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173 results for “carbon monoxide”
Products and Models for "Detection of carbon monoxide's 4.6 micron fundamental band structure in WASP-39b's atmosphere with JWST NIRSpec G395H"
<p>Overview:</p> <p>Carbon monoxide (CO) is predicted to be the dominant carbon-bearing molecule in giant planet atmospheres, and, along with water, is important for discerning the oxygen and therefore carbon-to-oxygen ratio of these planets. The fundamental absorption mode of CO has a broad double-branched structure composed of many individual absorption lines from 4.3 to 5.1 µm, which can now be spectroscopically measured with JWST. Here we present a technique for detecting the rotational sub-band structure of CO at medium resolution with the NIRSpec G395H instrument. We use a single transit observation of the hot Jupiter WASP-39b from the JWST Transiting Exoplanet Community Early Release Science (JTEC ERS) program at the native resolution of the instrument (R ~ 2700) to resolve the CO absorption structure. We robustly detect absorption by CO, with an increase in transit depth of 264 <span>\(\pm\)</span> 68 ppm, in agreement with the predicted CO contribution from the best-fit model at low resolution. This detection confirms our theoretical expectations that CO is the dominant carbon-bearing molecule in WASP-39b's atmosphere, and further supports the conclusions of low C/O and super-solar metallicities presented in the JTEC ERS papers for WASP-39b. </p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
Dataset for "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland"
<p>This is the dataset (ver. 2017.02.13) for the manuscript "Soil fluxes of carbonyl sulfide (COS), carbon monoxide, and carbon dioxide in a boreal forest in southern Finland" submitted to the journal <em>Atmospheric Chemistry and Physics</em>.</p>
Measurements of Carbon-14 of Carbon monoxide (14CO) in a global network
<p>This is a data set containing measurements of [14CO] in a new global network led by the University of Rochester. Measurements are from samples collected approximately biweekly during 2021 at Barrow, Mace Head, Mauna Loa, Barbados, American Samoa, Reunion Island and Baring Head atmospheric observatories.</p>
Long-term (2001-2020) global full-coverage daytime and nighttime carbon monoxide profile and total column
<p>Validation results are in preparation. The full dataset will be uploaded after the submission/acceptance of our paper.</p>
Global spatiotemporal continuous daily high-resolution total column carbon monoxide for TROPOMI
<p>A novel framework is developed to recover missing data in global TROPOMI TCCO product over land from Jun. 01 2018 to May. 31 2021 by fusing multisource data. Validation results show that the accuracy of recovered results is satisfactory and close to that of TROPOMI, with the R of 0.885 against NDACC and 0.918 against TCCON. Furthermore, the recovered results achieve a small (distinctly) better performance than those of MOPITT (CAMS). The spatial pattern of the recovered TCCO is consistent with that of the MOPITT TCCO and can specify much finer spatial details by comparison with CAMS.</p>
Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network
<p><strong>Ozone and Carbon Monoxide Dataset Collected by the OpenSense Zurich Mobile Sensor Network</strong></p> <p>This dataset contains ozone (O3) and carbon monoxide (CO) concentration measurements collected by the OpenSense (<a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a>) mobile senor network over the course of 4.5 years (2012/02-2016/09). The sensors are mounted on top of 10 streetcars in the city of Zurich, Switzerland.</p> <p><br> In particular, the dataset contains: </p> <ol> <li>Ozone (O3) data: 2012/02 - 2016/09 (19.9 Mio samples)</li> <li>Carbonmonoxide (CO) data: 2014/03 - 2016/09 (49.7Mio samples)</li> </ol> <p><strong>Hardware:</strong><br> --------------</p> <ol> <li>Ozone sensor: SGX (former e2V) MiCS-OZ-47 Ozone Sensing Head with Smart Transmitter PCB</li> <li>Carbon monoxide sensor: Alphasense CO-B4</li> <li>GPS receiver: u-blox EVK-6p</li> </ol> <p><strong>Data files format: </strong><br> -------------------------<br> co_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>WE_CHANNEL_SENSOR_1_MV: The voltage [in mV] at the working electrode of the electrochemical sensor (see Alphasense CO-B4 datasheet for more details)</li> </ol> <p>o3_data_*: </p> <ol> <li>Time of day: yyyy.mm.dd HH:MM</li> <li>Latitude WGS84</li> <li>Longitude WGS84</li> <li>HDOP: horizontal dilution of precision, uncertainty of the GPS position</li> <li>Tram ID</li> <li>Ozone [ppb]: On-device calibrated (according to manufacturer) ozone measurement [in parts-per-billion] </li> <li>Temperature [in °C]</li> <li>Relative Humidity [in %]</li> </ol> <p><strong>Data quality:</strong><br> ------------------<br> The data has NOT been post-processed!<br> In order to achieve high data quality, the data needs to be cleaned (e.g. outlier filtering) and, most importantly, the sensors need to be individually calibrated.<br> Reference data can be obtained from <a href="http://www.ostluft.ch">www.ostluft.ch</a>, the official air quality monitoring network in eastern Switzerland, which operates multiple monitoring stations in the city of Zurich.</p> <p><strong>Plot Coverage Map (MATLAB):</strong><br> --------------------------------------------<br> The provided MATLAB script plot_data_coverage.m plots the locations of the collected samples onto the map of Zurich (map_zurich.png). </p> <p><strong>References:</strong><br> -----------------<br> The dataset (and related aspects) has partly been used and is described in more detail in the following publications:</p> <ol> <li>Balz Maag et al. <strong>SCAN: Multi-Hop Calibration for Mobile Sensor Arrays</strong>. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol.1, No.2 (IMWUT), 2017.</li> <li>Olga Saukh et al. <strong>Reducing Multi-Hop Calibration Errors in Mobile Sensor Networks</strong>. In IEEE/ACM International Conference on Information Processing in Sensor Networks (IPSN), 2015. Best Paper Award!</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensor Nodes on Public Transport Networks</strong>. In Journal of Ambient Intelligence and Humanized Computing, 5(3), Springer, 2014.</li> <li>Olga Saukh et al. <strong>On Rendezvous in Mobile Sensing Networks</strong>. In Proceedings of the 5th Workshop on Real-World Wireless Sensor Networks (RealWSN), 2013.</li> <li>Jason Jingshi Li et al. <strong>Sensing the Air we Breathe – The OpenSense Zurich Dataset</strong>. In Proceedings of the 26th International Conference on Artificial Intelligence (AAAI), 2012.</li> <li>Olga Saukh et al. <strong>Route Selection for Mobile Sensors with Checkpointing Constraints</strong>. In Proceedings of the 8th International Workshop on Sensor Networks and Systems for Pervasive Computing (PerSeNS, in conjunction with IEEE PerCom), March 2012.</li> <li>David Hasenfratz et al. <strong>On-the-fly Calibration of Low-Cost Gas Sensors</strong>. In Proceedings of the 9th European Conference on Wireless Sensor Networks (EWSN), 2012. </li> </ol> <p><br> For further information, visit: <a href="http://www.opensense.ethz.ch">http://www.opensense.ethz.ch</a></p>
Global daily high-resolution surface carbon monoxide concentrations (2019-2020)
<p>A brand-new framework is developed to estimate daily globally distributed surface carbon monoxide (CO) concentrations (2019-2020) at a high spatial resolution (0.05º) through data-driven fusion. Evaluation results (historical) show that the proposed framework presents a desired estimation accuracy over the globe, with the Rs/RMSEs of 0.73/0.273 ppm and 0.77/0.215 ppm at daily and monthly scales, respectively. Compared to GEOS Composition Forecasting replay product, the proposed framework also performs distinctly better, of which the R increases by 0.3 and RMSE decreases by 0.185 ppm.</p>
ASRC Rooftop methane (CH<sub>4</sub>) observations and NYCMA observed and simulated ΔCH<sub>4</sub>, with coincident carbon monoxide (CO) observations and ΔCO
Open the record for dataset details and reuse information.
Impacts of improved cookstove interventions on personal exposure to carbon monoxide and particulate matter in Zambia
Open the record for dataset details and reuse information.
Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations
<p><strong>The description of the NetCDF files (12×200×350):</strong></p> <ol> <li> <p>The first dimension represents the months, the second represents latitude, and the third represents longitude.</p> </li> <li> <p>The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both.</p> </li> </ol> <p><strong>The units for all files are as follows:</strong></p> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Format</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>All emission data.zip</p> </td> <td> <p>netcdf</p> </td> <td> <p>kg·m<sup>-2</sup>·s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Emissions in seven regions.csv</p> </td> <td> <p>csv</p> </td> <td> <p>10<sup>3</sup> kt</p> </td> </tr> <tr> <td> <p>Simulated CO concentrations.zip</p> </td> <td> <p>txt</p> </td> <td> <p>μg·m<sup>-3</sup> </p> </td> </tr> </tbody> </table>
Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.1
<p>This data is based on the paper: Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE 1.1 (unpublished).</p>
Safety Study of Inhaled Carbon Monoxide to Treat Pneumonia and Sepsis-Induced Acute Respiratory Distress Syndrome (ARDS)
ClinicalTrials.gov study NCT04870125. IPD Sharing: YES. Countries: 1. Publications: 12.
Study of Inhaled Carbon Monoxide to Treat Idiopathic Pulmonary Fibrosis
ClinicalTrials.gov study NCT01214187. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Monitoring of Exhaled Carbon Monoxide to Promote Pre-operative Smoking Cessation
ClinicalTrials.gov study NCT01014455. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Safety and Efficacy Study of Inhaled Carbon Monoxide to Treat Acute Respiratory Distress Syndrome (ARDS)
ClinicalTrials.gov study NCT03799874. IPD Sharing: NO. Countries: 1. Publications: 14.
Carbon monoxide exposure inside UK road vehicles: a pilot study
Open the record for dataset details and reuse information.
Weekly Carbon Monoxide Anomalies over Maritime Southeast Asia and Weekly Climate Indices
This repository contains weekly atmospheric carbon monoxide (CO) anomalies over the Maritime Southeast Asia (MSEA) region from 2001 to 2019, as well as weekly climate index data. Total column CO from the MOPITT satellite instrument were converted to column average volume mixing ratios (VMR) and were averaged within the MSEA region on a weekly time scale. A climatological seasonal cycle for the weekly time series was created using all 19 years of MOPITT data and was subtracted from the weekly VMRs to create the anomalies. Five climate indices are also provided on a weekly timescale (Nino3.4, AAO, DMI, TSA, OLR proxy for MJO). For details on the MOPITT CO retrieval parameters and the geometry of the MSEA region, see the provided README file. The geometry of the MSEA region and the spatial range of influence of the five climate indices are also plotted on the provided map. These data are associated with the JGR-Atmos. manuscript "Predicting Fire Season Intensity in Maritime Southeast Asia with Interpretable Models" by Daniels et al., (submitted October 2021).
Global Carbon Monoxide (CO) Flux Estimates for 2001-2015
<p>This data set contains Global carbon monoxide (CO) flux estimates for 2001-2015 partitioned into biomass burning (BB), fossil fuel (FF) and biogenic (BG) sources. The estimates were created at JPL/Caltech by Anthony Bloom using a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) algorithm (Bloom et al., 2015) applied to top-down CO fluxes obtained from inverse modeling using the GEOS-Chem (with adjoint) model and data from the Terra/MOPITT satellite (Jiang et al., 2017). The spatial resolution is 4.0 x 5.0 degrees lat/lon.</p> <p>Examples of the use of this data are described in Worden, J., et al., 2017 and Worden, H. et al., 2019.</p> <p>References:</p> <p>Bloom, A. A., J. Worden, Z. Jiang, H. Worden, T. Kurosu, C. Frankenberg, D. Schimel, (2015), Remote sensing constraints on South America fire traits by Bayesian fusion of atmospheric and surface data, Geophysical Research Letters, doi:10.1002/2014GL062584</p> <p>Jiang, Z., J. R. Worden, H. Worden, M. Deeter, D. B. A. Jones, A. F. Arellano, and D. K. Henze (2017), A 15-year record of CO emissions constrained by MOPITT CO observations, Atmos. Chem. Phys., 17(7), 4565–4583, doi:10.5194/acp-17-4565-2017.</p> <p>Worden, J. R., A.A. Bloom, S. Pandey, Z. Jiang, H.M. Worden, T.W. Walker, S. Houweling, T. Röckmann, (2017), Reduced biomass burning emissions reconcile conflicting estimates of the post-2006 atmospheric methane budget, Nature Communications, 8:2227, doi:10.1038/s41467-017-02246-0.</p> <p>Worden, H. M., Bloom, A. A., Worden, J. R., Jiang, Z., Marais, E., Stavrakou, T., Gaubert, B., and Lacey, F.: New Constraints on Biogenic Emissions using Satellite-Based Estimates of Carbon Monoxide Fluxes, Atmos. Chem. Phys. Discuss., doi:10.5194/acp-2019-377, in review, 2019.</p>
Water-assisted generation of catalytic interface: The case of supported Pt-FeOx(OH)y catalysts for preferential carbon monoxide oxidation
<p>Open data for "Water-assisted generation of catalytic interface: The case of supported Pt-FeOx(OH)y catalysts for preferential carbon monoxide oxidation"</p>
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.