The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Spaceborne detection of localized carbon dioxide sources
Abstract
INTRODUCTION: Although the carbon budget is often presented in terms of global-scale fluxes, many of the contributing processes occur through localized point sources, which have been challenging to measure from space. Persistent anthropogenic carbon dioxide (CO 2 ) emissions have altered the natural balance of Earth’s carbon sources and sinks. These emissions are driven by a multitude of individual mobile and stationary point sources that combust fossil fuels, with urban areas accounting for more than 70% of anthropogenic emissions to the atmosphere. Natural point-source emissions are dominated by wildfires and persistent volcanic degassing. RATIONALE: Comprehensive global measurements from space could help to more completely characterize anthropogenic and natural point-source emissions. In global carbon cycle models, anthropogenic point-source information comes from bottom-up emission inventories, whereas natural point-source information comes from a sparse in situ measurement network. Whereas clusters of urban CO 2 point-source plumes merge together, isolated point sources (e.g., remote power plants, cement production plants, and persistently degassing volcanoes) create localized plumes. Because turbulent mixing and diffusion cause rapid downwind dilution, they are challenging to detect and analyze. Point-source detection from space is complicated by signal dilution: The observed values of Δ X CO 2 (enhancement of the column-averaged dry-air CO 2 mole fraction) correspond to in situ CO 2 enhancements of 10-fold or higher. Space-based sensors that detect and quantify CO 2 in plumes from individual point sources would enable validation of reported inventory fluxes for power plants. These sensors would also advance the detectability of volcanic eruption precursors and improve volcanic CO 2 emission inventories. RESULTS: Spaceborne measurements of atmospheric CO 2 using kilometer-scale data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) reveal distinct structures caused by known anthropogenic and natural point sources, including megacities and volcanoes. Continuous along-track sampling across Los Angeles (USA) by OCO-2 at its ~2.25-km spatial resolution exposes intra-urban spatial variability in the atmospheric X CO 2 distribution that corresponds to the structure of the urban dome, which is detectable under favorable wind conditions. Los Angeles X CO 2 peaks over the urban core and decreases through suburban areas to rural background values more than ~100 km away. Enhancements of X CO 2 in the Los Angeles urban CO 2 dome observed by OCO-2 vary seasonally from 4.4 to 6.1 parts per million (ppm). We also detected isolated CO 2 plumes from the persistently degassing Yasur, Ambrym, and Aoba volcanoes (Vanuatu), corroborated by near-simultaneous sulfur dioxide plume detections by NASA’s Ozone Mapping and Profiler Suite. An OCO-2 transect passing directly downwind of Yasur volcano yielded a narrow filament of enhanced X CO 2 ( Δ X CO 2 ≈ 3.4 ppm), consistent with plume modeling of a CO 2 point source emitting 41.6 ± 19.7 kilotons per day (15.2 ± 7.2 megatons per year). These highest continuous volcanic CO 2 emissions are collectively dwarfed by about 70 fossil fuel–burning power plants on Earth, which each emit more than 15 megatons per year of CO 2 . CONCLUSION: OCO-2’s sampling strategy was designed to characterize CO 2 sources and sinks on regional to continental and ocean-basin scales, but the unprecedented kilometer-scale resolution and high sensitivity enables detection of CO 2 from natural and anthropogenic localized emission sources. OCO-2 captures seasonal, intra-urban, and isolated plume signals. Capitalizing on OCO-2’s sensitivity, a much higher temporal resolution would capture anthropogenic emission signal variations from diurnal, weekly, climatic, and economic effects, and, for volcanoes, precursory emission variability. Future sampling strategies will benefit from a continuous mapping approach with the sensitivity of OCO-2 to systematically and repeatedly capture these smaller, urban to individual plume scales of CO 2 point sources. OCO-2 detects urban CO 2 signals with unprecedented detail over Los Angeles.: Individual “footprints” of OCO-2 X CO 2 data from early fall 2014 and summer 2015 over the city of Los Angeles strongly contrast with values over the distant, rural Antelope Valley. X CO 2 is the averaged dry-air molar CO 2 concentration between the spacecraft and Earth’s surface.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years
Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P., & White, J. W. C. (2012) · Nature · doi:10.1038/nature11299 · Cited by 784
Persistent growth of CO2 emissions and implications for reaching climate targets
Friedlingstein, P., Andrew, R. M., Rogelj, J., Peters, G. P., Canadell, J. G., Knutti, R., Luderer, G., Raupach, M. R., Schaeffer, M., van Vuuren, D. P., et al. (2014) · Nature Geoscience · doi:10.1038/ngeo2248 · Cited by 771
Emissions of greenhouse gases from a North American megacity
Wunch, D., Wennberg, P. O., Toon, G. C., Keppel‐Aleks, G., & Yavin, Y. G. (2009) · Geophysical Research Letters · doi:10.1029/2009gl039825 · Cited by 271
Cited by
Temporary reduction in daily global CO2 emissions during the covid-19 forced confinement
Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J. P., Abernethy, S., Andrew, R. M., De-Gol, A. J., Willis, D. R., Shan, Y., Canadell, J. G., et al. (2020) · Nature Climate Change · doi:10.1038/s41558-020-0797-x · Cited by 2,258
Plank, T., & Manning, C. E. (2019) · Nature · doi:10.1038/s41586-019-1643-z · Cited by 494
Satellite-based estimates of decline and rebound in China’s CO 2 emissions during COVID-19 pandemic
Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., Wu, N., Chevallier, F., Broquet, G., Boersma, F., et al. (2020) · Science Advances · doi:10.1126/sciadv.abd4998 · Cited by 282
Pallister, J., Papale, P., Eichelberger, J., Newhall, C., Mandeville, C., Nakada, S., Marzocchi, W., Loughlin, S., Jolly, G., Ewert, J., et al. (2019) · Journal of Applied Volcanology · doi:10.1186/s13617-019-0082-8 · Cited by 68
Heerah, S., Frausto‐Vicencio, I., Jeong, S., Marklein, A. R., Ding, Y., Meyer, A. G., Parker, H. A., Fischer, M. L., Franklin, J. E., Hopkins, F. M., et al. (2021) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2021jd034785 · Cited by 22
Metadata
- Authors
- Schwandner, F. M., Gunson, M. R., Miller, C. E., Carn, S. A., Eldering, A., Krings, T., Verhulst, K. R., Schimel, D. S., Nguyen, H. M., Crisp, D., O’Dell, C. W., Osterman, G. B., Iraci, L. T., & Podolske, J. R.
- Venue
- Science
- Published
- 2017-10-13
- Volume
- 358
- Issue
- 6360
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2763551617
- Cited by
- 238
- References
- 3
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Spaceborne detection of localized carbon dioxide sources. https://app.cochat.ai/papers/10.1126/science.aam5782
- The paper
- Schwandner, F. M., Gunson, M. R., Miller, C. E., Carn, S. A., Eldering, A., Krings, T., Verhulst, K. R., Schimel, D. S., Nguyen, H. M., Crisp, D., O’Dell, C. W., Osterman, G. B., Iraci, L. T., & Podolske, J. R. (2017). Spaceborne detection of localized carbon dioxide sources. Science. https://doi.org/10.1126/science.aam5782
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.