Articles | Volume 14, issue 2
https://doi.org/10.5194/amt-14-995-2021
© 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-14-995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Feb 2021
Research article |
| 09 Feb 2021
| 09 Feb 2021
Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration
Colby Buehler
Department of Chemical & Environmental Engineering, Yale
University, School of Engineering and
Applied Science, New Haven, Connecticut 06511, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale
University, New Haven,
Connecticut 06511, USA
Fulizi Xiong
Department of Chemical & Environmental Engineering, Yale
University, School of Engineering and
Applied Science, New Haven, Connecticut 06511, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale
University, New Haven,
Connecticut 06511, USA
Misti Levy Zamora
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale
University, New Haven,
Connecticut 06511, USA
Department of Environmental Health and Engineering, Johns Hopkins
Bloomberg School of Public
Health, Baltimore, Maryland 21205, USA
Kate M. Skog
Department of Chemical & Environmental Engineering, Yale
University, School of Engineering and
Applied Science, New Haven, Connecticut 06511, USA
Joseph Kohrman-Glaser
Department of Mechanical Engineering, Yale University, School of
Engineering and Applied Science,
New Haven, Connecticut 06511, USA
Stefan Colton
Department of Mechanical Engineering, Yale University, School of
Engineering and Applied Science,
New Haven, Connecticut 06511, USA
Michael McNamara
Department of Electrical Engineering, Yale University, School of
Engineering and Applied Science,
New Haven, Connecticut 06511, USA
Kevin Ryan
Department of Electrical Engineering, Yale University, School of
Engineering and Applied Science,
New Haven, Connecticut 06511, USA
Carrie Redlich
Department of Internal Medicine, Yale University, School of Medicine, New Haven, Connecticut 06510, USA
Department of Environmental Health Sciences, Yale University, School of Public Health, New Haven,
Connecticut 06511, USA
Matthew Bartos
Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin,
Cockrell School of Engineering, Austin, Texas 78712, USA
Brandon Wong
Civil and Environmental Engineering, University of Michigan, 2350
Hayward St, G.G. Brown Building,
Ann Arbor, Michigan 48109, USA
Branko Kerkez
Civil and Environmental Engineering, University of Michigan, 2350
Hayward St, G.G. Brown Building,
Ann Arbor, Michigan 48109, USA
Kirsten Koehler
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale
University, New Haven,
Connecticut 06511, USA
Department of Environmental Health and Engineering, Johns Hopkins
Bloomberg School of Public
Health, Baltimore, Maryland 21205, USA
Drew R. Gentner
CORRESPONDING AUTHOR
Department of Chemical & Environmental Engineering, Yale
University, School of Engineering and
Applied Science, New Haven, Connecticut 06511, USA
SEARCH (Solutions for Energy, Air, Climate and Health) Center, Yale
University, New Haven,
Connecticut 06511, USA
Multiphase Chemistry, Max Planck Institute for Chemistry, 55128 Mainz, Germany
Related authors
Misti Levy Zamora, Colby Buehler, Abhirup Datta, Drew R. Gentner, and Kirsten Koehler
Atmos. Meas. Tech., 16, 169–179, https://doi.org/10.5194/amt-16-169-2023, https://doi.org/10.5194/amt-16-169-2023, 2023
Short summary
Short summary
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.
Sunhye Kim, Jo Machesky, Drew R. Gentner, and Albert A. Presto
Atmos. Chem. Phys., 24, 1281–1298, https://doi.org/10.5194/acp-24-1281-2024, https://doi.org/10.5194/acp-24-1281-2024, 2024
Short summary
Short summary
Cooking emissions are often an overlooked source of air pollution. We used a mobile lab to measure the characteristics of particles emitted from cooking sites in two cities. Our findings showed that cooking releases a substantial number of fine particles. While most emissions were similar, a bakery site showed distinctive chemical compositions with higher nitrogen compound levels. Thus, understanding the particle emissions from different cooking activities is crucial.
Andrew T. Lambe, Bin Bai, Masayuki Takeuchi, Nicole Orwat, Paul M. Zimmerman, Mitchell W. Alton, Nga L. Ng, Andrew Freedman, Megan S. Claflin, Drew R. Gentner, Douglas R. Worsnop, and Pengfei Liu
Atmos. Chem. Phys., 23, 13869–13882, https://doi.org/10.5194/acp-23-13869-2023, https://doi.org/10.5194/acp-23-13869-2023, 2023
Short summary
Short summary
We developed a new method to generate nitrate radicals (NO3) for atmospheric chemistry applications that works by irradiating mixtures containing ceric ammonium nitrate with a UV light at room temperature. It has several advantages over traditional NO3 sources. We characterized its performance over a range of mixture and reactor conditions as well as other irradiation products. Proof of concept was demonstrated by generating and characterizing oxidation products of the β-pinene + NO3 reaction.
Benjamin N. Murphy, Darrell Sonntag, Karl M. Seltzer, Havala O. T. Pye, Christine Allen, Evan Murray, Claudia Toro, Drew R. Gentner, Cheng Huang, Shantanu Jathar, Li Li, Andrew A. May, and Allen L. Robinson
Atmos. Chem. Phys., 23, 13469–13483, https://doi.org/10.5194/acp-23-13469-2023, https://doi.org/10.5194/acp-23-13469-2023, 2023
Short summary
Short summary
We update methods for calculating organic particle and vapor emissions from mobile sources in the USA. Conventionally, particulate matter (PM) and volatile organic carbon (VOC) are speciated without consideration of primary semivolatile emissions. Our methods integrate state-of-the-science speciation profiles and correct for common artifacts when sampling emissions in a laboratory. We quantify impacts of the emission updates on ambient pollution with the Community Multiscale Air Quality model.
Misti Levy Zamora, Colby Buehler, Abhirup Datta, Drew R. Gentner, and Kirsten Koehler
Atmos. Meas. Tech., 16, 169–179, https://doi.org/10.5194/amt-16-169-2023, https://doi.org/10.5194/amt-16-169-2023, 2023
Short summary
Short summary
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.
Peeyush Khare, Jordan E. Krechmer, Jo E. Machesky, Tori Hass-Mitchell, Cong Cao, Junqi Wang, Francesca Majluf, Felipe Lopez-Hilfiker, Sonja Malek, Will Wang, Karl Seltzer, Havala O. T. Pye, Roisin Commane, Brian C. McDonald, Ricardo Toledo-Crow, John E. Mak, and Drew R. Gentner
Atmos. Chem. Phys., 22, 14377–14399, https://doi.org/10.5194/acp-22-14377-2022, https://doi.org/10.5194/acp-22-14377-2022, 2022
Short summary
Short summary
Ammonium adduct chemical ionization is used to examine the atmospheric abundances of oxygenated volatile organic compounds associated with emissions from volatile chemical products, which are now key contributors of reactive precursors to ozone and secondary organic aerosols in urban areas. The application of this valuable measurement approach in densely populated New York City enables the evaluation of emissions inventories and thus the role these oxygenated compounds play in urban air quality.
Katherine L. Hayden, Shao-Meng Li, John Liggio, Michael J. Wheeler, Jeremy J. B. Wentzell, Amy Leithead, Peter Brickell, Richard L. Mittermeier, Zachary Oldham, Cristian M. Mihele, Ralf M. Staebler, Samar G. Moussa, Andrea Darlington, Mengistu Wolde, Daniel Thompson, Jack Chen, Debora Griffin, Ellen Eckert, Jenna C. Ditto, Megan He, and Drew R. Gentner
Atmos. Chem. Phys., 22, 12493–12523, https://doi.org/10.5194/acp-22-12493-2022, https://doi.org/10.5194/acp-22-12493-2022, 2022
Short summary
Short summary
In this study, airborne measurements provided the most detailed characterization, to date, of boreal forest wildfire emissions. Measurements showed a large diversity of air pollutants expanding the volatility range typically reported. A large portion of organic species was unidentified, likely comprised of complex organic compounds. Aircraft-derived emissions improve wildfire chemical speciation and can support reliable model predictions of pollution from boreal forest wildfires.
Yun Lin, Yuan Wang, Bowen Pan, Jiaxi Hu, Song Guo, Misti Levy Zamora, Pengfei Tian, Qiong Su, Yuemeng Ji, Jiayun Zhao, Mario Gomez-Hernandez, Min Hu, and Renyi Zhang
Atmos. Chem. Phys., 22, 4951–4967, https://doi.org/10.5194/acp-22-4951-2022, https://doi.org/10.5194/acp-22-4951-2022, 2022
Short summary
Short summary
Severe regional haze events, which are characterized by exceedingly high levels of fine particulate matter (PM), occur frequently in many developing countries (such as China and India), with profound implications for human health, weather, and climate. Our work establishes a synthetic view for the dominant regional features during severe haze events, unraveling rapid in situ PM production and inefficient transport, both of which are amplified by atmospheric stagnation.
Jenna C. Ditto, Jo Machesky, and Drew R. Gentner
Atmos. Chem. Phys., 22, 3045–3065, https://doi.org/10.5194/acp-22-3045-2022, https://doi.org/10.5194/acp-22-3045-2022, 2022
Short summary
Short summary
We analyzed gases and aerosols sampled in summer and winter in a coastal region that is often downwind of urban areas and observed large contributions of nitrogen-containing organic compounds influenced by a mix of biogenic, anthropogenic, and/or marine sources as well as photochemical and aqueous-phase atmospheric processes. The results show the prevalence of key reduced and oxidized nitrogen functional groups and advance knowledge on the chemical structure of nitrogen-containing compounds.
Jenna C. Ditto, Megan He, Tori N. Hass-Mitchell, Samar G. Moussa, Katherine Hayden, Shao-Meng Li, John Liggio, Amy Leithead, Patrick Lee, Michael J. Wheeler, Jeremy J. B. Wentzell, and Drew R. Gentner
Atmos. Chem. Phys., 21, 255–267, https://doi.org/10.5194/acp-21-255-2021, https://doi.org/10.5194/acp-21-255-2021, 2021
Short summary
Short summary
Forest fires are an important source of reactive organic gases and aerosols to the atmosphere. We analyzed organic aerosols collected from an aircraft above a boreal forest fire and reported an increasing contribution from compounds containing oxygen, nitrogen, and sulfur as the plume aged, with sulfide and ring-bound nitrogen functionality. Our results demonstrated chemistry that is important in biomass burning but also in urban/developing regions with high local nitrogen and sulfur emissions.
Cited articles
Alphasense Ltd.: IRC-A1 Carbon Dioxide Infrared Sensor Data Sheet, available at: http://www.alphasense.com/WEB1213/wp-content/uploads/2018/04/IRC-A1.pdf (last access: 11 December 2020), 2018.
Alphasense Ltd.: CO-A4 Carbon Monoxide Sensor Data Sheet, available at:
http://www.alphasense.com/WEB1213/wp-content/uploads/2019/09/CO-A4.pdf (last access: 11 December 2020), 2019a.
Alphasense Ltd.: NO-A4 Nitric Oxide Sensor Data Sheet, available at:
http://www.alphasense.com/WEB1213/wp-content/uploads/2019/09/NO-A4.pdf (last access: 11 December 2020), 2019b.
Bigi, A., Mueller, M., Grange, S. K., Ghermandi, G., and Hueglin, C.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717–3735, https://doi.org/10.5194/amt-11-3717-2018, 2018.
Borrego, C., Costa, A. M., Ginja, J., Amorim, M., Coutinho, M., Karatzas,
K., Sioumis, T., Katsifarakis, N., Konstantinidis, K., De Vito, S.,
Esposito, E., Smith, P., André, N., Gérard, P., Francis, L. A.,
Castell, N., Schneider, P., Viana, M., Minguillón, M. C., Reimringer,
W., Otjes, R. P., von Sicard, O., Pohle, R., Elen, B., Suriano, D., Pfister,
V., Prato, M., Dipinto, S., and Penza, M.: Assessment of air quality
microsensors versus reference methods: The EuNetAir joint exercise, Atmos.
Environ., 147, 246–263, https://doi.org/10.1016/j.atmosenv.2016.09.050, 2016.
Brauer, M., Amann, M., Burnett, R. T., Cohen, A., Dentener, F., Ezzati, M.,
Henderson, S. B., Krzyzanowski, M., Martin, R. V., van Dingenen, R., van Donkelaar, A., and Thurston, G. D.: Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution, Environ.
Sci. Technol., 46, 652–660, https://doi.org/10.1021/es2025752, 2012.
Cao, T. and Thompson, J. E.: Personal monitoring of ozone exposure: A fully
portable device for under USD 150 cost, Sensors Actuators, B. Chem., 224,
936–943, https://doi.org/10.1016/j.snb.2015.10.090, 2016.
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain,
B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms
contribute to air quality monitoring and exposure estimates?, Environ. Int.,
99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007, 2017.
Chuang, K. J., Chan, C. C., Su, T. C., Lee, C. T., and Tang, C. S.: The
effect of urban air pollution on inflammation, oxidative stress,
coagulation, and autonomic dysfunction in young adults, Am. J. Resp. Crit. Care, 176, 370–376, https://doi.org/10.1164/rccm.200611-1627OC, 2007.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep,
K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y.,
Martin, R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G.,
Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L.,
and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of
disease attributable to ambient air pollution: an analysis of data from the
Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918,
https://doi.org/10.1016/S0140-6736(17)30505-6, 2017.
Cross, E. S., Williams, L. R., Lewis, D. K., Magoon, G. R., Onasch, T. B., Kaminsky, M. L., Worsnop, D. R., and Jayne, J. T.: Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements, Atmos. Meas. Tech., 10, 3575–3588, https://doi.org/10.5194/amt-10-3575-2017, 2017.
Datta, A., Saha, A., Zamora, M. L., Buehler, C., Hao, L., Xiong, F.,
Gentner, D. R., and Koehler, K.: Statistical field calibration of a low-cost
PM2.5 monitoring network in Baltimore, Atmos. Environ., 242, 117761, https://doi.org/10.1016/j.atmosenv.2020.117761, 2020.
Eugster, W. and Kling, G. W.: Performance of a low-cost methane sensor for ambient concentration measurements in preliminary studies, Atmos. Meas. Tech., 5, 1925–1934, https://doi.org/10.5194/amt-5-1925-2012, 2012.
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.
Forouzanfar, M. H., Afshin, A., Alexander, L. T., Biryukov, S.,
Brauer, M., Cercy, K., Charlson, F. J., Cohen, A. J., Dandona,
55 L., Estep, K., Ferrari, A. J., Frostad, J. J., Fullman, N., Godwin,
W. W., Griswold, M., Hay, S. I., Kyu, H. H., Larson, H. J., Lim,
S. S., Liu, P. Y., Lopez, A. D., Lozano, R., Marczak, L., Mokdad,
A. H., Moradi-Lakeh, M., Naghavi, M., Reitsma, M. B., Roth, G.
A., Sur, P. J., Vos, T., Wagner, J. A., Wang, H., Zhao, Y., Zhou,
M., Barber, R. M., Bell, B., Blore, J. D., Casey, D. C., Coates, 60
M. M., Cooperrider, K., Cornaby, L., Dicker, D., Erskine, H. E.,
Fleming, T., Foreman, K., Gakidou, E., Haagsma, J. A., Johnson,
C. O., Kemmer, L., Ku, T., Leung, J., Masiye, F., Millear,
A., Mirarefin, M., Misganaw, A., Mullany, E., Mumford, J. E.,
Ng, M., Olsen, H., Rao, P., Reinig, N., Roman, Y., Sandar, L., 65
Santomauro, D. F., Slepak, E. L., Sorensen, R. J. D., Thomas, B.
A., Vollset, S. E., Whiteford, H. A., Zipkin, B., Murray, C. J. L.,
Mock, C. N., Anderson, B. O., Futran, N. D., Anderson, H. R.,
Bhutta, Z. A., Nisar, M. I., Akseer, N., Krueger, H., Gotay, C.
C., Kissoon, N., Kopec, J. A., Pourmalek, F., Burnett, R., Abajo- 70
bir, A. A., Knibbs, L. D., Veerman, J. L., Lalloo, R., Scott, J. G.,
Alam, N. K. M., Gouda, H. N., Guo, Y., McGrath, J. J., Jeemon,
P., Dandona, R., Goenka, S., Kumar, G. A., Gething, P. W.,
Bisanzio, D., et al. (GBD 2015 Risk Factors Collaborators): Global, regional, and national comparative risk
assessment of 79 behavioural, environmental and occupational, and metabolic
risks or clusters of risks, 1990–2015: a systematic analysis for the Global
Burden of Disease Study 2015, Lancet, 388, 1659–1724,
https://doi.org/10.1016/S0140-6736(16)31679-8, 2016.
Hagan, D. H., Isaacman-VanWertz, G., Franklin, J. P., Wallace, L. M. M., Kocar, B. D., Heald, C. L., and Kroll, J. H.: Calibration and assessment of electrochemical air quality sensors by co-location with regulatory-grade instruments, Atmos. Meas. Tech., 11, 315–328, https://doi.org/10.5194/amt-11-315-2018, 2018.
Hodgkinson, J., Smith, R., Ho, W. O., Saffell, J. R., and Tatam, R. P.:
Non-dispersive infra-red (NDIR) measurement of carbon dioxide at 4.2 µm in a compact and optically efficient sensor, Sensors Actuators, B. Chem., 186, 580–588, https://doi.org/10.1016/j.snb.2013.06.006, 2013.
Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M.,
Lagler, F., Redon, N., Crunaire, S., and Borowiak, A.: Review of the
performance of low-cost sensors for air quality monitoring, Atmosphere-Basel, 10, 506, https://doi.org/10.3390/atmos10090506, 2019.
Kheirbek, I., Wheeler, K., Walters, S., Kass, D., and Matte, T.: PM2.5 and ozone health impacts and disparities in New York City: Sensitivity to
spatial and temporal resolution, Air Qual. Atmos. Hlth., 6, 473–486,
https://doi.org/10.1007/s11869-012-0185-4, 2013.
Kim, J., Shusterman, A. A., Lieschke, K. J., Newman, C., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: field calibration and evaluation of low-cost air quality sensors, Atmos. Meas. Tech., 11, 1937–1946, https://doi.org/10.5194/amt-11-1937-2018, 2018.
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.
Lewis, A. C., Lee, J. D., Edwards, P. M., Shaw, M. D., Evans, M. J., Moller,
S. J., Smith, K. R., Buckley, J. W., Ellis, M., Gillot, S. R., and White, A.:
Evaluating the performance of low cost chemical sensors for air pollution
research, Faraday Discuss., 189, 85–103, https://doi.org/10.1039/c5fd00201j, 2016.
Lewis, A. C., von Schneidemesser, E., and Peltier, R. E.: Low-cost sensors
for the measurement of atmospheric composition: overview of topic and future
applications, available at:
http://www.wmo.int/pages/prog/arep/gaw/documents/Draft_low_cost_sensors.pdf, (last access: 11 December 2020), 2018.
Mead, M. I., Popoola, O. A. M., Stewart, G. B., Landshoff, P., Calleja, M.,
Hayes, M., Baldovi, J. J., McLeod, M. W., Hodgson, T. F., Dicks, J., Lewis,
A., Cohen, J., Baron, R., Saffell, J. R., and Jones, R. L.: The use of
electrochemical sensors for monitoring urban air quality in low-cost,
high-density networks, Atmos. Environ., 70, 186–203,
https://doi.org/10.1016/j.atmosenv.2012.11.060, 2013.
Pang, X., Shaw, M. D., Gillot, S., and Lewis, A. C.: The impacts of water
vapour and co-pollutants on the performance of electrochemical gas sensors
used for air quality monitoring, Sensors Actuators, B. Chem., 266, 674–684,
https://doi.org/10.1016/j.snb.2018.03.144, 2018.
Piedrahita, R., Xiang, Y., Masson, N., Ortega, J., Collier, A., Jiang, Y., Li, K., Dick, R. P., Lv, Q., Hannigan, M., and Shang, L.: The next generation of low-cost personal air quality sensors for quantitative exposure monitoring, Atmos. Meas. Tech., 7, 3325–3336, https://doi.org/10.5194/amt-7-3325-2014, 2014.
Pope, C. A. and Dockery, D. W.: Health effects of fine particulate air
pollution: Lines that connect, J. Air Waste Manage., 56, 709–742,
https://doi.org/10.1080/10473289.2006.10464485, 2006.
Popoola, O. A. M., Stewart, G. B., Mead, M. I., and Jones, R. L.: Development
of a baseline-temperature correction methodology for electrochemical sensors
and its implications for long-term stability, Atmos. Environ., 147,
330–343, https://doi.org/10.1016/j.atmosenv.2016.10.024, 2016.
Ripoll, A., Viana, M., Padrosa, M., Querol, X., Minutolo, A., Hou, K. M., Barcelo-Ordinas, J. M., and Garcia-Vidal, J.: Testing the performance of sensors for ozone pollution monitoring in a citizen science approach, Sci. Total Environ., 651, 1166–1179, https://doi.org/10.1016/j.scitotenv.2018.09.257, 2019.
Schilling, K., Gentner, D. R., Wilen, L., Medina, A., Buehler, C.,
Perez-Lorenzo, L. J., Pollitt, K. J. G., Bergemann, R., Bernardo, N.,
Peccia, J., Wilczynski, V., and Lattanza, L.: An accessible method for
screening aerosol filtration identifies poor-performing commercial masks and
respirators, J. Expo. Sci. Env. Epid., online first, https://doi.org/10.1038/s41370-020-0258-7, 2020.
Shusterman, A. A., Teige, V. E., Turner, A. J., Newman, C., Kim, J., and Cohen, R. C.: The BErkeley Atmospheric CO2 Observation Network: initial evaluation, Atmos. Chem. Phys., 16, 13449–13463, https://doi.org/10.5194/acp-16-13449-2016, 2016.
Spinelle, L., Gerboles, M., Villani, M. G., Aleixandre, M., and Bonavitacola,
F.: Field calibration of a cluster of low-cost commercially available
sensors for air quality monitoring, Part B: NO, CO and CO2, Sensors
Actuators, B. Chem., 238, 706–715, https://doi.org/10.1016/j.snb.2016.07.036, 2017.
Thorson, J., Collier-Oxandale, A., and Hannigan, M.: Using A Low-Cost Sensor
Array and Machine Mixtures and Identify Likely Sources, Sensors, 19, 3723, https://doi.org/10.3390/s19173723, 2019.
van den Bossche, M., Rose, N. T., and De Wekker, S. F. J.: Potential of a
low-cost gas sensor for atmospheric methane monitoring, Sensors Actuators, B.
Chem., 238, 501–509, https://doi.org/10.1016/j.snb.2016.07.092, 2017.
Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., Robinson, A. L., and Subramanian, R.: A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring, Atmos. Meas. Tech., 11, 291–313, https://doi.org/10.5194/amt-11-291-2018, 2018.
Short summary
In this paper we develop a stationary and portable low-cost multipollutant monitor capable of measuring a variety of human-health- and climate-related pollutants. While traditional reference instrumentation is sparsely spaced, these monitors can be deployed as a network to gain insight into the spatial and temporal variability within an urban setting, or in other targeted studies. We also implement an online calibration system to address long-term drift of sensors and adjust calibrations.
In this paper we develop a stationary and portable low-cost multipollutant monitor capable of...
