Articles | Volume 15, issue 21
https://doi.org/10.5194/amt-15-6309-2022
https://doi.org/10.5194/amt-15-6309-2022
Research article
 | 
02 Nov 2022
Research article |  | 02 Nov 2022

Calibrating networks of low-cost air quality sensors

Priyanka deSouza, Ralph Kahn, Tehya Stockman, William Obermann, Ben Crawford, An Wang, James Crooks, Jing Li, and Patrick Kinney

Related authors

Siberian wildfire smoke observations from space-based multi-angle imaging: A multi-year regional analysis of smoke particle properties, their evolution, and comparisons with North American boreal fire plumes
Katherine T. Junghenn Noyes and Ralph A. Kahn
EGUsphere, https://doi.org/10.5194/egusphere-2025-395,https://doi.org/10.5194/egusphere-2025-395, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Biomass burning emission analysis based on MODIS aerosol optical depth and AeroCom multi-model simulations: implications for model constraints and emission inventories
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025,https://doi.org/10.5194/acp-25-1545-2025, 2025
Short summary
Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency of multisensor satellite aerosol products
Xin Xi, Jun Wang, Zhendong Lu, Andrew Sayer, Jaehwa Lee, Robert Levy, Yujie Wang, Alexei Lyapustin, Hongqing Liu, Istvan Laszlo, Changwoo Ahn, Omar Torres, Sabur Abdullaev, and Ralph Kahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3416,https://doi.org/10.5194/egusphere-2024-3416, 2024
Short summary
Light-absorbing black carbon and brown carbon components of smoke aerosol from DSCOVR EPIC measurements over North America and central Africa
Myungje Choi, Alexei Lyapustin, Gregory L. Schuster, Sujung Go, Yujie Wang, Sergey Korkin, Ralph Kahn, Jeffrey S. Reid, Edward J. Hyer, Thomas F. Eck, Mian Chin, David J. Diner, Olga Kalashnikova, Oleg Dubovik, Jhoon Kim, and Hans Moosmüller
Atmos. Chem. Phys., 24, 10543–10565, https://doi.org/10.5194/acp-24-10543-2024,https://doi.org/10.5194/acp-24-10543-2024, 2024
Short summary
MAGARA: a Multi-Angle Geostationary Aerosol Retrieval Algorithm
James A. Limbacher, Ralph A. Kahn, Mariel D. Friberg, Jaehwa Lee, Tyler Summers, and Hai Zhang
Atmos. Meas. Tech., 17, 471–498, https://doi.org/10.5194/amt-17-471-2024,https://doi.org/10.5194/amt-17-471-2024, 2024
Short summary

Related subject area

Subject: Aerosols | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
Performance evaluation of Atmotube PRO sensors for air quality measurements in an urban location
Aishah I. Shittu, Kirsty J. Pringle, Stephen R. Arnold, Richard J. Pope, Ailish M. Graham, Carly Reddington, Richard Rigby, and James B. McQuaid
Atmos. Meas. Tech., 18, 817–828, https://doi.org/10.5194/amt-18-817-2025,https://doi.org/10.5194/amt-18-817-2025, 2025
Short summary
Inversion Algorithm of Black Carbon Mixing State Based on Machine Learning
Zeyuan Tian, Jiandong Wang, Jiaping Wang, Chao Liu, Jinbo Wang, Zhouyang Zhang, Yuzhi Jin, Sunan Shen, Bin Wang, Wei Nie, Xin Huang, and Aijun Ding
EGUsphere, https://doi.org/10.5194/egusphere-2024-2496,https://doi.org/10.5194/egusphere-2024-2496, 2024
Short summary
Spatial analysis of PM2.5 using a concentration similarity index applied to air quality sensor networks
Rósín Byrne, John C. Wenger, and Stig Hellebust
Atmos. Meas. Tech., 17, 5129–5146, https://doi.org/10.5194/amt-17-5129-2024,https://doi.org/10.5194/amt-17-5129-2024, 2024
Short summary
A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization
Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach
Atmos. Meas. Tech., 17, 1251–1277, https://doi.org/10.5194/amt-17-1251-2024,https://doi.org/10.5194/amt-17-1251-2024, 2024
Short summary
Towards a hygroscopic growth calibration for low-cost PM2.5 sensors
Milan Y. Patel, Pietro F. Vannucci, Jinsol Kim, William M. Berelson, and Ronald C. Cohen
Atmos. Meas. Tech., 17, 1051–1060, https://doi.org/10.5194/amt-17-1051-2024,https://doi.org/10.5194/amt-17-1051-2024, 2024
Short summary

Cited articles

Anderson, G. B. and Peng, R. D.: weathermetrics: Functions to convert between weather metrics (R package), http://cran.r-project.org/web/packages/weathermetrics/index.html (last access: 26 October 2022), 2012. 
Apte, J. S., Messier, K. P., Gani, S., Brauer, M., Kirchstetter, T. W., Lunden, M. M., Marshall, J. D., Portier, C. J., Vermeulen, R. C. H., and Hamburg, S. P.: High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data, Environ. Sci. Technol., 51, 6999–7008, https://doi.org/10.1021/acs.est.7b00891, 2017. 
Barkjohn, K. K., Gantt, B., and Clements, A. L.: Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor, Atmos. Meas. Tech., 14, 4617–4637, https://doi.org/10.5194/amt-14-4617-2021, 2021. 
Bean, J. K.: Evaluation methods for low-cost particulate matter sensors, Atmos. Meas. Tech., 14, 7369–7379, https://doi.org/10.5194/amt-14-7369-2021, 2021. 
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. 
Download
Short summary
How sensitive are the spatial and temporal trends of PM2.5 derived from a network of low-cost sensors to the calibration adjustment used? How transferable are calibration equations developed at a few co-location sites to an entire network of low-cost sensors? This paper attempts to answer this question and offers a series of suggestions on how to develop the most robust calibration function for different end uses. It uses measurements from the Love My Air network in Denver as a test case.
Share