49 citations as recorded by crossref.

1. Optimizing a dynamic fossil fuel CO<sub>2</sub> emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO<sub>2</sub>, CO, NO<sub><i>x</i></sub>, and SO<sub>2</sub> I. Super et al. 10.5194/gmd-13-2695-2020
2. Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis G. Geng et al. 10.5194/acp-17-4131-2017
3. HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 2: The bottom–up module M. Guevara et al. 10.5194/gmd-13-873-2020
4. How to distinguish dwellings taking into account the information about supply from the district heating system? CO2 emission inventory in the Silesia, Poland D. Zasina & J. Zawadzki 10.1080/17583004.2018.1537515
5. Anthropogenic Osmium in Macroalgae from Tokyo Bay Reveals Widespread Contamination from Municipal Solid Waste A. Sproson et al. 10.1021/acs.est.0c01602
6. New York City greenhouse gas emissions estimated with inverse modeling of aircraft measurements J. Pitt et al. 10.1525/elementa.2021.00082
7. Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2emission in Vietnam, Cambodia and Laos A. Gaughan et al. 10.1088/2515-7620/ab3d91
8. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China X. Chuai & J. Feng 10.1016/j.scitotenv.2019.05.138
9. The impact of temporal variability in prior emissions on the optimization of urban anthropogenic emissions of CO2, CH4 and CO using in-situ observations I. Super et al. 10.1016/j.aeaoa.2021.100119
10. Evaluating Anthropogenic CO2 Bottom-Up Emission Inventories Using Satellite Observations from GOSAT and OCO-2 S. Zhang et al. 10.3390/rs14195024
11. Assimilation of atmospheric CO2 observations from space can support national CO2 emission inventories T. Kaminski et al. 10.1088/1748-9326/ac3cea
12. PMIF v1.0: assessing the potential of satellite observations to constrain CO<sub>2</sub> emissions from large cities and point sources over the globe using synthetic data Y. Wang et al. 10.5194/gmd-13-5813-2020
13. Coupling population dynamics with earth system models: the POPEM model A. Navarro et al. 10.1007/s11356-017-0127-7
14. Carbon Monoxide Emissions from the Washington, DC, and Baltimore Metropolitan Area: Recent Trend and COVID-19 Anomaly I. Lopez-Coto et al. 10.1021/acs.est.1c06288
15. EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012 G. Janssens-Maenhout et al. 10.5194/essd-11-959-2019
16. Lagrangian inversion of anthropogenic CO2 emissions from Beijing using differential column measurements K. Che et al. 10.1088/1748-9326/ac7477
17. Relationships in Diversity, Vegetation Indexes and Water Area in Terminal Lake of the Tarim River, Northwest China X. Zhao et al. 10.3390/ecologies2040019
18. Evaluating China's fossil-fuel CO<sub>2</sub> emissions from a comprehensive dataset of nine inventories P. Han et al. 10.5194/acp-20-11371-2020
19. Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China S. Dai et al. 10.3390/rs12233932
20. Uncertainty analysis of a European high-resolution emission inventory of CO<sub>2</sub> and CO to support inverse modelling and network design I. Super et al. 10.5194/acp-20-1795-2020
21. Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959–2018 M. Jones et al. 10.1038/s41597-020-00779-6
22. A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration Z. Ji et al. 10.3390/atmos15030323
23. A Vector Map of Carbon Emission Based on Point-Line-Area Carbon Emission Classified Allocation Method H. Liu et al. 10.3390/su122310058
24. Impacts of Horizontal Resolution on Global Data Assimilation of Satellite Measurements for Tropospheric Chemistry Analysis T. Sekiya et al. 10.1029/2020MS002180
25. Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique I. Lopez-Coto et al. 10.1021/acs.est.9b06619
26. Hourly disaggregation of industrial CO2 emissions from Shenzhen, China L. Ma et al. 10.1016/j.envpol.2018.01.090
27. Optimal design of surface CO2 observation network to constrain China’s land carbon sink Y. Wang et al. 10.1016/j.scib.2023.07.010
28. Constraining Fossil Fuel CO2 Emissions From Urban Area Using OCO‐2 Observations of Total Column CO2 X. Ye et al. 10.1029/2019JD030528
29. Change in Spatial Distribution Patterns and Regeneration of Populus euphratica under Different Surface Soil Salinity Conditions P. Zhang et al. 10.1038/s41598-019-42306-7
30. The potential of a constellation of low earth orbit satellite imagers to monitor worldwide fossil fuel CO2 emissions from large cities and point sources F. Lespinas et al. 10.1186/s13021-020-00153-4
31. A Method for Estimating the Background Column Concentration of CO2 Using the Lagrangian Approach Z. Pei et al. 10.1109/TGRS.2022.3176134
32. Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model J. Hedelius et al. 10.5194/acp-18-16271-2018
33. Evaluating China's anthropogenic CO<sub>2</sub> emissions inventories: a northern China case study using continuous surface observations from 2005 to 2009 A. Dayalu et al. 10.5194/acp-20-3569-2020
34. A Lagrangian approach towards extracting signals of urban CO<sub>2</sub> emissions from satellite observations of atmospheric column CO<sub>2</sub> (XCO<sub>2</sub>): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”) D. Wu et al. 10.5194/gmd-11-4843-2018
35. The Open-source Data Inventory for Anthropogenic CO<sub>2</sub>, version 2016 (ODIAC2016): a global monthly fossil fuel CO<sub>2</sub> gridded emissions data product for tracer transport simulations and surface flux inversions T. Oda et al. 10.5194/essd-10-87-2018
36. Potentially underestimated gas flaring activities—a new approach to detect combustion using machine learning and NASA’s Black Marble product suite S. Chakraborty et al. 10.1088/1748-9326/acb6a7
37. A comparison of estimates of global carbon dioxide emissions from fossil carbon sources R. Andrew 10.5194/essd-12-1437-2020
38. Potential of European <sup>14</sup>CO<sub>2</sub> observation network to estimate the fossil fuel CO<sub>2</sub> emissions via atmospheric inversions Y. Wang et al. 10.5194/acp-18-4229-2018
39. Comparison of Global Downscaled Versus Bottom‐Up Fossil Fuel CO2 Emissions at the Urban Scale in Four U.S. Urban Areas K. Gurney et al. 10.1029/2018JD028859
40. Disaggregating population data for assessing progress of SDGs: methods and applications Y. Qiu et al. 10.1080/17538947.2021.2013553
41. Bayesian inverse estimation of urban CO2 emissions: Results from a synthetic data simulation over Salt Lake City, UT L. Kunik et al. 10.1525/elementa.375
42. Designing surface CO2 monitoring network to constrain the Indian land fluxes K. Nalini et al. 10.1016/j.atmosenv.2019.117003
43. Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources R. Bun et al. 10.1007/s11027-018-9791-2
44. Estimating CO<sub>2</sub> emissions for 108 000 European cities D. Moran et al. 10.5194/essd-14-845-2022
45. On the impact of granularity of space-based urban CO2 emissions in urban atmospheric inversions: A case study for Indianapolis, IN T. Oda et al. 10.1525/elementa.146
46. Improving the representation of anthropogenic CO<sub>2</sub> emissions in climate models: impact of a new parameterization for the Community Earth System Model (CESM) A. Navarro et al. 10.5194/esd-9-1045-2018
47. High-Resolution Bayesian Inversion of Carbon Dioxide Flux Over Peninsular India S. Sijikumar et al. 10.1016/j.atmosenv.2023.119868
48. Errors and uncertainties in a gridded carbon dioxide emissions inventory T. Oda et al. 10.1007/s11027-019-09877-2
49. Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production Z. Liu et al. 10.1038/s41597-020-00708-7