, PDF, and XML)
Thereof 2,084 with geography defined
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Total article views: 654 (including HTML, PDF, and XML)
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We assessed five pairs of co-located reference and low-cost sensor data sets (PM2.5, O3, NO2, NO, and CO) to make recommendations for best practices regarding the field calibration of low-cost air quality sensors. We found diminishing improvements for calibration periods longer than about 6 weeks for all sensors and that co-location can be minimized if the period is strategically selected and monitored so that the calibration period is representative of the desired measurement setting.
We assessed five pairs of co-located reference and low-cost sensor data sets (PM2.5, O3, NO2,...
