Next Article in Journal
RMHIL: A Rule Matching Algorithm Based on Heterogeneous Integrated Learning in Software Defined Network
Previous Article in Journal
Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network
Article

Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021

1
Independent Researcher, Santa Rosa, CA 95049, USA
2
Independent Researcher, Milpitas, CA 95035, USA
3
Department of American Indian Studies, San Diego State University (SDSU), San Diego, CA 92182, USA
4
Education, Training, and Research, Inc. (ETR), Scotts Valley, CA 95066, USA
*
Author to whom correspondence should be addressed.
Sensors 2022, 22(13), 4741; https://doi.org/10.3390/s22134741
Received: 9 May 2022 / Revised: 13 June 2022 / Accepted: 21 June 2022 / Published: 23 June 2022
(This article belongs to the Section Environmental Sensing)
Large quantities of real-time particle data are becoming available from low-cost particle monitors. However, it is crucial to determine the quality of these measurements. The largest network of monitors in the United States is maintained by the PurpleAir company, which offers two monitors: PA-I and PA-II. PA-I monitors have a single sensor (PMS1003) and PA-II monitors employ two independent PMS5003 sensors. We determine a new calibration factor for the PA-I monitor and revise a previously published calibration algorithm for PA-II monitors (ALT-CF3). From the PurpleAir API site, we downloaded 83 million hourly average PM2.5 values in the PurpleAir database from Washington, Oregon, and California between 1 January 2017 and 8 September 2021. Daily outdoor PM2.5 means from 194 PA-II monitors were compared to daily means from 47 nearby Federal regulatory sites using gravimetric Federal Reference Methods (FRM). We find a revised calibration factor of 3.4 for the PA-II monitors. For the PA-I monitors, we determined a new calibration factor (also 3.4) by comparing 26 outdoor PA-I sites to 117 nearby outdoor PA-II sites. These results show that PurpleAir PM2.5 measurements can agree well with regulatory monitors when an optimum calibration factor is found. View Full-Text
Keywords: sensors; low-cost particle monitors; calibration factor; PurpleAir; particles; PM2.5; ALT-CF3; algorithm; PMS1003; PMS5003 sensors; low-cost particle monitors; calibration factor; PurpleAir; particles; PM2.5; ALT-CF3; algorithm; PMS1003; PMS5003
Show Figures

Figure 1

MDPI and ACS Style

Wallace, L.; Zhao, T.; Klepeis, N.E. Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021. Sensors 2022, 22, 4741. https://doi.org/10.3390/s22134741

AMA Style

Wallace L, Zhao T, Klepeis NE. Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021. Sensors. 2022; 22(13):4741. https://doi.org/10.3390/s22134741

Chicago/Turabian Style

Wallace, Lance, Tongke Zhao, and Neil E. Klepeis. 2022. "Calibration of PurpleAir PA-I and PA-II Monitors Using Daily Mean PM2.5 Concentrations Measured in California, Washington, and Oregon from 2017 to 2021" Sensors 22, no. 13: 4741. https://doi.org/10.3390/s22134741

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop