Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4617-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-4617-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Jun 2021
Research article |
| 22 Jun 2021
| 22 Jun 2021
Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor
Karoline K. Barkjohn
CORRESPONDING AUTHOR
Office of Research and Development, US Environmental Protection
Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
Brett Gantt
Office of Air Quality Planning and Standards, US Environmental
Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
Andrea L. Clements
Office of Research and Development, US Environmental Protection
Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA
Related authors
Stuart J. Illson and Karoline K. Barkjohn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1273, https://doi.org/10.5194/egusphere-2026-1273, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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This study investigates a generalizable method for identifying outliers within networks of air quality sensors, and discusses its challenges during wildland fire smoke events, which may obscure other approaches by introducing real variability. Tested using 9 months of data from over 19,000 devices, the methods developed distinguish malfunctioning sensors, localized conditions affecting a single sensor, and genuine air quality impacts across a range of network densities and smoke conditions.
Stuart J. Illson and Karoline K. Barkjohn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1273, https://doi.org/10.5194/egusphere-2026-1273, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This study investigates a generalizable method for identifying outliers within networks of air quality sensors, and discusses its challenges during wildland fire smoke events, which may obscure other approaches by introducing real variability. Tested using 9 months of data from over 19,000 devices, the methods developed distinguish malfunctioning sensors, localized conditions affecting a single sensor, and genuine air quality impacts across a range of network densities and smoke conditions.
Lena Francesca Weissert, Geoff Stephen Henshaw, Andrea Lee Clements, Rachelle Monique Duvall, and Carry Croghan
Atmos. Meas. Tech., 18, 3635–3645, https://doi.org/10.5194/amt-18-3635-2025, https://doi.org/10.5194/amt-18-3635-2025, 2025
Short summary
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This study evaluates a remote calibration tool, referred to as MOment MAtching (MOMA), for calibrating PurpleAir PM2.5 sensors, especially for varying PM sources, and compares it with the Environmental Protection Agency (EPA) correction. MOMA improved the accuracy of PurpleAir sensor data comparable to the EPA correction. Although reliant on nearby reference sites, MOMA offers valuable insights into potential PM sources, thereby increasing the overall value of PurpleAir sensor networks.
Cited articles
Air quality index reporting: 64 Fed. Reg 42530, Office of the Federal Register, National Archives and Records Administration, Washington, DC, USA, available at:
https://www.govinfo.gov/app/details/FR-1999-08-04/99-19433/summary (last access: 17 June 2021), 1991.
Al-Thani, H., Koç, M., and Isaifan, R. J.: A review on the direct
effect of particulate atmospheric pollution on materials and its mitigation
for sustainable cities and societies, Environ. Sci. Pollut. R., 25, 27839–27857, https://doi.org/10.1007/s11356-018-2952-8, 2018.
Apte, J. S., Marshall, J. D., Cohen, A. J., and Brauer, M.: Addressing
Global Mortality from Ambient PM2.5, Environ. Sci. Technol., 49, 8057–8066, https://doi.org/10.1021/acs.est.5b01236, 2015.
Ardon-Dryer, K., Dryer, Y., Williams, J. N., and Moghimi, N.: Measurements of PM2.5 with PurpleAir under atmospheric conditions, Atmos. Meas. Tech., 13, 5441–5458, https://doi.org/10.5194/amt-13-5441-2020, 2020.
Barkjohn, K. K.: Dataset Development and Application of a United States wide
correction for PM2.5 data collected with the PurpleAir sensor, U.S. EPA
Office of Research and Development (ORD) [Data set], https://doi.org/10.23719/1522388, 2021.
Barkjohn, K. K., Bergin, M. H., Norris, C., Schauer, J. J., Zhang, Y.,
Black, M., Hu, M., and Zhang, J.: Using Low-cost sensors to Quantify the
Effects of Air Filtration on Indoor and Personal Exposure Relevant PM2.5
Concentrations in Beijing, China, Aerosol Air Qual. Res., 20, 297–313,
https://doi.org/10.4209/aaqr.2018.11.0394, 2020.
Barkjohn, K. K., Norris, C., Cui, X., Fang, L., Zheng, T., Schauer, J. J.,
Zhang, Y., Black, M., Zhang, J., and Bergin, M. H.: Real-time Measurements
of PM2.5 and Ozone to Assess the Effectiveness of Residential Indoor Air
Filtration in Shanghai Homes, Indoor Air, 31, 74–87, https://doi.org/10.1111/ina.12716, 2021.
Bell, M. L., Ebisu, K., and Belanger, K.: Ambient air pollution and low
birth weight in Connecticut and Massachusetts, Environ. Health Persp., 115,
1118–1124, https://doi.org/10.1289/ehp.9759, 2007.
Bi, J., Wildani, A., Chang, H. H., and Liu, Y.: Incorporating Low-Cost
Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale, Environ. Sci. Technol., 54, 2152–2162, https://doi.org/10.1021/acs.est.9b06046, 2020.
Brook, R. D., Rajagopalan, S., Pope, C. A., Brook, J. R., Bhatnagar, A.,
Diez-Roux, A. V., Holguin, F., Hong, Y., Luepker, R. V., Mittleman, M. A.,
Peters, A., Siscovick, D., Smith, S. C., Whitsel, L., and Kaufman, J. D.:
Particulate Matter Air Pollution and Cardiovascular Disease, Circulation,
121, 2331–2378, https://doi.org/10.1161/CIR.0b013e3181dbece1, 2010.
Chakrabarti, B., Fine, P. M., Delfino, R., and Sioutas, C.: Performance
evaluation of the active-flow personal DataRAM PM2.5 mass monitor (Thermo Anderson pDR-1200) designed for continuous personal exposure measurements, Atmos. Environ., 38, 3329–3340, https://doi.org/10.1016/j.atmosenv.2004.03.007, 2004.
Chung, A., Chang, D. P. Y., Kleeman, M. J., Perry, K. D., Cahill, T. A.,
Dutcher, D., McDougall, E. M., and Stroud, K.: Comparison of Real-Time
Instruments Used To Monitor Airborne Particulate Matter,
J. Air Waste Manage., 51, 109–120, https://doi.org/10.1080/10473289.2001.10464254, 2001.
Clements, A. L., Reece, S., Conner, T., and Williams, R.: Observed data
quality concerns involving low-cost air sensors, Atmos. Environ., 3, 100034, https://doi.org/10.1016/j.aeaoa.2019.100034, 2019.
Davison, G., Barkjohn, K. K., Hagler, G. S. W., Holder, A. L., Coefield, S.,
Noonan, C., and Hassett-Sipple, B.: Creating Clean Air Spaces During
Wildland Fire Smoke Episodes: Web Summit Summary, Front. Public Health, 9, 508971, https://doi.org/10.3389/fpubh.2021.508971, 2021.
Day, D. E. and Malm, W. C.: Aerosol light scattering measurements as a
function of relative humidity: a comparison between measurements made at
three different sites, Atmos. Environ., 35, 5169–5176, https://doi.org/10.1016/S1352-2310(01)00320-X, 2001.
Delfino, R. J., Quintana, P. J. E., Floro, J., Gastañaga, V. M., Samimi,
B. S., Kleinman, M. T., Liu, L. J. S., Bufalino, C., Wu, C.-F., and McLaren,
C. E.: Association of FEV1 in asthmatic children with personal and
microenvironmental exposure to airborne particulate matter, Environ. Health Persp., 112, 932–941, https://doi.org/10.1289/ehp.6815, 2004.
Delp, W. W. and Singer, B. C.: Wildfire Smoke Adjustment Factors for
Low-Cost and Professional PM2.5 Monitors with Optical Sensors, Sensors-Basel, 20, 3683, https://doi.org/10.3390/s20133683, 2020.
Di, Q., Dai, L., Wang, Y., Zanobetti, A., Choirat, C., Schwartz, J. D., and
Dominici, F.: Association of Short-term Exposure to Air Pollution With
Mortality in Older Adults, JAMA, 318, 2446–2456, https://doi.org/10.1001/jama.2017.17923, 2017.
Dominici, F., Peng, R. D., Zeger, S. L., White, R. H., and Samet, J. M.:
Particulate air pollution and mortality in the United States: did the risks
change from 1987 to 2000?, Am. J. Epidemiol., 166, 880–888, https://doi.org/10.1093/aje/kwm222, 2007.
Durkin, A., Gonzalez, R., Isaksen, T. B., Walker, E., and Errett, N. A.:
Establishing a Community Air Monitoring Network in a Wildfire Smoke-Prone
Rural Community: The Motivations, Experiences, Challenges, and Ideas of
Clean Air Methow's Clean Air Ambassadors, Int. J. Env. Res. Pub. He., 17, 8393, https://doi.org/10.3390/ijerph17228393, 2020.
Duvall, R. M., Hagler, G. S. W., Clements, A. L., Benedict, K., Barkjohn, K. K., Kilaru, V., Hanley, T., Watkins, N., Kaufman, A., Kamal, A., Reece, S.,
Fransioli, P., Gerboles, M., Gillerman, G., Habre, R., Hannigan, M., Ning,
Z., Papapostolou, V., Pope, R., Quintana, P. J. E., and Lam Snyder, J.:
Deliberating Performance Targets: Follow-on workshop discussing PM10, NO2, CO, and SO2 air sensor targets, Atmos. Environ., 246, 118099, https://doi.org/10.1016/j.atmosenv.2020.118099, 2020.
Feenstra, B., Papapostolou, V., Hasheminassab, S., Zhang, H., Boghossian, B.
D., Cocker, D., and Polidori, A.: Performance evaluation of twelve low-cost
PM2.5 sensors at an ambient air monitoring site, Atmos. Environ.,
216, 116946, https://doi.org/10.1016/j.atmosenv.2019.116946, 2019.
Feinberg, S., Williams, R., Hagler, G. S. W., Rickard, J., Brown, R., Garver, D., Harshfield, G., Stauffer, P., Mattson, E., Judge, R., and Garvey, S.: Long-term evaluation of air sensor technology under ambient conditions in Denver, Colorado, Atmos. Meas. Tech., 11, 4605–4615, https://doi.org/10.5194/amt-11-4605-2018, 2018.
Ford, B., Martin, M. V., Zelasky, S. E., Fischer, E. V., Anenberg, S. C.,
Heald, C. L., and Pierce, J. R.: Future Fire Impacts on Smoke
Concentrations, Visibility, and Health in the Contiguous United States,
Geohealth, 2, 229–247, https://doi.org/10.1029/2018gh000144, 2018.
Ford, B., Pierce, J. R., Wendt, E., Long, M., Jathar, S., Mehaffy, J., Tryner, J., Quinn, C., van Zyl, L., L'Orange, C., Miller-Lionberg, D., and Volckens, J.: A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado, Atmos. Meas. Tech., 12, 6385–6399, https://doi.org/10.5194/amt-12-6385-2019, 2019.
Franklin, M., Zeka, A., and Schwartz, J.: Association between PM2.5 and
all-cause and specific-cause mortality in 27 US communities, J. Expo. Sci. Env. Epid., 17, 279–287, https://doi.org/10.1038/sj.jes.7500530, 2007.
Grande, G., Ljungman, P. L. S., Eneroth, K., Bellander, T., and Rizzuto, D.:
Association Between Cardiovascular Disease and Long-term Exposure to Air
Pollution With the Risk of Dementia, JAMA Neurol., 77, 801–809, https://doi.org/10.1001/jamaneurol.2019.4914, 2020.
He, M., Kuerbanjiang, N., and Dhaniyala, S.: Performance characteristics of
the low-cost Plantower PMS optical sensor, Aerosol Sci. Tech., 54, 232–241, https://doi.org/10.1080/02786826.2019.1696015, 2020.
Heintzenberg, J., Wiedensohler, A., Tuch, T. M., Covert, D. S., Sheridan,
P., Ogren, J. A., Gras, J., Nessler, R., Kleefeld, C., Kalivitis, N.,
Aaltonen, V., Wilhelm, R.-T., and Havlicek, M.: Intercomparisons and Aerosol
Calibrations of 12 Commercial Integrating Nephelometers of Three
Manufacturers, J. Atmos. Ocean. Tech., 23, 902–914, https://doi.org/10.1175/jtech1892.1, 2006.
Holder, A. L., Mebust, A. K., Maghran, L. A., McGown, M. R., Stewart, K. E.,
Vallano, D. M., Elleman, R. A., and Baker, K. R.: Field Evaluation of
Low-Cost Particulate Matter Sensors for Measuring Wildfire Smoke, Sensors-Basel,
20, 4796, https://doi.org/10.3390/s20174796, 2020.
Holm, S. M., Miller, M. D., and Balmes, J. R.: Health effects of wildfire
smoke in children and public health tools: a narrative review, J. Expo. Sci. Env. Epid., 31, 1–20, https://doi.org/10.1038/s41370-020-00267-4, 2020.
Jayaratne, R., Liu, X., Thai, P., Dunbabin, M., and Morawska, L.: The influence of humidity on the performance of a low-cost air particle mass sensor and the effect of atmospheric fog, Atmos. Meas. Tech., 11, 4883–4890, https://doi.org/10.5194/amt-11-4883-2018, 2018.
Jiao, W., Hagler, G., Williams, R., Sharpe, R., Brown, R., Garver, D., Judge, R., Caudill, M., Rickard, J., Davis, M., Weinstock, L., Zimmer-Dauphinee, S., and Buckley, K.: Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States, Atmos. Meas. Tech., 9, 5281–5292, https://doi.org/10.5194/amt-9-5281-2016, 2016.
Johnson, K. K., Bergin, M. H., Russell, A. G., and Hagler, G. S.: Field test
of several low-cost particulate matter sensors in high and low concentration
urban environments, Aerosol Air Qual. Res., 18, 565–578, 2018.
Karl, T. R. and Koss, W. J.: Regional and National Monthly, Seasonal, and
Annual Temperature Weighted by Area, 1895–1983, Historical Climatology
Series 4–3, National Climatic Data Center, Asheville, NC, USA, 38 pp., 1984.
Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D.,
Martin, R., and Butterfield, A.: Ambient and laboratory evaluation of a
low-cost particulate matter sensor, Environ. Pollut., 221, 491–500,
https://doi.org/10.1016/j.envpol.2016.12.039, 2017.
Kim, S., Park, S., and Lee, J.: Evaluation of Performance of Inexpensive
Laser Based PM2.5 Sensor Monitors for Typical Indoor and Outdoor Hotspots of South Korea, Appl. Sci.-Basel, 9, 1947, https://doi.org/10.3390/app9091947, 2019.
Kosmopoulos, G., Salamalikis, V., Pandis, S. N., Yannopoulos, P., Bloutsos,
A. A., and Kazantzidis, A.: Low-cost sensors for measuring airborne
particulate matter: Field evaluation and calibration at a South-Eastern
European site, Sci. Total Environ., 784, 141396, https://doi.org/10.1016/j.scitotenv.2020.141396, 2020.
Kuula, J., Mäkelä, T., Aurela, M., Teinilä, K., Varjonen, S., González, Ó., and Timonen, H.: Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors, Atmos. Meas. Tech., 13, 2413–2423, https://doi.org/10.5194/amt-13-2413-2020, 2020a.
Kuula, J., Friman, M., Helin, A., Niemi, J. V., Aurela, M., Timonen, H., and
Saarikoski, S.: Utilization of scattering and absorption-based particulate
matter sensors in the environment impacted by residential wood combustion,
J. Aerosol Sci., 150, 105671, https://doi.org/10.1016/j.jaerosci.2020.105671, 2020b.
Lal, R. M., Das, K., Fan, Y., Barkjohn, K. K., Botchwey, N., Ramaswami, A.,
and Russell, A. G.: Connecting Air Quality with Emotional Well-Being and
Neighborhood Infrastructure in a US City, Environmental Health Insights, 14,
1178630220915488, https://doi.org/10.1177/1178630220915488, 2020.
Levy Zamora, M., Xiong, F., Gentner, D., Kerkez, B., Kohrman-Glaser, J., and
Koehler, K.: Field and Laboratory Evaluations of the Low-Cost Plantower
Particulate Matter Sensor, Environ. Sci. Technol., 53,
838–849, https://doi.org/10.1021/acs.est.8b05174, 2019.
Li, J. Y., Mattewal, S. K., Patel, S., and Biswas, P.: Evaluation of Nine
Low-cost-sensor-based Particulate Matter Monitors, Aerosol Air Qual. Res., 20, 254–270, https://doi.org/10.4209/aaqr.2018.12.0485, 2020.
Liu, X. G., Cheng, Y. F., Zhang, Y. H., Jung, J. S., Sugimoto, N., Chang, S.
Y., Kim, Y. J., Fan, S. J., and Zeng, L. M.: Influences of relative humidity
and particle chemical composition on aerosol scattering properties during
the 2006 PRD campaign, Atmos. Environ., 42, 1525–1536, https://doi.org/10.1016/j.atmosenv.2007.10.077, 2008.
LRAPA: LRAPA PurpleAir Monitor Correction Factor History, available at: https://www.lrapa.org/DocumentCenter/View/4147/PurpleAir-Correction-Summary (last access: 10 June 2021), 2018.
Lu, Y., Giuliano, G., and Habre, R.: Estimating hourly PM2.5 concentrations
at the neighborhood scale using a low-cost air sensor network: A Los Angeles
Case Study, Environ. Res., 195, 110653, https://doi.org/10.1016/j.envres.2020.110653, 2021.
Magi, B. I., Cupini, C., Francis, J., Green, M., and Hauser, C.: Evaluation
of PM2.5 measured in an urban setting using a low-cost optical particle
counter and a Federal Equivalent Method Beta Attenuation Monitor, Aerosol Sci. Tech., 54, 147–159, https://doi.org/10.1080/02786826.2019.1619915, 2019.
Malings, C., Tanzer, R., Hauryliuk, A., Saha, P. K., Robinson, A. L.,
Presto, A. A., and Subramanian, R.: Fine particle mass monitoring with
low-cost sensors: Corrections and long-term performance evaluation, Aerosol Sci. Tech., 54, 160–174, https://doi.org/10.1080/02786826.2019.1623863, 2020.
Mannshardt, E., Benedict, K., Jenkins, S., Keating, M., Mintz, D., Stone, S., and Wayland, R.: Analysis of short-term ozone and PM2.5 measurements: Characteristics and relationships for air sensor messaging, J. Air Waste Manage. Assoc., 67, 462–474, https://doi.org/10.1080/10962247.2016.1251995, 2017.
McFarlane, C., Isevulambire, P. K., Lumbuenamo, R. S., Ndinga, A. M. E.,
Dhammapala, R., Jin, X., McNeill, V. F., Malings, C., Subramanian, R., and
Westervelt, D. M.: First Measurements of Ambient PM2.5 in Kinshasa,
Democratic Republic of Congo and Brazzaville, Republic of Congo Using
Field-calibrated Low-cost Sensors, Aerosol Air Qual. Res., 21, 200619, https://doi.org/10.4209/aaqr.200619, 2021.
Mehadi, A., Moosmüller, H., Campbell, D. E., Ham, W., Schweizer, D.,
Tarnay, L., and Hunter, J.: Laboratory and field evaluation of real-time and
near real-time PM2.5 smoke monitors, J. Air Waste Manage., 70, 158–179, https://doi.org/10.1080/10962247.2019.1654036, 2020.
Morawska, L., Thai, P. K., Liu, X., Asumadu-Sakyi, A., Ayoko, G., Bartonova,
A., Bedini, A., Chai, F., Christensen, B., Dunbabin, M., Gao, J., Hagler, G.
S. W., Jayaratne, R., Kumar, P., Lau, A. K. H., Louie, P. K. K., Mazaheri,
M., Ning, Z., Motta, N., Mullins, B., Rahman, M. M., Ristovski, Z., Shafiei,
M., Tjondronegoro, D., Westerdahl, D., and Williams, R.: Applications of
low-cost sensing technologies for air quality monitoring and exposure
assessment: How far have they gone?, Environ. Int., 116,
286–299, https://doi.org/10.1016/j.envint.2018.04.018, 2018.
Mukherjee, A., Brown, S. G., McCarthy, M. C., Pavlovic, N. R., Stanton, L.
G., Snyder, J. L., D'Andrea, S., and Hafner, H. R.: Measuring Spatial and
Temporal PM2.5 Variations in Sacramento, California, Communities Using a
Network of Low-Cost Sensors, Sensors-Basel, 19, 4701, https://doi.org/10.3390/s19214701, 2019.
NOAA: U.S. Climate Regions, available at: https://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php (last access: 10 June 2021), 2020.
OR DEQ: 2020 Oregon Annual Ambient Criteria Pollutant Air Monitoring Network Plan, available at: https://www.oregon.gov/deq/FilterDocs/AQmonitoringplan.pdf (last access: 15 June 2021), 2020.
Pawar, H. and Sinha, B.: Humidity, density and inlet aspiration efficiency
correction improve accuracy of a low-cost sensor during field calibration at
a suburban site in the north-western Indo-Gangetic Plain (NW-IGP), Aerosol Sci. Tech., 54, 685–703, https://doi.org/10.1080/02786826.2020.1719971, 2020.
Plantower: Digital universal particle concentration sensor: Plantower PMS5003 series data manual, available at: http://www.aqmd.gov/docs/default-source/aq-spec/resources-page/plantower-pms5003-manual_v2-3.pdf?sfvrsn=2 (last access: 10 June 2021), 2016.
Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D.,
Ito, K., and Thurston, G. D.: Lung cancer, cardiopulmonary mortality, and
long-term exposure to fine particulate air pollution, JAMA, 287, 1132–1141, https://doi.org/10.1001/jama.287.9.1132, 2002.
R Development Core Team: A Language and Environment for Statistical
Computing, Vienna, Austria, 2019.
Robinson, D. L.: Accurate, Low Cost PM2.5 Measurements Demonstrate the Large
Spatial Variation in Wood Smoke Pollution in Regional Australia and Improve
Modeling and Estimates of Health Costs, Atmosphere-Basel, 11, 856, https://doi.org/10.3390/atmos11080856, 2020.
Sayahi, T., Butterfield, A., and Kelly, K. E.: Long-term field evaluation of
the Plantower PMS low-cost particulate matter sensors, Environ. Pollut., 245, 932–940, https://doi.org/10.1016/j.envpol.2018.11.065, 2019.
Schulte, N., Li, X., Ghosh, J. K., Fine, P. M., and Epstein, S. A.:
Responsive high-resolution air quality index mapping using model, regulatory
monitor, and sensor data in real-time, Environ. Res. Lett., 15, 10, https://doi.org/10.1088/1748-9326/abb62b, 2020.
Schwartz, J., Dockery, D. W., and Neas, L. M.: Is daily mortality associated
specifically with fine particles?, J. Air Waste Manage., 46, 927–939,
1996.
Si, M., Xiong, Y., Du, S., and Du, K.: Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods, Atmos. Meas. Tech., 13, 1693–1707, https://doi.org/10.5194/amt-13-1693-2020, 2020.
Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R.
W., Hagler, G. S. W., Shelow, D., Hindin, D. A., Kilaru, V. J., and Preuss,
P. W.: The Changing Paradigm of Air Pollution Monitoring, Environ. Sci. Technol., 47, 11369–11377, https://doi.org/10.1021/es4022602, 2013.
Soneja, S., Chen, C., Tielsch, J. M., Katz, J., Zeger, S. L., Checkley, W.,
Curriero, F. C., and Breysse, P. N.: Humidity and gravimetric equivalency
adjustments for nephelometer-based particulate matter measurements of
emissions from solid biomass fuel use in cookstoves, Int. J. Env. Res. Pub. He., 11, 6400–6416, https://doi.org/10.3390/ijerph110606400, 2014.
Stampfer, O., Austin, E., Ganuelas, T., Fiander, T., Seto, E., and Karr, C.:
Use of low-cost PM monitors and a multi-wavelength aethalometer to
characterize PM2.5 in the Yakama Nation reservation, Atmos. Environ., 224, 117292, https://doi.org/10.1016/j.atmosenv.2020.117292, 2020.
Stavroulas, I., Grivas, G., Michalopoulos, P., Liakakou, E., Bougiatioti,
A., Kalkavouras, P., Fameli, K. M., Hatzianastassiou, N., Mihalopoulos, N.,
and Gerasopoulos, E.: Field Evaluation of Low-Cost PM Sensors (Purple Air
PA-II) Under Variable Urban Air Quality Conditions, in Greece, Atmosphere-Basel, 11, 926, https://doi.org/10.3390/atmos11090926, 2020.
Tryner, J., Quinn, C., Windom, B. C., and Volckens, J.: Design and
evaluation of a portable PM2.5 monitor featuring a low-cost sensor in line with an active filter sampler, Environ. Sci.-Proc. Imp., 21, 1403–1415, https://doi.org/10.1039/c9em00234k, 2019.
Tryner, J., L'Orange, C., Mehaffy, J., Miller-Lionberg, D., Hofstetter, J.
C., Wilson, A., and Volckens, J.: Laboratory evaluation of low-cost
PurpleAir PM monitors and in-field correction using co-located portable
filter samplers, Atmos. Environ., 220, 117067, https://doi.org/10.1016/j.atmosenv.2019.117067, 2020a.
Tryner, J., Mehaffy, J., Miller-Lionberg, D., and Volckens, J.: Effects of
aerosol type and simulated aging on performance of low-cost PM sensors,
J. Aerosol Sci., 150, 105654, https://doi.org/10.1016/j.jaerosci.2020.105654, 2020b.
U.S. EPA: PM2.5 Continuous Monitor Comparability Assessments, available at:
https://www.epa.gov/outdoor-air-quality-data/pm25-continuous-monitor-comparability-assessments, last access: 11 March 2020a.
U.S. EPA: PM2.5 Data Quality Dashboard, available at:
https://sti-r-shiny.shinyapps.io/QVA_Dashboard/, last access: 11 March 2020b.
U.S. EPA: List of designated reference and equivalent methods, available at: http://www.epa.gov/ttn/amtic/criteria.html, last access: 15 June 2021.
van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy,
R. C., Lyapustin, A., Sayer, A. M., and Winker, D. M.: Global Estimates of
Fine Particulate Matter using a Combined Geophysical-Statistical Method with
Information from Satellites, Models, and Monitors, Environ. Sci. Technol., 50, 3762–3772, https://doi.org/10.1021/acs.est.5b05833, 2016.
Wang, W.-C. V., Lung, S.-C. C., Liu, C. H., and Shui, C.-K.: Laboratory
Evaluations of Correction Equations with Multiple Choices for Seed Low-Cost
Particle Sensing Devices in Sensor Networks, Sensors-Basel, 20, 3661, https://doi.org/10.3390/s20133661, 2020.
Wang, Z., Delp, W. W., and Singer, B. C.: Performance of low-cost indoor air
quality monitors for PM2.5 and PM10 from residential sources, Build. Environ., 171, 106654, https://doi.org/10.1016/j.buildenv.2020.106654, 2020.
Williams, R., Duvall, R., Kilaru, V., Hagler, G., Hassinger, L., Benedict,
K., Rice, J., Kaufman, A., Judge, R., Pierce, G., Allen, G., Bergin, M.,
Cohen, R. C., Fransioli, P., Gerboles, M., Habre, R., Hannigan, M., Jack,
D., Louie, P., Martin, N. A., Penza, M., Polidori, A., Subramanian, R., Ray,
K., Schauer, J., Seto, E., Thurston, G., Turner, J., Wexler, A. S., and
Ning, Z.: Deliberating performance targets workshop: Potential paths for
emerging PM2.5 and O3 air sensor progress, Atmos. Environ., 2,
100031, https://doi.org/10.1016/j.aeaoa.2019.100031, 2019.
Zhang, X., Turpin, B. J., McMurry, P. H., Hering, S. V., and Stolzenburg, M.
R.: Mie Theory Evaluation of Species Contributions to 1990 Wintertime
Visibility Reduction in the Grand Canyon, J. Air Waste Manage., 44, 153–162, https://doi.org/10.1080/1073161X.1994.10467244, 1994.
Zheng, T., Bergin, M. H., Johnson, K. K., Tripathi, S. N., Shirodkar, S., Landis, M. S., Sutaria, R., and Carlson, D. E.: Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments, Atmos. Meas. Tech., 11, 4823–4846, https://doi.org/10.5194/amt-11-4823-2018, 2018.
Zikova, N., Masiol, M., Chalupa, D. C., Rich, D. Q., Ferro, A. R., and
Hopke, P. K.: Estimating Hourly Concentrations of PM2.5 across a
Metropolitan Area Using Low-Cost Particle Monitors, Sensors-Basel, 17, 1922, https://doi.org/10.3390/s17081922, 2017.
Zou, Y., Clark, J. D., and May, A. A.: A systematic investigation on the
effects of temperature and relative humidity on the performance of eight
low-cost particle sensors and devices, J. Aerosol Sci., 152, 105715,
https://doi.org/10.1016/j.jaerosci.2020.105715, 2020a.
Zou, Y., Young, M., Chen, J., Liu, J., May, A., and Clark, J. D.: Examining
the functional range of commercially available low-cost airborne particle
sensors and consequences for monitoring of indoor air quality in residences,
Indoor Air, 30, 213–234, https://doi.org/10.1111/ina.12621, 2020b.
Zusman, M., Schumacher, C. S., Gassett, A. J., Spalt, E. W., Austin, E.,
Larson, T. V., Carvlin, G., Seto, E., Kaufman, J. D., and Sheppard, L.:
Calibration of low-cost particulate matter sensors: Model development for a
multi-city epidemiological study, Environ. Int., 134, 105329,
https://doi.org/10.1016/j.envint.2019.105329, 2020.
Short summary
Although widely used, air sensor measurements are often biased. In this work we develop a correction with a relative humidity term that reduces the bias and improves consistency between different United States regions. This correction equation, along with proposed data cleaning criteria, has been applied to PurpleAir PM2.5 measurements across the US on the AirNow Fire and Smoke Map and has the potential to be successfully used in other air quality and public health applications.
Although widely used, air sensor measurements are often biased. In this work we develop a...
