Articles | Volume 10, issue 7
https://doi.org/10.5194/amt-10-2383-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-2383-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Jul 2017
Research article |
| 03 Jul 2017
Evaluation and environmental correction of ambient CO2 measurements from a low-cost NDIR sensor
Cory R. Martin
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, MD 20742, USA
Anna Karion
National Institute of Standards and Technology, Gaithersburg, MD
20899, USA
Russell R. Dickerson
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, MD 20742, USA
Xinrong Ren
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
Air Resources Laboratory, National Oceanic and Atmospheric
Administration, College Park, MD 20740, USA
Bari N. Turpie
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
Kristy J. Weber
Department of Atmospheric and Oceanic Science, University of Maryland,
College Park, MD 20742, USA
now at: Department of Geography, University of Colorado at Boulder,
Boulder, CO 80309, USA
Related authors
No articles found.
Qixiang Cai, Ning Zeng, Xiaoyu Yang, Chi Xu, Zhaojun Wang, and Pengfei Han
Atmos. Meas. Tech., 18, 4871–4884, https://doi.org/10.5194/amt-18-4871-2025, https://doi.org/10.5194/amt-18-4871-2025, 2025
Short summary
Short summary
Mid- and low-cost CO2 sensors are attractive in carbon monitoring and atmospheric inversions. They are useful in both fixed stations and mobile monitoring. Yet the performance faces great challenges due to environmental impacts and long-term drifts. Here, we conducted 30 months of co-located observations using such sensors with a reference instrument. After corrections of environmental impacts and drifts, the accuracy reached 1–3 ppm. We recommend standard gas calibration within 3–6 months.
Pengfei Han, Ning Zeng, Bo Yao, Wen Zhang, Weijun Quan, Pucai Wang, Ting Wang, Minqiang Zhou, Qixiang Cai, Yuzhong Zhang, Ruosi Liang, Wanqi Sun, and Shengxiang Liu
Atmos. Chem. Phys., 25, 4965–4988, https://doi.org/10.5194/acp-25-4965-2025, https://doi.org/10.5194/acp-25-4965-2025, 2025
Short summary
Short summary
Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4 emissions, yet large observation gaps exist. Here we compiled a comprehensive dataset, which is publicly available, that includes ground-based, satellite-based, inventory, and modeling results to show the CH4 concentrations, enhancements, and spatial–temporal variations. The data can benefit the research community and policy-makers for future observations, atmospheric inversions, and policy-making.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Anna Karion, Michael F. Link, Rileigh Robertson, Tyler Boyle, and Dustin Poppendieck
Atmos. Meas. Tech., 17, 7065–7075, https://doi.org/10.5194/amt-17-7065-2024, https://doi.org/10.5194/amt-17-7065-2024, 2024
Short summary
Short summary
Methane leaks into houses that use natural gas from appliances, pipes, and fittings. We measured methane emitted from a manufactured house under different ventilation conditions using indoor and outdoor concentration measurements. We injected methane at prescribed rates into the house and then measured the emissions using our method. We report the error in the calculation based on these tests. We also describe the method and provide guidance on conducting this type of experiment.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted
Short summary
Short summary
We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.
Rui Zhang, Yuying Wang, Zhanqing Li, Zhibin Wang, Russell R. Dickerson, Xinrong Ren, Hao He, Fei Wang, Ying Gao, Xi Chen, Jialu Xu, Yafang Cheng, and Hang Su
Atmos. Chem. Phys., 22, 14879–14891, https://doi.org/10.5194/acp-22-14879-2022, https://doi.org/10.5194/acp-22-14879-2022, 2022
Short summary
Short summary
Factors of cloud condensation nuclei number concentration (NCCN) profiles determined in the North China Plain include air mass sources, temperature structure, anthropogenic emissions, and terrain distribution. Cloud condensation nuclei (CCN) spectra suggest that the ability of aerosol activation into CCN is stronger in southeasterly than in northwesterly air masses and stronger in the free atmosphere than near the surface. A good method to parameterize NCCN from aerosol optical data is found.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
Short summary
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han
Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, https://doi.org/10.5194/esd-13-833-2022, 2022
Short summary
Short summary
We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.
Zhaohui Chen, Parvadha Suntharalingam, Andrew J. Watson, Ute Schuster, Jiang Zhu, and Ning Zeng
Biogeosciences, 18, 4549–4570, https://doi.org/10.5194/bg-18-4549-2021, https://doi.org/10.5194/bg-18-4549-2021, 2021
Short summary
Short summary
As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this global warming. The increased CO2 is mainly caused by emissions from fossil fuel use and land use. At the same time, the ocean is a significant sink in the carbon cycle. The North Atlantic is a critical ocean region in reducing CO2 concentration. We estimate the CO2 uptake in this region based on a carbon inverse system and atmospheric CO2 observations.
Nikita M. Fedkin, Can Li, Nickolay A. Krotkov, Pascal Hedelt, Diego G. Loyola, Russell R. Dickerson, and Robert Spurr
Atmos. Meas. Tech., 14, 3673–3691, https://doi.org/10.5194/amt-14-3673-2021, https://doi.org/10.5194/amt-14-3673-2021, 2021
Short summary
Short summary
This study presents a new volcanic sulfur dioxide (SO2) layer height retrieval algorithm for the Ozone Monitoring Instrument (OMI). We generated a large spectral dataset with a radiative transfer model and used it to train neural networks to predict SO2 height from OMI radiance data. The algorithm is fast and takes less than 10 min for a single orbit. Retrievals were tested on four eruption cases, and results had reasonable agreement (within 2 km) with other retrievals and previous studies.
Anna Karion, Israel Lopez-Coto, Sharon M. Gourdji, Kimberly Mueller, Subhomoy Ghosh, William Callahan, Michael Stock, Elizabeth DiGangi, Steve Prinzivalli, and James Whetstone
Atmos. Chem. Phys., 21, 6257–6273, https://doi.org/10.5194/acp-21-6257-2021, https://doi.org/10.5194/acp-21-6257-2021, 2021
Short summary
Short summary
Estimating city emissions based on atmospheric observations requires that the portion of observed greenhouse gases that originated in the city be separated from the portion that originated outside the city, also known as the background concentration. Here, we investigate different methods to determine background concentrations for the Washington, DC, and Baltimore, MD, region and evaluate how well those methods work and the uncertainties they involve.
Di Liu, Wanqi Sun, Ning Zeng, Pengfei Han, Bo Yao, Zhiqiang Liu, Pucai Wang, Ke Zheng, Han Mei, and Qixiang Cai
Atmos. Chem. Phys., 21, 4599–4614, https://doi.org/10.5194/acp-21-4599-2021, https://doi.org/10.5194/acp-21-4599-2021, 2021
Short summary
Short summary
It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather induced variations. Here, we determined the on-road CO2 concentration declines in Beijing using mobile observatory data before (BC), during (DC) and after COVID-19 (AC). We chose trips with the most similar weather and calculated the enhancement, the difference between on-road and the city “background”. We showed a clear on-road CO2 decrease in DC, which is consistent with the emissions reductions in DC.
Xiaohui Lin, Wen Zhang, Monica Crippa, Shushi Peng, Pengfei Han, Ning Zeng, Lijun Yu, and Guocheng Wang
Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, https://doi.org/10.5194/essd-13-1073-2021, 2021
Short summary
Short summary
CH4 is a potent greenhouse gas, and China’s anthropogenic CH4 emissions account for a large proportion of global total emissions. However, the existing estimates either focus on a specific sector or lag behind real time by several years. We collected and analyzed 12 datasets and compared them to reveal the spatiotemporal changes and their uncertainties. We further estimated the emissions from 1990–2019, and the estimates showed a robust trend in recent years when compared to top-down results.
Cited articles
Allan, D. W.: Statistics of Atomic Frequency Standards, Pr. Inst. Electr. Elect., 54, 221–230, https://doi.org/10.1109/proc.1966.4634, 1966.
Bosch Sensortec: BME280 Digital Pressure Sensor Datasheet, available at: https://cdn-shop.adafruit.com/datasheets/BST-BME280_DS001-10.pdf (last access: 7 June 2016), 2015.
Bréon, F. M., Broquet, G., Puygrenier, V., Chevallier, F., Xueref-Remy, I., Ramonet, M., Dieudonné, E., Lopez, M., Schmidt, M., Perrussel, O., and Ciais, P.: An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements, Atmos. Chem. Phys., 15, 1707–1724, https://doi.org/10.5194/acp-15-1707-2015, 2015.
Briber, B., Hutyra, L., Dunn, A., Raciti, S., and Munger, J.: Variations in Atmospheric CO2 Mixing Ratios across a Boston, MA Urban to Rural Gradient, Land, 2, 304–327, https://doi.org/10.3390/land2030304, 2013.
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.
Gas Sensing Solutions: COZIR Ultra Low Power Carbon Dioxide Sensor, available at: http://www.gassensing.co.uk/media/1050/cozir_ambient_datasheet_gss.pdf (last access: 29 December 2015), 2014.
General Electric: Telaire T6615 Sensor Dual Channel Module, available at: http://www.avnet-abacus.eu/fileadmin/user_upload/Products_Menu/Amphenol/AmphenolAdvancedSensors_CO2_double_channel_module.pdf (last access: 29 December 2015), 2011.
Holstius, D. M., Pillarisetti, A., Smith, K. R., and Seto, E.: Field calibrations of a low-cost aerosol sensor at a regulatory monitoring site in California, Atmos. Meas. Tech., 7, 1121–1131, https://doi.org/10.5194/amt-7-1121-2014, 2014.
Hurst, S., Durant, A. J., and Jones, R. L.: A low cost, disposable instrument for vertical profile measurements of atmospheric CO2, Chemistry Research Project Report, Centre for Atmospheric Science, Department of Chemistry, University of Cambridge, 2011.
Jones, E., Oliphant, E., Peterson, P., et al.: SciPy: Open Source Scientific Tools for Python, available at: http://www.scipy.org/ (last access: 10 October 2016), 2001.
Keeling, C. D., Piper, S. C., Bacastow, R. B., Wahlen, M., Whorf, T. P., Heimann, M., and Meijer, H. A.: Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and oceans from 1978 to 2000: Observations and carbon cycle implications, History of Atmospheric CO2 and Its Effects on Plants, Animals and Ecosystems, 177, 83–113, https://doi.org/10.1007/0-387-27048-5_5, 2005.
Kort, E. A., Angevine, W. M., Duren, R., and Miller, C. E.: Surface observations for monitoring urban fossil fuel CO2 emissions: Minimum site location requirements for the Los Angeles megacity, J. Geophys. Res.-Atmos., 118, 1–8, https://doi.org/10.1002/jgrd.50135, 2013.
Lopez-Coto, I., Ghosh, S., Prasad, K., and Whetstone, J.: Tower-Based Greenhouse Gas Measurement Network Design – the NIST North East Corridor Testbed, Adv. Atmos. Sci., https://doi.org/10.1007/s00376-017-6094-6, online first, 2017.
Los Gatos Research: Fast Greenhouse Gas Analyzer (Enhanced Performance Model) Datasheet, available at: http://www.lgrinc.com/documents/LGR_FGGA_Datasheet.pdf (last access: 29 December 2015), 2013.
Langridge, J. M., Ball, S. M., Shillings, A. J. L., and Jones, R. L.: A broadband absorption spectrometer using light emitting diodes for ultrasensitive, in situ trace gas detection, Rev. Sci. Instrum., 79, 123110, https://doi.org/10.1063/1.3046282, 2008.
Lauvaux, T., Miles, N. L., Deng, A., Richardson, S. J., Cambaliza, M. O., Davis, K. J., Gaudet, B., Gurney, K. R., Huang, J., and O'Keefe, D.: High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX), J. Geophys. Res.-Atmos., 121, 5213–5236, https://doi.org/10.1002/2015JD024473, 2016.
McKain, K., Wofsy, S. C., Nehrkorn, T., Eluszkiewicz, J., Ehleringer, J. R., and Stephens, B. B.: Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region, P. Natl. Acad. Sci. USA, 109, 8423–8428, https://doi.org/10.1073/pnas.1116645109, 2012.
Pataki, D. E., Bowling, D. R., and Ehleringer, J. R.: Seasonal cycle of carbon dioxide and its isotopic composition in an urban atmosphere: Anthropogenic and biogenic effects, J. Geophys. Res.-Atmos., 108, 4735, https://doi.org/10.1029/2003jd003865, 2003.
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.
Raspberry Pi Foundation: Raspberry Pi Hardware Documentation, available at: https://www.raspberrypi.org/documentation/hardware/raspberrypi/, last access: 29 December 2015.
SenseAir: CO2 Engine K30 Specification, available at: http://www.senseair.com/wp-content/uploads/2015/03/CO2-Engine-K30_PSP110-R7.pdf (last access: 29 December 2015), 2007.
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.
Turnbull, J. C., Sweeney, C., Karion, A., Newberger, T., Lehman, S. J., Tans, P. P., Davis, K. J., Lauvaux, T., Miles, N. L., Richardson, S. J., Cambaliza, M. O., Shepson, P. B., Gurney, K., Patarasuk, R., and Razlivanov, I.: Toward quantification and source sector identification of fossil fuel CO2 emissions from an urban area: Results from the INFLUX experiment, J. Geophys. Res.-Atmos., 120, 292–312, https://doi.org/10.1002/2014jd022555, 2015.
Turner, A. J., Shusterman, A. A., McDonald, B. C., Teige, V., Harley, R. A., and Cohen, R. C.: Network design for quantifying urban CO2 emissions: assessing trade-offs between precision and network density, Atmos. Chem. Phys., 16, 134650013475, https://doi.org/10.5194/acp-16-13465-2016, 2016.
Wang, Y., Li, J. Y., Jing, H., Zhang, Q., Jiang, J. K., and Biswas, P.: Laboratory Evaluation and Calibration of Three Low- Cost Particle Sensors for Particulate Matter Measurement, Aerosol Sci. Tech., 49, 1063–1077, https://doi.org/10.1080/02786826.2015.1100710, 2015.
World Meteorological Organization: GAW Report No. 213: 17th WMO/IAEA Meeting on Carbon Dioxide, Other Greenhouse Gases and Related Tracers Measurement Techniques (GGMT-2013), available at: http://www.wmo.int/pages/prog/arep/gaw/documents/Final_GAW_213_web.pdf (last access: 11 July 2016), 2013.
Wu, L., Broquet, G., Ciais, P., Bellassen, V., Vogel, F., Chevallier, F., Xueref-Remy, I., and Wang, Y.: What would dense atmospheric observation networks bring to the quantification of city CO2 emissions?, Atmos. Chem. Phys., 16, 7743–7771, https://doi.org/10.5194/acp-16-7743-2016, 2016.
Yasuda, T., Yonemura, S., and Tani, A.: Comparison of the Characteristics of Small Commercial NDIR CO2 Sensor Models and Development of a Portable CO2 Measurement Device, Sensors, 12, 3641–3655, https://doi.org/10.3390/s120303641, 2012.
Young, D. T., Chapman, L., Muller, C. L., Cai, X. M., and Grimmond, C. S. B.: A Low-Cost Wireless Temperature Sensor: Evaluation for Use in Environmental Monitoring Applications, J. Atmos. Ocean. Tech., 31, 938–944, https://doi.org/10.1175/jtech-d-13-00217.1, 2014.
Short summary
A low-cost sensor for measuring carbon dioxide is evaluated for its performance in detecting concentrations in Earth's atmosphere. After a multivariate regression correcting for environmental variables, the root mean square error between it and a research-grade gas analyzer is less than 0.5 % of the observed average value. This demonstrates the viability for using these sensors in certain real-world atmospheric observing applications.
A low-cost sensor for measuring carbon dioxide is evaluated for its performance in detecting...
