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1. Using Space‐Based CO 2 and NO 2 Observations to Estimate Urban CO 2 Emissions

   https://doi.org/10.1029/2022JD037736  

   Yang, Emily G. ; Kort, Eric A. ; Ott, Lesley E. ; Oda, Tomohiro ; Lin, John C. ( March 2023 , Journal of Geophysical Research: Atmospheres)

   As the majority of fossil fuel carbon dioxide (CO₂) emissions originate from cities, the use of novel techniques to leverage available satellite observations of CO₂and proxy species to constrain urban CO₂is of great importance. In this study, we seek to empirically determine relationships between satellite observations of CO₂and the proxy species nitrogen dioxide (NO₂), applying these relationships to NO₂fields to generate NO₂‐derived CO₂fields (NDCFs) from which CO₂emissions can be estimated. We first establish this method using simulations of CO₂and NO₂for the cities of Buenos Aires, Melbourne, and Mexico City, finding that the method is viable throughout the year. For the same three cities, we next calculate empirical relationships (slopes) between co‐located observations of NO₂from the Tropospheric Monitoring Instrument and Snapshot Area Mode observations of CO₂from Orbiting Carbon Observatory‐3. Applying varying combinations of slopes to generate NDCFs, we evaluate methodological uncertainties for each slope application method and use a simple mass balance method to estimate CO₂emissions from NDCFs. We demonstrate monthly urban CO₂emissions estimates that are comparable to emissions inventory estimates. We additionally prove the utility of our method by demonstrating how large uncertainties at a grid cell level (equivalent to ∼1–3 ppm) can be reduced substantially when aggregating emissions estimates from NDCFs generated from all NO₂swaths (about 1%–6%). Rather than rely on prior knowledge of emission ratios, our method circumvents such assumptions and provides a valuable observational constraint on urban CO₂emissions.

    

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2. Nonmigrating tidal impact on the CO 2  15 μm infrared cooling of the lower thermosphere during solar minimum conditions: TIDAL IMPACT ON THE CO 2  15 μm EMISSIONS

   https://doi.org/10.1002/2017JA024273  

   Nischal, N. ; Oberheide, J. ; Mlynczak, M. G. ; Hunt, L. A. ; Maute, A. ( June 2017 , Journal of Geophysical Research: Space Physics)
3. Large CO 2 effluxes at night and during synoptic weather events significantly contribute to CO 2 emissions from a reservoir

   https://doi.org/10.1088/1748-9326/11/6/064001  

   Liu, Heping ; Zhang, Qianyu ; Katul, Gabriel G. ; Cole, Jonathan J. ; Chapin, III, F. Stuart ; MacIntyre, Sally ( May 2016 , Environmental Research Letters)
4. Soil carbon dioxide emissions from the Mojave desert: Isotopic evidence for a carbonate source: Abiotic Soil CO 2 Emissions

   https://doi.org/10.1002/2016GL071198  

   Soper, Fiona M. ; McCalley, Carmody K. ; Sparks, Kimberlee ; Sparks, Jed P. ( January 2017 , Geophysical Research Letters)
5. Quantification and machine learning based N 2 O–N and CO 2 –C emissions predictions from a decomposing rye cover crop

   https://doi.org/10.1002/agj2.21185  

   Joshi, Deepak R. ; Clay, David E. ; Clay, Sharon A. ; Moriles‐Miller, Janet ; Daigh, Aaron L. M. ; Reicks, Graig ; Westhoff, Shaina ( November 2022 , Agronomy Journal)

   Cover crops improve soil health and reduce the risk of soil erosion. However, their impact on the carbon dioxide equivalence (CO_2e) is unknown. Therefore, the objective of this 2‐yr study was to quantify the effect of cover crop‐induced differences in soil moisture, temperature, organic C, and microorganisms on CO_2e, and to develop machine learning algorithms that predict daily N₂O–N and CO₂–C emissions. The prediction models tested were multiple linear regression, partial least square regression, support vector machine, random forest (RF), and artificial neural network. Models’ performance was accessed using R², RMSE and mean of absolute value of error. Rye (Secale cerealeL.) was dormant seeded in mid‐October, and in the following spring it was terminated at corn's (Zea maysL.) V4 growth stage. Soil temperature, moisture, and N₂O–N and CO₂–C emissions were measured near continuously from soil thaw to harvest in 2019 and 2020. Prior to termination, the cover crop decreased N₂O–N emissions by 34% (p = .05), and over the entire season, N₂O–N emissions from cover crop and no cover crop treatments were similar (p = .71). Based on N₂O–N and CO₂–C emissions over the entire season and the estimated fixed cover crop‐C remaining in the soil, the partial CO_2ewere −1,061 and 496 kg CO_2eha^–1in the cover crop and no cover crop treatments, respectively. The RF algorithm explained more of the daily N₂O–N (73%) and CO₂–C (85%) emissions variability during validation than the other models. Across models, the most important variables were temperature and the amount of cover crop‐C added to the soil.

    

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